diff --git a/.gitattributes b/.gitattributes deleted file mode 100644 index 080bd1d..0000000 --- a/.gitattributes +++ /dev/null @@ -1,3 +0,0 @@ -* text=auto eol=lf -*.npz binary -*.tar.gz binary diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index f1cdc89..1153259 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -4,74 +4,15 @@ on: push: pull_request: -permissions: - contents: read - jobs: - python: - runs-on: ubuntu-24.04 + test: + runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: python-version: "3.12" - cache: pip - - run: python -m pip install --upgrade pip - - run: python -m pip install -e '.[test,qualification]' ruff - - run: ruff format --check src tests tools setup.py modal_app.py qualification_modal.py - - run: ruff check src tests tools setup.py modal_app.py qualification_modal.py + - run: python -m pip install -e '.[dev]' + - run: ruff check src tests + - run: ruff format --check src tests - run: pytest - - run: python tools/freeze_manifests.py - - run: git diff --exit-code -- data/dev-v1.json data/ranked-v1.json - - minimum-dependencies: - runs-on: ubuntu-24.04 - steps: - - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 - with: - python-version: "3.12" - cache: pip - - run: python -m pip install -e . numpy==2.0.2 scipy==1.14.1 pytest - - run: pytest tests/test_dataset.py tests/test_pilot_data.py tests/test_ns_dominance.py tests/test_ns_refinement.py - - installed-wheel: - runs-on: ubuntu-24.04 - steps: - - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 - with: - python-version: "3.12" - cache: pip - - run: python -m pip install build - - run: python -m build - - name: Exercise installed resources outside the checkout - shell: bash - run: | - python -m venv "$RUNNER_TEMP/wheel-venv" - "$RUNNER_TEMP/wheel-venv/bin/python" -m pip install dist/*.whl - cd "$RUNNER_TEMP" - "$RUNNER_TEMP/wheel-venv/bin/linear-solver-bench" dataset summary - "$RUNNER_TEMP/wheel-venv/bin/python" -c 'from linear_solver_bench.paths import native_dir; assert (native_dir() / "src/driver.cpp").is_file(); assert (native_dir() / "include/nsl_hypre_solver.h").is_file()' - - native-smoke: - runs-on: ubuntu-24.04 - timeout-minutes: 20 - steps: - - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 - with: - python-version: "3.12" - cache: pip - - run: sudo apt-get update && sudo apt-get install -y ninja-build - - run: python -m pip install build - - run: python -m build --wheel - - run: python -m pip install dist/*.whl - - name: Build and validate the native runtime from the installed wheel - shell: bash - run: | - cd "$RUNNER_TEMP" - linear-solver-bench runtime build --output runtime --jobs 2 - linear-solver-bench runtime build --output runtime-check --jobs 2 - python -c 'import json; a=json.load(open("runtime/manifest.json")); b=json.load(open("runtime-check/manifest.json")); assert a == b, "clean runtime builds must be identical"' - linear-solver-bench candidate validate "$GITHUB_WORKSPACE/submissions/starter.c" --runtime runtime diff --git a/.gitignore b/.gitignore index 30692b8..ce6a6b9 100644 --- a/.gitignore +++ b/.gitignore @@ -1,17 +1,11 @@ .venv/ -.uv-cache/ -__pycache__/ .pytest_cache/ .ruff_cache/ -.coverage -htmlcov/ +__pycache__/ *.egg-info/ build/ dist/ results/ -workspace/ -secrets/ -data/prepared/ -data/downloads/ +.cache/ .env .DS_Store diff --git a/BENCHMARK.md b/BENCHMARK.md new file mode 100644 index 0000000..96f989d --- /dev/null +++ b/BENCHMARK.md @@ -0,0 +1,80 @@ +# Benchmark contract + +## Candidate interface + +A candidate is one UTF-8 C file of at most 65,536 bytes. It includes exactly +`HYPRE_parcsr_ls.h` and exports: + +```c +HYPRE_Int solver_create(HYPRE_Solver *solver, + HYPRE_Real relative_tolerance, + HYPRE_Real absolute_tolerance); +``` + +The returned object provides setup, solve, and destroy callbacks. Candidates may +assemble a solver from the HYPRE operations allowlisted by the benchmark. Stock +HYPRE Krylov outer solvers are reserved for the fixed reference. Candidates may +not perform I/O, networking, process creation, dynamic loading, or timing. Inputs +must not be modified. + +Submissions use a reviewed-source threat model. The compiler and symbol audit +enforce the ordinary C contract; they are not a security boundary against hostile +inline assembly or raw system calls. Modal still isolates the evaluation from the +host and blocks sandbox networking. + +## Paired execution + +The fixed reference and candidate are built against the same pinned HYPRE runtime +inside one environment. Each numerical case is run in two fresh processes: + +1. Run and verify the fixed reference. +2. Run and verify the candidate. +3. Compute `speedup = reference_seconds / candidate_seconds` if both pass. + +A reference failure invalidates the evaluation. A candidate failure is recorded +with `passed: false` and no speedup. Failed or omitted cases never disappear from +an aggregate. + +The native monotonic clock measures factory creation, setup, and solve. It excludes +compilation, input loading, transport, destruction, and trusted verification. +The reference is measured again for every candidate; timings are never reused +between sandboxes. Each solver process has a 600-second wall-time limit. + +The complete-split score is the geometric mean of scored-case speedups, provided +the candidate passes every scored and control case. Partial evaluations and +evaluations with failures have no aggregate score. + +## Accuracy + +For residual `r = b - A x`, the trusted evaluator computes normwise and +componentwise backward error and the relative residual. NS cases also compare +against the evaluator-held manufactured target. + +NS requires: + +- normwise backward error ≤ `1e-10`; +- componentwise backward error ≤ `1e-8`; +- relative L2 and Linf forward errors ≤ `1e-5`. + +FLASH requires: + +- normwise backward error ≤ `max(tolerance, 20 * float64_epsilon)`; +- relative residual ≤ `tolerance * (1 + 1e-6)`. + +Both families require a finite solution of the correct size, a successful solver +status, and unchanged inputs. + +## Modal venue + +Modal is the default and the only official scoring venue. Local runs are useful +development checks, but their reports are marked `official: false`. + +The Modal venue uses a versioned immutable image, exact CPU and memory +request/limit pairs, and a network-disabled sandbox. It compiles the two solvers +once and transfers one public case at a time. Only the numerical `CaseInput` +crosses the execution boundary; targets and correctness remain on the trusted +client. + +Changing the reference source, HYPRE runtime, native driver, image, resources, +case membership, timing boundary, or accuracy rules creates a new venue or +benchmark version. diff --git a/CITATION.cff b/CITATION.cff index c41a698..36000f9 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -1,9 +1,8 @@ cff-version: 1.2.0 -message: "If you use this benchmark, please cite this software and the source matrices." +message: "If you use this benchmark, please cite this software and its source matrices." title: "LinearSolverBench" type: software version: 0.1.0 -date-released: 2026-08-29 authors: - name: Autodidakt license: Apache-2.0 diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index da87fea..2c916b6 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,35 +1,16 @@ # Contributing -Changes should preserve the benchmark's scientific identity and keep the -trusted path small. Bug fixes, portability improvements, new public tests, and -proposals for future tracks are welcome. - -## Development checks +Keep the participant workflow small and readable. New abstractions need more than +one real use before they are introduced. ```bash -uv venv --python 3.12 -uv pip install -e '.[dev]' -ruff format --check . -ruff check . +python -m pip install -e '.[dev]' +ruff format --check src tests +ruff check src tests pytest ``` -To exercise the native boundary, build the pinned runtime and audit the starter -submission: - -```bash -linear-solver-bench runtime build --output build/runtime -linear-solver-bench candidate validate submissions/starter.c --runtime build/runtime -``` - -Run `python tools/freeze_manifests.py` after intentionally changing selection -logic. Both generated manifest digests and the reason for a dataset-version -change must be reviewed. Do not rewrite a published manifest in place; create a -new benchmark version. - -## Contract changes - -A pull request that changes the dataset, accuracy gates, timed boundary, -submission ABI, resource limits, HYPRE identity, or ranking rule must call that -out explicitly. Such changes normally belong in a new track or major benchmark -version so existing results remain comparable. +Changes to numerical inputs, accuracy, the native ABI, the fixed reference, +timing, or Modal resources change benchmark results and require an explicit +version decision. Keep private test data, credentials, full private reports, and +dataset-construction artifacts outside this repository. diff --git a/MANIFEST.in b/MANIFEST.in deleted file mode 100644 index 382023f..0000000 --- a/MANIFEST.in +++ /dev/null @@ -1,25 +0,0 @@ -include benchmark.toml -include modal_app.py -include qualification_modal.py -include CITATION.cff -include CONTRIBUTING.md -include SECURITY.md -include data/*.json -include data/*.md -recursive-include data/releases *.json *.md -recursive-include docs *.md *.json -include native/CMakeLists.txt -include native/include/*.h -include native/include/NOTICE-* -include native/include/LICENSE-* -include native/src/*.cpp -recursive-include submissions *.c -recursive-include tools *.py -prune build -prune dist -prune reports -prune report_notes -prune research -prune data/prepared -prune data/downloads -global-exclude .DS_Store *.py[cod] __pycache__ diff --git a/README.md b/README.md index 885ed50..045f876 100644 --- a/README.md +++ b/README.md @@ -1,156 +1,99 @@ # LinearSolverBench -LinearSolverBench measures how well AI models and systems can produce accurate, -fast solvers for sparse linear systems. A submission is one C source file using -approved [HYPRE](https://github.com/hypre-space/hypre) operations and -preconditioners. The evaluator compiles it once, runs independent cases, and -checks each returned solution against the original matrix and right-hand side. +LinearSolverBench measures the correctness and speed of C/HYPRE linear solvers. +Every candidate is compared with a fixed reference solver in the same process +environment—or the same Modal CPU sandbox—so results are normalized across +different hosts. -The pilot supports two separate tracks: +The benchmark has two public development families: -| Family | Track | Result | -| --- | --- | --- | -| `ns-mesh-pde` | `coverage` | Number of cases solved accurately within fixed budgets; equal counts tie | -| `magnetic_diffusion_flash` | `replay` | Speedup for individual captured solves against one qualified reference | +| Family | Input systems | +| --- | --- | +| `ns_mesh_pde` | Nonsymmetric SuiteSparse mesh and PDE matrices with manufactured right-hand sides | +| `flash` | Captured FLASH magnetic-diffusion matrices, right-hand sides, and initial guesses | -Each pilot case runs **once in one fresh native process**. Setup and solve time -both count. NS coverage needs no timing reference. FLASH replay requires every -scored case and correctness control to pass before reporting aggregate speedup. -SPD, NS performance, and FLASH trajectory tracks are not supported yet. The -original SuiteSparse v1 benchmark remains available under its separate identity -and retains three repetitions per case. +## Quick start -## Get started with NS coverage - -From a checkout: +Python 3.12, a C/C++ compiler, CMake, Ninja, Git, and `nm` are required. ```bash -uv venv --python 3.12 +python -m venv .venv source .venv/bin/activate -uv pip install -e '.[dev]' -linear-solver-bench dataset families -linear-solver-bench dataset summary --release ns-mesh-cohort-v2-dev -linear-solver-bench dataset list --release ns-mesh-cohort-v2-dev -linear-solver-bench dataset prepare \ - --release ns-mesh-cohort-v2-dev --output data/prepared/ns-dev -linear-solver-bench runtime build --output build/runtime -linear-solver-bench candidate validate submissions/starter.c --runtime build/runtime -linear-solver-bench run submissions/starter.c \ - --runtime build/runtime --cases data/prepared/ns-dev \ - --output results/ns-dev.json -linear-solver-bench score results/ns-dev.json +python -m pip install -e '.[dev]' + +linear-solver-bench init solver.c +linear-solver-bench run solver.c --family ns_mesh_pde --venue local \ + --case ss-225-rademacher-0 ``` -NS cohort v2 contains 49 qualified SuiteSparse mesh/PDE matrices: 19 development -cases in nine provenance groups and 30 ranked cases in 15 disjoint groups. It retains the -original 43 matrices and adds six source-reviewed operators. A frozen -matrix-only policy covers every shared application, spatial method, size band, -structural regime, and method-by-size combination supported by multiple groups -on both sides. Semiconductor devices and boundary elements are development-only. -The [cohort review](docs/NS_COHORT_V2.md) publishes the selection, sources, and -remaining single-source joint gaps; it does not claim to cover all NS PDE -problems or equalize solver difficulty. - -Read the [NS dataset card](docs/NS_DATASET_CARD.md) for download access, data -format, complete group membership, evaluation rules, limitations, and citation. - -Every fixed workload meets the unchanged tenfold qualification margins, using -46 refined sparse-LU witnesses, one PyAMG/GMRES witness with inexact refinement, -and two exact row-dominance certificates. All 49 numerical preparations also -reproduce with the minimum supported NumPy/SciPy versions. Full Modal runs give -**11/19 development and 14/30 ranked** for the public GMRES+AMG candidate. -All 49 cases run once; the two 90-second timeouts remain in the results without -retry. The 1.6-million-row Transport case passes in 5.81 seconds within 4 GiB. -Partial coverage is a valid benchmark result and requires no all-case timing -reference. - -The historical 43-case `ns-mesh-pilot` remains frozen with its original 11/32 -partition and GMRES+AMG results of 10/11 development and 14/32 ranked. The -two-case `ns-mesh-dev-pilot` remains a separate integration check. - -`--release` accepts an installed manifest name without `.json`, or an existing -manifest path such as `data/ns-mesh-cohort-v2-dev.json`. Optional `--family` and -`--track` selectors must agree with that release; `ns_mesh_pde` is an accepted -alias for `ns-mesh-pde`. The `run` command selects the evaluator from the prepared -manifest, so it needs no repeated family or track flags. - -Local runs are labeled `local-uncontrolled`; their CPU and memory allocations -are not an official evaluation venue. For isolated execution from a checkout: +The local venue is a development smoke test. Its first run downloads the selected +inputs and builds the pinned HYPRE runtime; both are cached under +`~/.cache/linear-solver-bench`. + +## Standardized Modal evaluation + +Install the Modal extra and configure your Modal account, then select the Modal +venue: ```bash -modal run modal_app.py \ - --source submissions/starter.c --cases data/prepared/ns-dev \ - --output results/ns-modal-dev.json -linear-solver-bench score results/ns-modal-dev.json +python -m pip install -e '.[modal]' +linear-solver-bench run solver.c --family ns_mesh_pde --venue modal \ + --output result.json ``` -The trusted evaluator loads cases one at a time. The Modal route streams only -the current numerical input into a network-disabled sandbox and verifies the -returned solution outside it. See [operator instructions](docs/OPERATIONS.md) -for trusted release checks, resource limits, and reference calibration. - -## FLASH replay data - -FLASH cases are available from the public, ungated -[LinearSolveBench dataset on Hugging Face](https://huggingface.co/datasets/hgarud/LinearSolveBench). -The published release contains 96 development cases and 248 ranked cases, including -88 correctness controls. Each downloadable case contains `matrix.npz`, `b.npy`, -`x0.npy`, and `case.json`. The numerical inputs retain their captured values; -using them requires neither FLASH nor access to its source. - -The manifests `flash-replay-dev-pilot` and `flash-replay-ranked-pilot` pin dataset -commit `3da5eda0ce3e85da1808d4b62d28d9111127e354`. Prepare either split with -`dataset prepare --release --output `. - -The fixed public reference `submissions/gmres_amg.c` passes all 344 cases in the -official Modal venue, including every correctness control. Candidate runs reuse -the frozen [development](data/releases/flash-replay-dev-reference.json) or -[ranked](data/releases/flash-replay-ranked-reference.json) reference timings; -there is no reference rerun for each submission. Follow the -[replay instructions](docs/OPERATIONS.md#run-and-score-flash-replay) to evaluate -and score a candidate with the matching calibration. Local timings remain -development results. See [validation evidence](docs/PILOT_VALIDATION.md), -[dataset details](docs/DATASET.md), and the -[FLASH task statement](docs/task-flash-replay.md). - -## Submit a solver - -Candidate source includes exactly `HYPRE_parcsr_ls.h` and exports only: - -```c -HYPRE_Int solver_create(HYPRE_Solver *solver, - HYPRE_Real relative_tolerance, - HYPRE_Real absolute_tolerance, - HYPRE_Int maximum_iterations); +Modal is the default and the only official, cross-candidate venue. The reference +and candidate are compiled once in one network-disabled sandbox. +For every case, the evaluator launches a fresh reference process followed by a +fresh candidate process and independently checks both solutions. A passing case +reports: + +```text +speedup = reference seconds / candidate seconds ``` -The evaluator force-includes `native/include/nsl_hypre_solver.h`; candidates -must not include it explicitly. The factory provides setup, solve, and destroy -callbacks and owns its allocations and preconditioners. Approved HYPRE -preconditioners are building blocks; stock outer solvers are excluded from the -candidate allowlist. Only numerical inputs reach candidate code, without case -names, provenance, manufactured targets, or numerical reference vectors. +The report records the immutable image, runtime, reference, candidate, and +manifest identities. Exact sandbox hardware is not part of the score because the +reference is remeasured beside every candidate. + +> **FLASH data status:** publishing the dev-only FLASH archive at the URLs pinned +> by its manifest remains a release prerequisite. The implementation and tests do +> not require private or mixed-split data. -Read the [specification](docs/SPEC.md), -[NS coverage task statement](docs/task-ns-coverage.md), and -[FLASH replay task statement](docs/task-flash-replay.md) before submitting. +> **Reference status:** the fixed reference compiles and passes native smoke tests +> plus the first 10 NS cases. Full NS and FLASH qualification is still a release +> gate; the current ILUT reference is too slow on `ss-896` to accept yet. -## Existing v1 benchmark +## Write a solver -Commands without `--release` keep their existing v1 meaning: +Candidates are one C file exporting `solver_create`. Start with +[`examples/solver.c`](examples/solver.c) and read [BENCHMARK.md](BENCHMARK.md) for +the ABI, allowed HYPRE operations, timing boundary, and accuracy rules. + +## Submit ```bash -linear-solver-bench dataset summary -linear-solver-bench dataset prepare --split dev --output data/prepared/dev-v1 +linear-solver-bench submit solver.c --name my-solver \ + --family ns_mesh_pde --license Apache-2.0 --submitter 'Your Name' \ + --output submissions/my-solver +``` + +Commit the generated three-file directory and open a pull request. Private test +data and result publication are maintained outside this participant repository. + +## Code map + +The normal path is deliberately short: + +```text +CLI/API -> benchmark.py -> data.py -> build.py or modal.py -> verify.py ``` -V1 has its own accuracy, repetition, qualification, and scoring rules. A pilot -selector cannot reinterpret v1 prepared data. Its 203-case ranked inventory is -not an NS mesh coverage release. +Family-specific input recipes live in `families.py`; native build and protocol +code live in `build.py`. There is no plugin system, compatibility layer, training +framework, or public operator service. ## License -Benchmark code is Apache-2.0. HYPRE-derived declarations retain HYPRE's MIT -notice. Preserve SuiteSparse metadata and matrix-specific attribution when -using its matrices; see [dataset attribution](docs/DATASET.md). The Hugging Face -dataset has its own dataset card and license metadata. +Benchmark code is Apache-2.0. HYPRE-derived declarations retain their MIT notice. +SuiteSparse matrices retain their original attribution; see the benchmark-family +README files. diff --git a/benchmark.toml b/benchmark.toml deleted file mode 100644 index d663b40..0000000 --- a/benchmark.toml +++ /dev/null @@ -1,89 +0,0 @@ -[benchmark] -id = "linear-solver-bench-cpu-v1" -schema_version = 1 -numeric_type = "float64" -ranked_case_count = 203 -repetitions = 3 -ranking = "solved_count_then_penalized_geomean" - -[accuracy] -contract = "three-gate-v1" -normwise_factor = 1.0 -componentwise_factor = 5.0 -forward_factor = 5.0 -forward_floor = 2.0e-12 -forward_ceiling = 2.0e-1 -tolerance_tiers = [1.0e-4, 1.0e-3, 1.0e-2, 4.0e-2] - -[candidate] -source_filename = "policy.c" -factory_export = "solver_create" -maximum_source_bytes = 65536 -maximum_iterations = 10000 -compile_timeout_seconds = 60 - -[execution] -cpu_physical_cores = 2.0 -cpu_limit_physical_cores = 2.0 -memory_mib = 4096 -memory_limit_mib = 4096 -network = false -mpi_ranks = 1 -openmp_threads = 1 -default_case_timeout_seconds = 120 - -[hypre] -repository = "https://github.com/hypre-space/hypre.git" -commit = "e0ce7986941ce27111587ee7ea635ef7ca2eca94" -tree = "c985eacc03d590011ab7652fe6fc4e9b25f1064d" -build = "sequential-cpu-float64-int32-static" - -[dataset] -suite_sparse_index_sha256 = "f040a883a523c4621c5986aa66c39eeaa21bcae79c45596e953e7ea5e7ccbdb4" -suite_sparse_last_revision = "31-Oct-2023 18:12:37" -catalogue_sha256 = "8226ebe538dcd0590d1415a2057da8df5e332d5b51886654a3d9a0c0181b92cd" -qualification_sha256 = "30c7b765ed3f8c9712897c0be1d84890921cec90f5c6da122e9108100c8e3044" -tolerance_policy = "condition-aware-four-tier-v1" - -[qualification] -condition_norm = "1" -method = "spqr-guarded-onenormest-v1" -rank_method = "SuiteSparseQR" -estimator_repeats = 3 -estimator_guard_factor = 3.0 -onenormest_t = 4 -onenormest_itmax = 10 - -# Pilot settings are resolved from each frozen release manifest. The sections -# above continue to describe the original v1 benchmark. -[pilot] -id = "linear-solver-bench-pilot-cpu-v2" -schema_version = 2 -execution_contract = "independent-case-once-v1" -repetitions = 1 -settings_source = "release_manifest" - -[pilot.ns_mesh] -family = "ns-mesh-pde" -track = "coverage" -accuracy_contract = "ns-mesh-accuracy-v1" -scoring_contract = "ns-coverage-count-v1" -development_manifest = "ns-mesh-cohort-v2-dev" -ranked_manifest = "ns-mesh-cohort-v2-ranked" -normwise_backward_limit = 1.0e-10 -componentwise_backward_limit = 1.0e-8 -forward_limit = 1.0e-5 -ranking = "solved_count" -timing_reference_required = false - -[pilot.flash_replay] -family = "magnetic_diffusion_flash" -track = "replay" -accuracy_contract = "flash-replay-accuracy-v1" -scoring_contract = "flash-replay-speedup-v1" -dataset_repository = "hgarud/LinearSolveBench" -development_manifest = "flash-replay-dev-pilot" -ranked_manifest = "flash-replay-ranked-pilot" -relative_residual_slack = 1.0e-6 -ranking = "weighted_geometric_speedup" -timing_reference_required = true diff --git a/data/NOTICE.md b/data/NOTICE.md deleted file mode 100644 index 00e51cc..0000000 --- a/data/NOTICE.md +++ /dev/null @@ -1,22 +0,0 @@ -# Dataset notices - -## SuiteSparse - -The catalogue and qualification evidence identify matrices from the -[SuiteSparse Matrix Collection](https://sparse.tamu.edu/). The matrices are -licensed under CC BY 4.0. This repository does not commit matrix payloads. - -Prepared systems are modified derivatives: storage is canonicalized to -float64 CSR and the benchmark constructs a right-hand side from an -evaluator-owned manufactured solution. Preserve original matrix metadata and -matrix-specific attribution whenever prepared payloads are distributed. - -## FLASH replay - -The standalone numerical captures are published in the public -[LinearSolveBench dataset](https://huggingface.co/datasets/hgarud/LinearSolveBench) -under Apache-2.0. The release manifests identify the exact dataset commit and -archive hashes. FLASH software is not redistributed with these captures, and -the dataset license does not grant a license to FLASH itself. Public capture -context, split assignments, and numerical validation are recorded in the dataset -catalogue and card. diff --git a/data/catalogue.json b/data/catalogue.json deleted file mode 100644 index 59d1897..0000000 --- a/data/catalogue.json +++ /dev/null @@ -1 +0,0 @@ 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"group": "Embree", - "index": 202, - "matrix_id": 2830, - "matrix_sha256": "c88348f63b03e0c8528eaa5d60d30fc6bc466b5e3ea5619f76a3cdeffd0deb1f", - "minimum_safe_tolerance": 1.0774397409143562e-09, - "n": 96307, - "name": "ifiss_mat", - "nnz": 3599932, - "source_url": "https://sparse.tamu.edu/MM/Embree/ifiss_mat.tar.gz", - "tolerance": 0.0001 - } - ], - "catalogue_sha256": "8226ebe538dcd0590d1415a2057da8df5e332d5b51886654a3d9a0c0181b92cd", - "manifest_sha256": "85ad37de2d7fc4b787e7eb0d5248eaebccf7ccaa04fd6f24e58637d37d6ef69f", - "qualification_sha256": "30c7b765ed3f8c9712897c0be1d84890921cec90f5c6da122e9108100c8e3044", - "rhs_scheme": "rademacher-hmac-pcg64-v1", - "schema_version": 1, - "selection": "spqr-full-rank-and-condition-supported-v1", - "split": "ranked", - "tolerance_policy": { - "condition_method": "spqr-guarded-onenormest-v1", - "condition_norm": "1", - "criterion": "minimum_safe_tolerance<=case_tolerance", - "id": "condition-aware-four-tier-v1", - "tiers": [ - 0.0001, - 0.001, - 0.01, - 0.04 - ] - } -} diff --git a/data/releases/README.md b/data/releases/README.md deleted file mode 100644 index ee092b4..0000000 --- a/data/releases/README.md +++ /dev/null @@ -1,61 +0,0 @@ -# Frozen validation and reference artifacts - -## NS cohort v2 validation - -`ns-cohort-v2-dev-validation.json` and -`ns-cohort-v2-ranked-validation.json` contain complete coverage evaluation -reports for the fixed public GMRES+AMG example. They bind the selected releases, -fresh prepared inputs, runtime, resource limits, and every individual outcome. -They are coverage examples; NS scoring does not use their elapsed times as a -speedup reference. Candidate failures and timeouts remain in each report. - -`ns-cohort-v2-preparation-reproduction.json` records exact matrix and complete -system identity agreement for all 49 fresh workloads under the minimum -supported NumPy 2.0.2 and SciPy 1.14.1 versions. Only source acquisition and -manufactured-input preparation were repeated for that check. - -`ns-cohort-v2-offline-reference-attempts.json` records the large-case offline -reference attempts, including failed or cancelled attempts on unchanged inputs. -These use separate qualification budgets and are not candidate timing results. - -See the [cohort review](../../docs/NS_COHORT_V2.md) for selection, qualification -and remaining representation gaps, and -[pilot validation](../../docs/PILOT_VALIDATION.md) for the measured results. - -## FLASH replay reference - -`flash-replay-dev-reference.json` and `flash-replay-ranked-reference.json` -contain the complete qualification reports and frozen timings for the pilot. -The fixed public solver, `submissions/gmres_amg.c`, passed all 96 development -cases and all 248 ranked cases, including 88 required correctness controls. -Each split compiled the same source once and executed each case exactly once. - -Use the artifact matching the candidate's split when running or scoring FLASH -replay. These timings apply to `modal-sandbox-pilot-cpu-v2`, with two CPUs, -4 GiB RAM, a 90-second case deadline, and at most 5,000 iterations. Creation, -setup, and solve time count toward the score. Transfer and archive loading do -not. Local timings cannot be compared against this reference. - -`cpu-runtime-v2.json` records the exact Linux compiler, HYPRE commit, native -artifacts, and runtime identity. Two independent clean Linux builds reproduced -this manifest. A changed runtime needs a newly qualified reference. - -| Identity | SHA-256 | -| --- | --- | -| Reference C source | `89f8ff13a1c21448692c1df9cfc363e180dc0838f031d531ca51573b43ea16b0` | -| Runtime manifest | `4b09e68ce2fc17261c1d776bb01d63ec40f8f92388b9aff95c046f7c1f8bb076` | -| Compiled reference executable | `4d6d411793e5d33d4fd24aa08301867a872473f8056a328ae7394a265c3a237f` | - -Each reference file carries its own canonical `calibration_sha256`; the scorer -registers that exact value for its release, runtime, and venue. The embedded -report also binds every prepared numerical input, accuracy gate, resource -limit, case role, weight, and timing. Hashes establish artifact identity; -official evaluation still requires trusted operator execution. - -The dataset and reference files are also available from the public -[Hugging Face dataset](https://huggingface.co/datasets/hgarud/LinearSolveBench). -Reference artifacts are pinned at commit -[`cbb06d2735b38d4078182d9db92fbc63790fe87b`](https://huggingface.co/datasets/hgarud/LinearSolveBench/tree/cbb06d2735b38d4078182d9db92fbc63790fe87b/references). -Numerical release manifests retain their original data commit. -See [operations](../../docs/OPERATIONS.md) for commands and -[pilot validation](../../docs/PILOT_VALIDATION.md) for the release evidence. diff --git a/data/releases/cpu-runtime-v2.json b/data/releases/cpu-runtime-v2.json deleted file mode 100644 index 3ae50c5..0000000 --- a/data/releases/cpu-runtime-v2.json +++ /dev/null @@ -1,42 +0,0 @@ -{ - "build_kind": "sequential-cpu-float64-int32-static", - "candidate_factory_export": "solver_create", - "files": { - "driver_object": { - "path": "trusted/driver.o", - "sha256": "475dff40d6b8378476332cacf5f906e5f1590371b495c075b34166a0596c5570" - }, - "hypre_library": { - "path": "lib/libHYPRE.a", - "sha256": "4e474d9dab14564d856e21193a4148e8a0417b8a9e991da3ae541da3ed5e8cca" - }, - "policy_header": { - "path": "include/nsl_hypre_solver.h", - "sha256": "04219a73af84e14349ee482c0bc486927f73c30c73a0cdf7aeef533763756e1e" - } - }, - "hypre_commit": "e0ce7986941ce27111587ee7ea635ef7ca2eca94", - "hypre_repository": "https://github.com/hypre-space/hypre.git", - "hypre_tree": "c985eacc03d590011ab7652fe6fc4e9b25f1064d", - "include_tree_sha256": "9cbae3f8087e1c5651269ecc95ef343b1210af84e3fbcdb82c379e1ff437aa4f", - "krylov_primitive_abi": "hypre-native-krylov-solver-v4", - "krylov_primitive_surface_sha256": "4524a309eb6503155829c08922e8e7b2db9ea1dc6cc998909a5c6e36808115e7", - "manifest_sha256": "4b09e68ce2fc17261c1d776bb01d63ec40f8f92388b9aff95c046f7c1f8bb076", - "schema_version": 1, - "tool_versions": { - "cc": "cc (Debian 14.2.0-19) 14.2.0", - 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"execution": "independent-case-once-v1", - "scoring": "ns-coverage-count-v1" - }, - "execution": { - "case_timeout_seconds": 90.0, - "cpus": 2, - "max_iterations": 5000, - "memory_bytes": 4294967296, - "repetitions": 1 - }, - "family": "ns-mesh-pde", - "kind": "linear-solver-bench-pilot-release", - "manifest_sha256": "a86a2d42765364bcadd48fdef22dc2f0900d04e98fe4fe49fe551b84d591ad52", - "release_id": "ns-mesh-cohort-v2", - "rhs": { - "draw_index": 0, - "key_id": "0341a5b546c640c3a44c65e88aa72ff7063a0684924daf0ff0128b6f6de86eb6", - "public_key_hex": null, - "scheme": "rademacher-hmac-sha256-v2" - }, - "schema_version": 2, - "split": "ranked", - "track": "coverage" - }, - "release_id": "ns-mesh-cohort-v2", - "release_manifest_sha256": "a86a2d42765364bcadd48fdef22dc2f0900d04e98fe4fe49fe551b84d591ad52", - "repetitions": 1, - "report_sha256": "9033cdf5169667905eed5d4711cabac4acc6e3e8a9a5241ff0fcd29cb09df07a", - "runtime_manifest_sha256": "4b09e68ce2fc17261c1d776bb01d63ec40f8f92388b9aff95c046f7c1f8bb076", - "schema_version": 2, - "solved_count": 14, - "source_sha256": "89f8ff13a1c21448692c1df9cfc363e180dc0838f031d531ca51573b43ea16b0", - "split": "ranked", - "track": "coverage", - "venue": { - "cpus": 2, - "id": "modal-sandbox-pilot-cpu-v2", - "limits_enforced": true, - "memory_bytes": 4294967296 - } -} diff --git a/docs/DATASET.md b/docs/DATASET.md deleted file mode 100644 index 3f142ee..0000000 --- a/docs/DATASET.md +++ /dev/null @@ -1,314 +0,0 @@ -# Dataset and provenance - -For a standalone overview of the current NS release, see the -[NS Mesh PDE dataset card](NS_DATASET_CARD.md), including access, schema, -curation, split membership, known gaps, and citation. - -## Pilot releases - -A release manifest fixes its family, track, development or ranked split, ordered -case inventory, scientific admission, numerical identities, and execution and -scoring contracts. Names alone do not establish scientific qualification. - -The replacement NS release `ns-mesh-cohort-v2` contains 49 qualified real -mesh/PDE operators. Both releases are frozen and their complete venue runs -are validated. - -| Manifest | Split | Cases | Provenance groups | Unknowns | -| --- | --- | ---: | ---: | ---: | -| `ns-mesh-cohort-v2-dev` | Development | 19 | 9 | 317–1,602,111 | -| `ns-mesh-cohort-v2-ranked` | Ranked | 30 | 15 | 240–1,489,752 | - -The [cohort review](NS_COHORT_V2.md) documents the source evidence, exhaustive -group selection, and explicit coverage limits. Every shared application and -spatial-method category, size band, structural regime, and method-by-size -combination with multiple supporting groups occurs in both splits. Unknown -spatial methods do not establish known-method coverage. Semiconductor devices -and boundary elements have one supporting group each and remain development-only. -The ranked 40,816-row finite-volume case lacks development support in that same -size-and-method combination; other single-group combinations are also reported. -This is conditional coverage of a declared corpus, not a guarantee of equal -solver difficulty or representation of all nonsymmetric PDE systems. - -Groups do not cross the split boundary. The largest operator has 23,487,281 -nonzeros. Both splits use the same numerical gates and execution contract: -90 seconds per case, two CPU cores, 4 GiB memory, a 5,000-iteration request, and -relative stopping tolerance `1e-12`. Qualification uses fresh targets under -the new release identity; neither acceptance gates nor targets may be changed -to obtain passing qualification. - -The complete cohort v2 venue runs give **11/19 development and 14/30 ranked** -for the public GMRES+AMG candidate. CoupCons3D and Goodwin_095 reached their -90-second deadlines; both failures were retained without retry. All 49 cases -ran once, with no infrastructure failures or crashes. The -[development](../data/releases/ns-cohort-v2-dev-validation.json) and -[ranked](../data/releases/ns-cohort-v2-ranked-validation.json) validation reports -record every execution. Transport's 1,602,111-row case passed in 5.81 seconds -within 4 GiB. Offline qualification may use more resources -than the candidate contract: large-case admission allows up to 32 GiB and one -hour without changing the candidate's 4 GiB/90-second budget. - -The previous `ns-mesh-pilot` remains a historical 43-case release with -11-development/32-ranked cases in 4/17 groups. All its workloads were qualified -and executed in Modal; the public GMRES+AMG solver passed 10/11 and 14/32, -without crashes, timeouts, infrastructure failures, or retries. Those results -belong to that frozen release, not cohort v2. Its -[historical split review](NS_SPLIT_REVIEW.md) explains why it was replaced. - -The word NS means nonsymmetric and is not -restricted to Navier–Stokes equations. The existing v1 catalogue's generic -nonsymmetry screen does not establish mesh/PDE provenance. - -The earlier `ns-mesh-dev-pilot` is retained separately as a two-case integration -check using `DRIVCAV/cavity01` (317 unknowns) and `FEMLAB/poisson2D` (367 unknowns). -It is not the main pilot's development or ranked split. - -Preparation obtains original archives from the -[SuiteSparse Matrix Collection](https://sparse.tamu.edu/), checking both archive -and canonical matrix hashes. Each case preserves its public matrix identifier, -source citation, and scientific admission statement. Sparse storage is -canonicalized by expanding declared symmetry, summing duplicate coordinates, -removing exact zeros, and sorting each row's `(column index, value)` pairs. -Sorting storage pairs does not permute matrix columns or change the operator. -Lossy numeric conversions, nonfinite entries, and unsupported dimensions are -rejected. No scaling, transpose, shift, or variable reordering is introduced. - -Each matrix receives one deterministic Rademacher target with entries ±1 and -exactly unit RMS. The versioned HMAC recipe separates draws by release, family, -canonical matrix, RHS kind, and draw index. Development keys are public; ranked -keys remain with the operator. The target and `b = fl(A x_target)` are frozen -in trusted prepared archives. A new draw changes the numerical system identity -and requires new qualification. - -NS qualification supports three kinds of evidence, each tied to the exact -stored system and requiring a tenfold margin against the four acceptance gates: - -- **Refined sparse LU.** An independent float64 factorization solution - receives exactly one correction using a higher-precision residual and the - same factors. Extended precision is used when available, otherwise 80-digit - decimal arithmetic. The final metrics and RHS formation audit are empirical - numerical evidence, not verified forward-error bounds. -- **Refined iterative reference.** The admitted Transport workload uses a fixed - PyAMG/GMRES method with an initial relative tolerance of `1e-13` and two - higher-precision residual corrections solved to relative tolerance `1e-2`. - Absolute tolerance remains zero. Metrics use the original matrix, RHS, and - target; the four final gates remain unchanged. This is empirical feasibility - evidence, not a verified inverse or forward bound. A fixed equilibrated - GMRES/ILU reference is also supported, with its own versioned configuration. -- **Strict row-dominance certificate.** Exact integer accumulation of binary64 - coefficients establishes every row's dominance margin and the exact RHS - formation residual. Their ratio certifies the exact stored-system solution's - relative L2 and Linf distance from the manufactured target. Separately, - binary64 verifier metrics at the target are measured as a feasible witness; - they are not claimed to be interval bounds or an independent factorization. - The certified forward bound must also satisfy the tenfold margin. - -The historical 43-case release used 41 refined sparse-LU witnesses and two -dominance certificates. Cohort v2 has 46 refined-LU witnesses, one PyAMG/GMRES -v2 witness, and two dominance certificates. The -[offline attempt history](../data/releases/ns-cohort-v2-offline-reference-attempts.json) -preserves failed and stopped reference attempts on the same fixed inputs. -The original unrefined LU method remains supported for older frozen development -evidence. See -[the accuracy contract](SPEC.md#independent-accuracy-checks) for the common -acceptance gates and the distinction between targets and exact solutions. - -## FLASH replay downloads - -The publication destination is the public, ungated -[LinearSolveBench dataset on Hugging Face](https://huggingface.co/datasets/hgarud/LinearSolveBench). -The published inventory contains 344 admitted captured systems: 96 development -cases and 248 ranked cases, of which 88 are correctness controls. Manifests -`flash-replay-dev-pilot` and `flash-replay-ranked-pilot` share release identity -`flash-replay-pilot` and pin dataset commit -`3da5eda0ce3e85da1808d4b62d28d9111127e354`. Archive and canonical matrix/vector -hashes are frozen in each manifest. - -```bash -linear-solver-bench dataset prepare \ - --release flash-replay-dev-pilot --output data/prepared/flash-dev -``` - -The captures come from FLASH 4.8 AlWire, driven two-dimensional ZPinch, and -MagDiff test-problem variants, primarily during startup. There are 13 source -runs and 10 provenance groups. Dimensions range from 4,224 to 196,608 and -nonzero counts from 16,420 to 2,153,480. The catalogue documents the limited -near-duplicate audit: it compares identical canonical sparsity patterns and -does not claim permutation invariance. - -Every case is a standalone ZIP archive with exactly: - -```text -matrix.npz # float64 CSR, readable by scipy.sparse.load_npz -b.npy # captured float64 right-hand side -x0.npy # captured float64 initial guess -case.json # public identity, provenance grouping, tolerance, and hashes -``` - -Downloading one case requires neither the benchmark package nor FLASH. The -catalogue maps public case IDs to archive paths, byte sizes, SHA-256 hashes, -dimensions, source-run and provenance-group IDs, time steps, and within-step -solve positions. A release assigns scored/control roles and freezes aggregation -weights. Additional Matrix Market exports and bulk bundles are outside the -pilot format. - -The captured matrix, RHS, initial guess, and relative tolerance are preserved. -Cases requiring nonzero absolute tolerance are rejected. This four-file pilot -format contains no reference solution. The numerical verifier can compute -diagnostics against a separately supplied numerical reference, without treating -it as exact truth. -Captured and offline numerical solutions establish case feasibility. The -fixed public reference `submissions/gmres_amg.c` additionally passes all 344 -cases in the official Modal venue, including every correctness control. Its -complete reports and single-execution timings are frozen in the -[development](../data/releases/flash-replay-dev-reference.json) and -[ranked](../data/releases/flash-replay-ranked-reference.json) calibration -artifacts. Candidates reuse the artifact for their split and must match its -runtime and venue. Local timings are development results. -The same reference artifacts are downloadable from the -[frozen Hugging Face reference release](https://huggingface.co/datasets/hgarud/LinearSolveBench/tree/cbb06d2735b38d4078182d9db92fbc63790fe87b/references). -This reference publication has its own immutable commit; numerical case -downloads remain pinned to the original data commit above. - -Both development and ranked numerical inputs are public. Related source runs, -refinements, exact duplicates, and documented near-duplicate relationships must -remain within the same split. Ranked is an evaluation designation, not a claim -that those input matrices are hidden. Replay evaluates individual captured -solves and does not claim complete-trajectory coverage or rerun the simulation. - -## Preparation and cache - -```bash -linear-solver-bench dataset prepare \ - --release ns-mesh-cohort-v2-dev --output data/prepared/ns-dev -linear-solver-bench dataset prepare \ - --release ns-mesh-cohort-v2-dev --offline --output data/prepared/ns-dev-offline -``` - -Preparation validates source identity, safely decodes each source archive, and -writes a trusted schema-v2 prepared archive per case. NS archives declare a -manufactured reference; FLASH pilot archives declare no reference. Candidate -input payloads contain only numerical solve inputs. Frozen NS qualification is -reused after verifying its exact numerical identity, so ordinary preparation -does not repeat offline factorizations. - -All 49 newly generated cohort v2 numerical systems reproduced exactly under -NumPy 2.0.2 and SciPy 1.14.1 on Python 3.12.11, macOS arm64. The -[preparation reproduction receipt](../data/releases/ns-cohort-v2-preparation-reproduction.json) -records matching canonical-matrix and numerical-system hashes for every case. -This is a preparation reproducibility check, independent of solver accuracy or -candidate venue results. - -Downloads use a content-addressed cache with bounded sizes, resumable transfer, -and hash validation before reuse. `--offline` uses verified cached archives and -fails clearly for missing or corrupt data. Set `--cache` or -`LINEAR_SOLVER_BENCH_CACHE` to choose writable storage. Installed package -resources are read-only inputs and need no checkout for the Python CLI. - -`--case CASE_ID` may select a case for a development check. A subset is marked -incomplete and cannot receive a complete-split score. Local and trusted Modal -evaluation load prepared cases one at a time to limit evaluator memory. - -## Existing SuiteSparse v1 dataset - -The following inventory and qualification procedure describe the original v1 -benchmark, not the NS mesh pilot. Its manifests and archive identities remain -supported without relabeling their scientific scope. - -### Source snapshot - -The source catalogue is the SuiteSparse Matrix Collection -[`ss_index.mat`](https://sparse.tamu.edu/files/ss_index.mat); field definitions -are documented on the collection's -[statistics page](https://sparse.tamu.edu/statistics). The frozen snapshot has: - -- `LastRevisionDate`: `31-Oct-2023 18:12:37` -- SHA-256: `f040a883a523c4621c5986aa66c39eeaa21bcae79c45596e953e7ea5e7ccbdb4` - -The complete metadata screen is frozen in `data/catalogue.json`. All filters -are cumulative: - -```text -RBtype == "rua" -nrows == ncols -0 <= numerical_symmetry < 0.99 -isGraph == 0 -sprank == nrows -500 <= nrows <= 1,000,000 -nnz <= 20,000,000 -3 <= nnz / nrows <= 500 -nnzdiag / nrows >= 0.50 -1 <= nblocks <= 0.25 * nrows -amd_vnz >= 0 -amd_rnz >= 0 -amd_vnz + amd_rnz <= 100,000,000 -``` - -The result contains 350 matrices. `RBtype == "rua"` means real-valued, -unsymmetric, assembled; the explicit square check remains in the frozen filter -for readability and replay safety. - -### Numerical qualification - -`data/qualification.json` freezes numerical rank and condition evidence for all -350 matrices. Every matrix is processed by the same method. SuiteSparseQR uses -an explicit cutoff of -`40 * n * float64_epsilon * max_column_2_norm(A)` and reports 213 numerically -full-rank matrices and 137 rank-deficient matrices. For each full-rank matrix, -three deterministic `onenormest` runs (`t=4`, `itmax=10`) reuse the QR factor's -ordinary and transpose solves to estimate `norm(A^-1, 1)`. The largest estimate -is multiplied by a guard factor of 3 and then by `norm(A, 1)` to produce the -frozen condition estimate. - -The ranked v1 set contains the 203 full-rank cases supported by at least one -accuracy tier. Each case receives the smallest tolerance in `1e-4`, `1e-3`, -`1e-2`, and `4e-2` for which: - -```text -100 * estimated_condition * float64_epsilon <= 5 * case_tolerance -``` - -The tier counts are 183, 9, 7, and 4 respectively. Ten other full-rank matrices -exceed the `4e-2` tier's condition ceiling and are excluded from v1. The -evaluator continues to compute actual relative forward error in the 2-norm; -it also computes relative infinity-norm forward error and requires both gates -to pass. The 1-norm condition estimate assigns the tolerance but does not -replace either direct check or claim to upper-bound the 2-norm condition -number. - -`data/qualification-inputs-v1.json` freezes every downloaded archive identity, -and `qualification_modal.py` reproduces the qualification campaign. The -content hash of the resulting evidence is bound into both dataset manifests. -The runner first fills a hash-addressed Modal volume over authenticated HTTPS, -then performs all QR factorizations and condition estimates in network-disabled -workers. Qualification may use more memory and CPU than the solver-evaluation -venue because it is offline dataset preparation, not measured candidate work. - -```bash -modal run qualification_modal.py \ - --output data/qualification-replay.json -``` - -### Matrix transformation - -Preparation downloads `https://sparse.tamu.edu/MM/{Group}/{Name}.tar.gz`, -verifies the frozen archive digest, safely extracts the Matrix Market member, -expands declared symmetry, converts to float64 CSR, sums duplicate entries, -removes exact zeros, and sorts column indices. It performs no reordering, -scaling, or transposition. - -Every prepared case is content-addressed. Ranked right-hand sides are derived -only during trusted preparation from an operator-held key. The key and manufactured -target are never copied into the candidate sandbox workspace. - -## Attribution - -SuiteSparse matrices are licensed CC BY 4.0. Redistributions must preserve the -original Matrix Market metadata and matrix-specific citation instructions, -identify the canonicalization and manufactured-right-hand-side modifications, -and cite: - -- Timothy A. Davis and Yifan Hu, *The University of Florida Sparse Matrix - Collection*, ACM TOMS 38(1), 2011, DOI 10.1145/2049662.2049663. -- Scott P. Kolodziej et al., *The SuiteSparse Matrix Collection Website - Interface*, JOSS 4(35), 2019, DOI 10.21105/joss.01244. diff --git a/docs/FAMILY_SUPPORT_PLAN.md b/docs/FAMILY_SUPPORT_PLAN.md deleted file mode 100644 index 5ce6c20..0000000 --- a/docs/FAMILY_SUPPORT_PLAN.md +++ /dev/null @@ -1,469 +0,0 @@ -# First pilot: NS mesh coverage and FLASH replay - -Status: family implementation, numerical qualification, and complete venue -validation are complete, updated 13 September 2026. -NS cohort v2 freezes 49 operators in 19-development/30-ranked splits, selected -by a documented matrix-only policy with disjoint provenance groups. The -[cohort review](NS_COHORT_V2.md) records the sources, hard coverage criteria, -selection, and remaining single-source joint gaps. All 49 fresh workloads are -qualified; complete venue runs give 11/19 development and 14/30 ranked passes, -with two preserved timeouts and no retries. The historical 43-case -NS release remains reproducible. FLASH has 344 publicly downloadable captures -and a fixed public reference that passes every case in the official venue; -its release, runtime, and replay calibration identities are frozen. Future -tracks remain separate work. - -## Implementation snapshot - -| Area | Verified pilot evidence | Requirement for later changes | -| --- | --- | --- | -| Family and execution contracts | Two explicit pairs; `ns_mesh_pde` alias; schema-v2 identities; one execution per case; registered split digests and fixed venue | Version changed inputs, contracts, and venues | -| Numerical verification | Fixed NS gates, FLASH residual/backward gates, explicit reference semantics, stable ratios, JSON-safe diagnostics, v1 compatibility | Retain qualification and identity checks for later releases | -| NS coverage | Cohort v2 contains 49 qualified cases in 19/30 splits; complete venue runs give 11/19 and 14/30, retaining both timeouts | Retain explicit singleton joint limitations and revalidate changed releases | -| FLASH inputs | 344 standalone archives published on Hugging Face; commit-pinned manifests; captured arrays verified; fresh installed public download succeeds | Preserve immutable bytes and provenance split boundaries | -| FLASH reference and scores | Fixed public `gmres_amg.c` passes all 344 Modal cases; registered single-execution timings and weighted scoring | Requalify when reference, inputs, runtime, or venue changes | -| Execution and distribution | Full Modal split runs; hard resource limits; fresh wheel/sdist builds, isolated installed resources, downloads, offline reuse, and native compilation | Repeat package/content and venue checks for each release | - -The replacement `ns-mesh-cohort-v2` selects 49 matrices in 24 groups, with 19 -development cases in nine groups and 30 ranked cases in 15 groups. Dimensions -range from 240 to 1,602,111. Shared known application, spatial-method, size, -structural, and multiply-supported method-by-size regimes occur on both sides. -Semiconductor devices and boundary elements are development-only because each -has one source group. The ranked medium-sized finite-volume case has no -development case in that same joint regime; this and other singleton joint -limitations remain explicit. These facts establish the declared coverage -policy, not an exhaustive or universally representative scientific inventory. - -The historical `ns-mesh-pilot` contains 43 matrices in 21 groups with 11/32 -cases. Its qualification uses one fixed higher-precision refinement after -sparse LU for 41 cases and exact dominance certificates for two. Every frozen -workload meets the unchanged tenfold margins. Cohort v2 independently meets the -same margins on fresh targets, using 46 refined-LU witnesses, one PyAMG/GMRES -witness with inexact refinement, and two exact dominance certificates. The earlier two-case -`ns-mesh-dev-pilot` remains an integration check; the generic v1 inventory is a -separate benchmark. - -The complete cohort v2 Modal check executed all 49 cases exactly once. The public -GMRES+AMG candidate passes 11/19 development and 14/30 ranked cases. CoupCons3D -and Goodwin_095 reach the 90-second limit and remain in the results; there are -no crashes, infrastructure failures, or retries. Transport's 1,602,111-row -system passes in 5.81 seconds within 4 GiB. - -The historical 43-case NS Modal check completed every case once without crashes, timeouts, -infrastructure failures, or retries. Both matrices with over a million unknowns -passed within 4 GiB. A partial solved-case count is a valid coverage result; -NS coverage does not require discovery of an all-case performance reference. - -The published FLASH splits are 96 development scored cases and 248 ranked cases -(160 scored, 88 correctness controls). Public inputs cover 13 source runs in 10 -provenance groups. Captured solutions and stored independent numerical references -pass the public accuracy gates for all 344 cases. The fixed public reference -also passes all 344 in the official Modal venue. Complete reports and reference -timings are frozen in the [release artifacts](../data/releases/README.md). -The ungated -[Hugging Face dataset](https://huggingface.co/datasets/hgarud/LinearSolveBench) -is pinned by both manifests to commit -`3da5eda0ce3e85da1808d4b62d28d9111127e354`. - -## Pilot scope - -The implementation supports exactly two family/track pairs through one -independent-case evaluator: - -| Family | Track | Primary result | Timing reference | -| --- | --- | --- | --- | -| `ns-mesh-pde` | `coverage` | Number of cases solved accurately within fixed budgets | Not required | -| `magnetic_diffusion_flash` | `replay` | Speedup on individual captured solves | One frozen reference qualified on the entire replay split | - -`ns_mesh_pde` is accepted as a CLI alias. Manifests and reports use the public -ID `ns-mesh-pde`. - -SPD mesh, NS mesh performance, and FLASH trajectory are later phases. Their -agreed designs remain in [FAMILY_SUPPORT_ROADMAP.md](FAMILY_SUPPORT_ROADMAP.md). -They do not add pilot implementation or acceptance requirements. In particular, -the pilot needs no SPD qualification pipeline, trajectory datasets or driver, -persistent solver ABI, retained-matrix API, or NS performance activation flow. -The CLI rejects unsupported family/track pairs. - -Keep family, track, release, accuracy contract, and venue identities explicit -so later additions can use new versioned contracts. Implement only the two -supported pairs; do not build a generic plugin system or empty future adapters. -Preserve the existing public v1 manifests and replay behavior under their -original identity. Give the pilot a distinct release and benchmark identity. - -## Shared execution and accuracy - -Both pilot tracks fit the existing `solver_create` interface and numerical input -protocol. Compile one C submission once, then evaluate each case exactly once -in one fresh native process. Solver objects, workspace, and preconditioners are -recreated for every case. No state carries between cases. Freeze -`repetitions = 1` in the pilot execution contract to keep evaluation costs down; -do not add timed warm-ups, hidden repetitions, or candidate-failure retries. - -The timed boundary remains factory creation, candidate setup/preconditioner -construction, and solve. Input decoding and HYPRE object construction precede -timing. Candidate destruction, output extraction, mutation checks, and trusted -verification follow timing. Report phase timings and the measured complete -native solve time from that single execution. Count a case as passing when -that execution satisfies every required accuracy and execution check. - -Use the existing compiler capability restrictions, pinned sequential float64 -HYPRE runtime, and candidate isolation. Only numerical `A,b,x0,tau` inputs reach -candidate code. Keep source names, public case IDs, provenance, reference -vectors, and preparation keys outside the candidate boundary. - -Preserve original operators after documented storage canonicalization: expand -declared Matrix Market symmetry, sum duplicates, remove exact zeros, and sort -each row's `(column index, value)` storage pairs. This does not permute matrix -columns or variables. Do not silently scale, transpose, reorder, shift, or replace the -operator with another problem during preparation. Verification uses the original -serialized matrix and RHS, regardless of the candidate's internal algorithm. - -| Input or required metric | NS mesh coverage | FLASH replay | -| --- | --- | --- | -| Scientific admission | Real, materially nonsymmetric original PDE discretization operators with verified mesh/PDE provenance and numerical qualification | Valid captured scalar magnetic-diffusion systems with qualified capture evidence | -| RHS | One Rademacher manufactured target per matrix at unit RMS; `b = fl(A x_target)` | Preserve captured `b` | -| Initial guess | Zero | Preserve captured `x0` | -| Relative residual `\|\|b-Ax\|\|2 / \|\|b\|\|2` | Diagnostic | `<= tau * (1 + 1e-6)` | -| Normwise backward error | `<= 1e-10` | `<= max(tau, 20 eps)` | -| Componentwise backward error | `<= 1e-8` | Diagnostic | -| Relative forward error in L2 and Linf | Each `<= 1e-5` against the manufactured target | Numerical-reference diagnostics only | - -Use the normwise/componentwise definitions in [SPEC.md](SPEC.md). Both tracks -also require valid shape, finite output, successful native status, unchanged -inputs, and compliance with time, memory, and iteration limits. Invalid required -metrics fail; unavailable or infinite diagnostics use JSON `null` with a reason. - -`ns` means nonsymmetric, not exclusively Navier–Stokes. The current generic -SuiteSparse v1 screen does not establish mesh/PDE provenance and must not be -relabelled as this family. Validate scientific source evidence and numerical -feasibility independently; solver failure alone is not grounds for excluding -an otherwise qualified NS coverage case. - -Distinguish manufactured targets from numerical reference solutions. Because -RHS formation rounds in float64, the manufactured target is not necessarily the -exact solution of the stored `A,b`; qualify formation uncertainty relative to -the forward-error limits. NS forward checks must never be disabled by missing -truth. FLASH numerical references, when available, support diagnostics without -becoming exact-solution claims or required forward-error gates. - -The NS acceptance limits above are fixed benchmark requirements shared by every -case. Numerical qualification checks their feasibility for each frozen -`A,b,x_target`; it does not calculate a different tolerance for each matrix. -Require offline qualification evidence at one tenth of every required error -limit. Record whether that evidence consists of independently computed -reference-solve metrics or conservative mathematical bounds. A successful -float64 reference solve establishes empirical feasibility; it does not certify -the distance between the exact stored-system solution and the manufactured -target. Audit sensitive cases with higher-precision residuals/refinement or -verified error bounds before release, documenting the remaining uncertainty. -Condition estimates are diagnostics, not certified upper bounds or formulas for -the acceptance limits. This preparation adds no candidate evaluation processes. - -Implemented qualification methods distinguish their evidence explicitly. Sparse -LU can perform one fixed higher-precision residual correction using the same -factors; its measured result remains empirical. A fixed equilibrated GMRES/ILU -reference with exactly two higher-precision corrections is another empirical -feasibility method, evaluated against the original stored system. Large-case -offline qualification may use 32 GiB and one hour; candidate limits remain -4 GiB and 90 seconds. An alternate strict row-diagonal -dominance path accumulates coefficients and RHS formation residuals exactly as -binary64 integers and certifies a bound on stored-system forward discrepancy. -It separately checks measured target-witness metrics at the tenfold limits and -also requires the certified forward bound to meet that margin. It does not claim -that those measured floating-point metrics are themselves interval bounds. - -The admitted Transport witness uses a versioned PyAMG/GMRES reference with -initial relative tolerance `1e-13` and two correction solves at `1e-2`, with -zero absolute tolerance. The four final qualification gates are unchanged. -The [attempt history](../data/releases/ns-cohort-v2-offline-reference-attempts.json) -preserves earlier failures and the stopped v1 attempt; it does not establish -a Transport-specific floating-point accuracy floor. - -For FLASH, retain raw componentwise diagnostics even when weak rows make them -large; do not introduce row floors or reject on that diagnostic. Define a zero -RHS residual ratio as zero only for an exactly zero residual, otherwise infinity. -The pilot supports zero absolute tolerance; reject incompatible captures rather -than silently discarding their requested absolute tolerance. - -Keep the frozen NS acceptance limits separate from the solver's stopping -request. Start NS feasibility runs with a supplied relative tolerance of -`1e-12` and 5,000 iterations; freeze those execution settings with the release. -Neither that stopping request nor the diagnostic relative-residual guidance of -`1e-8` replaces or changes the backward/forward acceptance limits. FLASH receives -its captured relative tolerance and a separately published iteration cap. Use -numerically stable norm evaluation and detect overflowing residuals or -denominators before accepting any metric. - -## Track scores and the replay reference - -NS coverage ranks by raw solved-case count: - -```text -passed_i = the single execution of case i passes -solved_count = sum_i passed_i -coverage_fraction = solved_count / expected_case_count -ranking_key = -solved_count -``` - -Equal solved counts tie; timing is diagnostic and does not break ties. Retain -outcomes for every expected case. A candidate failure on one case does not stop -other cases from being evaluated. Provenance-group breakdowns can accompany the -raw score but do not change its meaning. This path requires no timing reference -or calibration artifact, although numerical qualification remains mandatory. - -FLASH replay measures each captured solve independently. Where one physical -time step contains multiple magnetic solves, expose them as distinct cases -with public within-step ordering. The pilot does not reconstruct or score -complete trajectories. - -Qualify one public reference solver/configuration on every case in the frozen -replay split, including correctness controls, under the same runtime, accuracy, -single-execution lifecycle, and venue as candidates. A stock GMRES+AMG -configuration requires measured passing evidence. The current public -`submissions/gmres_amg.c` implementation passes the full 344-case inventory -in the official Modal venue, including every correctness control. -Publish its source/configuration, complete qualification report, and timing -artifact. Reference qualification runs each case once and freezes that measured -time. Reuse the frozen reference timings for subsequent submissions rather than -rerunning the reference alongside each candidate. No private runtime or source -dependencies may be needed to reproduce it. - -For a scored case, define `speedup_i = reference_time_i / candidate_time_i`, -using each solver's single measured execution time. -Aggregate with a weighted geometric mean, assigning equal mass to provenance -groups, equal source runs within each group, and equal scored cases within each -run. Freeze weights before timing or candidate evaluation. A value above one -means faster than the reference. - -Require all scored cases and mandatory correctness controls to pass their -single candidate execution for an official aggregate speedup. A failure yields -`eligible = false` and `speedup = null`, while preserving per-case diagnostics. -Run correctness-only controls but exclude their timings from the speed mean. -Never average only successful cases, use a budget as a substitute reference, -or substitute per-case winners from a portfolio for the declared reference. - -Bind calibration and reports to the exact family/track, ordered cases, roles and -weights, prepared numerical inputs, accuracy/execution/scoring contracts, -reference identity, compiler/runtime, execution count, and venue. Reject incomplete reports or -mismatches. Infrastructure failure invalidates a run under a fixed retry policy; -it does not silently shrink the scored set. Keep coverage and replay leaderboards -separate, with no combined score. - -## Implemented modules - -The pilot extends the existing evaluator through a fixed registry. The original -v1 dataset and scoring paths retain their identities and three-repetition -behavior. No generic plugin framework or future-family runtime is introduced. - -| Area | Implemented pilot behavior | -| --- | --- | -| `families.py`, `manifests.py` | Two supported pairs, strict identities/settings, source and qualification validation, published-release digest checks | -| `pilot_dataset.py`, `sources/`, `workloads.py` | Public SuiteSparse and FLASH loaders, deterministic manufactured vectors, qualification, resumable verified downloads and offline cache reuse | -| `models.py`, `archive.py` | Required manufactured targets, optional numerical references, explicit schema-v2 archives, legacy decoding and byte compatibility | -| `accuracy.py`, `verify.py` | Shared stable metrics with separate NS/FLASH gates and diagnostic handling | -| `pilot_runner.py`, `runner.py`, `modal_app.py` | One execution per case, shared verification/report construction, release budgets and streamed numerical inputs | -| `pilot_scoring.py` | Reference-free coverage and all-pass qualified-reference replay speedup with frozen group/run/case weights | -| `cli.py`, `paths.py`, packaging | Release selection, automatic prepared-schema dispatch, installed resources, public cache locations and CLI documentation | -| `submissions/gmres_amg.c` | Fixed public reference passes all 344 FLASH cases in the official venue; timings are frozen for reuse | - -Load prepared cases one at a time in local and trusted operator execution. -Retain a small case specification, family/track contract, optional reference -solver identity, and release record. Do not add `TrajectorySpec`, trajectory -callbacks, or placeholder SPD runtime types to pilot code. Retain the current -single-case C ABI and force-included header; change the native driver only if -pilot validation exposes a necessary correctness or settings-propagation fix. - -Centralize effective settings so manifest values, Python execution, and native -limits cannot disagree. A user-authored self-consistent manifest is insufficient -for official evaluation: bind it to a trusted published pilot release. Keep -acquisition and qualification dependencies out of the candidate runtime. - -## Public data and publication boundary - -Freeze a declared pilot inventory and provenance-based development/ranked -splits. Use tiny synthetic fixtures and a small real case from each family for -the first integration checks; then validate all cases in the declared pilot. -Exact pilot counts are chosen after asset inventory and qualification, not -implicitly inherited from another corpus or chosen from candidate success. -Document the pilot's limited scope rather than claiming full-family coverage. - -For NS mesh, download original SuiteSparse archives by public identifiers and -URLs, verify archive and canonical-matrix checksums, preserve source metadata, -and generate the declared manufactured workloads using a versioned recipe. -Freeze numerical bytes and qualification evidence. Domain-separate new draws by -release, family, matrix, RHS kind, and draw index. Keep operator-held ranked -keys and reference vectors outside candidate-visible storage. New draws require -new prepared identities and numerical qualification. - -For FLASH replay, publish a standalone numerical dataset in a public, ungated -Hugging Face dataset repository with commit-pinned downloads. Hugging Face is -the only required publication host; Zenodo can be added later if needed. -Publish a catalogue mapping stable public IDs -to URLs, byte sizes, SHA-256 hashes, matrix dimensions, tolerances, scientific -provenance, roles, and citations. Provide one downloadable archive per case: - -```text -matrix.npz # Canonical CSR, readable with scipy.sparse.load_npz -b.npy # Captured float64 RHS -x0.npy # Captured float64 initial guess -case.json # Public metadata, tolerance, identity, and hashes -``` - -These per-case archives are the required public format for FLASH replay; -additional Matrix Market exports and bulk bundles are outside the pilot scope. -Users must be able to download a single case directly without installing the -benchmark or obtaining FLASH. Preparation -resolves IDs, verifies downloads and canonical arrays, extracts safely, and -caches by content hash. Support download resumption and offline cache reuse. -[Pinned Hub downloads](https://huggingface.co/docs/huggingface_hub/main/guides/download). - -Publish FLASH development and ranked numerical inputs; ranked denotes the -evaluation split, not secret data. Keep related source runs, time steps, -refinements, exact duplicates, and documented near-duplicates in the same split. -Preserve public source-run grouping so a future trajectory release can respect -these exposure boundaries, but do not require full-trajectory capture, -publication, or execution for the replay pilot. Publicly downloadable inputs -cannot be claimed to be hidden merely because reference vectors are private. - -All 49 selected NS sources have verified original public archives and reproduce -the same numerical systems with the minimum supported NumPy/SciPy versions. -Fresh cohort v2 qualification has passed all 49 fixed workloads. -The historical 43-case release has complete -qualification and preparation evidence. FLASH assets are publicly -downloadable with immutable commit identities and frozen manifests. Both pilot -tracks have working public data paths and complete venue validation; FLASH -also has a qualified reference in the declared comparison venue. - -Construct new public manifests and qualification reports through explicit -allowlisted exports. Exclude private package imports, repository paths/names, -account/deployment/storage identifiers, experiment receipts, prompts, model -outputs, and private source hashes. Keep conversion tools and source-to-public -mappings outside the public repository. Audit the source tree, Git history, -wheel/sdist, container context, embedded archive metadata, and documentation. -Public CI can validate allowed schemas and imports; literal private identifiers -used in release scans must remain in private tooling. - -Preserve legitimate upstream citations and notices. SuiteSparse matrices are -CC BY 4.0, with matrix metadata retained and modifications identified. -[SuiteSparse licensing guidance](https://sparse.tamu.edu/about). -Establish redistribution terms for captured FLASH numerical data and metadata -before uploading. Do not bundle FLASH source or assume that its capture patches -are redistributable. [FLASH license agreement](https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node3.html). - -## Pilot sequence and acceptance criteria - -| Step | Status | Completed evidence | -| --- | --- | --- | -| 1. Freeze contracts and inventory | Implemented; replacement NS selection frozen | Two pairs and public schemas are explicit. NS cohort v2 selects 49 cases in disjoint 19/30 splits. FLASH has 344 cases in published 96/248 splits. | -| 2. Deliver NS coverage end to end | Implemented; 49/49 fresh workloads qualified | Cohort v2 retains every original operator and adds six. All 49 meet the unchanged tenfold margins and reproduce with minimum supported dependencies. | -| 3. Deliver FLASH replay end to end | Implemented; public assets published | Captured arrays round-trip unchanged across 344 archives; both manifests pin an immutable Hugging Face commit. | -| 4. Complete replay qualification and scoring | Complete | One fixed public solver passes every case and control in both Modal splits; complete reports and registered timings are frozen. | -| 5. Validate corpus and venue | Complete | Cohort v2 gives 11/19 development and 14/30 ranked with two preserved 90-second timeouts, no retries, and no crashes or infrastructure failures. FLASH and historical NS runs remain complete. | -| 6. Validate distribution and publish | Implemented and checked | Fresh wheel/sdist builds, isolated installation, installed trusted manifests, public downloads, offline reuse, and native compilation pass; public data and reference artifacts have frozen identities. | - -Begin with NS coverage, then FLASH replay. Keep numerical-reference quality -separate from performance-reference qualification. Do not wait for an NS -all-case solver and do not implement its performance track during this pilot. -The replay reference qualification path should be small and reusable later, -without adding an unused NS activation workflow now. - -Test the existing venue against the declared pilot before retaining or changing -its CPU, memory, and deadline settings. Measure the largest admitted cases as -well as small controls. Freeze one resource contract for every compared -candidate/reference pair; local timings remain diagnostic. Do not expand pilot -work into full-corpus or trajectory capacity studies. Changes to official -resources or numerical inputs require a new calibration identity. - -Update `README.md`, `SPEC.md`, `DATASET.md`, `OPERATIONS.md`, `benchmark.toml`, -CLI help, packaging, and examples together. Provide one versioned AI task -statement per pilot pair: available development data, permitted tools/libraries, -accuracy gates, C artifact contract, and run commands. Record model/system and -prompt versions, generation budget, attempt count, feedback access, and artifact -selection rule in experiment reports. Select final artifacts using development -feedback before ranked evaluation; model invocation adapters stay optional. - -Implemented NS development commands: - -```bash -linear-solver-bench dataset families -linear-solver-bench dataset prepare --release ns-mesh-cohort-v2-dev \ - --output data/prepared/ns-mesh-dev -linear-solver-bench run submissions/starter.c \ - --runtime build/runtime --cases data/prepared/ns-mesh-dev \ - --output results/ns-coverage.json -linear-solver-bench score results/ns-coverage.json -``` - -The same CLI accepts `flash-replay-dev-pilot` and `flash-replay-ranked-pilot` -by installed name or file path. Prepare the complete split and evaluate the -candidate using the bundled calibration for that split. Reuse those reference -times when scoring; reference regeneration is an optional operation for -development or a new release. Concrete operator commands are in -[OPERATIONS.md](OPERATIONS.md). No invented commit or placeholder release ID is -an official data source. - -Require the selected pair to match its verified release, prepared case set, -accuracy/scoring contracts, and reference artifact. CLI options cannot override -scientific gates or reinterpret another track's results. - -## Pilot verification and completion - -Implemented tests cover required/diagnostic metric separation, malformed and -rehashed input rejection, reference semantics, deterministic draws, resume/offline -cache behavior, one-case streaming and object release, failure continuation, -report/weight/reference identity binding, and Modal transport using a test -sandbox. Native development checks and public-array round trips complement -those tests. A mocked sandbox is not evidence of a full official venue run. - -Completed release validation includes: - -1. All 43 qualified `ns-mesh-pilot` workloads prepared from public sources and executed once - in the official venue. The public GMRES+AMG solver passes 10/11 development - and 14/32 ranked cases, with both large atmospheric matrices fitting 4 GiB. - These are historical release results. Cohort v2 has 49 freshly qualified - workloads and complete venue runs giving 11/19 development and 14/30 ranked, - with both 90-second timeouts retained and no retries. All 49 numerical - preparations reproduce exactly under NumPy 2.0.2/SciPy 1.14.1. See - [NS_COHORT_V2.md](NS_COHORT_V2.md) for the separate release evidence. -2. The fixed public replay reference passes all 96 development and all 248 - ranked cases, including controls. Complete reports, single-execution timings, - published split digests, and the runtime identity are frozen and registered. -3. Fresh wheel and source-distribution builds, isolated wheel installation, - all four installed release IDs, public NS/FLASH single-case downloads, - identical offline preparation, and installed native compilation pass. - Archive scans detect no private identifiers, credential patterns, prepared - data, or raw matrix payloads. See [PILOT_VALIDATION.md](PILOT_VALIDATION.md). - -The acceptance coverage to retain as implementation evolves is: - -- Numerical: hand-computed required gates; each NS backward/forward failure; - FLASH residual failures and non-rejecting diagnostics; nonzero captured warm - starts, zero RHS, rounding uncertainty, nonfinite values and overflow. -- Data: deterministic manufactured inputs, exact captured-array preservation, - missing NS targets, hash corruption, invalid CSR/dtypes/dimensions, unsafe - archive paths, duplicate case IDs, incompatible contracts and split overlap. -- Native: an actual verified solve from each pilot family, not just compilation; - exactly one fresh evaluation process per case; status, mutation, timeout, memory and invalid-output - failures; release iteration settings reaching the native execution. -- Scores: raw NS solved-count ties; no calibration needed for coverage; complete - replay reference qualification with one execution per case; speedups from - single measured times; controls, partial - solutions, missing cases and mismatched identities handled correctly. -- Distribution: documented downloads and direct numerical loading, offline - cache reuse, clean wheel/resource lookup, minimal container context, reference - isolation, and no dependency on private repositories or artifacts. - -Use small synthetic fixtures for offline CI and small real inputs for separate -integration checks. Run the full declared pilot in the official venue before -publishing reference timing claims. Do not add SPD, NS performance, or trajectory -execution tests to the pilot acceptance gate. - -Pilot completion means NS mesh coverage and FLASH replay can both be prepared -from documented public sources, evaluated with their correct numerical gates, -and reported reproducibly from a clean installation. Coverage ranks accurate -case counts; replay reports qualified-reference speedups. Implementation may -be reviewed before data publication, but a two-track public pilot release also -requires available numerical assets, established data-use terms, frozen -manifests/resources, and a passing replay reference. Future work remains in the -separate roadmap and does not block this milestone. diff --git a/docs/FAMILY_SUPPORT_ROADMAP.md b/docs/FAMILY_SUPPORT_ROADMAP.md deleted file mode 100644 index b2f9c8d..0000000 --- a/docs/FAMILY_SUPPORT_ROADMAP.md +++ /dev/null @@ -1,810 +0,0 @@ -# Matrix-family roadmap beyond the first pilot - -Status: future-track design roadmap, updated 13 September 2026. The active -implementation and validation evidence are recorded in -[FAMILY_SUPPORT_PLAN.md](FAMILY_SUPPORT_PLAN.md). NS coverage and FLASH replay -are implemented in the shared pilot evaluator. SPD, NS performance, and FLASH -trajectory remain unsupported; their design below is not a pilot launch gate. - -NS cohort v2 contains 49 qualified matrices in 19-development/30-ranked splits -under a frozen matrix-only policy, retaining all 43 original cases and adding six -source-reviewed operators. It satisfies the declared shared application, -spatial-method, size, structural, and multiply-supported method-by-size coverage -rules. Single-source joint gaps and development-only semiconductor and boundary -element categories remain explicit in the [cohort review](NS_COHORT_V2.md). -This is conditional coverage of a declared pool, not an exhaustive family claim. - -Fresh qualification has passed all 49 workloads: 46 refined-LU witnesses, one -PyAMG/GMRES witness with inexact refinement, and two strict row-dominance -certificates. Complete venue runs give 11/19 development and 14/30 ranked for -GMRES+AMG. All 49 cases ran exactly once, with two preserved 90-second timeouts, -no retries, and no crashes or infrastructure failures. The 1,602,111-row -Transport case passes in 5.81 seconds within 4 GiB. All 49 numerical preparations -reproduce with the minimum supported NumPy/SciPy versions. The historical -`ns-mesh-pilot` retains its original 11/32 split and 10/11, 14/32 venue results. - -FLASH has 344 published captures in 96-case development and 248-case ranked -splits, pinned to an immutable Hugging Face commit. Its fixed public reference -passes all 344 in the official Modal venue, with registered timings for both -splits. The pilot plan and [validation record](PILOT_VALIDATION.md) record -completed checks and each release's scope. - -Pilot cases and replay reference qualification each use exactly one fresh -native process per case. The original v1 benchmark retains three repetitions. -Any repetition/median proposals below for deferred tracks are provisional and -must be decided in those tracks' own versioned contracts. They do not change -implemented pilot execution. - -## Retained broader design - -Add `magnetic_diffusion_flash`, `ns-mesh-pde`, and `spd-mesh` as three explicit -dataset families served by one evaluator. Give each family a documented input -and accuracy contract, with separate evaluation tracks: - -| Family | Track | Primary measurement | Reference requirement | -| --- | --- | --- | --- | -| `ns-mesh-pde` | `coverage` | Number of cases solved accurately within fixed budgets | No performance reference required | -| `ns-mesh-pde` | `performance` | Speedup relative to one frozen solver that solves every case | Awaiting discovery and qualification of that solver | -| `magnetic_diffusion_flash` | `replay` | Speedup for individual captured time-step solves | A qualified reference under the replay lifecycle | -| `magnetic_diffusion_flash` | `trajectory` | Speedup over a complete trajectory | A qualified reference under the trajectory lifecycle | -| `spd-mesh` | Proposed independent-case design | Previously proposed accuracy and timing results | Qualify independently; no additional tracks requested | - -Share compiler restrictions, the HYPRE runtime, numerical metrics, and trusted -verification. Give each track its own scoring and solver-lifecycle contract. -In particular, trajectory execution needs persistent state within a trajectory, -while replay and NS mesh cases are independent solves. - -Introduce these contracts as a new benchmark version. Preserve the existing v1 -manifests and results; do not relabel its nonsymmetric SuiteSparse corpus as -`ns-mesh-pde`. Matrix family, dataset release, submission track, and execution -venue are separate concepts and need separate identities. - -The public implementation must stand alone. Its dependencies are public -libraries and independently published data artifacts. Public source files, -documentation, tests, manifests, packages, and container images must contain -only material intended for publication. - -## 1. Implemented foundation and remaining extensions - -The pilot now provides the shared pieces that later tracks should reuse: - -- Strict float64 CSR inputs, preserved initial guesses, native int32 bounds, - and separate manufactured/numerical/absent reference semantics. -- Restricted `solver_create` C interface, symbol auditing, pinned sequential - HYPRE, timing of creation/setup/solve, and trusted mutation and accuracy checks. -- Explicit family/track contracts, release/prepared/report hashes, one native - execution per pilot case, and shared local/Modal result construction. -- Public SuiteSparse and FLASH loaders, deterministic manufactured workloads, - resumable verified downloads, offline cache reuse, and case-by-case streaming. -- Reference-free NS coverage, qualified-reference FLASH replay scoring, package - resources, release-aware CLI commands, and per-track candidate documentation. - -| Area | Implemented pilot behavior | Remaining future work | -| --- | --- | --- | -| Registry and schemas | Two independent-case pairs with strict versioned release identities | Add only requested future tracks and their actual contracts | -| References and metrics | Required NS targets, optional FLASH references, separate required/diagnostic gates | SPD qualification and any independently justified new accuracy contracts | -| Dataset preparation | NS/FLASH acquisition, new workload identities, qualification and streaming | Broader NS scientific inventory, SPD inventory, and complete trajectory acquisition | -| Native execution | One fresh process per pilot case; release iteration requests and deadlines reach execution | Ordered persistent-state trajectory ABI, ownership, and cumulative budgets | -| Scoring | NS raw coverage and all-pass FLASH replay reference qualification | NS performance activation and complete-trajectory scoring/reference lifecycle | -| Distribution | Installed resources and wheel/native CI paths | Validate each additional track's public data, package, venue, and release | - -The original v1 SuiteSparse screen still has its own counts, digests, and -500–1,000,000 dimension bounds. Those filters do not define the new mesh -families. NS and SPD admission must rely on documented scientific provenance -and numerical qualification, including valid cases outside the old size range -when their released venue supports them. - -## 2. Family contracts - -Preserve the original operator after documented storage canonicalization: -expand declared Matrix Market symmetry, sum duplicates, remove exact zeros, -and sort `(column index, value)` pairs within each row. This canonicalizes -storage without permuting variables. Do not silently scale, transpose, shift, symmetrize, reorder, -or replace an operator with normal equations during dataset preparation. -Candidates may implement permitted mathematical transformations inside their -timed solve, but verification always uses the original serialized `A` and `b`. - -| Public family ID | Scientific scope | RHS and initial guess | Reference | -| --- | --- | --- | --- | -| `magnetic_diffusion_flash` | Captured scalar magnetic-diffusion systems from FLASH | Preserve captured `b`, `x0`, and requested tolerance | Independently qualified numerical solution for diagnostics; never label it exact | -| `ns-mesh-pde` | Real, materially nonsymmetric original PDE discretization operators with verified mesh/PDE provenance and numerical qualification | Initially one Rademacher manufactured solution per matrix, at unit RMS; `b = fl(A x_target)`, `x0 = 0` | Manufactured target, accompanied by RHS formation and reference-quality evidence | -| `spd-mesh` | Real, exactly symmetric positive-definite mesh operators; describe any initial scalar-PDE cohort restriction explicitly | Rademacher and uniform `[-1,1]` manufactured targets, one draw each; `b = fl(A x_target)`, `x0 = 0` | Manufactured target, accompanied by qualification evidence | - -`ns` means nonsymmetric, and does not restrict the family to Navier–Stokes. -An original PDE operator may come from a source eigenmode problem, with that -history disclosed and a fresh manufactured linear workload qualified separately; -this does not permit squaring, shifting, or otherwise replacing the operator. -SuiteSparse metadata is a discovery aid, not proof of mesh provenance, -nonsingularity, or positive definiteness. In particular, a positive diagonal -alone is not an SPD certificate. Require original-matrix symmetry checks and a -documented numerical positive-definiteness assessment, such as bounded sparse -Cholesky qualification, with failures and inconclusive cases recorded. - -Use the following acceptance rules as the starting scientific specification, -then verify their feasibility before freezing a public release: - -| Required quantity | `magnetic_diffusion_flash` | `ns-mesh-pde` | `spd-mesh` | -| --- | --- | --- | --- | -| Relative residual, `\|\|b-Ax\|\|2 / \|\|b\|\|2` | `<= tau * (1 + 1e-6)` | Diagnostic | `<= 1e-9` | -| Normwise backward error | `<= max(tau, 20 eps)` | `<= 1e-10` | `<= 1e-9` | -| Componentwise backward error | Diagnostic | `<= 1e-8` | `<= 1e-8` | -| Relative forward error in both L2 and Linf | Numerical-reference diagnostic | Each `<= 1e-5` | Each `<= 1e-6` | - -Use the existing definitions of normwise and componentwise backward error in -`SPEC.md`. All families require valid output shape, finite solution values, -successful native status, unchanged inputs, and compliance with execution -budgets. Pilot cases require their single execution to pass. A future track -must specify its own execution count; no additional repetitions are implied. - -For FLASH, small weak rows can make componentwise relative error large even -when residual accuracy is satisfactory. Preserve the diagnostic value without -adding row floors or turning it into an undocumented rejection gate. Numerical -reference disagreement is also diagnostic, rather than a forward-error -guarantee. State that limitation in the family documentation. - -For a zero RHS, define the relative residual as zero only when the residual is -exactly zero; otherwise it is infinity. The first release supports zero absolute -tolerance. Reject captures requiring a nonzero absolute tolerance until a -versioned transport and evaluator contract support it. Never silently discard -such a tolerance. - -Distinguish solver stopping guidance from acceptance criteria. Start mesh -feasibility runs with a solver tolerance of `1e-12`, with iteration caps of 5,000 -for NS mesh and 16,000 for SPD mesh. These are proposed execution settings to -freeze after venue validation, not formulas for the terminal gates. FLASH uses -the captured relative tolerance and a separately declared iteration budget. - -Compute norms with scaling where needed; detect overflowing residuals and -denominators rather than allowing an infinite denominator to produce a false -zero error. Serialize unavailable or unbounded diagnostics as JSON `null` with -an explicit reason. Invalid required metrics always fail. - -## 3. A small shared implementation - -Prefer a fixed registry and ordinary typed records over a plugin framework or -three copies of the evaluator. A family should select an input recipe, an -accuracy contract, and public documentation. A track selects the execution -lifecycle, scoring contract, and reference requirements. Neither should own a -duplicate compiler, sandbox implementation, or report serializer. - -Implemented modules under `src/linear_solver_bench/` should be extended where -the new behavior fits; trajectory execution remains a separate future module: - -```text -families.py # Implemented pilot pairs and contracts -manifests.py # Implemented strict release, case, and asset schemas -pilot_dataset.py # Implemented preparation and streamed case loading -pilot_runner.py # Implemented independent-case evaluation -pilot_scoring.py # Implemented coverage and replay scoring -sources/ - suitesparse.py # Implemented public acquisition and canonicalization - flash.py # Implemented public numerical capture loader -workloads.py # Implemented manufactured vectors and NS qualification -trajectory.py # Deferred: ordered sessions and whole-trajectory results -``` - -Retain and extend the existing `models.py`, `archive.py`, `dataset.py`, -`accuracy.py`, `verify.py`, `runner.py`, and `scoring.py`. Keep qualification -tools separate from evaluation dependencies. Sparse factorization libraries, -capture tooling, or file formats needed only to construct datasets should not -be required to run submissions against prepared numerical inputs. - -The core records should express these responsibilities: - -- `FamilyDefinition`: public family ID, workload kinds, accuracy contract ID, - and permitted reference kinds. -- `TrackDefinition`: family/track pair, evaluation unit, state lifetime, - scoring contract, execution contract, and performance-reference requirements. -- `DatasetRelease`: release ID, family manifests, split policy, ordered cases, - public artifact identities, and execution/scoring contract references. -- `CaseSpec`: globally unique ID, matrix identity, split, provenance group, - workload kind, asset references, and resolved solver settings. Timing roles - belong to track membership: a replay control is still a timed trajectory step. -- `TrajectorySpec`: public trajectory ID, complete ordered step references, - physical start/end, expected solve count, split/provenance group, and - trajectory accuracy, lifecycle, and budget contracts. Each step identifies - its physical time step, within-step solve ordinal, and mesh/order identity. -- `TrackRelease`: immutable family/track/release identity, allowed numerical - corpus, readiness state, budgets, weights, and optional qualified reference. -- `ReferenceSolution`: `kind = manufactured | numerical`, values, and - qualification identity. A missing reference is explicit and allowed only - for FLASH diagnostics; it cannot disable required mesh forward checks. -- `ReferenceSolver`: source/configuration, runtime and compiler identities, - qualification report, timing artifact, and exact dataset/track binding. - This is distinct from a reference solution used to check numerical accuracy. - It is absent for NS coverage and initially absent for NS performance. -- `EvaluationCase`: numerical candidate input plus evaluator-owned case - specification, accuracy contract, and optional reference. -- `AccuracyContract`: named required thresholds and diagnostic definitions, - validated against a closed set of supported contract versions. - -Separate metric calculation from deciding whether a metric passes. Local and -Modal execution must call the same function to evaluate an outcome and build -its report. The transport layer should only launch work, enforce budgets, and -return native outputs. - -Preserve `solver_create` and the current numerical input protocol where possible: -they already accept an arbitrary `x0`. The pilot's versioned case metadata and -references provide the independent-case foundation; future tracks need their own -trusted archive schema, not extra candidate-visible identifiers. Trajectory -adds a separate streaming protocol and lifecycle ABI, with public per-step -numerical settings and explicit ownership rules. Document the family -and its contract in the problem statement, while passing only numerical inputs -to candidate code. Resolve and validate the family/track pair before any -candidate is compiled. Keep leaderboards separate by family and track; a -coverage count and a trajectory speedup must not be collapsed into one score. - -Give new hashes a source-neutral, versioned domain. Keep v1 hash domains and -decoders intact for v1 replay. Centralize effective execution settings so that -changing a configuration file cannot leave hard-coded Python and native values -silently inconsistent. - -## 4. Public dataset publication - -Create a new public schema instead of copying another system's manifests. Use -an explicit field allowlist. A public matrix record should contain only: - -- A public source ID and URL or dataset accession, author citations, license, - dimensions, nonzero count, and archive and canonical-array hashes. -- Scientific classification and the evidence supporting admission. -- A public provenance-group ID, split assignment, and duplicate relationships. -- Named canonicalization steps and versioned workload-generation information. - -A release should bind matrix, `b`, and `x0` hashes; required reference and -qualification hashes; accuracy and execution contracts; case roles and weights; -and the ordered case list. Hashes establish identity, so an official evaluator -must also check them against the selected trusted release. A self-consistent -user-authored manifest alone must not qualify as an official benchmark run. - -For SuiteSparse, acquire original archives from published upstream URLs and -retain their Matrix Market metadata. Recompute public qualification artifacts -using public scripts. Reuse scientific selection conclusions only when their -supporting public evidence is available and matches the operator bytes. - -For FLASH, publish an independently documented numerical dataset with `A`, `b`, -`x0`, requested tolerances, and enough scientific provenance to understand the -capture. Put large arrays in immutable, checksummed dataset releases, outside -the code repository. Benchmark installation and evaluation must not require -building FLASH. Acquisition reproducibility and solver-evaluation -reproducibility are separate documented procedures. - -### FLASH download and hosting design - -The benchmark maintainers must publish and maintain this dataset: there is no -SuiteSparse accession or download endpoint assumed for these captures. The -publication host is the public, ungated -[LinearSolveBench dataset on Hugging Face](https://huggingface.co/datasets/hgarud/LinearSolveBench). -The replay pilot already publishes 344 per-case archives there. Hugging Face is -the only required host; Zenodo archival is an optional later addition if needed. -Complete trajectory manifests and bundles remain future work. - -Hugging Face supports individual file downloads pinned to a full commit hash. -Use that capability for reproducible downloads; never resolve a benchmark -release through a mutable `main` branch. [Hugging Face download documentation](https://huggingface.co/docs/huggingface_hub/main/guides/download). -If Zenodo archival is added later, preserve the same payload bytes, cite the -specific version DOI, and publish its download URLs as optional mirrors. -Neither a DOI nor mirror support is required for the Hugging Face release. -[Zenodo DOI versioning](https://zenodo.org/help/versioning). - -For a future release including trajectories, publish metadata covering the -following responsibilities. These proposed files extend the replay format; -the current required files and manifest IDs are documented in [DATASET.md](DATASET.md). - -- `catalogue.json`: stable public matrix and case IDs, dimensions, nonzero - counts, scientific provenance, license/citations, download URLs, byte sizes, - SHA-256 checksums, and case-to-matrix relationships. -- `release.json`: frozen membership, split assignments, roles, tolerance and - accuracy contracts, supported tracks, catalogue hash, and pinned host revision. -- `trajectories/.json`: complete ordered case references, physical - step and solve ordinals, start/end times, layout-change information, expected - solve count, numerical checksums, and public capture-completeness evidence. -- `README.md` and `DATA_LICENSE`: numerical format documentation, a small - download/load example, capture description, and established data-use terms. - -An existing replay case ID is `flash-replay-0001`; its numerical -identity is also bound to hashes of `A`, `b`, and `x0`. Allocate IDs independently -of acquisition-directory names or private run identifiers. Repeated matrices -may share a matrix ID while different RHS/warm-start pairs have distinct case -IDs. Never reuse an ID for changed bytes. - -Make each case downloadable separately as a compressed archive containing: - -```text -matrix.npz # Canonical float64 CSR, readable with scipy.sparse.load_npz -b.npy # Captured float64 right-hand side -x0.npy # Captured float64 initial guess -case.json # Public case ID, tolerance, schema, scientific metadata, hashes -``` - -These per-case binary archives are the required public format for FLASH replay -and remain authoritative for benchmark identity. A user must be able to obtain -one case without downloading the full corpus. Additional Matrix Market exports -and bulk bundles are outside the replay pilot scope. - -Publish full-trajectory bundles as well as individual cases. A trajectory -download must resolve its entire sequence and preserve every repeated entry; -content-addressed storage can avoid downloading the same array blob twice. -The existing replay selection is not a sufficient trajectory inventory: recover -or acquire missing steps and independently attest sequence completeness before -publication. A case omitted from replay merely because it is a duplicate must -still appear in its trajectory. A numerically invalid required step prevents -that entire trajectory from qualifying until resolved. - -Extend `dataset prepare` with case and trajectory selectors. Resolve IDs from -the frozen catalogue, download only selected archives, verify compressed and -canonical-array hashes, extract safely, and cache by content hash. Support -resuming interrupted downloads. Mirror fallback can be added later if needed. -An offline cache must be sufficient to repeat preparation. For example: - -```bash -linear-solver-bench dataset prepare --release flash-replay-dev-pilot \ - --family magnetic_diffusion_flash --track replay \ - --case flash-replay-0001 \ - --output data/prepared/flash-example -``` - -This replay command is supported now; trajectory selection remains deferred. -Users can also follow a direct catalogue link and load the data without installing the -benchmark or obtaining FLASH. This package contains captured numerical inputs; -it does not contain FLASH source, raw simulation dumps, private acquisition -receipts, or evaluator reference solutions. - -For the fully public release, publish numerical inputs for both development and -ranked splits. `ranked` denotes the evaluation split, not a secret dataset. -Preserve the provenance separation and document prior exposure, but do not -claim that publicly downloadable cases are hidden. Any future evaluation with -unreleased systems must be a separate, explicitly identified track. Keeping -reference vectors private does not make a published `A,b,x0` system secret. - -Publication is complete only after an unauthenticated machine can download a -single case and a complete split, verify all identities, and solve the example -using the documented public commands. Require established redistribution terms -for every included numerical artifact before uploading it. Dataset files remain -outside Git; their catalogue, checksums, and format stay reviewable in the code -repository. - -Publish development inputs and worked examples. Retain ranked evaluation -references and any ranked manufactured-vector key in trusted operator storage. -Domain-separate new manufactured draws by release, family, matrix, RHS kind, -and draw index. Freeze the algorithm, normalization, tool versions, vector -hashes, and serialized bytes. Do not put a library version into an implicit -seed recipe that changes the workload after a dependency upgrade. - -Changing a seed or key produces different workloads and requires new numerical -qualification, prepared identities, and calibration. A manufactured target is -not necessarily the exact solution of the rounded serialized `A,b` system: -record formation error and verify that this uncertainty is small relative to -the forward-error requirement. For NS mesh, the acceptance limits are fixed -release requirements, not values calculated from each matrix's condition -estimate. Require offline qualification evidence at one tenth of each required -error limit and distinguish empirical reference-solve metrics from certified -bounds. A passing float64 reference alone does not certify the exact -stored-system solution's distance from the target. Audit sensitive cases using -higher-precision residuals/refinement or verified error bounds and document -remaining uncertainty. These are preparation checks, not additional candidate -executions. - -Use provenance groups to split data before tuning or baseline selection. Keep -related simulation steps, mesh refinements, parameter variants, exact -duplicates, and documented near-duplicates together. Check overlaps across -families, tracks, and previously published development sets. A ranked FLASH -trajectory must not contribute steps to the replay development split: apply -one provenance-group split consistently to both FLASH tracks. New RHS draws do -not make a previously exposed matrix unseen. Public matrix availability also means -the benchmark cannot claim to rule out model pretraining exposure. - -Publish a development split, a ranked split, and an exclusion ledger. Treat -quarantine as a disposition, not a scored split. FLASH correctness controls -must retain their role even if their initial guess already satisfies accuracy; -exclude them from replay speed aggregation and report their pass rate separately. -Every required step, including such controls, contributes to trajectory time. - -## 5. Track execution, references, and scores - -### NS mesh: coverage - -The implemented pilot evaluates every case in the frozen NS mesh split once -in one fresh native process. Count a case as solved only when that execution -satisfies the fixed accuracy and execution requirements. A future performance -track must preserve the numerical contract and declare its execution lifecycle -separately; it must not change existing coverage results. - -```text -solved_count = sum_i single_execution_passed_i -coverage_fraction = solved_count / expected_case_count -ranking_key = -solved_count -``` - -Use the raw solved-case count as the primary score, as requested. Equal counts -tie: execution time does not break coverage ties. Report timings, failure -reasons, matrix/group breakdowns, and optionally a provenance-balanced coverage -diagnostic, but these do not change the ranking. Candidate failure on one case -does not prevent accurate solutions on other cases from counting. - -Coverage requires no performance reference solver or timing calibration. -Numerical targets and qualification evidence are still required to enforce the -accuracy gates. Freeze independent per-case execution budgets before evaluating -candidate submissions. Preserve hard cases even when available baselines fail. - -### NS mesh: performance - -Measure speedup against **one versioned reference solver that solves every -case** in the selected frozen split, under the same accuracy and execution -contract. Use the same numerical corpus as coverage; do not remove cases or -change their tolerances to obtain a passing reference. - -The track initially has `status = awaiting_reference`, with no reference solver -or timing artifact. In the later NS performance phase, implement its execution, qualification, -scoring, CLI, and report schemas before discovering the reference. An official performance evaluation must fail clearly before -compilation when no qualified reference is registered; it must not fabricate -reference times or fall back to another scoring rule. - -When a suitable solver is discovered: - -1. Freeze its public source, configuration, compiler/runtime identity, and - reference-solver ID. One reference artifact may contain adaptive algorithms, - but all cases must run that artifact without case-name dispatch or an - evaluator choosing a different best solver for each case. -2. Run it on the entire frozen split in the official venue for all three - repetitions. Independently verify every solution and retain every result. -3. Require complete case membership, all passing repetitions, finite positive - timings, and matching dataset, accuracy, lifecycle, and resource identities. - Missing cases, timeouts, or numerical failures reject qualification. -4. Publish the qualification report and frozen reference timings, then create - an active performance-track release bound to those artifacts. Coverage - remains usable throughout and retains its existing release identity. - -For case `i`, let `R_i` be the qualified reference median and `C_i` the candidate -median, both including creation, setup, and solve. Define `speedup_i = R_i/C_i`. -Require candidates to solve all scored cases and mandatory controls for an -official aggregate speedup. If any fails, retain coverage and timing diagnostics -but return `eligible = false` and `speedup = null`; never average only the -successful cases or replace failed timings with a purported speedup. - -Use a weighted geometric mean of case speedups, with equal mass for provenance -groups, equal matrices within each group, and equal RHS workloads within each -matrix. Freeze these weights independently of timing and candidate results: - -```text -speedup = exp(sum_i weight_i * log(R_i / C_i)) -ranking_key = -speedup -``` - -The weights sum to one. A value above one means faster than the reference. -Changing the reference, resource budget, corpus, or accuracy contract creates -a new performance release; scores against different references are not mixed. -Use an all-passing synthetic reference in CI to test activation in that phase. Discovering -the real reference is a separate research task, not a prerequisite for -implementing the track or releasing coverage. - -### FLASH: replay - -Treat each case as one captured magnetic-diffusion solve at a time step. If a -physical step contains multiple magnetic solves, expose their identities and -ordering explicitly rather than silently combining or omitting them. - -Each solve starts with its captured `A,b,x0,tau`, a fresh native process, and a -new solver object. Time factory creation, setup/preconditioner construction, -and solve. Destroy the object afterward. No solver state, preconditioner, -workspace, or candidate solution carries from one replay case to another. -The implemented pilot uses exactly one execution and its complete measured -solve time, including one execution per case during reference qualification. - -Qualify one public reference solver across the replay split under this exact -lifecycle. A stock GMRES+AMG configuration is a starting candidate for that -qualification, not an assumed valid timing artifact. Compute per-case speedup -and a frozen provenance-balanced geometric mean, with equal weight for source -trajectories within a provenance group and scored steps within a trajectory. -Apply the same complete-accuracy eligibility rule as NS performance. Run -designated correctness-only controls but exclude their times from replay speed -aggregation. Require the reference and candidate to pass those controls. - -Replay measures independent time-step solves. Its speedups cannot serve as -trajectory reference times or be summed to claim trajectory performance. - -### FLASH: trajectory - -The agreed scope is an **ordered captured sequence with persistent solver -state**. The driver streams each captured `A_t,b_t,x0_t,tau_t` in order to one -candidate process and solver state. Captured numerical inputs remain fixed; -candidate outputs do not regenerate future matrices or RHS vectors and do not -advance a live FLASH simulation. The reported quantity is cumulative linear -solver time across the complete captured trajectory. It makes no claim about -full simulation wall-clock speedup or error propagation in a recoupled PDE run. - -Allow candidate-owned workspaces, preconditioners, Krylov recycling data, and -past numerical information to persist between steps. Supply the captured initial -guess at every step; a solver may choose an alternative initial iterate using -its own past state as part of its timed work. Do not automatically replace the -captured guess with the previous candidate solution, particularly when the mesh -dimension or degree-of-freedom ordering changes. - -Require every magnetic solve from the declared simulation start through its -declared terminal time, in recorded order, including repeated matrices, -duplicate inputs, controls, substeps, and multiple solves per physical step. -A sampled set of replay cases or a cropped window is not a complete trajectory. -If the available capture omits necessary solves, acquire a complete trajectory -before enabling this track. Missing or invalid steps cannot be repaired by -dropping them. Duplicate numerical blobs may share storage, but all their -positions in the sequence remain executable. - -Provide a small, separate versioned trajectory ABI, leaving the existing -single-case submission ABI unchanged. The interface needs three operations: - -```text -create(state) # Once at trajectory start -step(state, A, b, x, solve_settings) # Once per captured solve, in order -destroy(state) # Once at trajectory end -``` - -`x` is initialized from captured `x0`; `solve_settings` carries the current -relative/absolute tolerances and iteration cap. The step operation includes -all candidate decisions about retaining, rebuilding, or updating state, and -must work across changed values, patterns, dimensions, and orderings. Freeze -exact C signatures and exported symbols in a public header before implementation. -Audit the trajectory artifact against its own small entrypoint allowlist while -retaining the existing capability restrictions. - -Specify HYPRE object ownership explicitly. Current-step inputs are borrowed; -retaining an old matrix for a cached preconditioner requires an explicit -read-only retain/release handle supported by the driver. Retained matrices and -candidate-owned allocations count toward the process memory limit, and the -driver audits retained inputs for mutation. Vector histories must be copied -into candidate-owned workspace. Do not keep every previous HYPRE object alive -implicitly or assume that a matching dimension means matching mesh ordering. -Test this lifetime contract with actual HYPRE preconditioners before freezing it. - -Do not expose future step payloads to candidate code. Stream only the current -input, enforce its integrity and accuracy, then advance. Candidate state resets -between trajectories and between repetitions. Each repetition runs the entire -trajectory from a new process; a sandbox cannot silently restart the candidate -mid-trajectory or carry caches from another trajectory. - -For each full-trajectory repetition, measure: - -```text -T = create_time + sum_t step_time_t + destroy_time -``` - -This includes candidate setup, reuse decisions, matrix-retention calls, workspace -changes, preconditioner rebuilds, solves, and candidate cleanup. Exclude trusted -input decoding, HYPRE input assembly, transport, solution extraction, and -independent verification. Report these excluded phases separately. Compared to -single-case replay, final candidate destruction is explicitly included here -to account for the complete persistent solver lifecycle. Enforce a cumulative -native-time budget, a total process watchdog, and the trajectory memory limit. - -Independently verify each returned step solution against that step's original -`A_t,b_t` and FLASH accuracy gates. A failed, missing, repeated-out-of-order, or -nonfinite output invalidates the trajectory; abort it and record the remaining -steps as unexecuted. No speedup is assigned to a partial trajectory. Correctness -controls and duplicates contribute their execution time in this track because -they are part of the full trajectory. - -Qualify a reference using the same trajectory ABI, streamed inputs, allowed -reuse, memory limits, and timed boundary. A published stateless adapter can -serve as a reference, but all of its per-step creation/setup/destruction must -be charged inside its step callbacks. Qualify it by executing full sequences; -do not construct its timing from replay measurements. - -For trajectory `j`, take the median of three complete totals for each solver: - -```text -trajectory_speedup_j = median(T_reference_j) / median(T_candidate_j) -``` - -Do not sum per-step medians or average per-step speedups. Aggregate eligible -trajectory ratios geometrically, giving equal mass to provenance groups and -equal trajectories within each group. Require every trajectory and every step -to pass all repetitions for an official aggregate; report partial completion -only as diagnostics. Publish step traces and whole-trajectory totals so setup -amortization and regressions remain visible. - -### SPD and shared venue rules - -SPD retains its proposed single-case, coverage-first and calibrated-time -ordering, with its separate accuracy contract and qualification. No additional -SPD tracks are introduced by this update. Keep its results separate from the -four explicitly requested track leaderboards. Existing public v1 results and -ranking remain unchanged as well. - -For clarity, the retained SPD proposal uses provenance/matrix/RHS-balanced -weights, ranks weighted passing coverage first, and uses the penalized geometric -mean of normalized times second. Its frozen normalization can use the best -passing median from a predeclared public baseline portfolio, or a budget value -explicitly labelled as such when none passes. Failures contribute twice their -deadline divided by that normalization. This SPD cost diagnostic is not the NS -performance or FLASH speedup formula, and budget normalization must never be -accepted by those speedup tracks. - -Do a venue feasibility sweep before choosing limits for additional tracks. The -pilot's 4 GiB, 90-second venue does not establish that new workloads fit. Measure -peak memory, wall time, initialization overhead, and retained-state growth. -Start independent-solve feasibility runs with memory headroom up to 32 GiB and -cases up to 300 seconds; these are experiment allowances, not leaderboard -limits. Measure whole trajectories to establish their separate total budgets. - -Load one prepared numerical input at a time. Budget total sandbox lifetime for -repetitions, compilation, transfer, and cleanup. Partition long evaluations -only at independent case or trajectory boundaries; never split a trajectory -to fit a shorter sandbox lifetime. Use the same frozen runtime and resources -for a candidate/reference comparison. Venue changes invalidate calibration. - -Provide understandable public GMRES/FGMRES and PCG examples with appropriate -preconditioners. Any trusted stock-HYPRE calibration executable needs a separate -build route and published configuration; it must not broaden the candidate -allowlist. A reference solver is public benchmark infrastructure and must not -carry private source dependencies or identifiers. - -Bind results to family, track, evaluation unit, release, prepared inputs, -accuracy/lifecycle/scoring contracts, reference identity when applicable, -compiler/runtime, venue, repetitions, budgets, and frozen weights. Infrastructure -failure invalidates a run and follows a fixed retry policy; it never changes -the scored case set. Candidate failures remain explicit outcomes. Local runs -are diagnostics and cannot be submitted as official venue results. - -## 6. Open-source boundary - -Prepare publication material through an explicit export into a clean directory. -Export numerical data and public scientific evidence, and write new public -schemas and documentation around them. Keep any one-time source-conversion -tool and source-to-public mapping outside the public repository. Public loaders -must consume the public format without knowing where it was assembled. - -Exclude private package imports, repository names and paths, deployment or -account IDs, storage names, experiment logs, prompts, model outputs, historical -receipts, and private source hashes. Do not copy whole directories and then -attempt string replacement. Recompute hashes after constructing public -metadata; retain original upstream artifact hashes where they identify public -scientific sources. Preserve required third-party copyright notices. - -Audit the actual release contents: source archive, wheel, container build -context, embedded archive metadata, generated documentation, and Git history. -Use public schema/import allowlists in CI. Keep any check containing literal -private identifiers in private release tooling, so the check itself cannot -publish those identifiers. Legitimate attribution is not an internal detail -and must not be removed. - -Keep code and data licensing separate. SuiteSparse states that its matrices -are CC BY 4.0 and requires preserving matrix-specific metadata and identifying -modifications. [SuiteSparse licensing and redistribution guidance](https://sparse.tamu.edu/about). - -FLASH's published license restricts redistribution of its source code and -components. Do not bundle that source or assume capture patches are freely -redistributable. Establish the redistribution terms for the numerical dataset -and its accompanying metadata before publishing it; source-code terms alone -do not establish the terms for those outputs. [FLASH license agreement](https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node3.html). - -## 7. Future implementation sequence and acceptance criteria - -This retained sequence describes the broader, deferred scope. Several shared -pieces are already delivered by the pilot, as listed in section 1; extend them -instead of recreating those changes. The completed pilot's official venue, -reference registration, and distribution evidence are recorded in its plan. -Each additional track should be a small, separately reviewable extension. - -| Step | Deliverable | Acceptance criteria | -| --- | --- | --- | -| 1. Freeze the design and inventory | Family/track specifications, source list, provenance groups, asset availability, trajectory completeness, and redistribution status | Every proposed case/trajectory has a disposition; public data locations and unresolved rights are explicit; no assumed complete corpus | -| 2. Separate v1 from v2 configuration | Typed release/contracts, family/track registry, strict manifests, readiness states, version dispatch | Existing v1 behavior remains unchanged; unknown family/track pairs fail closed; NS performance represents a missing reference explicitly | -| 3. Extend reference and accuracy handling | New trusted archive schema, shared metrics and family gate selection | Tiny analytic cases exercise all contracts, warm starts, and reference types; missing mesh truth cannot pass | -| 4. Add public dataset preparation | SuiteSparse and FLASH loaders, generator, complete trajectory manifests, qualification commands | Prepare one small case per family and one complete sequence from public artifacts; hashes, order, completeness and provenance validate; captured inputs are preserved | -| 5. Complete independent-case execution | Streaming case loading, iteration limits, shared local/Modal reporting | NS coverage, FLASH replay and SPD native solves succeed; local and Modal verdicts agree; candidate sees no reference or identifying case metadata | -| 6. Implement trajectory execution | Versioned ABI and streaming driver, retained-object ownership, whole-sequence timing, trace validation | Actual HYPRE reuse works across changing inputs; all steps execute in order; state resets between repetitions; missing steps or memory/time failures invalidate the trajectory | -| 7. Implement track scores and reference qualification | Raw coverage count, qualified-reference speedups, activation command, track-bound reports | Coverage runs with no timing reference; a synthetic all-passing reference activates NS performance in CI; incomplete references and partial candidate speedups are rejected | -| 8. Freeze datasets and venue | Qualified case/trajectory releases, official execution budgets and available reference reports | All admitted numerical workloads qualify, full trajectories fit, splits are consistent across tracks, and each speedup track is explicitly active or awaiting data/reference | -| 9. Validate distribution and publish | Documentation, package resources, CI, citations and notices, readiness reporting | Clean checkout/wheel/container runs without sibling repositories; content audit passes; documented coverage/replay/trajectory paths work; missing NS reference is handled clearly | -| 10. Activate NS performance after discovery | Public all-case reference artifact, complete official qualification, new active track release | Every existing NS case passes every reference repetition; published speedups bind to the frozen reference; coverage data and results remain unchanged | - -After the pilot, a first additional vertical slice can use a small SPD mesh -case with its own scientific qualification and the existing SuiteSparse loader. -A trajectory slice then needs a tiny complete sequence with repeated and changed -matrices, a stateful solver, and a reference adapter. Validate each new track -before publishing that track's corpus; these extensions do not delay release of -independent-case pilot data. Implement NS performance's activation mechanism -when implementing that deferred track. Discovering its real all-case reference -remains a separate milestone. - -Update `README.md`, `SPEC.md`, `DATASET.md`, `OPERATIONS.md`, `benchmark.toml`, -packaging metadata, and CLI help together. Add three concise family documents, -track specifications, and both independent-case and trajectory submission -examples. Label scientific background, -candidate instructions, and evaluator operation procedures clearly. - -For comparisons of AI models and systems, publish a versioned task statement -per family and track: available development cases, permitted tools and libraries, -accuracy requirements, artifact format, and evaluation commands. Record the -model/system version, prompt version, generation budget, number of independent -attempts, development feedback access, and final artifact-selection rule in -experiment reports. Select artifacts using development feedback before ranked -evaluation. Keep model invocation adapters optional and separate from the -numerical evaluator so any AI system can submit the same C artifact. - -Proposed CLI behavior: - -```bash -linear-solver-bench dataset families -linear-solver-bench dataset summary --release mesh-cpu-v2 -linear-solver-bench dataset list --release mesh-cpu-v2 --family spd-mesh --split dev -linear-solver-bench dataset prepare --release mesh-cpu-v2 --family spd-mesh \ - --split dev --output data/prepared/spd-mesh-dev -linear-solver-bench run submissions/pcg.c --runtime build/runtime \ - --cases data/prepared/spd-mesh-dev --output results/pcg-spd.json - -linear-solver-bench run submissions/gmres.c --family ns-mesh-pde \ - --track coverage --runtime build/runtime --cases data/prepared/ns-mesh-dev \ - --output results/ns-coverage.json -linear-solver-bench track status --family ns-mesh-pde --track performance - -linear-solver-bench dataset prepare --release mesh-cpu-v2 \ - --family magnetic_diffusion_flash --track trajectory --split dev \ - --trajectory flash-trajectory-v1/000001 --output data/prepared/flash-trajectory -linear-solver-bench run submissions/trajectory.c \ - --family magnetic_diffusion_flash --track trajectory --runtime build/runtime \ - --cases data/prepared/flash-trajectory --output results/flash-trajectory.json - -# After an NS reference has been discovered: -linear-solver-bench reference qualify references/ns-reference.c \ - --family ns-mesh-pde --track performance --runtime build/runtime \ - --cases data/prepared/ns-mesh-ranked --output references/ns-qualification.json -``` - -These commands and filenames are proposed interfaces, not currently available -features. Require an explicit `--track` when a family has multiple tracks. Runs -resolve that selection against the verified release and prepared manifest; the -CLI cannot override a gate, change case membership, use a reference from another -track, or silently reinterpret an existing release. The official reference -qualification command must use the registered official venue; local evidence -cannot activate an official track. - -## 8. Verification needed for future-track completion - -Existing pilot tests already cover many independent-case numerical, data, -streaming, report, and scoring checks. The list below describes the broader -acceptance surface after the additional families and lifecycles are implemented; -it does not establish that those future checks already run. - -- Numerical tests: hand-computed residual/backward/forward errors; a perturbed - mesh solution failing each required gate; FLASH diagnostic errors that do not - reject an otherwise valid solution; zero RHS, nonzero warm starts, rounding - uncertainty, nonfinite values, and overflowing denominators. -- Data tests: exact public-array preservation, deterministic generation, - corrupted hashes, wrong dimensions/dtypes, unsafe paths, duplicate case IDs, - missing references, unknown fields, and an intentionally overlapping split. -- Native tests: a successful solve from every family; status, mutation, timeout, - memory-limit and invalid-output failures; repeated fresh-process execution; - observed iteration settings matching the release. -- Track tests: identical NS accuracy verdicts in coverage and performance; - coverage ranks raw case counts and retains time ties; absence of a timing - reference never blocks coverage; absent, failing, mismatched, or incomplete - references cannot activate speedup scoring; a synthetic qualified reference - exercises activation and replacement via a new release. -- Trajectory tests: persistent workspace/preconditioner reuse; retained HYPRE - matrix lifetimes and mutation checks; value/pattern/dimension/order changes; - per-step tolerance changes; duplicates and controls retained; no future-input - exposure; wrong order, missing terminal step, failure at the last step, and - state leaking across repetitions; memory and cumulative-time limits. -- Scoring tests: exact reference/candidate speedup examples, invalid partial - solves, failed controls, and distinct timing rules for replay and trajectory. - Verify median-of-whole-trajectory sums, not sum-of-step-medians, and that no - replay timing or budget fallback is accepted as a trajectory reference. - Reject mismatched family/track/release/runtime/contract identities. -- Distribution tests: install from a wheel outside the repository; exercise - package resource lookup; build a sandbox from its minimal allowlisted context; - confirm no evaluator references are included; run all documented examples. - -Keep routine CI small and offline using synthetic fixtures. Run public small -real-data integration checks separately from the full official-venue sweep. -The existing CI native job only compiles and audits the starter; extend it to -execute and independently verify actual solves. - -Completion of this broader roadmap means all three families and the four -requested tracks have documented public formats, execution contracts, numerical checks, -scoring, distribution support, and tested readiness/activation behavior. Dataset -and leaderboard readiness are tracked separately: NS coverage can open before -NS performance, and NS performance remains awaiting reference until the real -all-case qualification succeeds. FLASH trajectory requires complete captured -sequences plus a separately qualified trajectory reference; independent replay -results do not establish its readiness. The main release dependencies are data -availability and redistribution terms, defensible split coverage, numerical -qualification, and measured venue capacity. diff --git a/docs/NS_COHORT_V2.md b/docs/NS_COHORT_V2.md deleted file mode 100644 index b73b325..0000000 --- a/docs/NS_COHORT_V2.md +++ /dev/null @@ -1,278 +0,0 @@ -# NS mesh cohort v2 - -The [dataset card](NS_DATASET_CARD.md) is the reader's guide to this release. -This review records the detailed source and selection decisions behind it. - -The replacement release `ns-mesh-cohort-v2` contains 49 qualified original -operators in 24 reviewed provenance groups. It retains all 43 matrices from -`ns-mesh-pilot` and adds six public SuiteSparse operators. Selection, -manufactured-workload qualification, and execution validation are separate -steps; source admission alone does not make this an active benchmark release. -The original 11-development/32-ranked release remains reproducible, with its -limitations recorded in the [earlier split review](NS_SPLIT_REVIEW.md). - -Current status: all 49 fixed workloads are qualified, both releases are -frozen, and both complete venue runs are validated. The public GMRES+AMG -candidate passes 11/19 development and 14/30 ranked cases. - -## Scientific scope and added sources - -This family uses real, materially nonsymmetric original PDE discretization -operators for manufactured linear-system workloads. Source provenance must -identify the PDE or mesh construction. Preparation canonicalizes storage and -preserves the operator; it does not square, shift, project, scale, or replace it. -A source may originally have used the operator for eigenmode analysis. That -history is disclosed, and the benchmark makes no claim to reproduce its native -physical right-hand side. - -| Added operator | SuiteSparse ID | Unknowns | Numerical nonzeros | Source-supported spatial method and purpose | -| --- | ---: | ---: | ---: | --- | -| [HB/plskz362](https://math.nist.gov/MatrixMarket/data/Harwell-Boeing/platz/platz.html) | 231 | 362 | 1,760 | Finite-difference shallow-water ocean operator, originally used for natural-mode eigenanalysis | -| [Hohn/fd12](https://sparse.tamu.edu/Hohn/fd12) | 917 | 7,500 | 28,462 | Finite-difference single-material crack problem on a logarithmic grid | -| [Janna/CoupCons3D](https://sparse.tamu.edu/Janna/CoupCons3D) | 2647 | 416,800 | 17,277,420 | Finite-element, fully coupled three-dimensional poroelastic consolidation | -| [Janna/Transport](https://sparse.tamu.edu/Janna/Transport) | 2649 | 1,602,111 | 23,487,281 | Tetrahedral finite-element, density-driven coupled flow and transport | -| [Grueninger/windtunnel_evap3d](https://sparse.tamu.edu/Grueninger/windtunnel_evap3d) | 2815 | 40,816 | 803,978 | Coupled Navier–Stokes/Darcy evaporation with cell-centred finite volumes and a marker-and-cell scheme | -| [Goodwin/Goodwin_095](https://sparse.tamu.edu/Goodwin/Goodwin_095) | 2825 | 100,037 | 3,226,066 | Finite-element Navier–Stokes and related transport equations | - -The original skew-symmetric PLSK ocean matrix is retained. The related squared -PLAT operators are excluded. CoupCons3D uses finite differences in **time**; -that does not establish spatial finite-difference coverage. Numerical nonzero -counts exclude explicitly stored zeros after canonicalization. - -The Janna pair stays in one group because the sources share a specific study -and their mesh/code overlap is unresolved. Goodwin_095 joins the existing -Goodwin group, so all four related Goodwin cases stay together. Hohn/fd12 and -the existing fd15 case share one crack-model group. These groups -describe reviewed model, study, mesh, and generator exposure. A common author -or collector alone does not force unrelated physical models into one group. -The [source descriptors](analysis/ns-cohort-v2-descriptors.json) record the -evidence, unknown methods, and remaining independence uncertainties. - -The input pool was revised from 47 to 48 operators before new manufactured -targets or candidate executions. A joint size-by-method review identified the -need for another finite-element source in the 100,000-to-1,000,000 band. -Botonakis/FEM_3D_thermal2 was investigated first, but its original coefficients -differ from their transpose only at roundoff scale. Its maximum relative -asymmetry is approximately `1.11e-16`, below the unchanged `1e-12` admission -threshold. The [screening record](analysis/ns-cohort-v2-screening.json) preserves -that exclusion and archive identities. Goodwin_095 passes the same gate and -provides another source group in that size-by-method stratum. No candidate -convergence or timing result entered either decision. - -The 48-operator pool then exposed incompatible finite-difference support -constraints: small finite-difference cases, medium finite-difference cases, -and all-zero-diagonal cases tied three groups into pairwise opposite-split -requirements. Adding fd12 supplies another reviewed group in the small -finite-difference stratum, while keeping both Hohn matrices together. This -produces the 49-operator input pool without relaxing the coverage policy or -using a solver outcome to select a case. - -## Selection contract - -The [frozen policy](analysis/ns-cohort-v2-policy.json) applies to the -[matrix-only inventory](analysis/ns-cohort-v2-inventory.json). It keeps each -provenance group indivisible and retains every admitted operator. Development -must contain 25–50% of cases, with one third as the balance target; ranking must -retain at least eight groups. - -Hard coverage rules precede numerical balance. Each known application, spatial -method, size band, and declared structural regime with at least two source -groups must occur in both splits. A category represented by only one group is -development-only. Unknown method labels never establish known-method coverage. -Size bands are below 10,000; 10,000 to below 100,000; 100,000 to below 1,000,000; -and at least 1,000,000 unknowns. The zero-diagonal and weak-row-dominance -fractions each distinguish zero, strictly between zero and one, and one. - -The same both-split requirement applies to spatial-method-by-size combinations -supported by at least two groups. Single-group combinations are reported as -limitations, because marginal coverage does not establish every joint regime. -The weak-dominance feature is a floating-point structural diagnostic, separate -from exact certificates used in numerical qualification. - -Among feasible group partitions, the selector minimizes an equally weighted -combination of marginal Wasserstein distance, multivariate energy distance, -known-tag incidence differences, and deviation from the development-count -target. The five matrix features are `log10(n)`, `log10(nnz/n)`, relative -Frobenius nonsymmetry, zero-diagonal fraction, and weakly diagonally dominant -row fraction. Feature ranges, energy normalization, rounding, and tie-breaking -are explicit in the policy. The selector examines every group partition; -candidate outcomes and reference solutions are not selector inputs. - -These are declared benchmark design choices, not statistical estimates of an -optimal split. Meeting them establishes coverage conditional on this input -pool, taxonomy, and source review. It does not establish representativeness of -all nonsymmetric PDE systems, equal solver difficulty, complete code -independence, or coverage of every physical and numerical combination. - -## Frozen partition and remaining gaps - -The [selection report](analysis/ns-cohort-v2-selection.json) records all -16,777,216 group partitions considered and the 6,531 that satisfy the hard -rules. The selected partition contains 19 development cases in nine groups and -30 ranked cases in 15 groups. Development contains 38.8% of the cases. - -| Unknowns | Development | Ranked | -| --- | ---: | ---: | -| Below 10,000 | 11 | 15 | -| 10,000 to below 100,000 | 4 | 12 | -| 100,000 to below 1,000,000 | 3 | 1 | -| At least 1,000,000 | 1 | 2 | - -Development groups are `crack-log-grid`, `driven-cavity-fem`, -`finite-volume-rans`, `janna-coupled-fe`, `nonlinear-diffusion`, `reservoir-ors`, -`semiconductor-devices`, `square-inlet-flow`, and `torso-electrophysiology`. - -Ranked groups are `airfoil-flow`, `atmospheric-elliptic`, `cavity-vorticity`, -`circle-surface-poisson`, `femlab-langemyr`, `fidap-flow-transport`, -`goodwin-flow-transport`, `heart-quasistatic`, `platzman-ocean-model`, -`reservoir-sherman`, `reservoir-steam`, `thermal-fem`, `unstructured-euler`, -`unstructured-stokes`, and `windtunnel-evaporation`. The report lists every -SuiteSparse matrix ID in each split. - -All shared application and spatial-method categories, all four size bands, -both structural features' three regimes, and every method-by-size combination -supported by multiple groups occur in both splits. In particular, both sides -have all-zero-diagonal cases and finite-element cases in the -100,000-to-1,000,000 band. Semiconductor devices and boundary elements have only -one supporting source group each and are development-only; they are not -claimed as ranked regimes represented by independent development sources. - -Four single-group size-by-method combinations remain one-sided: - -| Combination | Development | Ranked | Source group | -| --- | ---: | ---: | --- | -| Finite volume, 10,000 to below 100,000 unknowns | 0 | 1 | Wind-tunnel evaporation | -| Finite difference, 100,000 to below 1,000,000 | 2 | 0 | Torso electrophysiology | -| Boundary element, 100,000 to below 1,000,000 | 1 | 0 | Torso electrophysiology | -| Finite element, at least 1,000,000 | 1 | 0 | Coupled flow/transport | - -The ranked 40,816-row finite-volume case therefore has no development case -with the same size-and-method combination. Development's confirmed -finite-volume cases are smaller than 10,000 rows. Adding an independent source -in that joint regime is future cohort work; known-method marginal coverage -does not remove this limitation. Unknown spatial methods are kept unknown. - -The selected weighted objective is approximately `0.0524530`: marginal -Wasserstein `0.0559072`, normalized energy `0.0128166`, tag-incidence difference -`0.0866667`, and development-fraction difference `0.0544218`. These numbers -are descriptive comparisons under the declared objective, not quality -certificates. The report includes the next five feasible partitions, whose -scores range from approximately `0.0527210` to `0.0551028`, with development -counts from 17 to 20. It also reports nearest development feature neighbours -for each ranked case. Those diagnostics do not establish equal difficulty or -robustness to every alternative policy. - -Reproduce the exhaustive selection from a checkout with the package installed: - -```bash -python tools/select_ns_cohort.py \ - --inventory docs/analysis/ns-cohort-v2-inventory.json \ - --policy docs/analysis/ns-cohort-v2-policy.json \ - --output results/ns-cohort-v2-selection.json -``` - -This command reads the frozen public feature inventory and policy. It needs -neither matrix downloads nor manufactured targets, RHS keys, or solver runs. -The expected `selection_sha256` is -`606ca7f230261c72d49287da8d4600ea123707e302928ecff13e3b374d6eb024`. - -The underlying matrix properties and descriptors can also be audited directly -from public archives: - -```bash -python tools/audit_ns_split.py \ - --release data/ns-mesh-cohort-v2-dev.json \ - --release data/ns-mesh-cohort-v2-ranked.json \ - --descriptors docs/analysis/ns-cohort-v2-descriptors.json \ - --output results/ns-cohort-v2-matrix-audit.json -``` - -Use `--cache` to choose writable archive storage and `--offline` to require -cached archives. This audit processes the original operators without ranked -RHS keys or candidate execution. Its descriptive distances do not replace the -selection policy or establish a universal acceptable threshold. - -## Release qualification - -Selection is followed by fresh deterministic manufactured targets under the -new release identity. Each fixed workload must meet the existing tenfold -qualification margins without changing its operator, target, or acceptance -thresholds. Removing a case that cannot be qualified requires an explicit -inventory/release revision; choosing another target because it passes is not -permitted. A failed reference algorithm does not establish that a workload is -infeasible: another documented reference method may qualify the same fixed -inputs, with the failed attempt retained. - -The 49 completed qualifications comprise 46 independent sparse-LU witnesses -with one higher-precision residual correction each, one PyAMG-preconditioned -GMRES witness with two inexact corrections, and two exact -row-diagonal-dominance certificates. The solver witnesses establish empirical -feasibility at the tenfold margins; they are not verified forward-error bounds. -The dominance method separately certifies the exact stored-system solution's -forward discrepancy and measures verifier metrics at the manufactured target. -All four qualification gates retain their tenfold margins: normwise backward -error `1e-11`, componentwise backward error `1e-9`, and both target-relative -forward errors `1e-6`. - -Offline qualification has a separate resource budget from candidate evaluation. -The large-case qualification venue allows up to 32 GiB and one hour; candidate -cases still have two CPU cores, 4 GiB, and a 90-second deadline. CoupCons3D's -fixed ILU reference attempt broke down, and a full-LU qualification on the same -stored inputs passed using 8.68 GiB and 268.54 seconds. That is valid offline -feasibility evidence, not a claim that this reference satisfies the candidate -budget. Failure receipts remain part of the qualification record. Neither -the target nor the acceptance gates changed between reference methods. - -Transport qualified with the versioned -`independent-pyamg-sa-gmres-refined-v2` method. It keeps the initial relative -GMRES tolerance at `1e-13`, uses `1e-2` for each of two correction solves, and -keeps absolute tolerance zero throughout. The hierarchy and zero initial -guesses are fixed, and the manufactured target is never a reference-solver -input. Inexact correction solves are a standard iterative-refinement option; -the attainable improvement depends on the system and is checked independently -here. [Carson and Higham, 2018](https://eprints.maths.manchester.ac.uk/2629/1/cahi18.pdf) - -Its final normwise and componentwise backward errors were approximately -`1.11e-16` and `3.31e-16`; L2 and Linf target errors were `2.06e-14` and -`5.65e-14`. Qualification took 174.38 seconds with 2,471,686,144 bytes peak RSS. -These are offline reference measurements, not candidate performance results. -The [public attempt history](../data/releases/ns-cohort-v2-offline-reference-attempts.json) -also preserves the earlier ILU timeout and the stopped PyAMG v1 attempt, which -had no final accuracy verdict. The cause of that attempt's slow correction -convergence was not established; no Transport-specific floating-point accuracy -floor is claimed. V2 changes the reference algorithm's inner stopping rule, -without changing the frozen system, cohort membership, or acceptance gates. - -All 49 freshly generated numerical systems reproduce exactly with the minimum -supported NumPy 2.0.2 and SciPy 1.14.1 on Python 3.12.11, macOS arm64. The -[preparation receipt](../data/releases/ns-cohort-v2-preparation-reproduction.json) -binds each original archive, canonical matrix, and resulting numerical-system -digest to this inventory. This verifies deterministic preparation for those -versions and platform; it does not substitute for numerical qualification or -claim reproducibility on every possible platform. - -## Candidate venue validation - -The public `gmres_amg.c` candidate completed both full split evaluations in -the declared Modal venue: - -| Split | Cases | Passed | Timeouts | -| --- | ---: | ---: | ---: | -| Development | 19 | 11 | 1 | -| Ranked | 30 | 14 | 1 | - -All 49 cases received exactly one fresh native execution. CoupCons3D in -development and Goodwin_095 in ranking reached their 90-second deadlines and -remain failed coverage cases. Neither was retried or removed. There were no -crashes, infrastructure failures, or candidate retries. The complete -[development report](../data/releases/ns-cohort-v2-dev-validation.json) and -[ranked report](../data/releases/ns-cohort-v2-ranked-validation.json) preserve -every outcome and input/runtime identity. Both scores meet the registered -official release and venue requirements. - -Transport, with 1,602,111 unknowns, passed in 5.81 seconds of native elapsed -time, including 2.59 seconds of setup and 3.22 seconds of solving, under the -normal two-CPU/4-GiB/90-second contract. This is a measured candidate result; -the offline qualification algorithm and its 174.38-second preparation cost -are separate. NS ranking uses solved counts, with equal counts tied. These -partial coverage results do not establish an all-case performance reference. diff --git a/docs/NS_DATASET_CARD.md b/docs/NS_DATASET_CARD.md deleted file mode 100644 index f57c2e4..0000000 --- a/docs/NS_DATASET_CARD.md +++ /dev/null @@ -1,445 +0,0 @@ -# NS Mesh PDE — dataset card - -**49 original sparse operators · 24 provenance groups · 19 development / 30 ranked cases** - -NS Mesh PDE is the nonsymmetric mesh/PDE family of **LinearSolverBench**, a -benchmark for AI models and systems that produce linear solvers. Each case asks -a solver to solve `A x = b` accurately within a fixed resource budget. This -release measures **coverage: how many cases pass every accuracy check**. - -**NS means nonsymmetric**, not exclusively Navier–Stokes. The matrices span -fluid flow and transport, reservoir simulation, diffusion and thermal problems, -biomedical models, semiconductor devices, and solid mechanics. - -| Property | Frozen release | -| --- | --- | -| Release identity | `ns-mesh-cohort-v2` | -| Family / track | `ns-mesh-pde` / `coverage`; CLI alias `ns_mesh_pde` | -| Status | All 49 workloads qualified; both complete venue runs validated | -| Numerical data | Real square float64 sparse matrices; one manufactured RHS per matrix; zero initial guess | -| Matrix dimensions | 240–1,602,111 unknowns | -| Numerical nonzeros per matrix | 1,760–23,487,281, after storage canonicalization | -| Development | 19 cases in 9 provenance groups; fully reproducible with public inputs | -| Ranked | 30 cases in 15 disjoint groups; public matrices, operator-held RHS key and targets | -| Candidate budget | 2 CPU cores, 4 GiB, 90 seconds per case; 5,000-iteration request | -| Execution | One fresh native process per case; one execution, no retained solver state | -| Distribution | Original matrices from SuiteSparse; manifests, recipes, and evidence in this repository | -| Licensing | SuiteSparse matrices: CC BY 4.0; benchmark code: Apache-2.0 | - -> **Representation limit.** The development split covers every shared declared -> application and method category, but has no confirmed finite-volume case with -> 10,000–100,000 unknowns. The ranked wind-tunnel case falls in that range. -> Coverage of categories does not establish equal solver difficulty or represent -> every nonsymmetric PDE problem. - -[Access](#access-and-quickstart) · [Data format](#what-one-case-contains) · -[Curation](#where-the-data-come-from) · [Splits](#development-and-ranked-splits) · -[Evaluation](#accuracy-and-evaluation) · [Limitations](#limitations-and-responsible-interpretation) · -[Citation](#license-attribution-and-citation) - -## Intended use - -Use development cases to design, debug, and compare solver-generation systems. -Reserve ranked cases for final evaluation and disclose any prior use of their -matrices or published results. A benchmark submission is one C source file -implementing the public `solver_create` interface with approved HYPRE operations -and preconditioners. See the [candidate task](task-ns-coverage.md) and -[specification](SPEC.md) for the executable contract. - -The dataset also supports separately labeled numerical-method research. Runs -with different solvers, tolerances, resources, or workloads should report those -changes and their own results. This release does not measure speedup against an -all-case reference, physical simulation fidelity, or generalization to unseen -PDE families. It has no training split. - -## Access and quickstart - -All 49 **original matrices** are publicly downloadable from the -[SuiteSparse Matrix Collection](https://sparse.tamu.edu/). The two release -manifests specify exact matrix IDs, archive URLs, byte sizes, SHA-256 checksums, -canonical matrix identities, citations, and numerical qualification: - -| Manifest | Cases | Original compressed archive bytes | -| --- | ---: | ---: | -| [ns-mesh-cohort-v2-dev](../data/ns-mesh-cohort-v2-dev.json) | 19 | 562,789,795 | -| [ns-mesh-cohort-v2-ranked](../data/ns-mesh-cohort-v2-ranked.json) | 30 | 253,520,899 | - -These byte totals describe downloads, not peak RAM or prepared-data disk usage. -No raw matrix payloads are committed to this repository. The benchmark's -Hugging Face dataset hosts the separate FLASH family; it is not the download -source for these NS matrices. - -From a checkout, prepare development data with Python 3.12 or newer: - -```bash -uv venv --python 3.12 -source .venv/bin/activate -uv pip install -e . -linear-solver-bench dataset list --release ns-mesh-cohort-v2-dev -linear-solver-bench dataset prepare \ - --release ns-mesh-cohort-v2-dev --output data/prepared/ns-dev -``` - -Preparation downloads and verifies the original archives, canonicalizes sparse -storage, generates the fixed workload, and checks its numerical identity against -frozen qualification. It does not repeat offline factorizations. Downloads are -cached by content; use `--offline` to reuse an already populated, verified cache. -Use `--cache` to choose its location. - -For a small development check, add `--case ss-225-rademacher-0` to preparation -and choose a separate output directory. A subset cannot receive a complete-split -coverage score. The [README](../README.md#get-started-with-ns-coverage) continues -from preparation to compiling, running, and scoring a candidate. - -**Ranked access differs from development.** Anyone can download the ranked -matrices and inspect the manifests and scalar qualification evidence. Preparing -the exact frozen ranked RHS and targets requires the operator-held key matching -the manifest commitment. It is not included in the package. Public matrix -availability therefore does not imply public access to every ranked numerical -input. See [operator preparation](OPERATIONS.md#prepare-and-run-ns-coverage). - -## What one case contains - -A case is one square operator and one numerical workload, not a mesh, a complete -simulation, or a collection of alternative right-hand sides. For example, the -first development case is `ss-225-rademacher-0`: SuiteSparse ID **225**, -[HB/orsirr_1](https://sparse.tamu.edu/HB/orsirr_1), with 1,030 unknowns and 6,858 -numerical nonzeros, assigned to provenance group `reservoir-ors`. - -The source archive contains the original Matrix Market file and source -metadata. Benchmark preparation produces `manifest.json` and one trusted -schema-v2 compressed NumPy archive per case. The prepared manifest maps each -case ID to its relative archive path, hashes, dimensions, and qualification. -This archive has the benchmark's own schema; it is not a SciPy `save_npz` file. - -| Prepared archive field | Type / shape | Meaning | -| --- | --- | --- | -| `schema_version` | uint32 scalar | `2` | -| `case_id` | String scalar | Public workload identifier | -| `row_offsets` | uint64, `(n + 1,)` | Zero-based CSR row offsets | -| `column_indices` | uint32, `(nnz,)` | Zero-based columns, strictly increasing within each row | -| `values` | float64, `(nnz,)` | Original operator coefficients after canonicalization | -| `b` | float64, `(n,)` | Stored manufactured RHS | -| `x0` | float64, `(n,)` | All zeros | -| `tolerance` | float64 scalar | Relative stopping request, `1e-12` | -| `reference_kind` | String scalar | `manufactured` | -| `x_star` | float64, `(n,)` | Manufactured target, with entries −1 or +1 | - -The native HYPRE build uses signed 32-bit indices, so admitted dimensions and -nonzero counts must also fit that limit. Metadata and `x_star` remain with the -trusted evaluator; candidate code receives the numerical solve inputs and -stopping requests. The trusted NPZ archive is never given to a candidate. - -Read one prepared development case using the package's validating decoder: - -```python -import json -from pathlib import Path - -from linear_solver_bench.archive import decode_system - -root = Path("data/prepared/ns-dev") -manifest = json.loads((root / "manifest.json").read_text()) -record = manifest["cases"][0] -case = decode_system((root / record["relpath"]).read_bytes()) -A = case.public.matrix.to_scipy() -b, x0 = case.public.b, case.public.x0 -print(case.case_id, A.shape, A.nnz) -# ss-225-rademacher-0 (1030, 1030) 6858 -``` - -## Where the data come from - -The cohort reuses operators deposited by scientific contributors in SuiteSparse. -Source review identifies their PDE, mesh, discretization, and related models; -the benchmark does not regenerate their original simulations. Public citations -and grouping rationales are recorded in the -[source descriptors](analysis/ns-cohort-v2-descriptors.json), with computed -matrix properties in the [inventory](analysis/ns-cohort-v2-inventory.json). -Missing or ambiguous spatial methods remain labeled `unknown`. - -Cohort v2 retains the 43 operators from the historical `ns-mesh-pilot` and adds -six: HB/plskz362, Hohn/fd12, Janna/CoupCons3D, Janna/Transport, -Grueninger/windtunnel_evap3d, and Goodwin/Goodwin_095. The additions address -documented gaps and incompatible group-coverage constraints. The -[cohort review](NS_COHORT_V2.md) records their primary sources and selection -history. All additions and the final split were selected before the fresh v2 -targets and candidate evaluations; solver outcomes are not selector inputs. - -Admission and preparation follow three separate steps: - -1. **Scientific and matrix admission.** Require a real, square, original PDE - operator with documented provenance and material nonsymmetry: - `max(abs(A - A.T)) > 1e-12 * max(abs(A))`. Roundoff-scale asymmetry alone is - insufficient. The original PLSK ocean operator is admitted with its historical - eigenmode purpose disclosed; its squared PLAT relatives are excluded. -2. **Storage and workload construction.** Expand declared Matrix Market - symmetry, sum duplicate coordinates, remove exact zeros, and sort each row's - `(column index, value)` pairs. This sorting does not permute variables. - No scaling, shift, projection, normal equations, or replacement operator is - introduced. A versioned HMAC recipe generates one fixed Rademacher target - `x_target` with entries ±1 and exactly unit RMS; float64 arithmetic forms - `b = fl(A x_target)`, with `x0 = 0` and draw index zero. -3. **Numerical qualification.** Bind feasibility evidence to the complete stored - system identity and require a tenfold margin against each acceptance limit. - A new target or numerical operator changes that identity and needs new - qualification. Inputs are not redrawn or altered to obtain a passing witness. - -The target is a benchmark label, **not necessarily the exact solution of the -stored `A,b`**: rounding when forming `b` can separate the two. Qualification -audits RHS formation and tests feasibility relative to the gates; the available -guarantees depend on the recorded evidence type. -Of the 49 workloads, 46 use refined sparse-LU witnesses, one uses a PyAMG/GMRES -witness with inexact refinement, and two use exact strict-row-dominance -certificates with separate measured feasibility witnesses. The first two methods -provide empirical numerical evidence; they do not certify forward-error bounds. -See [qualification details](DATASET.md#pilot-releases) and the preserved -[offline attempt history](../data/releases/ns-cohort-v2-offline-reference-attempts.json). - -## Development and ranked splits - -The split unit is a **provenance group**: related models, studies, meshes, or -generators stay together. Shared authorship alone does not establish a shared -model. Unresolved overlap is documented and related cases are grouped -conservatively; group separation is not proof of complete code independence. - -The [frozen selection policy](analysis/ns-cohort-v2-policy.json) first requires -both splits to contain each known application, spatial method, size band, and -structural regime supported by at least two groups. Single-group categories -go to development. The same both-split rule applies to method-by-size -combinations supported by multiple groups; single-group combinations are -reported as gaps. Unknown method labels do not establish known-method coverage. - -Among feasible partitions, a deterministic selector balances five matrix -features, known-tag proportions, and development size. Its features are -`log10(n)`, `log10(nnz/n)`, relative Frobenius nonsymmetry, zero-diagonal fraction, -and weakly diagonally dominant row fraction. These are structural descriptors, -not difficulty labels. Exhaustive search considers 16,777,216 group partitions, -of which 6,531 satisfy the hard rules. The chosen partition has 19 development -and 30 ranked cases; the [selection report](analysis/ns-cohort-v2-selection.json) -also records alternative feasible partitions and remaining gaps. - -| Size band: unknowns `n` | Development | Ranked | -| --- | ---: | ---: | -| `n < 10,000` | 11 | 15 | -| `10,000 <= n < 100,000` | 4 | 12 | -| `100,000 <= n < 1,000,000` | 3 | 1 | -| `n >= 1,000,000` | 1 | 2 | -| **Total** | **19** | **30** | - -| Source-supported spatial method | Development | Ranked | -| --- | ---: | ---: | -| Finite element | 8 | 13 | -| Finite difference | 4 | 4 | -| Finite volume | 2 | 3 | -| Boundary element | 1 | 0 | -| Unknown | 5 | 10 | - -Method counts may overlap within a case; columns need not sum to split sizes. -They describe spatial methods, so finite differences used only for time -integration do not count as spatial finite differences. - -
-Complete group membership and SuiteSparse IDs - -Each ID below identifies one matrix and one workload. Resolve names, source -URLs, and archive hashes through the linked release manifests or `dataset list`. - -| Development provenance group | SuiteSparse IDs | -| --- | --- | -| `crack-log-grid` | 917, 918 | -| `driven-cavity-fem` | 380, 389, 395 | -| `finite-volume-rans` | 2334, 2335 | -| `janna-coupled-fe` | 2647, 2649 | -| `nonlinear-diffusion` | 1916 | -| `reservoir-ors` | 225, 227 | -| `semiconductor-devices` | 830, 832, 833 | -| `square-inlet-flow` | 444, 445 | -| `torso-electrophysiology` | 896, 897 | - -| Ranked provenance group | SuiteSparse IDs | -| --- | --- | -| `airfoil-flow` | 1319 | -| `atmospheric-elliptic` | 2265, 2267 | -| `cavity-vorticity` | 828, 829 | -| `circle-surface-poisson` | 2562, 2565, 2567 | -| `femlab-langemyr` | 864, 925, 926, 927 | -| `fidap-flow-transport` | 428, 431, 435 | -| `goodwin-flow-transport` | 446, 2817, 2822, 2825 | -| `heart-quasistatic` | 891 | -| `platzman-ocean-model` | 231 | -| `reservoir-sherman` | 246 | -| `reservoir-steam` | 251, 252 | -| `thermal-fem` | 377 | -| `unstructured-euler` | 820, 821, 822 | -| `unstructured-stokes` | 1862 | -| `windtunnel-evaporation` | 2815 | - -
- -Reproduce the split without matrix downloads, numerical targets, or solver runs: - -```bash -python tools/select_ns_cohort.py \ - --inventory docs/analysis/ns-cohort-v2-inventory.json \ - --policy docs/analysis/ns-cohort-v2-policy.json \ - --output results/ns-cohort-v2-selection.json -``` - -## Accuracy and evaluation - -The trusted verifier uses the original stored matrix and RHS. For the returned -solution `x`, it computes `r = b - A x` and requires all four acceptance gates: - -| Required metric | Candidate acceptance | Offline qualification | -| --- | --- | --- | -| Normwise backward error | `<= 1e-10` | `<= 1e-11` | -| Componentwise backward error | `<= 1e-8` | `<= 1e-9` | -| Relative target error, L2 | `<= 1e-5` | `<= 1e-6` | -| Relative target error, Linf | `<= 1e-5` | `<= 1e-6` | - -```text -normwise backward error = ||r||inf / (||A||inf ||x||inf + ||b||inf) -componentwise backward error = max_i |r_i| / (|A||x| + |b|)_i -relative target error, L2 = ||x - x_target||2 / ||x_target||2 -relative target error, Linf = ||x - x_target||inf / ||x_target||inf -relative residual = ||r||2 / ||b||2 [diagnostic only] -``` - -The requested relative stopping tolerance `1e-12` is guidance for the solver, -not a replacement for these acceptance gates. Output must also be finite, -correctly shaped, successful, leave inputs unchanged, and meet the deadline. -The [specification](SPEC.md#independent-accuracy-checks) defines zero-denominator -and nonfinite behavior. - -Compile the submission once and execute each case once in a fresh process. -Factory creation, setup/preconditioner construction, and solve count toward -measured time. Cases are loaded one at a time. Coverage is `solved_count` out -of the complete expected split, with equal counts tied and timing diagnostic. -Every case has equal weight; provenance groups are not equally weighted. -Solver failures and timeouts remain in the denominator. Missing cases or -infrastructure failures prevent a valid complete-split score. - -### Published example results - -The public [GMRES+AMG candidate](../submissions/gmres_amg.c) has these frozen -results in the two-core, 4 GiB, 90-second Modal venue: - -| Split | Solved | Coverage | Complete report | -| --- | ---: | ---: | --- | -| Development | 11 / 19 | 57.9% | [Development validation](../data/releases/ns-cohort-v2-dev-validation.json) | -| Ranked | 14 / 30 | 46.7% | [Ranked validation](../data/releases/ns-cohort-v2-ranked-validation.json) | - -All 49 cases ran once. CoupCons3D and Goodwin_095 timed out; their failures were -retained without retry. There were no crashes or infrastructure failures. -The [runtime manifest](../data/releases/cpu-runtime-v2.json) binds the environment. -These results demonstrate a valid partial-coverage evaluation, not an all-case -reference or a claim about the best attainable score. Offline qualification may -use more resources and different methods than a candidate; its success does not -guarantee success under the candidate budget. - -## Limitations and responsible interpretation - -- **Conditional representation.** This is a curated SuiteSparse cohort, not a - random sample of all PDE operators. Public availability, source documentation, - numerical admission, and the chosen taxonomy shape its scope. The split policy - balances declared descriptors; it does not establish matched condition numbers, - equal solver difficulty, or coverage of every joint regime. -- **Known gaps.** The ranked 40,816-row finite-volume wind-tunnel case has no - development counterpart in its size/method combination. Semiconductor and - boundary-element categories are development-only. Other development-only - method/size combinations are finite differences and boundary elements at - 100,000–1,000,000 unknowns, and finite elements at at least 1,000,000 unknowns. - Unknown methods remain unknown. The [cohort review](NS_COHORT_V2.md#frozen-partition-and-remaining-gaps) - gives exact support counts. -- **Public-data exposure.** Matrix IDs, coefficients, provenance, and example - results are public and may have appeared in model training or prior research. - Group separation and an operator-held ranked RHS key do not establish an - uncontaminated test set. Disclose benchmark exposure and ranked-set tuning; - use development data for iterative design. -- **Manufactured workloads.** One ±1 target and zero initial guess per matrix - do not reproduce native physical forcing, multiple RHS behavior, warm starts, - or trajectories. Target-based forward errors are not universally certified - errors against the exact stored-system solution. The release publishes the - evidence type and its uncertainty for every case. -- **Score interpretation.** Groups have different case counts, so related cases - can contribute more total score than a singleton group. Compare scores only - under the same frozen workloads and enforced venue. Single-execution timings - are diagnostics, not estimates of timing variability; local runs are labeled - `local-uncontrolled` and do not establish official resource enforcement. -- **Data subjects and context.** The records are numerical operators and - scientific provenance, not person-level observations. Some sources model - biomedical systems; that label describes the equations, not a clinical - prediction task. Public contributor metadata are retained for attribution. - This card does not certify the absence of sensitive information in every - upstream source archive. - -## Reproducibility and maintenance - -The release manifests are the authority for ordered cases, scientific admission, -workload identities, gates, budgets, and scoring. Their `manifest_sha256` values -are semantic content identities checked by the package, not byte checksums of -the pretty-printed JSON files: - -```text -ns-mesh-cohort-v2-dev -9927ea72bac431d871b0547b966f9ce78b550ca0d1285c7060cdcb2e3e688ecc - -ns-mesh-cohort-v2-ranked -a86a2d42765364bcadd48fdef22dc2f0900d04e98fe4fe49fe551b84d591ad52 -``` - -Per-case source-archive hashes, canonical-matrix hashes, and complete-system -hashes distinguish download integrity from numerical identity. Hashes bind -content; a self-authored consistent manifest does not establish an official -release. The package's trusted registry fixes the accepted release digests. - -All 49 prepared systems reproduced the same canonical-matrix and complete-system -hashes with NumPy 2.0.2, SciPy 1.14.1, and Python 3.12.11 on macOS arm64. The -[reproduction receipt](../data/releases/ns-cohort-v2-preparation-reproduction.json) -records the comparison. This validates that tested preparation environment, -not every platform or the reproducibility of every offline solver's metrics. - -LinearSolverBench maintainers curate the manifests, code, and this card; -SuiteSparse contributors and maintainers provide the original matrices and -source records. Report issues through the benchmark repository's issue tracker, -following the [contribution guidelines](../CONTRIBUTING.md) and including release -ID, case ID, the observed discrepancy, and public evidence. -Do not attach ranked keys or trusted target archives to public issues. - -Documentation corrections can update this card. Changes to numerical inputs, -split membership, or evaluation contracts require a new frozen release and -relevant qualification/validation; they must not silently replace this release. -The historical 43-case `ns-mesh-pilot` remains available with its original 11/32 -split. Its scores are not directly comparable to cohort v2: both membership and -manufactured workloads differ. There is no scheduled refresh cadence. - -## License, attribution, and citation - -SuiteSparse states that its matrices are distributed under **CC BY 4.0** and -requests preservation of matrix metadata and matrix-specific citations. See the -[collection's license and attribution guidance](https://sparse.tamu.edu/about#license). -Prepared workloads document their modifications: canonical float64 CSR storage -and a manufactured RHS/target instead of a native physical workload. Retain -original source attribution and identify prepared workloads as derivatives when -redistributing them. Benchmark software is separately -[Apache-2.0 licensed](../LICENSE); see [dataset notices](../data/NOTICE.md). - -For publications, cite the benchmark using [CITATION.cff](../CITATION.cff), include -the code revision, release ID, split, and manifest digest, and cite SuiteSparse -and the original matrix sources: - -- Timothy A. Davis and Yifan Hu. *The University of Florida Sparse Matrix - Collection*. ACM Transactions on Mathematical Software, 38(1), 2011. - [DOI: 10.1145/2049662.2049663](https://doi.org/10.1145/2049662.2049663). -- Scott P. Kolodziej et al. *The SuiteSparse Matrix Collection Website - Interface*. Journal of Open Source Software, 4(35), 1244, 2019. - [DOI: 10.21105/joss.01244](https://doi.org/10.21105/joss.01244). -- Matrix-specific references in the original Matrix Market headers and the - [source descriptors](analysis/ns-cohort-v2-descriptors.json). - -*Card last reviewed: 2026-09-13. Organization follows the motivation, -composition, provenance, use, and maintenance questions in -[Datasheets for Datasets](https://arxiv.org/abs/1803.09010) and the -[Hugging Face dataset-card guidance](https://huggingface.co/docs/hub/datasets-cards). -These are documentation references, not papers introducing this cohort.* diff --git a/docs/NS_SPLIT_REVIEW.md b/docs/NS_SPLIT_REVIEW.md deleted file mode 100644 index 7c84b1c..0000000 --- a/docs/NS_SPLIT_REVIEW.md +++ /dev/null @@ -1,186 +0,0 @@ -# NS development/ranked split review - -This is the historical review of `ns-mesh-pilot`. The -[cohort v2 review](NS_COHORT_V2.md) records its replacement selection, additional -source screening, qualification status, and remaining limitations. The frozen -43-case inventory and the findings below remain unchanged for reproducibility. - -Historical status: **the 11-development/32-ranked partition is provisional for -representativeness**. Its 43 workloads remain numerically qualified and its full -Modal execution remains validated. Those checks establish feasible accuracy -requirements and working evaluation; they do not establish that development -feedback covers the scientific and numerical regimes encountered in ranking. -That partition does not satisfy the representative-split requirement; the -replacement cohort's declared coverage and limitations are reviewed separately. - -This review uses the frozen manifests, the -[matrix-property audit](analysis/ns-split-audit.json), and the -[public-source descriptors](analysis/ns-source-descriptors.json). The audit -reads matrix properties without evaluating candidate solvers. Source descriptors -record application and discretization evidence from original public metadata, -with unresolved claims labeled explicitly. These descriptors are a draft for -scientific review; the counts below reflect the evidence established so far, -and unresolved classifications require further source review. - -## Gaps in the current partition - -Development contains four provenance groups and ranked contains 17. Keeping -groups disjoint limits related-data exposure, but does not by itself balance -applications, discretizations, dimensions, or matrix structure. - -| Number of unknowns | Development cases | Ranked cases | Available provenance support | -| --- | ---: | ---: | --- | -| Below 10,000 | 7 | 17 | Multiple groups | -| 10,000 to below 100,000 | 4 | 11 | Multiple groups | -| 100,000 to below 1,000,000 | 0 | 2 | Both cases belong to one torso group | -| 1,000,000 or more | 0 | 2 | Both cases belong to one atmospheric group | - -The largest development matrix has 26,068 unknowns; the largest ranked matrix -has 1,489,752. Neither of the two largest size bands can appear on both sides -through a different partition of the current inventory while keeping provenance -groups disjoint: each band has only one group. Adding independent groups is -necessary if the replacement split is to represent both bands in development. - -The source review also finds one-sided application coverage: - -| Broad application tag | Development cases | Ranked cases | -| --- | ---: | ---: | -| Fluid flow and coupled transport | 4 | 17 | -| Elliptic, diffusion, or thermal systems | 3 | 6 | -| Reservoir simulation | 0 | 5 | -| Biomedical systems | 0 | 3 | -| Semiconductor devices | 3 | 0 | -| Solid mechanics or fracture | 1 | 1 | - -These are coarse descriptions of the documented application, not solver -difficulty classes. Development has no reservoir or biomedical group. All three -semiconductor cases occupy one development group. A change in case counts alone -cannot address the absence of independent provenance support for a category. - -Confirmed discretization evidence differs as well: - -| Source-supported method | Development cases | Ranked cases | -| --- | ---: | ---: | -| Finite element | 8 | 10 | -| Finite volume | 0 | 4 | -| Finite difference | 0 | 6 | -| Boundary element | 0 | 1 | -| Unresolved by the reviewed metadata | 3 | 12 | - -One ranked case has both finite-difference and boundary-element tags, so these -rows are not mutually exclusive. An unresolved method is not evidence that a -missing method is covered. A group introduction or generic collection label -must not override more specific matrix metadata. - -Numerical structure also has uncovered extremes. The largest zero-diagonal -fraction in development is about 0.2628, while ranked includes -[Hohn/fd15](https://sparse.tamu.edu/Hohn/fd15), whose entire diagonal is zero. -The audit additionally compares dimension, average nonzeros per row, relative -nonsymmetry, and the fraction of weakly diagonally dominant rows. These traits -help identify gaps even when broad application names overlap. - -## What the matrix audit establishes - -[audit_ns_split.py](../tools/audit_ns_split.py) computes five features from each -canonical matrix: `log10(n)`, `log10(nnz/n)`, the relative Frobenius difference -between the matrix and its transpose, the zero-diagonal fraction, and the -weakly diagonally dominant row fraction. It reports split ranges, medians, and -per-feature Wasserstein distances normalized by the pooled feature range. - -Reproduce the audit directly from public SuiteSparse matrices, without ranked -RHS keys or candidate executions: - -```bash -python tools/audit_ns_split.py \ - --release data/ns-mesh-pilot-dev.json \ - --release data/ns-mesh-pilot-ranked.json \ - --descriptors docs/analysis/ns-source-descriptors.json \ - --output results/ns-split-audit.json -``` - -Use `--cache` to choose an archive cache and `--offline` to require cached -archives. Operators may instead supply the two existing prepared directories -with repeated `--prepared` arguments. Both input paths reproduced the same -audit for all 43 matrices. Matrices are processed individually; targets, RHS -values, qualification outcomes, and solver results do not enter the comparison. - -These distances describe the observed marginal distributions. There is no -universal acceptable-distance threshold in this audit. An improved average -distance can still hide an absent application, an uncovered extreme, or a -different joint distribution. Matching individual feature distributions would -not establish equal solver difficulty. The audit provides no condition-number, -nonnormality, convergence, or forward-error certificate. Its floating-point -dominance diagnostic is separate from the exact dominance certificates used -to qualify two manufactured workloads. - -## Requirements for a replacement split - -The development count and number of development groups are open design choices; -neither 11 cases nor four groups is a target to preserve. Define the intended -scientific scope and preparation/evaluation budget before selecting a new -inventory and partition. -Declare the application/discretization taxonomy, size bands, and structural -regimes before selection so those definitions cannot be adjusted afterward to -make the chosen split appear balanced. - -1. Keep related matrices, refinements, duplicates, and source runs within one - provenance group, and keep those groups disjoint across development and - ranking. Distinct collection group names alone do not establish independence. -2. For each broad application, confirmed discretization, size band, and - numerical structural regime declared as represented in ranking, require - independent development support wherever the release claims representative - development coverage. Explicitly review important combinations, such as - large finite-volume systems or zero-diagonal flow operators. Unknown labels - do not satisfy a coverage requirement. -3. Identify requirements that the available provenance groups cannot satisfy. - Acquire and qualify independent groups when necessary. Any scientific scope - restriction must be explicit and justified before selection; moving related - cases across the split boundary is not a solution. -4. Freeze a documented balance objective, multivariate review procedure, and - resource budget before choosing the partition. Separate required regime - coverage from descriptive distribution summaries, explain feature scaling - and weights, and inspect tails and joint structure rather than optimizing - only a mean marginal distance. Report unresolved tradeoffs and sensitivity - to reasonable alternative partitions. No numerical cutoff is implied here. -5. Select from scientific provenance and matrix properties without candidate - convergence, speed, or coverage outcomes. Qualify frozen manufactured inputs - and validate their execution budgets separately; solver failures must remain - valid coverage outcomes rather than becoming a selection rule. -6. Publish the proposed membership and evidence for review. A new partition - requires a new release identity, fresh deterministic RHS generation and - qualification, updated registered digests, and complete venue validation. - Preserve the original frozen manifests and reports for reproducibility. - -## Acquisition candidates - -Three public matrices have useful source evidence for further investigation. -They have not been downloaded or numerically qualified for this replacement -release, and are not admitted benchmark cases: - -| Candidate | SuiteSparse ID | Unknowns | Reported nonzeros | Source evidence and possible contribution | -| --- | ---: | ---: | ---: | --- | -| [Janna/Transport](https://sparse.tamu.edu/Janna/Transport) | 2649 | 1,602,111 | 23,487,281 | Real nonsymmetric, tetrahedral finite-element discretization of coupled flow and transport; a potential additional large-matrix provenance group | -| [Janna/CoupCons3D](https://sparse.tamu.edu/Janna/CoupCons3D) | 2647 | 416,800 | 17,277,420 | Real nonsymmetric, fully coupled three-dimensional poroelastic consolidation with spatial finite elements; potential support in the 100,000-to-1,000,000 band | -| [Grueninger/windtunnel_evap3d](https://sparse.tamu.edu/Grueninger/windtunnel_evap3d) | 2815 | 40,816 | 803,978 | Real nonsymmetric coupled Navier–Stokes/Darcy system with finite-volume and MAC discretization; potential additional finite-volume support | - -The CoupCons3D source identifies finite differences for time discretization; -that does not establish a spatial finite-difference regime. Its reported -17,277,420 numerical nonzeros exclude 5,044,916 explicitly stored zeros. Use -matrix-specific descriptions and symmetry records when they differ from a -generic group introduction. - -Transport and CoupCons3D share authors and a cited paper. Their mesh and code -overlap is unresolved, so conservatively treat them as one Janna provenance -group and keep them in the same split. If qualified and admitted together in -development, they could supply additional source support in both missing large -size bands while retaining the existing torso and atmospheric groups in -ranking. That would address size-band absence; it would not establish coverage -of the same physics, discretizations, or joint numerical regimes. - -Each still needs original archive/hash verification, independent provenance -review, admission and numerical qualification, and measured resource checks. -Further semiconductor leads in the 100,000-to-1,000,000 band have unresolved -provenance or usage questions and are not ready data. Broader application and -discretization coverage remains unresolved. Additional acquisition and an -explicit partition review are required before claiming representative -development coverage. diff --git a/docs/OPERATIONS.md b/docs/OPERATIONS.md deleted file mode 100644 index fdfc8e8..0000000 --- a/docs/OPERATIONS.md +++ /dev/null @@ -1,195 +0,0 @@ -# Evaluator operations - -Preparation and verification run on the trusted operator side. Manufactured -targets, optional numerical reference vectors, and ranked RHS keys stay there. -The candidate receives only the current numerical input in a network-disabled -Modal sandbox. Pilot cases are loaded and streamed one at a time, compiled -candidate code is reused, and each case runs once in a fresh native process. - -## Prepare and run NS coverage - -NS cohort v2 contains 19 development and 30 ranked cases in disjoint provenance -groups. All 49 workloads are qualified, both releases are frozen, and both -complete venue runs are validated. See the -[cohort review](NS_COHORT_V2.md) for current status. From a checkout: - -```bash -linear-solver-bench dataset prepare \ - --release ns-mesh-cohort-v2-dev --output data/prepared/ns-dev -modal run modal_app.py \ - --source submissions/candidate.c --cases data/prepared/ns-dev \ - --official --output results/ns-coverage.json -linear-solver-bench score results/ns-coverage.json --output results/ns-score.json -``` - -Coverage requires no timing reference or calibration. Partial solver coverage -is a valid result: every expected case remains in the report and solved counts -determine ranking. All expected cases must be evaluated, even when some fail. - -Prepare `ns-mesh-cohort-v2-ranked` with its operator-held key using -`dataset prepare --rhs-key-file`. The key must match the release's commitment; -it is not packaged with the benchmark. Changing it changes numerical inputs -and requires a new release and qualification. Store it outside candidate-visible -paths and images. The earlier `ns-mesh-pilot` remains a historical 43-case -release, and `ns-mesh-dev-pilot` remains a separate two-case integration check. - -Frozen NS qualification is reused only after binding it to the exact prepared -numerical inputs. Normal downloads do not rerun sparse factorizations. The 49 -qualified cohort v2 workloads use 46 empirical refined-LU records, one -PyAMG/GMRES v2 record with inexact refinement, and two exact row-dominance -certificates. The iterative witness uses relative tolerance `1e-13` initially -and `1e-2` for two correction solves; all final qualification gates are unchanged. -Each evidence type has explicit witness/reference semantics. No candidate -repetitions are added by offline qualification. The large-case offline -qualification budget of 32 GiB and one hour is separate from the candidate's -4 GiB and 90-second limits. - -The complete cohort v2 Modal runs give 11/19 development and 14/30 ranked for -the public GMRES+AMG solver. CoupCons3D in development and Goodwin_095 in ranked -reached their 90-second deadlines and remain failed cases, with no retries. -All 49 cases ran exactly once; there were no crashes or infrastructure failures. -The [development report](../data/releases/ns-cohort-v2-dev-validation.json) and -[ranked report](../data/releases/ns-cohort-v2-ranked-validation.json) record -every execution. Transport's 1,602,111-row case passed in 5.81 seconds within -4 GiB. A solver need not pass every NS case to obtain a valid official coverage -score. Historical 10/11-development and 14/32-ranked results belong to -`ns-mesh-pilot`. - -## Run and score FLASH replay - -The public FLASH manifests are `flash-replay-dev-pilot` and -`flash-replay-ranked-pilot`. The fixed reference implementation is -`submissions/gmres_amg.c`. Use the frozen calibration for the selected split -when evaluating candidates. It contains the measured reference time for every -case and binds those timings to the released numerical inputs, runtime, and -Modal venue. The reference is evaluated once when freezing a release; ordinary -candidate evaluation reuses its timings. - -The fixed reference passed all 96 development and 248 ranked cases, including -88 correctness controls, in the official Modal venue. Its registered calibration -artifacts are bundled with the release. From a checkout: - -```bash -linear-solver-bench dataset prepare \ - --release flash-replay-ranked-pilot --output data/prepared/flash-ranked -modal run modal_app.py \ - --source submissions/candidate.c --cases data/prepared/flash-ranked \ - --calibration data/releases/flash-replay-ranked-reference.json \ - --official --output results/flash-candidate.json -linear-solver-bench score results/flash-candidate.json \ - data/releases/flash-replay-ranked-reference.json \ - --output results/flash-score.json -``` - -For development, select `flash-replay-dev-pilot` and use -`data/releases/flash-replay-dev-reference.json`. Calibration files are trusted -operator inputs and stay outside the candidate sandbox. - -Scoring rejects a different numerical release, case order, prepared corpus, -accuracy/execution/scoring contract, runtime, venue, roles, or weights. A failed -candidate case or control leaves aggregate speedup null. Without a reference, -a replay report remains useful for accuracy diagnostics but scores as -`awaiting_reference`. - -Local runs can provide development references, but their `local-uncontrolled` -timings cannot be mixed with Modal results or treated as official venue timings. - -## Regenerate a reference for development or a new release - -Requalify a reference when intentionally changing its source, numerical release, -runtime, or venue. For a complete prepared split: - -```bash -modal run modal_app.py \ - --source submissions/gmres_amg.c --cases data/prepared/flash-ranked \ - --output results/flash-reference.json -linear-solver-bench calibrate results/flash-reference.json \ - --output operator/flash-calibration.json -``` - -`calibrate` accepts only a complete replay report with every scored case and -correctness control passing. It freezes the one measured execution per case, -including the reference source identity and complete report. It does not adjust -pilot deadlines or run the reference again. Publish the reference source and -configuration with its report and calibration artifact. - -Register the exact published calibration digest for its release, runtime, and -venue before accepting official replay scores. An unregistered calibration can -produce a development score, but reports `reference_status: unregistered` and -`official: false` even when its solver passes every case. - -## Frozen settings and official evaluation - -Pilot execution takes deadlines, maximum iteration requests, CPU cores, and -memory from the release manifest. Modal applies matching CPU and memory -requests and hard limits. Networking is disabled. Local evaluation reports the -same requested settings but does not claim those resource limits are enforced. -The candidate factory receives the frozen iteration request and tolerance. -Both pilot releases use two CPU cores, 4 GiB, a 90-second case deadline, and a -5,000-iteration request. The published -[runtime manifest](../data/releases/cpu-runtime-v2.json) fixes the runtime used -for official replay timings in `modal-sandbox-pilot-cpu-v2`. - -Add `--official` to `modal run modal_app.py` when evaluating a trusted published -release. This checks its exact digest against the packaged release registry -and requires the complete split. A draft manifest may be evaluated without -that flag, but hashing a user-authored manifest does not make it an official -release. The resulting score records whether release trust and venue conditions -qualify it as official. Official replay also requires the registered calibration -digest for that exact release, runtime, and venue. Hashing or copying a custom -passing reference cannot make it the registered benchmark reference. - -Candidate failures are recorded without retrying or skipping other cases. -Malformed inputs, missing expected cases, and infrastructure failures invalidate -the evaluation; they do not reduce its denominator. Preserve the full report -and original source bytes, whose SHA-256 is bound into the report. - -## Runtime and installed packages - -```bash -linear-solver-bench runtime build --output build/runtime -linear-solver-bench candidate validate submissions/candidate.c --runtime build/runtime -``` - -The runtime pins HYPRE, compiler/build information, native driver, and the -`solver_create` entry point. After changing the candidate interface or runtime, -build a new runtime, recompile submissions, and requalify references before -using new timing artifacts. Calibration from the previous runtime is rejected. - -The Python CLI includes packaged native sources, public headers, benchmark -configuration, and dataset manifests. Installed release IDs work outside a -checkout. A writable cache is separate from installed resources; use -`LINEAR_SOLVER_BENCH_CACHE` or `dataset prepare --cache` when needed. Modal -examples use the checkout's `modal_app.py` and the optional Modal dependency. -Fresh wheel installation, installed manifest discovery, public single-case -downloads, offline reuse, and installed native compilation have been checked; -see [pilot validation](PILOT_VALIDATION.md). - -## Existing v1 operations - -V1 remains separate and retains 203 ranked cases, three repetitions, its own -condition-dependent tolerances, and calibration-based timing tie-breaks. -Commands without a pilot `--release` retain the v1 preparation route: - -```bash -mkdir -p secrets -openssl rand 32 > secrets/ranked-rhs-v1.bin -linear-solver-bench dataset prepare --split ranked \ - --rhs-key-file secrets/ranked-rhs-v1.bin --output data/prepared/ranked-v1 -modal run modal_app.py \ - --source operator/reference.c --cases data/prepared/ranked-v1 \ - --deadline 60 --output results/reference-modal-ranked-v1.json -linear-solver-bench calibrate results/reference-modal-ranked-v1.json \ - --output operator/calibration-cpu-v1.json -modal run modal_app.py \ - --source submissions/candidate.c --cases data/prepared/ranked-v1 \ - --calibration operator/calibration-cpu-v1.json \ - --output results/candidate-modal-ranked-v1.json -linear-solver-bench score results/candidate-modal-ranked-v1.json \ - operator/calibration-cpu-v1.json -``` - -Keep the persistent ranked key and all prepared reference vectors on the trusted -operator side. Freeze the benchmark commit, runtime, dataset identities, venue, -reference source, and calibration before accepting official results. Changes to -scientific requirements or venue require a new version. diff --git a/docs/PILOT_VALIDATION.md b/docs/PILOT_VALIDATION.md deleted file mode 100644 index 69e449d..0000000 --- a/docs/PILOT_VALIDATION.md +++ /dev/null @@ -1,124 +0,0 @@ -# Pilot validation - -The coverage and replay evaluations use the fixed public candidate -[`gmres_amg.c`](../submissions/gmres_amg.c). Each split compiled the candidate -once, then evaluated each case once in a fresh native process. The Modal venue -enforced 2 CPUs, 4 GiB RAM, a 90-second case budget, and 5,000 iterations. Creation, -setup, and solve time counted; the accuracy gates and manufactured targets were -unchanged throughout validation. - -| Track and split | Cases | Accurate solves | -| --- | ---: | ---: | -| NS cohort v2 coverage, development | 19 | 11 | -| NS cohort v2 coverage, ranked | 30 | 14 | -| FLASH replay, development | 96 | 96 | -| FLASH replay, ranked | 248 | 248 | - -The [NS cohort review](NS_COHORT_V2.md) records the new 49-case inventory, -source evidence, selection policy, and remaining representation gaps. All -49 cases received one execution, with no crashes or infrastructure failures. -Janna/CoupCons3D in development and Goodwin/Goodwin_095 in ranking reached the -90-second limit and count as unsuccessful solves; neither was retried or -removed. The other 22 unsuccessful solver outcomes also remain in the coverage -count. The 1,602,111-row Transport case passed in 5.81 seconds of native elapsed -time under the same 4 GiB limit. -The full scalar reports preserve every outcome, resource limit, release -identity, and prepared-system identity: -[`development`](../data/releases/ns-cohort-v2-dev-validation.json) and -[`ranked`](../data/releases/ns-cohort-v2-ranked-validation.json). - -All 49 fresh workloads independently passed the unchanged tenfold -qualification margins: 46 refined sparse-LU witnesses, one refined PyAMG/GMRES -witness, and two exact row-dominance certificates. The -[offline attempt history](../data/releases/ns-cohort-v2-offline-reference-attempts.json) -preserves earlier unsuccessful reference attempts and their separate resource -budgets. Those offline timings do not define a candidate performance baseline. - -These are examples of coverage evaluation. This candidate is not a universal -NS reference, and the NS performance track remains unsupported. The older -`ns-mesh-pilot` release remains frozen with its historical 10/11 development -and 14/32 ranked results; those runs had no crashes, timeouts, infrastructure -failures, or retries. The replacement release uses fresh manufactured targets, -so results from the two releases should not be treated as a paired solver -comparison. - -The FLASH reference passed all 344 cases: 96 development cases and 248 ranked -cases, including all 88 correctness controls. Each case ran once, with one -compilation per split. - -The fixed identities used for these runs are: - -| Item | SHA-256 | -| --- | --- | -| Candidate source | `89f8ff13a1c21448692c1df9cfc363e180dc0838f031d531ca51573b43ea16b0` | -| Modal CPU runtime manifest | `4b09e68ce2fc17261c1d776bb01d63ec40f8f92388b9aff95c046f7c1f8bb076` | - -The frozen runtime manifest is -[`data/releases/cpu-runtime-v2.json`](../data/releases/cpu-runtime-v2.json). -The FLASH reference artifacts are -[`flash-replay-dev-reference.json`](../data/releases/flash-replay-dev-reference.json) -and -[`flash-replay-ranked-reference.json`](../data/releases/flash-replay-ranked-reference.json). -Each reference artifact embeds its complete verified report, including every -scored case and correctness control, source and executable identities, exact -prepared inputs, resource limits, and per-case timings. A replay candidate must -match the registered reference's release, runtime, and venue and pass all cases -before receiving an aggregate speedup. - -The data release pins Hugging Face commit -[`3da5eda0ce3e85da1808d4b62d28d9111127e354`](https://huggingface.co/datasets/hgarud/LinearSolveBench/tree/3da5eda0ce3e85da1808d4b62d28d9111127e354). -All 344 published archive digests were checked against the release inventory; -anonymous downloads of sample cases also passed checksum and numerical-input -validation. NS preparation uses the original public SuiteSparse archives. A -freshly installed wheel successfully prepared NS and FLASH data without -depending on a source checkout. - -The reference source, runtime manifest, and frozen calibrations are published -at Hugging Face commit -[`cbb06d2735b38d4078182d9db92fbc63790fe87b`](https://huggingface.co/datasets/hgarud/LinearSolveBench/tree/cbb06d2735b38d4078182d9db92fbc63790fe87b/references). -Anonymous downloads of all eight files added or updated in that publication -matched their local release bytes exactly. - -Two independent clean Linux runtime builds produced identical complete -manifests, including the HYPRE library, trusted driver, and header tree. Two -clean macOS builds also matched each other. This is reproducibility within each -platform and toolchain; it does not require Linux and macOS binaries to match. -Preparing all 49 fresh NS workloads with the minimum supported NumPy 2.0.2 and -SciPy 1.14.1 reproduced every canonical matrix and complete numerical-system -identity obtained with the newer validation environment. The -[reproduction receipt](../data/releases/ns-cohort-v2-preparation-reproduction.json) -records all 49 matches. This repeats source decoding and input preparation; -ordinary public preparation reuses the frozen qualification evidence and does -not rerun expensive reference factorizations. - -All 167 automated tests pass. They cover the selector against independent -exhaustive and distance calculations, release-to-selection bindings, -qualification, complete venue reports, and loading public resources from a -built wheel outside the checkout. -Both registered FLASH references score exactly 1.0 against themselves. The -original v1 split manifests and earlier frozen family releases retain their -identities. Ruff checks pass. - -To repeat the public development evaluation from a checkout: - -```bash -uv pip install -e '.[dev,qualification]' -python -m pytest -linear-solver-bench dataset prepare \ - --release ns-mesh-cohort-v2-dev --output data/prepared/ns-dev -modal run modal_app.py --source submissions/gmres_amg.c \ - --cases data/prepared/ns-dev --output results/ns-dev.json \ - --score-output results/ns-dev-score.json -linear-solver-bench dataset prepare \ - --release flash-replay-dev-pilot --output data/prepared/flash-dev -modal run modal_app.py --source submissions/gmres_amg.c \ - --cases data/prepared/flash-dev --output results/flash-dev.json \ - --calibration data/releases/flash-replay-dev-reference.json \ - --score-output results/flash-dev-score.json -``` - -These are single measurements, not estimates of timing variance or stable -hardware-level precision. Rebuilt runtimes must match the frozen identity before -using its timing reference. Hashes bind artifact contents and detect changes; -they do not authenticate who performed an evaluation. Official results depend -on the trusted operator running the declared venue and publishing its reports. diff --git a/docs/SPEC.md b/docs/SPEC.md deleted file mode 100644 index 63a4338..0000000 --- a/docs/SPEC.md +++ /dev/null @@ -1,182 +0,0 @@ -# Benchmark specification - -## Supported contracts - -The pilot benchmark identity is `linear-solver-bench-pilot-cpu-v2`. It supports: - -| Family / track | Accuracy contract | Scoring contract | -| --- | --- | --- | -| `ns-mesh-pde` / `coverage` | `ns-mesh-accuracy-v1` | `ns-coverage-count-v1` | -| `magnetic_diffusion_flash` / `replay` | `flash-replay-accuracy-v1` | `flash-replay-speedup-v1` | - -Both use `independent-case-once-v1`: one execution per case with no retained -solver state between cases. The release fixes case order, roles, weights, -tolerances, deadlines, iteration requests, and resources. Unsupported family -and track combinations are rejected. The existing v1 contract is described -separately below and retains its original identity. - -## Candidate artifact and inputs - -A submission is one regular UTF-8 C source file with LF endings, at most 65,536 -bytes. It contains exactly one include directive, for `HYPRE_parcsr_ls.h`, and -exports exactly `solver_create`: - -```c -HYPRE_Int solver_create(HYPRE_Solver *solver, - HYPRE_Real relative_tolerance, - HYPRE_Real absolute_tolerance, - HYPRE_Int maximum_iterations); -``` - -The evaluator force-includes `nsl_hypre_solver.h` and audits object symbols -before linking. Allowed capabilities include selected vector, reduction, and -matrix-vector operations and approved preconditioner lifecycle/configuration -calls. I/O, clocks, processes, networking, dynamic linking, and stock outer -solvers are excluded. Operating-system isolation remains a separate boundary. - -Each input contains a square float64 CSR matrix `A`, vectors `b,x0`, and relative -tolerance `tau`. Matrix dimensions and nonzero counts must fit the native int32 -build. The factory receives the release's iteration limit and absolute tolerance -zero. The candidate must honor those requests and preserve `A,b`. - -NS uses a manufactured target with entries in `{−1,+1}`, exactly unit RMS, -`b = fl(A x_target)`, and `x0 = 0`. FLASH preserves captured `b,x0,tau`; captures -requiring nonzero absolute tolerance are incompatible with this pilot. Case IDs, -source names, provenance, keys, and reference vectors stay outside candidate code. - -## Lifecycle and timing - -Compile the candidate once. For every pilot case, start one fresh native process -and construct new solver objects and workspace. There are no warm-ups, repeated -evaluations, or candidate-failure retries. A candidate failure on one case does -not prevent evaluation of the remaining cases. - -Input decoding and trusted HYPRE object construction precede timing. The measured -elapsed time covers factory creation, setup/preconditioner construction, and -solve. Destruction, solution extraction, input-mutation checks, and trusted -verification follow timing. Reports retain elapsed time and phase timings from -that single execution. Local and Modal evaluators load numerical cases one at -a time and retain scalar results between cases. - -## Independent accuracy checks - -Verification always uses the original serialized operator and RHS. For -`r = b - A x`, compute: - -```text -relative_residual = ||r||2 / ||b||2 -eta_norm = ||r||inf / (||A||inf ||x||inf + ||b||inf) -eta_comp = max_i |r_i| / (|A||x| + |b|)_i -forward_l2 = ||x - x_reference||2 / ||x_reference||2 -forward_linf = ||x - x_reference||inf / ||x_reference||inf -``` - -| Metric | NS coverage | FLASH replay | -| --- | --- | --- | -| Relative residual | Diagnostic | `<= tau * (1 + 1e-6)` | -| Normwise backward error | `<= 1e-10` | `<= max(tau, 20 eps)` | -| Componentwise backward error | `<= 1e-8` | Diagnostic, without row floors | -| Relative forward error, L2 | `<= 1e-5` against manufactured target | Optional numerical-reference diagnostic | -| Relative forward error, Linf | `<= 1e-5` against manufactured target | Optional numerical-reference diagnostic | - -Here `eps` is float64 machine epsilon. NS acceptance limits are fixed across -cases; the solver stopping request, initially `1e-12`, is a separate setting. -Relative-residual guidance of `1e-8` does not replace the NS backward/forward gates. - -Manufactured targets are required for NS and cannot be replaced by numerical -references or omitted. Floating-point RHS formation means a target need not be -the exact solution of the stored system. Offline qualification requires one tenth -of every error limit and records RHS formation uncertainty. Numerical references -are labeled as such; they carry no exact-truth claim and do not add forward-error -gates to FLASH. - -NS qualification has two explicit evidence paths. Refined sparse LU performs -one higher-precision residual correction with the same factors and measures the -result; this remains empirical evidence. Strict row dominance can instead -certify stored-system forward consistency. For positive -`d = min_i (|a_ii| - sum_{j != i}|a_ij|)` and exact formation residual -`e = b - A x_target`, the bound `||A^-1||inf <= 1/d` gives both relative forward -discrepancies at most `||e||inf/d`, because the target has entries ±1. Coefficient -sums and formation residuals are computed exactly as integers in binary64 units, -then reported bounds are rounded outward. This certified bound must be at most -`1e-6`. Separate binary64 measurements at the target demonstrate a feasible -output witness and must satisfy all four tenfold limits; they are not claimed -as rigorous bounds for the verifier's floating-point arithmetic. - -Zero divided by zero is defined as zero for these ratios. A nonzero numerator -over zero is infinity; in particular, a zero RHS passes the residual test only -with an exactly zero residual. Stable norm ratios avoid overflow from squaring -large values. Nonfinite residuals or required denominators cannot yield a pass. -Unavailable or nonfinite diagnostics serialize as JSON `null` with a reason. - -Output must have the expected shape, be finite, return status zero, leave inputs -unchanged, and complete within the fixed deadline. Required numerical failures -make the case fail even if the candidate declares convergence. - -## Scores and reference identity - -NS coverage reports `solved_count`, `coverage_fraction`, and ranking key -`-solved_count`. Equal counts tie; timing is diagnostic. Scoring requires the -complete expected split and no calibration artifact. - -For FLASH scored cases, `speedup_i = reference_elapsed_i / candidate_elapsed_i`. -The aggregate is the weighted geometric mean, with equal mass assigned to -provenance groups, then source runs within each group, then scored cases within -each run. Weights are frozen before evaluation. Correctness controls execute -and must pass, but have zero aggregate timing weight. - -One declared reference must pass every scored case and control on the complete -split. Qualification freezes its single measured time for each case. Candidate -runs reuse these timings. Reference artifacts bind the source, numerical inputs, -case order, roles, weights, contracts, runtime, and venue. A collection of per-case -portfolio winners is not a reference solver. - -Any failed candidate case makes replay `eligible = false` and `speedup = null`. -Missing reference calibration gives reason `awaiting_reference`. No failure is -replaced by a budget time, and successful subsets are not scored. Infrastructure -failures invalidate evaluation rather than removing expected cases. - -## Venue and release trust - -Local evaluation is `local-uncontrolled`: it supports development, with no claim -of hard CPU or memory enforcement. Modal uses `modal-sandbox-pilot-cpu-v2`, with -CPU request/limit and memory request/limit taken from the release, networking -disabled, and one sandbox per submission. Native child processes handle cases -individually. HYPRE is the pinned sequential float64/int32 build. - -An official score requires the full split, enforced venue limits, and the exact -release digest in the packaged trusted registry. A self-consistent user-authored -manifest or report does not establish official release identity. A local timing -reference cannot be mixed with Modal candidate timings. Official replay also -requires the registered calibration digest for that release, runtime, and venue; -a custom passing reference remains useful for development but is not official. - -The pilot freezes two CPU cores, 4 GiB, 90 seconds per case, and a -5,000-iteration request. Its published -[runtime manifest](../data/releases/cpu-runtime-v2.json) has identity -`4b09e68ce2fc17261c1d776bb01d63ec40f8f92388b9aff95c046f7c1f8bb076`. -The fixed replay reference passes every case in both splits under that runtime -and `modal-sandbox-pilot-cpu-v2`. Reuse the registered -[development](../data/releases/flash-replay-dev-reference.json) or -[ranked](../data/releases/flash-replay-ranked-reference.json) calibration; -candidate evaluation adds no reference executions. - -## Original SuiteSparse v1 compatibility - -V1 retains benchmark identity `linear-solver-bench-cpu-v1` and accuracy contract -`three-gate-v1`. It uses manufactured RHS and a zero initial guess, three fresh -processes per case, and requires every repetition to pass. Its limits remain: - -```text -eta_norm <= max(tau, 20 eps) -eta_comp <= max(5 tau, 100 eps) -max(forward_l2, forward_linf) <= min(2e-1, max(5 tau, 2e-12)) -``` - -Frozen condition estimates select one tolerance tier from `1e-4`, `1e-3`, `1e-2`, -and `4e-2`. They do not replace measured errors. Ranking maximizes completely -solved cases, then minimizes a penalized geometric mean: successful cases use -the median time divided by calibrated reference time, and failures use twice -the deadline divided by that reference time. V1 scoring requires calibration. -Its Modal venue remains two physical CPU cores and 4096 MiB memory. Existing -v1 archive decoding and manufactured archive bytes remain supported. diff --git a/docs/analysis/ns-cohort-v2-descriptors.json b/docs/analysis/ns-cohort-v2-descriptors.json deleted file mode 100644 index 1825449..0000000 --- a/docs/analysis/ns-cohort-v2-descriptors.json +++ /dev/null @@ -1,2062 +0,0 @@ -{ - "application_tag_definitions": { - "biomedical_systems": "Explicit cardiac, torso, or other biomedical models without unsupported physical subtyping.", - "elliptic_diffusion_thermal": "Poisson, diffusion, thermal, or atmospheric elliptic operators; a deliberately broad application/operator category.", - "fluid_flow_transport": "Flow, Navier-Stokes, Stokes, Euler, vorticity, and flow-coupled transport.", - "reservoir_simulation": "Oil/black-oil/steam reservoir models.", - "semiconductor_devices": "Explicit diode/electron-continuity/MOSFET applications.", - "solid_mechanics_fracture": "Explicit structural-mechanics or material-crack applications." - }, - "cross_group_review": [ - { - "citations": [ - "https://sparse.tamu.edu/Wang" - ], - "decision": "retain-distinct-model-groups", - "evidence": "The source has separate model descriptions, physical equations, meshes and discretization references for the vorticity and device subgroups.", - "groups": [ - "cavity-vorticity", - "semiconductor-devices" - ], - "uncertainty": "Shared author/collaborator and implementation-level overlap remain possible; model-source separation is not a statistical independence certificate." - }, - { - "citations": [ - "https://sparse.tamu.edu/Norris" - ], - "decision": "retain-distinct-model-groups", - "evidence": "The matrix-specific source distinguishes a quasistatic FE heart model from FD or FD/BEM torso electrophysiology.", - "groups": [ - "heart-quasistatic", - "torso-electrophysiology" - ], - "uncertainty": "Shared laboratory, contributor and possible common libraries do not establish shared model or mesh; full code independence remains unverified." - }, - { - "citations": [ - "https://sparse.tamu.edu/HB/orsirr_1", - "https://sparse.tamu.edu/HB/steam1", - "https://sparse.tamu.edu/HB/sherman5" - ], - "decision": "retain-distinct-model-groups", - "evidence": "Source descriptions distinguish the ORS oil-reservoir grid models, three-dimensional steam-injection models, and Sherman black-oil simulator.", - "groups": [ - "reservoir-ors", - "reservoir-steam", - "reservoir-sherman" - ], - "uncertainty": "Historical generator lineage is incomplete. A common collector alone does not warrant merging physically distinct source descriptions." - }, - { - "citations": [ - "https://sparse.tamu.edu/DRIVCAV", - "https://sparse.tamu.edu/FIDAP" - ], - "decision": "retain-distinct-generators", - "evidence": "DRIVCAV identifies its own driven-cavity FE code; FIDAP identifies matrices extracted from FIDAP examples despite a common collection/extraction source.", - "groups": [ - "driven-cavity-fem", - "fidap-flow-transport" - ], - "uncertainty": "This supports distinct generating codes, not a proof of complete implementation or statistical independence." - } - ], - "discretization_tag_definitions": { - "boundary_element": "Identified in applicable source metadata; may coexist with FD.", - "finite_difference": "Spatial finite differences established by applicable source metadata; time differencing alone does not earn this tag.", - "finite_element": "Identified in matrix-specific or applicable source metadata, including a specific method reference.", - "finite_volume": "Identified in applicable source metadata.", - "unknown": "The reviewed primary metadata does not uniquely establish the broad method family. Candidate interpretations are not confirmed tags." - }, - "kind": "linear-solver-bench-ns-cohort-source-descriptors", - "matrices": [ - { - "application_tags": [ - "reservoir_simulation" - ], - "citations": [ - { - "archive_sha256": "b8a2035bb0c2f538014017d40c4644de49d80c40d5381933a6c615f80eb31897", - "archive_url": "https://sparse.tamu.edu/MM/HB/orsirr_1.tar.gz", - "header_sha256": "c3c2b8326383d92d2515f0c353a5da2e7d7cfee1d0e21a968af4f1404ee22199", - "kind": "suitesparse_matrix_metadata", - "locator": "original Matrix Market title/notes", - "member": "orsirr_1/orsirr_1.mtx", - "url": "https://sparse.tamu.edu/HB/orsirr_1" - } - ], - "discretization_candidates": [], - "discretization_status": "unknown", - "discretization_tags": [ - "unknown" - ], - "evidence_summary": "Original titles identify oil-reservoir equations on irregular and full spatial grids. They do not specify the spatial discretization method.", - "full_name": "HB/orsirr_1", - "matrix_id": 225, - "provenance_group": "reservoir-ors", - "uncertainties": [] - }, - { - "application_tags": [ - "reservoir_simulation" - ], - "citations": [ - { - "archive_sha256": "0f51a31d3a0ae1e6fbbb8d01bef8a2b88d50dfd4f09f8de8c6f55c3788fc4446", - "archive_url": "https://sparse.tamu.edu/MM/HB/orsreg_1.tar.gz", - "header_sha256": "c5b14295a72d2707ff9c10e4f15435b75b2b38d5576a94df444bbcc115bcc3d5", - "kind": "suitesparse_matrix_metadata", - "locator": "original Matrix Market title/notes", - "member": "orsreg_1/orsreg_1.mtx", - "url": "https://sparse.tamu.edu/HB/orsreg_1" - } - ], - "discretization_candidates": [], - "discretization_status": "unknown", - "discretization_tags": [ - "unknown" - ], - "evidence_summary": "Original titles identify oil-reservoir equations on irregular and full spatial grids. They do not specify the spatial discretization method.", - "full_name": "HB/orsreg_1", - "matrix_id": 227, - "provenance_group": "reservoir-ors", - "uncertainties": [] - }, - { - "application_tags": [ - "fluid_flow_transport" - ], - "citations": [ - { - "kind": "public_primary_source", - "locator": "Matrix-specific title, type and author notes", - "url": "https://sparse.tamu.edu/HB/plskz362" - }, - { - "kind": "public_primary_source", - "locator": "PLATZ model description distinguishes original PLSK operators from derived squared PLAT matrices", - "url": "https://math.nist.gov/MatrixMarket/data/Harwell-Boeing/platz/platz.html" - } - ], - "discretization_candidates": [], - "discretization_status": "confirmed", - "discretization_tags": [ - "finite_difference" - ], - "evidence_summary": "The primary collection sources identify the original skew-symmetric finite-difference shallow-water operator for a North Atlantic ocean submodel. Its source application was natural-mode eigenanalysis; the benchmark manufactures a linear workload on the unchanged PLSK operator.", - "full_name": "HB/plskz362", - "matrix_id": 231, - "operator_origin": "original PDE operator used for source eigenmode analysis", - "provenance_group": "platzman-ocean-model", - "time_discretization_tags": [], - "uncertainties": [ - "Do not substitute the derived squared PLAT operator. No physical source RHS or time-step solve is claimed." - ] - }, - { - "application_tags": [ - "reservoir_simulation" - ], - "citations": [ - { - "archive_sha256": "d6a023da04e86097bd2ec52ce5008d1c671c5fa66be4ddc7dca505a186400000", - "archive_url": "https://sparse.tamu.edu/MM/HB/sherman5.tar.gz", - "header_sha256": "c98ed8761d74a2be6a4a9831993127f9dd91b4cde62dd9e28dac0a47ea0c6a58", - "kind": "suitesparse_matrix_metadata", - "locator": "original Matrix Market title/notes", - "member": "sherman5/sherman5.mtx", - "url": "https://sparse.tamu.edu/HB/sherman5" - } - ], - "discretization_candidates": [], - "discretization_status": "unknown", - "discretization_tags": [ - "unknown" - ], - "evidence_summary": "Original title identifies a fully implicit black-oil simulator on a spatial grid. Fully implicit describes time/nonlinear treatment, not an established spatial discretization.", - "full_name": "HB/sherman5", - "matrix_id": 246, - "provenance_group": "reservoir-sherman", - "uncertainties": [] - }, - { - "application_tags": [ - "reservoir_simulation" - ], - "citations": [ - { - "archive_sha256": "90007bbd731335e4d721aa1ffba2084571028035219d7ba1d887082bedb4f1dd", - "archive_url": "https://sparse.tamu.edu/MM/HB/steam1.tar.gz", - "header_sha256": "43773a998cc646a76aecb98bb2422036b0685be072367790b2dd43d162f140bf", - "kind": "suitesparse_matrix_metadata", - "locator": "original Matrix Market title/notes", - "member": "steam1/steam1.mtx", - "url": "https://sparse.tamu.edu/HB/steam1" - } - ], - "discretization_candidates": [], - "discretization_status": "unknown", - "discretization_tags": [ - "unknown" - ], - "evidence_summary": "Original titles identify three-dimensional steam-injection reservoir models with explicit grid dimensions. Spatial discretization is not specified.", - "full_name": "HB/steam1", - "matrix_id": 251, - "provenance_group": "reservoir-steam", - "uncertainties": [] - }, - { - "application_tags": [ - "reservoir_simulation" - ], - "citations": [ - { - "archive_sha256": "dde4911dcf2c84e0878afe3da2c2b453733f4b890352b1a66e9f039d850e5220", - "archive_url": "https://sparse.tamu.edu/MM/HB/steam2.tar.gz", - "header_sha256": "e2acbcbf5daf05c229d4f80fa9d0a14fd16e9e71c834d813b4b6b317eb93d374", - "kind": "suitesparse_matrix_metadata", - "locator": "original Matrix Market title/notes", - "member": "steam2/steam2.mtx", - "url": "https://sparse.tamu.edu/HB/steam2" - } - ], - "discretization_candidates": [], - "discretization_status": "unknown", - "discretization_tags": [ - "unknown" - ], - "evidence_summary": "Original titles identify three-dimensional steam-injection reservoir models with explicit grid dimensions. Spatial discretization is not specified.", - "full_name": "HB/steam2", - "matrix_id": 252, - "provenance_group": "reservoir-steam", - "uncertainties": [] - }, - { - "application_tags": [ - "elliptic_diffusion_thermal" - ], - "citations": [ - { - "archive_sha256": "e39b6ff33334db95faf6291451b79d3adfaa35a63b54dc22dc903bb0a8e10edd", - "archive_url": "https://sparse.tamu.edu/MM/Brunetiere/thermal.tar.gz", - "header_sha256": "172a0773ce8f85ba95c47a4d7626e25db2cc44fc32227b3aa1d590afb298b263", - "kind": "suitesparse_matrix_metadata", - "locator": "original Matrix Market title/notes", - "member": "thermal/thermal.mtx", - "url": "https://sparse.tamu.edu/Brunetiere/thermal" - } - ], - "discretization_candidates": [], - "discretization_status": "documented", - "discretization_tags": [ - "finite_element" - ], - "evidence_summary": "Original title explicitly identifies a thermal FEM problem.", - "full_name": "Brunetiere/thermal", - "matrix_id": 377, - "provenance_group": "thermal-fem", - "uncertainties": [] - }, - { - "application_tags": [ - "fluid_flow_transport" - ], - "citations": [ - { - "archive_sha256": "b1481371e07e7997910216f3c395bb7898f96243df8f16a5a46174410b19f41d", - "archive_url": "https://sparse.tamu.edu/MM/DRIVCAV/cavity01.tar.gz", - "header_sha256": "34d20e0507e2a7c72a61d1ae9dc81b35211e646a656867bca808c9becde2cecc", - "kind": "suitesparse_matrix_metadata", - "locator": "original Matrix Market title/notes", - "member": "cavity01/cavity01.mtx", - "url": "https://sparse.tamu.edu/DRIVCAV/cavity01" - }, - { - "kind": "suitesparse_author_description", - "locator": "matrix title and group description", - "url": "https://sparse.tamu.edu/DRIVCAV" - } - ], - "discretization_candidates": [], - "discretization_status": "documented", - "discretization_tags": [ - "finite_element" - ], - "evidence_summary": "Matrix titles identify driven-cavity cases; 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Unknown spatial methods remain unknown; possible interpretations are not confirmed tags.", - "A common author, collector, institution, or broad numerical method alone does not establish shared model, mesh, or generator provenance.", - "Provenance groups are conservative source/model exposure units, not certified statistical independence. Residual uncertainties are recorded in group and cross-group reviews.", - "The original PDE operator may have been assembled for a source eigenmode problem; the benchmark uses a newly manufactured linear workload without squaring, shifting, projecting or otherwise replacing that operator.", - "Source review is separate from archive verification, numerical qualification and admission of frozen manufactured workloads." - ], - "provenance_group_reviews": [ - { - "citations": [ - "https://sparse.tamu.edu/Engwirda", - "https://sparse.tamu.edu/Engwirda/airfoil_2d" - ], - "independence_uncertainties": [ - "Spatial discretization and generating-code details remain unresolved." - ], - "known_evidence": [ - "The group documents use in a Navier-Stokes solver on an unstructured airfoil mesh, but does not identify the spatial discretization method." - ], - "matrix_ids": [ - 1319 - ], - "provenance_group": "airfoil-flow", - "rationale": "The source identifies an unstructured airfoil mesh used in a Navier-Stokes solver." - }, - { - "citations": [ - "https://sparse.tamu.edu/Bourchtein", - "https://sparse.tamu.edu/Bourchtein/atmosmodd", - "https://sparse.tamu.edu/Bourchtein/atmosmodl" - ], - "independence_uncertainties": [ - "The general discussion mentions both FD and FE but does not bind either to these individual matrices." - ], - "known_evidence": [ - "Original notes describe elliptic subproblems from semi-implicit atmospheric models and discuss both finite differences and finite elements without choosing one for these matrices.", - "Original notes describe elliptic subproblems from semi-implicit atmospheric models and discuss both finite differences and finite elements without choosing one for these matrices." - ], - "matrix_ids": [ - 2265, - 2267 - ], - "provenance_group": "atmospheric-elliptic", - "rationale": "The two matrices are submitted together as atmospheric semi-implicit elliptic subproblems." - }, - { - "citations": [ - "https://sparse.tamu.edu/Wang", - "https://sparse.tamu.edu/Wang/swang1", - "https://sparse.tamu.edu/Wang/swang2" - ], - "independence_uncertainties": [ - "Shared authorship and fitted-method references with the device subgroup do not establish a shared physical model or mesh; implementation overlap is unresolved." - ], - "known_evidence": [ - "The author description identifies driven-cavity vorticity equations and an exponentially fitted finite-volume scheme.", - "The author description identifies driven-cavity vorticity equations and an exponentially fitted finite-volume scheme." - ], - "matrix_ids": [ - 828, - 829 - ], - "provenance_group": "cavity-vorticity", - "rationale": "The source explicitly separates two driven-cavity vorticity matrices on a triangular mesh using finite volumes." - }, - { - "citations": [ - "https://sparse.tamu.edu/CPM", - "https://sparse.tamu.edu/CPM/cz10228", - "https://sparse.tamu.edu/CPM/cz1268", - "https://sparse.tamu.edu/CPM/cz40948" - ], - "independence_uncertainties": [ - "The exact relationship between every grid/interpolation variant is not reconstructed." - ], - "known_evidence": [ - "Original notes identify a surface-Poisson embedding on a Cartesian band solved with finite differences, including a source-assembled constraint row.", - "Original notes identify a surface-Poisson embedding on a Cartesian band solved with finite differences, including a source-assembled constraint row.", - "Original notes identify a surface-Poisson embedding on a Cartesian band solved with finite differences, including a source-assembled constraint row." - ], - "matrix_ids": [ - 2562, - 2565, - 2567 - ], - "provenance_group": "circle-surface-poisson", - "rationale": "The source identifies a common closest-point surface-Poisson embedding method with grid and method variants." - }, - { - "citations": [ - "https://sparse.tamu.edu/Hohn", - "https://sparse.tamu.edu/Hohn/fd12", - "https://sparse.tamu.edu/Hohn/fd15" - ], - "independence_uncertainties": [ - "Other Hohn variants would share this source lineage and would not add independent support." - ], - "known_evidence": [ - "Original title identifies a single-material crack problem on a logarithmic finite-difference grid.", - "The matrix-specific title and author group description identify a finite-difference approximation to a single-material crack problem on a logarithmic grid." - ], - "matrix_ids": [ - 917, - 918 - ], - "provenance_group": "crack-log-grid", - "rationale": "The matrix-specific source identifies a single-material crack model on a logarithmic finite-difference grid." - }, - { - "citations": [ - "https://sparse.tamu.edu/DRIVCAV", - "https://sparse.tamu.edu/DRIVCAV/cavity01", - "https://sparse.tamu.edu/DRIVCAV/cavity10", - "https://sparse.tamu.edu/DRIVCAV/cavity16" - ], - "independence_uncertainties": [ - "The group shares a collection/extraction source with FIDAP, but FIDAP identifies a different generating code." - ], - "known_evidence": [ - "Matrix titles identify driven-cavity cases; the collection group explicitly identifies the generating code as finite element.", - "Matrix titles identify driven-cavity cases; the collection group explicitly identifies the generating code as finite element.", - "Matrix titles identify driven-cavity cases; the collection group explicitly identifies the generating code as finite element." - ], - "matrix_ids": [ - 380, - 389, - 395 - ], - "provenance_group": "driven-cavity-fem", - "rationale": "The group documents one driven-cavity finite-element generator and related matrix variants." - }, - { - "citations": [ - "https://sparse.tamu.edu/FEMLAB", - "https://sparse.tamu.edu/FEMLAB/ns3Da", - "https://sparse.tamu.edu/FEMLAB/poisson2D", - "https://sparse.tamu.edu/FEMLAB/poisson3Da", - "https://sparse.tamu.edu/Langemyr/comsol" - ], - "independence_uncertainties": [ - "The structural and PDE examples use distinct physical models; exact shared code and mesh details are not reconstructed." - ], - "known_evidence": [ - "Original title explicitly identifies finite-element structural mechanics.", - "Original title identifies three-dimensional Navier-Stokes equations; the FEMLAB group identifies its source toolbox as finite element.", - "Original titles identify Poisson equations; the FEMLAB group identifies its source toolbox as finite element.", - "Original titles identify Poisson equations; the FEMLAB group identifies its source toolbox as finite element." - ], - "matrix_ids": [ - 864, - 925, - 926, - 927 - ], - "provenance_group": "femlab-langemyr", - "rationale": "FEMLAB and the Langemyr COMSOL source are conservatively one software-lineage exposure group." - }, - { - "citations": [ - "https://sparse.tamu.edu/FIDAP", - "https://sparse.tamu.edu/FIDAP/ex31", - "https://sparse.tamu.edu/FIDAP/ex35", - "https://sparse.tamu.edu/FIDAP/ex40" - ], - "independence_uncertainties": [ - "The code version and complete mesh relations are not reconstructed." - ], - "known_evidence": [ - "The matrix is FIDAP example31; its source describes species deposition on a heated plate and identifies FIDAP matrices as finite element.", - "The matrix is FIDAP example35, turbulent flow through a heated channel; the group identifies finite-element discretization.", - "The matrix is FIDAP example40; the group identifies the source as finite-element coupled flow equations." - ], - "matrix_ids": [ - 428, - 431, - 435 - ], - "provenance_group": "fidap-flow-transport", - "rationale": "All selected cases are documented examples from the same FIDAP finite-element code." - }, - { - "citations": [ - "https://sparse.tamu.edu/Fluorem", - "https://sparse.tamu.edu/Fluorem/DK01R", - "https://sparse.tamu.edu/Fluorem/GT01R" - ], - "independence_uncertainties": [ - "Different source geometries remain grouped under their shared generating framework." - ], - "known_evidence": [ - "Original notes explicitly identify automatically differentiated steady-flow Jacobians from finite-volume equations.", - "Original notes explicitly identify automatically differentiated steady-flow Jacobians from finite-volume equations." - ], - "matrix_ids": [ - 2334, - 2335 - ], - "provenance_group": "finite-volume-rans", - "rationale": "The source documents a common differentiated finite-volume steady-flow Jacobian framework." - }, - { - "citations": [ - "https://sparse.tamu.edu/Goodwin", - "https://sparse.tamu.edu/Goodwin/Goodwin_010", - "https://sparse.tamu.edu/Goodwin/Goodwin_040", - "https://sparse.tamu.edu/Goodwin/Goodwin_095", - "https://sparse.tamu.edu/Goodwin/goodwin" - ], - "independence_uncertainties": [ - "Source-code continuity and exact mesh relations across releases are not fully established; the existing group is retained conservatively." - ], - "known_evidence": [ - "Original title explicitly identifies a finite-element fluid-mechanics problem.", - "Original titles explicitly identify finite-element Navier-Stokes and transport equations.", - "Original titles explicitly identify finite-element Navier-Stokes and transport equations.", - "The matrix-specific author notes identify a finite-element discretization of Navier-Stokes and related transport equations on various geometries." - ], - "matrix_ids": [ - 446, - 2817, - 2822, - 2825 - ], - "provenance_group": "goodwin-flow-transport", - "rationale": "The selected matrices form the published Goodwin finite-element flow/transport family across source releases and geometries." - }, - { - "citations": [ - "https://sparse.tamu.edu/Norris", - "https://sparse.tamu.edu/Norris/heart2" - ], - "independence_uncertainties": [ - "The common laboratory and contributor do not prove shared heart/torso model or mesh; library-level overlap is unresolved." - ], - "known_evidence": [ - "Original title identifies a quasistatic finite-element heart model." - ], - "matrix_ids": [ - 891 - ], - "provenance_group": "heart-quasistatic", - "rationale": "The source identifies a quasistatic finite-element heart model, distinct from its torso electrophysiology descriptions." - }, - { - "citations": [ - "https://sparse.tamu.edu/Janna/CoupCons3D", - "https://sparse.tamu.edu/Janna/Transport" - ], - "independence_uncertainties": [ - "They have different physics; exact mesh and code overlap is unresolved. They must remain in the same split." - ], - "known_evidence": [ - "The source identifies a real nonsymmetric three-dimensional coupled poroelastic consolidation operator, with finite elements in space and finite differences in time.", - "The source identifies a real nonsymmetric tetrahedral finite-element discretization of density-driven coupled flow and transport." - ], - "matrix_ids": [ - 2647, - 2649 - ], - "provenance_group": "janna-coupled-fe", - "rationale": "The two new matrices share named authors and a specific cited preconditioning study and are conservatively one study exposure group." - }, - { - "citations": [ - "https://sparse.tamu.edu/TKK", - "https://sparse.tamu.edu/TKK/t2d_q4" - ], - "independence_uncertainties": [ - "The mesh terminology suggests but does not explicitly establish the spatial discretization family." - ], - "known_evidence": [ - "Original title and the group entry identify nonlinear diffusion on a bilinear mesh. They do not explicitly name the spatial discretization method." - ], - "matrix_ids": [ - 1916 - ], - "provenance_group": "nonlinear-diffusion", - "rationale": "The matrix belongs to the source-described nonlinear-diffusion sequence on a bilinear mesh." - }, - { - "citations": [ - "https://math.nist.gov/MatrixMarket/data/Harwell-Boeing/platz/platz.html", - "https://sparse.tamu.edu/HB/plskz362" - ], - "independence_uncertainties": [ - "Natural-mode analysis was the original task; only the unchanged original PLSK operator is in scope for a manufactured linear solve." - ], - "known_evidence": [ - "The primary collection sources identify the original skew-symmetric finite-difference shallow-water operator for a North Atlantic ocean submodel. Its source application was natural-mode eigenanalysis; the benchmark manufactures a linear workload on the unchanged PLSK operator." - ], - "matrix_ids": [ - 231 - ], - "provenance_group": "platzman-ocean-model", - "rationale": "The primary NIST source identifies one finite-difference ocean-model family, with original skew and derived squared variants." - }, - { - "citations": [ - "https://sparse.tamu.edu/HB/orsirr_1", - "https://sparse.tamu.edu/HB/orsreg_1" - ], - "independence_uncertainties": [ - "Underlying generator lineage is not fully documented; a shared historical collector alone does not link this group to the steam or Sherman models." - ], - "known_evidence": [ - "Original titles identify oil-reservoir equations on irregular and full spatial grids. They do not specify the spatial discretization method.", - "Original titles identify oil-reservoir equations on irregular and full spatial grids. They do not specify the spatial discretization method." - ], - "matrix_ids": [ - 225, - 227 - ], - "provenance_group": "reservoir-ors", - "rationale": "Oil-reservoir regular and irregular-grid variants are retained as one source-model group." - }, - { - "citations": [ - "https://sparse.tamu.edu/HB/sherman5" - ], - "independence_uncertainties": [ - "Implementation and mesh independence from other historical reservoir models is not certified." - ], - "known_evidence": [ - "Original title identifies a fully implicit black-oil simulator on a spatial grid. Fully implicit describes time/nonlinear treatment, not an established spatial discretization." - ], - "matrix_ids": [ - 246 - ], - "provenance_group": "reservoir-sherman", - "rationale": "The source specifies a fully implicit black-oil simulator and a named matrix source distinct from the ORS and steam descriptions." - }, - { - "citations": [ - "https://sparse.tamu.edu/HB/steam1", - "https://sparse.tamu.edu/HB/steam2" - ], - "independence_uncertainties": [ - "The shared collector with ORS is not evidence of a shared simulator; code lineage remains unresolved." - ], - "known_evidence": [ - "Original titles identify three-dimensional steam-injection reservoir models with explicit grid dimensions. Spatial discretization is not specified.", - "Original titles identify three-dimensional steam-injection reservoir models with explicit grid dimensions. Spatial discretization is not specified." - ], - "matrix_ids": [ - 251, - 252 - ], - "provenance_group": "reservoir-steam", - "rationale": "The two three-dimensional steam-injection reservoir variants belong to one documented source-model group." - }, - { - "citations": [ - "https://sparse.tamu.edu/Wang", - "https://sparse.tamu.edu/Wang/wang1", - "https://sparse.tamu.edu/Wang/wang3", - "https://sparse.tamu.edu/Wang/wang4" - ], - "independence_uncertainties": [ - "The higher-resolution spatial-method family and implementation overlap with the distinct vorticity model remain unresolved." - ], - "known_evidence": [ - "The author describes electron-continuity equations for a three-dimensional diode and cites the exponentially fitted finite-element discretization.", - "The author describes the higher-resolution diode case, but cites either the earlier finite-element method paper or a Scharfetter-Gummel box-method paper for its scheme.", - "The author identifies a MOSFET and gives either a finite-element method reference or a Scharfetter-Gummel box-method reference for discretization." - ], - "matrix_ids": [ - 830, - 832, - 833 - ], - "provenance_group": "semiconductor-devices", - "rationale": "Diode resolution variants and a MOSFET from the documented device simulator are kept together." - }, - { - "citations": [ - "https://sparse.tamu.edu/Garon/garon1", - "https://sparse.tamu.edu/Garon/garon2" - ], - "independence_uncertainties": [ - "No further cross-source shared mesh or generator evidence was established." - ], - "known_evidence": [ - "Original titles explicitly identify two-dimensional finite-element Navier-Stokes equations with opposed inlet and outlet.", - "Original titles explicitly identify two-dimensional finite-element Navier-Stokes equations with opposed inlet and outlet." - ], - "matrix_ids": [ - 444, - 445 - ], - "provenance_group": "square-inlet-flow", - "rationale": "The two source matrices discretize the same two-dimensional flow geometry with opposed inlet and outlet." - }, - { - "citations": [ - "https://sparse.tamu.edu/Brunetiere/thermal" - ], - "independence_uncertainties": [ - "A unique public code or mesh lineage was not recovered in this bounded review." - ], - "known_evidence": [ - "Original title explicitly identifies a thermal FEM problem." - ], - "matrix_ids": [ - 377 - ], - "provenance_group": "thermal-fem", - "rationale": "The source identifies a thermal finite-element problem from its named contributor." - }, - { - "citations": [ - "https://sparse.tamu.edu/Norris", - "https://sparse.tamu.edu/Norris/torso1", - "https://sparse.tamu.edu/Norris/torso2" - ], - "independence_uncertainties": [ - "Exact shared meshes and implementation details are not reconstructed; no independent BEM source exists in this candidate inventory." - ], - "known_evidence": [ - "Original title and the matrix-specific group entry identify a coupled finite-difference/boundary-element torso model.", - "Original title and the matrix-specific group entry identify a finite-difference electrophysiological torso model." - ], - "matrix_ids": [ - 896, - 897 - ], - "provenance_group": "torso-electrophysiology", - "rationale": "The source identifies related torso electrophysiology models using FD and coupled FD/BEM." - }, - { - "citations": [ - "https://sparse.tamu.edu/Simon", - "https://sparse.tamu.edu/Simon/venkat01", - "https://sparse.tamu.edu/Simon/venkat25", - "https://sparse.tamu.edu/Simon/venkat50" - ], - "independence_uncertainties": [ - "The broad spatial method and full generating-code lineage remain unresolved." - ], - "known_evidence": [ - "Original titles identify an unstructured two-dimensional Euler solver at different time steps. The reviewed metadata does not specify FE, FV, or FD.", - "Original titles identify an unstructured two-dimensional Euler solver at different time steps. The reviewed metadata does not specify FE, FV, or FD.", - "Original titles identify an unstructured two-dimensional Euler solver at different time steps. The reviewed metadata does not specify FE, FV, or FD." - ], - "matrix_ids": [ - 820, - 821, - 822 - ], - "provenance_group": "unstructured-euler", - "rationale": "The source identifies related unstructured Euler-solver matrices at successive time steps with the same sparsity pattern." - }, - { - "citations": [ - "https://sparse.tamu.edu/Szczerba/Ill_Stokes" - ], - "independence_uncertainties": [ - "A tetrahedral mesh alone does not establish a finite-element method or full generator lineage." - ], - "known_evidence": [ - "Original notes identify unreduced Stokes momentum and continuity equations on a tetrahedral mesh with velocity and pressure variables. They do not explicitly name the discretization method." - ], - "matrix_ids": [ - 1862 - ], - "provenance_group": "unstructured-stokes", - "rationale": "The source describes a coupled Stokes velocity-pressure formulation on an unstructured tetrahedral mesh." - }, - { - "citations": [ - "https://elib.uni-stuttgart.de/handle/11682/9377", - "https://sparse.tamu.edu/Grueninger/windtunnel_evap3d" - ], - "independence_uncertainties": [ - "The two-dimensional and three-dimensional variants, if both acquired, must remain one group." - ], - "known_evidence": [ - "The source identifies a coupled Navier-Stokes/Darcy evaporation model, with cell-centred finite volumes combined with a marker-and-cell scheme in one monolithic system." - ], - "matrix_ids": [ - 2815 - ], - "provenance_group": "windtunnel-evaporation", - "rationale": "The source and author report identify one coupled Navier-Stokes/Darcy evaporation model and generating software." - } - ], - "review_date": "2026-09-13", - "schema_version": 1, - "source_descriptor_sha256": "7afaf3ecc844fbf9c2292897198255f4d3cb4ec191a1fc79a4fbe473c081d8a4", - "status": "reviewed-with-explicit-unknowns" -} diff --git a/docs/analysis/ns-cohort-v2-infeasibility.json b/docs/analysis/ns-cohort-v2-infeasibility.json deleted file mode 100644 index 18c6748..0000000 --- a/docs/analysis/ns-cohort-v2-infeasibility.json +++ /dev/null @@ -1,168 +0,0 @@ -{ - "all_assignments": [ - { - "group_assignments": { - "circle-surface-poisson": "dev", - "crack-log-grid": "dev", - "platzman-ocean-model": "dev" - }, - "violated_constraint_indices": [ - 0, - 1, - 2 - ] - }, - { - "group_assignments": { - "circle-surface-poisson": "dev", - "crack-log-grid": "dev", - "platzman-ocean-model": "ranked" - }, - "violated_constraint_indices": [ - 1 - ] - }, - { - "group_assignments": { - "circle-surface-poisson": "dev", - "crack-log-grid": "ranked", - "platzman-ocean-model": "dev" - }, - "violated_constraint_indices": [ - 0 - ] - }, - { - "group_assignments": { - "circle-surface-poisson": "dev", - "crack-log-grid": "ranked", - "platzman-ocean-model": "ranked" - }, - "violated_constraint_indices": [ - 2 - ] - }, - { - "group_assignments": { - "circle-surface-poisson": "ranked", - "crack-log-grid": "dev", - "platzman-ocean-model": "dev" - }, - "violated_constraint_indices": [ - 2 - ] - }, - { - "group_assignments": { - "circle-surface-poisson": "ranked", - "crack-log-grid": "dev", - "platzman-ocean-model": "ranked" - }, - "violated_constraint_indices": [ - 0 - ] - }, - { - "group_assignments": { - "circle-surface-poisson": "ranked", - "crack-log-grid": "ranked", - "platzman-ocean-model": "dev" - }, - "violated_constraint_indices": [ - 1 - ] - }, - { - "group_assignments": { - "circle-surface-poisson": "ranked", - "crack-log-grid": "ranked", - "platzman-ocean-model": "ranked" - }, - "violated_constraint_indices": [ - 0, - 1, - 2 - ] - } - ], - "certificate_sha256": "34c43a16826a418dc60abaeef4e254937fc62b2af220a909cb4a00f300f33774", - "conflicting_constraints": [ - { - "kind": "size_method_joint", - "matrix_ids": [ - 231, - 2562 - ], - "required_placement": "both-splits", - "stratum": "n<10000 / finite_difference", - "supporting_groups": [ - "circle-surface-poisson", - "platzman-ocean-model" - ] - }, - { - "kind": "size_method_joint", - "matrix_ids": [ - 918, - 2565, - 2567 - ], - "required_placement": "both-splits", - "stratum": "10000<=n<100000 / finite_difference", - "supporting_groups": [ - "circle-surface-poisson", - "crack-log-grid" - ] - }, - { - "kind": "zero_diagonal_fraction", - "matrix_ids": [ - 231, - 918 - ], - "required_placement": "both-splits", - "stratum": "1", - "supporting_groups": [ - "crack-log-grid", - "platzman-ocean-model" - ] - } - ], - "constraint_indices_are_zero_based": true, - "diagnosis": "Three required pairwise opposite-split constraints form an odd cycle. These constraints alone are infeasible, independently of case budgets, minimum ranked groups, singleton placement, and the objective.", - "disposition": "Preserve the frozen policy. No cohort was selected and no new manufactured targets were generated from this inventory. Scientific source-pool expansion requires a newly sealed inventory before rerunning selection.", - "enumerated_partition_count": 16777216, - "feasible_partition_count": 0, - "inventory_case_count": 48, - "inventory_group_count": 24, - "inventory_sha256": "0028644b8785958df1739aafb9c0322378892c812d354bd19befb16efbd1f2c0", - "kind": "ns-cohort-infeasibility-certificate", - "minimality_witnesses": [ - { - "omitted_constraint_index": 0, - "satisfying_group_assignments": { - "circle-surface-poisson": "dev", - "crack-log-grid": "ranked", - "platzman-ocean-model": "dev" - } - }, - { - "omitted_constraint_index": 1, - "satisfying_group_assignments": { - "circle-surface-poisson": "dev", - "crack-log-grid": "dev", - "platzman-ocean-model": "ranked" - } - }, - { - "omitted_constraint_index": 2, - "satisfying_group_assignments": { - "circle-surface-poisson": "dev", - "crack-log-grid": "ranked", - "platzman-ocean-model": "ranked" - } - } - ], - "policy_sha256": "e5c7bfd1f224621c12101fb38a07a7cba1d1f19304901e235c2826f8a4d9188c", - 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Numerical qualification uses fixed fresh targets after selection; failed qualification is reported and requires a versioned inventory revision, never a replacement draw chosen to pass." - ], - "objective": { - "components": [ - "mean-marginal-wasserstein", - "normalized-energy-distance", - "mean-known-tag-incidence-gap", - "absolute-dev-fraction-gap" - ], - "energy_normalization": "twice-maximum-pooled-pair-distance", - "features": [ - "log10_n", - "log10_nnz_per_row", - "relative_nonsymmetry", - "zero_diagonal_fraction", - "weakly_diagonally_dominant_row_fraction" - ], - "normalization": "pooled-feature-range", - "tie_break": "lexicographic-sorted-dev-group-tuple", - "tie_round_decimals": 12, - "weights": [ - 0.25, - 0.25, - 0.25, - 0.25 - ] - }, - "policy_id": "ns-cohort-v2-selection-v2", - "revision_note": "Before RHS generation or candidate execution, add both-sided method-by-size coverage whenever at least two provenance groups support that joint stratum. Additional sources are screened against the unchanged scientific and material-nonsymmetry admission requirements before joining the eligible pool. Unsupported singleton joint strata remain explicit limitations. The original 47-operator policy and inventory remain in version history.", - "schema_version": 1, - "search": { - "algorithm": "exhaustive-group-partitions", - "maximum_groups": 26 - } -} diff --git a/docs/analysis/ns-cohort-v2-screening.json b/docs/analysis/ns-cohort-v2-screening.json deleted file mode 100644 index 1235a7c..0000000 --- a/docs/analysis/ns-cohort-v2-screening.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "kind": "linear-solver-bench-ns-source-screening", - "manifest_sha256": "9478b1540be57432cc2f6d480ea68c76f61186dbb7f37139eddbd45c3043f635", - "records": [ - { - "archive_bytes": 34619774, - "archive_sha256": "8081649d168c1fbcad725252b9f5649f2c065f4c73a46c63d3b1c0c543f1fbb7", - "archive_url": "https://sparse.tamu.edu/MM/Botonakis/FEM_3D_thermal2.tar.gz", - "decision": "exclude-from-ns-input-pool", - "full_name": "Botonakis/FEM_3D_thermal2", - "material_nonsymmetry_threshold": 1e-12, - "matrix_id": 1852, - "matrix_market_header": "%%MatrixMarket matrix coordinate real general", - "matrix_sha256": "17e9657b2d691dc25a7ae7485eb57955b75390a91be1c34c402c9446e77f7bb5", - "n": 147900, - "nnz": 3489300, - "reason": "The canonical original operator differs from its transpose only at roundoff scale and fails the fixed material-nonsymmetry admission threshold. 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"reservoir-ors", - "reservoir-sherman", - "reservoir-steam", - "semiconductor-devices", - "unstructured-euler", - "unstructured-stokes" - ] - } - } - } - }, - "descriptor_sha256": "651bfdbd5b777dd8d6afdc931dfe7535dc638e5aa16c3a5d9101df3a088b77f7", - "kind": "ns-split-descriptive-audit", - "limitations": [ - "Marginal feature similarity does not establish joint similarity or equal solver difficulty.", - "No condition-number, nonnormality, or solver-success guarantee is inferred.", - "Dominance and nonsymmetry are floating-point descriptive diagnostics, not mathematical certificates.", - "Application and discretization tags reflect the cited source review; unknown labels do not establish regime coverage.", - "No universal acceptable-distance threshold is specified by this audit." - ], - "method": "matrix-only-marginal-wasserstein-range-normalized-v1", - "schema_version": 1, - "source_release_sha256": [ - "25e79afb724d2f33c2438513f3ea13aeaebfced24fb8c4bba6d5c78e5edfa296", - "61fb1ec691400d0e1bc83d8524f5e90f1e6479941e31e4f8e0ccd840163da241" - ] -} diff --git a/docs/task-flash-replay.md b/docs/task-flash-replay.md deleted file mode 100644 index e9e7230..0000000 --- a/docs/task-flash-replay.md +++ /dev/null @@ -1,60 +0,0 @@ -# FLASH replay: candidate task - -Write one C solver that accurately solves captured scalar magnetic-diffusion -systems as quickly as possible. Each case is an independent replay of one -captured solve. Factory and preconditioner setup costs count toward speedup. -No solver state carries between cases, and the simulation is not rerun. - -Submit a single UTF-8 C source file, at most 65,536 bytes. Include exactly -`HYPRE_parcsr_ls.h` and export only: - -```c -HYPRE_Int solver_create(HYPRE_Solver *solver, - HYPRE_Real relative_tolerance, - HYPRE_Real absolute_tolerance, - HYPRE_Int maximum_iterations); -``` - -The evaluator force-includes the public solver header. Implement setup, solve, -and destroy callbacks using the approved HYPRE operations and preconditioners. -Stock outer solvers, I/O, clocks, networking, process creation, and dynamic -linking are unavailable. Release all owned workspace and preconditioners in -destruction. `submissions/starter.c` demonstrates the ABI. - -Each input preserves the captured float64 matrix `A`, right-hand side `b`, -initial guess `x0`, and relative tolerance `tau`. The initial guess may be useful; -do not assume it is zero. The factory receives the release's iteration limit -and absolute tolerance zero. Preserve `A,b` and write the solution to the -supplied solution vector. Case IDs, source-run names, scored/control roles, -weights, and numerical reference vectors are not supplied to candidate code. - -For `r = b - A x`, trusted verification requires: - -```text -||r||2 / ||b||2 <= tau * (1 + 1e-6) -||r||inf / (||A||inf ||x||inf + ||b||inf) <= max(tau, 20 eps) -``` - -`eps` is float64 machine epsilon. With a zero RHS, relative residual is zero -only when the residual is exactly zero. Output must have the expected shape, -be finite, return success, leave inputs unchanged, and complete within its -fixed deadline. Componentwise backward error is diagnostic, including for weak -rows. Forward errors against any available numerical reference are also -optional diagnostics, not acceptance gates. - -The evaluator compiles the source once and runs each case once in one fresh -native process. The measured time includes factory creation, setup, and solve. -Each scored case's speedup is qualified reference time divided by candidate -time. The aggregate is a weighted geometric mean: equal mass per provenance -group, then source run, then scored case within that run. - -Every scored case and mandatory correctness control must pass for aggregate -speedup eligibility. Controls have no aggregate timing weight. One failed case -makes speedup null; successful subsets are not averaged. The benchmark reuses -frozen timings from one reference solver qualified on the complete split. -Until that reference exists, reports provide accuracy diagnostics and score as -`awaiting_reference`. - -The public dataset and its timing reference are separate release artifacts. -See [DATASET.md](DATASET.md) for download formats and [OPERATIONS.md](OPERATIONS.md) -for qualification and scoring commands. diff --git a/docs/task-ns-coverage.md b/docs/task-ns-coverage.md deleted file mode 100644 index cbc2ced..0000000 --- a/docs/task-ns-coverage.md +++ /dev/null @@ -1,58 +0,0 @@ -# NS mesh coverage: candidate task - -Write one C solver that accurately solves as many supplied nonsymmetric mesh/PDE -linear systems as possible within the release's fixed per-case budgets. NS means -nonsymmetric; the systems are not restricted to Navier–Stokes equations. The -cohort v2 selects 19 development matrices and 30 ranked matrices, separated by -provenance groups. Develop against `ns-mesh-cohort-v2-dev`; the ranked split is -for final evaluation. See the [cohort review](NS_COHORT_V2.md) for release status, -the declared coverage criteria, and remaining single-source joint gaps. This -cohort does not exhaust the scientific family. - -Submit a single UTF-8 C source file, at most 65,536 bytes. Include exactly -`HYPRE_parcsr_ls.h` and export only: - -```c -HYPRE_Int solver_create(HYPRE_Solver *solver, - HYPRE_Real relative_tolerance, - HYPRE_Real absolute_tolerance, - HYPRE_Int maximum_iterations); -``` - -The evaluator force-includes the public solver header. Use its approved HYPRE -vector/matrix operations and preconditioners to implement setup, solve, and -destroy callbacks. Stock outer solvers, I/O, clocks, networking, process -creation, and dynamic linking are unavailable. Free all owned workspace and -preconditioners in destruction. `submissions/starter.c` demonstrates the ABI. - -The solver receives a real square float64 sparse matrix `A`, a vector `b`, and -zero initial guess. Use the supplied relative tolerance and iteration limit; -the development release requests `1e-12` and 5,000 iterations. Absolute tolerance -is zero. Preserve `A,b` and write the solution to the supplied solution vector. -Case names, provenance, and manufactured targets are not supplied to the solver. - -Trusted verification computes `r = b - A x` and requires all four limits: - -```text -||r||inf / (||A||inf ||x||inf + ||b||inf) <= 1e-10 -max_i |r_i| / (|A||x| + |b|)_i <= 1e-8 -||x-x_target||2 / ||x_target||2 <= 1e-5 -||x-x_target||inf / ||x_target||inf <= 1e-5 -``` - -The target has entries ±1, with `b = fl(A x_target)`. Offline qualification -accounts for formation uncertainty; candidate acceptance always checks the -stored target. Relative residual is diagnostic. A solver's stopping declaration -or requested tolerance does not replace these fixed acceptance gates. Output -must be finite, correctly shaped, successful, and within the deadline. - -Your source is compiled once and each case runs exactly once in a fresh native -process. Factory creation, setup, preconditioner construction, and solve all -count toward its measured solve time. No state persists between cases. - -The score is the number of cases passing every required check. Equal counts tie; -timing does not break ties. All expected cases are evaluated, including after a -solver failure. No timing reference or calibration is used for coverage. - -See [SPEC.md](SPEC.md) for the full contract and [README.md](../README.md) for -preparation and execution commands. diff --git a/examples/solver.c b/examples/solver.c new file mode 100644 index 0000000..082401f --- /dev/null +++ b/examples/solver.c @@ -0,0 +1,32 @@ +#include "HYPRE_parcsr_ls.h" + +/* A deliberately small starting point. Participants may replace BoomerAMG + with any solver assembled from the functions documented in BENCHMARK.md. */ + +HYPRE_Int +solver_create(HYPRE_Solver *solver, HYPRE_Real relative_tolerance, + HYPRE_Real absolute_tolerance) +{ + HYPRE_Int error; + HYPRE_Real target = 0.1 * relative_tolerance; + (void) absolute_tolerance; + + error = HYPRE_BoomerAMGCreate(solver); + if (error != 0) + { + return error; + } + + error |= HYPRE_BoomerAMGSetTol( + *solver, target < 1.0e-12 ? target : 1.0e-12); + error |= HYPRE_BoomerAMGSetMaxIter(*solver, 200); + error |= HYPRE_BoomerAMGSetCoarsenType(*solver, 10); + error |= HYPRE_BoomerAMGSetInterpType(*solver, 6); + error |= HYPRE_BoomerAMGSetPMaxElmts(*solver, 4); + error |= HYPRE_BoomerAMGSetStrongThreshold(*solver, 0.25); + error |= HYPRE_BoomerAMGSetRelaxType(*solver, 6); + error |= HYPRE_BoomerAMGSetNumSweeps(*solver, 1); + error |= HYPRE_BoomerAMGSetMaxLevels(*solver, 25); + error |= HYPRE_BoomerAMGSetMaxCoarseSize(*solver, 9); + return error; +} diff --git a/families/flash/README.md b/families/flash/README.md new file mode 100644 index 0000000..1b039ac --- /dev/null +++ b/families/flash/README.md @@ -0,0 +1,8 @@ +# FLASH magnetic-diffusion replays + +This family replays linear systems captured from FLASH magnetic-diffusion +solves. Each capture preserves its sparse matrix, right-hand side, warm start, +and relative tolerance exactly. + +[`dev.json`](dev.json) is the complete public development set. It pins every +archive and each decoded numerical array by SHA-256. diff --git a/data/flash-replay-dev-pilot.json b/families/flash/dev.json similarity index 87% rename from data/flash-replay-dev-pilot.json rename to families/flash/dev.json index b077091..007bc53 100644 --- a/data/flash-replay-dev-pilot.json +++ b/families/flash/dev.json @@ -5,7 +5,6 @@ "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -30,15 +29,13 @@ "x0_sha256": "e78a0b1f90e249ec2a6fd3b0dfbfbb5c60845a482f2833dee0eda93582d8581b" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -63,15 +60,13 @@ "x0_sha256": "8fd54d115ed11befb9fd4f7dfa56b086b0b173f3da3b3a17cc1999bdd8857917" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -96,15 +91,13 @@ "x0_sha256": "8bd3fdeac0083d4cb4d0b5430a1f84c3ea282201edc0d43821e2d254ef5e3cdd" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -129,15 +122,13 @@ "x0_sha256": "f8f6ed1e761941da61fc0604473f47deb4b7708af08e8887221a920c4e50fb31" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -162,15 +153,13 @@ "x0_sha256": "eae04ee23d3da0316076b993b1d404e7197e32fa16e3d1a21bf124772e93d23c" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -195,15 +184,13 @@ "x0_sha256": "b1c3affc8333dbbf046b483b7bd7512beb19dbd40b52280b9d69cdd2eaa77a1e" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -228,15 +215,13 @@ "x0_sha256": "721979e912b536be96e65b578dedff89b270138d08586c6ef01e123e547f84c7" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -261,15 +246,13 @@ "x0_sha256": "5bceface51c931c8ebe99d6d6a030a0ab12e41e95a481c3e1826190da8caeb57" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -294,15 +277,13 @@ "x0_sha256": "baf280bfa26d907c4192dc2187bdbda6ad8be56d4ea2fef9b0464ed12719d929" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -327,15 +308,13 @@ "x0_sha256": "fff7dd419b1d3693266e37b9c637aeb6dd635e29527f7e0f7f86ec3c5466061b" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -360,15 +339,13 @@ "x0_sha256": "2677baac90f744ed2df9924db3725ef4bca09ed2adc9da726f0c75bdc85501bd" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -393,15 +370,13 @@ "x0_sha256": "fc68ea9a01a3322b44df28d9b61dfc6d259f2debc086b6ae9d24facc74991df6" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -426,15 +401,13 @@ "x0_sha256": "ca9131891b397bfc5911cc989153a7f272aa636d8d9b5c983f9821a8207523e2" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -459,15 +432,13 @@ "x0_sha256": "0c3ae365d67941d00d23083e434b87a5374289f2192dd87fd225f3562f300090" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -492,15 +463,13 @@ "x0_sha256": "6f05bb707fa4511092a2dd0b1e67eaed30015b7719cfa32c4f090f85c2356835" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -525,15 +494,13 @@ "x0_sha256": "f2bbfbf13ee7e8f0c578f10045f81e4bf798aad86f2611220687b3ac9df58dec" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -558,15 +525,13 @@ "x0_sha256": "50ae9f18265de9b0230789dbef055a8c9823fb0fe368b77222b1203bd985e68f" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -591,15 +556,13 @@ "x0_sha256": "61fda62b6f422f782310c0aba3b33186b98e8c0f5056b753cce2a6b346225955" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -624,15 +587,13 @@ "x0_sha256": "1584b05ad69437dbd0e8879db816765b5a8d038658745fe9dca5c96c113aadf9" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -657,15 +618,13 @@ "x0_sha256": "d266f2dabc0e2ce516dc20e1494748771804173a0e5f11407d1af517d3ef5005" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -690,15 +649,13 @@ "x0_sha256": "2a054ac05eb350615a6d095a0d9ffd12dc3656cc4ae4f10b691bc13aef97e1a2" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -723,15 +680,13 @@ "x0_sha256": "2cc1205904c00589a029e62cce80b1335e9f9500aad1102a4aa5db6170b85892" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -756,15 +711,13 @@ "x0_sha256": "7af9cb66a792d91153e9e340857b57e89622d30b11e8abe8c86efd19bfd40f01" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -789,15 +742,13 @@ "x0_sha256": "84165338581df11ace746153175d67cd2458c5cd1aa95adc4c15c7361ae08a8b" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -822,15 +773,13 @@ "x0_sha256": "33564104c8d421a42557424cba1f0288f65ab99fed4c2ef5b553b01ab3a09f73" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -855,15 +804,13 @@ "x0_sha256": "784765328247a201f2a2e394ea31d391c457688e8b2a50f0962f96bd0c438639" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -888,15 +835,13 @@ "x0_sha256": "781e6c847906cdf79e1c686de9e3df2fc282de0ce5270c58524fe6878edef69f" }, "source_run": "flash-run-01", - "tolerance": 1e-10, - "weight": 0.015625 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -921,15 +866,13 @@ "x0_sha256": "c39b28bad63e6661e8f1c171f24e6b3d5156ae47134a850f29126dfdbbe6feef" }, "source_run": "flash-run-01", - 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"tolerance": 1e-10, - "weight": 0.0078125 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -2934,15 +2757,13 @@ "x0_sha256": "52fc3cb52caaf61ab25109358ca8d1cc08feccb10824426ea2f8ff6e38c9cdf9" }, "source_run": "flash-run-12", - "tolerance": 1e-10, - "weight": 0.0078125 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -2967,15 +2788,13 @@ "x0_sha256": "ef6eecf0491ad0dbcaa226b6eb40778199f481e315792dd824da6ccb73824631" }, "source_run": "flash-run-12", - "tolerance": 1e-10, - "weight": 0.0078125 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -3000,15 +2819,13 @@ "x0_sha256": "11a75f5540436abfe9f3a7273071403ae256431a4a52957925eb310e9f7d14bc" }, "source_run": "flash-run-12", - "tolerance": 1e-10, - "weight": 0.0078125 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -3033,15 +2850,13 @@ "x0_sha256": "198f82eacabe9ebc97e64e1911808e3e6c4b5472ad50174241c5574ff8e4f917" }, "source_run": "flash-run-12", - "tolerance": 1e-10, - "weight": 0.0078125 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -3066,15 +2881,13 @@ "x0_sha256": "10bea0d465f52b50959f261eeeb5b3cf7ac5b3d2878fd4678f5a5ee04ee4a71d" }, "source_run": "flash-run-12", - "tolerance": 1e-10, - "weight": 0.0078125 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -3099,15 +2912,13 @@ "x0_sha256": "42ccd2310d20df18ccd4044596913e312ad9b61c2679c44ade46a1a4dac64f32" }, "source_run": "flash-run-12", - "tolerance": 1e-10, - "weight": 0.0078125 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -3132,15 +2943,13 @@ "x0_sha256": "ecbf623b8b17f8a5f65cb67fdc4cda4643655f1694d5edb708ec9252ac2623eb" }, "source_run": "flash-run-12", - "tolerance": 1e-10, - "weight": 0.0078125 + "tolerance": 1e-10 }, { "admission": { "absolute_tolerance": 0, "capture_kind": "scalar-magnetic-diffusion", "citations": [ - "https://huggingface.co/datasets/hgarud/LinearSolveBench/resolve/3da5eda0ce3e85da1808d4b62d28d9111127e354/catalogue.json", "https://flash.rochester.edu/site/flashcode/user_support/flash_ug_devel/node128.html" ], "relative_tolerance": 1e-10, @@ -3165,28 +2974,25 @@ "x0_sha256": "39c6f3e6090a15ac8fdf57b0ae8f71617e5e752fcbd3b89c5eda9fde80d7a2aa" }, "source_run": "flash-run-12", - "tolerance": 1e-10, - "weight": 0.0078125 + "tolerance": 1e-10 } ], "contracts": { "accuracy": "flash-replay-accuracy-v1", - "execution": "independent-case-once-v1", - "scoring": "flash-replay-speedup-v1" + "execution": "independent-case-unbounded-v1", + "scoring": "candidate-time-v1" }, "execution": { - "case_timeout_seconds": 90.0, "cpus": 2, - "max_iterations": 5000, "memory_bytes": 4294967296, "repetitions": 1 }, "family": "magnetic_diffusion_flash", - "kind": "linear-solver-bench-pilot-release", - "manifest_sha256": "02a8e24e667b96029f0c8db627f87c237549778a63f3342a73010e7278876669", - "release_id": "flash-replay-pilot", + "kind": "linear-solver-bench-release", + "manifest_sha256": "0746f3d4c419ce91469252407588eb585de08766b5425c5796cd1f76f1f54168", + "release_id": "magdiff-flash", "rhs": null, - "schema_version": 2, + "schema_version": 3, "split": "dev", "track": "replay" } diff --git a/families/ns_mesh_pde/README.md b/families/ns_mesh_pde/README.md new file mode 100644 index 0000000..6a125f1 --- /dev/null +++ b/families/ns_mesh_pde/README.md @@ -0,0 +1,9 @@ +# Nonsymmetric mesh/PDE systems + +This family uses original matrices from the SuiteSparse Matrix Collection. For +each matrix, the benchmark deterministically manufactures a hidden Rademacher +target and forms `b = A @ target`. Candidate code receives only `A`, `b`, the +zero initial guess, and the requested tolerance. + +[`dev.json`](dev.json) is the complete public development set. It pins every +source archive and canonical matrix by SHA-256. diff --git a/data/ns-mesh-cohort-v2-dev.json b/families/ns_mesh_pde/dev.json similarity index 96% rename from data/ns-mesh-cohort-v2-dev.json rename to families/ns_mesh_pde/dev.json index 4c281ce..72304b7 100644 --- a/data/ns-mesh-cohort-v2-dev.json +++ b/families/ns_mesh_pde/dev.json @@ -41,8 +41,7 @@ "url": "https://sparse.tamu.edu/MM/HB/orsirr_1.tar.gz" }, "source_run": "ss-225", - "tolerance": 1e-12, - "weight": 1 + "tolerance": 1e-12 }, { "admission": { @@ -85,8 +84,7 @@ "url": "https://sparse.tamu.edu/MM/HB/orsreg_1.tar.gz" }, "source_run": "ss-227", - "tolerance": 1e-12, - "weight": 1 + "tolerance": 1e-12 }, { "admission": { @@ -130,8 +128,7 @@ "url": "https://sparse.tamu.edu/MM/DRIVCAV/cavity01.tar.gz" }, "source_run": "ss-380", - "tolerance": 1e-12, - "weight": 1 + "tolerance": 1e-12 }, { "admission": { @@ -175,8 +172,7 @@ "url": "https://sparse.tamu.edu/MM/DRIVCAV/cavity10.tar.gz" }, "source_run": "ss-389", - "tolerance": 1e-12, - "weight": 1 + "tolerance": 1e-12 }, { "admission": { @@ -220,8 +216,7 @@ "url": "https://sparse.tamu.edu/MM/DRIVCAV/cavity16.tar.gz" }, "source_run": "ss-395", - "tolerance": 1e-12, - "weight": 1 + "tolerance": 1e-12 }, { "admission": { @@ -264,8 +259,7 @@ "url": "https://sparse.tamu.edu/MM/Garon/garon1.tar.gz" }, "source_run": "ss-444", - "tolerance": 1e-12, - "weight": 1 + "tolerance": 1e-12 }, { "admission": { @@ -308,8 +302,7 @@ "url": "https://sparse.tamu.edu/MM/Garon/garon2.tar.gz" }, "source_run": "ss-445", - "tolerance": 1e-12, - "weight": 1 + "tolerance": 1e-12 }, { "admission": { @@ -353,8 +346,7 @@ "url": "https://sparse.tamu.edu/MM/Wang/wang1.tar.gz" }, "source_run": "ss-830", - "tolerance": 1e-12, - "weight": 1 + "tolerance": 1e-12 }, { "admission": { @@ -398,8 +390,7 @@ "url": "https://sparse.tamu.edu/MM/Wang/wang3.tar.gz" }, "source_run": "ss-832", - "tolerance": 1e-12, - "weight": 1 + "tolerance": 1e-12 }, { "admission": { @@ -443,8 +434,7 @@ "url": "https://sparse.tamu.edu/MM/Wang/wang4.tar.gz" }, "source_run": "ss-833", - "tolerance": 1e-12, - "weight": 1 + "tolerance": 1e-12 }, { "admission": { @@ -488,8 +478,7 @@ "url": "https://sparse.tamu.edu/MM/Norris/torso1.tar.gz" }, "source_run": "ss-896", - "tolerance": 1e-12, - "weight": 1 + "tolerance": 1e-12 }, { "admission": { @@ -533,8 +522,7 @@ "url": "https://sparse.tamu.edu/MM/Norris/torso2.tar.gz" }, "source_run": "ss-897", - "tolerance": 1e-12, - "weight": 1 + "tolerance": 1e-12 }, { "admission": { @@ -578,8 +566,7 @@ "url": "https://sparse.tamu.edu/MM/Hohn/fd12.tar.gz" }, "source_run": "ss-917", - "tolerance": 1e-12, - "weight": 1 + "tolerance": 1e-12 }, { "admission": { @@ -622,8 +609,7 @@ "url": "https://sparse.tamu.edu/MM/Hohn/fd15.tar.gz" }, "source_run": "ss-918", - "tolerance": 1e-12, - "weight": 1 + "tolerance": 1e-12 }, { "admission": { @@ -667,8 +653,7 @@ "url": "https://sparse.tamu.edu/MM/TKK/t2d_q4.tar.gz" }, "source_run": "ss-1916", - "tolerance": 1e-12, - "weight": 1 + "tolerance": 1e-12 }, { "admission": { @@ -712,8 +697,7 @@ "url": "https://sparse.tamu.edu/MM/Fluorem/DK01R.tar.gz" }, "source_run": "ss-2334", - "tolerance": 1e-12, - "weight": 1 + "tolerance": 1e-12 }, { "admission": { @@ -757,8 +741,7 @@ "url": "https://sparse.tamu.edu/MM/Fluorem/GT01R.tar.gz" }, "source_run": "ss-2335", - "tolerance": 1e-12, - "weight": 1 + "tolerance": 1e-12 }, { "admission": { @@ -801,8 +784,7 @@ "url": "https://sparse.tamu.edu/MM/Janna/CoupCons3D.tar.gz" }, "source_run": "ss-2647", - "tolerance": 1e-12, - "weight": 1 + "tolerance": 1e-12 }, { "admission": { @@ -944,33 +926,30 @@ "url": "https://sparse.tamu.edu/MM/Janna/Transport.tar.gz" }, "source_run": "ss-2649", - "tolerance": 1e-12, - "weight": 1 + "tolerance": 1e-12 } ], "contracts": { "accuracy": "ns-mesh-accuracy-v1", - "execution": "independent-case-once-v1", - "scoring": "ns-coverage-count-v1" + "execution": "independent-case-unbounded-v1", + "scoring": "candidate-time-v1" }, "execution": { - "case_timeout_seconds": 90.0, "cpus": 2, - "max_iterations": 5000, "memory_bytes": 4294967296, "repetitions": 1 }, "family": "ns-mesh-pde", - "kind": "linear-solver-bench-pilot-release", - "manifest_sha256": "9927ea72bac431d871b0547b966f9ce78b550ca0d1285c7060cdcb2e3e688ecc", - "release_id": "ns-mesh-cohort-v2", + "kind": "linear-solver-bench-release", + "manifest_sha256": "4f40ea3871e0e7c8cc9aa221793a21c87ecdd2909c2e48155ede06d3648844c0", + "release_id": "ns-mesh", "rhs": { "draw_index": 0, "key_id": "c55715b2cda3547981ef9fe643dc65eab64ea683649a40fee8cbed670a654ea6", "public_key_hex": "6c696e6561722d736f6c7665722d62656e63682d6e732d6d6573682d636f686f72742d76322d646576656c6f706d656e74", "scheme": "rademacher-hmac-sha256-v2" }, - "schema_version": 2, + "schema_version": 3, "split": "dev", "track": "coverage" } diff --git a/modal_app.py b/modal_app.py deleted file mode 100644 index 71e59e1..0000000 --- a/modal_app.py +++ /dev/null @@ -1,670 +0,0 @@ -"""Official single-Sandbox CPU venue. - -Run with: - - modal run modal_app.py --source submissions/starter.c \ - --cases data/prepared/dev-v1 --output results/modal-dev.json - -The trusted client retains evaluator archives and exact solutions. The Sandbox -receives only candidate source, the pinned runtime, and one encoded public -matrix input at a time. -""" - -from __future__ import annotations - -import json -import math -import pathlib -import time -import zlib -from collections.abc import Iterable, Mapping - -import modal - -from linear_solver_bench.dataset import load_prepared, load_prepared_manifest -from linear_solver_bench.models import EvaluationSystem -from linear_solver_bench.protocol import decode_output, encode_input -from linear_solver_bench.runner import ( - PROCESS_OVERHEAD_ALLOWANCE_S, - CaseResult, - RepeatResult, - build_report, -) -from linear_solver_bench.scoring import validate_calibration -from linear_solver_bench.verify import verify_solution - -ROOT = pathlib.Path(__file__).resolve().parent -APP_NAME = "linear-solver-bench-cpu-v1" -BASE_IMAGE = ( - "debian:trixie-slim@sha256:" - "abc9cb88a5587630d7f915f47b23b0668fe250fbfc6457aa4d52b534c1bbf73f" -) -REMOTE_BENCHMARK = "/opt/LinearSolverBench" -REMOTE_RUNTIME = "/opt/lsb-runtime" -MAXIMUM_SANDBOX_LIFETIME_S = 24 * 60 * 60 - -# Decompress only the current public input, then replace this process with the -# native driver. Transport and preparation remain outside the driver's timer. -# Bound both total output and each temporary buffer by trusted client sizes. -_PILOT_INPUT_ERROR = "linear-solver-bench input preparation failed:" -_PILOT_INPUT_EXEC = r""" -import os -import sys -import zlib - -try: - remaining = int(sys.argv[1]) - decoder = zlib.decompressobj() - with open(sys.argv[2], "rb") as source, open(sys.argv[4], "wb") as target: - while chunk := source.read(1024 * 1024): - while chunk: - decoded = decoder.decompress(chunk, min(1024 * 1024, remaining + 1)) - if len(decoded) > remaining or decoder.unused_data: - raise ValueError("compressed input exceeds its declared size") - target.write(decoded) - remaining -= len(decoded) - chunk = decoder.unconsumed_tail - if remaining or not decoder.eof: - raise ValueError("compressed input is incomplete") - os.unlink(sys.argv[2]) - os.execv(sys.argv[3], sys.argv[3:]) -except (OSError, ValueError, zlib.error) as error: - print("linear-solver-bench input preparation failed:", error, file=sys.stderr) - sys.exit(125) -""" - -app = modal.App(APP_NAME) -image = ( - modal.Image.from_registry(BASE_IMAGE, add_python="3.12") - .apt_install("build-essential", "cmake", "git", "ninja-build") - .pip_install("numpy==2.5.1", "scipy==1.18.0") - # Keep evaluator archives out of the image. In particular, never add the - # repository's data/ tree: prepared archives contain x_star. - .add_local_dir(ROOT / "src", f"{REMOTE_BENCHMARK}/src", copy=True) - .add_local_dir(ROOT / "native", f"{REMOTE_BENCHMARK}/native", copy=True) - .add_local_file( - ROOT / "pyproject.toml", f"{REMOTE_BENCHMARK}/pyproject.toml", copy=True - ) - .add_local_file(ROOT / "README.md", f"{REMOTE_BENCHMARK}/README.md", copy=True) - .add_local_file(ROOT / "LICENSE", f"{REMOTE_BENCHMARK}/LICENSE", copy=True) - .add_local_file(ROOT / "setup.py", f"{REMOTE_BENCHMARK}/setup.py", copy=True) - .add_local_file( - ROOT / "benchmark.toml", f"{REMOTE_BENCHMARK}/benchmark.toml", copy=True - ) - .env({"LINEAR_SOLVER_BENCH_ROOT": REMOTE_BENCHMARK}) - .run_commands(f"python -m pip install --no-deps {REMOTE_BENCHMARK}") - .run_commands( - f"linear-solver-bench runtime build --output {REMOTE_RUNTIME} --jobs 2" - ) -) - - -def _stream_tail(stream, limit: int = 8000) -> str: - tail = "" - for chunk in stream: - tail = (tail + chunk)[-limit:] - return tail - - -def _pilot_case_executor(sandbox): - """Transfer one public system and verify its single execution on the client.""" - case_number = 0 - - def execute( - executable: pathlib.Path, - system: EvaluationSystem, - *, - index: int = 0, - deadline_s: float, - maximum_iterations: int, - accuracy_contract_id: str, - ) -> RepeatResult: - nonlocal case_number - case_root = f"/work/cases/{case_number:04d}" - case_number += 1 - input_path = f"{case_root}/input.bin" - compressed_path = f"{case_root}/input.bin.zlib" - output_path = f"{case_root}/output.bin" - # write_bytes creates parents. Targets and evaluator archives stay local. - payload = encode_input(system.public) - input_size = len(payload) - compressed = zlib.compress(payload, level=1) - del payload - sandbox.filesystem.write_bytes(compressed, compressed_path) - del compressed - started = time.monotonic() - try: - try: - process = sandbox.exec( - "python", - "-c", - _PILOT_INPUT_EXEC, - str(input_size), - compressed_path, - str(executable), - input_path, - output_path, - str(maximum_iterations), - format(deadline_s, ".17g"), - env={ - "OMP_NUM_THREADS": "1", - "OPENBLAS_NUM_THREADS": "1", - "MKL_NUM_THREADS": "1", - }, - timeout=math.ceil(deadline_s + PROCESS_OVERHEAD_ALLOWANCE_S), - ) - diagnostics = ( - _stream_tail(process.stdout) + _stream_tail(process.stderr) - )[-8000:] - returncode = process.wait() - except modal.exception.ExecTimeoutError: - return RepeatResult( - index, - "timeout", - None, - time.monotonic() - started, - "sandbox execution timeout", - None, - None, - ) - wall_s = time.monotonic() - started - if returncode == 125 and _PILOT_INPUT_ERROR in diagnostics: - raise RuntimeError(diagnostics) - # Modal's process.wait() also represents an exec timeout as -1. - if returncode in {124, -1}: - return RepeatResult( - index, "timeout", returncode, wall_s, diagnostics, None, None - ) - if returncode != 0: - return RepeatResult( - index, "crash", returncode, wall_s, diagnostics, None, None - ) - try: - payload = sandbox.filesystem.read_bytes(output_path) - except modal.exception.SandboxFilesystemNotFoundError: - return RepeatResult( - index, - "invalid_output", - returncode, - wall_s, - f"{diagnostics}\nmissing driver output".strip(), - None, - None, - ) - try: - driver = decode_output(payload, expected_n=system.public.matrix.n) - except ValueError as exc: - return RepeatResult( - index, - "invalid_output", - returncode, - wall_s, - f"{diagnostics}\n{exc}".strip(), - None, - None, - ) - verification = verify_solution( - system, - driver.solution, - status=driver.status, - input_mutated=driver.input_mutated, - contract_id=accuracy_contract_id, - ) - return RepeatResult( - index, - "completed", - returncode, - wall_s, - diagnostics, - driver, - verification, - ) - finally: - # Retain only the current public case in the sandbox filesystem. - # Filesystem or sandbox failures propagate and invalidate the run. - sandbox.filesystem.remove(case_root, recursive=True) - - return execute - - -def _evaluate_pilot_sandbox( - source: pathlib.Path, - systems: Iterable[EvaluationSystem], - prepared: Mapping, - *, - official: bool = False, - reference: Mapping | None = None, -) -> dict: - from linear_solver_bench.manifests import validate_release - from linear_solver_bench.pilot_dataset import validate_pilot_prepared_manifest - from linear_solver_bench.pilot_runner import evaluate_pilot - from linear_solver_bench.pilot_scoring import validate_replay_reference - - validate_pilot_prepared_manifest(prepared, official=official) - release = validate_release(prepared["source_release"], official=official) - execution = release["execution"] - memory_mib, remainder = divmod(execution["memory_bytes"], 1024**2) - if remainder or memory_mib < 1: - raise ValueError("Modal memory_bytes must be an exact positive number of MiB") - timeout = math.ceil( - 300 - + len(prepared["cases"]) - * (execution["case_timeout_seconds"] + PROCESS_OVERHEAD_ALLOWANCE_S + 5) - ) - if timeout > MAXIMUM_SANDBOX_LIFETIME_S: - raise ValueError("selected cases exceed Modal's 24-hour Sandbox lifetime") - venue = { - "id": "modal-sandbox-pilot-cpu-v2", - "cpus": execution["cpus"], - "memory_bytes": execution["memory_bytes"], - "limits_enforced": True, - } - if reference is not None: - if release["track"] != "replay": - raise ValueError("coverage scoring does not use a timing reference") - validate_replay_reference(reference) - anchor = reference["reference_report"] - expected = { - "family": release["family"], - "track": release["track"], - "release_manifest_sha256": release["manifest_sha256"], - "prepared_manifest_sha256": prepared["manifest_sha256"], - "contracts": release["contracts"], - "repetitions": 1, - "venue": venue, - "split": release["split"], - } - for name, value in expected.items(): - if anchor[name] != value: - raise ValueError(f"candidate and reference {name} differ") - if len(anchor["cases"]) != len(prepared["cases"]): - raise ValueError("candidate and reference case sets differ") - for entry, baseline in zip(prepared["cases"], anchor["cases"], strict=True): - if ( - entry["case_id"] != baseline["case_id"] - or entry["system_sha256"] != baseline["system_sha256"] - or entry["sha256"] != baseline["archive_sha256"] - ): - raise ValueError("candidate and reference numerical inputs differ") - sandbox = modal.Sandbox.create( - app=app, - image=image, - cpu=(float(execution["cpus"]), float(execution["cpus"])), - memory=(memory_mib, memory_mib), - timeout=timeout, - block_network=True, - ) - try: - if sandbox.exec("mkdir", "-p", "/work").wait() != 0: - raise RuntimeError("cannot initialize sandbox working directory") - sandbox.filesystem.copy_from_local(source, "/work/policy.c") - compile_process = sandbox.exec( - "linear-solver-bench", - "candidate", - "build", - "/work/policy.c", - "--runtime", - REMOTE_RUNTIME, - "--output", - "/work/candidate", - timeout=120, - ) - compile_stdout = _stream_tail(compile_process.stdout, 256 * 1024) - compile_stderr = _stream_tail(compile_process.stderr) - if compile_process.wait() != 0: - raise RuntimeError( - "candidate build failed:\n" + compile_stdout + compile_stderr - ) - build = json.loads(compile_stdout) - runtime = json.loads( - sandbox.filesystem.read_text(f"{REMOTE_RUNTIME}/manifest.json") - ) - if ( - reference is not None - and reference["reference_report"]["runtime_manifest_sha256"] - != runtime["manifest_sha256"] - ): - raise ValueError("candidate and reference runtime_manifest_sha256 differ") - return evaluate_pilot( - pathlib.Path(build["executable"]), - systems, - prepared, - source_sha256=build["source_sha256"], - executable_sha256=build["executable_sha256"], - runtime_manifest_sha256=runtime["manifest_sha256"], - venue=venue, - execute=_pilot_case_executor(sandbox), - ) - finally: - sandbox.terminate() - - -def _run_pilot( - source: pathlib.Path, - cases: pathlib.Path, - output: pathlib.Path, - *, - family: str, - track: str, - official: bool, - calibration: str, - score_output: str, -) -> None: - from linear_solver_bench.families import resolve_track - from linear_solver_bench.pilot_dataset import ( - iter_pilot_prepared, - load_pilot_prepared_manifest, - ) - from linear_solver_bench.pilot_scoring import score_pilot_report - - prepared = load_pilot_prepared_manifest(cases, official=official) - release = prepared["source_release"] - if bool(family) != bool(track): - raise ValueError("provide both family and track selectors") - if family: - contract = resolve_track(family, track) - if (contract.family, contract.track) != (release["family"], release["track"]): - raise ValueError("family/track differs from the prepared release") - if score_output and [case["case_id"] for case in prepared["cases"]] != [ - case["case_id"] for case in release["cases"] - ]: - raise ValueError("scoring requires the complete frozen split") - if score_output and pathlib.Path(score_output).expanduser().resolve() == output: - raise ValueError("report and score output paths must differ") - reference = None - if calibration: - reference = json.loads( - pathlib.Path(calibration).expanduser().read_text(encoding="utf-8") - ) - report = _evaluate_pilot_sandbox( - source, - iter_pilot_prepared(cases), - prepared, - official=official, - reference=reference, - ) - output.parent.mkdir(parents=True, exist_ok=True) - output.write_text( - json.dumps(report, indent=2, sort_keys=True, allow_nan=False) + "\n", - encoding="utf-8", - ) - print(output) - if score_output: - score_path = pathlib.Path(score_output).expanduser().resolve() - score_path.parent.mkdir(parents=True, exist_ok=True) - score_path.write_text( - json.dumps( - score_pilot_report(report, reference), - indent=2, - sort_keys=True, - allow_nan=False, - ) - + "\n", - encoding="utf-8", - ) - print(score_path) - - -def _calibration(calibration_path: pathlib.Path | None) -> dict[str, object] | None: - if calibration_path is None: - return None - value = json.loads(calibration_path.read_text(encoding="utf-8")) - validate_calibration(value) - return value - - -@app.local_entrypoint() -def main( - source: str, - cases: str, - output: str, - calibration: str = "", - deadline: float = 120.0, - family: str = "", - track: str = "", - official: bool = False, - score_output: str = "", -) -> None: - cases_path = pathlib.Path(cases).expanduser().resolve() - schema = json.loads((cases_path / "manifest.json").read_text(encoding="utf-8")).get( - "schema_version" - ) - if schema == 2: - _run_pilot( - pathlib.Path(source).expanduser().resolve(), - cases_path, - pathlib.Path(output).expanduser().resolve(), - family=family, - track=track, - official=official, - calibration=calibration, - score_output=score_output, - ) - return - if family or track or official or score_output: - raise ValueError( - "family, track, official, and score-output options require pilot cases" - ) - if not math.isfinite(deadline) or not 0.0 < deadline <= 120.0: - raise ValueError("case deadline must lie in (0, 120]") - source_path = pathlib.Path(source).expanduser().resolve() - cases_path = pathlib.Path(cases) - prepared_manifest = load_prepared_manifest(cases_path) - systems = load_prepared(cases_path) - calibration_value = _calibration( - pathlib.Path(calibration).expanduser().resolve() if calibration else None - ) - deadline_by_case = None - if calibration_value is not None: - observed_cases = [str(case["case_id"]) for case in calibration_value["cases"]] - expected_cases = [system.case_id for system in systems] - if observed_cases != expected_cases: - raise ValueError("calibration case order differs from prepared cases") - expected_identity = { - "venue_id": "modal-sandbox-cpu-v1", - "dataset_manifest_sha256": prepared_manifest["source_manifest_sha256"], - "prepared_manifest_sha256": prepared_manifest["manifest_sha256"], - } - mismatches = sorted( - key - for key, expected in expected_identity.items() - if calibration_value.get(key) != expected - ) - if mismatches: - raise ValueError("calibration identity mismatch: " + ", ".join(mismatches)) - deadline_by_case = { - str(case["case_id"]): float(case["deadline_s"]) - for case in calibration_value["cases"] - } - case_deadlines = [ - deadline_by_case[system.case_id] if deadline_by_case is not None else deadline - for system in systems - ] - total_timeout = int( - 300 - + 3 * sum(value + PROCESS_OVERHEAD_ALLOWANCE_S + 5 for value in case_deadlines) - ) - if total_timeout > MAXIMUM_SANDBOX_LIFETIME_S: - raise ValueError( - "case deadlines exceed Modal's 24-hour Sandbox lifetime; " - "provide a valid venue calibration artifact" - ) - sandbox = modal.Sandbox.create( - app=app, - image=image, - cpu=(2.0, 2.0), - memory=(4096, 4096), - timeout=total_timeout, - block_network=True, - ) - try: - sandbox.exec("mkdir", "-p", "/work").wait() - sandbox.filesystem.copy_from_local(source_path, "/work/policy.c") - compile_process = sandbox.exec( - "linear-solver-bench", - "candidate", - "build", - "/work/policy.c", - "--runtime", - REMOTE_RUNTIME, - "--output", - "/work/candidate", - timeout=120, - ) - compile_stdout = compile_process.stdout.read() - compile_stderr = compile_process.stderr.read() - if compile_process.wait() != 0: - raise RuntimeError( - "candidate build failed:\n" + compile_stdout + compile_stderr - ) - build_metadata = json.loads(compile_stdout) - remote_executable = str(build_metadata["executable"]) - runtime_manifest = json.loads( - sandbox.filesystem.read_text(f"{REMOTE_RUNTIME}/manifest.json") - ) - if ( - calibration_value is not None - and calibration_value["runtime_manifest_sha256"] - != runtime_manifest["manifest_sha256"] - ): - raise ValueError("calibration runtime identity mismatch") - results = [] - environment = { - "OMP_NUM_THREADS": "1", - "OPENBLAS_NUM_THREADS": "1", - "MKL_NUM_THREADS": "1", - } - for case_index, system in enumerate(systems): - case_root = f"/work/cases/{case_index:04d}" - sandbox.exec("mkdir", "-p", case_root).wait() - input_path = f"{case_root}/input.bin" - sandbox.filesystem.write_bytes(encode_input(system.public), input_path) - case_deadline = ( - deadline_by_case[system.case_id] - if deadline_by_case is not None - else deadline - ) - repeats = [] - for repeat_index in range(3): - output_path = f"{case_root}/output-{repeat_index}.bin" - started = time.monotonic() - try: - process = sandbox.exec( - remote_executable, - input_path, - output_path, - "10000", - format(case_deadline, ".17g"), - env=environment, - timeout=case_deadline + PROCESS_OVERHEAD_ALLOWANCE_S, - ) - stdout = process.stdout.read() - stderr = process.stderr.read() - returncode = process.wait() - wall_s = time.monotonic() - started - except ( - modal.exception.ExecTimeoutError, - modal.exception.SandboxTimeoutError, - ): - repeats.append( - RepeatResult( - repeat_index, - "timeout", - None, - time.monotonic() - started, - "sandbox execution timeout", - None, - None, - ) - ) - break - diagnostics = (stdout + stderr)[-8000:] - if returncode == 124: - repeats.append( - RepeatResult( - repeat_index, - "timeout", - returncode, - wall_s, - diagnostics, - None, - None, - ) - ) - break - if returncode != 0: - repeats.append( - RepeatResult( - repeat_index, - "crash", - returncode, - wall_s, - diagnostics, - None, - None, - ) - ) - break - try: - driver = decode_output( - sandbox.filesystem.read_bytes(output_path), - expected_n=system.public.matrix.n, - ) - except ValueError as exc: - repeats.append( - RepeatResult( - repeat_index, - "invalid_output", - returncode, - wall_s, - f"{diagnostics}\n{exc}".strip(), - None, - None, - ) - ) - break - verification = verify_solution( - system, - driver.solution, - status=driver.status, - input_mutated=driver.input_mutated, - ) - repeats.append( - RepeatResult( - repeat_index, - "completed", - returncode, - wall_s, - diagnostics, - driver, - verification, - ) - ) - results.append( - CaseResult( - case_id=system.case_id, - n=system.public.matrix.n, - nnz=system.public.matrix.nnz, - tolerance=system.public.tolerance, - deadline_s=case_deadline, - repetitions=tuple(repeats), - ) - ) - report = build_report( - tuple(results), - source_sha256=str(build_metadata["source_sha256"]), - executable_sha256=str(build_metadata["executable_sha256"]), - runtime_manifest_sha256=str(runtime_manifest["manifest_sha256"]), - dataset_manifest_sha256=str(prepared_manifest["source_manifest_sha256"]), - prepared_manifest_sha256=str(prepared_manifest["manifest_sha256"]), - venue_id="modal-sandbox-cpu-v1", - ) - output_path = pathlib.Path(output).expanduser().resolve() - output_path.parent.mkdir(parents=True, exist_ok=True) - output_path.write_text( - json.dumps(report, indent=2, sort_keys=True) + "\n", - encoding="utf-8", - ) - print(output_path) - finally: - sandbox.terminate() diff --git a/native/CMakeLists.txt b/native/CMakeLists.txt index ab2dda4..c13f71a 100644 --- a/native/CMakeLists.txt +++ b/native/CMakeLists.txt @@ -3,12 +3,22 @@ project(linear_solver_bench_driver LANGUAGES C CXX) set(CMAKE_C_STANDARD 17) set(CMAKE_C_STANDARD_REQUIRED ON) +set(CMAKE_C_EXTENSIONS OFF) set(CMAKE_CXX_STANDARD 17) set(CMAKE_CXX_STANDARD_REQUIRED ON) +set(CMAKE_CXX_EXTENSIONS OFF) find_path(HYPRE_INCLUDE_DIR HYPRE.h REQUIRED) -find_library(HYPRE_LIBRARY NAMES HYPRE REQUIRED) +set(LSB_RUNTIME_ROOT "" CACHE PATH "Runtime root to normalize in compiled diagnostics") add_library(lsb_driver OBJECT src/driver.cpp) target_include_directories(lsb_driver PRIVATE ${HYPRE_INCLUDE_DIR}) target_compile_options(lsb_driver PRIVATE -O3 -DNDEBUG -fno-omit-frame-pointer) +target_compile_options(lsb_driver PRIVATE + "-ffile-prefix-map=${CMAKE_CURRENT_SOURCE_DIR}=/linear-solver-bench/native") +if(LSB_RUNTIME_ROOT) + target_compile_options(lsb_driver PRIVATE + "-ffile-prefix-map=${LSB_RUNTIME_ROOT}=/linear-solver-bench/runtime") +endif() + +install(FILES $ DESTINATION trusted RENAME driver.o) diff --git a/native/allowed-symbols.txt b/native/allowed-symbols.txt new file mode 100644 index 0000000..90681b8 --- /dev/null +++ b/native/allowed-symbols.txt @@ -0,0 +1,164 @@ +hypre_ParKrylovCAlloc +hypre_ParKrylovFree +hypre_ParKrylovCommInfo +hypre_ParKrylovCreateVector +hypre_ParKrylovCreateVectorArray +hypre_ParKrylovDestroyVector +hypre_ParKrylovCopyVector +hypre_ParKrylovClearVector +hypre_ParKrylovScaleVector +hypre_ParKrylovAxpy +hypre_ParKrylovInnerProd +hypre_ParKrylovInnerProdTagged +hypre_ParKrylovMassInnerProd +hypre_ParKrylovMassDotpTwo +hypre_ParKrylovMassAxpy +hypre_ParKrylovMatvecCreate +hypre_ParKrylovMatvec +hypre_ParKrylovMatvecT +hypre_ParKrylovMatvecDestroy +hypre_ParKrylovIdentitySetup +hypre_ParKrylovIdentity +HYPRE_ParCSRMatrixGetComm +HYPRE_ParCSRMatrixGetDims +HYPRE_ParCSRMatrixGetLocalRange +HYPRE_ParCSRDiagScaleSetup +HYPRE_ParCSRDiagScale +HYPRE_BoomerAMGCreate +HYPRE_BoomerAMGDestroy +HYPRE_BoomerAMGSetup +HYPRE_BoomerAMGSolve +HYPRE_BoomerAMGSolveT +HYPRE_BoomerAMGSetTol +HYPRE_BoomerAMGSetMaxIter +HYPRE_BoomerAMGSetMinIter +HYPRE_BoomerAMGSetMaxCoarseSize +HYPRE_BoomerAMGSetMinCoarseSize +HYPRE_BoomerAMGSetMaxLevels +HYPRE_BoomerAMGSetStrongThreshold +HYPRE_BoomerAMGSetMaxRowSum +HYPRE_BoomerAMGSetNonGalerkinTol +HYPRE_BoomerAMGSetCoarsenType +HYPRE_BoomerAMGSetMeasureType +HYPRE_BoomerAMGSetAggNumLevels +HYPRE_BoomerAMGSetNumPaths +HYPRE_BoomerAMGSetInterpType +HYPRE_BoomerAMGSetInterpRefine +HYPRE_BoomerAMGSetTruncFactor +HYPRE_BoomerAMGSetPMaxElmts +HYPRE_BoomerAMGSetAggInterpType +HYPRE_BoomerAMGSetAggTruncFactor +HYPRE_BoomerAMGSetAggPMaxElmts +HYPRE_BoomerAMGSetCycleType +HYPRE_BoomerAMGSetNumSweeps +HYPRE_BoomerAMGSetRelaxType +HYPRE_BoomerAMGSetRelaxOrder +HYPRE_BoomerAMGSetCycleNumSweeps +HYPRE_BoomerAMGSetCycleRelaxType +HYPRE_BoomerAMGSetRelaxWt +HYPRE_BoomerAMGSetOuterWt +HYPRE_BoomerAMGSetChebyOrder +HYPRE_BoomerAMGSetChebyScale +HYPRE_BoomerAMGSetChebyVariant +HYPRE_BoomerAMGSetChebyEigEst +HYPRE_BoomerAMGSetChebyFraction +HYPRE_BoomerAMGSetSmoothType +HYPRE_BoomerAMGSetSmoothNumLevels +HYPRE_BoomerAMGSetSmoothNumSweeps +HYPRE_BoomerAMGDDCreate +HYPRE_BoomerAMGDDDestroy +HYPRE_BoomerAMGDDSetup +HYPRE_BoomerAMGDDSolve +HYPRE_BoomerAMGDDSetFACNumRelax +HYPRE_BoomerAMGDDSetFACNumCycles +HYPRE_BoomerAMGDDSetFACCycleType +HYPRE_BoomerAMGDDSetFACRelaxType +HYPRE_BoomerAMGDDSetFACRelaxWeight +HYPRE_BoomerAMGDDSetStartLevel +HYPRE_BoomerAMGDDSetPadding +HYPRE_BoomerAMGDDSetNumGhostLayers +HYPRE_FSAICreate +HYPRE_FSAIDestroy +HYPRE_FSAISetup +HYPRE_FSAISolve +HYPRE_FSAISetAlgoType +HYPRE_FSAISetMaxSteps +HYPRE_FSAISetMaxStepSize +HYPRE_FSAISetMaxNnzRow +HYPRE_FSAISetNumLevels +HYPRE_FSAISetMaxIterations +HYPRE_FSAISetEigMaxIters +HYPRE_FSAISetZeroGuess +HYPRE_FSAISetThreshold +HYPRE_FSAISetKapTolerance +HYPRE_FSAISetOmega +HYPRE_FSAISetTolerance +HYPRE_ParChebyCreate +HYPRE_ParChebyDestroy +HYPRE_ParChebySetup +HYPRE_ParChebySolve +HYPRE_ParChebySetMaxIterations +HYPRE_ParChebySetOrder +HYPRE_ParChebySetVariant +HYPRE_ParChebySetScale +HYPRE_ParChebySetEigEst +HYPRE_ParChebySetZeroGuess +HYPRE_ParChebySetTolerance +HYPRE_ParChebySetEigRatio +HYPRE_ParChebySetMinMaxEigEst +HYPRE_ParaSailsCreate +HYPRE_ParaSailsDestroy +HYPRE_ParaSailsSetup +HYPRE_ParaSailsSolve +HYPRE_ParaSailsSetParams +HYPRE_ParaSailsSetFilter +HYPRE_ParaSailsSetSym +HYPRE_ParaSailsSetLoadbal +HYPRE_ParaSailsSetReuse +HYPRE_EuclidCreate +HYPRE_EuclidDestroy +HYPRE_EuclidSetup +HYPRE_EuclidSolve +HYPRE_EuclidSetLevel +HYPRE_EuclidSetBJ +HYPRE_EuclidSetRowScale +HYPRE_EuclidSetSparseA +HYPRE_EuclidSetILUT +HYPRE_ParCSRPilutCreate +HYPRE_ParCSRPilutDestroy +HYPRE_ParCSRPilutSetup +HYPRE_ParCSRPilutSolve +HYPRE_ParCSRPilutSetMaxIter +HYPRE_ParCSRPilutSetFactorRowSize +HYPRE_ParCSRPilutSetDropTolerance +HYPRE_ILUCreate +HYPRE_ILUDestroy +HYPRE_ILUSetup +HYPRE_ILUSolve +HYPRE_ILUSetMaxIter +HYPRE_ILUSetTriSolve +HYPRE_ILUSetLowerJacobiIters +HYPRE_ILUSetUpperJacobiIters +HYPRE_ILUSetLevelOfFill +HYPRE_ILUSetMaxNnzPerRow +HYPRE_ILUSetSchurMaxIter +HYPRE_ILUSetType +HYPRE_ILUSetLocalReordering +HYPRE_ILUSetTol +HYPRE_ILUSetDropThreshold +HYPRE_ILUSetNSHDropThreshold +HYPRE_SchwarzCreate +HYPRE_SchwarzDestroy +HYPRE_SchwarzSetup +HYPRE_SchwarzSolve +HYPRE_SchwarzSetVariant +HYPRE_SchwarzSetOverlap +HYPRE_SchwarzSetDomainType +HYPRE_SchwarzSetNonSymm +HYPRE_SchwarzSetLocalSolverType +HYPRE_SchwarzSetILUKLevelOfFill +HYPRE_SchwarzSetILUTMaxNnzPerRow +HYPRE_SchwarzSetMaxIter +HYPRE_SchwarzSetRelaxWeight +HYPRE_SchwarzSetILUTDroptol +HYPRE_SchwarzSetTol diff --git a/native/include/NOTICE-HYPRE b/native/include/NOTICE-HYPRE old mode 100755 new mode 100644 diff --git a/native/include/nsl_hypre_solver.h b/native/include/nsl_hypre_solver.h old mode 100755 new mode 100644 index 17f6aa5..91bdeed --- a/native/include/nsl_hypre_solver.h +++ b/native/include/nsl_hypre_solver.h @@ -60,8 +60,7 @@ HYPRE_Int hypre_ParKrylovIdentity(void *vdata, void *A, void *b, void *x); HYPRE_Int solver_create(HYPRE_Solver *solver, HYPRE_Real relative_tolerance, - HYPRE_Real absolute_tolerance, - HYPRE_Int maximum_iterations); + HYPRE_Real absolute_tolerance); #ifdef __cplusplus } diff --git a/native/src/driver.cpp b/native/src/driver.cpp index 656cbb1..002c163 100644 --- a/native/src/driver.cpp +++ b/native/src/driver.cpp @@ -19,13 +19,11 @@ #include #include -#include #include extern "C" HYPRE_Int solver_create(HYPRE_Solver* solver, HYPRE_Real relative_tolerance, - HYPRE_Real absolute_tolerance, - HYPRE_Int maximum_iterations); + HYPRE_Real absolute_tolerance); namespace { @@ -36,11 +34,6 @@ constexpr std::uint32_t kProtocolVersion = 1; volatile std::sig_atomic_t g_phase = 0; void fatal_signal_handler(int signal_number) { - if (signal_number == SIGALRM) { - constexpr char message[] = "LSB_TIMING_TIMEOUT\n"; - static_cast(::write(STDERR_FILENO, message, sizeof(message) - 1)); - ::_exit(124); - } const char* message = "LSB_FATAL_PHASE=unknown\n"; switch (g_phase) { case 1: message = "LSB_FATAL_PHASE=read_input\n"; break; @@ -156,42 +149,6 @@ Input read_input(const std::string& path) { return input; } -int positive_int(const char* text) { - std::size_t consumed = 0; - const long value = std::stol(text, &consumed); - if (text[consumed] != '\0' || value <= 0 || - value > std::numeric_limits::max()) { - throw std::runtime_error("invalid maximum iteration count"); - } - return static_cast(value); -} - -double positive_double(const char* text) { - std::size_t consumed = 0; - const double value = std::stod(text, &consumed); - if (text[consumed] != '\0' || !std::isfinite(value) || value <= 0.0) { - throw std::runtime_error("invalid timing limit"); - } - return value; -} - -void set_timing_limit(double seconds) { - struct itimerval timer {}; - const auto microseconds = static_cast(std::ceil(seconds * 1e6)); - timer.it_value.tv_sec = static_cast(microseconds / 1000000U); - timer.it_value.tv_usec = static_cast(microseconds % 1000000U); - if (::setitimer(ITIMER_REAL, &timer, nullptr) != 0) { - throw std::runtime_error("cannot arm timing limit"); - } -} - -void clear_timing_limit() { - struct itimerval timer {}; - if (::setitimer(ITIMER_REAL, &timer, nullptr) != 0) { - throw std::runtime_error("cannot clear timing limit"); - } -} - void check(HYPRE_Int status, const char* operation) { if (status != 0) { throw std::runtime_error(std::string(operation) + " failed: " + @@ -322,14 +279,12 @@ struct SolveResult { bool input_mutated = false; }; -SolveResult solve(const Input& input, HypreObjects& objects, - HYPRE_Int maximum_iterations, double timing_limit_s) { +SolveResult solve(const Input& input, HypreObjects& objects) { HYPRE_Solver solver = nullptr; HYPRE_Int status = 0; - set_timing_limit(timing_limit_s); const auto start = std::chrono::steady_clock::now(); g_phase = 4; - status |= solver_create(&solver, input.tolerance, 0.0, maximum_iterations); + status |= solver_create(&solver, input.tolerance, 0.0); const auto create_stop = std::chrono::steady_clock::now(); auto setup_stop = create_stop; if (status == 0 && solver != nullptr) { @@ -348,7 +303,6 @@ SolveResult solve(const Input& input, HypreObjects& objects, } } const auto stop = std::chrono::steady_clock::now(); - clear_timing_limit(); SolveResult result; result.elapsed_ns = static_cast( @@ -402,18 +356,15 @@ void write_output(const std::string& path, const SolveResult& result) { } // namespace int main(int argc, char** argv) { - if (argc != 5) { - std::cerr << "usage: lsb_driver INPUT OUTPUT MAX_ITERATIONS TIME_LIMIT_S\n"; + if (argc != 3) { + std::cerr << "usage: lsb_driver INPUT OUTPUT\n"; return 2; } HypreObjects objects; bool initialized = false; std::signal(SIGSEGV, fatal_signal_handler); std::signal(SIGABRT, fatal_signal_handler); - std::signal(SIGALRM, fatal_signal_handler); try { - const int maximum_iterations = positive_int(argv[3]); - const double timing_limit_s = positive_double(argv[4]); g_phase = 1; const Input input = read_input(argv[1]); g_phase = 2; @@ -421,7 +372,7 @@ int main(int argc, char** argv) { initialized = true; g_phase = 3; objects = create_objects(input); - const SolveResult result = solve(input, objects, maximum_iterations, timing_limit_s); + const SolveResult result = solve(input, objects); g_phase = 9; write_output(argv[2], result); g_phase = 10; diff --git a/pyproject.toml b/pyproject.toml index cdf3e0f..0c60b37 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,22 +5,18 @@ build-backend = "setuptools.build_meta" [project] name = "linear-solver-bench" version = "0.1.0" -description = "A benchmark for AI-written HYPRE linear solvers" +description = "A benchmark for C/HYPRE linear solvers" readme = "README.md" requires-python = ">=3.12" license = "Apache-2.0" -license-files = ["LICENSE"] -authors = [{name = "Autodidakt"}] dependencies = [ "numpy>=2.0", "scipy>=1.14", ] [project.optional-dependencies] -qualification = ["pyamg==5.3.0"] modal = ["modal==1.5.5"] -test = ["pytest>=8.3", "pytest-cov>=6.0", "build>=1.2", "setuptools>=77"] -dev = ["linear-solver-bench[modal,test]", "ruff>=0.9"] +dev = ["linear-solver-bench[modal]", "pytest>=8.3", "ruff>=0.9"] [project.scripts] linear-solver-bench = "linear_solver_bench.cli:main" @@ -28,20 +24,36 @@ linear-solver-bench = "linear_solver_bench.cli:main" [tool.setuptools.packages.find] where = ["src"] -[tool.setuptools.package-data] -linear_solver_bench = ["py.typed"] +[tool.setuptools.data-files] +"share/linear-solver-bench/families/ns_mesh_pde" = [ + "families/ns_mesh_pde/README.md", + "families/ns_mesh_pde/dev.json", +] +"share/linear-solver-bench/families/flash" = [ + "families/flash/README.md", + "families/flash/dev.json", +] +"share/linear-solver-bench/examples" = ["examples/solver.c"] +"share/linear-solver-bench/reference" = ["reference/solver.c"] +"share/linear-solver-bench/native" = [ + "native/CMakeLists.txt", + "native/allowed-symbols.txt", +] +"share/linear-solver-bench/native/include" = [ + "native/include/LICENSE-HYPRE-MIT", + "native/include/NOTICE-HYPRE", + "native/include/nsl_hypre_solver.h", +] +"share/linear-solver-bench/native/src" = ["native/src/driver.cpp"] [tool.pytest.ini_options] addopts = "-q" testpaths = ["tests"] +pythonpath = ["src"] [tool.ruff] line-length = 88 target-version = "py312" -[tool.ruff.format] -# This file's exact bytes are bound into data/qualification.json. -exclude = ["src/linear_solver_bench/qualification.py"] - [tool.ruff.lint] select = ["E", "F", "I", "UP", "B"] diff --git a/qualification_modal.py b/qualification_modal.py deleted file mode 100644 index 6cae0c1..0000000 --- a/qualification_modal.py +++ /dev/null @@ -1,413 +0,0 @@ -"""Run the uniform 1-norm condition campaign on locked SuiteSparse inputs.""" - -from __future__ import annotations - -import hashlib -import importlib.metadata -import json -import os -import pathlib -import platform -import subprocess -import urllib.request -import uuid -from collections.abc import Mapping - -import modal - -ROOT = pathlib.Path(__file__).resolve().parent -APP_NAME = "linear-solver-bench-condition-v2" -BASE_IMAGE = ( - "debian:trixie-slim@sha256:" - "abc9cb88a5587630d7f915f47b23b0668fe250fbfc6457aa4d52b534c1bbf73f" -) -APT_PACKAGES = ( - "build-essential=12.12", - "libsuitesparse-dev=1:7.10.1+dfsg-1", - "pkg-config=1.8.1-4", -) -CACHE_APT_PACKAGES = ("ca-certificates=20250419",) -PYTHON_PACKAGES = ( - "numpy==2.5.1", - "scipy==1.18.0", - "scikit-sparse==0.5.0", -) -REMOTE_ROOT = "/opt/LinearSolverBench" -REMOTE_SOURCE = "/opt/LinearSolverBench/src" -REMOTE_CACHE = "/condition-cache" -CACHE_VOLUME_NAME = "linear-solver-bench-qualification-cache-v1" -SMALL_FILL_MAX = 1_000_000 -MEDIUM_FILL_MAX = 10_000_000 -MAX_ARCHIVE_BYTES = 8 * 1024**3 - -app = modal.App(APP_NAME) -archive_volume = modal.Volume.from_name(CACHE_VOLUME_NAME, create_if_missing=True) -image = ( - modal.Image.from_registry(BASE_IMAGE, add_python="3.12") - .apt_install(*APT_PACKAGES) - .env( - { - "CC": "gcc", - "CXX": "g++", - "PYTHONPATH": f"{REMOTE_ROOT}:{REMOTE_SOURCE}", - } - ) - .pip_install(*PYTHON_PACKAGES) - .add_local_dir(ROOT / "src", REMOTE_SOURCE, copy=True) - .add_local_file( - ROOT / "qualification_modal.py", - f"{REMOTE_ROOT}/qualification_modal.py", - copy=True, - ) -) -cache_image = ( - modal.Image.from_registry(BASE_IMAGE, add_python="3.12") - .apt_install(*CACHE_APT_PACKAGES) - .env({"PYTHONPATH": REMOTE_ROOT}) - .add_local_file( - ROOT / "qualification_modal.py", - f"{REMOTE_ROOT}/qualification_modal.py", - copy=True, - ) -) - - -def _remote_archive(case: Mapping[str, object]) -> pathlib.Path: - return ( - pathlib.Path(REMOTE_CACHE) - / str(case["archive_sha256"]) - / str(case["archive_filename"]) - ) - - -def _sha256(path: pathlib.Path) -> str: - digest = hashlib.sha256() - with path.open("rb") as handle: - for block in iter(lambda: handle.read(1024 * 1024), b""): - digest.update(block) - return digest.hexdigest() - - -@app.function( - image=cache_image, - volumes={REMOTE_CACHE: archive_volume}, - cpu=0.25, - memory=512, - timeout=1800, - max_containers=16, - serialized=True, -) -def cache_archive(job: Mapping[str, object]) -> int: - """Populate one hash-addressed archive without changing the worker image.""" - case = job["case"] - if not isinstance(case, Mapping): - raise TypeError("condition cache job case is invalid") - expected = str(case["archive_sha256"]) - target = _remote_archive(case) - if target.is_file() and _sha256(target) == expected: - return int(case["index"]) - target.parent.mkdir(parents=True, exist_ok=True) - temporary = target.parent / f".{target.name}.{uuid.uuid4().hex}.tmp" - request = urllib.request.Request( - str(case["source_url"]), - headers={"User-Agent": "linear-solver-bench-qualification/0.1"}, - ) - try: - with urllib.request.urlopen(request, timeout=120) as response: - declared = response.headers.get("Content-Length") - if declared is not None and int(declared) > MAX_ARCHIVE_BYTES: - raise ValueError("qualification archive exceeds the size limit") - digest = hashlib.sha256() - total = 0 - with temporary.open("wb") as handle: - while block := response.read(1024 * 1024): - total += len(block) - if total > MAX_ARCHIVE_BYTES: - raise ValueError("qualification archive exceeds the size limit") - digest.update(block) - handle.write(block) - if digest.hexdigest() != expected: - raise ValueError("qualification archive digest mismatch") - os.replace(temporary, target) - archive_volume.commit() - finally: - temporary.unlink(missing_ok=True) - return int(case["index"]) - - -def _package_version(name: str) -> str: - try: - return importlib.metadata.version(name) - except importlib.metadata.PackageNotFoundError: - return "unavailable" - - -def _command_output(command: list[str]) -> str: - try: - result = subprocess.run( - command, check=True, capture_output=True, text=True, timeout=30 - ) - except (OSError, subprocess.SubprocessError): - return "unavailable" - return result.stdout.strip() or result.stderr.strip() or "unavailable" - - -def _toolchain() -> dict[str, object]: - import numpy - import scipy - - import linear_solver_bench.qualification as qualification - - source = pathlib.Path(qualification.__file__) - return { - "python": platform.python_version(), - "platform": platform.platform(), - "numpy": numpy.__version__, - "scipy": scipy.__version__, - "scikit_sparse": _package_version("scikit-sparse"), - "suite_sparse_debian": _command_output( - ["dpkg-query", "--show", "--showformat=${Version}", "libsuitesparse-dev"] - ), - "compiler": _command_output(["gcc", "--version"]).splitlines()[0], - "image_contract": { - "base": BASE_IMAGE, - "apt_packages": list(APT_PACKAGES), - "python_packages": list(PYTHON_PACKAGES), - }, - "source_sha256": { - "qualification.py": hashlib.sha256(source.read_bytes()).hexdigest(), - }, - } - - -def _estimate(job: Mapping[str, object]) -> dict[str, object]: - from linear_solver_bench.dataset import ( - load_matrix_market_archive, - matrix_sha256, - sha256_file, - ) - from linear_solver_bench.models import CsrMatrix - from linear_solver_bench.qualification import ( - indeterminate_condition, - qualify_matrix, - ) - - case = job["case"] - if not isinstance(case, Mapping): - raise TypeError("condition job case is invalid") - archive_volume.reload() - archive = _remote_archive(case) - if sha256_file(archive) != case["archive_sha256"]: - raise ValueError("condition source archive digest mismatch") - matrix = load_matrix_market_archive(archive, case) - canonical_sha256 = matrix_sha256(CsrMatrix.from_scipy(matrix)) - try: - condition, inverse = qualify_matrix(matrix, case_id=str(case["case_id"])) - except Exception as exc: # noqa: BLE001 - preserve per-case failure evidence - condition = indeterminate_condition(f"{type(exc).__name__}: {exc}") - inverse = None - return { - **{ - key: case[key] - for key in ( - "index", - "matrix_id", - "case_id", - "group", - "name", - "n", - "nnz", - "archive_sha256", - ) - }, - "matrix_sha256": canonical_sha256, - "inverse_norm_evidence": inverse.as_record() if inverse else None, - "condition": condition, - } - - -@app.function( - image=image, - volumes={REMOTE_CACHE: archive_volume}, - cpu=4.0, - memory=8192, - timeout=1800, - max_containers=8, - block_network=True, - serialized=True, -) -def estimate_small(job: Mapping[str, object]) -> dict[str, object]: - return {"result": _estimate(job), "toolchain": _toolchain()} - - -@app.function( - image=image, - volumes={REMOTE_CACHE: archive_volume}, - cpu=8.0, - memory=32768, - timeout=3600, - max_containers=8, - block_network=True, - serialized=True, -) -def estimate_medium(job: Mapping[str, object]) -> dict[str, object]: - return {"result": _estimate(job), "toolchain": _toolchain()} - - -@app.function( - image=image, - volumes={REMOTE_CACHE: archive_volume}, - cpu=16.0, - memory=65536, - timeout=7200, - max_containers=8, - block_network=True, - serialized=True, -) -def estimate_large(job: Mapping[str, object]) -> dict[str, object]: - return {"result": _estimate(job), "toolchain": _toolchain()} - - -TIER_FUNCTIONS = { - "small": estimate_small, - "medium": estimate_medium, - "large": estimate_large, -} - - -def _load_inputs(path: pathlib.Path) -> dict[str, object]: - from linear_solver_bench.dataset import identity_sha256 - - value = json.loads(path.read_text(encoding="utf-8")) - digest = value.get("manifest_sha256") - body = {key: item for key, item in value.items() if key != "manifest_sha256"} - if ( - value.get("schema_version") != 1 - or value.get("kind") != "linear-solver-bench-condition-inputs-v1" - or digest != identity_sha256(body) - or value.get("case_count") != len(value.get("cases", ())) - ): - raise ValueError("qualification input lock is invalid") - return value - - -def _tier(record: Mapping[str, object]) -> str: - fill = int(record["amd_vnz"]) + int(record["amd_rnz"]) - if fill <= SMALL_FILL_MAX: - return "small" - if fill <= MEDIUM_FILL_MAX: - return "medium" - return "large" - - -def _write(path: pathlib.Path, value: Mapping[str, object]) -> None: - path.parent.mkdir(parents=True, exist_ok=True) - temporary = path.parent / f".{path.name}.{uuid.uuid4().hex}.tmp" - temporary.write_text( - json.dumps(value, indent=2, sort_keys=True, allow_nan=False) + "\n", - encoding="utf-8", - ) - os.replace(temporary, path) - - -@app.local_entrypoint() -def main( - output: str = "data/qualification-1norm-v2.json", - inputs: str = "data/qualification-inputs-v1.json", - case_limit: int = 0, -) -> None: - from linear_solver_bench.qualification import qualification_document - - if case_limit < 0: - raise ValueError("case_limit cannot be negative") - input_path = pathlib.Path(inputs).expanduser().resolve() - output_path = pathlib.Path(output).expanduser().resolve() - locked = _load_inputs(input_path) - catalogue = json.loads((ROOT / "data/catalogue.json").read_text()) - records = catalogue["records"] - if len(records) != locked["case_count"]: - raise ValueError("qualification inputs disagree with the catalogue") - completed = [] - expected_toolchain = None - if output_path.is_file(): - checkpoint = json.loads(output_path.read_text()) - completed = list(checkpoint["results"]) - expected_toolchain = checkpoint["toolchain"] - completed_indices = {int(result["index"]) for result in completed} - jobs = {tier: [] for tier in TIER_FUNCTIONS} - for case, record in zip(locked["cases"], records, strict=True): - if case["index"] in completed_indices: - continue - jobs[_tier(record)].append({"case": case}) - if case_limit: - remaining = case_limit - for tier in jobs: - jobs[tier] = jobs[tier][:remaining] - remaining -= len(jobs[tier]) - print( - json.dumps( - { - "pending": {tier: len(items) for tier, items in jobs.items()}, - "completed": len(completed), - "output": str(output_path), - }, - sort_keys=True, - ), - flush=True, - ) - cache_jobs = [job for tier in jobs.values() for job in tier] - for cached in cache_archive.map( - cache_jobs, order_outputs=False, return_exceptions=False - ): - print(f"cached qualification input {cached}", flush=True) - for tier, pending in jobs.items(): - if not pending: - continue - for packet in TIER_FUNCTIONS[tier].map( - pending, order_outputs=False, return_exceptions=False - ): - toolchain = packet["toolchain"] - if expected_toolchain is None: - expected_toolchain = toolchain - elif toolchain != expected_toolchain: - raise RuntimeError("qualification worker toolchains differ") - result = packet["result"] - index = int(result["index"]) - if index in completed_indices: - raise RuntimeError("qualification worker returned a duplicate case") - completed.append(result) - completed_indices.add(index) - _write( - output_path, - qualification_document( - catalogue_sha256=str(locked["catalogue_sha256"]), - input_manifest_sha256=str(locked["manifest_sha256"]), - expected_case_count=int(locked["case_count"]), - results=completed, - toolchain=expected_toolchain, - ), - ) - print(f"qualified {len(completed)}/{locked['case_count']}", flush=True) - if expected_toolchain is None: - raise RuntimeError("qualification campaign produced no toolchain evidence") - document = qualification_document( - catalogue_sha256=str(locked["catalogue_sha256"]), - input_manifest_sha256=str(locked["manifest_sha256"]), - expected_case_count=int(locked["case_count"]), - results=completed, - toolchain=expected_toolchain, - ) - _write(output_path, document) - print( - json.dumps( - { - "complete": document["complete"], - "condition_results_sha256": document["condition_results_sha256"], - "status_counts": document["status_counts"], - "method_counts": document["method_counts"], - "output": str(output_path), - }, - indent=2, - sort_keys=True, - ) - ) diff --git a/reference/solver.c b/reference/solver.c new file mode 100644 index 0000000..75317b7 --- /dev/null +++ b/reference/solver.c @@ -0,0 +1,148 @@ +#include "HYPRE_parcsr_ls.h" + +/* Fixed timing reference: HYPRE GMRES(200), preconditioned by ILUT. + This file is compiled by build_reference(), whose audit allows the + stock outer-GMRES calls that participant sources intentionally cannot use. */ + +typedef struct +{ + hypre_Solver base; + HYPRE_Real relative_tolerance; + HYPRE_Real absolute_tolerance; + HYPRE_Solver gmres; + HYPRE_Solver amg; +} reference_Solver; + +static HYPRE_Int +reference_destroy_objects(reference_Solver *data) +{ + HYPRE_Int error = 0; + if (data->gmres != NULL) + { + error |= HYPRE_ParCSRGMRESDestroy(data->gmres); + data->gmres = NULL; + } + if (data->amg != NULL) + { + error |= HYPRE_ILUDestroy(data->amg); + data->amg = NULL; + } + data->base.is_setup = 0; + return error; +} + +static HYPRE_Int +reference_destroy(HYPRE_Solver solver) +{ + reference_Solver *data = (reference_Solver *) solver; + HYPRE_Int error; + if (data == NULL) + { + return 0; + } + error = reference_destroy_objects(data); + error |= hypre_ParKrylovFree(data); + return error; +} + +static HYPRE_Int +reference_setup(HYPRE_Solver solver, HYPRE_Matrix matrix, + HYPRE_Vector rhs, HYPRE_Vector solution) +{ + reference_Solver *data = (reference_Solver *) solver; + HYPRE_ParCSRMatrix par_matrix = (HYPRE_ParCSRMatrix) matrix; + HYPRE_ParVector par_rhs = (HYPRE_ParVector) rhs; + HYPRE_ParVector par_solution = (HYPRE_ParVector) solution; + MPI_Comm communicator; + HYPRE_Int error; + + if (data == NULL || matrix == NULL || rhs == NULL || solution == NULL) + { + return HYPRE_ERROR_GENERIC; + } + error = reference_destroy_objects(data); + error |= HYPRE_ParCSRMatrixGetComm(par_matrix, &communicator); + error |= HYPRE_ILUCreate(&data->amg); + if (error != 0) + { + reference_destroy_objects(data); + return error; + } + + error |= HYPRE_ILUSetType(data->amg, 1); + error |= HYPRE_ILUSetLocalReordering(data->amg, 1); + error |= HYPRE_ILUSetMaxIter(data->amg, 1); + error |= HYPRE_ILUSetMaxNnzPerRow(data->amg, 1000); + error |= HYPRE_ILUSetDropThreshold(data->amg, 1.0e-4); + error |= HYPRE_ILUSetTol(data->amg, 0.0); + + error |= HYPRE_ParCSRGMRESCreate(communicator, &data->gmres); + if (error == 0) + { + error |= HYPRE_ParCSRGMRESSetKDim(data->gmres, 200); + error |= HYPRE_ParCSRGMRESSetMaxIter(data->gmres, 10000); + error |= HYPRE_ParCSRGMRESSetTol( + data->gmres, data->relative_tolerance); + error |= HYPRE_ParCSRGMRESSetAbsoluteTol( + data->gmres, data->absolute_tolerance); + error |= HYPRE_ParCSRGMRESSetPrecond( + data->gmres, HYPRE_ILUSolve, + HYPRE_ILUSetup, data->amg); + } + if (error == 0) + { + error = HYPRE_ParCSRGMRESSetup( + data->gmres, par_matrix, par_rhs, par_solution); + } + if (error != 0) + { + reference_destroy_objects(data); + return error; + } + data->base.is_setup = 1; + return 0; +} + +static HYPRE_Int +reference_solve(HYPRE_Solver solver, HYPRE_Matrix matrix, + HYPRE_Vector rhs, HYPRE_Vector solution) +{ + reference_Solver *data = (reference_Solver *) solver; + if (data == NULL || !data->base.is_setup || data->gmres == NULL) + { + return HYPRE_ERROR_GENERIC; + } + return HYPRE_ParCSRGMRESSolve( + data->gmres, (HYPRE_ParCSRMatrix) matrix, + (HYPRE_ParVector) rhs, (HYPRE_ParVector) solution); +} + +HYPRE_Int +solver_create(HYPRE_Solver *solver, HYPRE_Real relative_tolerance, + HYPRE_Real absolute_tolerance) +{ + reference_Solver *data; + HYPRE_Real target; + if (solver == NULL || !isfinite((double) relative_tolerance) || + !isfinite((double) absolute_tolerance) || + relative_tolerance < 0.0 || absolute_tolerance < 0.0) + { + return HYPRE_ERROR_GENERIC; + } + *solver = NULL; + data = (reference_Solver *) hypre_ParKrylovCAlloc( + 1, sizeof(*data), HYPRE_MEMORY_HOST); + if (data == NULL) + { + return HYPRE_ERROR_GENERIC; + } + target = 0.1 * relative_tolerance; + data->relative_tolerance = target < 1.0e-12 ? target : 1.0e-12; + data->absolute_tolerance = absolute_tolerance; + data->base.setup = reference_setup; + data->base.solve = reference_solve; + data->base.destroy = reference_destroy; + data->base.is_setup = 0; + *solver = (HYPRE_Solver) data; + return 0; +} diff --git a/report_notes/group_taxonomy/aggregate_summary.json b/report_notes/group_taxonomy/aggregate_summary.json deleted file mode 100644 index 24db4fd..0000000 --- a/report_notes/group_taxonomy/aggregate_summary.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "revision": "31-Oct-2023 18:12:37", - "matrices": 2904, - "groups": 203, - "group_size": { - "wide (>100\u00d7)": 52, - "moderate (4\u2013100\u00d7)": 60, - "one size": 60, - "narrow (\u22644\u00d7)": 31 - }, - "group_shape": { - "mixed": 10, - "square only": 159, - "rectangular only": 34 - }, - "group_symmetry": { - "mixed among square matrices": 32, - "no square matrix exactly symmetric/Hermitian": 76, - "all square matrices symmetric/Hermitian": 61, - "N/A (rectangular only)": 34 - }, - "group_values": { - "real + complex": 10, - "real only": 184, - "complex only": 9 - }, - "groups_mixing_curator_value_type": 39, - "groups_mixing_curator_structure": 36, - "group_cohesion": { - "multi-domain/mixed": 15, - "coherent family/series": 94, - "one broad domain; mixed matrix forms": 43, - "single matrix": 51 - }, - "single_matrix_groups": 51, - "multi_kind_groups": 31, - "multi_family_groups_interpretive": 15 -} \ No newline at end of file diff --git a/report_notes/group_taxonomy/analyze_groups.py b/report_notes/group_taxonomy/analyze_groups.py deleted file mode 100644 index 520187a..0000000 --- a/report_notes/group_taxonomy/analyze_groups.py +++ /dev/null @@ -1,268 +0,0 @@ -#!/usr/bin/env python3 -"""Build a reproducible group-level view of the SuiteSparse CSV index. - -The public ssstats.csv lacks column headers; the first twelve fields are -documented at https://sparse.tamu.edu/statistics. A thirteenth current field -is retained as ``nentries`` but is not needed by this analysis. -""" - -from __future__ import annotations - -import csv -import json -import math -import re -from collections import Counter, defaultdict -from pathlib import Path - - -ROOT = Path(__file__).resolve().parent -GROUP_SOURCE = Path("/tmp/ssweb.o4KLNI/db/collection_data/groups") -MATRIX_SOURCE = Path("/tmp/ssweb.o4KLNI/db/collection_data/matrices") - - -def clean_kind(kind: str) -> str: - kind = re.sub(r"^(subsequent|duplicate)\s+", "", kind.strip()) - kind = re.sub(r"\s+sequence$", "", kind) - return kind - - -def family(kind: str) -> str: - k = clean_kind(kind).lower() - if "power network" in k: - return "electrical/circuit/device" - if "biochemical network" in k: - return "chemistry/biology" - if re.search(r"\b(?:graph|multigraph)\b", k) or "term/document" in k: - return "graph/network data" - if any(x in k for x in ("linear programming", "optimization", "optimal control", "combinatorial")): - return "optimization/control" - if any(x in k for x in ("structural", "materials")): - return "structures/materials" - if any(x in k for x in ("circuit", "semiconductor")): - return "electrical/circuit/device" - if any(x in k for x in ("fluid", "oceanography")): - return "fluids/transport" - if any(x in k for x in ("electromagnet", "acoustic")): - return "electromagnetics/acoustics" - if any(x in k for x in ("chemistry", "chemical")): - return "chemistry/biology" - if any(x in k for x in ("model reduction", "eigenvalue")): - return "eigenvalue/model reduction" - if any(x in k for x in ("graphics", "vision", "robotics", "tomography")): - return "imaging/graphics/robotics" - if any(x in k for x in ("2d/3d", "thermal")): - return "generic spatial PDE/thermal" - if any(x in k for x in ("statistical", "counter-example", "mathematical")): - return "mathematics/statistics" - if "data analytics" in k: - return "data analytics" - return "other" - - -def official_notes() -> dict[str, str]: - result = {} - for p in sorted(GROUP_SOURCE.glob("*.rb")): - text = p.read_text(errors="replace") - match = re.search(r"notes:\s*'(.*)'\s*,?\s*\n?\s*}\s*$", text, re.S) - note = match.group(1) if match else "" - note = note.replace("\\'", "'") - result[p.stem] = note.strip() - return result - - -def current_website_metadata() -> dict[tuple[str, str], dict[str, str]]: - """Read current curator labels from the official website source. - - At analysis time these differ from the downloadable 2023 CSV for only the - four Chevron matrices (current: 2D/3D problem; CSV: other problem). - """ - result = {} - if not MATRIX_SOURCE.exists(): - return result - for p in MATRIX_SOURCE.glob("*/*.rb"): - text = p.read_text(errors="replace") - name_match = re.search(r"^\s*name:\s*'([^']+)'", text, re.M) - fields = {} - for field in ("kind", "rb_type", "structure"): - match = re.search(rf"^\s*{field}:\s*'([^']+)'", text, re.M) - if match: - fields[field] = match.group(1) - if name_match and "kind" in fields: - result[(p.parent.name, name_match.group(1))] = fields - return result - - -def synopsis(note: str, limit: int = 210) -> str: - lines = [re.sub(r"\s+", " ", line.strip()) for line in note.splitlines() if line.strip()] - text = " ".join(lines[:2]) - text = re.sub(r"https?://\S+", "", text).strip() - return text if len(text) <= limit else text[: limit - 1].rstrip() + "…" - - -def label_span(lo: int, hi: int) -> str: - if lo == hi: - return "one size" - ratio = hi / max(lo, 1) - if ratio <= 4: - return "narrow (≤4×)" - if ratio <= 100: - return "moderate (4–100×)" - return "wide (>100×)" - - -def main() -> None: - raw = list(csv.reader((ROOT / "ssstats.csv").open(newline=""))) - declared_count, revision = int(raw[0][0]), raw[1][0] - rows = raw[2:] - assert len(rows) == declared_count - website_metadata = current_website_metadata() - - grouped: dict[str, list[dict[str, object]]] = defaultdict(list) - for row in rows: - group, name, nrows, ncols, nnz = row[:5] - current = website_metadata.get((group, name), {}) - fallback_rb_type = "binary" if int(row[6]) else "real" if int(row[5]) else "complex" - fallback_structure = "rectangular" if nrows != ncols else "symmetric/Hermitian" if float(row[10]) == 1 else "unsymmetric" - grouped[group].append( - { - "name": name, - "nrows": int(nrows), - "ncols": int(ncols), - "nnz": int(nnz), - "is_real": int(row[5]), - "is_binary": int(row[6]), - "is_nd": int(row[7]), - "posdef": int(row[8]), - "pattern_symmetry": float(row[9]), - "numerical_symmetry": float(row[10]), - "kind": current.get("kind", row[11]), - "rb_type": current.get("rb_type", fallback_rb_type), - "structure": current.get("structure", fallback_structure), - } - ) - - notes = official_notes() - output = [] - for group, members in grouped.items(): - n = len(members) - dims = [max(x["nrows"], x["ncols"]) for x in members] - nnzs = [x["nnz"] for x in members] - square = [x for x in members if x["nrows"] == x["ncols"]] - rect = n - len(square) - # The official statistic compares A with A' (conjugate transpose in - # MATLAB), so it means symmetric for real matrices and Hermitian for - # complex matrices. - exact_sym = sum(x["numerical_symmetry"] == 1 for x in square) - structurally_sym = sum(x["pattern_symmetry"] == 1 for x in square) - kinds = Counter(clean_kind(str(x["kind"])) for x in members) - families = Counter(family(str(x["kind"])) for x in members) - rb_types = Counter(str(x["rb_type"]) for x in members) - structures = Counter(str(x["structure"]) for x in members) - family_names = sorted(families) - shape_profile = "square only" if not rect else "rectangular only" if not square else "mixed" - if not square: - symmetry_profile = "N/A (rectangular only)" - elif exact_sym == len(square): - symmetry_profile = "all square matrices symmetric/Hermitian" - elif exact_sym == 0: - symmetry_profile = "no square matrix exactly symmetric/Hermitian" - else: - symmetry_profile = "mixed among square matrices" - value_profile = ( - "real only" if all(x["is_real"] for x in members) - else "complex only" if all(not x["is_real"] for x in members) - else "real + complex" - ) - form_mixed = len(structures) > 1 or len(rb_types) > 1 - if n == 1: - cohesion = "single matrix" - elif len(families) > 1: - cohesion = "multi-domain/mixed" - elif form_mixed or len(kinds) > 2: - cohesion = "one broad domain; mixed matrix forms" - else: - cohesion = "coherent family/series" - - dominant_family, dominant_count = families.most_common(1)[0] - note = notes.get(group, "") - output.append( - { - "group": group, - "count": n, - "documented_meaning": synopsis(note), - "source_url": f"https://sparse.tamu.edu/{group}", - "coarse_kind_families_interpretive": "; ".join(family_names), - "dominant_coarse_family_interpretive": dominant_family, - "dominant_coarse_family_share": dominant_count / n, - "normalized_kind_count": len(kinds), - "normalized_kinds": "; ".join(f"{k} ({v})" for k, v in kinds.most_common()), - "curator_value_type_count": len(rb_types), - "curator_value_types": "; ".join(f"{k} ({v})" for k, v in rb_types.most_common()), - "curator_structure_count": len(structures), - "curator_structures": "; ".join(f"{k} ({v})" for k, v in structures.most_common()), - "min_dimension": min(dims), - "max_dimension": max(dims), - "dimension_ratio": max(dims) / max(min(dims), 1), - "size_span": label_span(min(dims), max(dims)), - "min_nnz": min(nnzs), - "max_nnz": max(nnzs), - "shape_profile": shape_profile, - "square": len(square), - "rectangular": rect, - "symmetry_profile": symmetry_profile, - "exact_symmetric_square": exact_sym, - "structurally_symmetric_square": structurally_sym, - "known_spd": sum(x["posdef"] == 1 for x in members), - "value_profile": value_profile, - "binary": sum(x["is_binary"] for x in members), - "geometric_2d3d": sum(x["is_nd"] for x in members), - "cohesion": cohesion, - "official_notes": note, - } - ) - - fields = list(output[0]) - with (ROOT / "group_metrics.csv").open("w", newline="") as f: - writer = csv.DictWriter(f, fields) - writer.writeheader() - writer.writerows(output) - (ROOT / "group_metrics.json").write_text(json.dumps({"revision": revision, "matrix_count": declared_count, "groups": output}, indent=2)) - - with (ROOT / "group_catalogue.md").open("w") as f: - f.write("# SuiteSparse group catalogue\n\n") - f.write(f"Generated from the public `ssstats.csv` index ({declared_count:,} matrices; revision {revision}), current curator kind labels in the official website source, and official group notes.\n\n") - f.write("`Numerically self-adjoint` means numerical symmetry = 1 in the official index (A=Aᵀ for real A; A=A* for complex A); rectangular matrices are excluded from that denominator. The exact curator `structure` column separately preserves complex-symmetric and skew-symmetric classes. `Dimension` is max(rows, columns). Kinds have only the index's `subsequent`, `duplicate`, and `sequence` qualifiers collapsed.\n\n") - f.write("| Group | N | Documented meaning/source | Exact curator kind(s) | Coarse kind family (interpretive) | Dimension range | Curator structure | Numerically self-adjoint | Curator value type | Assessment (heuristic) |\n") - f.write("|---|---:|---|---|---|---:|---|---:|---|---|\n") - for x in sorted(output, key=lambda x: x["group"].lower()): - desc = str(x["documented_meaning"]).replace("|", "\\|") - link = f"[{x['group']}]({x['source_url']})" - f.write( - f"| {link} | {x['count']} | {desc} | {x['normalized_kinds']} | {x['coarse_kind_families_interpretive']} | " - f"{x['min_dimension']:,}–{x['max_dimension']:,} ({x['size_span']}) | " - f"{x['curator_structures']} | {x['exact_symmetric_square']}/{x['square']} square | " - f"{x['curator_value_types']} | {x['cohesion']} |\n" - ) - - summary = { - "revision": revision, - "matrices": declared_count, - "groups": len(output), - "group_size": Counter(x["size_span"] for x in output), - "group_shape": Counter(x["shape_profile"] for x in output), - "group_symmetry": Counter(x["symmetry_profile"] for x in output), - "group_values": Counter(x["value_profile"] for x in output), - "groups_mixing_curator_value_type": sum(x["curator_value_type_count"] > 1 for x in output), - "groups_mixing_curator_structure": sum(x["curator_structure_count"] > 1 for x in output), - "group_cohesion": Counter(x["cohesion"] for x in output), - "single_matrix_groups": sum(x["count"] == 1 for x in output), - "multi_kind_groups": sum(x["normalized_kind_count"] > 1 for x in output), - "multi_family_groups_interpretive": sum(";" in x["coarse_kind_families_interpretive"] for x in output), - } - (ROOT / "aggregate_summary.json").write_text(json.dumps(summary, indent=2)) - print(json.dumps(summary, indent=2)) - - -if __name__ == "__main__": - main() diff --git a/report_notes/group_taxonomy/group_catalogue.md b/report_notes/group_taxonomy/group_catalogue.md deleted file mode 100644 index 1f63d8b..0000000 --- a/report_notes/group_taxonomy/group_catalogue.md +++ /dev/null @@ -1,211 +0,0 @@ -# SuiteSparse group catalogue - -Generated from the public `ssstats.csv` index (2,904 matrices; revision 31-Oct-2023 18:12:37), current curator kind labels in the official website source, and official group notes. - -`Numerically self-adjoint` means numerical symmetry = 1 in the official index (A=Aᵀ for real A; A=A* for complex A); rectangular matrices are excluded from that denominator. The exact curator `structure` column separately preserves complex-symmetric and skew-symmetric classes. `Dimension` is max(rows, columns). Kinds have only the index's `subsequent`, `duplicate`, and `sequence` qualifiers collapsed. - -| Group | N | Documented meaning/source | Exact curator kind(s) | Coarse kind family (interpretive) | Dimension range | Curator structure | Numerically self-adjoint | Curator value type | Assessment (heuristic) | -|---|---:|---|---|---|---:|---|---:|---|---| -| [ACUSIM](https://sparse.tamu.edu/ACUSIM) | 1 | Computational fluid dynamics matrices from ACUSIM, Inc. from Farzin Shakib, ACUSIM, Inc. | computational fluid dynamics problem (1) | fluids/transport | 14,822–14,822 (one size) | symmetric (1) | 1/1 square | real (1) | single matrix | -| [AG-Monien](https://sparse.tamu.edu/AG-Monien) | 32 | AG-Monien Graph Collection, Ralf Diekmann and Robert Preis | 2D/3D problem (26); undirected graph (6) | generic spatial PDE/thermal; graph/network data | 224–1,048,576 (wide (>100×)) | symmetric (32) | 32/32 square | binary (32) | multi-domain/mixed | -| [Alemdar](https://sparse.tamu.edu/Alemdar) | 1 | A finite-element matrix from Bulent Alemdar, RAM International. | 2D/3D problem (1) | generic spatial PDE/thermal | 6,245–6,245 (one size) | symmetric (1) | 1/1 square | integer (1) | single matrix | -| [AMD](https://sparse.tamu.edu/AMD) | 2 | Circuit simulation matrices from Ufuk Okuyucu, AMD, Inc. | circuit simulation problem (2) | electrical/circuit/device | 150,102–1,585,478 (moderate (4–100×)) | symmetric (2) | 2/2 square | real (2) | coherent family/series | -| [Andrews](https://sparse.tamu.edu/Andrews) | 1 | Large eigenvalue problem from Stuart Andrews, Brown University, Rhode Island. stu at cs.brown.edu | computer graphics/vision problem (1) | imaging/graphics/robotics | 60,000–60,000 (one size) | symmetric (1) | 1/1 square | integer (1) | single matrix | -| [Andrianov](https://sparse.tamu.edu/Andrianov) | 16 | Matrices from Alexander Andrianov, SAS Institute Inc. | optimization problem (16) | optimization/control | 5,026–534,388 (wide (>100×)) | symmetric (16) | 16/16 square | binary (16) | coherent family/series | -| [ANSYS](https://sparse.tamu.edu/ANSYS) | 3 | Underdetermined systems needing well-conditioned bases to be found. The goal is to find a permutation or factorization that places A in upper | least squares problem (3) | other | 887,058–1,823,928 (narrow (≤4×)) | rectangular (3) | 0/0 square | real (3) | coherent family/series | -| [Arenas](https://sparse.tamu.edu/Arenas) | 4 | Networks from Alex Arenas, Univeristy Rovira i Virgili, Tarragona, Spain | undirected multigraph (3); undirected graph (1) | graph/network data | 198–10,680 (moderate (4–100×)) | symmetric (4) | 4/4 square | integer (3); binary (1) | one broad domain; mixed matrix forms | -| [ATandT](https://sparse.tamu.edu/ATandT) | 4 | Frequency domain analysis of large nonlinear analog circuits, AT&T. See ATT.ps for a paper describing these matrices, in the Other directory: | frequency-domain circuit simulation problem (4) | electrical/circuit/device | 36,057–659,033 (moderate (4–100×)) | unsymmetric (4) | 0/4 square | real (4) | coherent family/series | -| [Averous](https://sparse.tamu.edu/Averous) | 4 | Matrices from David Averous, Institut National Polytechnique de Toulouse (INPT). -------------------------------------------------------------------------------- | thermal problem (4) | generic spatial PDE/thermal | 1,794–84,617 (moderate (4–100×)) | unsymmetric (4) | 0/4 square | real (4) | coherent family/series | -| [Bai](https://sparse.tamu.edu/Bai) | 78 | Matrices from Z. Bai, D. Day, J. Demmel, and J. Dongarra. located at ftp.ms.uky.edu : pub/misc/bai/Collection. | computational fluid dynamics problem (31); electromagnetics problem (26); 2D/3D problem (8); statistical/mathematical problem (4); power network problem (3); chemical process simulation problem (2); robotics problem (2); materials problem (2) | chemistry/biology; electrical/circuit/device; electromagnetics/acoustics; fluids/transport; generic spatial PDE/thermal; imaging/graphics/robotics; mathematics/statistics; structures/materials | 62–23,560 (wide (>100×)) | unsymmetric (66); symmetric (11); Hermitian (1) | 8/78 square | real (70); complex (6); integer (2) | multi-domain/mixed | -| [Barabasi](https://sparse.tamu.edu/Barabasi) | 3 | Notre Dame networks, A. Barabasi. ================================================================================ | bipartite graph (1); directed graph (1); undirected graph (1) | graph/network data | 2,114–392,400 (wide (>100×)) | rectangular (1); unsymmetric (1); symmetric (1) | 1/2 square | binary (3) | one broad domain; mixed matrix forms | -| [Bates](https://sparse.tamu.edu/Bates) | 3 | Statistical matrices, D. Bates, Univ. Wisconsin | statistical/mathematical problem (2); least squares problem (1) | mathematics/statistics; other | 2,541–1,748,122 (wide (>100×)) | rectangular (2); symmetric (1) | 1/1 square | real (2); binary (1) | multi-domain/mixed | -| [Belcastro](https://sparse.tamu.edu/Belcastro) | 3 | Gene networks from V. Belcastro, TIGEM, Naples, Italy Currently working on gene regulatory network inference. Obtained | undirected weighted graph (3) | graph/network data | 14,340–45,101 (narrow (≤4×)) | symmetric (3) | 3/3 square | real (3) | coherent family/series | -| [BenElechi](https://sparse.tamu.edu/BenElechi) | 1 | Matrix from S. Ben Elechi, Arcelor, Inc. with many sparse right-hand sides. | 2D/3D problem (1) | generic spatial PDE/thermal | 245,874–245,874 (one size) | symmetric (1) | 1/1 square | real (1) | single matrix | -| [Bindel](https://sparse.tamu.edu/Bindel) | 6 | Coupled linear thermoeslasticity equations. D Bindel, UC Berkeley | thermal problem (6) | generic spatial PDE/thermal | 10,605–10,605 (one size) | unsymmetric (4); symmetric (2) | 2/6 square | real (4); complex (2) | one broad domain; mixed matrix forms | -| [Bodendiek](https://sparse.tamu.edu/Bodendiek) | 5 | Curl-Curl operator of 2nd order Maxwell's equations, A. Bodendiek From Andre' Bodendiek, Institut Computational Mathematics, | model reduction problem (5) | eigenvalue/model reduction | 11,083–2,380,515 (wide (>100×)) | symmetric (5) | 5/5 square | real (5) | coherent family/series | -| [Boeing](https://sparse.tamu.edu/Boeing) | 32 | Structural engineering matrices from Roger Grimes, Boeing. The files have been renamed to fit my naming scheme (matrixname.type), | structural problem (24); materials problem (8) | structures/materials | 588–217,918 (wide (>100×)) | symmetric (32) | 32/32 square | real (27); binary (5) | one broad domain; mixed matrix forms | -| [Bomhof](https://sparse.tamu.edu/Bomhof) | 4 | Matrices from circuit simulation problems. Wim Bomhof, Univ. Utrecht. Modelled as a system of differential algebraic equations. | circuit simulation problem (4) | electrical/circuit/device | 2,624–80,209 (moderate (4–100×)) | unsymmetric (4) | 0/4 square | real (4) | coherent family/series | -| [Botonakis](https://sparse.tamu.edu/Botonakis) | 6 | FEM thermal/mechanical problems from Ioannis Botonakis, Germany. FEM_3D_thermal1 and FEM_3D_thermal2 are | thermal problem (6) | generic spatial PDE/thermal | 17,880–204,316 (moderate (4–100×)) | unsymmetric (3); symmetric (3) | 3/6 square | real (6) | one broad domain; mixed matrix forms | -| [Bourchtein](https://sparse.tamu.edu/Bourchtein) | 4 | Atmospheric modeling problems from Andrei Bourchtein These matrices arise in the numerical weather prediction and atmospheric | computational fluid dynamics problem (4) | fluids/transport | 1,270,432–1,489,752 (narrow (≤4×)) | unsymmetric (4) | 0/4 square | real (3); integer (1) | one broad domain; mixed matrix forms | -| [Bova](https://sparse.tamu.edu/Bova) | 1 | Model of Charleston Harbor. Steve Bova, Nichols Research Corporation. 2524 S. I-20 Frontage Road, Suite A, P.0. Box 820186, | computational fluid dynamics problem (1) | fluids/transport | 46,835–46,835 (one size) | unsymmetric (1) | 0/1 square | real (1) | single matrix | -| [Brethour](https://sparse.tamu.edu/Brethour) | 2 | Numerical simulation of coating flows, from Jim Brethour. Original data was sent with 12 decimal digits in the mantissa, thus | computational fluid dynamics problem (2) | fluids/transport | 1,348–9,540 (moderate (4–100×)) | unsymmetric (2) | 0/2 square | real (2) | coherent family/series | -| [Brogan](https://sparse.tamu.edu/Brogan) | 1 | specular surface reconstruction problem, Submitted by Joel Brogan, Notre Dame, July 2014 | computer vision problem (1) | imaging/graphics/robotics | 477,976–477,976 (one size) | rectangular (1) | 0/0 square | real (1) | single matrix | -| [Brunetiere](https://sparse.tamu.edu/Brunetiere) | 1 | A matrix from Noel Brunetiere. Finite-element discretization of thermal problem. (Noel.Brunetiere :at the domain: lms.univ-poitiers.fr), | thermal problem (1) | generic spatial PDE/thermal | 3,456–3,456 (one size) | unsymmetric (1) | 0/1 square | real (1) | single matrix | -| [Buss](https://sparse.tamu.edu/Buss) | 2 | Website recommendation system, Steven Buss, Univ. Florida connectus: | undirected bipartite graph (2) | graph/network data | 394,792–872,622 (narrow (≤4×)) | rectangular (2) | 0/0 square | binary (2) | coherent family/series | -| [Bydder](https://sparse.tamu.edu/Bydder) | 2 | MRI reconstruction matrices from Mark Bydder, UCSD. Note that that A is sparse and rectangular, and b is complex. | computer graphics/vision problem (2) | imaging/graphics/robotics | 147,456–147,456 (one size) | rectangular (2) | 0/0 square | real (2) | coherent family/series | -| [Cannizzo](https://sparse.tamu.edu/Cannizzo) | 1 | Structural engineering matrix (finite-element) from Fabio Cannizzo. | structural problem (1) | structures/materials | 4,098–4,098 (one size) | symmetric (1) | 1/1 square | real (1) | single matrix | -| [Castrillon](https://sparse.tamu.edu/Castrillon) | 1 | Counter-example matrix (denormal in A+alpha*I), J. Castrillon, Teledyne The problem is x=(A+alpha*I)\\b as alpha varies from 0 to 100. However | counter-example problem (1) | mathematics/statistics | 89,400–89,400 (one size) | symmetric (1) | 1/1 square | real (1) | single matrix | -| [CEMW](https://sparse.tamu.edu/CEMW) | 4 | Electromagnetic problems from D. Isaak, Computational_EM_Works - cemw_vfem is a vector finite element complex symmetric matrix with 93,476 | electromagnetics problem (4) | electromagnetics/acoustics | 93,476–921,632 (moderate (4–100×)) | unsymmetric (3); symmetric (1) | 1/4 square | real (2); integer (1); complex (1) | one broad domain; mixed matrix forms | -| [Chen](https://sparse.tamu.edu/Chen) | 14 | Finite element matrices from Pu Chen, Peking University. 14 symmetric positive-definite matrices (patterns only). | structural problem (14) | structures/materials | 10,800–151,926 (moderate (4–100×)) | symmetric (14) | 14/14 square | binary (14) | coherent family/series | -| [Chevron](https://sparse.tamu.edu/Chevron) | 4 | 2D Helmholtz; Temporal freq. domain seismic modeling. J. Washbourne, Chevron Linearized for Gauss-Newton method. | 2D/3D problem (4) | generic spatial PDE/thermal | 37,365–711,450 (moderate (4–100×)) | unsymmetric (4) | 0/4 square | complex (4) | coherent family/series | -| [Clark](https://sparse.tamu.edu/Clark) | 1 | Tomographic reconstruction of a particle field in fluid flow, T. Clark See | computer graphics/vision problem (1) | imaging/graphics/robotics | 73,159–73,159 (one size) | rectangular (1) | 0/0 square | real (1) | single matrix | -| [Cote](https://sparse.tamu.edu/Cote) | 2 | Vibroacoustic matrices from Andre Cote, Univ. Sherbrooke, Quebec. Andre Cote | acoustics problem (2) | electromagnetics/acoustics | 5,962–12,328 (narrow (≤4×)) | symmetric (2) | 1/2 square | complex (1); real (1) | one broad domain; mixed matrix forms | -| [CPM](https://sparse.tamu.edu/CPM) | 9 | Closest Point Method, Yujia Chen, Andy Wathen, Shengxin Zhu A method for computing on surfaces. For more information see: | 2D/3D problem (9) | generic spatial PDE/thermal | 148–40,948 (wide (>100×)) | unsymmetric (9) | 0/9 square | real (9) | coherent family/series | -| [Cunningham](https://sparse.tamu.edu/Cunningham) | 4 | Finite element matrices from Andrew Cunningham, Vibro-Acoustic Sciences, Inc. | acoustics problem (4) | electromagnetics/acoustics | 11,107–66,127 (moderate (4–100×)) | symmetric (3); unsymmetric (1) | 3/4 square | real (4) | one broad domain; mixed matrix forms | -| [Cylshell](https://sparse.tamu.edu/Cylshell) | 7 | Finite element analysis of cylindrical shells, R. Kouhia, Helsinki. Set CYLSHELL | structural problem (7) | structures/materials | 5,357–5,489 (narrow (≤4×)) | symmetric (7) | 7/7 square | real (7) | coherent family/series | -| [Dattorro](https://sparse.tamu.edu/Dattorro) | 3 | Dattorro Convex Optimization of Eternity II, Jon Dattoro | optimization problem (3) | optimization/control | 150,638–262,144 (narrow (≤4×)) | rectangular (3) | 0/0 square | integer (3) | coherent family/series | -| [Davis](https://sparse.tamu.edu/Davis) | 1 | Analysis of the MATLAB FileExchange, March 2010 For the 8th Edition of the MATLAB Primer (published by CRC Press) | term/document graph (1) | graph/network data | 9,498–9,498 (one size) | rectangular (1) | 0/0 square | integer (1) | single matrix | -| [Dehghani](https://sparse.tamu.edu/Dehghani) | 1 | Light transport in soft tissue, Hamid Dehghani, Univ. of Exeter, UK | electromagnetics problem (1) | electromagnetics/acoustics | 29,282–29,282 (one size) | unsymmetric (1) | 0/1 square | complex (1) | single matrix | -| [DIMACS10](https://sparse.tamu.edu/DIMACS10) | 151 | 10th DIMACS Implementation Challenge Updated July 2012 | undirected graph (67); undirected weighted graph (50); random unweighted graph (15); undirected random graph (10); undirected multigraph (6); random undirected graph (3) | graph/network data | 39–50,912,018 (wide (>100×)) | symmetric (151) | 151/151 square | binary (95); integer (56) | one broad domain; mixed matrix forms | -| [DNVS](https://sparse.tamu.edu/DNVS) | 15 | Positive definite matrices from Christian Damhaug, DNV Software. Originally collected by Nick Gould, Yifan Hu, & Jennifer Scott, | structural problem (15) | structures/materials | 18,010–224,617 (moderate (4–100×)) | symmetric (15) | 15/15 square | real (8); binary (7) | one broad domain; mixed matrix forms | -| [DRIVCAV](https://sparse.tamu.edu/DRIVCAV) | 26 | Driven cavity, finite element matrices from Yousef Saad (SPARSKIT2 collection). Matrix types appended to file names by Tim Davis. | computational fluid dynamics problem (26) | fluids/transport | 317–4,562 (moderate (4–100×)) | unsymmetric (26) | 0/26 square | real (26) | coherent family/series | -| [Dziekonski](https://sparse.tamu.edu/Dziekonski) | 5 | High order vector finite element method in electromagnetics The matrices came from analysis of a 9-th order microwave | electromagnetics problem (5) | electromagnetics/acoustics | 420,408–1,157,456 (narrow (≤4×)) | symmetric (5) | 3/5 square | real (3); complex (2) | one broad domain; mixed matrix forms | -| [Embree](https://sparse.tamu.edu/Embree) | 1 | Fluid stability problem from IFISS package, Mark Embree, Virginia Tech The matrix comes from a shift-invert eigenvalue computation that arises | computational fluid dynamics (1) | fluids/transport | 96,307–96,307 (one size) | unsymmetric (1) | 0/1 square | real (1) | single matrix | -| [Engwirda](https://sparse.tamu.edu/Engwirda) | 1 | Unstructured 2D mesh (an airfoil) from Darren Engwirda. Includes coordinates of the mesh. Used in a Navier-Stokes solver. | computational fluid dynamics problem (1) | fluids/transport | 14,214–14,214 (one size) | unsymmetric (1) | 0/1 square | real (1) | single matrix | -| [FEMLAB](https://sparse.tamu.edu/FEMLAB) | 9 | Test matrices from FEMLAB, a finite-element method toolbox for MATLAB. developed by Comsol, Inc. See for more details. | computational fluid dynamics problem (4); structural problem (3); 2D/3D problem (1); electromagnetics problem (1) | electromagnetics/acoustics; fluids/transport; generic spatial PDE/thermal; structures/materials | 367–85,623 (wide (>100×)) | unsymmetric (9) | 0/9 square | real (8); complex (1) | multi-domain/mixed | -| [FIDAP](https://sparse.tamu.edu/FIDAP) | 35 | Fluid dynamics, finite element matrices from Yousef Saad (SPARSKIT2 collection). Names converted to Univ. of Florida naming convention (.mat changed to | computational fluid dynamics problem (35) | fluids/transport | 27–19,716 (wide (>100×)) | unsymmetric (22); symmetric (13) | 13/35 square | real (35) | one broad domain; mixed matrix forms | -| [FlowIPM22](https://sparse.tamu.edu/FlowIPM22) | 11 | FlowIPM22: Laplacians from Newton-Steps of Maxflow Short-step IPM on Chimera Graphs A collection of matrices arising from solving maximum flow problems on a | undirected weighted graph (11) | graph/network data | 100,000–72,180,402 (wide (>100×)) | symmetric (11) | 11/11 square | real (11) | coherent family/series | -| [Fluorem](https://sparse.tamu.edu/Fluorem) | 5 | CFD matrices from Francois Pacull, FLUOREM, in Lyon, France We are dealing with CFD and more precisely steady flow | computational fluid dynamics problem (5) | fluids/transport | 903–2,017,169 (wide (>100×)) | unsymmetric (5) | 0/5 square | real (5) | coherent family/series | -| [FreeFieldTechnologies](https://sparse.tamu.edu/FreeFieldTechnologies) | 1 | 3D vibro-acoustic problem, aircraft engine nacelle, from M. Gontier Free Field Technologies, Mont-Saint Guibert, Belguim ( | acoustics problem (1) | electromagnetics/acoustics | 169,410–169,410 (one size) | unsymmetric (1) | 0/1 square | complex (1) | single matrix | -| [Freescale](https://sparse.tamu.edu/Freescale) | 8 | Circuit simulation matrices K. Gullapalli, Freescale Semiconductor | circuit simulation problem (7); circuit simulation matrix (1) | electrical/circuit/device | 178,866–5,558,326 (moderate (4–100×)) | unsymmetric (8) | 0/8 square | real (8) | coherent family/series | -| [Gaertner](https://sparse.tamu.edu/Gaertner) | 3 | Matrices from Dr. Klaus Gaertner, Weierstrass Institute Berlin. | directed weighted graph (2); undirected weighted graph (1) | graph/network data | 10,774–13,209 (narrow (≤4×)) | unsymmetric (2); symmetric (1) | 1/3 square | integer (3) | one broad domain; mixed matrix forms | -| [GAP](https://sparse.tamu.edu/GAP) | 5 | GAP Benchmark matrices: Scott Beamer, Krste Asanovic', and David Patterson. as described in "The GAP Benchmark Suite", . | directed weighted graph (3); random undirected weighted graph (2) | graph/network data | 23,947,347–134,217,728 (moderate (4–100×)) | symmetric (3); unsymmetric (2) | 3/5 square | integer (5) | one broad domain; mixed matrix forms | -| [Garon](https://sparse.tamu.edu/Garon) | 2 | Matrices from Andre Garon, Univ. of Montreal. 2D Navier-Stokes. (andreg :at the domain: CERCA.UMontreal.CA). | computational fluid dynamics problem (2) | fluids/transport | 3,175–13,535 (moderate (4–100×)) | unsymmetric (2) | 0/2 square | real (2) | coherent family/series | -| [GenBank](https://sparse.tamu.edu/GenBank) | 5 | Protein k-mer graphs generated using data from GenBank are available below. | undirected graph (5) | graph/network data | 55,042,369–214,005,017 (narrow (≤4×)) | symmetric (5) | 5/5 square | binary (5) | coherent family/series | -| [GHS_indef](https://sparse.tamu.edu/GHS_indef) | 60 | Indefinite matrices collected by Nick Gould, Yifan Hu, and Jennifer Scott. The GHS_indef and GHS_psdef collection includes matrices from the Matrix | optimization problem (30); 2D/3D problem (17); computational fluid dynamics problem (4); materials problem (4); structural problem (3); statistical/mathematical problem (2) | fluids/transport; generic spatial PDE/thermal; mathematics/statistics; optimization/control; structures/materials | 3,002–466,316 (wide (>100×)) | symmetric (60) | 60/60 square | real (57); integer (3) | multi-domain/mixed | -| [GHS_psdef](https://sparse.tamu.edu/GHS_psdef) | 32 | Positive definite matrices collected by Nick Gould, Yifan Hu, & Jennifer Scott. some in unassembled finite-element form (and some in both forms). | structural problem (19); optimization problem (8); computational fluid dynamics problem (3); random 2D/3D problem (2) | fluids/transport; generic spatial PDE/thermal; optimization/control; structures/materials | 15,449–952,203 (moderate (4–100×)) | symmetric (32) | 32/32 square | real (22); binary (10) | multi-domain/mixed | -| [Gleich](https://sparse.tamu.edu/Gleich) | 10 | Web matrices and wikipedia matrices from David Gleich, Stanford. dgleich at standford dot edu. | directed graph (7); undirected graph (3) | graph/network data | 2,642–9,845,725 (wide (>100×)) | unsymmetric (7); symmetric (3) | 3/10 square | binary (10) | one broad domain; mixed matrix forms | -| [Goodwin](https://sparse.tamu.edu/Goodwin) | 12 | Fluid mechanics matrices from Ralph Goodwin Matrices from 2018: | computational fluid dynamics (10); computational fluid dynamics problem (2) | fluids/transport | 1,182–178,437 (wide (>100×)) | unsymmetric (12) | 0/12 square | real (12) | coherent family/series | -| [Graham](https://sparse.tamu.edu/Graham) | 1 | Finite element matrix from Dave Graham, Univ. of Illinois. GRAHAM1 Obtained Feb. 18, 1998. | computational fluid dynamics problem (1) | fluids/transport | 9,035–9,035 (one size) | unsymmetric (1) | 0/1 square | real (1) | single matrix | -| [Grueninger](https://sparse.tamu.edu/Grueninger) | 2 | Wind tunnel evaporation, Christoph Grueninger, Univ. Stuttgart Source: Christoph Grueninger, pr at grueninger.de | computational fluid dynamics problem (2) | fluids/transport | 8,256–40,816 (moderate (4–100×)) | unsymmetric (2) | 0/2 square | real (2) | coherent family/series | -| [Grund](https://sparse.tamu.edu/Grund) | 22 | Matrices from Friedrich Grund, WIAS Berlin. Chemical process simulation. -------------------------------------------------------------------------------- | chemical process simulation problem (16); economic problem (4); circuit simulation problem (2) | chemistry/biology; electrical/circuit/device; other | 7–57,735 (wide (>100×)) | unsymmetric (21); symmetric (1) | 1/22 square | real (22) | multi-domain/mixed | -| [Gset](https://sparse.tamu.edu/Gset) | 67 | Random graphs from Yinyu Ye, Univ. of Iowa. Obtained from ftp://dollar.biz.uiowa.edu/pub/yyye/Gset/ | undirected random graph (34); undirected weighted random graph (33) | graph/network data | 800–10,000 (moderate (4–100×)) | symmetric (67) | 67/67 square | binary (34); integer (33) | one broad domain; mixed matrix forms | -| [Guettel](https://sparse.tamu.edu/Guettel) | 3 | 3D Transient Electromagnetic Modelleing, Stefan Guettel, Univ Manchester The TEM problem relates to the time-dependent modelling of a transient | electromagnetics problem (3) | electromagnetics/acoustics | 27,623–181,302 (moderate (4–100×)) | symmetric (3) | 3/3 square | real (3) | coherent family/series | -| [Gupta](https://sparse.tamu.edu/Gupta) | 3 | Linear programming matrices from Anshul Gupta, IBM T. J. Watson Research Center. Discussed in "Fast and Effective Algorithms for Graph Partitioning and Sparse | optimization problem (3) | optimization/control | 16,783–62,064 (narrow (≤4×)) | symmetric (3) | 3/3 square | binary (3) | coherent family/series | -| [Hamm](https://sparse.tamu.edu/Hamm) | 6 | Semiconductor simulation matrices from Steve Hamm, Motorola, Inc. Semiconductor Systems Design Technology | circuit simulation problem (6) | electrical/circuit/device | 2,395–170,998 (moderate (4–100×)) | unsymmetric (6) | 0/6 square | real (6) | coherent family/series | -| [Hamrle](https://sparse.tamu.edu/Hamrle) | 3 | Circuit simulation matrices from Jaroslav Hamrle, RIKEN, Japan. -------------------------------------------------------------------------------- | circuit simulation problem (3) | electrical/circuit/device | 32–1,447,360 (wide (>100×)) | unsymmetric (3) | 0/3 square | real (3) | coherent family/series | -| [Hardesty](https://sparse.tamu.edu/Hardesty) | 3 | Surface fitting problem for visualization, Sean Hardesty Visualization of 3D structures in the earth | computer graphics/vision problem (3) | imaging/graphics/robotics | 929,901–8,217,820 (moderate (4–100×)) | rectangular (2); symmetric (1) | 1/1 square | real (2); integer (1) | one broad domain; mixed matrix forms | -| [Harvard_Seismology](https://sparse.tamu.edu/Harvard_Seismology) | 1 | Earth's crust underneath Japan, Harvard Seismology Group. This matrix represents the forward operator of a linearized seismic | tomography problem (1) | imaging/graphics/robotics | 87,616–87,616 (one size) | rectangular (1) | 0/0 square | real (1) | single matrix | -| [HB](https://sparse.tamu.edu/HB) | 292 | The original Harwell-Boeing collection. Iain Duff, Roger Grimes, and John Lewis. Duff, I. S. and R. G. Grimes and J. G. Lewis, | structural problem (121); 2D/3D problem (36); computational fluid dynamics problem (27); power network problem (18); optimization problem (17); chemical process simulation problem (16); economic problem (14); thermal problem (12); least squares problem (11); directed weighted graph (8); acoustics problem (4); counter-example problem (3); semiconductor device problem (2); materials problem (1); statistical/mathematical problem (1); directed graph (1) | chemistry/biology; electrical/circuit/device; electromagnetics/acoustics; fluids/transport; generic spatial PDE/thermal; graph/network data; mathematics/statistics; optimization/control; other; structures/materials | 9–44,609 (wide (>100×)) | symmetric (169); unsymmetric (105); rectangular (16); skew-symmetric (2) | 169/276 square | real (170); binary (112); integer (7); complex (3) | multi-domain/mixed | -| [Hohn](https://sparse.tamu.edu/Hohn) | 6 | Single-material crack problem, Michael Hohn, Univ. of Utah. The included files contain the matrices and rhs vectors arising in | materials problem (6) | structures/materials | 7,500–16,428 (narrow (≤4×)) | unsymmetric (6) | 0/6 square | real (6) | coherent family/series | -| [Hollinger](https://sparse.tamu.edu/Hollinger) | 49 | Economic models from Peter Holinger, Intex Solutions, Inc. from MULTIMOD, a multi-country forward-looking macroeconometric model | economic problem (49) | other | 2,880–64,089 (moderate (4–100×)) | unsymmetric (49) | 0/49 square | real (49) | coherent family/series | -| [HVDC](https://sparse.tamu.edu/HVDC) | 2 | Power simulation matrices from Alex Wang, Manitoba HVDC, Canada. | power network problem (2) | electrical/circuit/device | 24,842–189,860 (moderate (4–100×)) | unsymmetric (2) | 0/2 square | real (2) | coherent family/series | -| [IBM_Austin](https://sparse.tamu.edu/IBM_Austin) | 1 | Circuit simulation matrix from Emrah Acar, IBM Austin. High degree of coupling. From Emrah Acar, IBM Research-Austin | circuit simulation problem (1) | electrical/circuit/device | 11,341–11,341 (one size) | unsymmetric (1) | 0/1 square | real (1) | single matrix | -| [IBM_EDA](https://sparse.tamu.edu/IBM_EDA) | 7 | Circuit simulation matrices from Tim Lehner, IBM EDA. Added March, 2006. | circuit simulation problem (7) | electrical/circuit/device | 49,702–116,835 (narrow (≤4×)) | unsymmetric (7) | 0/7 square | real (7) | coherent family/series | -| [INPRO](https://sparse.tamu.edu/INPRO) | 1 | Parasol matrices, collected by Jacko Koster | structural problem (1) | structures/materials | 415,863–415,863 (one size) | symmetric (1) | 1/1 square | real (1) | single matrix | -| [IPSO](https://sparse.tamu.edu/IPSO) | 7 | Power simulation matrices, Li Peijie, Inst. of Power Systems, Guangxi Univ. Three different kinds of optimization methods (OPF, TSCOPF, HTC) | power network problem (7) | electrical/circuit/device | 8,140–226,340 (moderate (4–100×)) | symmetric (7) | 7/7 square | real (7) | coherent family/series | -| [Janna](https://sparse.tamu.edu/Janna) | 18 | Finite element simulations: gas reservoir and structural problems. Univ Padova. C. Janna, Univ. of Padova. Authors: Carlo Janna and Massimiliano Ferronato | structural problem (14); 2D/3D problem (3); computational fluid dynamics problem (1) | fluids/transport; generic spatial PDE/thermal; structures/materials | 377,002–4,147,110 (moderate (4–100×)) | symmetric (14); unsymmetric (4) | 14/18 square | real (18) | multi-domain/mixed | -| [JGD_BIBD](https://sparse.tamu.edu/JGD_BIBD) | 21 | Balanced Incomplete Block Design from Mark Giesbrecht, Univ. of Waterloo From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, | combinatorial problem (21) | optimization/control | 84–319,770 (wide (>100×)) | rectangular (20); symmetric (1) | 1/1 square | binary (21) | one broad domain; mixed matrix forms | -| [JGD_CAG](https://sparse.tamu.edu/JGD_CAG) | 3 | CAG matrix set from Michael Monagan, Simon Fraser Univ., Canada From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, | combinatorial problem (3) | optimization/control | 72–1,916 (moderate (4–100×)) | unsymmetric (3) | 0/3 square | integer (3) | coherent family/series | -| [JGD_Forest](https://sparse.tamu.edu/JGD_Forest) | 10 | Forests and Trees from Nicolas Thiery From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, | combinatorial problem (10) | optimization/control | 107–317,955 (wide (>100×)) | rectangular (10) | 0/0 square | integer (10) | coherent family/series | -| [JGD_Franz](https://sparse.tamu.edu/JGD_Franz) | 11 | Cohomology of various rings, from Matthias Franz, Univ. Konstanz, Germany From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, | combinatorial problem (11) | optimization/control | 2,240–47,104 (moderate (4–100×)) | rectangular (11) | 0/0 square | integer (11) | coherent family/series | -| [JGD_G5](https://sparse.tamu.edu/JGD_G5) | 13 | Decomposable subspaces at degree d of the invariant ring of G5, Nicolas Thiery. Univ. Paris Sud. | combinatorial problem (13) | optimization/control | 77–47,894 (wide (>100×)) | rectangular (13) | 0/0 square | integer (13) | coherent family/series | -| [JGD_GL6](https://sparse.tamu.edu/JGD_GL6) | 5 | Differentials of the Voronoi complex of perfect forms of rank 6 mod GL_6(Z), from Philippe Elbaz-Vincent, Institut Fourier, Grenoble, France. | combinatorial problem (5) | optimization/control | 341–637 (narrow (≤4×)) | rectangular (5) | 0/0 square | integer (5) | coherent family/series | -| [JGD_GL7d](https://sparse.tamu.edu/JGD_GL7d) | 17 | Differentials of the Voronoi complex of perfect forms of rank 7 mod GL_7(Z) equivalences, (related to the cohomology of GL_7(Z) and the K-theory of Z). | combinatorial problem (17) | optimization/control | 60–1,955,309 (wide (>100×)) | rectangular (17) | 0/0 square | integer (17) | coherent family/series | -| [JGD_Groebner](https://sparse.tamu.edu/JGD_Groebner) | 8 | Gro"bner basis from Jean-Charles Fauge`res, From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, | combinatorial problem (8) | optimization/control | 404–5,761 (moderate (4–100×)) | rectangular (8) | 0/0 square | integer (7); binary (1) | one broad domain; mixed matrix forms | -| [JGD_Homology](https://sparse.tamu.edu/JGD_Homology) | 128 | Simplicial complexes from Homology from Volkmar Welker. From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, | combinatorial problem (128) | optimization/control | 15–564,480 (wide (>100×)) | rectangular (122); unsymmetric (6) | 0/6 square | integer (128) | one broad domain; mixed matrix forms | -| [JGD_Kocay](https://sparse.tamu.edu/JGD_Kocay) | 12 | Brute force disjoint product matrices in tree algebra on n nodes, Nicolas Thiery From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, | combinatorial problem (12) | optimization/control | 2–15,905 (wide (>100×)) | rectangular (12) | 0/0 square | integer (11); binary (1) | one broad domain; mixed matrix forms | -| [JGD_Margulies](https://sparse.tamu.edu/JGD_Margulies) | 23 | Combinatorial optimization as polynomial eqns, Susan Margulies, UC Davis From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, | combinatorial problem (23) | optimization/control | 25–902,103 (wide (>100×)) | rectangular (22); unsymmetric (1) | 0/1 square | binary (23) | one broad domain; mixed matrix forms | -| [JGD_Relat](https://sparse.tamu.edu/JGD_Relat) | 15 | Relations from Florent Hiver, Univ. Paris-Est Marne-La-Valle'e From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, | combinatorial problem (15) | optimization/control | 12–12,360,060 (wide (>100×)) | rectangular (15) | 0/0 square | integer (15) | coherent family/series | -| [JGD_SL6](https://sparse.tamu.edu/JGD_SL6) | 7 | Differentials of the Voronoi complex of perfect forms from Philippe Elbaz-Vincent, Institut Fourier, Grenoble, France. | combinatorial problem (7) | optimization/control | 434–1,271 (narrow (≤4×)) | rectangular (7) | 0/0 square | integer (7) | coherent family/series | -| [JGD_SPG](https://sparse.tamu.edu/JGD_SPG) | 7 | Symmetric powers of graphs from Gordon Royle, Univ Western Australia From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, | combinatorial problem (7) | optimization/control | 300–6,545 (moderate (4–100×)) | symmetric (6); unsymmetric (1) | 6/7 square | binary (6); integer (1) | one broad domain; mixed matrix forms | -| [JGD_Taha](https://sparse.tamu.edu/JGD_Taha) | 2 | Taha matrix set from Abdul Salam Jarrah, New Mexico State Univ. From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, | combinatorial problem (2) | optimization/control | 14,596–37,932 (narrow (≤4×)) | rectangular (2) | 0/0 square | integer (2) | coherent family/series | -| [JGD_Trefethen](https://sparse.tamu.edu/JGD_Trefethen) | 11 | Diagonal matrices with primes, Nick Trefethen, Oxford Univ. From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, | combinatorial problem (11) | optimization/control | 19–20,000 (wide (>100×)) | symmetric (11) | 11/11 square | integer (11) | coherent family/series | -| [Kamvar](https://sparse.tamu.edu/Kamvar) | 2 | Web matrices from Sep Kamvar, Stanford University. * Stanford Web Matrix: | directed graph (2) | graph/network data | 281,903–683,446 (narrow (≤4×)) | unsymmetric (2) | 0/2 square | binary (2) | coherent family/series | -| [Kemelmacher](https://sparse.tamu.edu/Kemelmacher) | 1 | Computer vision problem from Ira Kemelmacher, Weizmann Inst., Israel. Identifying 3D objects. | computer graphics/vision problem (1) | imaging/graphics/robotics | 28,452–28,452 (one size) | rectangular (1) | 0/0 square | real (1) | single matrix | -| [Kim](https://sparse.tamu.edu/Kim) | 2 | 2D complex meshes, from Wonsik Kim, Korea. | 2D/3D problem (2) | generic spatial PDE/thermal | 38,415–456,976 (moderate (4–100×)) | unsymmetric (2) | 0/2 square | complex (2) | coherent family/series | -| [Koutsovasilis](https://sparse.tamu.edu/Koutsovasilis) | 2 | Symmetric indefinite matrix from P. Koutsovasilis, Tech. Univ. Dresden | structural problem (2) | structures/materials | 71,505–343,791 (moderate (4–100×)) | symmetric (2) | 2/2 square | real (2) | coherent family/series | -| [Langemyr](https://sparse.tamu.edu/Langemyr) | 1 | FEM structural mechanics matrix from Lars Langemyr, COMSOL. This is one of the few matrices for which a pivot tolerance of 0.1 (in UMFPACK | structural problem (1) | structures/materials | 1,500–1,500 (one size) | unsymmetric (1) | 0/1 square | real (1) | single matrix | -| [LAW](https://sparse.tamu.edu/LAW) | 15 | Laboratory for Web Algorithmics (LAW), Università degli Studi di Milano | directed graph (13); undirected graph (2) | graph/network data | 69,244–118,142,155 (wide (>100×)) | unsymmetric (13); symmetric (2) | 2/15 square | binary (15) | one broad domain; mixed matrix forms | -| [Lee](https://sparse.tamu.edu/Lee) | 2 | FEM, Electromagnetics, Center for Computational Electromagnetics, UIUC From the Univ of Illinois at Urbana-Champaign, Center for Computational | electromagnetics problem (2) | electromagnetics/acoustics | 74,062–491,100 (moderate (4–100×)) | unsymmetric (2) | 0/2 square | complex (2) | coherent family/series | -| [LeGresley](https://sparse.tamu.edu/LeGresley) | 3 | Power flow analyses of electrical grids. Submitted by Patrick LeGresley, Stanford, July 2013 | power network problem (3) | electrical/circuit/device | 2,508–87,936 (moderate (4–100×)) | unsymmetric (3) | 0/3 square | real (3) | coherent family/series | -| [Li](https://sparse.tamu.edu/Li) | 2 | 3D magnetohydrodynamics finite element matrix from Ben Li, Washington State Univ. A magnetohydrodynamics problem with 5 variables. Collected and converted | electromagnetics problem (2) | electromagnetics/acoustics | 22,695–22,695 (one size) | unsymmetric (1); symmetric (1) | 1/2 square | real (1); binary (1) | one broad domain; mixed matrix forms | -| [Lin](https://sparse.tamu.edu/Lin) | 1 | Large sparse eigenvalue problem from Chun-hung (Martin) Lin. NCHC, Taiwan. MartinLin AT nchc dot org dot tw. National Center for High-Performance | structural problem (1) | structures/materials | 256,000–256,000 (one size) | symmetric (1) | 1/1 square | real (1) | single matrix | -| [LiuWenzhuo](https://sparse.tamu.edu/LiuWenzhuo) | 1 | Power system simulation matrix from Wenzhou Liu, EPRI, China. | power network problem (1) | electrical/circuit/device | 15,838–15,838 (one size) | unsymmetric (1) | 0/1 square | real (1) | single matrix | -| [Lourakis](https://sparse.tamu.edu/Lourakis) | 1 | 3D vision matrices, M. Lourakis, Foundation for Research & Technology - Hellas. Inst. of Computer Science | computer graphics/vision problem (1) | imaging/graphics/robotics | 10,581–10,581 (one size) | symmetric (1) | 1/1 square | real (1) | single matrix | -| [LPnetlib](https://sparse.tamu.edu/LPnetlib) | 138 | NETLIB Linear Programming problems, www.netlib.org. matrices/LPnetlib/README. Tim Davis, May 15, 1997. (davis :at the domain: | linear programming problem (138) | optimization/control | 13–243,246 (wide (>100×)) | rectangular (138) | 0/0 square | real (108); integer (30) | one broad domain; mixed matrix forms | -| [Lucifora](https://sparse.tamu.edu/Lucifora) | 2 | GSM cell traffic matrices from Salvatore Lucifora, Telecom Italia Mobile. Ing. Salvatore Lucifora | directed weighted graph (2) | graph/network data | 7,055–7,055 (one size) | unsymmetric (2) | 0/2 square | real (2) | coherent family/series | -| [Luong](https://sparse.tamu.edu/Luong) | 2 | Photogrammetry problem from Bruno Luong, FOGALE nanotech, France. The problem of interest is: | computer graphics/vision problem (2) | imaging/graphics/robotics | 1,388–4,472 (narrow (≤4×)) | rectangular (2) | 0/0 square | real (2) | coherent family/series | -| [Mallya](https://sparse.tamu.edu/Mallya) | 18 | Chemical process simulation matrices, J. Mallya, S. Zitney, M. Stadtherr. Reference: | chemical process simulation problem (18) | chemistry/biology | 1,477–70,304 (moderate (4–100×)) | unsymmetric (18) | 0/18 square | real (18) | coherent family/series | -| [Mancktelow](https://sparse.tamu.edu/Mancktelow) | 1 | FEM viscoelastic behavior of rocks, Neil Mancktelow, ETH-Zentrum | materials problem (1) | structures/materials | 37,762–37,762 (one size) | unsymmetric (1) | 0/1 square | real (1) | single matrix | -| [Marini](https://sparse.tamu.edu/Marini) | 1 | Economic Time Series, T. Di Fonzo (Univ Padua) and M. Marini, (ISTAT, Italy) Description of the Sparse Linear System arising from the | economic problem (1) | other | 7,245–7,245 (one size) | symmetric (1) | 1/1 square | real (1) | single matrix | -| [Martin](https://sparse.tamu.edu/Martin) | 1 | Martin/marine1: chemical oceanography; a marine nitrogen cycle inverse model A matrix submitted by Taylor Martin, Stanford, discussed the following paper: | chemical oceanography problem (1) | fluids/transport | 400,320–400,320 (one size) | unsymmetric (1) | 0/1 square | real (1) | single matrix | -| [MathWorks](https://sparse.tamu.edu/MathWorks) | 11 | Matrices contributed by The Mathworks, Inc. Some matrices come from other sources. | counter-example problem (6); structural problem (2); statistical/mathematical problem (1); directed graph (1); computer graphics/vision problem (1) | graph/network data; imaging/graphics/robotics; mathematics/statistics; structures/materials | 102–12,406 (wide (>100×)) | unsymmetric (7); rectangular (2); symmetric (2) | 2/9 square | real (10); binary (1) | multi-domain/mixed | -| [MAWI](https://sparse.tamu.edu/MAWI) | 5 | MAWI Working Group Traffic Archive ( The MAWI (Measurement and Analysis on the WIDE Internet) Working | undirected weighted graph (5) | graph/network data | 18,571,154–226,196,185 (moderate (4–100×)) | symmetric (5) | 5/5 square | integer (5) | coherent family/series | -| [MaxPlanck](https://sparse.tamu.edu/MaxPlanck) | 2 | Weather shallow water equations, Max-Planck Institute of Meteoroloy The two shallow_water* matrices arise from weather shallow water equations | computational fluid dynamics problem (2) | fluids/transport | 81,920–81,920 (one size) | symmetric (2) | 2/2 square | real (2) | coherent family/series | -| [Mazaheri](https://sparse.tamu.edu/Mazaheri) | 1 | Sparse bundle adjustment problem from Mehdi Mazaheri, University of Calgary A is symmetric positive definite, coming from bundle adjustment of 1700 images | computer vision problem (1) | imaging/graphics/robotics | 513,351–513,351 (one size) | symmetric (1) | 1/1 square | real (1) | single matrix | -| [McRae](https://sparse.tamu.edu/McRae) | 2 | circuitscape: circuit theory applied to animal/gene flow. B. McRae, UCSB Landscape ecology problem, using electrical network theory to model | 2D/3D problem (2) | generic spatial PDE/thermal | 999,999–1,000,000 (narrow (≤4×)) | symmetric (2) | 2/2 square | real (2) | coherent family/series | -| [Meng](https://sparse.tamu.edu/Meng) | 1 | Jacobian matrix from a computational chemistry problem, iChem, China' Lingyi Meng, Collaborative Innovation Center of Chemistry for | computational chemistry problem (1) | chemistry/biology | 274,087–274,087 (one size) | unsymmetric (1) | 0/1 square | complex (1) | single matrix | -| [Meszaros](https://sparse.tamu.edu/Meszaros) | 166 | Linear programming problems from C. Me'sza'ros email: mcsaba at oplab.sztaki.hu | linear programming problem (166) | optimization/control | 16–1,113,780 (wide (>100×)) | rectangular (165); unsymmetric (1) | 0/1 square | real (107); integer (41); binary (18) | one broad domain; mixed matrix forms | -| [Mittelmann](https://sparse.tamu.edu/Mittelmann) | 30 | Linear programming problems from H. Mittelmann 12 Jul 2006 | linear programming problem (30) | optimization/control | 24,460–1,961,394 (moderate (4–100×)) | rectangular (30) | 0/0 square | integer (20); real (10) | one broad domain; mixed matrix forms | -| [MKS](https://sparse.tamu.edu/MKS) | 1 | 2-D Fokker Planck equations, electron dynamics. Dan Smith, MKS Inst. | electromagnetics problem (1) | electromagnetics/acoustics | 7,548–7,548 (one size) | unsymmetric (1) | 0/1 square | real (1) | single matrix | -| [ML_Graph](https://sparse.tamu.edu/ML_Graph) | 36 | ML_Graph: adjacency matrices from machine learning datasets, Olaf Schenk. The following adjacency matrices were used in the numerical experiments in: | undirected weighted graph (36) | graph/network data | 106–38,547 (wide (>100×)) | symmetric (36) | 36/36 square | real (36) | coherent family/series | -| [Moqri](https://sparse.tamu.edu/Moqri) | 1 | Document similarity graph for MIS articles, including metadata KnowledgeMap -- Visualizing Invisible Networks of Knowledge | undirected weighted graph (1) | graph/network data | 2,427–2,427 (one size) | symmetric (1) | 1/1 square | real (1) | single matrix | -| [Morandini](https://sparse.tamu.edu/Morandini) | 3 | Multibody simulation problems from Marco Morandini. Added March, 2006. | structural problem (2); robotics problem (1) | imaging/graphics/robotics; structures/materials | 100–791 (moderate (4–100×)) | unsymmetric (3) | 0/3 square | real (3) | multi-domain/mixed | -| [Muite](https://sparse.tamu.edu/Muite) | 4 | Ill-conditioned Chebyshev integration matrices from Benson Muite, Oxford. | structural problem (4) | structures/materials | 261–68,121 (wide (>100×)) | unsymmetric (4) | 0/4 square | real (4) | coherent family/series | -| [Mulvey](https://sparse.tamu.edu/Mulvey) | 2 | Multistage stochastic financial modelling. J. Mulvey, Princeton. Matrices provided by Ed Rothberg, Silicon Graphics, Inc. | economic problem (2) | other | 74,752–74,752 (one size) | symmetric (2) | 2/2 square | real (1); binary (1) | one broad domain; mixed matrix forms | -| [Mycielski](https://sparse.tamu.edu/Mycielski) | 19 | Mycielski graph adjacency matrices matrices/Mycielski/README. Scott Kolodziej, June 27, 2018. | undirected graph (19) | graph/network data | 2–786,431 (wide (>100×)) | symmetric (19) | 19/19 square | binary (19) | coherent family/series | -| [Nasa](https://sparse.tamu.edu/Nasa) | 11 | NASA matrices obtained from Alex Pothen, ICASE and Old Dominion Univ. Converted to HB format by Tim Davis. The following is the original | structural problem (11) | structures/materials | 1,824–54,870 (moderate (4–100×)) | symmetric (8); unsymmetric (3) | 8/11 square | binary (6); real (5) | one broad domain; mixed matrix forms | -| [ND](https://sparse.tamu.edu/ND) | 4 | ND problem set. 3D mesh problems. May 7, 2003. | 2D/3D problem (4) | generic spatial PDE/thermal | 9,000–72,000 (moderate (4–100×)) | symmetric (4) | 4/4 square | real (4) | coherent family/series | -| [Negre](https://sparse.tamu.edu/Negre) | 1 | Negre: phenyl dendrimer density matrix, for graph partitioning The phenyl dendrimer structure is composed of 22 covalently bonded | computational chemistry problem (1) | chemistry/biology | 730–730 (one size) | symmetric (1) | 1/1 square | real (1) | single matrix | -| [Nemeth](https://sparse.tamu.edu/Nemeth) | 26 | A set of matrices from Karoly Nemeth, theoretical chemistry. currently postdoctoral fellow at Rice University with Prof. G.E. Scuseria | theoretical/quantum chemistry problem (26) | chemistry/biology | 9,506–9,506 (one size) | symmetric (26) | 26/26 square | real (26) | coherent family/series | -| [Newman](https://sparse.tamu.edu/Newman) | 16 | Network graph collection from Mark Newman, University of Michigan | undirected weighted graph (7); undirected graph (6); directed weighted graph (1); undirected multigraph (1); directed multigraph (1) | graph/network data | 34–40,421 (wide (>100×)) | symmetric (14); unsymmetric (2) | 14/16 square | binary (6); real (6); integer (4) | one broad domain; mixed matrix forms | -| [Norris](https://sparse.tamu.edu/Norris) | 12 | Finite element modelling of human body parts. Stuart Norris, Univ. Aukland. email: s.norris :at the domain: auckland.ac.nz | 2D/3D problem (10); computational fluid dynamics problem (2) | fluids/transport; generic spatial PDE/thermal | 1,650–259,156 (wide (>100×)) | unsymmetric (9); symmetric (3) | 3/12 square | real (12) | multi-domain/mixed | -| [NYPA](https://sparse.tamu.edu/NYPA) | 8 | Rank-deficient least squares problems. D. Maragal, New York Power Authority These matrices are both structurally and numerically singular. The numerical | least squares problem (8) | other | 32–75,077 (wide (>100×)) | rectangular (8) | 0/0 square | real (8) | coherent family/series | -| [Oberwolfach](https://sparse.tamu.edu/Oberwolfach) | 38 | Oberwolfach model reduction benchmark collection ================================================================================ | model reduction problem (38) | eigenvalue/model reduction | 14–986,703 (wide (>100×)) | symmetric (33); unsymmetric (5) | 32/38 square | real (37); complex (1) | one broad domain; mixed matrix forms | -| [Okunbor](https://sparse.tamu.edu/Okunbor) | 2 | Acoustic radiation around aft duct fan. Walter Eversman and Daniel Okunbor. These matrices are generated by a finite element code. | acoustics problem (2) | electromagnetics/acoustics | 8,184–8,205 (narrow (≤4×)) | symmetric (1); unsymmetric (1) | 1/2 square | real (1); complex (1) | one broad domain; mixed matrix forms | -| [Pajek](https://sparse.tamu.edu/Pajek) | 76 | Pajek data sets, V. Batagelj and A. Mrvar. Pajek data sets, from | directed graph (28); undirected graph (13); directed weighted graph (11); directed multigraph (11); undirected weighted graph (7); bipartite graph (3); weighted bipartite graph (2); undirected multigraph (1) | graph/network data | 10–3,774,768 (wide (>100×)) | unsymmetric (49); symmetric (22); rectangular (5) | 22/71 square | binary (44); integer (28); real (4) | one broad domain; mixed matrix forms | -| [PARSEC](https://sparse.tamu.edu/PARSEC) | 21 | PARSEC matrices, density functional theory. Zhou, Saad, Tiago, Chelikowsky, UMN. ------------------------------------------------------------------ | theoretical/quantum chemistry problem (21) | chemistry/biology | 769–268,096 (wide (>100×)) | symmetric (21) | 21/21 square | real (21) | coherent family/series | -| [Pereyra](https://sparse.tamu.edu/Pereyra) | 1 | From Victor Pereyra, Stanford University. | least squares problem (1) | other | 71,952–71,952 (one size) | rectangular (1) | 0/0 square | real (1) | single matrix | -| [POLYFLOW](https://sparse.tamu.edu/POLYFLOW) | 2 | Parasol matrices, collected by Jacko Koster | computational fluid dynamics problem (2) | fluids/transport | 29,957–30,412 (narrow (≤4×)) | unsymmetric (2) | 0/2 square | real (2) | coherent family/series | -| [Pothen](https://sparse.tamu.edu/Pothen) | 22 | Structrual engineering matrices from NASA, collected by Alex Pothen. Each matrix has xy or xyz coordinates for each node (row/column) in the matrix. | structural problem (22) | structures/materials | 48–94,069 (wide (>100×)) | symmetric (22) | 22/22 square | binary (12); real (10) | one broad domain; mixed matrix forms | -| [PowerSystem](https://sparse.tamu.edu/PowerSystem) | 1 | Power system matrix | power network problem (1) | electrical/circuit/device | 197,156–197,156 (one size) | unsymmetric (1) | 0/1 square | real (1) | single matrix | -| [Precima](https://sparse.tamu.edu/Precima) | 1 | Precima: data analytics problem Submitted by Carl Ugaz, Precima, Inc. CUgaz@precima.com | data analytics problem (1) | data analytics | 303,813–303,813 (one size) | symmetric (1) | 1/1 square | real (1) | single matrix | -| [Priebel](https://sparse.tamu.edu/Priebel) | 6 | Quadratic sieve matrices from David Priebel, Tenn. Tech. Univ. Each column in the matrix corresponds to a number in the factor base | combinatorial problem (6) | optimization/control | 584–24,430 (moderate (4–100×)) | rectangular (6) | 0/0 square | binary (6) | coherent family/series | -| [Puri](https://sparse.tamu.edu/Puri) | 4 | ABAQUS benchmarks: pt.loaded fluid-filled shell. Srinivasan Puri ROM benchmark test matrices. | model reduction problem (4) | eigenvalue/model reduction | 23,412–23,412 (one size) | unsymmetric (4) | 0/4 square | complex (3); real (1) | one broad domain; mixed matrix forms | -| [Qaplib](https://sparse.tamu.edu/Qaplib) | 8 | Linear programming, lower bounds for quadratic assignment problem, M. Resende. In the paper "Computing lower bounds for the quadratic assignment problem | linear programming problem (8) | optimization/control | 225–379,350 (wide (>100×)) | rectangular (8) | 0/0 square | integer (8) | coherent family/series | -| [QCD](https://sparse.tamu.edu/QCD) | 14 | Quantum Chromodynamics, B. Medeke, Univ. Wuppertal, Germany Source: Bjoern Medeke, Department of Mathematics, Institute of Applied | theoretical/quantum chemistry problem (14) | chemistry/biology | 3,072–49,152 (moderate (4–100×)) | unsymmetric (14) | 0/14 square | complex (14) | coherent family/series | -| [QLi](https://sparse.tamu.edu/QLi) | 3 | Mixed complementarity optimization problems from Qaun Li and Michael Ferris. QLi/crashbasis and QLi/majorbasis have the same nonzero pattern. However, | optimization problem (3) | optimization/control | 160,000–440,020 (narrow (≤4×)) | unsymmetric (3) | 0/3 square | real (3) | coherent family/series | -| [Quaglino](https://sparse.tamu.edu/Quaglino) | 2 | FEM discretization of a viscoplastic collision problem, Alessio Quaglino. | materials problem (2) | structures/materials | 4,326–32,769 (moderate (4–100×)) | unsymmetric (2) | 0/2 square | real (2) | coherent family/series | -| [QY](https://sparse.tamu.edu/QY) | 2 | Transient stabilty constrained interior pt. optimal power flow, J. Quanyuan Two problem sets from Dr. Jiang Quanyuan from Zhejiang University, | power network problem (2) | electrical/circuit/device | 14,454–40,216 (narrow (≤4×)) | symmetric (2) | 2/2 square | real (2) | coherent family/series | -| [Rajat](https://sparse.tamu.edu/Rajat) | 32 | Circuit simulation matrices from Rajat and Raj. From a company that develops commerical circuit simulation tools. | circuit simulation problem (32) | electrical/circuit/device | 135–4,690,002 (wide (>100×)) | unsymmetric (26); symmetric (6) | 6/32 square | real (25); binary (7) | one broad domain; mixed matrix forms | -| [Raju](https://sparse.tamu.edu/Raju) | 1 | 3D laminar flow, M. Raju, Case Western Univ. A finite element discretization (u,v,w,p) of 3D laminar | computational fluid dynamics problem (1) | fluids/transport | 67,173–67,173 (one size) | unsymmetric (1) | 0/1 square | real (1) | single matrix | -| [Rommes](https://sparse.tamu.edu/Rommes) | 35 | Power system models from Joost Rommes, Nelson Martins, Francisco Freitas This collection of power system models originates from real power systems, | eigenvalue/model reduction problem (35) | eigenvalue/model reduction | 66–40,366 (wide (>100×)) | unsymmetric (35) | 0/35 square | real (35) | coherent family/series | -| [Ronis](https://sparse.tamu.edu/Ronis) | 2 | Crystalline compounds (zeolites,sodalites), D. Ronis, Univ. of McGill. Two matrices from David Ronis, Chemistry Dept, Univ of McGill, Canada. | materials problem (2) | structures/materials | 48,600–157,464 (narrow (≤4×)) | unsymmetric (2) | 0/2 square | real (2) | coherent family/series | -| [Rost](https://sparse.tamu.edu/Rost) | 1 | semiconductor device simulation, Johann Rost, Politechnia Univ., Bucharest. Numerical Methods Laboratory, Politehnica Univ., Bucharest, Romania. | semiconductor device problem (1) | electrical/circuit/device | 74,104–74,104 (one size) | unsymmetric (1) | 0/1 square | complex (1) | single matrix | -| [Rothberg](https://sparse.tamu.edu/Rothberg) | 6 | Structural engineering matrices from Ed Rothberg, Silicon Graphics, Inc. Some of the matrix names and file names don't match. | computational fluid dynamics problem (3); structural problem (3) | fluids/transport; structures/materials | 4,350–153,746 (moderate (4–100×)) | symmetric (6) | 6/6 square | binary (4); real (2) | multi-domain/mixed | -| [Rucci](https://sparse.tamu.edu/Rucci) | 1 | ill-conditioned least-squares problem, A. Rucci | least squares problem (1) | other | 1,977,885–1,977,885 (one size) | rectangular (1) | 0/0 square | real (1) | single matrix | -| [Rudnyi](https://sparse.tamu.edu/Rudnyi) | 1 | 3D fluid flow: water in a tank. Evgenii Rudnyi, IMTEK | computational fluid dynamics problem (1) | fluids/transport | 60,740–60,740 (one size) | unsymmetric (1) | 0/1 square | real (1) | single matrix | -| [Sandia](https://sparse.tamu.edu/Sandia) | 192 | Circuit simulation matrices from Sandia National Laboratory. | circuit simulation problem (192) | electrical/circuit/device | 430–682,862 (wide (>100×)) | unsymmetric (192) | 0/192 square | real (192) | coherent family/series | -| [Sanghavi](https://sparse.tamu.edu/Sanghavi) | 1 | ECL32, a semiconductor device simulation matrix. Jagesh Sanghavi, UC Berkeley. Obtained by Christof Voemel, Lawrence Berkeley National Lab. | semiconductor device problem (1) | electrical/circuit/device | 51,993–51,993 (one size) | unsymmetric (1) | 0/1 square | real (1) | single matrix | -| [Schenk](https://sparse.tamu.edu/Schenk) | 5 | Symmetric indefinite KKT matrices, O. Schenk, Univ. of Basel, Switzerland Nonlinear programming problems for a 3D PDE-constrained optimization | optimization problem (5) | optimization/control | 1,062,400–27,993,600 (moderate (4–100×)) | symmetric (5) | 5/5 square | real (5) | coherent family/series | -| [Schenk_AFE](https://sparse.tamu.edu/Schenk_AFE) | 16 | Sheet metal forming matrices, Olaf Schenk, Univ. of Basel. Univ. of Basel collection, Olaf Schenk (semiconductors, sheet metal forming | structural problem (16) | structures/materials | 503,625–1,508,065 (narrow (≤4×)) | symmetric (16) | 16/16 square | real (16) | coherent family/series | -| [Schenk_IBMNA](https://sparse.tamu.edu/Schenk_IBMNA) | 52 | Non-linear optimization matrices, Olaf Schenk, Univ. of Basel. Univ. of Basel collection, Olaf Schenk (semiconductors, sheet metal forming | optimization problem (52) | optimization/control | 2,169–345,241 (wide (>100×)) | symmetric (52) | 52/52 square | real (52) | coherent family/series | -| [Schenk_IBMSDS](https://sparse.tamu.edu/Schenk_IBMSDS) | 7 | Semiconductor device simulation matrices, Olaf Schenk, Univ. of Basel. Univ. of Basel collection, Olaf Schenk (semiconductors, sheet metal forming | semiconductor device problem (7) | electrical/circuit/device | 27,628–125,329 (moderate (4–100×)) | unsymmetric (7) | 0/7 square | real (7) | coherent family/series | -| [Schenk_ISEI](https://sparse.tamu.edu/Schenk_ISEI) | 18 | Semiconductor device simulation matrices, Olaf Schenk, Univ. of Basel. Univ. of Basel collection, Olaf Schenk (semiconductors, sheet metal forming | semiconductor device problem (18) | electrical/circuit/device | 10,938–181,343 (moderate (4–100×)) | unsymmetric (18) | 0/18 square | real (18) | coherent family/series | -| [Schmid](https://sparse.tamu.edu/Schmid) | 2 | Unstructured FEM, steady state thermal problem. D. Schmid, Univ. Oslo See for more details. See also: | thermal problem (2) | generic spatial PDE/thermal | 82,654–1,228,045 (moderate (4–100×)) | symmetric (2) | 2/2 square | real (2) | coherent family/series | -| [Schulthess](https://sparse.tamu.edu/Schulthess) | 3 | Biochemical networks from Pascal Schulthess, Inst. Pathology, Berlin Matrices from Pascal Schulthess, Institute for Pathology, | biochemical network (3) | chemistry/biology | 1,996–16,559 (moderate (4–100×)) | rectangular (3) | 0/0 square | integer (3) | coherent family/series | -| [Shen](https://sparse.tamu.edu/Shen) | 4 | Matrices from Kai Shen, UCSB (now at Univ. of Rochester, NY). Kai Shen | 2D/3D problem (4) | generic spatial PDE/thermal | 3,432–18,510 (moderate (4–100×)) | unsymmetric (4) | 0/4 square | real (4) | coherent family/series | -| [Shyy](https://sparse.tamu.edu/Shyy) | 2 | Computational fluid dynamics, viscous flow, Wei Shyy, Univ. Florida. Matrices: shyy161.rua, shyy41.rua | computational fluid dynamics problem (2) | fluids/transport | 4,720–76,480 (moderate (4–100×)) | unsymmetric (2) | 0/2 square | real (2) | coherent family/series | -| [Simon](https://sparse.tamu.edu/Simon) | 12 | Structural engineering, computational fluid dynamics. Horst Simon. Matrices from Horst Simon, | computational fluid dynamics problem (7); structural problem (4); directed weighted random graph (1) | fluids/transport; graph/network data; structures/materials | 3,242–62,424 (moderate (4–100×)) | unsymmetric (10); symmetric (2) | 2/12 square | real (12) | multi-domain/mixed | -| [Sinclair](https://sparse.tamu.edu/Sinclair) | 2 | 3-D spectral-element elastic wave modelling in freq. domain, C. Sinclair, Univ. Adelaide, Australia. | materials problem (2) | structures/materials | 292,008–680,943 (narrow (≤4×)) | Hermitian (2) | 2/2 square | complex (2) | coherent family/series | -| [SNAP](https://sparse.tamu.edu/SNAP) | 68 | Networks from SNAP (Stanford Network Analysis Platform) Network Data Sets, Jure Leskovec | directed graph (30); undirected graph (14); directed temporal multigraph (8); undirected graph with communities (6); directed weighted graph (5); directed graph with communities (2); directed weighted temporal graph (2); directed weighted temporal multigraph (1) | graph/network data | 1,005–65,608,366 (wide (>100×)) | unsymmetric (47); symmetric (21) | 21/68 square | binary (53); integer (15) | one broad domain; mixed matrix forms | -| [Sorensen](https://sparse.tamu.edu/Sorensen) | 1 | Call graph of the Linux kernel. The sparse matrix represents a call-graph made from the source code of | directed weighted graph (1) | graph/network data | 324,085–324,085 (one size) | unsymmetric (1) | 0/1 square | integer (1) | single matrix | -| [Springer](https://sparse.tamu.edu/Springer) | 1 | Tim Springer, Solenix (www.solenix.ch), Navigation Support Office, OPS-GN (www.nng.esoc.de), European Space Operations Centre, Germany. | least squares problem (1) | other | 327,062–327,062 (one size) | rectangular (1) | 0/0 square | real (1) | single matrix | -| [Stevenson](https://sparse.tamu.edu/Stevenson) | 1 | Circuit optimization for digital circuits. John Peter Stevenson, Stanford. LargeRegFile is a matrix representing an optimization problem. | circuit simulation problem (1) | electrical/circuit/device | 2,111,154–2,111,154 (one size) | rectangular (1) | 0/0 square | real (1) | single matrix | -| [Sumner](https://sparse.tamu.edu/Sumner) | 1 | A computer graphics problem from Bob Sumner, PhD student at MIT (as of 2003). From sumner :at the domain: graphics.lcs.mit.edu Fri Aug 8 15:28:05 2003 | computer graphics/vision problem (1) | imaging/graphics/robotics | 29,493–29,493 (one size) | rectangular (1) | 0/0 square | real (1) | single matrix | -| [Sybrandt](https://sparse.tamu.edu/Sybrandt) | 2 | Sybrandt: machine learning graphs from document analysis ---------------------------------------------------------------------- | undirected weighted graph (1); undirected graph (1) | graph/network data | 30,239,687–183,964,077 (moderate (4–100×)) | symmetric (2) | 2/2 square | real (1); binary (1) | one broad domain; mixed matrix forms | -| [Szczerba](https://sparse.tamu.edu/Szczerba) | 1 | Ill-conditioned CFD matrix from D. Szczerba, ETH Computer Vision Lab, Zurich. | computational fluid dynamics problem (1) | fluids/transport | 20,896–20,896 (one size) | unsymmetric (1) | 0/1 square | real (1) | single matrix | -| [TAMU_SmartGridCenter](https://sparse.tamu.edu/TAMU_SmartGridCenter) | 3 | Texas A&M Smart Grid Center: synthetic (yet realistic) power system grids Group name: TAMU_SmartGridCenter | power network problem (3) | electrical/circuit/device | 4,000–69,999 (moderate (4–100×)) | unsymmetric (2); symmetric (1) | 1/3 square | real (3) | one broad domain; mixed matrix forms | -| [TKK](https://sparse.tamu.edu/TKK) | 12 | Matrices from Reijo Kouhia, Structural Mechanics, Helsinki Univ. of Technology (Teknillinen Korkeakoulo) | structural problem (12) | structures/materials | 1,282–143,571 (wide (>100×)) | symmetric (10); unsymmetric (2) | 10/12 square | real (5); integer (4); binary (3) | one broad domain; mixed matrix forms | -| [TOKAMAK](https://sparse.tamu.edu/TOKAMAK) | 4 | TOKAMAK test matrices from the SPARSKIT2 collection, Yousef Saad, Univ. MN. (saad :at the domain: cs.umn.edu) | electromagnetics problem (4) | electromagnetics/acoustics | 300–5,940 (moderate (4–100×)) | unsymmetric (4) | 0/4 square | real (4) | coherent family/series | -| [Toledo](https://sparse.tamu.edu/Toledo) | 1 | Null space problem, computer graphics domain. Matrices from Sivan Toledo. Matrix from Sivan Toledo. | counter-example problem (1) | mathematics/statistics | 68,600–68,600 (one size) | rectangular (1) | 0/0 square | real (1) | single matrix | -| [Tromble](https://sparse.tamu.edu/Tromble) | 1 | Finite-state machine model for natural language processing. Roy Tromble. A matrix from Roy Tromble | directed weighted graph (1) | graph/network data | 399,130–399,130 (one size) | unsymmetric (1) | 0/1 square | real (1) | single matrix | -| [TSOPF](https://sparse.tamu.edu/TSOPF) | 27 | Transient stability-constrained optimal power flow, G. Geng, Zhejiang Univ. comments on the TSOPF_RS_b2303 matrix: | power network problem (27) | electrical/circuit/device | 2,454–120,216 (moderate (4–100×)) | unsymmetric (15); symmetric (12) | 12/27 square | real (27) | one broad domain; mixed matrix forms | -| [Um](https://sparse.tamu.edu/Um) | 2 | FEM, electromagnetics, 2 cubes in a sphere. Evan Um, Geophysics, Stanford A matrix from Evan Um, Geophysics, Stanford. Studying finite-element | electromagnetics problem (2) | electromagnetics/acoustics | 101,492–259,789 (narrow (≤4×)) | symmetric (2) | 2/2 square | real (2) | coherent family/series | -| [UTEP](https://sparse.tamu.edu/UTEP) | 3 | Matrices from Lenka Dubcova, Jakub Cerveny, and Pavel Solin Univ. of Texas at El Paso. From a PDE solver. The fill-in when working on | 2D/3D problem (3) | generic spatial PDE/thermal | 16,129–146,689 (moderate (4–100×)) | symmetric (3) | 3/3 square | real (3) | coherent family/series | -| [vanHeukelum](https://sparse.tamu.edu/vanHeukelum) | 13 | DNA electrophoresis model. Alexander van Heukelum, Utrect Univ, The Netherlands. Date: Wed, 5 Jun 2002 14:12:02 +0200 (MET DST) | directed weighted graph (13) | graph/network data | 5–5,154,859 (wide (>100×)) | unsymmetric (13) | 0/13 square | real (13) | coherent family/series | -| [VanVelzen](https://sparse.tamu.edu/VanVelzen) | 10 | Chemical process simulation matrices from Nils van Velzen, VORtech Computing. Added March, 2006. | chemical process simulation problem (10) | chemistry/biology | 21,982–22,835 (narrow (≤4×)) | unsymmetric (10) | 0/10 square | real (10) | coherent family/series | -| [Vavasis](https://sparse.tamu.edu/Vavasis) | 1 | Irregular finite-element matrix from Steve Vavasis, Cornell. (copy provided by Sherry Li). | 2D/3D problem (1) | generic spatial PDE/thermal | 41,092–41,092 (one size) | unsymmetric (1) | 0/1 square | real (1) | single matrix | -| [VDOL](https://sparse.tamu.edu/VDOL) | 91 | Optimal control problems, Vehicle Dynamics & Optimization Lab, UF Anil Rao and Begum Senses, University of Florida | optimal control problem (91) | optimization/control | 99–18,476 (wide (>100×)) | symmetric (91) | 91/91 square | real (91) | coherent family/series | -| [VLSI](https://sparse.tamu.edu/VLSI) | 14 | VLSI: semiconductor device and process simulation Process and Device Simulation matrices: nv1 and nv2 are very ill-conditioned. | semiconductor device problem (8); semiconductor process problem (6) | electrical/circuit/device | 46,994–11,449,533 (wide (>100×)) | unsymmetric (14) | 0/14 square | real (14) | coherent family/series | -| [Wang](https://sparse.tamu.edu/Wang) | 6 | 3D semiconductor device, 2D incompressible flow. S. Wang, Univ. New South Wales. The simulators solve the corresponding coupled nonlinear PDE | semiconductor device problem (6) | electrical/circuit/device | 2,903–26,068 (moderate (4–100×)) | unsymmetric (6) | 0/6 square | real (6) | coherent family/series | -| [Watson](https://sparse.tamu.edu/Watson) | 2 | Chemical reaction simulation matrices from Layne Watson and Jingwei Zhang. Virginia Tech, ltw at cs dot vtu doe yes, and jwzhang at the same domain. | 2D/3D problem (2) | generic spatial PDE/thermal | 40,401–112,211 (narrow (≤4×)) | unsymmetric (2) | 0/2 square | real (2) | coherent family/series | -| [Williams](https://sparse.tamu.edu/Williams) | 7 | Matrices used by S. Williams et al for sparse matrix multiplication on GPUs. 14 matrices were used in the following paper: | 2D/3D problem (4); weighted undirected graph (1); economic problem (1); weighted directed graph (1) | generic spatial PDE/thermal; graph/network data; other | 36,417–1,000,005 (moderate (4–100×)) | symmetric (4); unsymmetric (3) | 4/7 square | real (6); integer (1) | multi-domain/mixed | -| [Wissgott](https://sparse.tamu.edu/Wissgott) | 1 | Parabolic FEM problem from Phillip Wissgott, TU Vienna. A diffusion-convection reaction. The parabolic_fem matrix uses constant | computational fluid dynamics problem (1) | fluids/transport | 525,825–525,825 (one size) | symmetric (1) | 1/1 square | real (1) | single matrix | -| [YCheng](https://sparse.tamu.edu/YCheng) | 3 | Power system state simulation matrices Yunzhi Cheng, UT Arlington. In MATLAB, the solution to x=A\\b is desired, but this can be slow in | power network problem (3) | electrical/circuit/device | 14,318–28,634 (narrow (≤4×)) | rectangular (3) | 0/0 square | real (3) | coherent family/series | -| [Yoshiyasu](https://sparse.tamu.edu/Yoshiyasu) | 2 | Image editting and mesh deformation problem from Y. Yoshiyasu, Keio Univ., Japan I uploaded 5 matrices A1,b1,A2,b2 and D (for x=(A1'*A1)/(A1'*b1) and | computer graphics/vision problem (2) | imaging/graphics/robotics | 234,023–240,000 (narrow (≤4×)) | rectangular (2) | 0/0 square | real (2) | coherent family/series | -| [YZhou](https://sparse.tamu.edu/YZhou) | 1 | Circuit simulation problem, Y. Zhou, Carleton Univ, Canada | circuit simulation problem (1) | electrical/circuit/device | 1,020–1,020 (one size) | unsymmetric (1) | 0/1 square | real (1) | single matrix | -| [Zaoui](https://sparse.tamu.edu/Zaoui) | 1 | Optimal power flow, nonlinear optimization (KKT), Fabrice Zaoui, RTE, France | optimization problem (1) | optimization/control | 2,063,494–2,063,494 (one size) | symmetric (1) | 1/1 square | real (1) | single matrix | -| [Zhao](https://sparse.tamu.edu/Zhao) | 2 | Reduced-order modeling, electromagnetics. A. Cangellaris and L. Zhao. Matrices from A. C. Cangellaris and Li Zhao, "Reduced-order | electromagnetics problem (2) | electromagnetics/acoustics | 33,861–33,861 (one size) | unsymmetric (2) | 0/2 square | real (2) | coherent family/series | -| [Zitney](https://sparse.tamu.edu/Zitney) | 8 | Chemical engineering applications. S. Zitney, Cray Research. Matrices from Dr. Stephen E. Zitney | chemical process simulation problem (8) | chemistry/biology | 1,048–5,308 (moderate (4–100×)) | unsymmetric (8) | 0/8 square | real (8) | coherent family/series | diff --git a/report_notes/group_taxonomy/group_metrics.csv b/report_notes/group_taxonomy/group_metrics.csv deleted file mode 100644 index d616d2f..0000000 --- a/report_notes/group_taxonomy/group_metrics.csv +++ /dev/null @@ -1,14416 +0,0 @@ -group,count,documented_meaning,source_url,coarse_kind_families_interpretive,dominant_coarse_family_interpretive,dominant_coarse_family_share,normalized_kind_count,normalized_kinds,curator_value_type_count,curator_value_types,curator_structure_count,curator_structures,min_dimension,max_dimension,dimension_ratio,size_span,min_nnz,max_nnz,shape_profile,square,rectangular,symmetry_profile,exact_symmetric_square,structurally_symmetric_square,known_spd,value_profile,binary,geometric_2d3d,cohesion,official_notes -HB,292,"The original Harwell-Boeing collection. Iain Duff, Roger Grimes, and John Lewis. Duff, I. S. and R. G. Grimes and J. G. Lewis,",https://sparse.tamu.edu/HB,chemistry/biology; electrical/circuit/device; electromagnetics/acoustics; fluids/transport; generic spatial PDE/thermal; graph/network data; mathematics/statistics; optimization/control; other; structures/materials,structures/materials,0.4178082191780822,16,structural problem (121); 2D/3D problem (36); computational fluid dynamics problem (27); power network problem (18); optimization problem (17); chemical process simulation problem (16); economic problem (14); thermal problem (12); least squares problem (11); directed weighted graph (8); acoustics problem (4); counter-example problem (3); semiconductor device problem (2); materials problem (1); statistical/mathematical problem (1); directed graph (1),4,real (170); binary (112); integer (7); complex (3),4,symmetric (169); unsymmetric (105); rectangular (16); skew-symmetric (2),9,44609,4956.555555555556,wide (>100×),24,2043492,mixed,276,16,mixed among square matrices,169,184,60,real + complex,112,203,multi-domain/mixed,"The original Harwell-Boeing collection. Iain Duff, Roger Grimes, and John Lewis. - -Duff, I. S. and R. G. Grimes and J. G. Lewis, -Sparse Matrix Problems, ACM Trans. on Mathematical Software, -vol 14, no. 1, pp 1-14, 1989." -ATandT,4,"Frequency domain analysis of large nonlinear analog circuits, AT&T. See ATT.ps for a paper describing these matrices, in the Other directory:",https://sparse.tamu.edu/ATandT,electrical/circuit/device,electrical/circuit/device,1.0,1,frequency-domain circuit simulation problem (4),1,real (4),1,unsymmetric (4),36057,659033,18.277532795296338,moderate (4–100×),222596,5834044,square only,4,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,0,coherent family/series,"Frequency domain analysis of large nonlinear analog circuits, AT&T. - -See ATT.ps for a paper describing these matrices, in the Other directory: - -@inproceedings{FeldmannMelvilleLong96, - author={Feldmann, P. and Melville, R. and Long, D.}, - title={Efficient frequency domain analysis of large nonlinear analog circuits}, - booktitle={Proceedings of the IEEE Custom Integrated Circuits Conference}, - year={1996} - ,address={Santa Clara, CA} - ,annote={online, in matrices/ATandT/Documentation/ATT.ps} - } - - --------------------------------------------------------------------------------- - -rich.m converted to onetone1.rua, and sparse.m converted to onetone2.rua: - - Date: Wed, 8 May 96 07:30:09 EDT - From: David Long - To: davis at cise.ufl.edu - Subject: matrices - - I put two matrices in incoming/rich.m.gz and incoming/sparse.m.gz. - Both are for the same medium-sized example. The first one has a - relatively rich collection of off-diagonal entries and is typical of - what we might use for an SSOR-like preconditioner. The second is - sparser, but probably more typical of what would be used with direct - factorization. Both of these are fairly typical of the matrices that - arise in ""one-tone"" problems, in that the off-diagonal entries are - generally confined in some band around the diagonal. I'll also try to - find an appropriate matrix from a multi-tone problem. Those tend to - have one set of off-diagonal entries close to the diagonal and another - set relatively far away. - - David - --------------------------------------------------------------------------------- - -The tt3.m (multitone) matrix was converted to twotone.rua: - - Date: Thu, 9 May 96 07:12:46 EDT - From: David Long - To: davis at cise.ufl.edu - Subject: Re: matrices - - A matrix from a two-tone problem is in incoming/tt3.m.gz. - --------------------------------------------------------------------------------- -Regarding a question about the paper (ATT.ps): --------------------------------------------------------------------------------- - - Date: Fri, 10 May 96 14:07:22 EDT - From: David Long - To: davis at cise.ufl.edu - Subject: Re: matrices - - Yes, figure 2 is supposed to be a diagram of the Jacobian, but let me - add a few comments that may help clarify things. First, you can just - ignore the Y part. It's included in the formulation for completeness, - but is not generally needed. None of the matrices that I sent have a - Y part. Second, the matrices can be naturally structured in one of - two ways, which we call nN format and Nn format. In nN format, you - view the matrix as an n by n block matrix, with each block having size - N by N. Similarly, Nn format indicates an N by N block matrix, with - each block having size n by n. The two formats are related by the - obvious permutations. The picture of the Jacobian in figure 2 is for - the matrix in nN format. However, to confuse matters, our program - actually builds the preconditioner matrix in Nn format, and that's the - format of the matrices that I sent. (Also to confuse matters, the - paper is a bit inconsistent. For example, the statement before - equation 15 about the Jacobian being block-diagonal for a linear - circuit assumes that Jacobian is in Nn format.) The almost-circulant - structure in figure 2 is in the dense N by N blocks, i.e., each N by N - block is basically a circulant matrix. I say almost-circulant because - the multiplication of Gamma C Gamma^-1 by Omega breaks the circulant - structure. For simplicity, in the paper, the discussion of the - Jacobian structure is carried out assuming that the problem is - formulated in the complex domain. In the complex case though, half of - the coefficients that define the soluation are just conjugates of the - other half. Thus, if you translate everything into the real domain, - you can save some memory. The program uses this observation, and - that's why the matrices that I sent are real. The Jacobian in the - real case is - - Omega T^-1 Gamma C Gamma^-1 T + T^-1 Gamma G Gamma^-1 T - - where T is a matrix that translates from the real domain to the - complex domain. It turns out that when you apply T^-1 and T to the - complex circulant matrix, each pair of conjugate stripes in the - circulant maps into one of those tilted boxes that we discussed. You - can see these clearly in the tt3.m preconditioner matrix. - - The paper appeared in the 1996 Custom Integrated Circuits Conference - (CICC'96). The conference just ended Wednesday, and I haven't - actually seen the proceedings yet, so I can't give you page numbers - and such. - - David - --------------------------------------------------------------------------------- -My response to the message just above: --------------------------------------------------------------------------------- - -To: David Long -cc: davis at cise.ufl.edu, rcm at allegra.att.com -Subject: Re: matrices -Date: Fri, 10 May 1996 17:22:19 -0400 -From: Tim Davis - - -In a previous message, David Long wrote -> Yes, figure 2 is supposed to be a diagram of the Jacobian, but let me -> add a few comments that may help clarify things... - -Yes - that clarifies things a lot. I'll have to think it through. -In the nN form, the N-by-N blocks are dense? - -So in the Nn format, the n-by-n blocks have a sparse structure like the -left hand side of Figure 2 (the ""MNA"" matrix)? And the n-by-n matrix -has a rather irregular structure, right? - -The dense blocks, by the way, are why UMFPACK gets good performance on these -matrices. You may have observed this ... as you increase N, the mflop -rate should increase nicely. - -The dense blocks are still there, in nN or Nn format, at least in the -sense that UMFPACK will find them no matter how you contort the input -matrix. In one sense this is an ideal matrix for UMFPACK ... unsymmetric -in the n-by-n structure (UMFPACK handles asymmetry and irregular structure -fairly well, but there are better methods when the structure is regular), -with each element being a dense N-by-N matrix (UMFPACK likes dense blocks, -since it gets good performance on them in the dense BLAS kernels). - -Here are some results so far, on my UltraSparc (128 MB memory, two level -cache (Level 1: 16K data and 16K instruction, Level 2: 512K), and 2GB swap -space). Peak performance of the BLAS is about 80 mflops in double precision, -160 mflops in single. Theoretical peak is 333 mflops. - -I've renamed the matrices (rich.m = onetone1, sparse.m = onetone2, -and tt3 = twotone). - -Methods used: - UMFPACK2-defaults: with default parameters - UMFPACK2-nondef.: no BTF, prefer symmetric pivots - UMFPACK2 is the same as MA38 (to appear in Harwell Subr. Library). - SuperLU: a sparse partial pivoting code, from Berkeley. Also uses - the BLAS. - MA48: a successor to MA28. Lots faster than MA28. Does not make as - extensive use of the BLAS as does SuperLU and UMFPACK. Better - suited to more irregular problems (I've seen it much faster - than UMFPACK for some matrices). - -The following is the factorization time (including ordering, -symbolic factorization, etc.): - - time(cpu) nz in LU flops Mflops - in seconds - -onetone1: - -UMFPACK2-defaults 60.8 4693767 0.2148D+10 35.3 -UMFPACK2-nondef. 61.0 4907410 0.2337D+10 38.2 -SuperLU 108.2 4674848 0.2525D+10 23.3 -MA48 331.0 5109483 0.4564D+10 13.8 - - -onetone2: - -UMFPACK2-defaults 10.5 1269790 0.1588D+09 15.1 -UMFPACK2-nondef. 9.6 1439817 0.2200D+09 22.9 -SuperLU 8.6 1319194 0.1306D+09 15.2 -MA48 38.3 1260893 0.2229D+09 5.8 - -twotone: - -UMFPACK2-defaults 217.3 9746806 0.6988D+10 32.2 -UMFPACK2-nondef. 238.9 15730302 0.9601D+10 40.2 -SuperLU failed** -MA48 734.1 10860368 1.4675D+10 20.0 - -For twotone, SuperLU ran out of memory in a preordering part that I wrote, -not in SuperLU proper (I need to try it again, and give it more memory in -that part). - -I haven't included pure factorization time - all of these methods can use -prior orderings, for example (UMD2FA vs. UMD2RF, for example). -Do you use UMD2RF? It would be used if the values of the -Jacobian changes, but the pattern remains the same (and the pivot order -remains the same, and stays numerical acceptable). In that case, UMFPACK2, -SuperLU, and MA48 would all be faster in the 2nd factorization than they would -in the first. - -Do these numbers compare with the results you have? Actually, your BLAS on -the RS/6000 should get closer to the theoretical peak than does the -UltraSparc. - -Oh - and thanks for the matrices. Nice to have larger ones in which UMFPACK2 -does so nicely. - -Thanks, -Tim - - --------------------------------------------------------------------------------- -Matrix pre2.m, converted to pre2.rua" -Averous,4,"Matrices from David Averous, Institut National Polytechnique de Toulouse (INPT). --------------------------------------------------------------------------------",https://sparse.tamu.edu/Averous,generic spatial PDE/thermal,generic spatial PDE/thermal,1.0,1,thermal problem (4),1,real (4),1,unsymmetric (4),1794,84617,47.166666666666664,moderate (4–100×),7764,463625,square only,4,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,4,coherent family/series,"Matrices from David Averous, Institut National Polytechnique de Toulouse (INPT). - --------------------------------------------------------------------------------- - -Date: Thu, 19 Feb 1998 15:13:41 +0100 -To: davis at cise.ufl.edu -From: David Averous -Subject: UMFPACK - -Dear Prof. T. DAVIS, -We work at ENSIGCT (in Toulouse (France) which is in the same university as -ENSEEIHT)in Laboratory of Chemical Engineering. Ours fields are simulation, -dynamic and static, and modeling of chemical processes. So we deal with -matrices that can be small and quiet full (N=100) up to very large, very -sparse and unsymmetric (N=100000, NNZ=500000). -We have tested various codes and according to literature and also workshops -(ILAY 95 CERFACS) we use UMF (2.*). For most of ours cases we have very -good results, fast integration and acceptable fill in. - -We use it with a general DAE (differential Algebraic Equations) systems -solver using Gear, write in Fortran 77. Due to its general aspect we don't -know a priori the structure of matrix. So for static allocation of -integrator work vector, does a method (or a function) exist to evaluate an -upper limit of LVALUE and LINDEX with N(nb of equations) and NNZ(nb of non -zero) ? (Do you intend to make a f90 version of UMF in the future.. ?) - -We have another problem due to fill in. For static simulation of plate fin -heat exchanger, we deal with matrices -from N~10000 and NNZ~50000 to N~100000 and NNZ~500000. But we can't reach -the upper limit of N due to fill in, the factor range from 5 to 50 , -depending of cases. It depends on matrix structure. But perhaps there is a -matrix structure (large or small bandwidth, with or without border ...) -that reduce fill-in in UMF ?(we tried to change the number of columns -examined during pivot search but the behavior was unpredictable.) - -Thanking you by advance for your courtesy, - - Best regards. Prof. J.M. LE LANN - D. AVEROUS - K. HAMMADI - A. SARGOUSSE - - --------------------------------------------------------------------------- -David AVEROUS -Institut National Polytechnique de Toulouse (INPT) -Ecole Nationale Sup=E9rieure d'Ing=E9nieurs de G=E9nie Chimique (ENSIGC) -Laboratoire de G=E9nie Chimique (UMR 5503 CNRS) -Equipe Analyse Fonctionnelle des Proc=E9d=E9s -18, Chemin de la Loge - F31078 TOULOUSE CEDEX (France)=09 - T=E9l.: 33 (0)5 62 25 23 00 Poste 2585 Fax : 33 (0)5 62 25 23 18 - E-mail : David.Averous at ensigct.fr --------------------------------------------------------------------------- - -Date: Mon, 02 Mar 1998 10:47:35 +0100 -To: davis at cise.ufl.edu -From: David Averous -Subject: Matrices - -I have sent you matrices (epb0.mat, epb1.mat, epb2.mat, epb3.mat). I use -gunzip for compression.=20 - -epb0 : N = 1794 ; NE = 8960 Plate-Fin Heat Exchanger with Simple Model -epb1 : N = 14734 ; NE = 95053 PFHE Model (simple case) -epb2 : N = 25228 ; NE = 175027 PFHE model ( case with flow redistributio= -n -represent by the extra diagonal blocs) -epb3 : N = 84617 ; NE = 463625 PFHE model (large case) - -I have made some tests on memory requirements as function of the amalgation -parameter. Normalized memory requirements increases faster than in your -tests (TR-97-016) unfortunately for me I have an extremun at default value -(2.0), I reach 1.6 in some case. A value of 1.1 for amalgation seems better -for my kind of matrices, of course it means a little increase in -factorization time. - -Best wishes - -David Averous" -Bai,78,"Matrices from Z. Bai, D. Day, J. Demmel, and J. Dongarra. located at ftp.ms.uky.edu : pub/misc/bai/Collection.",https://sparse.tamu.edu/Bai,chemistry/biology; electrical/circuit/device; electromagnetics/acoustics; fluids/transport; generic spatial PDE/thermal; imaging/graphics/robotics; mathematics/statistics; structures/materials,fluids/transport,0.3974358974358974,8,computational fluid dynamics problem (31); electromagnetics problem (26); 2D/3D problem (8); statistical/mathematical problem (4); power network problem (3); chemical process simulation problem (2); robotics problem (2); materials problem (2),3,real (70); complex (6); integer (2),3,unsymmetric (66); symmetric (11); Hermitian (1),62,23560,380.0,wide (>100×),342,463360,square only,78,0,mixed among square matrices,8,39,4,real + complex,0,69,multi-domain/mixed,"Matrices from Z. Bai, D. Day, J. Demmel, and J. Dongarra. -located at ftp.ms.uky.edu : pub/misc/bai/Collection. - -Modifications: -Header line (line 1) of AF23560 modified - 11 null characters removed. -Names changed to reflect the naming conventions at this site -(lower case, followed by a 3 character extension with the matrix type - .rua, -.rsa, etc.). - -Jan, 2003: Matrices with unsorted columns were sorted (originals in -Jumbled directory). -Apr 2003: Matrices with ""(5E16)"" format changed to ""(5E16.8)"". Originals in -5e16 directory. - --------------------------------------------------------------------------------- -Original README file: --------------------------------------------------------------------------------- - -This directory contains the data files for the test matrices. - - -Title Key ------------------------------------------------------------------------+-------- - -square dielectric waveguid, H. Dong DW256A - DW245B - DW1024 - DW4096 - -bounded finline waveguide eigenmodes, B. Shultz and S. Gedney BFWA62 - BFWB62 - BFWA398 - BFWB398 - BFWA782 - BFWB782 - -Magneto-hydro-dynamics Alfven spectral problem, MHDA416 - MHDB416 - -Airfoil (A. Mahajan) AF23560 - -Quebec Hydro Power system, Deo QH882 - -Chuck matrix (multiple eigenvalues) J. Cullum CK104 - CK400 - CK656 - -LOPSI Stochastic Test Matrix LOP163 - - -Model ordinary differential eigenvalue problem ODEPA400 - ODEPB400 - -Model convection-diffusion differential equations CDDE1 - CDDE2 - CDDE3 - CDDE4 - CDDE5 - CDDE6 - -Brusselator wave model in transport interaction of chemical solutions BWM200 - BWM2000 - - -Markov Chain Modeling, Random Walk (m = 15) G. W. Stewart RW136 - (m = 30) RW496 - (m = 100) RW5151 - -model PDE problem, H. Elman nx = ny = 15 PDE225 - nx = ny = 30 PDE900 - nx=47, ny=63 PDE2961 - - -Quantum chemistry (complex symmetric) QC324 - QC2534 - ----------------- to be included ------------------------------------------------ -Orr-Sommerfeld (complex matrix) - -Ising mode (size? and parameters?) - -Robotic control (CAD in C program) ROB480 - -Taylor-Couette flow - -NASA AMES arc2d problems (16,256 by 16,256)" -Boeing,32,"Structural engineering matrices from Roger Grimes, Boeing. The files have been renamed to fit my naming scheme (matrixname.type),",https://sparse.tamu.edu/Boeing,structures/materials,structures/materials,1.0,2,structural problem (24); materials problem (8),2,real (27); binary (5),1,symmetric (32),588,217918,370.60884353741494,wide (>100×),10485,11524432,square only,32,0,all square matrices symmetric/Hermitian,32,32,15,real only,5,32,one broad domain; mixed matrix forms,"Structural engineering matrices from Roger Grimes, Boeing. -The files have been renamed to fit my naming scheme (matrixname.type), -where type = rsa, rua, psa, etc. (via the script ""fix"" in this directory). - -NOTE: the BCSSTK39 matrix has been recently added (Dec 2002). - -The NASA* matrices are almost duplicates of ../Nasa/*: - -nasa1824.rsa: Boeing version has different numerical values (same pattern). - I think the Nasa/nasa1824.rsa copy is the true version (do a diff - between the two, and you'll see what I mean). - -nasa2910.?sa and nasa4704.?sa: Boeing versions are just the pattern - The Nasa/ versions include numerical values, and right-hand side. - The patterns are the same. - --------------------------------------------------------------------------------- -The following matrices appear to be corrupted: - - ct20mass.rsa - mn12k.rsa - mn12m.rsa - pwtm.rsa - --------------------------------------------------------------------------------- -Date: Fri, 2 Jun 95 09:23:48 PDT -From: Roger Grimes 206-865-3585 -Message-Id: <9506021623.AA02721 :at: espresso.rt.cs.boeing.com> -To: davis :at: cis.ufl.edu -Subject: Re: sparse matrices - -Tim, - -I am sending you (u.s. snail mail) today an 8 mm tape. -It has a copy of 2 tar tapes. -+ -The first tar tape has the following matrices. - -BCSSTK34 -BCSSTK35 -BCSSTK36 -BCSSTK37 -BCSSTK38 -BCSSTK39 -BCSSTM34 -BCSSTM35 -BCSSTM36 -BCSSTM37 -BCSSTM38 -BCSSTM39 -CRYSTK01 -CRYSTK02 -CRYSTK03 -CRYSTM01 -CRYSTM02 -CRYSTM03 -CT20MASS -CT20STIF -MSC00726 -MSC01050 -MSC01440 -MSC04515 -MSC10848 -MSC23052 -NASA1824 -NASA2910 -NASA4704 - -The second tar tape has - -mn12.K -mn12.M -pwt.K -pwt.M - -I believe I already have sent you the headers for all of these matrices. -If not let me know and I will do so. - -To extract the 2 different tar files from the 8 mm tape you will need -to know some about the 'dd' and 'mt' command on unix. I had to learn -this recently so, in case you need it, here are commands that will let -you get the table of contents from the two tar files. Note that our -8 mm tape drive is referenced by 'rst1'. 'nrst1' says look at tape -device rst1 but do not rewind the tape. - -mt -f /dev/rst1 rewind ""positions tape to beginning"" -tar tvf /dev/rst1 ""gets table of contents of first tar file"" -mt -f /dev/rst1 rewind ""positions tape to beginning"" -mt -f /dev/nrst1 fsf 1 ""skips past first tar file"" -tar tvf /dev/rst1 ""gets table of contents of second tar file"" - -If you are interested only working on ordering/symbolic factorization -for these problems you may find the following program useful. It -reads the H-B matrix and puts out a H-B representation of only -the pattern. - - program pattern -c -c--------------------------------------------------------------------- -c -c this program creates a pattern only representation of a sparse -c matrix in the harwell/boeing sparse matrix format. -c -c--------------------------------------------------------------------- -c - character* 3 mtxtyp, rhstyp - character* 8 key - character*16 ptrfmt, indfmt - character*20 valfmt, rhsfmt - character*72 title - character*80 line -c - integer lintot, linptr, linind, linval, linrhs, - 1 nrow, ncol, nnzero, nelmnt, nrhs, nrhsi -c -c--------------------------------------------------------------------- -c - read ( 5, 1000, end = 900 ) title, key, - 1 lintot, linptr, linind, linval, linrhs, - 2 mtxtyp, nrow, ncol, nnzero, nelmnt, - 3 ptrfmt, indfmt, valfmt, rhsfmt - 1000 format ( a72, a8 / 5i14 / a3, 11x, 4i14 / 2a16, 2a20 ) -c - if ( linrhs .gt. 0 ) read ( 5, 1100 ) rhstyp, nrhs, nrhsi - 1100 format ( a3, 11x, 2i14 ) -c - if ( mtxtyp(1:1) .ne. 'P' ) then - lintot = linptr + linind - linval = 0 - linrhs = 0 - mtxtyp(1:1) = 'P' - valfmt(1:20) = ' ' - rhsfmt(1:20) = ' ' - end if -c - write ( 6, 2000) title, key, - 1 lintot, linptr, linind, linval, linrhs, - 2 mtxtyp, nrow, ncol, nnzero, nelmnt, - 3 ptrfmt, indfmt, valfmt, rhsfmt - 2000 format ( a72, a8 / 5i14 / a3, 11x, 4i14 / 2a16, 2a20 ) -c - do 200 i = 1, lintot - read ( 5, '(a80)' ) line - write ( 6, '(a80)' ) line - 200 continue -c -c--------------------------------------------------------------------- -c - 900 stop - end - -Just a reminder. You will need some significant disk space to deal with -mn12.K and pwt.K. As a compress file, mn12.K requires 28M and pwt.K -requires 54M. Uncompressed files will need 2.5 to 3 times as much. -They are definitely not files for the weak of heart or for those short -on disk space. - -Roger Grimes - --- -------------------------------+------------------------------------- -Roger G. Grimes | E-mail: rgrimes :at: espresso.rt.cs.boeing.com -Math and Eng. Analysis | Tel.: (206) 865-3585 -Boeing Information and | FAX: (206) 865-2966 - Support Services | -------------------------------+------------------------------------- -The comments in this message are mine and not necessarily those of -The Boeing Company. ----------------------------+----------------------------------------" -Bomhof,4,"Matrices from circuit simulation problems. Wim Bomhof, Univ. Utrecht. Modelled as a system of differential algebraic equations.",https://sparse.tamu.edu/Bomhof,electrical/circuit/device,electrical/circuit/device,1.0,1,circuit simulation problem (4),1,real (4),1,unsymmetric (4),2624,80209,30.567454268292682,moderate (4–100×),21199,307604,square only,4,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,0,coherent family/series,"Matrices from circuit simulation problems. Wim Bomhof, Univ. Utrecht. - -Modelled as a system of differential algebraic equations. -Normally solved with a BDF method, and Newton's method, which -give rise to linear systems that need to be solved. A, x, and b -are provided. - -Wim Bomhof, Univ. Utrecht (now at PLAXIS (www.plaxis.nl), a small company -that develops a finite element code for geomechanical problems. -email: (first name in lower case).(current directory name in lower case) at hetnet.nl. -circuit matrices posted at http://www.math.uu.nl/people/bomhof/. - -Added to the UF sparse matrix collection in December 2001. - - -Citations: - -Wim Bomhof and Henk van der Vorst, A parallel linear system solver -for circuit simulation problems. Numer. Linear Algebra Appl. 7 (2000), pp. -649-665. - -Wim Bomhof and Henk van der Vorst, A parallelizable GMRES-type method -for p-cyclic matrices, with applications in circuit simulation. To appear -in the proceedings of the SCEE-2000 Workshop, August 20-23, 2000, Warnemunde. - -Wim Bomhof, Iterative and parallel methods for linear systems, with -applications in circuit simulation. PhD thesis, Mathematical Institute, Utrecht -University, 2001." -Bova,1,"Model of Charleston Harbor. Steve Bova, Nichols Research Corporation. 2524 S. I-20 Frontage Road, Suite A, P.0. Box 820186,",https://sparse.tamu.edu/Bova,fluids/transport,fluids/transport,1.0,1,computational fluid dynamics problem (1),1,real (1),1,unsymmetric (1),46835,46835,1.0,one size,2329092,2329092,square only,1,0,no square matrix exactly symmetric/Hermitian,0,1,0,real only,0,1,single matrix,"Model of Charleston Harbor. Steve Bova, Nichols Research Corporation. -2524 S. I-20 Frontage Road, Suite A, P.0. Box 820186, -Vicksburg, MS 39182-0186. email: swb :at the domain: nrcpet3.wes.army.mil. -Located at the U.S. Army Engineer Waterways Experiment -Station, Vicksburg, MS. - -RMA10: A 3D finite-element element matrix from a CFD code. - -From Steve Bova: This is a discretization of Charleston -Harbor, which has an island in the middle of it." -Brethour,2,"Numerical simulation of coating flows, from Jim Brethour. Original data was sent with 12 decimal digits in the mantissa, thus",https://sparse.tamu.edu/Brethour,fluids/transport,fluids/transport,1.0,1,computational fluid dynamics problem (2),1,real (2),1,unsymmetric (2),1348,9540,7.077151335311573,moderate (4–100×),19457,207308,square only,2,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,2,coherent family/series,"Numerical simulation of coating flows, from Jim Brethour. - -Original data was sent with 12 decimal digits in the mantissa, thus -the 4E20.12 format for the numerical values. - -Jim Brethour writes: - -""Problems such as ours, incompressible -free-surface flows, with (in my case) deformable boundaries, represent -practical problems where direct solvers are typically the best way to go. -The large system is for the tail end of a converging slot -coater (418 elements) and the smaller is for a 64 element lid-drvien cavity."" - --------------------------------------------------- -Jim Brethour, brethour :at the domain: cems.umn.edu -Box #13, 151 Amundson Hall, -Dept. of Chemical Engineering & Materials Science, -University of Minnesota, -421 Washington Avenue S.E. -Minneapolis, MN 55455 -(612)-625-3083 --------------------------------------------------- - -(PhD student of Prof. Scriven). - -Added November 3, 1997." -Brunetiere,1,"A matrix from Noel Brunetiere. Finite-element discretization of thermal problem. (Noel.Brunetiere :at the domain: lms.univ-poitiers.fr),",https://sparse.tamu.edu/Brunetiere,generic spatial PDE/thermal,generic spatial PDE/thermal,1.0,1,thermal problem (1),1,real (1),1,unsymmetric (1),3456,3456,1.0,one size,66528,66528,square only,1,0,no square matrix exactly symmetric/Hermitian,0,1,0,real only,0,1,single matrix,"A matrix from Noel Brunetiere. Finite-element discretization of thermal problem. -(Noel.Brunetiere :at the domain: lms.univ-poitiers.fr), -a PhD student (as of Spring 2001) in University of Poitiers (France). - -Finite-element discretization of a thermal problem. -The original matrix in triplet form had 100,512 duplicate entries -(not present in the RUA form of the matrix). - -The mesh is regular." -Cote,2,"Vibroacoustic matrices from Andre Cote, Univ. Sherbrooke, Quebec. Andre Cote",https://sparse.tamu.edu/Cote,electromagnetics/acoustics,electromagnetics/acoustics,1.0,1,acoustics problem (2),2,complex (1); real (1),1,symmetric (2),5962,12328,2.067762495806776,narrow (≤4×),142190,301700,square only,2,0,mixed among square matrices,1,2,0,real + complex,0,2,one broad domain; mixed matrix forms,"Vibroacoustic matrices from Andre Cote, Univ. Sherbrooke, Quebec. - - Andre Cote - GAUS, genie mecanique - Universite de Sherbrooke | E-mail : - Andre.Cote :at the domain: gme.usherb.ca - 2500, boul. Universite | Tel : (819) 821-8000 ext. 3166 - Sherbrooke ( Quebec ) | Fax : (819) 821-7163 - J1K 2R1 | Web : http://www-gaus.gme.usherb.ca/ - - -Andre Cote writes: - -Depending on the type of problems we solve ( structure only, fluid only, -fluid-structure, etc.), and the type of solutions we are interested in -(eigenvalue or forced problems), we use different calculations schemes. -But, basically, the main bottleneck for every path comes down to two -situations: - -1. Solving Ax = b for A symmetric and real (double precision) - - file name: vibrobox.rsa - - How large ? 12328 DOFS - What is the structure like ? symmetric with large bandwidth - What structural problem ? flexible box (structure only) - - -2. Solving Ax = b for A symmetric and complex (double precision) - - file name: mplate.csa - - How large ? 5962 DOFS - What is the structure like ? symmetric - What structural problem ? multi-layer plate (that is - a plate-air-poroelastic-air-plate system) - -Many solves are required (a few to 5000)." -DRIVCAV,26,"Driven cavity, finite element matrices from Yousef Saad (SPARSKIT2 collection). Matrix types appended to file names by Tim Davis.",https://sparse.tamu.edu/DRIVCAV,fluids/transport,fluids/transport,1.0,1,computational fluid dynamics problem (26),1,real (26),1,unsymmetric (26),317,4562,14.391167192429021,moderate (4–100×),5923,138040,square only,26,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,26,coherent family/series,"Driven cavity, finite element matrices from Yousef Saad (SPARSKIT2 collection). -Matrix types appended to file names by Tim Davis. - -c---------------------------------------------------------------------------- -c Code for Driven Cavity (Finite Element Method) -c -c Author: Andrew Chapman (chapman :at the domain: msi.umn.edu) -c -c Matrices extracted by: Abdelkader Baggag (baggag :at the domain: cs.umn.edu) -c -c For more information, contact Prof. -c Yousef Saad (saad :at the domain: cs.umn.edu) -c -c---------------------------------------------------------------------------- -c Directories -c---------------------------------------------------------------------------- -c src : source code -c Block_fmt : Matrices in Block format -c NoBlock_fmt : Matrices in Non Block format -c -c----------------------------------------------------------------------------" -FIDAP,35,"Fluid dynamics, finite element matrices from Yousef Saad (SPARSKIT2 collection). Names converted to Univ. of Florida naming convention (.mat changed to",https://sparse.tamu.edu/FIDAP,fluids/transport,fluids/transport,1.0,1,computational fluid dynamics problem (35),1,real (35),2,unsymmetric (22); symmetric (13),27,19716,730.2222222222222,wide (>100×),279,1096948,square only,35,0,mixed among square matrices,13,35,8,real only,0,35,one broad domain; mixed matrix forms,"Fluid dynamics, finite element matrices from Yousef Saad (SPARSKIT2 collection). -Names converted to Univ. of Florida naming convention (.mat changed to -.rua). - -c------------------------------------------------------------------------c -c FIDAP MATRICES: c -c------------------------------------------------------------------------c -c These matrices are extracted from the examples provided by Fidap c -c The matrices are stored in Harwell-Boeing format c -c For the physic of the problems, see detailed description in Fidap c -c c -c Matrices extracted by: Abdelkader Baggag (baggag at cs.umn.edu) -c with the help of Barry Rackner (bnr at msc.edu) -c c -c For help or more informations, contact: c -c Prof. Yousef Saad (saad at cs.umn.edu) -c c -c------------------------------------------------------------------------c -c GENERAL INFORMATION ON THE PROBLEMS c -c------------------------------------------------------------------------c -c The elements used are Q2 for the velocity and P1 for the pressure c -c for 2D problems and the so-called Brick elements (8 nodes) for 3D c -c problems (unless specified) c -c c -c We are solving the fully coupled Navier-Stokes Equations (unless c -c specified), including the Temperature Equation for the Heat Transfer c -c Problems and the Chemical Convection-Diffusion Equations for different c -c species c -c The Mesh is regular structured but not uniform c -c c -c | Kmax c -c | c -c | c -c | c -c |__________________ Jmax c -c / c -c / c -c / c -c / c -c / Imax c -c------------------------------------------------------------------------c -c This version is dated: June 09, 1994 c -c------------------------------------------------------------------------c -c ex1.mat : 2D Transient Poiseuille Flow in a Pipe -c Reynolds = 1.0 -c Imax = 9, Jmax = 5 -c -c ex2.mat : 3D Steady Couette Flow -c Reynolds = 1.0 -c Imax = 9, Jmax = 3 -c -c ex3.mat : 2D Flow Past a Cylinder in Freestream -c Reynolds = 40.0 -c Imax = 19, Jmax = 9 -c -c ex4.mat : 2D Hamel Flow -c Reynolds = 0.0 -c Imax = 9, Jmax = 5 -c -c ex5.mat : Non-newtonian Flow in a Channel (Power Law Poiseuille Flow) -c Reynolds = Power Law -c -c ex6.mat : 2D Die Swell Problem -c Capillary number = 1.0 -c Imax = 43, Jmax = 15 -c -c ex7.mat : 2D natural Convection in a Square Enclosure -c Rayleigh = 1000 -c Imax = 9, Jmax = 9 -c -c ex8.mat : Developing Flow, Vertical Channel, Angle = 0.0 -c Rayleigh = 1000 -c Imax = 5, Jmax = 13 -c -c ex9.mat : 2D Jet Impingement Cooling -c Reynolds = 0.0 ; (Jet) = 300 -c Imax = 21, Jmax = 89 -c -c ex10.mat : 2D Flow over Multisteps in a Channel -c Reynolds = 100 ; Prandtl = 0.6896 -c Imax = 13, Jmax = 5 -c -c ex11.mat : 3D Steady Flow; Cylinder and Flat Plate Heat Exchanger -c Imax = 21; Jmax = 7; Kmax = 5 -c Density = 1.0; Viscosity = 0.025; SpecifiHeat = 1.0; -c Conductivity = 3.760e-3 -c -c ex12.mat : 2D Flow Stokes Flow in Lid-Driven Wedge (28.5 Degree Angle) -c Imax = 7; Jmax = 5 -c Density = 1.0; Viscosity = 5.0 -c -c ex13.mat : Axisymmetric Flow through a Poppet Valve -c Imax = 12; Jmax = 13 -c Steady, Newtonian, Axi-Symmetric, Isothermal, Nonlinear -c Density = 3.122e-2; Viscosity = 6.77e-4 -c -c ex14.mat : 2D Isothermal Seepage Flow -c Imax = 23; Jmax = 23 -c Density = 1000.0; Permeability = 100; Porosity = 0.5 -c -c ex15.mat : 2D Spin up of a Liquid in an Annulus -c Imax = 7; Jmax = 7 -c Transient, Nonlinear, Newtonian, Cylindrical -c Density = 1.0; Viscosity = 0.02 -c -c ex16.mat : 3D Flow Past a Heated Obstacle -c Imax = 13; Jmax = 13; Kmax = 9 -c Nonlinear -c Density = 1.0; Viscosity = 0.1; Conductivity = 0.1 -c -c ex17.mat : 3D Flow in a Rectangular Duct Around a 120 Degree Bend -c Imax = 5; Jmax = 3; Kmax = 3 -c Nonlinear, Turbulent -c Density = 1.74e-3; Viscosity = 1.84e-4 1.0 (Mixing) -c -c ex18.mat : 2D Turbulent Flow Over a backward-Facing Step -c Imax = 5; Jmax = 9 -c Nonlinear, Turbulent (k-epsilon) -c Density = 1.0; Viscosity = 2.2222e-5 -c -c ex19.mat : 2D Developing Pipe Flow, Turbulent k-epsilon -c Imax = 5; Jmax = 5 -c Nonlinear, Axi-Symmetric, Turbulent -c Density = 1.0; Viscosity = 1.0e-5 -c -c ex20.mat : 2D Attenuation of a Surface Disturbance -c Imax = 7; Jmax = 5 -c Transient, Nonlinear, free -c Density = 1.0; Viscosity = 0.1 -c -c ex21.mat : 2D Growth of a Drop from a Nozzle -c Imax = 5; Jmax = 3 -c Transient, Nonlinear, free, axi-symmetric -c Density = 1.0; Viscosity = 1.0 -c -c ex22.mat : 2D Slot Coater -c Imax = 6; Jmax = 3 -c Steady, Nonlinear, free -c Density = 1.0; Viscosity = 0.1; SurfaceTension = 1.0 -c -c ex23.mat : 2D Fountain Flow in Injection Molding -c Imax = 4; Jmax = 3 -c Nonlinear, Free -c Density = 1.0; Viscosity = 2706.0 -c -c ex24.mat : 2D Unsymmetric Forward Roll Coating -c Imax = 5; Jmax = 3 -c Nonlinear, Free -c Viscosity = 1.0; Density = 1.0; SurfaceTension = 10 -c -c ex25.mat : 2D Drop Growth Resulting from a Moving Piston -c Imax = 3; Jmax = 3 -c Transient, Nonlinear, Free, AxiSymmetric -c Density = 1.0; Viscosity = 1.0; SurfaceTension = 10.0 -c -c ex26.mat : 2D surface Tension Gradient Driven Thermal Convection -c Imax = 3; Jmax = 3 -c Steady, Nonlinear, Free, Weakly -c Density = 1.0; Viscosity = 1.0; Surface Tension = variable; -c Conductivity = 0.565 -c -c ex27.mat : 2D Crystal Growth Simulation -c Imax = 3; Jmax = 3 -c Axisymmetric, Transient, Nonlinear, Free, Strongly -c Density = 1.0; Viscosity = 1.0; Conductivity = 0.051282 -c -c ex28.mat : 2D Two Merging Liquids with one External and an interior Interface -c Imax = 7; Jmax = 6 -c Nonlinear, Steady, Free -c Viscosity = 0.50; density = -c -c ex29.mat : 2D Turbulent flow in AxiSymmetric U-Bend: k-epsilon -c Imax = 5; Jmax = 3 -c Nonlinear, Turbulent, Axisymmetric -c Density = 1.0; Viscosity = 1.0e-5 -c -c ex30.mat : 3D Crystal Growth: Bridgman Technique -c Imax = 11; Jmax = 7; Kmax = 3; MaxPoints = 999 -c Nonlinear, Strongly -c Density = 1.0; Conductivity = 0.6; SpecifiHeat = 1.0 (Solid region) -c density = 306; Viscosity = 4; Enthalpy = 4; Conductivity = 4 (Fluid Region) -c -c ex31.mat : 2D Dilute Species Deposition on a Titled heated Plate -c Imax = 7; Jmax = 3 -c Nonlinear, Weakly -c Density = 1.0; Viscosity = 0.16; Conductivity = variable; Diffusivity = 0.15 -c -c ex32.mat : 2D radiation Heat Transfer in an Open Channel -c Imax = 5; Jmax = 7 -c Nonlinear, weakly -c Density = 1.3698; Conductivity = variable; Emissivity = variable -c -c ex33.mat : 2D Radiation Heat Transfer in a Square Cavity -c Imax = 7; Jmax = 7 -c Ra = 1.6e7; Rad = 5.88; Density = 1.3698; SpecifiHeat = 0.7300 -c Emissivity = 0.45--0.88 -c Nonlinear, Strongly -c -c ex34.mat : 2D Conduction Melting -c Imax = 3; Jmax = 3 -c Transient, Nonlinear, Strongly -c Density = 1.0; Conductivity = 0.1; Diffusivity = 1.0 (NonIsotropic) -c Viscosity and Enthalpy definec on curves -c -c ex35.mat : 2D Turbulent Flow in a Heated Channel -c Imax = 41; Jmax = 7 -c Reynolds = 25960; k-epsilon -c Nonlinear, Turbulent -c -c ex36.mat : 2D Chemical Vapor Deposition in a Horizontal Reactor -c Imax = 7; Jmax = 3 -c Nonlinear, Strongly -c Density = variable; Viscosity = variable defined on a curve -c All the other properties are variable and defined on curves -c -c ex37.mat : 3D Flow of Plastic in a profile extrusion Die -c Imax = 7; Jmax = 9; Kmax = 5 -c Nonlinear, NonNewtonian, Weakly -c Viscosity = variable; Conductivity = 1.0 -c -c ex38.mat : Multiple Species Reaction A + 2B -----> 2C + D -c Imax = 5; Jmax = 5 -c axisymmetric, nonlinear, weakly -c all the properties are variable -c -c ex39.mat : 3D Mixing Tank 2 Blades -c Imax = 9; Jmax = 5; Kmax =7 -c Nonlinear -c Density = 1.0; Viscosity = 1.0 -c -c ex40.mat : 3D Die Swell Problem on a Square Die -c Imax = 5; Jmax = 11; Kmax = 5 -c Re = 1.0; Ca = inf -c Viscosity = 1.0; Density = 0.5 -c -c----------------------------------------------------------------------- -c-----------------------------------------------------------------------" -Gaertner,3,"Matrices from Dr. Klaus Gaertner, Weierstrass Institute Berlin.",https://sparse.tamu.edu/Gaertner,graph/network data,graph/network data,1.0,2,directed weighted graph (2); undirected weighted graph (1),1,integer (3),2,unsymmetric (2); symmetric (1),10774,13209,1.2260070540189345,narrow (≤4×),70842,91465,square only,3,0,mixed among square matrices,1,3,0,real only,0,0,one broad domain; mixed matrix forms,"Matrices from Dr. Klaus Gaertner, Weierstrass Institute Berlin." -Garon,2,"Matrices from Andre Garon, Univ. of Montreal. 2D Navier-Stokes. (andreg :at the domain: CERCA.UMontreal.CA).",https://sparse.tamu.edu/Garon,fluids/transport,fluids/transport,1.0,1,computational fluid dynamics problem (2),1,real (2),1,unsymmetric (2),3175,13535,4.262992125984252,moderate (4–100×),84723,373235,square only,2,0,no square matrix exactly symmetric/Hermitian,0,2,0,real only,0,2,coherent family/series,"Matrices from Andre Garon, Univ. of Montreal. 2D Navier-Stokes. - -(andreg :at the domain: CERCA.UMontreal.CA). -2D Finite-Element discretisation of the Navier-Stokes -Equations. The geometry is a simply a square, with inlet and outlet on -opposing sides. - -The ""matrix_big"" file got truncated in transmission... - -Performance of various solvers: - --------------------------------------------------------------------------------- -To: andreg :at the domain: CERCA.UMontreal.CA -cc: bramley :at the domain: cs.indiana.edu, davis :at the domain: cise.ufl.edu -Date: Mon, 13 May 1996 13:48:24 -0400 -From: Tim Davis - -Andre, - -Here are some initial results with your matrices. -Run time is the complete factorization time (ordering, -symbolic factorization, and numerical factorization). - -This is on a Sun UltraSparc, with 128MB of memory, -2GB of swap space. Peak performance in the BLAS is about -80 mflops in double precision, 160 mflops in single -(I'm using double prec.). - -The methods used: - - UMFPACK2: default parameters, version 2.1 (at my ftp site, - but very similar to version 2.0 in netlib). - SuperLU-mmd: SuperLU with MMD preordering on A'*A - SuperLU-camd: SuperLU with COLAMD preodering on A'*A - (COLAMD is a code I'm working on). - MA48-def: from Harwell Subr. Library. default parameters, - successor to MA28. - MA48-sym: MA48 but strictly with symmetric pivoting only. - MA42: from Harwell Subr. Library. A unifrontal code. - -Both matrix_small and matrix_medium are easily factorizable. -Perhaps not as fast as an iterative method might work, but -they can be factorized. - -""matrix_small"" n=3175, nz=88927. - - time(sec) nz in LU flop count -UMFPACK2 3.95 754158 0.1318D+09 -SuperLU-mmd 4.62 700626 0.1061D+09 -SuperLU-camd 3.94 625623 0.0788D+09 -MA48-def 17.8 646982 0.1371D+09 -MA48-sym 11.9 485733 0.0693D+09 -MA42 10.9 1044187 0.0772D+09 - -""matrix_medium"" n=13535, nz=390607 - - time(sec) nz in LU flop count -UMFPACK2 116.9 8298907 0.5235D+10 -SuperLU-mmd 48.1 4725883 0.1251D+10 -SuperLU-camd 54.5 4673004 0.1280D+10 -MA48-def 472.1 6297647 0.4531D+10 -MA48-sym 293.9 3826925 0.1520D+10 -MA42 128.1 9020565 1.3587D+10 - -It looks like UMFPACK2 is getting unacceptable fill-in. The -diagonal is good - MA48-sym works better than MA48-def. -SuperLU seems to work the best ... HOWEVER ... you can't use -just SuperLU alone. It needs a column preordering. I shudder to -think what would happen if you didn't preordering the columns -(maybe I should try it). - -This is from a fluid flow problem, right? UMFPACK seems to have -trouble with those. Can you email me the details? - - - - - - --------------------------------------------------------------------------------- -Subject: more results -Date: Fri, 17 May 1996 16:23:22 -0400 -From: Tim Davis - - -Andre, - -I ran MA41, the ""symmetric-pattern"" multifrontal method (a new version, -to appear in the next release of the Harwell Subroutine Library), on -your matrices. - -Here are the results. Basically, MA41 is quite a bit faster for these -matrices than UMFPACK (=MA38). I doubt there's much I can do to beat -these run times. It would be possible to improve UMFPACK, I think, -so it wouldn't be as slow as it is. MA41 is also faster than -SuperLU, MA42, and MA48 for these matrices. MA41 is also more -accurate than UMFPACK2, probably because of the smaller flop count. - -These matrices have symmetric nonzero pattern. - -Do you have problems that lead to matrices with unsymmetric nonzero -pattern? - -Thanks, -Tim - -p.s., you'll need a wide screen to read these results. --------------------------------------------------------------------------------- - -This is on a lightly loaded UltraSparc, May 15-17, 1996, -128MB memory, 2GB swap space. Large differences between CPU and -WALL CLOCK time indicate swap-space thrashing of the method. - -Method.A is the method using DEFAULT parameters, except UMFPACK uses u=0.01. -Method.B uses non-default parameters. - - MA41.A: no max transversal, u=0.01 (defaults) - MA41.B: max transversal, u=0.01 - UMF*.A: BTF and no symmetric preference (defaults), u=0.01 - UMF*.B: no BTF, and with symmetric preference, u=0.01 - - -""total time"" is analysis+factorize, not including solve time. -All times in seconds. - -method total time | num. factorize | solve time | nz in | flop | error - cpu wall | cpu wall | cpu wall | LU | count | (max norm) - - -matrices/Garon/garon1.rua -MA41.A 0.835 0.938 0.729 0.810 0.036 0.078 357037 2.6470e+07 5.15D-12 -MA41.B 0.869 0.941 0.730 0.790 0.036 0.037 357037 2.6470e+07 5.15D-12 -UMF2.A 3.844 3.951 3.304 3.332 0.068 0.082 728318 1.3160e+08 0.6468E-04 -UMF2.B 2.501 2.504 2.103 2.110 0.057 0.057 606371 8.2460e+07 0.3974E-07 -UMF1.A 4.077 4.087 3.863 3.889 0.083 0.083 821459 1.1397e+08 0.1419E-03 -UMF1.B 2.102 2.109 1.641 1.643 0.058 0.058 558734 5.5743e+07 0.7491E-06 - - -matrices/Garon/garon2.rua -MA41.A 7.645 8.046 7.133 7.492 0.215 0.217 2396585 3.4220e+08 2.76D-11 -MA41.B 7.785 8.187 7.141 7.499 0.216 0.224 2396585 3.4220e+08 2.76D-11 -UMF2.A 76.076 81.396 82.050 167.636 1.013 27.110 7322867 3.2870e+09 0.6202E+02 -UMF2.B 31.693 31.801 30.761 31.005 0.386 0.386 4556143 1.2800e+09 0.5692E-04 -UMF1.A 73.480 82.058 86.705 239.346 1.098 28.960 7100507 2.9960e+09 0.7279E-01 -UMF1.B 25.236 25.665 25.874 26.828 0.408 0.408 4099241 9.2035e+08 0.1846E-04" -Goodwin,12,Fluid mechanics matrices from Ralph Goodwin Matrices from 2018:,https://sparse.tamu.edu/Goodwin,fluids/transport,fluids/transport,1.0,2,computational fluid dynamics (10); computational fluid dynamics problem (2),1,real (12),1,unsymmetric (12),1182,178437,150.96192893401016,wide (>100×),32282,5778545,square only,12,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,12,coherent family/series,"Fluid mechanics matrices from Ralph Goodwin - -Matrices from 2018: - - Finite element discretization of the Navier-Stokes and similar transport - equations on various geometries: - - Goodwin_010 - Goodwin_013 - Goodwin_017 - Goodwin_023 - Goodwin_030 - Goodwin_040 - Goodwin_054 - Goodwin_071 - Goodwin_095 - Goodwin_127 - -Matrices from 1995: - -The goodwin.rua matrix: - - A finite-element matrix from Ralph Goodwin, Chemical Engineering Dept., - Univ. of Illinois at Urbana-Champaign. A fluid mechanics problem. - From a nonlinear solver (Newton iterations. Uses several back - substitutions for the Sherman-Morrison formula to deal with a dense - row (not given in the matrix). - - The matrix was originally obtained in triplet (i,j,a_ij) format, with - lots of duplicate entries (from unassembled finite elements). - Converted (and duplicates assembled) into RUA format by Tim Davis. - - email: ralph :at the domain: wsnext.scs.uiuc.edu - -The rim.rua matrix: n=22560, nz=1014951. Originally in triplet format with - duplicate entries. See comments below: - - - From ralph :at the domain: wsnext.scs.uiuc.edu Mon May 22 16:31:26 1995 - - Tim, - - I have another matrix that may interest you. It is similar - to the matrix that you added to the Boeing-Harwell library. - THere are 22560 equations. The frontwidth is about 144. - However pivoting consideration cause the frontwidth to grow - to 2174. I have not tried using UMFPACK with this matrix - because my code requires 287 Mbytes to store the LU factors, - and therefore goes out-of-core. Still if you are - interested in a matrix that appears to have severe pivoting - problems and is big then I have one for you. - - Ralph Goodwin - - From ralph :at the domain: wsnext Fri May 26 08:03:45 1995 - - Tim, - - I solved my problem of excessive front width growth by rescaling - the rows of the matrix, so I guess I sounded a false alarm. - Previously, the front would grow from 155 to 2300, now it stays - at 144. I rescaled by the 1 norm of each row. - - Ralph - - From ralph :at the domain: wsnext.scs.uiuc.edu Fri May 26 14:27:21 1995 - - Tim, - - I ftped the matrix to you. It is in the - directory incoming and is named rim.mat.gz. It is - a 15Mb file that uncompresses to about 42Mb. The matrix - is stored in triple format with the first two lines - being the order of the matrix and the number of nonzeroes. - There are duplicate nonzeroes that must be summed. - - Earlier I said that I scaled the rows by their 1 norm. - This is not exactly correct. I scaled by the 1 norm - of the *unsummed* nonzeroes in the row, which is of - course different than the 1 norm of the row. - - Ralph - - - From ralph :at the domain: wsnext.scs.uiuc.edu Fri May 26 14:53:18 1995 - - - Yes, it is from the same application. The physical properties - are different (viscosity, density, ...) but otherwise it is - the same problem. One more thing about this problem is that - it involves not only solving the steady Navier-Stokes (N-S) equations - but also solving a pair of elliptic mesh generation (EMG) equations. - The EMG equations are coupled to the N-S equations by - boundary conditions. So the system of equations represented - by the matrix I sent you (and also goodwin.rua) is both - N-S equations and EMG equations all discretized using quadrilateral - biquadratic finite elements. The main point is that the matrix - is not just a discretization of the N-S equations. - - Ralph - - -Minor change, 3/31/03: ""rua"" changed to ""RUA"", in rim.rua header." -Graham,1,"Finite element matrix from Dave Graham, Univ. of Illinois. GRAHAM1 Obtained Feb. 18, 1998.",https://sparse.tamu.edu/Graham,fluids/transport,fluids/transport,1.0,1,computational fluid dynamics problem (1),1,real (1),1,unsymmetric (1),9035,9035,1.0,one size,335472,335472,square only,1,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,1,single matrix,"Finite element matrix from Dave Graham, Univ. of Illinois. - -GRAHAM1 Obtained Feb. 18, 1998. - Jacobian from a Galerkin finite element discretization of - the Navier-Stokes equations applied to a two-phase fluid flow - problem. - - Original problem was in finite-element (unassembled) form. - When converted to RUA type, 93877 entries with duplicate - row & column indices were summed." -Grund,22,"Matrices from Friedrich Grund, WIAS Berlin. Chemical process simulation. --------------------------------------------------------------------------------",https://sparse.tamu.edu/Grund,chemistry/biology; electrical/circuit/device; other,chemistry/biology,0.7272727272727273,3,chemical process simulation problem (16); economic problem (4); circuit simulation problem (2),1,real (22),2,unsymmetric (21); symmetric (1),7,57735,8247.857142857143,wide (>100×),15,275094,square only,22,0,mixed among square matrices,1,1,0,real only,0,0,multi-domain/mixed,"Matrices from Friedrich Grund, WIAS Berlin. Chemical process simulation. - --------------------------------------------------------------------------------- - -To: davis :at the domain: cise.ufl.edu -Subject: Matrices to your subdirectory -Date: Tue, 07 Oct 97 10:53:02 +0200 -From: ""F. Grund, WIAS Berlin"" - - -I put the files - -bayer.tar.gz -chem.tar.gz -poli.tar.gz - -in your subdirectory pub/faculty/davis/incoming - -The matrices in bayer.tar.gz are of the german chemical industry - (chemical process simulation). - -In chem.tar.gz you find matrices of demonstration examples of the - chemical process simulator SPEEDUP. - -The two matrices in poli.tar.gz are of account of capital links. The - problem is: - -Determine the inverse matrix of I-A. I is the unity matrix and A a matrix - with elements a(i,j) and - - 0 <= a(i,j) < 1 - -The elements b(i,j) of B = (I-A)**(-1) are >= 0. These matrices are - very sparse and the fill in is very small. - - - -Best Regards, -Friedrich Grund" -Gset,67,"Random graphs from Yinyu Ye, Univ. of Iowa. Obtained from ftp://dollar.biz.uiowa.edu/pub/yyye/Gset/",https://sparse.tamu.edu/Gset,graph/network data,graph/network data,1.0,2,undirected random graph (34); undirected weighted random graph (33),2,binary (34); integer (33),1,symmetric (67),800,10000,12.5,moderate (4–100×),3200,82918,square only,67,0,all square matrices symmetric/Hermitian,67,67,0,real only,34,0,one broad domain; mixed matrix forms,"Random graphs from Yinyu Ye, Univ. of Iowa. - -Obtained from ftp://dollar.biz.uiowa.edu/pub/yyye/Gset/ -See also http://dollar.biz.uiowa.edu/col/ye/ - -From 'rudy', an automatic graph generator. - - Three types of patterns: - even: evenly, randomly distributed entries - tor: 2D torus. All nodes have degree 4. Wrapped in both directions - skew: randomly distributed entries, but the avg degree of nodes 1:n - appears to be a decaying function, from high (node 1) to low - (node n) - - Matrices are binary or have values 0,1,-1. The distribution of +/-1 are - random. Entries are in the strictly lower triangular part only. - The original G* files in ftp://dollar.biz.uiowa.edu/pub/yyye/Gset/ - contain no duplicate entries, and are strictly upper triangular. - - mat n nz type binary comments - ------------------------------------------------------------------------------- - Matrices G1-G10: exactly 3% of the upper tri. entries are nonzero - G1 800 19176 even yes = spones (G6) - G2 800 19176 even yes = spones (G7) - G3 800 19176 even yes = spones (G8) - G4 800 19176 even yes = spones (G9) - G5 800 19176 even yes = spones (G10) - G6 800 19176 even no - G7 800 19176 even no - G8 800 19176 even no - G9 800 19176 even no - G10 800 19176 even no - - ------------------------------------------------------------------------------- - G11 800 1600 tor no 8-by-100 - G12 800 1600 tor no 16-by-50 - G13 800 1600 tor no 32-by-25 - - ------------------------------------------------------------------------------- - G14 800 4694 skew yes = spones (G18) - G15 800 4661 skew yes = spones (G19) - G16 800 4672 skew yes = spones (G20) - G17 800 4667 skew yes = spones (G21) - G18 800 4694 skew no - G19 800 4661 skew no - G20 800 4672 skew no - G21 800 4667 skew no - - ------------------------------------------------------------------------------- - Matrices G22-G31: exactly 1% of the upper tri. entries are nonzero - G22 2000 19990 even yes = spones (27) - G23 2000 19990 even yes NOT = spones (28), a unique pattern - G24 2000 19990 even yes = spones (29) - G25 2000 19990 even yes = spones (30) - G26 2000 19990 even yes = spones (31) - G27 2000 19990 even no - G28 2000 19990 even no - G29 2000 19990 even no - G30 2000 19990 even no - G31 2000 19990 even no - - ------------------------------------------------------------------------------- - G32 2000 4000 tor no 20-by-100 - G33 2000 4000 tor no 25-by-80 - G34 2000 4000 tor no 40-by-50 - - ------------------------------------------------------------------------------- - G35 2000 11778 skew yes = spones (G39) - G36 2000 11766 skew yes = spones (G40) - G37 2000 11785 skew yes = spones (G41) - G38 2000 11779 skew yes = spones (G42) - G39 2000 11778 skew no - G40 2000 11766 skew no - G41 2000 11785 skew no - G42 2000 11779 skew no - - ------------------------------------------------------------------------------- - Matrices G43-G47: exactly 1% of the upper tri. entries are nonzero - G43 1000 9990 even yes - G44 1000 9990 even yes - G45 1000 9990 even yes - G46 1000 9990 even yes - G47 1000 9990 even yes - - ------------------------------------------------------------------------------- - G48 3000 6000 tor yes 60-by-50 - G49 3000 6000 tor yes 100-by-30 - G50 3000 6000 tor yes 120-by-25 - - ------------------------------------------------------------------------------- - G51 1000 5909 skew yes - G52 1000 5916 skew yes - G53 1000 5914 skew yes - G54 1000 5916 skew yes - - ------------------------------------------------------------------------------- - Matrices G55-G56: approximately 0.05% of the upper tri. entries are nonzero - (0.05% is 12497.5, which rounds up to 12498) - G55 5000 12498 even yes = spones (56) - G56 5000 12498 even no - - ------------------------------------------------------------------------------- - G57 5000 10000 tor no 50-by-100 - - ------------------------------------------------------------------------------- - G58 5000 29570 skew yes = spones (58) - G59 5000 29570 skew no - - ------------------------------------------------------------------------------- - Matrices G60-G61: approximately 0.035% of the upper tri. entries are nonzero - (0.035% is 17147.55, which rounds up to 17148) - G60 7000 17148 even yes = spones (61) - G61 7000 17148 even no - - ------------------------------------------------------------------------------- - G62 7000 14000 tor no 70-by-100 - - ------------------------------------------------------------------------------- - G63 7000 41459 skew yes = spones (64) - G64 7000 41459 skew - - ------------------------------------------------------------------------------- - G65 8000 16000 tor no 80-by-100 - - ------------------------------------------------------------------------------- - G66 9000 18000 tor no 100-by-90 - - ------------------------------------------------------------------------------- - G67 10000 20000 tor no 100-by-100 - - -------------------------------------------------------------------------------" -Gupta,3,"Linear programming matrices from Anshul Gupta, IBM T. J. Watson Research Center. Discussed in ""Fast and Effective Algorithms for Graph Partitioning and Sparse",https://sparse.tamu.edu/Gupta,optimization/control,optimization/control,1.0,1,optimization problem (3),1,binary (3),1,symmetric (3),16783,62064,3.69802776619198,narrow (≤4×),2164210,9323427,square only,3,0,all square matrices symmetric/Hermitian,3,3,0,real only,3,0,coherent family/series,"Linear programming matrices from Anshul Gupta, IBM T. J. Watson Research Center. - -Discussed in ""Fast and Effective Algorithms for Graph Partitioning and Sparse -Matrix Ordering,"" Ansul Gupta, IBM Research Division, T.J. Watson Research -Center, PO Box 218, Yorktown Heights, New York, 10598, USA, -anshul :at the domain: watson.ibm.com. Tech Report RC 20496 (90799) -July 10, 1996 -Computer Science / Mathematics. The report discusses the WGPP graph -partitioner. - -All three matrices come from linear programming problems. They -are matrices of the form A times A transpose, where A is a rectangular -matrix with N rows (not provided). All three matrices are positive definite, -but values are not provided. - - N NZ (in all of matrix, not just lower triangular part) - -Gupta1.psa 31,802 2,164,210 - -Gupta2.psa 62,064 4,248,286 - -Gupta3.psa 16,783 9,323,427 - - -If you ""spy"" these matrices in Matlab, you can see that -the Gupta1 and Gupta2 matrices have a structure roughly of the form: - - A D - D A D - D A D - D ... - -where D is banded, and A is an arrowhead matrix of the form - - x x x x x x x x x - x x - x x - x x - x x - x x - x x - x x - x x - -where the ""x"" above, is a dense block. Gupta1 has 3 of these arrowheads, -and Gupta2 has 6 of them. The Gupta1 matrix is not used in the tech. report, -but it is similar to Gupta2, which is used in the experiments reported there. - -The Gupta3 matrix has a form - - x x - x x - x x - x x - x x - x x - x x - x x - x x x x x x x x x <=== the border, with nearly all the nonzeros - -where the ""x""'s are block submatrices (dense, or with some sparse pattern). - -The leading 15343-by-15343 submatrix of Gupta3 has only 596,765 nonzeros. -This leading submatrix appears to be block diagonal, with dense blocks of -size 39, 40, or 41. If a natural ordering is used, L has the same number of -nonzeros in its leading 15343-by-15343 submatrix as does the lower triangular -part of the leading 15343-by-15343 submatrix of Gupta3. That is, no fill-in -occurs in this part of the matrix. - -The remaining 8.72 million nonzeros are in the border (which is very dense). -Cholesky factorization of Gupta3, using the natural ordering, results in a -factor L with 5.4 million nonzeros, requiring 2.262 billion floating-point -operations to compute. This compares with 2.181 billion flops when using an -ordering from WGPP. Essentially, the matrix does not need any permutation to -reduce fill-in. - -These dense rows cause minimum degree ordering algorithms (AMD, for example) -to take a long time to run. A simple preprocessing can remove them from -the matrix prior to calling AMD. The dense rows/columns can then be -ordered last. This technique can greatly improve AMD's run time." -Hamm,6,"Semiconductor simulation matrices from Steve Hamm, Motorola, Inc. Semiconductor Systems Design Technology",https://sparse.tamu.edu/Hamm,electrical/circuit/device,electrical/circuit/device,1.0,1,circuit simulation problem (6),1,real (6),1,unsymmetric (6),2395,170998,71.39791231732777,moderate (4–100×),13151,958936,square only,6,0,no square matrix exactly symmetric/Hermitian,0,4,0,real only,0,0,coherent family/series,"Semiconductor simulation matrices from Steve Hamm, Motorola, Inc. -Semiconductor Systems Design Technology -3501 Ed Bluestein Blvd., MD-M2, Austin TX 78762 USA -Internet: hamm :at the domain: ssdt-bluestein.sps.mot.com -Ph: 512-928-6612 -Fax: 512-928-7662 - -In Harwell/Boeing format. One dense right-hand side with each matrix. - -add20.rua 20-bit adder -add32.rua 32-bit adder -memplus.rua memory circuit - - -Matrices added July 2001. New address: - - email Steve.Hamm :at the domain: motorola.com - Motorola Inc. Circuit Simulation Group - TX32 PL29, 7700 W. Parmer Lane, Austin TX 78729 USA - -bcircuit.rua with parasitics -hcircuit.rua no parasitics -scircuit.rua many parasitics" -Hollinger,49,"Economic models from Peter Holinger, Intex Solutions, Inc. from MULTIMOD, a multi-country forward-looking macroeconometric model",https://sparse.tamu.edu/Hollinger,other,other,1.0,1,economic problem (49),1,real (49),1,unsymmetric (49),2880,64089,22.253125,moderate (4–100×),18229,717620,square only,49,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,0,coherent family/series,"Economic models from Peter Holinger, Intex Solutions, Inc. - -from MULTIMOD, a multi-country forward-looking macroeconometric model -(see http://www.imf.org/external/np/res/mmod/mark3/index.htm). - -mark3jacTTT[sc]: - - These matrices are the large block of a Jacobian matrix from the IMF's - MULTIMOD Mark3 model stacked for 'TTT' periods. In each 'TTT' pair, - the 'sc' version is the same matrix after row and column scaling. - -jan99jac*: - - Jacobians from a stacked forward-looking macroeconomic model, - in this case from the Bank of Canada. - -g7jac*: - - This set of matrices is from an ""Overlapping Generations Model"" - used to study the social security systems of the G7 nations. - It's from CEPII, a research institute in Paris. - - --=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- - Peter Hollinger Tel:781-449-6222 Fax:781-444-2318 - peterh :at the domain: intex.com - Intex Solutions, Inc. 110 A Street Needham, MA 02494-2807 --=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-" -LPnetlib,138,"NETLIB Linear Programming problems, www.netlib.org. matrices/LPnetlib/README. Tim Davis, May 15, 1997. (davis :at the domain:",https://sparse.tamu.edu/LPnetlib,optimization/control,optimization/control,1.0,1,linear programming problem (138),2,real (108); integer (30),1,rectangular (138),13,243246,18711.23076923077,wide (>100×),22,1408073,rectangular only,0,138,N/A (rectangular only),0,0,0,real only,0,0,one broad domain; mixed matrix forms,"NETLIB Linear Programming problems, www.netlib.org. - -matrices/LPnetlib/README. Tim Davis, May 15, 1997. (davis :at the domain: -cise.ufl.edu) - -The files in this directory are a translation of the linear programming -problems in Netlib. You can obtain more information about the Netlib LP -problems by sending email to netlib :at the domain: -ornl.gov with the message -""send index from lp"". - - -++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ -NOTICE! The Netlib LP problems, in MPS format, should be considered the -""canonical"" problems. If you find any problems with different optimal -objectives than what is reported in Netlib, then please let me know. -++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - -The Netlib LP problems are in MPS or compressed MPS format. Any compressed -MPS files were uncompressed using emps.c in the Netlib lp/data directory. -The MPS files were then converted to the KAR format using the mpsrd program -by M.G.C. Resende and G. Veiga. The mpsrd program does not process RANGES, -so five problems (boeing1, boeing2, forplan, nesm, and seba) do not appear -here. The KAR format is written out by mpsrd as follows: - -c name - problem name (same as mps name card) - write(out,'(a8)') name -c m - number or rows (input) -c n - number of columns (input) - write(out,*) m,n -c ia - pointers to the begining of storage of column - write(out,*) (ia(i),i=1,n+1) -c ja - row indices for each non zero entry (input) - write(out,*) (ja(i),i=1,ia(n+1)-1) -c a - non zero entries (input) - write(out,*) (a(i),i=1,ia(n+1)-1) -c b - right hand side (input) - write(out,*) (b(i),i=1,m) -c c - objective vector (minimize) - write(out,*) (c(i),i=1,n) -c z0 - initial fixed value for objective - write(out,*) z0 -c lobnd - lower bounds on variables - write(out,*) (lobnd(i),i=1,n) -c upbnd - upper bounds on variables - write(out,*) (upbnd(i),i=1,n) - -Mpsrd converts the MPS problem into a single form: - - minimize c'*x, subject to Ax=b and l<=x<=u. - (and given an initial value of the objective, z0). - -Next, the KAR formatted version of the LP problem was converted into -two formats by the readkar.f program, written by Tim Davis. It generates -a single Matlab script, such as lp_afiro.m for the AFIRO problem. -For infeasible (lp/infeas) problems, the prefix is lpi_ (lpi_galenet.m for -the problem GALENET, for example). This prefix was chosen so that you -are sure to notice that the problem is infeasible, and also because there -are two versions of the same problem in Netlib (GREENBEA), one feasible -and the other not. - - - --------------------------------------------------------------------------------- - - -The *.clu files contain the c, l, and u vectors, -and the scalar z0, for the LP problems held in -RRA format in the LPnetlib directory. The dense vectors -appear in order: c, l, u, and then z0. One entry is held -on each line. - -The RRA files contain A and b, in Harwell/Boeing format. - -The LP problems are all of the form - - minimize c'*x, subject to Ax=b and l<=x<=u - and where z0 is the initial value of the - objective." -Li,2,"3D magnetohydrodynamics finite element matrix from Ben Li, Washington State Univ. A magnetohydrodynamics problem with 5 variables. Collected and converted",https://sparse.tamu.edu/Li,electromagnetics/acoustics,electromagnetics/acoustics,1.0,1,electromagnetics problem (2),2,real (1); binary (1),2,unsymmetric (1); symmetric (1),22695,22695,1.0,one size,1215181,1350309,square only,2,0,mixed among square matrices,1,2,0,real only,1,2,one broad domain; mixed matrix forms,"3D magnetohydrodynamics finite element matrix from Ben Li, Washington State Univ. -A magnetohydrodynamics problem with 5 variables. Collected and converted -to Harwell/Boeing format by Tim Davis. - -Email: li :at the domain: -mme.wsu.edu. http://www.mme.wsu.edu/people/bql.htm - -Order: 22,695 with 1,350,309 entries. 1,215,181 nonzero entries. -If the specified zero entries (135,128 of them) -in li.rua are included, the pattern is symmetric. - -Doing a Cholesky factorization (if it were possible) would lead -to 19,896,926 entries in L if *no* reordering is done, and about 2e10 -floating-point operations. A symmetric-pattern LU code, such as MA41, -could get close to that (with about 4e10 ops), I would think, assuming -that the diagonal entries are reasonably large in value. The smallest -nonzero entry on the diagonal is 0.09, which is not too bad. - -For comparison, a skyline solver got 40,262,417 entries -in L and U. That's almost the same as a non-skyline code -(L+L' for Cholesky would have about 39.7 million entries). -So a skyline solver is doing very well for this matrix. - -On an 167 Mhz UltraSPARC-I, AMDBAR takes only 2.06 seconds to get a -permutation P such that the L (for Cholesky factorization) has 30,408,134 -entries ... much worse than when using the given ordering, which is unusual. - -Doing a nested dissection ordering using Chaco (from Sandia) as the -graph partitioner gives an ordering such that the Cholesky factorization -has 13,142,711 nonzeros in L. It took 81.7 seconds to get this -ordering on an UltraSPARC-1. I used John Gilbert's mesh partitioner code at -ftp://parcftp.xerox.com in /pub/gilbert/meshpart.uu which as a Matlab -interface to Chaco, and P = ndperm ('chaco', 8, B) in Matlab, -where B is the pattern that includes the explicit zeros. -This ordering results in only 1.4e10 flops for Cholesky, or about -twice that for a symmetric-pattern LU factorization, assuming no ""delayed -pivoting"" becaose of small entries on the diagonal. - -li.psa is the symmetric pattern of li.rua, where all specified entries of -li.rua are included, including entries with zero value. - -Using the li.psa version, Bruce Hendrickson reports the following. -Using BEND on an SGI Onyx, in 8 seconds and ordering was obtained -in which L has 13,052,539 nonzeros and about 1.37e10 flops for a -Cholesky factorization. - -Tim Davis, June 23, 1999" -Mallya,18,"Chemical process simulation matrices, J. Mallya, S. Zitney, M. Stadtherr. Reference:",https://sparse.tamu.edu/Mallya,chemistry/biology,chemistry/biology,1.0,1,chemical process simulation problem (18),1,real (18),1,unsymmetric (18),1477,70304,47.599187542315505,moderate (4–100×),18427,1528092,square only,18,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,0,coherent family/series,"Chemical process simulation matrices, J. Mallya, S. Zitney, M. Stadtherr. - -Reference: -Zitney, S. E. and Mallya, J. and Davis, T. A. and Stadtherr, M. A., -Multifrontal Techniques for Chemical Process Simulation on Supercomputers, -Fifth International Symposium on Process Systems Engineering, May 1994, -Kyongju, Korea (available via anonymous ftp to ftp.cis.ufl.edu as the -file cis/tech-reports/tr94/tr94-002.ps.Z). - -Light hydrocarbon recovery problems - large-scale chemical process -simulation using rigorous equation-based models. Note that these matrices -seem to be well-suited to unifrontal methods. - - ---Mallya, Jayarama U - Dept of Chemical Engg - U of Illinois. - mally :at the domain: turing.scs.uiuc.edu - - NOTE: The 1k problem is the same as the old lhr (light hydrocarbon) - recovery problem. The other problems are constructed from the 1k - problem. Some of the matrices are ill-conditioned, but this is OK, - since even with a poor residual the nonlinear solver rapidly - converges. Subsequent linear systems (not provided - these are - the first in the sequences) can be solved with a much lower residual. - -These matrices occur in steady-state chemical process simulation problems. -The problems involve a light hydrocarbon recovery process. The larger -problems are extensions of the smallest, involving more chemical species -with larger and/or more chemical processing units. These problems were -developed by Jayarama Mallya and Mark Stadtherr at the University of -Illinois (Department of Chemical Engineering). The matrices are taken -from an early iteration in a Newton-based solution procedure starting -from a poor initial guess. Automatic scaling options in the simulation -code were turned off. The matrices are ill-conditioned and contain hard -zeros. Please relate experiences with these matrices to Mark Stadtherr -(m-stadtherr :at the domain: uiuc.edu). - -The rank of lhr71 was estimated at 70301 (it is 70304-by-70304) by MA48. - -================================================================================ - -May 16, 1997: - -Some of the ill-conditioned behaviour of these matrices is due to a modelling -error. Better-conditioned versions have been derived (lhr04c, lhr07c, etc.), -with permission of the creators of these matrices. The two smallest -matrices (lhr01 and lhr02) are already reasonably well-conditioned and do not -require any modifications. - -The corrections were made as follows (see the FIX subdirectory for the -code to do this): - - 1) the matrices were converted to triplet format, with explicit zeros - replaced by the value 1e-300. The modified matrices were - then loaded into Matlab. Zeros were changed to 1e-300 so - that Matlab would not drop the explicit zeros. - - 2) the matrices were fixed, in Matlab. First, the matrix A is - permuted to block triangular form (PAQ), with a zero-free - diagonoal. Next, 0.001 is - added to each entry (PAQ)_ii for which (PAQ)_ii => 0, - and -0.001 if added to each (PAQ)_ii for which (PAQ)_ii is - < 0. The correction to A is then P'*F*Q', where F is - diagonal, and F_ii is the correction made to (PAQ)_ii. - - 3) the corrected matrix C = A+(P'*F*Q') was then written in the - Harwell/Boeing format. It has the same pattern of its - entries as A." -Mulvey,2,"Multistage stochastic financial modelling. J. Mulvey, Princeton. Matrices provided by Ed Rothberg, Silicon Graphics, Inc.",https://sparse.tamu.edu/Mulvey,other,other,1.0,1,economic problem (2),2,real (1); binary (1),1,symmetric (2),74752,74752,1.0,one size,596992,596992,square only,2,0,all square matrices symmetric/Hermitian,2,2,1,real only,1,0,one broad domain; mixed matrix forms,"Multistage stochastic financial modelling. J. Mulvey, Princeton. -Matrices provided by Ed Rothberg, Silicon Graphics, Inc. -From the paper: - - A. Berger, J. Mulvey, E. Rothberg, and R. Vanderbei, - ""Solving multistage stochastic programs using tree dissection,"" - tech. report SOR-97-07, Program in Statistics and Operations - Research - -Ed Rothberg, email: rothberg :at the domain: multnomah.engr.sgi.com -John Mulvey, email: mulvey :at the domain: macbeth.Princeton.edu - -A stochastic programming matrix in an interior-point algorithm -for financial portfolio optimization. - -Random nonzero values added (finan512.rsa) by Tim Davis. The values were -then modified to ensure that the matrix was symmetric positive definite. - -This matrix is quite strange, and appears to be highly sensitive to -tie-breaking issues, or some other phenomenon... See the paper, above, -for a description of the phenomenon. - -The following ordering algorithms were run -on a Sparc-10. AMDperm is the Approximate Minimum Degree method, with -various ""elbow room"" sizes (see TR-94-038), -based on upper bounds on the external degree. -AMDtry3 is AMD with a different tie-breaking strategy. oldAMDt is the same -as MA27, except that exact degree computations are replaced with an upper -bound on the true degree. initMD is MA27, and origMD is a modification in MUPS -that ignores the computation of degrees above a certain threshold. MMD is -Joseph Liu's multiple minimum degree algorithm (with delta = 0, 1, and 2). -ND is the nested dissection algorithm in Sparspak, and QMD is the minimum -degree ordering algorithm in Sparspak. The columns in the table are -the number of nonzeros in L, the Mflops needed to compute L, the ordering -time, the number of garbage collections, the memory usage (excluding O(n)-size -arrays), and the number of size-n work arrays. - -NOTE: Lnz is the number of nonzeros in the strictly lower diagonal part. -The A+AT number is the number of off-diagonal nonzeros in A+A^T. -Since this is a symmetric matrix, that's just the nonzeros in A minus n. - -Matrix Portfolio/finan512.psa.gz n 74752 Anz 596992 A+AT 522240 - -Method:delta/elbow Lnz LL^Tmflops time comp memory O(n)usage -AMDperm: 3361993 1035.385920 5.53 0 833747 8 -AMDperm:4 3361993 1035.385920 5.47 0 833747 8 -AMDperm:3 3361993 1035.385920 5.63 1 776090 8 -AMDperm:2 3361993 1035.385920 5.66 1 671745 8 -AMDperm:1 3361993 1035.385920 5.64 1 671745 8 -AMDtry3: 2522769 536.937792 5.18 0 797305 9 -oldAMDt: 9285069 14968.698880 5.90 0 996344 8 - initMD: 9285069 14968.315904 10.07 0 1000263 7 - origMD: 9237356 14654.392320 6.56 0 1036206 7 - MMD:0 6432433 5933.023232 292.65 0 522240 7 - MMD:1 6432433 5933.025280 291.97 0 522240 7 - MMD:2 6432433 5933.025280 278.82 0 522240 7 - ND: 8530875 3412.739840 6.20 0 522240 5 - QMD: 4345895 2121.354368 970.03 0 522240 9 - - -These minimum degree algorithms do show large variations in ordering times for -other matrices, but they usually produce comparable results. This matrix -(finan512) is a very unusual exception. The bcsstk30 matrix shows -more typical relative results, for example: - - -Matrix HB/bcsstk30.psa n 28924 Anz 2043492 A+AT 2014568 - -Method:delta/elbow Lnz LL^Tmflops time comp memory O(n)usage -AMDperm: 3768625 899.876992 2.30 0 2088703 8 -AMDperm:4 3768625 899.876992 2.31 0 2088703 8 -AMDperm:3 3768625 899.876992 2.29 0 2088703 8 -AMDperm:2 3768625 899.876992 2.31 0 2088703 8 -AMDperm:1 3768625 899.876992 2.76 1 2072417 8 -AMDtry3: 3764679 916.359040 2.32 0 2088942 9 -oldAMDt: 4518625 1363.998720 2.23 0 2107723 8 - initMD: 4583033 1409.133568 2.66 0 2107538 7 - origMD: 4487456 1336.230912 2.73 0 2135673 7 - MMD:0 3849328 938.700288 4.63 0 2014568 7 - MMD:1 3863974 955.433984 4.53 0 2014568 7 - MMD:2 3886867 963.582848 4.29 0 2014568 7 - ND: 5743266 2204.590848 12.11 0 2014568 5 - QMD: 4689554 1586.877184 59.55 0 2014568 9" -Nasa,11,"NASA matrices obtained from Alex Pothen, ICASE and Old Dominion Univ. Converted to HB format by Tim Davis. The following is the original",https://sparse.tamu.edu/Nasa,structures/materials,structures/materials,1.0,1,structural problem (11),2,binary (6); real (5),2,symmetric (8); unsymmetric (3),1824,54870,30.082236842105264,moderate (4–100×),23492,2677324,square only,11,0,mixed among square matrices,8,8,5,real only,6,11,one broad domain; mixed matrix forms,"NASA matrices obtained from Alex Pothen, ICASE and Old Dominion Univ. -Converted to HB format by Tim Davis. The following is the original -README file, and email discussing the *.nas format. - -The original *.nas files have coordinate information for the nodes of the -graph. They are located in the Conversion/ subdirectory, if you're interested. - --------------------------------------------------------------------------------- - -ftp://ftp.cs.odu.edu/pub/incoming/kumfert/README - -WARNING: -The contents of this ftp site are temporary and subject -to change without notice. - -DISCLAIMER: -Nothing free is guaranteed. - -CONTENTS: -The current contents are matrices in various formats. -All are compressed and should be ftp'd using binary mode. -The two major formats are .nas and .hb for NASA and -Harwell-Boeing formats respectively. The current list includes: - - barth.nas.Z - barth4.nas.Z - barth5.nas.Z - nasa1824.hb.Z - nasa2146.hb.Z - nasa2910.hb.Z - nasa4704.hb.Z - nasasrb.hb.Z - pwt.nas.Z - shuttle_eddy.nas.Z - skirt.nas.Z - -Also included are some files for reading in these problems into -the Matlab(R) environment. - - hbo2mat.f - hbo.m - readnasa.m - -Nasa files can be read in using the readnasa.m utility. Loading -in Harwell-Boeing files is a two step process. First one must -convert the .hb file to a .mat file using hbo2mat. Compilation -instructions are included in the file. Then within Matlab, -use hbo.m to properly load the .mat file. The latter two -files were written by Cleve Moler, The MathWorks. The readnasa.m -file was writtem by Gary Kumfert, Old Dominion University. - -Comments/suggestions can be sent to Gary Kumfert . - -Matlab is a registered trademark of The MathWorks Inc. - --------------------------------------------------------------------------------- - - -Date: Thu, 1 Jun 1995 12:59:00 -0400 -From: Pothen Alex -To: davis :at the domain: cis.ufl.edu -Subject: Re: problems - -problem.nas is an inhouse format used by us. -It lists n coordinates of the nodes -x_i y_i (z_i) -and then tuples or triples -i j a_{ij} -The first line lists n (no. of nodes) and -nnz (the no. of nonzeros). - ---Alex - - -From: Alex Pothen -Date: Thu, 1 Jun 1995 18:25:15 -0400 -To: davis :at the domain: cis.ufl.edu -Subject: Re: matrices - -Tim, -I am working at Icase and don't have access to barth -I will check and let you know tomorrow. ---Alex -P.S. If it is a nas file, I trust you are using -readnasa.m file to read it. -It is an airfoil problem, so you should be able to -visualize it using gplot. ---Alex - -From: Alex Pothen -Date: Fri, 2 Jun 1995 10:06:46 -0400 -To: davis :at the domain: cis.ufl.edu -Subject: Re: matrices - -It is possible that the original numbering of barth makes -it look random in a spy plot. ---Alex" -Nemeth,26,"A set of matrices from Karoly Nemeth, theoretical chemistry. currently postdoctoral fellow at Rice University with Prof. G.E. Scuseria",https://sparse.tamu.edu/Nemeth,chemistry/biology,chemistry/biology,1.0,1,theoretical/quantum chemistry problem (26),1,real (26),1,symmetric (26),9506,9506,1.0,one size,394808,1511760,square only,26,0,all square matrices symmetric/Hermitian,26,26,0,real only,0,0,coherent family/series,"A set of matrices from Karoly Nemeth, theoretical chemistry. -currently postdoctoral fellow at Rice University with Prof. G.E. Scuseria -(as of Summer 1999). - -Symmetric-positive semidefinite matrices. - - --------------------------------------------------------------------------------- - -Dear Prof Davis, - -The problem, where my matrices come from, is the Newton-Schultz iteration -(NSI), which is used for the calculation of the matrix sign function. -If we denote the ""sign"" of a hermitian matrix ""F"" by ""sign(F)"", -sign(F) can be calculated by the NSI, like: - - Z_{k+1} = Z_{k} + 1/2 * (I - Z_{k}^2) * Z_{k} . - -""Z"" is a general symmetric matrix, ""k"" is the iteration step, ""I"" is the -identity, Z_{0} = F/||F||, where ||F|| is the absolute value of the -largest eigenvalue of F. In my problems NSI converged in most cases in -less than 20 iterations. - -Currently I am very much interested in the Cholesky-decomposition of -(I - Z_{k}^2)^2: (I - Z_{k}^2)^2 = T^{t}T (T is upper triangular) . This -would help me to construct some orthogonal vectors, which are important -for some applications, the vectors are generated by : -(I - Z_{k}^2)*T^{-1}. - -As NSI converges, the eigenvalues in Z_{k} tend to +1 or -1. (I - Z_{k}^2) -gets more and more rank deficient (Z_{k}^2 tend to I). Thus normal Cholesky -cannot be used for the decomposition of (I - Z_{k}^2)^2. This is why I -need Cholesky factorization for positive semidefinite matrices. Thus I -expect to be able to generate ""r"" orthonormal vectors from (I - Z_{k}^2), -where ""r"" is the rank of (I - Z_{k}^2). - -Here I used a somewhat -modified version of NSI, where the trace of the matrices is kept fixed. -However the matrices needed to be Cholesky decomposed are the corresponding -(1-Z_{k}^2)^2 matrices. - -Best regards: - -Karoly - - -(The Nemeth{K}.rsa file holds the Z_{k} matrix.) - - - -*********************************** -Karoly Nemeth, Ph.D. ----------------------------------- -Rice University -Department of Chemistry - MS 60 -6100 Main Street -Houston, TX 77005-1892 - -e-mail: karoly :at the domain: celaeno.rice.edu - -office phone: before 5.30pm : +713-527-8101/2826 - after 5.30pm : +713-527-8750/2826 - fax: +713-285-5155 -***********************************" -Okunbor,2,Acoustic radiation around aft duct fan. Walter Eversman and Daniel Okunbor. These matrices are generated by a finite element code.,https://sparse.tamu.edu/Okunbor,electromagnetics/acoustics,electromagnetics/acoustics,1.0,1,acoustics problem (2),2,real (1); complex (1),2,symmetric (1); unsymmetric (1),8184,8205,1.0025659824046922,narrow (≤4×),125567,127762,square only,2,0,mixed among square matrices,1,2,1,real + complex,0,2,one broad domain; mixed matrix forms,"Acoustic radiation around aft duct fan. Walter Eversman and Daniel Okunbor. -These matrices are generated by a finite element code. - -(C)1997 Copyright by -Walter Eversman and Daniel Okunbor -University of Missouri-Rolla - -okunbor :at the domain: cs.umr.edu - --------------------------------------------------------------------------------- - -NOTE (added Feb 9, 2012): The aft01 matrix is numerically rank deficient. It -is tagged in the UF Sparse Matrix Collection as 'positive definite' because -chol works on it, but some eigensolvers report negative eigenvalues. See below -for details. - --------------------------------------------------------------------------------- - -On 01/26/2012 11:37 AM, Alexander Andrianov wrote: -Just a quick note somewhat related to the previous discussion. It looks like -aft01 instance is not PD: its smallest eigenvalue is about -2.0312E-03. Just -confirmed that by running dense eigensolvers including one from MKL library -(LAPACK’s DSYEV with 'N' option). - -A modified Cholesky factorization however does not choke and factorizes it -nicely, with normalized residual of about 1.0e-17. - -Not sure about its numerical rank though. - -Best regards, Alex - --------------------------------------------------------------------------------- - -From: Tim Davis -Sent: Friday, January 27, 2012 5:50 PM -To: Alexander Andrianov -Cc: Leslie Foster -Subject: Re: UF collection matrices info - -This is a wierd matrix. Its numerical rank is one. - -chol in MATLAB succeeds, unmodified, with a low residual - -[L,p,s]=chol(A,'lower','vector'); -norm (L*L'-A(s,s),1) - -the diagonal of L has elements in the range 0.1325 to 3.16e7 - -Maybe the eigenvalue solver is the one giving the 'wrong' answer. -This problem might be close to PD, and chol and eig are giving -different results. I tried this: - -e = eig (full(A)) ; - -and got a largest eigenvalue of 1e15, and a smallest one as 1.493e-4. -I don't see the -2e-3 that you got. - -So eig in MATLAB (also uses LAPACK), says the matrix (barely) PD. - -Numerically, the positive definiteness of this matrix looks like a -toss up. - -Thanks, -Tim - - --------------------------------------------------------------------------------- - -From: Alexander Andrianov at SAS.com -Subject: RE: UF collection matrices info -Date: January 30, 2012 9:52:55 AM EST -To: Davis Tim -Cc: Leslie Foster - -You may be right. I just ran it under various dense MKL solvers and got the -following: - - Smallest -DSYEV -2.0311917e-3 eigenvalues only ('N' option) -DYSEV -2.3789104e-1 complete eigendecomposition ('V' option) -DSYEVR 1.5329287e-4 complete eigendecomposition -DSYEVD -2.2634604e-3 complete eigendecomposition - -Also ran with my version of DSYEVD and got: -1.5259751e-4 for eigenvalues only -9.0656595e-3 for complete eigendecomposition - -The normalized maximum residual was 6.423e-4 (excellent) but the normalized -orthogonality loss was 1.347e11 (very bad). This is probably due to the fact -that it is rank-deficient. - -I don't know if this helps: the LAPACK team labels DSYEV and DSYEVD as being in -general more accurate (see LAPACK working note 183, page 16) but it doesn't -imply that they will beat DSYEVR all the time. - -I guess this instance can be labeled as 'numerically PD' due to the rounding -error during Cholesky factorization which helps make it PD after all. - -Thank you for running MATLAB on it, -Alex" -Qaplib,8,"Linear programming, lower bounds for quadratic assignment problem, M. Resende. In the paper ""Computing lower bounds for the quadratic assignment problem",https://sparse.tamu.edu/Qaplib,optimization/control,optimization/control,1.0,1,linear programming problem (8),1,integer (8),1,rectangular (8),225,379350,1686.0,wide (>100×),1050,1567800,rectangular only,0,8,N/A (rectangular only),0,0,0,real only,0,0,coherent family/series,"Linear programming, lower bounds for quadratic assignment problem, M. Resende. - -In the paper ""Computing lower bounds for the quadratic assignment problem -with an interior point algorithm for linear programming,"" (Resende, -Ramakrishnan, and Drezner, O.R. 43 (781-791) 1995), compute an LP-based lower -bound for the QAP using a interior point algorithm on numerous -problems from the QAPLIB. These linear programs are very difficult -for direct factorization based LP solvers. - -See http://www.research.att.com/~mgcr/ for more information. -The above paper is at http://www.research.att.com/~mgcr/doc/qaplp.ps. - --------------------------------------------------------------------------------- - -See ../LPnetlib/README.txt for a description of the *.clu files." -Ronis,2,"Crystalline compounds (zeolites,sodalites), D. Ronis, Univ. of McGill. Two matrices from David Ronis, Chemistry Dept, Univ of McGill, Canada.",https://sparse.tamu.edu/Ronis,structures/materials,structures/materials,1.0,1,materials problem (2),1,real (2),1,unsymmetric (2),48600,157464,3.24,narrow (≤4×),1181120,3866688,square only,2,0,no square matrix exactly symmetric/Hermitian,0,2,0,real only,0,2,coherent family/series,"Crystalline compounds (zeolites,sodalites), D. Ronis, Univ. of McGill. - -Two matrices from David Ronis, Chemistry Dept, Univ of McGill, Canada. -Email: ronis :at the domain: onsager.chem.mcgill.ca. - -Added to the UF sparse matrix collection on March 19, 2001. - -His comments: - - The matrix is generated on the fly in a large code that analyses the - elastic properties (and many other things) in a class of complicated - crystalline compounds called zeolites or sodalites. - - Note that in the actual inversions, I add a positive number times the - unit matrix to this matrix. The typical value of this scalar is - anywhere from 1e24 to 1e28--it really doesn't matter from the point of - view of the calculation I want to do--the matrix is nonsingular in all - cases. - -(in the two matrices, xenon1.rua and xenon2.rua, 1e24 has already been -added to the diagonal)." -Rothberg,6,"Structural engineering matrices from Ed Rothberg, Silicon Graphics, Inc. Some of the matrix names and file names don't match.",https://sparse.tamu.edu/Rothberg,fluids/transport; structures/materials,fluids/transport,0.5,2,computational fluid dynamics problem (3); structural problem (3),2,binary (4); real (2),1,symmetric (6),4350,153746,35.34390804597701,moderate (4–100×),237798,9080404,square only,6,0,all square matrices symmetric/Hermitian,6,6,2,real only,4,6,multi-domain/mixed,"Structural engineering matrices from Ed Rothberg, Silicon Graphics, Inc. - -Some of the matrix names and file names don't match. -This might be changed soon." -Shyy,2,"Computational fluid dynamics, viscous flow, Wei Shyy, Univ. Florida. Matrices: shyy161.rua, shyy41.rua",https://sparse.tamu.edu/Shyy,fluids/transport,fluids/transport,1.0,1,computational fluid dynamics problem (2),1,real (2),1,unsymmetric (2),4720,76480,16.203389830508474,moderate (4–100×),20042,329762,square only,2,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,2,coherent family/series,"Computational fluid dynamics, viscous flow, Wei Shyy, Univ. Florida. - -Matrices: shyy161.rua, shyy41.rua - -From: - -Prof. Wei Shyy -Aerospace Engineering, Mechanics & Engineering Science -231 Aerospace Building -P.O. Box 116250 -University of Florida -Gainesville, FL 32611-6250 -USA -email: wss :at the domain: tiger.aero.ufl.edu -phone: (904) 392-6416 - -Matrices are derived from a direct, fully-coupled method for solving -the Navier-Stokes equations for viscous flow calculations (in which the -pressure and velocity fields are coupled within the same linear system). -A body-fitted co-ordinate system is used. This method is less sensitive -to variations in parameters (such as the Reynolds number) than decoupled -methods, in which the pressure equation is artificially derived by combining -the momentum and continuity equations. - -Reference: -Braaten, M.E. and Shyy, W., ""Comparison of Iterative and Direct Solution -Methods for Viscous Flow Calculations in Body-Fitted Coordinates,"" -Inter. J. Numer. Meths. in Fluids, vol 6, pp. 325-349, 1986. - - -NOTE: these matrices give large errors and residuals when factorized. -They are linear systems from early on in the nonlinear iteration. A seemingly -poor solution to these linear systems is OK, since the solution still moves the -nonlinear iteration in the right direction. For example, Mups (multifrontal -code) gives the following for shyy41.rua (with a right-hand-side of b(i)=n/i): - - maxnorm (Ax-b) 2.2e+68 - maxnorm (A) 1.9e+02 - maxnorm (x) 2.9e+67 - maxnorm(b-Ax)/maxnorm(Ax) 3.9E-02 - - - -Minor change 3/31/03: ""rua"" changed to ""RUA"" in both files." -Simon,12,"Structural engineering, computational fluid dynamics. Horst Simon. Matrices from Horst Simon,",https://sparse.tamu.edu/Simon,fluids/transport; graph/network data; structures/materials,fluids/transport,0.5833333333333334,3,computational fluid dynamics problem (7); structural problem (4); directed weighted random graph (1),1,real (12),2,unsymmetric (10); symmetric (2),3242,62424,19.254780999383097,moderate (4–100×),130371,1853104,square only,12,0,mixed among square matrices,2,9,2,real only,0,11,multi-domain/mixed,"Structural engineering, computational fluid dynamics. Horst Simon. - -Matrices from Horst Simon, -NAS Systems Division -NASA Ames Research Center -Moffett Field, CA 94035 -USA - -email: simon :at the domain: nas.nasa.gov -phone: (415) 604-4322 - - -The following matrices have the same nonzero pattern: - 1) VENKAT01, VENKAT25, and VENKAT50 - 2) RAEFSKY1 and RAEFSKY2 - -Names changed (see ""fix"" script). - -Minor change 3/31/03: ""rua"" changed to ""RUA"" in bbmat.rua header." -TOKAMAK,4,"TOKAMAK test matrices from the SPARSKIT2 collection, Yousef Saad, Univ. MN. (saad :at the domain: cs.umn.edu)",https://sparse.tamu.edu/TOKAMAK,electromagnetics/acoustics,electromagnetics/acoustics,1.0,1,electromagnetics problem (4),1,real (4),1,unsymmetric (4),300,5940,19.8,moderate (4–100×),3155,83842,square only,4,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,4,coherent family/series,"TOKAMAK test matrices from the SPARSKIT2 collection, Yousef Saad, Univ. MN. - - (saad :at the domain: cs.umn.edu) - -Names changed (see ""fix"" script). Keys in first lines of -header fixed, also." -Vavasis,1,"Irregular finite-element matrix from Steve Vavasis, Cornell. (copy provided by Sherry Li).",https://sparse.tamu.edu/Vavasis,generic spatial PDE/thermal,generic spatial PDE/thermal,1.0,1,2D/3D problem (1),1,real (1),1,unsymmetric (1),41092,41092,1.0,one size,1683902,1683902,square only,1,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,1,single matrix,"Irregular finite-element matrix from Steve Vavasis, Cornell. - -(copy provided by Sherry Li). -Converted to Harwell/Boeing format by Tim Davis. - -This matrix has also been refered to as ""Vavasis3""." -Wang,6,"3D semiconductor device, 2D incompressible flow. S. Wang, Univ. New South Wales. The simulators solve the corresponding coupled nonlinear PDE",https://sparse.tamu.edu/Wang,electrical/circuit/device,electrical/circuit/device,1.0,1,semiconductor device problem (6),1,real (6),1,unsymmetric (6),2903,26068,8.979676197037547,moderate (4–100×),19093,177196,square only,6,0,no square matrix exactly symmetric/Hermitian,0,6,0,real only,0,6,coherent family/series,"3D semiconductor device, 2D incompressible flow. S. Wang, Univ. New South Wales. - -The simulators solve the corresponding coupled nonlinear PDE -system (i.e. the semiconductor device equations or the Navier-Stokes equations) -iteratively and in each nonlinear iteration unsymmetric sparse matrices -with the same sparse pattern are formed. - -The size of the matrices depends on the mesh used. Typical size (3d problems) -is 60,000. Presently the CGS is used. These represent smaller test matrices. - -The swang1.rua and swang2.rua matrices: ---------------------------------------- -They are from the discretisation of the vorticity equation based on -a arbitrary triangular mesh with 3489 mesh nodes. The problem -is a square driven cavity problem and the Reynolds number is 1000. -The discretisation used is an exponentially fitted finite volume -method. Reference: ``An exponentially fitted finite volume method -for the solution of incomopressible flow problems'' by Miller J.J.H -and Wang S. in Proceedings of the 4th international symposium -on computational fluid dynamics, vol.3, Davis, California, 1991 pp49-54. - -swang1.rua and swang2.rua have the same nonzero pattern. - -The wang1.rua and wang2.rua matrices: -------------------------------------- -These two files are from the discretisation of the electron continuity -equations of a 3d diode with piecewise doping function N = 10e16 in -n-region and -10e16 in p-region. The mesh is a non-uniform mesh with -14x14x16 (=3136) mesh nodes, and the forward bias is 1 volt. -Reference: ``An exponentially fitted finite element method for -a stationary convection-diffusion problem'' by Miller J.J.H. and Wang S. -in Computatioal methods for boundary and interior layers in -several dimensions (ed. Miller J.J.H.), Boole Press, Dublin, 1991 -pp 120-137. - -wang1.rua and wang2.rua have the same nonzero pattern. - -The wang3.rua matrix: ---------------------- -The same 3d diode semiconductor device problem as the wang1.rua and -wang2.rua matrices, but at a higher resolution. - -The wang4.rua matrix: ---------------------- -A MOSFET (metal on semiconductor field-effect device). The doping profile -is 1.0e19 in the two n-regions and 1.0e15 in the p-region (it is an -n-channel device). The applied biases are 0 on substrate and source -terminals, 0.2 on drain terminal and 5.0 on gate terminal. - -Meshes for the wang3.rua and wang4.rua problems: 30 X 30 X 30 uniform mesh is -used for both. The orders of the two linear systems are about 26,000 -(due to the Dirichlet boundaries). - -Reference for the discretisation scheme (wang3 and wang4): -either the previous reference or `An analysis of the Scharfetter-Gummel box -method for the stationary semiconductor device equations' by J.J.H. Miller -and S. Wang, RIORO Model. Math. Nal. Numer., vol. 28, No.2, 1994, pp123-140 - - -Dr. Song Wang -School of Mathematics -University of New South Wales -Sydney, 2052 -Australia -email: S.Wang :at the domain: unsw.edu.au - - -Minor change, 3/31/03: ""rua"" changed to ""RUA"" 4 of the matrices." -Zhao,2,"Reduced-order modeling, electromagnetics. A. Cangellaris and L. Zhao. Matrices from A. C. Cangellaris and Li Zhao, ""Reduced-order",https://sparse.tamu.edu/Zhao,electromagnetics/acoustics,electromagnetics/acoustics,1.0,1,electromagnetics problem (2),1,real (2),1,unsymmetric (2),33861,33861,1.0,one size,166453,166453,square only,2,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,2,coherent family/series,"Reduced-order modeling, electromagnetics. A. Cangellaris and L. Zhao. - -Matrices from A. C. Cangellaris and Li Zhao, ""Reduced-order -modeling of electromagnetic systems with Pade' via Lanczos -Approximations,"" Applied Computational Electromagnetics -Symposium, 1997 (ACES '97)." -Zitney,8,"Chemical engineering applications. S. Zitney, Cray Research. Matrices from Dr. Stephen E. Zitney",https://sparse.tamu.edu/Zitney,chemistry/biology,chemistry/biology,1.0,1,chemical process simulation problem (8),1,real (8),1,unsymmetric (8),1048,5308,5.064885496183206,moderate (4–100×),10965,94408,square only,8,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,0,coherent family/series,"Chemical engineering applications. S. Zitney, Cray Research. - - -Matrices from Dr. Stephen E. Zitney -Cray Research, Inc. -655-E Lone Oak Drive -Eagan, MN 55121-1560 USA -Email: sez :at the domain: cray.com -phone:(612) 683-3690 -fax:(612) 683-3099 - -Chemical engineering applications. The rdist* matrices are discussed in: - -Zitney, S. E., ""Sparse matrix methods for chemical process separation -calculations on supercomputers,"" in Proc. Supercomputing '92, pp. 414-423, -(Minneapolis, MN, November 16-20, 1992), IEEE Computer Society Press, Los -Alamitos, CA, 1992. - - -These matrices come from a nonlinear solver, so they represent a selection -from a sequence of matrices with similar values and pattern. - -Minor change: 3/31/03. ""rua"" changed to ""RUA"" in the headers of each matrix." -Cunningham,4,"Finite element matrices from Andrew Cunningham, Vibro-Acoustic Sciences, Inc.",https://sparse.tamu.edu/Cunningham,electromagnetics/acoustics,electromagnetics/acoustics,1.0,1,acoustics problem (4),1,real (4),2,symmetric (3); unsymmetric (1),11107,66127,5.953632844152336,moderate (4–100×),6639,1660579,square only,4,0,mixed among square matrices,3,4,1,real only,0,4,one broad domain; mixed matrix forms,"Finite element matrices from Andrew Cunningham, Vibro-Acoustic Sciences, Inc. -http://www.vasci.com - -qa8fk: Finite element stiffness matrix for a 3D acoustic problem. -qa8fm: Mass matrix for the above problem. -Both matrices are symmetric. - -The MATLAB version of qa8fk and qa8fm includes the 3D coordinates of -the nodes of the matrix as Problem.coord. They appear here as *.xyz.gz -files. See ../Pothen/README.txt for a description of the xyz file format. - -k3plates: Finite element mass matrix, 3 plates meeting at a line -m3plates: mass matrix for the above problem. - -Submitted to the UF sparse matrix collection in January, 2002." -Chen,14,"Finite element matrices from Pu Chen, Peking University. 14 symmetric positive-definite matrices (patterns only).",https://sparse.tamu.edu/Chen,structures/materials,structures/materials,1.0,1,structural problem (14),1,binary (14),1,symmetric (14),10800,151926,14.067222222222222,moderate (4–100×),810000,14836504,square only,14,0,all square matrices symmetric/Hermitian,14,14,0,real only,14,14,coherent family/series,"Finite element matrices from Pu Chen, Peking University. - -14 symmetric positive-definite matrices (patterns only). - -Except for pkustk07, pkustk08, and pkustk13, all problems -arose in civil engineering. There is more than 1 degree -of freedom at each node in the finite-element mesh. - -pkustk07 and pkustk08 are regular 3D meshes, with one side fixed -in the original FE models. - -Problem Description n nnz in lower triangular - part (incl. diagonal) -PKUSTK01 Beijing botanical exhibition hall 22,044 500,712 -PKUSTK02 Feiyue twin tower building 10,800 410,400 -PKUSTK03 Dalian group silo 63,336 1,596,876 -PKUSTK04 Yunsan Plaza 55,590 2,137,125 -PKUSTK05 Cofferdam (reduced model) 37,164 1,121,154 -PKUSTK06 Cofferdam (reduced model) 43,164 1,307,466 -PKUSTK07 Cubic 21 nodes solid, 10x10x10 mesh 16,860 1,217,832 -PKUSTK08 Cubic 21 nodes solid, 11x11x11 mesh 22,209 1,624,440 -PKUSTK09 Group silo 33,960 808,800 -PKUSTK10 4 tower silo 80,676 2,194,830 -PKUSTK11 Cofferdam (full size) 87,804 2,652,858 -PKUSTK12 Jijian Plaza, tall building 94,653 3,803,485 -PKUSTK13 Machine element, 21 nodes solid 94,893 3,355,860 -PKUSTK14 Tall building 151,926 7,494,215 - -Permanent address: - -Pu Chen, Dr.-Ing. -Dept of Mechanics and Engineering -Peking University -Beijing 100871, China -e-mail: chenpu :at the domain: pku.edu.cn - -Currently address: - -Pu Chen, Dr.-Ing. -Dept of Civil Engineering -The University of Akron -Akron, OH 44325-3905 -USA -e-mail: chenpu :at the domain: pku.edu.cn" -MathWorks,11,"Matrices contributed by The Mathworks, Inc. Some matrices come from other sources.",https://sparse.tamu.edu/MathWorks,graph/network data; imaging/graphics/robotics; mathematics/statistics; structures/materials,mathematics/statistics,0.6363636363636364,5,counter-example problem (6); structural problem (2); statistical/mathematical problem (1); directed graph (1); computer graphics/vision problem (1),2,real (10); binary (1),3,unsymmetric (7); rectangular (2); symmetric (2),102,12406,121.62745098039215,wide (>100×),306,340200,mixed,9,2,mixed among square matrices,2,4,2,real only,1,3,multi-domain/mixed,"Matrices contributed by The Mathworks, Inc. -Some matrices come from other sources. - -pivtol: A small matrix from the spline toolbox that illustrates the dramatic - growth factor that can occur with a pivot tolerance of 0.1 (in - UMFPACK v4.0). Needs a tolerance of 0.26, for off-diagonal pivoting. - -Pd: An unsymmetric matrix (x=P\\d) with many sparse right-hand-sides. The Pd.A - matrix is 8081-by-8081. Pd.b and Pd_rhs.A are the same 8081-by-12406 matrix - (the right-hand-sides). It is highly reducible to block triangular form, - with 7877 diagonal blocks, the largest of which is 30-by-30. Only 9 of - the blocks are bigger than 1-by-1. This is a good candidate for a solver - based on dmperm. - -Harvard500: Web connectivity matrix from Cleve Moler - (Numerical Computing with MATLAB, 2004). A(i,j) is nonzero if there - is a link from page j to page i. The URL of page j is in Problem.url{j} - in the MATLAB form of this problem. For more details, see Cleve Moler's - book at http://www.mathworks.com/moler . The matrix is 500-by-500, with - a structural rank of 233 and numerical rank of 170. - -tomography: An earth science problem from David Yang. Contributed by - Bobby Cheng. Much like a medical tomography problem. ""UMFPACK 4.0 - is consistently slower than MATLAB v5."" In MATLAB 7.1, - [L,U,P] = lu (A) takes 0.07 seconds (using GPLU), but UMFPACK v4.3 with - [L,U,P,Q] = lu (A) takes 0.11 seconds. - -Kuu, Muu: symmetric positive definite matrices which caused problems with - UMFPACK v4.0 (in MATLAB 6.5). chol, or UMFPACK v4.3 or later, work fine. - -QRpivot is a counter-example problem from The MathWorks, Pat Quillen - - This matrix was obtained from a MATLAB user. It illustrates the - limitations inherent in computing a basic solution to an under- - determined system without the use of column pivoting. - - With column pivoting (which can only be done in MATLAB with full - matrices), the problem is solved properly. - - When finding the min 2-norm solution (ignoring fill-in): - - [Q,R] = qr (A') ; - x = Q*(R'\\b) ; - - a good solution is found. To reduce fill-in: - - p = colamd (A') ; - [Q,R] = qr (A (p,:)') ; - x = Q*(R'\\b(p)) ; - - which also finds a good solution. - - However, x=A\\b computes a basic solution, using this algorithm: - - q = colamd (A) ; - [Q,R] = qr (A (:,q)) ; - x = R\\(Q'*b) ; - x (q) = q ; - - which finds an error with norm(A*x-b) of 1e-9 in MATLAB 7.6. - - With random permutations, and determining the cond(R1) of the leading - trianglar part (R is ""squeezed"" and the columns can be partitioned into - [R1 R2] where R1 is square and upper triangular) leads to the following - results. - - Note that the error is high when condest(R1) is high. Note in - particular the last trial. - - So this clinches the question. MATLAB's QR, and my new sparse QR, both - use a rank-detection method (by Heath) that does not do column pivoting, - and which is known to fail for some problems - for which Grimes & Lewis' - method will likely succeed. - - The advantage of my QR is that I now always return R as upper - trapezoidal, so if the user is concerned, he/she can easily check - condest(R(:,1:m)) if m < n. - - err 7.71e-07 condest R1 2.18e+12 - err 1.25e-09 condest R1 9.82e+08 - err 2.47e-09 condest R1 2.46e+11 - err 4.00e-09 condest R1 4.03e+09 - err 9.88e-10 condest R1 4.73e+09 - err 2.25e-08 condest R1 5.34e+09 - err 2.00e-08 condest R1 1.04e+09 - err 1.09e-09 condest R1 6.83e+08 - err 6.18e-08 condest R1 8.13e+10 - err 3.13e-10 condest R1 4.23e+09 - err 6.64e-10 condest R1 2.46e+10 - err 5.76e-09 condest R1 4.31e+09 - err 7.61e-07 condest R1 5.08e+10 - err 2.27e-09 condest R1 4.94e+09 - err 3.99e-10 condest R1 2.80e+09 - err 1.37e-09 condest R1 3.13e+09 - err 6.93e-05 condest R1 1.84e+14 - err 1.35e-08 condest R1 7.18e+09 - err 1.09e-08 condest R1 1.79e+11 - err 1.81e-09 condest R1 2.99e+08 - err 1.55e-01 condest R1 2.45e+18 - - In summary, this is a ""feature"" not a ""bug"". If you want a reliable - solution to an underdetermined system, find the min 2norm solution - via a QR factorization of A'. - -TS: a counter-example that triggers a bug in MATLAB R2009a. Fixed in R2009b." -Kim,2,"2D complex meshes, from Wonsik Kim, Korea.",https://sparse.tamu.edu/Kim,generic spatial PDE/thermal,generic spatial PDE/thermal,1.0,1,2D/3D problem (2),1,complex (2),1,unsymmetric (2),38415,456976,11.895769881556683,moderate (4–100×),933195,11330020,square only,2,0,no square matrix exactly symmetric/Hermitian,0,0,0,complex only,0,2,coherent family/series,"2D complex meshes, from Wonsik Kim, Korea." -Langemyr,1,"FEM structural mechanics matrix from Lars Langemyr, COMSOL. This is one of the few matrices for which a pivot tolerance of 0.1 (in UMFPACK",https://sparse.tamu.edu/Langemyr,structures/materials,structures/materials,1.0,1,structural problem (1),1,real (1),1,unsymmetric (1),1500,1500,1.0,one size,97645,97645,square only,1,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,1,single matrix,"FEM structural mechanics matrix from Lars Langemyr, COMSOL. - -This is one of the few matrices for which a pivot tolerance of 0.1 (in UMFPACK -V4.0) is not sufficient. An inaccurate solution is found with that tolerance. - -Penny Anderson, of The MathWorks, Inc, solved the problem in MATLAB 6.5 by -detecting a poor solution and automatically refactorizing with a pivot -tolerance of 1.0." -Pothen,22,"Structrual engineering matrices from NASA, collected by Alex Pothen. Each matrix has xy or xyz coordinates for each node (row/column) in the matrix.",https://sparse.tamu.edu/Pothen,structures/materials,structures/materials,1.0,1,structural problem (22),2,binary (12); real (10),1,symmetric (22),48,94069,1959.7708333333333,wide (>100×),306,460493,square only,22,0,all square matrices symmetric/Hermitian,22,22,10,real only,12,22,one broad domain; mixed matrix forms,"Structrual engineering matrices from NASA, collected by Alex Pothen. -Each matrix has xy or xyz coordinates for each node (row/column) in the matrix. - -These are all symmetric positive definite matrices. Some have only their -nonzero patterns supplied. The diagonal entries were not specified, so -for non-binary matrices they have been defined so that the diagonal A(i,i) -is equal to the sum of the absolute values of the off-diagonal entries in -row i, plus one. - -The xy or xyz coordinates for each matrix are in seperate files. -For example, for the bodyy4.rsa matrix, the coordinates are in bodyy4.xyz. -Row i of the bodyy4.xyz file lists the xy or xyz coordinates of node i. -2D problems list just 2 values per line, 3D problems have 3 values per line. - -Converted to Harwell/Boeing format by Tim Davis, January 2003." -Norris,12,"Finite element modelling of human body parts. Stuart Norris, Univ. Aukland. email: s.norris :at the domain: auckland.ac.nz",https://sparse.tamu.edu/Norris,fluids/transport; generic spatial PDE/thermal,generic spatial PDE/thermal,0.8333333333333334,2,2D/3D problem (10); computational fluid dynamics problem (2),1,real (12),2,unsymmetric (9); symmetric (3),1650,259156,157.06424242424242,wide (>100×),7419,8516500,square only,12,0,mixed among square matrices,3,6,3,real only,0,12,multi-domain/mixed,"Finite element modelling of human body parts. Stuart Norris, Univ. Aukland. - -email: s.norris :at the domain: auckland.ac.nz -Bioengineering Institute, University of Auckland -http://www.esc.auckland.ac.nz/People/Staff/Norris wk: +(64 9) 373 7599 x3055 - -Finite element matrices from bioengineering problems. - - -Matrices: -fv1 FV_100x100_works finite element, Laplace eqn. on a 2D mesh -fv2 FV_101x101_fails finite element, Laplace eqn. on a 2D mesh, causes - UMFPACK V4.0 with default pivot tolerance to fail. - Requires a larger tolerance. -fv3 FV_101x101_works finite element, Laplace eqn. on a 2D mesh - -lung1 Lung_1 Coupled temperature and water vapour transport in a lung -lung2 Lung_2 Ditto, with finer mesh resolution - -heart1 Heart_1 Quasi-static finite-element model of a heart -heart2 Heart_2 Quasi-static finite-element model of a heart -heart3 Heart_3 Quasi-static finite-element model of a heart - -stomach Stomach3D Electro-physiological model of a Duodenum - -torso1 Torso_FDandBE Coupled Finite difference/Boundary element - electro-physiological model of a torso (2D) - -torso2 Torso_FDin2D Finite difference electro-physiological model of - a torso (2D) -torso3 Torso_FDin3D Finite difference electro-physiological model of - a torso (3D)" -Shen,4,"Matrices from Kai Shen, UCSB (now at Univ. of Rochester, NY). Kai Shen",https://sparse.tamu.edu/Shen,generic spatial PDE/thermal,generic spatial PDE/thermal,1.0,1,2D/3D problem (4),1,real (4),1,unsymmetric (4),3432,18510,5.393356643356643,moderate (4–100×),25220,553562,square only,4,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,4,coherent family/series,"Matrices from Kai Shen, UCSB (now at Univ. of Rochester, NY). - -Kai Shen -Assistant Professor -Department of Computer Science -University of Rochester, Rochester, NY 14627 -Office: CSB 714 -Email: kshen :at the domain: cs.rochester.edu -585-275-5426(voice), 585-273-4556(fax) - - -Added to the UF sparse matrix collection Jan. 2003." -Pereyra,1,"From Victor Pereyra, Stanford University.",https://sparse.tamu.edu/Pereyra,other,other,1.0,1,least squares problem (1),1,real (1),1,rectangular (1),71952,71952,1.0,one size,1146848,1146848,rectangular only,0,1,N/A (rectangular only),0,0,0,real only,0,0,single matrix,"From Victor Pereyra, Stanford University." -vanHeukelum,13,"DNA electrophoresis model. Alexander van Heukelum, Utrect Univ, The Netherlands. Date: Wed, 5 Jun 2002 14:12:02 +0200 (MET DST)",https://sparse.tamu.edu/vanHeukelum,graph/network data,graph/network data,1.0,1,directed weighted graph (13),1,real (13),1,unsymmetric (13),5,5154859,1030971.8,wide (>100×),19,99199551,square only,13,0,no square matrix exactly symmetric/Hermitian,0,13,0,real only,0,0,coherent family/series,"DNA electrophoresis model. Alexander van Heukelum, Utrect Univ, The Netherlands. - -Date: Wed, 5 Jun 2002 14:12:02 +0200 (MET DST) -From: Alexander van Heukelum -To: davis :at the domain: cise.ufl.edu -Subject: Sparse matrix library - - --- Alexander van Heukelum -- Institute for Theoretical Physics -Room: Minnaert Building 307 Utrecht University -Phone: (+31) (30) 253 4622 Leuvenlaan 4 -Fax: (+31) (30) 253 5937 3584 CE Utrecht -Email: heukelum :at the domain: phys.uu.nl The Netherlands - - ------------------------------------------------------------------------ - -The cage model of DNA electrophoresis - -The cage model of DNA electrophoresis describes the drift, induced by a -constant electric field, of homogeneously charged polymers through a -gel. The gel forms an effective ""tube"", from which the polymers can only -escape by reptation, i.e., motion of stored length along the chain. The -polymer chain is modeled as a random walk on a cubic lattice, and stored -length is represented by two adjacent segments with opposite directions -(a kink). The dynamics consists of single-monomer moves: only -configurations which differ by the direction of a kink or the direction -of an end segment have a finite transition probability. - -The direction of the field is chosen in the direction of the main body -diagonal of the lattice, such that many polymer configurations become -equivalent. The matrices presented here describe the transition -probabilities between such equivalence-classes of the configurations, -for an applied field of E = (0.1, 0.1, 0.1) for polymers of L = 3 to 15 -monomers. - - name length rows columns nonzeros --------- -------- -------- -------- -------- - cage3 3 5 5 19 - cage4 4 9 9 49 - cage5 5 37 37 233 - cage6 6 93 93 785 - cage7 7 340 340 3084 - cage8 8 1015 1015 11003 - cage9 9 3534 3534 41594 - cage10 10 11397 11397 150645 - cage11 11 39082 39082 559722 - cage12 12 130228 130228 2032536 - cage13 13 445315 445315 7479343 - cage14 14 1505785 1505785 27130349 - cage15 15 5154859 5154859 99199551 - -For more information on the matrices, see A. van Heukelum, G. T. -Barkema, and R. H. Bisseling, ""DNA electrophoresis studied with -the cage model"", J. Comp. Phys. 180, 313-326 (2002), or -http://arxiv.org/format/cond-mat/0101467." -Hohn,6,"Single-material crack problem, Michael Hohn, Univ. of Utah. The included files contain the matrices and rhs vectors arising in",https://sparse.tamu.edu/Hohn,structures/materials,structures/materials,1.0,1,materials problem (6),1,real (6),1,unsymmetric (6),7500,16428,2.1904,narrow (≤4×),28462,948696,square only,6,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,6,coherent family/series,"Single-material crack problem, Michael Hohn, Univ. of Utah. - -The included files contain the matrices and rhs vectors arising in -two ways of discretizing a single-material crack problem. - -The *coord files are in zero-indexed coordinate format (row, column, value); -the *vec files contain - -- the size N - -- the entries from first to last - -The fd-* files come from a finite difference approximation; -the sinc-* files from a sinc-basis approximation. - -Both use the same logarithmic grid. - -The fd matrices are very sparse, and factor in a few seconds, so -they're not terribly interesting, but the sinc matrices are another matter... - -Larger matrices available on demand :) - --- Michael Hohn, hohn :at the domain: math.utah.edu" -Alemdar,1,"A finite-element matrix from Bulent Alemdar, RAM International.",https://sparse.tamu.edu/Alemdar,generic spatial PDE/thermal,generic spatial PDE/thermal,1.0,1,2D/3D problem (1),1,integer (1),1,symmetric (1),6245,6245,1.0,one size,42581,42581,square only,1,0,all square matrices symmetric/Hermitian,1,1,0,real only,0,1,single matrix,"A finite-element matrix from Bulent Alemdar, RAM International." -Andrews,1,"Large eigenvalue problem from Stuart Andrews, Brown University, Rhode Island. stu at cs.brown.edu",https://sparse.tamu.edu/Andrews,imaging/graphics/robotics,imaging/graphics/robotics,1.0,1,computer graphics/vision problem (1),1,integer (1),1,symmetric (1),60000,60000,1.0,one size,760154,760154,square only,1,0,all square matrices symmetric/Hermitian,1,1,1,real only,0,1,single matrix,"Large eigenvalue problem from Stuart Andrews, Brown University, Rhode Island. -stu at cs.brown.edu - -In MATLAB, the problem statement is: - - LL = load('LL.mat'); - L = LL.L; - opts.tol = 1e-9; - opts.issym=1; - opts.disp = 5; - [E,V] = eigs(L, 5, 'sm', opts); - - - - - - - - -Stuart Andrews -Brown University, Computer Science, Box 1910, Prov, RI, 02912 -stu at cs.brown.edu - - - - - - - -A large symmetric positive definite matrix, with seemingly random - sparsity pattern. LU (using UMFPACK v4.6) fails, because it uses AMD. - AMD obtains an ordering with nnz(L) of 117,039,940. METIS gets 36,595,274. - CHOLMOD's NESDIS gets 37,089,917. CHOLMOD can solve it with either the - METIS or NESDIS ordering, but not with AMD (x = cholmod(A,b) works if METIS - is installed, but not otherwise)." -FEMLAB,9,"Test matrices from FEMLAB, a finite-element method toolbox for MATLAB. developed by Comsol, Inc. See for more details.",https://sparse.tamu.edu/FEMLAB,electromagnetics/acoustics; fluids/transport; generic spatial PDE/thermal; structures/materials,fluids/transport,0.4444444444444444,4,computational fluid dynamics problem (4); structural problem (3); 2D/3D problem (1); electromagnetics problem (1),2,real (8); complex (1),1,unsymmetric (9),367,85623,233.30517711171663,wide (>100×),2417,3148656,square only,9,0,no square matrix exactly symmetric/Hermitian,0,5,0,real + complex,0,9,multi-domain/mixed,"Test matrices from FEMLAB, a finite-element method toolbox for MATLAB. -developed by Comsol, Inc. See http://www.femlab.com for more details." -Lucifora,2,"GSM cell traffic matrices from Salvatore Lucifora, Telecom Italia Mobile. Ing. Salvatore Lucifora",https://sparse.tamu.edu/Lucifora,graph/network data,graph/network data,1.0,1,directed weighted graph (2),1,real (2),1,unsymmetric (2),7055,7055,1.0,one size,30082,30082,square only,2,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,0,coherent family/series,"GSM cell traffic matrices from Salvatore Lucifora, Telecom Italia Mobile. - -Ing. Salvatore Lucifora -TIM R/SR-TI -via L. Rizzo, 22 -00136 Roma - -tel. +39 06 3900 9241 -fax. +39 06 3900 9315 -cell. +39 335 7534668 -email: slucifora :at the domain: -mail.tim.it - - -These 2 matrices come from a software tool that evaluates the correct -dimensioning for GSM cell with the coexistence of 2 kinds of traffic channels -(Full-Rate and Half-Rate: the latter requires only half bandwidth with -respect to the FR channel). The tool requires the solution of a sparse linear -system Ax=b. - -The cell1.rua and cell2.rua matrices have the same nonzero pattern, and -very similar values. The 1-norm of C1-C2 is only 0.002, compared with -a 1-norm of about 416 for both C1 and C2. MATLAB's condest reports a -condition number of 10^12 for C1 and 10^20 for C2." -ND,4,"ND problem set. 3D mesh problems. May 7, 2003.",https://sparse.tamu.edu/ND,generic spatial PDE/thermal,generic spatial PDE/thermal,1.0,1,2D/3D problem (4),1,real (4),1,symmetric (4),9000,72000,8.0,moderate (4–100×),3279690,28715634,square only,4,0,all square matrices symmetric/Hermitian,4,4,4,real only,0,4,coherent family/series,"ND problem set. 3D mesh problems. -May 7, 2003." -Schenk_AFE,16,"Sheet metal forming matrices, Olaf Schenk, Univ. of Basel. Univ. of Basel collection, Olaf Schenk (semiconductors, sheet metal forming",https://sparse.tamu.edu/Schenk_AFE,structures/materials,structures/materials,1.0,1,structural problem (16),1,real (16),1,symmetric (16),503625,1508065,2.9944204517249937,narrow (≤4×),17550675,52259885,square only,16,0,all square matrices symmetric/Hermitian,16,16,10,real only,0,16,coherent family/series,"Sheet metal forming matrices, Olaf Schenk, Univ. of Basel. - -Univ. of Basel collection, Olaf Schenk (semiconductors, sheet metal forming -problems, and non-linear optimization) - -http://www.computational.unibas.ch/computer_science/scicomp/matrices - -The following restriction applies (see the above web site): - -------------------------------------------------------------------------------- -Sparse Matrix Collection University of Basel -------------------------------------------------------------------------------- - -This page contains links to test data that we used to test our iterative and -direct parallel solvers projects. Within these projects, new parallel -algorithms and software for the solution of sparse systems of linear equations -will be developed. These include algorithms for direct solution, iterative -solution based on multilevel and domain decomposition methods, and for -preconditioning. - -THIS MATERIAL IS PROVIDED AS IS, -WITH ABSOLUTELY NO WARRANTY EXPRESSED OR IMPLIED. -ANY USE IS AT YOUR OWN RISK - -The user shall acknowledge the providers of this data in any publication of -material dependent upon the use of the data. The user shall use reasonable -endeavours to notify the providers of the data of this publication. Data -may be used for research purposes only. - -Dr. Olaf Schenk -Department of Computer Science -University of Basel -Klingelbergstrasse 50 -CH-4056 Basel -Switzerland - -Direct dial: +41 61 2671465 -Secretary: +41 61 2671460 -Fax: +41 61 2671461 - -E-Mail: olaf.schenk :at the domain: unibas.ch -URL: http://informatik.unibas.ch/personen/schenk_o.html - - -All matrices come with right-hand-sides. Many of the matrices have -many explicit zero entries. - -In the Schenk_IBMSDS directory: - - Provider: IBM Thomas Watson Research Center, Yorktown, USA, - Semiconductor Device Simulation Group - - Name Description Order No. entries - 2D_27628_bjtcai Semiconductor Device 27,628 442,898 - 2D_54019_highK Semiconductor Device 54,019 996,414 - 3D_28984_Tetra Semiconductor Device 28,984 599,170 - 3D_51448_3D Semiconductor Device 51,448 1,056,610 - ibm_matrix_2 Semiconductor Device 51,448 1,056,610 - matrix-new_3 Semiconductor Device 125,329 2,678,750 - matrix_9 Semiconductor Device 103,430 2,121,550 - -In the Schenk_ISEI directory: - - Provider: Integrated Systems Engineering Inc., San Jose, USA, - Semiconductor Device Simulation Group - - Name Description Order No. entries - barrier2-1 Semiconductor Device 113,076 3,805,068 - barrier2-2 Semiconductor Device 113,076 3,805,068 - barrier2-3 Semiconductor Device 113,076 3,805,068 - barrier2-4 Semiconductor Device 113,076 3,805,068 - barrier2-9 Semiconductor Device 115,625 3,897,557 - barrier2-10 Semiconductor Device 115,625 3,897,557 - barrier2-11 Semiconductor Device 115,625 3,897,557 - barrier2-12 Semiconductor Device 115,625 3,897,557 - igbt3 Semiconductor Device 10,938 234,006 - nmos3 Semiconductor Device 18,558 386,594 - ohne2 Semiconductor Device 181,343 11,063,545 - para-4 Semiconductor Device 153,226 5,326,228 - para-5 Semiconductor Device 155,924 5,416,358 - para-6 Semiconductor Device 155,924 5,416,358 - para-7 Semiconductor Device 155,924 5,416,358 - para-8 Semiconductor Device 155,924 5,416,358 - para-9 Semiconductor Device 155,924 5,416,358 - para-10 Semiconductor Device 155,924 5,416,358 - -In the Schenk_AFE directory: - - Provider:AutoForm Engineering GmbH, Zurich, Switzerland, Sheet Metal - Forming Simulation Group - - Name Description Order No. entries - AF_SHELL1 Indefinite 504,855 9,046,865 - AF_SHELL2 Indefinite 504,855 9,046,865 - AF_SHELL3 Positive Definite 504,855 9,046,865 - AF_SHELL4 Positive Definite 504,855 9,046,865 - AF_SHELL5 Indefinite 504,855 9,046,865 - AF_SHELL6 Indefinite 504,855 9,046,865 - AF_SHELL7 Positive Definite 504,855 9,046,865 - AF_SHELL8 Positive Definite 504,855 9,046,865 - AF_SHELL9 Indefinite 504,855 9,046,865 - -In the Schenk_IBMNA directory: - - Provider:IBM Thomas Watson Research Center, Yorktown, USA, - Numerical Analysis Group - - Name Description Order No. entries - C-62 Non-linear Optimization (IP-OPT) 41,731 300,537 - C-64 Non-linear Optimization (IP-OPT) 51,035 384,438 - C-66 Non-linear Optimization (IP-OPT) 49,989 274,498 - C-67 Non-linear Optimization (IP-OPT) 57,975 294,955 - C-73 Non-linear Optimization (IP-OPT) 169,422 724,348" -Schenk_IBMNA,52,"Non-linear optimization matrices, Olaf Schenk, Univ. of Basel. Univ. of Basel collection, Olaf Schenk (semiconductors, sheet metal forming",https://sparse.tamu.edu/Schenk_IBMNA,optimization/control,optimization/control,1.0,1,optimization problem (52),1,real (52),1,symmetric (52),2169,345241,159.17058552328263,wide (>100×),15145,2340859,square only,52,0,all square matrices symmetric/Hermitian,52,52,0,real only,0,0,coherent family/series,"Non-linear optimization matrices, Olaf Schenk, Univ. of Basel. - -Univ. of Basel collection, Olaf Schenk (semiconductors, sheet metal forming -problems, and non-linear optimization) - -http://www.computational.unibas.ch/computer_science/scicomp/matrices - -The following restriction applies (see the above web site): - -------------------------------------------------------------------------------- -Sparse Matrix Collection University of Basel -------------------------------------------------------------------------------- - -This page contains links to test data that we used to test our iterative and -direct parallel solvers projects. Within these projects, new parallel -algorithms and software for the solution of sparse systems of linear equations -will be developed. These include algorithms for direct solution, iterative -solution based on multilevel and domain decomposition methods, and for -preconditioning. - -THIS MATERIAL IS PROVIDED AS IS, -WITH ABSOLUTELY NO WARRANTY EXPRESSED OR IMPLIED. -ANY USE IS AT YOUR OWN RISK - -The user shall acknowledge the providers of this data in any publication of -material dependent upon the use of the data. The user shall use reasonable -endeavours to notify the providers of the data of this publication. Data -may be used for research purposes only. - -Dr. Olaf Schenk -Department of Computer Science -University of Basel -Klingelbergstrasse 50 -CH-4056 Basel -Switzerland - -Direct dial: +41 61 2671465 -Secretary: +41 61 2671460 -Fax: +41 61 2671461 - -E-Mail: olaf.schenk :at the domain: unibas.ch -URL: http://informatik.unibas.ch/personen/schenk_o.html - - -All matrices come with right-hand-sides. Many of the matrices have -many explicit zero entries. - -In the Schenk_IBMSDS directory: - - Provider: IBM Thomas Watson Research Center, Yorktown, USA, - Semiconductor Device Simulation Group - - Name Description Order No. entries - 2D_27628_bjtcai Semiconductor Device 27,628 442,898 - 2D_54019_highK Semiconductor Device 54,019 996,414 - 3D_28984_Tetra Semiconductor Device 28,984 599,170 - 3D_51448_3D Semiconductor Device 51,448 1,056,610 - ibm_matrix_2 Semiconductor Device 51,448 1,056,610 - matrix-new_3 Semiconductor Device 125,329 2,678,750 - matrix_9 Semiconductor Device 103,430 2,121,550 - -In the Schenk_ISEI directory: - - Provider: Integrated Systems Engineering Inc., San Jose, USA, - Semiconductor Device Simulation Group - - Name Description Order No. entries - barrier2-1 Semiconductor Device 113,076 3,805,068 - barrier2-2 Semiconductor Device 113,076 3,805,068 - barrier2-3 Semiconductor Device 113,076 3,805,068 - barrier2-4 Semiconductor Device 113,076 3,805,068 - barrier2-9 Semiconductor Device 115,625 3,897,557 - barrier2-10 Semiconductor Device 115,625 3,897,557 - barrier2-11 Semiconductor Device 115,625 3,897,557 - barrier2-12 Semiconductor Device 115,625 3,897,557 - igbt3 Semiconductor Device 10,938 234,006 - nmos3 Semiconductor Device 18,558 386,594 - ohne2 Semiconductor Device 181,343 11,063,545 - para-4 Semiconductor Device 153,226 5,326,228 - para-5 Semiconductor Device 155,924 5,416,358 - para-6 Semiconductor Device 155,924 5,416,358 - para-7 Semiconductor Device 155,924 5,416,358 - para-8 Semiconductor Device 155,924 5,416,358 - para-9 Semiconductor Device 155,924 5,416,358 - para-10 Semiconductor Device 155,924 5,416,358 - -In the Schenk_AFE directory: - - Provider:AutoForm Engineering GmbH, Zurich, Switzerland, Sheet Metal - Forming Simulation Group - - Name Description Order No. entries - AF_SHELL1 Indefinite 504,855 9,046,865 - AF_SHELL2 Indefinite 504,855 9,046,865 - AF_SHELL3 Positive Definite 504,855 9,046,865 - AF_SHELL4 Positive Definite 504,855 9,046,865 - AF_SHELL5 Indefinite 504,855 9,046,865 - AF_SHELL6 Indefinite 504,855 9,046,865 - AF_SHELL7 Positive Definite 504,855 9,046,865 - AF_SHELL8 Positive Definite 504,855 9,046,865 - AF_SHELL9 Indefinite 504,855 9,046,865 - -In the Schenk_IBMNA directory: - - Provider:IBM Thomas Watson Research Center, Yorktown, USA, - Numerical Analysis Group - - Name Description Order No. entries - C-62 Non-linear Optimization (IP-OPT) 41,731 300,537 - C-64 Non-linear Optimization (IP-OPT) 51,035 384,438 - C-66 Non-linear Optimization (IP-OPT) 49,989 274,498 - C-67 Non-linear Optimization (IP-OPT) 57,975 294,955 - C-73 Non-linear Optimization (IP-OPT) 169,422 724,348" -Schenk_IBMSDS,7,"Semiconductor device simulation matrices, Olaf Schenk, Univ. of Basel. Univ. of Basel collection, Olaf Schenk (semiconductors, sheet metal forming",https://sparse.tamu.edu/Schenk_IBMSDS,electrical/circuit/device,electrical/circuit/device,1.0,1,semiconductor device problem (7),1,real (7),1,unsymmetric (7),27628,125329,4.536303749819024,moderate (4–100×),206670,1205518,square only,7,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,7,coherent family/series,"Semiconductor device simulation matrices, Olaf Schenk, Univ. of Basel. - -Univ. of Basel collection, Olaf Schenk (semiconductors, sheet metal forming -problems, and non-linear optimization) - -http://www.computational.unibas.ch/computer_science/scicomp/matrices - -The following restriction applies (see the above web site): - -------------------------------------------------------------------------------- -Sparse Matrix Collection University of Basel -------------------------------------------------------------------------------- - -This page contains links to test data that we used to test our iterative and -direct parallel solvers projects. Within these projects, new parallel -algorithms and software for the solution of sparse systems of linear equations -will be developed. These include algorithms for direct solution, iterative -solution based on multilevel and domain decomposition methods, and for -preconditioning. - -THIS MATERIAL IS PROVIDED AS IS, -WITH ABSOLUTELY NO WARRANTY EXPRESSED OR IMPLIED. -ANY USE IS AT YOUR OWN RISK - -The user shall acknowledge the providers of this data in any publication of -material dependent upon the use of the data. The user shall use reasonable -endeavours to notify the providers of the data of this publication. Data -may be used for research purposes only. - -Dr. Olaf Schenk -Department of Computer Science -University of Basel -Klingelbergstrasse 50 -CH-4056 Basel -Switzerland - -Direct dial: +41 61 2671465 -Secretary: +41 61 2671460 -Fax: +41 61 2671461 - -E-Mail: olaf.schenk :at the domain: unibas.ch -URL: http://informatik.unibas.ch/personen/schenk_o.html - - -All matrices come with right-hand-sides. Many of the matrices have -many explicit zero entries. - -In the Schenk_IBMSDS directory: - - Provider: IBM Thomas Watson Research Center, Yorktown, USA, - Semiconductor Device Simulation Group - - Name Description Order No. entries - 2D_27628_bjtcai Semiconductor Device 27,628 442,898 - 2D_54019_highK Semiconductor Device 54,019 996,414 - 3D_28984_Tetra Semiconductor Device 28,984 599,170 - 3D_51448_3D Semiconductor Device 51,448 1,056,610 - ibm_matrix_2 Semiconductor Device 51,448 1,056,610 - matrix-new_3 Semiconductor Device 125,329 2,678,750 - matrix_9 Semiconductor Device 103,430 2,121,550 - -In the Schenk_ISEI directory: - - Provider: Integrated Systems Engineering Inc., San Jose, USA, - Semiconductor Device Simulation Group - - Name Description Order No. entries - barrier2-1 Semiconductor Device 113,076 3,805,068 - barrier2-2 Semiconductor Device 113,076 3,805,068 - barrier2-3 Semiconductor Device 113,076 3,805,068 - barrier2-4 Semiconductor Device 113,076 3,805,068 - barrier2-9 Semiconductor Device 115,625 3,897,557 - barrier2-10 Semiconductor Device 115,625 3,897,557 - barrier2-11 Semiconductor Device 115,625 3,897,557 - barrier2-12 Semiconductor Device 115,625 3,897,557 - igbt3 Semiconductor Device 10,938 234,006 - nmos3 Semiconductor Device 18,558 386,594 - ohne2 Semiconductor Device 181,343 11,063,545 - para-4 Semiconductor Device 153,226 5,326,228 - para-5 Semiconductor Device 155,924 5,416,358 - para-6 Semiconductor Device 155,924 5,416,358 - para-7 Semiconductor Device 155,924 5,416,358 - para-8 Semiconductor Device 155,924 5,416,358 - para-9 Semiconductor Device 155,924 5,416,358 - para-10 Semiconductor Device 155,924 5,416,358 - -In the Schenk_AFE directory: - - Provider:AutoForm Engineering GmbH, Zurich, Switzerland, Sheet Metal - Forming Simulation Group - - Name Description Order No. entries - AF_SHELL1 Indefinite 504,855 9,046,865 - AF_SHELL2 Indefinite 504,855 9,046,865 - AF_SHELL3 Positive Definite 504,855 9,046,865 - AF_SHELL4 Positive Definite 504,855 9,046,865 - AF_SHELL5 Indefinite 504,855 9,046,865 - AF_SHELL6 Indefinite 504,855 9,046,865 - AF_SHELL7 Positive Definite 504,855 9,046,865 - AF_SHELL8 Positive Definite 504,855 9,046,865 - AF_SHELL9 Indefinite 504,855 9,046,865 - -In the Schenk_IBMNA directory: - - Provider:IBM Thomas Watson Research Center, Yorktown, USA, - Numerical Analysis Group - - Name Description Order No. entries - C-62 Non-linear Optimization (IP-OPT) 41,731 300,537 - C-64 Non-linear Optimization (IP-OPT) 51,035 384,438 - C-66 Non-linear Optimization (IP-OPT) 49,989 274,498 - C-67 Non-linear Optimization (IP-OPT) 57,975 294,955 - C-73 Non-linear Optimization (IP-OPT) 169,422 724,348" -Schenk_ISEI,18,"Semiconductor device simulation matrices, Olaf Schenk, Univ. of Basel. Univ. of Basel collection, Olaf Schenk (semiconductors, sheet metal forming",https://sparse.tamu.edu/Schenk_ISEI,electrical/circuit/device,electrical/circuit/device,1.0,1,semiconductor device problem (18),1,real (18),1,unsymmetric (18),10938,181343,16.57917352349607,moderate (4–100×),130500,6869939,square only,18,0,no square matrix exactly symmetric/Hermitian,0,18,0,real only,0,18,coherent family/series,"Semiconductor device simulation matrices, Olaf Schenk, Univ. of Basel. - -Univ. of Basel collection, Olaf Schenk (semiconductors, sheet metal forming -problems, and non-linear optimization) - -http://www.computational.unibas.ch/computer_science/scicomp/matrices - -The following restriction applies (see the above web site): - -------------------------------------------------------------------------------- -Sparse Matrix Collection University of Basel -------------------------------------------------------------------------------- - -This page contains links to test data that we used to test our iterative and -direct parallel solvers projects. Within these projects, new parallel -algorithms and software for the solution of sparse systems of linear equations -will be developed. These include algorithms for direct solution, iterative -solution based on multilevel and domain decomposition methods, and for -preconditioning. - -THIS MATERIAL IS PROVIDED AS IS, -WITH ABSOLUTELY NO WARRANTY EXPRESSED OR IMPLIED. -ANY USE IS AT YOUR OWN RISK - -The user shall acknowledge the providers of this data in any publication of -material dependent upon the use of the data. The user shall use reasonable -endeavours to notify the providers of the data of this publication. Data -may be used for research purposes only. - -Dr. Olaf Schenk -Department of Computer Science -University of Basel -Klingelbergstrasse 50 -CH-4056 Basel -Switzerland - -Direct dial: +41 61 2671465 -Secretary: +41 61 2671460 -Fax: +41 61 2671461 - -E-Mail: olaf.schenk :at the domain: unibas.ch -URL: http://informatik.unibas.ch/personen/schenk_o.html - - -All matrices come with right-hand-sides. Many of the matrices have -many explicit zero entries. - -In the Schenk_IBMSDS directory: - - Provider: IBM Thomas Watson Research Center, Yorktown, USA, - Semiconductor Device Simulation Group - - Name Description Order No. entries - 2D_27628_bjtcai Semiconductor Device 27,628 442,898 - 2D_54019_highK Semiconductor Device 54,019 996,414 - 3D_28984_Tetra Semiconductor Device 28,984 599,170 - 3D_51448_3D Semiconductor Device 51,448 1,056,610 - ibm_matrix_2 Semiconductor Device 51,448 1,056,610 - matrix-new_3 Semiconductor Device 125,329 2,678,750 - matrix_9 Semiconductor Device 103,430 2,121,550 - -In the Schenk_ISEI directory: - - Provider: Integrated Systems Engineering Inc., San Jose, USA, - Semiconductor Device Simulation Group - - Name Description Order No. entries - barrier2-1 Semiconductor Device 113,076 3,805,068 - barrier2-2 Semiconductor Device 113,076 3,805,068 - barrier2-3 Semiconductor Device 113,076 3,805,068 - barrier2-4 Semiconductor Device 113,076 3,805,068 - barrier2-9 Semiconductor Device 115,625 3,897,557 - barrier2-10 Semiconductor Device 115,625 3,897,557 - barrier2-11 Semiconductor Device 115,625 3,897,557 - barrier2-12 Semiconductor Device 115,625 3,897,557 - igbt3 Semiconductor Device 10,938 234,006 - nmos3 Semiconductor Device 18,558 386,594 - ohne2 Semiconductor Device 181,343 11,063,545 - para-4 Semiconductor Device 153,226 5,326,228 - para-5 Semiconductor Device 155,924 5,416,358 - para-6 Semiconductor Device 155,924 5,416,358 - para-7 Semiconductor Device 155,924 5,416,358 - para-8 Semiconductor Device 155,924 5,416,358 - para-9 Semiconductor Device 155,924 5,416,358 - para-10 Semiconductor Device 155,924 5,416,358 - -In the Schenk_AFE directory: - - Provider:AutoForm Engineering GmbH, Zurich, Switzerland, Sheet Metal - Forming Simulation Group - - Name Description Order No. entries - AF_SHELL1 Indefinite 504,855 9,046,865 - AF_SHELL2 Indefinite 504,855 9,046,865 - AF_SHELL3 Positive Definite 504,855 9,046,865 - AF_SHELL4 Positive Definite 504,855 9,046,865 - AF_SHELL5 Indefinite 504,855 9,046,865 - AF_SHELL6 Indefinite 504,855 9,046,865 - AF_SHELL7 Positive Definite 504,855 9,046,865 - AF_SHELL8 Positive Definite 504,855 9,046,865 - AF_SHELL9 Indefinite 504,855 9,046,865 - -In the Schenk_IBMNA directory: - - Provider:IBM Thomas Watson Research Center, Yorktown, USA, - Numerical Analysis Group - - Name Description Order No. entries - C-62 Non-linear Optimization (IP-OPT) 41,731 300,537 - C-64 Non-linear Optimization (IP-OPT) 51,035 384,438 - C-66 Non-linear Optimization (IP-OPT) 49,989 274,498 - C-67 Non-linear Optimization (IP-OPT) 57,975 294,955 - C-73 Non-linear Optimization (IP-OPT) 169,422 724,348" -Kamvar,2,"Web matrices from Sep Kamvar, Stanford University. * Stanford Web Matrix:",https://sparse.tamu.edu/Kamvar,graph/network data,graph/network data,1.0,1,directed graph (2),1,binary (2),1,unsymmetric (2),281903,683446,2.424401301156781,narrow (≤4×),2312497,7583376,square only,2,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,2,0,coherent family/series,"Web matrices from Sep Kamvar, Stanford University. - -* Stanford Web Matrix: - 281903 pages, ~2.3 million links. From a September 2002 crawl. - -* Stanford-Berkeley Web Matrix: - 683446 pages, ~7.6 million links. From a December 2002 crawl. - -The data comes fro the Stanford WebBase project, -http://www-diglib.stanford.edu/~testbed/doc2/WebBase . - -The original data is posted at http://www.stanford.edu/~sdkamvar/research.html . -In the data posted there, one of the matrices is normalized, and the other is -not (but includes MATLAB code to produce the normalized matrix). Both have -some non-binary values in their connectivity, which represents pages with more -than one link to another specific page. The matrices posted here are purely -binary, and correspond to the matrix G discussed in Chapter 2 of -""Numerical Computing with MATLAB"" by Cleve Moler and Kathryn Moler, -http://www.mathworks.com/moler . - -In these matrices, if G(i,j)=1 then there is a link from page j to page i. -Thus, column j of G reflects the links that you can click on, if you are -visiting page j. - -A basic statement of the pagerank algorithm is given at -http://www.mathworks.com/moler/ncm/pagerank.m , but that algorithm is not -suited for these large problems. The methods posted at -http://www.mathworks.com/moler/ncm/pagerankpow.m or -http://www.stanford.edu/~sdkamvar/research.html -are more suitable for these problems. - -The related eigenvalue problem is to find the vector x such that x=A*x, -where A = (p*G*D + delta*e*e'), and: - - p = a scalar damping factor less than one (typically 0.85) - G = the binary connectivity matrix (Problem.A in the MATLAB .mat files, - or the .pua files). Don't confuse Problem.A with the matrix - A = (p*G*D + delta*e*e'). Problem.A is the matrix G, not A. - D = a diagonal matrix with D (i,i) equal to the sum of column i of G - delta = (1-p)/n - e = the column vector of all one's - -The urls of the Stanford_Berkeley matrix are given in a cell array, -Problem.colname. The url associate with column j is Problem.colname {j}. -Only a few of these are given, corresponding to root urls. -No urls are given for the Stanford matrix." -Sumner,1,"A computer graphics problem from Bob Sumner, PhD student at MIT (as of 2003). From sumner :at the domain: graphics.lcs.mit.edu Fri Aug 8 15:28:05 2003",https://sparse.tamu.edu/Sumner,imaging/graphics/robotics,imaging/graphics/robotics,1.0,1,computer graphics/vision problem (1),1,real (1),1,rectangular (1),29493,29493,1.0,one size,117954,117954,rectangular only,0,1,N/A (rectangular only),0,0,0,real only,0,1,single matrix,"A computer graphics problem from Bob Sumner, PhD student at MIT (as of 2003). - -From sumner :at the domain: graphics.lcs.mit.edu Fri Aug 8 15:28:05 2003 - -A computer graphics problem from Bob Sumner, PhD student at MIT. - -http://graphics.lcs.mit.edu/~sumner" -Tromble,1,Finite-state machine model for natural language processing. Roy Tromble. A matrix from Roy Tromble,https://sparse.tamu.edu/Tromble,graph/network data,graph/network data,1.0,1,directed weighted graph (1),1,real (1),1,unsymmetric (1),399130,399130,1.0,one size,1216334,1216334,square only,1,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,0,single matrix,"Finite-state machine model for natural language processing. Roy Tromble. - -A matrix from Roy Tromble -(royt :at the domain: cs.jhu.edu) -a PhD student in Computer Science -at Johns Hopkins University (in Jan 2003). For this matrix, UMFPACK v4.0 and -v4.1 perform an unusually large number of symbolic defragmentations in the -ordering / analysis phase. - -The matrix comes from finite-state machines for natural-language processing. -It has a zero-free diagonal, but a very unsymmetric nonzero pattern. -Factorizing A instead of A' may give better results." -Sanghavi,1,"ECL32, a semiconductor device simulation matrix. Jagesh Sanghavi, UC Berkeley. Obtained by Christof Voemel, Lawrence Berkeley National Lab.",https://sparse.tamu.edu/Sanghavi,electrical/circuit/device,electrical/circuit/device,1.0,1,semiconductor device problem (1),1,real (1),1,unsymmetric (1),51993,51993,1.0,one size,380415,380415,square only,1,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,1,single matrix,"ECL32, a semiconductor device simulation matrix. Jagesh Sanghavi, UC Berkeley. -Obtained by Christof Voemel, Lawrence Berkeley National Lab." -Sandia,192,Circuit simulation matrices from Sandia National Laboratory.,https://sparse.tamu.edu/Sandia,electrical/circuit/device,electrical/circuit/device,1.0,1,circuit simulation problem (192),1,real (192),1,unsymmetric (192),430,682862,1588.0511627906976,wide (>100×),1544,2638997,square only,192,0,no square matrix exactly symmetric/Hermitian,0,10,0,real only,0,0,coherent family/series,Circuit simulation matrices from Sandia National Laboratory. -IBM_Austin,1,"Circuit simulation matrix from Emrah Acar, IBM Austin. High degree of coupling. From Emrah Acar, IBM Research-Austin",https://sparse.tamu.edu/IBM_Austin,electrical/circuit/device,electrical/circuit/device,1.0,1,circuit simulation problem (1),1,real (1),1,unsymmetric (1),11341,11341,1.0,one size,97193,97193,square only,1,0,no square matrix exactly symmetric/Hermitian,0,1,0,real only,0,0,single matrix,"Circuit simulation matrix from Emrah Acar, IBM Austin. High degree of coupling. - -From Emrah Acar, IBM Research-Austin - -this matrix failed in umd4[umfpack4.0], ma28 takes forever, sparse1.3 -is slower than umd2, haven't tested in 4.3. The matrix is from transient -simulation of a timing path. The circuit contains a high level of coupling. - -For more info contact Emrah Acar, emrah (at sign) us dot ibm dotcom -IBM Austin Research Lab - -Results from Tim Davis: -On a 3.2Ghz Pentium 4, UMFPACK v4.0 takes 7.4 seconds (backslash in -MATLAB 6.5). UMFPACK v4.3 takes 0.20 seconds." -ACUSIM,1,"Computational fluid dynamics matrices from ACUSIM, Inc. from Farzin Shakib, ACUSIM, Inc.",https://sparse.tamu.edu/ACUSIM,fluids/transport,fluids/transport,1.0,1,computational fluid dynamics problem (1),1,real (1),1,symmetric (1),14822,14822,1.0,one size,715804,715804,square only,1,0,all square matrices symmetric/Hermitian,1,1,1,real only,0,1,single matrix,"Computational fluid dynamics matrices from ACUSIM, Inc. - -from Farzin Shakib, ACUSIM, Inc." -Rajat,32,Circuit simulation matrices from Rajat and Raj. From a company that develops commerical circuit simulation tools.,https://sparse.tamu.edu/Rajat,electrical/circuit/device,electrical/circuit/device,1.0,1,circuit simulation problem (32),2,real (25); binary (7),2,unsymmetric (26); symmetric (6),135,4690002,34740.75555555556,wide (>100×),665,20316253,square only,32,0,mixed among square matrices,6,13,0,real only,7,0,one broad domain; mixed matrix forms,"Circuit simulation matrices from Rajat and Raj. -From a company that develops commerical circuit simulation tools." -Hamrle,3,"Circuit simulation matrices from Jaroslav Hamrle, RIKEN, Japan. --------------------------------------------------------------------------------",https://sparse.tamu.edu/Hamrle,electrical/circuit/device,electrical/circuit/device,1.0,1,circuit simulation problem (3),1,real (3),1,unsymmetric (3),32,1447360,45230.0,wide (>100×),98,5514242,square only,3,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,0,coherent family/series,"Circuit simulation matrices from Jaroslav Hamrle, RIKEN, Japan. - --------------------------------------------------------------------------------- - -Date: Tue, 24 Feb 2004 17:33:31 +0900 -From: Jaroslav Hamrle <(first initial, then last name) at the domain - postman dot riken dot go - dot jp > -To: Cleve Moler <(last name) at mathworks dotcom> -Subject: Re: How to solve A\\y with large ( 60MB) sparse matrices ? - -Hello, - -the matrix is coming from solving of somewhat-like very large electrical -network. So, it is like a large graph, where matrix elements gives -connections between graph points. - -I'm sending you 'spy' of three matrices of this type. - -Maybe the most clear is the smallest 'spy', named Amatrix-small.pdf. -In this matrix, I changed colors depending on values: -red ..... +1 -green ... -1 -blue .... different from 1 and -1, usually number between 0.01 to 100 - -First two upper pars of the matrix given relation between values of nodes -inside the graph. More precisely, they express what is the relation between -current and voltage between both sides of one resistor. - -The first part treats resistors, which have special boundary conditions -(resistors whith connected only one end to the circuity). In this case, the -sub-matrices goes by on 'diagonal'. I used quotes here, because this -'diagonal' is not exactly diagonal, by goes in the way that skip of one -line is followed by skip of two raws. - -The second part treats relationship between both ends of the resistors. -In this part, the matrix 4x4 together with four adjective '-1' goes also -on 'diagonal'. Note that for large matrices, this second part is much -larger that the first one. - -The division between first two parts and second two part is exactly in the -half of the matrix. - -The thrid part of the matrix provide relationship, that the sum of the current -in the each electrical node is zero (sum(j_i))=0. Hence, each line of this -part of the matrix contains severals (in presented matrix 4x) '1' or '-1'. - -The fourth part of the matrix says that the voltage at each nod is the same -for all resistors ending/starting at given node. Hence, this part of matrix -contains in each line just once '1' and once '-1'. Hence, this part only -express that that some pairs of elements in resulting vector are identical. -As this part takes more that 1/4 of total matrix, maybe by some reordiring -it is possible to reduce significantly number of raws/lines. - - -In conclusion, the most of the matrix contain only '1' and '-1' elements. And -half of the lines (all third and fourt part) contains only '1' and '-1' -elements. More that 1/4 of lines contains just one pair of '1' and '-1'. - -Also, it is important to say, that the vector x (when solving A\\x) contains -mainly zeros. Only 1%-5% of the vector x is non-zero. (And these zeros -appears only in start of the x-vector, in part corresponding to the first -part of the matrix.) - -If you are interested, I can send you my matrix by ftp. - -I thank you very much for any help. - -Jaroslav Hamrle" -Oberwolfach,38,Oberwolfach model reduction benchmark collection ================================================================================,https://sparse.tamu.edu/Oberwolfach,eigenvalue/model reduction,eigenvalue/model reduction,1.0,1,model reduction problem (38),2,real (37); complex (1),2,symmetric (33); unsymmetric (5),14,986703,70478.78571428571,wide (>100×),46,47851783,square only,38,0,mixed among square matrices,32,37,13,real + complex,0,38,one broad domain; mixed matrix forms,"Oberwolfach model reduction benchmark collection - - -================================================================================ -==> bone010/README.txt <== -================================================================================ - --------------------------------------------------------------------------------- -Trabecular Bone Micro-Finite Element Models (bone* problems) --------------------------------------------------------------------------------- - -B. van Rietbergen, E. B. Rudnyi, J. G. Korvink - -Three-dimensional serial reconstruction techniques allow us to develop very -detailed micro-finite element (micro-FE) model of bones that can very -accurately represent the porous bone micro-architecture. Fig. 1 sketches the -micro finite element analysis [1]. Micro computed tomography (CT) is employed -to make 3D high-resolution images (~50 microns) of a bone. Then the 3D -reconstruction is directly transformed into an equally shaped micro finite -element model by simply converting all bone voxels to equally sized 8-node -brick elements. This results in finite element (FE) models with a very large -number of elements. Such models can be used, for example to study differences -in bone tissue loading between healthy and osteoporotic human bones during -quasi static loading [2]. - -Fig. 1: see http://www.imtek.de/simulation/benchmark/ - -Fig. 1. Micro finite element analysis. - -There is increasing evidence, however, that bone responds in particular to -dynamic loads [3]. It has been shown that the application of high-frequency, -very low magnitude strains to a bone can prevent bone loss due to osteoporosis -and can even result in increased bone strength in bones that are already -osteoporotic. In order to better understand this phenomenon, it is necessary to -determine the strain as sensed by the bone cells due to this loading. This -would be possible with the micro-FE analysis, but then such an analysis need to -be a dynamic one. - -The present benchmark presents 6 bone models varying in dimension from about -200 thousand to 12 millions equations with the goal to research on scalability -of model reduction software. Each model represent a second order system in the -form - -M d2x/dt2 + K x = B - -y = Cx - -where the matrices M and K are symmetric and positive definite. The goal of -model reduction is to speed up harmonic response analysis in the frequency -range 1-100 Hz. - -The matrix properties are given in Table 1 below. - -Table 1. - -Bone micro-finite -element models. - -BS01 BS10 B010 B025 B050 B120 -number of elements 20098 192539 278259 606253 1378782 3387547 -number of nodes 42508 305066 329001 719987 1644848 3989996 -number of DoFs 127224 914898 986703 2159661 4934244 11969688 -nnz in half M 1182804 9702186 12437739 27150810 61866069 151251738 -nnz in half K 3421188 28191660 36326514 79292769 180663963 441785526 - -Fig. 2 and 3 : see http://www.imtek.de/simulation/benchmark/ - -Fig. 2. Bone models for BS01 and BS10. - -It should be stressed that the first two models have been obtained differently -and they are much simpler to deal with than the last four. The connectivity in -the last four models is about four times higher. This can be seen by comparing -models BS10 and B010. Although models look similar by number of nonzeros in the -system matrices, the model B010 is much harder to solve: the number of nonzero -elements in the factor for model B010 is about four times more than for BS10. - -The method allows for the compact representation of the models, as the element -mass and stiffness matrices are the same for all elements. As a result, a file -describing the node indices for each element is enough to assemble the global -matrix. Each node has three degrees of freedom (UX, UY, UZ) and it contributes -three consecutive entries to the state vector. The node numbering is natural -from the first to the last. The assembly procedure as a pseudo-code is -presented below (indices start from one). It is assumed that the last 300 -degrees of freedom are fixed as zero Dirichlet boundary conditions. For -simplicity, the pseudo-code does not take into account that the matrix is -symmetric. - -The data file for each model contains the number of elements, nel, and the -number of nodes, nnod, in the first line and then nel lines with eight numbers -for node indices in each line. - -The input matrix contains a single column with B(1) = 1. The output matrix -takes first three components of the state vector, that is, three displacements -UX, UY and UZ for the first node. - -The archive assemble.tar.gz contains the element mass and stiffness matrices as -well as the sample code in C++ to assemble the dynamic system. The code can -write the dynamic system in the Matrix Market format or can be used as a hook -to transform the global matrices to an appropriate format. The gzipped data -files for element assembly as described above can be downloaded from Table 1. - -Model reduction for models BS010 and BS10 was performed in [4]. The -benchmarking of the parallel MUMPS direct solver [5] for the stiffness matrices -is described in [6]. References - -[1] B. van Rietbergen, H. Weinans, R. Huiskes, A. Odgaard, A new method to -determine trabecular bone elastic properties and loading using micromechanical -finite-elements models. J. Biomechanics, v. 28, N 1, p. 69-81, 1995. - -[2] B. van Rietbergen, R. Huiskes, F. Eckstein, P. Rueegsegger, Trabecular Bone -Tissue Strains in the Healthy and Osteoporotic Human Femur, J. Bone Mineral -Research, v. 18, N 10, p. 1781-1787, 2003. - -[3] L. E. Lanyon, C. T. Rubin, Static versus dynamic loads as an influence on -bone remodelling, Journal of Biomechanics, v. 17, p. 897-906, 1984. - -[4] E. B. Rudnyi, B. van Rietbergen, J. G. Korvink. Efficient Harmonic -Simulation of a Trabecular Bone Finite Element Model by means of Model -Reduction. 12th Workshop ""The Finite Element Method in Biomedical Engineering, -Biomechanics and Related Fields"", University of Ulm, 20-21 July 2005. -Proceedings of the 12th FEM Workshop, p. 61-68. ISBN: 3-9806183-8-2. - -[5] P. R. Amestoy and A. Guermouche and J.-Y. L'Excellent, S. Pralet, Hybrid -scheduling for the parallel solution of linear systems. Parallel Computing, v. -32, N 2, pp. 136-156 (2006). http://graal.ens-lyon.fr/MUMPS/ - -[6] E. B. Rudnyi, B. van Rietbergen, J. G. Korvink. Model Reduction for High -Dimensional Micro-FE Models. TAM'06, The Third HPC-Europa Transnational Access -Meeting, Barcelona, 14 - 16 June 2006. - -==> bone010/bone010.C.names <== -Node1_UX -Node1_UY -Node1_UZ -==> boneS01/boneS01.C.names <== -Node1_UX -Node1_UY -Node1_UZ -==> boneS10/boneS10.C.names <== -Node1_UX -Node1_UY -Node1_UZ - -================================================================================ -==> chipcool0/README.txt <== -================================================================================ - --------------------------------------------------------------------------------- -Convective Thermal Flow Problems (chipcool*, flowmeter* problems) --------------------------------------------------------------------------------- - -Christian Moosmann, moosmann@imtek.uni-freiburg.de Andreas Greiner, -greiner@imtek.uni-freiburg.de - -Many thermal problems require simulation of heat exchange between a solid body -and a fluid flow. The most elaborate approach to this problem is computational -fluid dynamics (CFD). However, CFD is computationally expensive. A popular -solution is to exclude the flow completely from the computational domain and to -use convection boundary conditions for the solid model. However, caution has to -be taken to select the film coefficient. - -An intermediate level is to include a flow region with a given velocity -profile, that adds convective transport to the model. Compared to convection -boundary conditions this approach has the advantage that the film coefficient -has not to be specified and that information about the heat profile in the flow -can be obtained. A drawback of the method is the greatly increased number of -elements needed to perform a physically valid simulation, because the solution -accuracy when employing upwind finite element schemes depends on the element -size. While this problem still is linear, due to the forced convection, the -conductivity matrix changes from a symmetric matrix to an un-symmetric one. So -this problem type can be used as a benchmark for problems containing -un-symmetric matrices. - -Fig. 1a. Fig. 1b. see http://www.imtek.de/simulation/benchmark - -Fig. 1. Convective heat flow examples: 2D anemometer model (left), 3D cooling -structure (right) - -Two different designs are tested: a 2D model of an anemometer-like structure -mainly consisting of a tube and a small heat source (Fig 1 left) [1]. The solid -model has been generated and meshed in ANSYS. Triangular PLANE55 elements have -been used for meshing and discretizing by the finite element method, resulting -in 19 282 elements and 9710 nodes. The second design is a 3D model of a chip -cooled by forced convection (Fig 1 right) [2]. In this case the tetrahedral -element type SOLID70 was used, resulting in 107 989 elements and 20542 nodes. -Since the implementation of the convective term in ANSYS does not allow for -definition of the fluid speed on a per element, but on a per region basis, the -flow profile has to be approximated by piece-wise step functions. The -approximation used for this benchmarks is shown in figure 1. - -The Dirichlet boundary conditions are applied to the original system. In both -models the reference temperature is set to 300 K, Dirichlet boundary conditions -as well as initial conditions are set to 0 with respect to the reference. The -specified Dirichlet boundary conditions are in both cases the inlet of the -fluid and the outer faces of the solids. Matrices are supplied for the -symmetric case (fluid speed is zero; no convection), and the unsymmetric case -(with forced convection). Table 1 shows the output nodes specified for the two -benchmarks, table 2 links the filenames according to the different cases. - -Matrices are in the Matrix Market format. The matrix name is used as an -extension of the matrix file. *.C.names contains a list of ouput names written -consecutively. The system matrices have been extracted from ANSYS models by -means of mor4ansys. - -Table 1: Output nodes for the two models - -Model Number Code Comment -Flow Meter 1 out1 outlet position - 2 out2 outlet position - 3 SenL left sensor position - 4 Heater within the heater - 5 SenR right sensor position -cooling Structure 1 out1 outlet position - 2 out2 outlet position - 3 out3 outlet position - 4 out4 outlet position - 5 Heater within the heater - -Table 2: Provided files - -Model fluid speed (m/s) name -Flow Meter - 0 flow_meter_model_v0.tgz renamed flowmeter0 - 0.5 flow_meter_model_v0.5.tgz renamed flowmeter5 -cooling Structure - 0 chip_cooling_model_v0.tgz renamed chipcool0 - 0.1 chip_cooling_model_v0.1.tgz renamed chipcool1 - - -Further information on the models can be found in [3] where model reduction by -means of the Arnoldi algorithm is also presented. Bibliography - -1 H. Ernst : High-Resolution Thermal Measurements in Fluids, PhD thesis, -University of Freiburg, Germany(2001). - -2 C. A. Harper : Electronic packaging and interconnection handbook, New York -McGraw- Hill, USA (1997) - -3 C. Moosmann, E. B. Rudnyi, A. Greiner, J. G. Korvink: Model Order Reduction -for Linear Convective Thermal Flow, Proceedings of 10th International Workshops -on THERMal INvestigations of ICs and Systems, THERMINIC2004, 29 Sept - 1 Oct , -2004, Sophia Antipolis, France. Preprint is at -http://www.imtek.uni-freiburg.de/simulation/mor4ansys/ - -==> chipcool0/chip_cooling_model_v0.C.names <== -out1 -out2 -out3 -out4 -Heater - -==> chipcool1/chip_cooling_model_v0.1.C.names <== -out1 -out2 -out3 -out4 -Heater - -==> flowmeter0/flow_meter_model_v0.C.names <== -out1 -out2 -SenL -Heater -SenR - -==> flowmeter5/flow_meter_model_v0.5.C.names <== -out1 -out2 -SenL -Heater -SenR - -================================================================================ -==> filter2D/README.txt <== -================================================================================ - --------------------------------------------------------------------------------- -Tunable Optical Filter (filter2D, filter3D problems) --------------------------------------------------------------------------------- - -Dennis Hohlfeld, hohlfeld@imtek.uni-freiburg.de Tamara Bechtold, -bechtold@imtek.uni-freiburg.de Hans Zappe, zappe@imtek.uni-freiburg.de - -The DFG project AFON (funded under grant ZA 276/2-1) aimed at the development -of an optical filter, which is tunable by thermal means. The thin-film filter -is configured as a membrane in order to improve thermal isolation. Fabrication -is based on silicon technology. Wavelength tuning is achieved through thermal -modulation of resonator optical thickness, using metal resistor deposited onto -the membrane. The devices features low power consumption, high tuning speed and -excellent optical performance [1]. - -Fig. 1. see http://www.imtek.de/simulation/benchmark - -Fig. 1. Tunable optical filter. - -The benchmark contains a simplified thermal model of a filter device. It helps -designers to consider important thermal issues, such as what electrical power -should be applied in order to reach the critical temperature at the membrane or -homogeneous temperature distribution over the membrane. The original model is -the heat transfer partial differential equation. - -There are two different benchmarks, 2D model and 3D model (see Table 1). Due to -modeling differences, their simulation results cannot be compared with each -other directly. - -Table 1: Tunable optical filter benchmarks -Code comment dimension nnz(A) nnz(E) -filter2D 2D, linear elements, PLANE55 1668 6209 1668 -filter3D 3D, linear elements, SOLID90 108373 1406808 1406791 - -The device solid models have been made, meshed and discretized in ANSYS 6.1 by -the finite element method. All material properties are considered as -temperature independent. Temperature is assumed to be in Celsius with the -initial state of 0 C. The Dirichlet boundary conditions of T = 0 C have been -applied at the bottom of the chip. - -The output nodes for the models are described in Table 2 and schematically -displayed in Fig 2. Output 1 is located at the very center of the membrane. By -simulating itÕs temperature one can prove what input power is needed to reach -the critical membrane temperature for each wavelength. Furthermore, the output -2 to 5 must be very close to output 1 (homogenous temperature distribution) in -order to provide the same optical properties across the complete diameter of -the laser beam. - -Table 2: Outputs for the optical filter model -Number Code Comment -1 Memb1 Membrane center -2 Memb2 Membrane node with radius 25E-6, theta 90 -3 Memb3 Membrane node with radius 50E-6 theta 90 -4 Memb4 Membrane node with radius 25E-6, theta 135 -5 Memb5 Membrane node with radius 50E-6 theta 135 - -Fig. 2. see http://www.imtek.de/simulation/benchmark - -Fig. 2. Schematical position of the chosen output nodes. - -The benchmark contains a constant load vector. The input function equal to 1 -corresponds to the constant input power of of 1 mW for 2D model and 10 mW for -3D model. The linear ordinary differential equations of the first order are -written as: - -E dT/dt = A T + B u y = C T - -where E and A are the symmetric sparse system matrices (heat capacity and heat -conductivity matrix), B is the load vector, C is the output matrix, and T is -the vector of unknown temperatures. - -The output of the transient simulation for node 1 over the rise time of the -device (0.25 s) for 3D model can be find in Filter3DTransResults. The results -can be used to compare the solution of a reduced model with the original one. -The time integration has been performed in ANSYS with accuracy of about 0.1 %. -The results are given as matrices where the first row is made of times, the -second of the temperatures. - -Download matrices in the Matrix Market format: filter2D.tar.gz, 106502 bytes; -filter3D.tar.gz, 37417415 bytes. The matrix name is used as an extension of the -matrix file. File *.C.names contains a list of ouput names written -consecutively. The system matrices have been extracted from ANSYS models by -means of mor4ansys. - -The discussion of electro-thermal modeling related to the benchmark can be -found in [2]. - -Bibliography - -1. D. Hohlfeld, H. Zappe, All-dielectric tunable optical filter based on the -thermo-optic effect, Journal of Optics A: Pure and Applied Optics, 6(6), 504- -511 (2003). - -2. T. Bechtold, Model Order Reduction of Electro-Thermal MEMS, PhD thesis, -University of Freiburg, Germany (In preparation). - -==> filter2D/filter2D.C.names <== -Memb1 -Memb2 -Memb3 -Memb4 -Memb5 - -==> filter3D/filter3D.C.names <== -Memb1 -Memb2 -Memb3 -Memb4 -Memb5 - -================================================================================ -==> gas_sensor/README.txt <== -================================================================================ - --------------------------------------------------------------------------------- -Microhotplate Gas sensor (gas_sensor problem) --------------------------------------------------------------------------------- - -Jürgen Hildenbrand, hildenbr@imtek.uni-freiburg.de Tamara Bechtold, -bechtold@imtek.uni-freiburg.de Jürgen Wöllenstein, woellen@ipm.fhg.de - -The goal of European project Glassgas (IST-99-19003) was to develop a novel -metal oxide low power microhotplate gas sensor [1]. In order to assure a robust -design and good thermal isolation of the membrane from the surrounding wafer, -the silicon microhotplate is supported by glass pillars emanating from a glass -cap above the silicon wafer, as shown in Fig 1. In present design, four -different sensitive layers can be deposited on the membrane. The thermal -management of a microhotplate gas sensor is of crucial importance. - -Fig. 1. see http://www.imtek.de/simulation/benchmark/ - -Fig. 1. Micromashined metal oxide gas sensor array; Bottom view (left), top -view (right). - -The benchmark contains a thermal model of a single gas sensor device with three -main components: a silicon rim, a silicon hotplate and glass structure [2]. It -allows us to simulate important thermal issues, such as the homogeneous -temperature distribution over gas sensitive regions or thermal decoupling -between the hotplate and the silicon rim. The original model is the heat -transfer partial differential equation. - -The device solid model has been made and then meshed and discretized in ANSYS -6.1 by means of the finite element method (SOLID70 elements were used). It -contains 68000 elements and 73955 nodes. Material properties were considered as -temperature independent. Temperature is assumed to be in degree Celsius with -the initial state of 0 C. The Dirichlet boundary conditions of T = 0 C is -applied at the top and bottom of the chip (at 7038 nodes). - -The output nodes are described in Table 1. In Fig 2 the red marked nodes are -positioned on the silicon rim. Their temperature should be close to the initial -temperature in the case of good thermal decoupling between the membrane and the -silicon rim. The black marked nodes are placed on the sensitive layers above -the heater and are numbered from left to right row by row, as schematically -shown in Fig 2. They allow us to prove whether the temperature distribution -over the gas sensitive layers is homogeneous (maximum difference of 10C is -allowed by design). - -Table 1: Outputs for the gas sensor model -Number Code Comment -1 aHeater within a heater, to be used for nonlinear input -2-7 SiRim1 to SiRim7 silicon rim -8-28 Memb1 to Memb21 gas sensitive layer - -Fig. 2. see http://www.imtek.de/simulation/benchmark/ - -Fig. 2. Masks disposition (left) and the schematical position of the chosen -output nodes (right). - -The benchmark contains a constant load vector. The input function equal to 1 -corresponds to the constant input power of 340 mW. One can insert a weak input -nonlinearity related to the dependence of heater's resistivity on temperature -given as: - -R(T)=R{0}(1 + alpha T) - -where alpha =1.469e-3 K^-1. To this end, one has to multiply the load vector by -a function: - -{U^2 274.94 (1 + alpha T)}/{0.34 (274.94 (1 + alpha T)+148.13)^2} - -where U is a desired constant voltage. The temperature in equation above should -be replaced by the temperature at the output 1. - -The linear ordinary differential equations of the first order are written as: - -E dT/dt = A T + B u y = C T - -where E and A are the symmetric sparse system matrices (heat capacity and heat -conductivity matrix), B is the load vector, C is the output matrix, and T is -the vector of unknown temperatures. The dimension of the system is 66917, the -number of nonzero elements in matrix E is 66917, in matrix A is 885141. - -The outputs of the transient simulation at output 18 (Memb11) over the rise -time of the device of 5 s for the original linear (with constant input power of -340 mW) and nonlinear (with constant voltage of 14 V) model are placed in files -LinearResults and NonlinearResults respectively. The results can be used to -compare the solution of a reduced model with the original one. The time -integration has been performed in ANSYS with accuracy of about 0.1 %. The -results are given as matrices where the first row is made of times, the second -of the temperatures. - -Download matrices in the Matrix Market format: GasSensor.tar.gz, 8407057 bytes. -The matrix name is used as an extension of the matrix file. File *.C.names -contains a list of ouput names written consecutively. The system matrices have -been extracted from ANSYS models by means of mor4ansys. - -The discussion of electro-thermal modeling related to the benchmark including -the nonlinear input function can be found in [3]. Bibliography - -1. J. Wöllenstein H. Böttner, J. A. Pláza, Carlos Carné, Y. Min, H. L. Tuller : -A novel single chip thin film metal oxide array, Sensors and Actuators B: -Chemical 93 (1-3) 350-355 (2003). - -2. J. Hildenbrand : Simulation and Characterisation of a Gas sensor and -Preparation for Model Order Reduction, Diploma Thesis, University of Freiburg, -Germany (2003). - -3. T. Bechtold, J. Hildenbrand, J. Wöllenstein, J. G. Korvink : Model Order -Reduction of 3D Electro-Thermal Model for a Novel, Micromachined Hotplate Gas -Sensor, Proceedings of 5th International conference on thermal and mechanical -simulation and experiments in microelectronics and microsystems, EUROSIME2004, -May 10-12, 2004, Brussels, Belgium, p. 263-267. Preprint is at -http://www.imtek.uni-freiburg.de/simulation/mor4ansys/ - -==> gas_sensor/GasSensor.C.names <== -aHeater -SiRim1 -SiRim2 -SiRim3 -SiRim4 -SiRim5 -SiRim6 -Memb1 -Memb2 -Memb3 -Memb4 -Memb5 -Memb6 -Memb7 -Memb8 -Memb9 -Memb10 -Memb11 -Memb12 -Memb13 -Memb14 -Memb15 -Memb16 -Memb17 -Memb18 -Memb19 -Memb20 -Memb21 - -================================================================================ -==> gyro/README.txt <== -================================================================================ - -Oberwolfach model reduction benchmark collection ------------------------------------------------- - -Contributions by Evgenii Rudnyi and Dag Billger. - -See also: - -http://www.imtek.de/simulation/benchmark -http://www.imtek.de/simulation/benchmark/wb/38847 -http://www.imtek.de/simulation/benchmark/wb/35889 - - --------------------------------------------------------------------------------- -IMEGO gyroscope --------------------------------------------------------------------------------- - -The Butterfly Gyro -Dag Billger, The Imego Institute, Arvid Hedvalls Backe 4, SE-411 33, -Goteborg, Sweden, Tel. +46 317 501 853, Fax. +46 317 501 801, -Email: dag dot billger at imego dotcom - -Figure 1: see http://www.imtek.de/simulation/benchmark/wb/35889/fig1.jpg - -Fig. 1. The Butterfly and micro-SIC mounted together. - -The Butterfly gyro is developed at the Imego Institute in an ongoing -project with Saab Bofors Dynamics AB. The Butterfly is a -vibrating micro-mechanical gyro that has sufficient theoretical -performance characteristics to make it a promising candidate for use in -inertial navigation applications. The goal of the current project is to -develop a micro unit for inertial navigation that can be commercialized -in the high-end segment of the rate sensor market. This project has -reached the final stage of a three-year phase where the development and -research efforts have ranged from model based signal processing, via -electronics packaging to design and prototype manufacturing of the -sensor element. The project has also included the manufacturing of an -ASIC, named micro-SIC, that has been especially designed for the -sensor (see Fig. 1). - -The gyro chip consists of a three-layer silicon wafer stack, in -which the middle layer contains the sensor element. The sensor consists -of two wing pairs that are connected to a common frame by a set of beam -elements (see Fig. 2 and 3); this is the reason the gyro is called the -Butterfly. Since the structure is manufactured using an anisotropic -wet-etch process, the connecting beams are slanted. This makes it -possible to keep all electrodes, both for capacitive excitation and -detection, confined to one layer beneath the two wing pairs. The -excitation electrodes are the smaller dashed areas shown in Fig. 2. The -detection electrodes correspond to the four larger ones. - -Figure 2: see http://www.imtek.de/simulation/benchmark/wb/35889/fig2.jpg - -Fig. 2. Schematic layout of the Butterfly design. - -By applying DC-biased AC-voltages to the four pairs of small -electrodes, the wings are forced to vibrate in anti-phase in the wafer -plane. This is the excitation mode. As the structure rotates -about the axis of sensitivity (see Fig. 2), each of the masses will be -affected by a Coriolis acceleration. This acceleration can be -represented as an inertial force that is applied at right angles with -the external angular velocity and the direction of motion of the mass. -The Coriolis force induces an anti-phase motion of the wings out of the -wafer plane. This is the detection mode. The external angular velocity -can be related to the amplitude of the detection mode, which is -measured via the large electrodes. - -When planning for and making decisions on future improvements of the -Butterfly, it is of importance to improve the efficiency of the gyro -simulations. Repeated analyses of the sensor structure have to be -conducted with respect to a number of important issues. Examples of -such are sensitivity to shock, linear and angular vibration -sensitivity, reaction to large rates and/or acceleration, different -types of excitation load cases and the effect of force-feedback. - -The use of model order reduction indeed decreases runtimes for -repeated simulations. Moreover, the reduction technique enables a -transformation of the FE representation of the gyro into a state space -equivalent formulation. This will prove helpful in testing the model -based Kalman signal processing algorithms that are being designed for -the Butterfly gyro. - -The structural model of the gyroscope has been done in ANSYS using -quadratic tetrahedral elements (SOLID187, see Fig. 3). The model shown -is a simplified one -with a coarse mesh as it is designed to test the model reduction -approaches. It includes the pure structural mechanics problem only. The -load vector is composed from time-varying nodal forces applied at -the centres of the excitation electrodes (see Fig. 2). The amplitude and -frequency of each force is equal to 0.055 micro-Newtons and 2384 Hz, -respectively. The Dirichlet boundary conditions have been applied to -all degree of freedom of the nodes -belonging to the top and bottom surfaces of the frame. The output nodes -are listed in Table 2 and correspond to the centres of the detection -electrodes (see Fig. 3). - -Figure 3: see http://www.imtek.de/simulation/benchmark/wb/35889/fig3.jpg - -Fig. 3. Finite element mesh of the gyro with a background photograph -of the gyro wafer pre-bonding. - -The structural model - -M d2x/dt2 + E dx/dt + K x = B -y = C x - -contains the mass M and stifness matrices K. The -damping matrix E can be modeled as E = alpha M + beta K, where the typical -values of alpha and beta are 0 and 1e-6 respectively. The nature of the -damping matrix is in reality more complex (squeeze film damping, thermo -elastic damping, etc.) but this simple approach has been chosen with respect -to the model reduction test. B is the load vector, C is the output matrix. - -The dynamic model has been converted to -(http://math.nist.gov/MatrixMarket/) Matrix Market format by -means of (http://www.imtek.de/simulation/mor4ansys/) mor4ansys. The -statistics for the matrices is shown in Table 1. - -Table 1. System matrices for the gyroscope. - - matrix m n nnz Is Symmetric? - M 17361 17361 178896 yes - K 17361 17361 519260 yes - B 17361 1 8 no - C 12 17361 12 no - -Table 2. Outputs for the Butterfly Gyro Model - - # Code Comment - - 1-3 det1m_Ux, det1m_Uy, det1m_Uz - Displacements of detection electrode 1, - (bottom left large electrode of Fig. 2) - - 4-6 det1p_Ux, det1p_Uy, det1p_Uz - Displacements of detection electrode 2, - (bottom right large electrode of Fig. 2) - - 7-9 det2m_Ux, det2m_Uy, det2m_Uz - Displacements of detection electrode 3, - (top left large electrode of Fig. 2) - - - 10-12 det2p_Ux, det2p_Uy, det2p_Uz - Displacements of detection electrode 4, - (top right large electrode of Fig. 2) - -(Note: the original matrices, in Matrix Market format, are at -http://www.imtek.de/simulation/benchmark). - -The model reduction of the gyroscope model by means of -(http://www.imtek.de/simulation/mor4ansys/) mor4ansys is described in Ref [1]. - -[1] Jan Lienemann, Dag Billger, Evgenii B. Rudnyi, Andreas Greiner, -and Jan G. Korvink, MEMS Compact Modeling Meets Model Order Reduction: -Examples of the Application of Arnoldi Methods to Microsystem Devices, -the Technical Proceedings of the 2004 Nanotechnology Conference and -Trade Show, Nanotech 2004, March 7-11, 2004, Boston, Massachusetts, USA - -Preprint is at http://www.imtek.uni-freiburg.de/simulation/mor4ansys/ - - - - -==> gyro/gyro.C.names <== -det1pUX -det1pUY -det1pUZ -det1mUX -det1mUY -det1mUZ -det2pUX -det2pUY -det2pUZ -det2mUX -det2mUY -det2mUZ - -================================================================================ -==> inlet/README.txt <== -================================================================================ - --------------------------------------------------------------------------------- -Active Control of a Supersonic Engine Inlet (Inlet problem) --------------------------------------------------------------------------------- - -Karen Willcox, Guillaume Lassaux, and David Gratton - -http://web.mit.edu/kwillcox/www/home.htm - -================================================================================ -==> LF10/README.txt <== -================================================================================ - --------------------------------------------------------------------------------- -Linear 1D Beam Models (LF* problems) --------------------------------------------------------------------------------- - -Jan Lienemann , Andreas Greiner , Jan G. Korvink Introduction - -Moving structures are an essential part for many microsystem devices, among -them fluidic components like pumps and electrically controllable valves, -sensing cantilevers, and optical structures. - -Several actuation principles can be employed on microscopic length scales, the -most frequent certainly the electromagnetic forces. While electrostatic -actuation falls behind at the macro scale, the effect of charged bodies -outperforms magnetic forces in the micro scale both in terms of performance and -fabrication expense. - -While the single component can easily be simulated on a usual desktop computer, -the calculation of a system of many coupled devices still presents a challenge. -This challenge is raised by the fact that many of these devices show a -nonlinear behavior. Especially for electrostatic structures, a further -difficulty is the large reach of the electrostatic forces, leading to a strong -spatial coupling of charges. - -Accurate modelling of such a system typically leads to high order models. The -tasks of simulation, analysis and controller design of high order nonlinear -control systems can be simplified by reducing the order of the original system -and approximate it by a lower order model. - -A application of electrostatic moving structures are e.g. RF switches or -filters. Given a simple enough shape, they often can be modelled as -one-dimensional beams embedded in two or three dimensional space. Model -description - -Setup of beam - -This model describes a slender beam with four degrees of freedom per node: - -x Axial displacement thetax Axial rotation y Flexural displacement thetaz -Flexural rotation - -Degree of Freedom x - -Degree of Freedom theta x - -Degrees of Freedom y and theta z - -The beam is supported either on the left side or on both sides. For the left -side (fixed) support, the force is applied on the rightmost node in y -direction, whereas for the support on both sides (simply supported), a node in -the middle is loaded. The damping matrix is calculated by a linear combination -of the mass matrix M and the stiffness matrix K. Benchmark examples - -Based on the finite element discretization presented in [1], an interactive -matrix generator has been created using Wolfram Research's webMathematica. It -is available online at -http://www.imtek.uni-freiburg.de/simulation/mstkmpkt/models/LinearBeam.html, -""Linear beam model"". However, models produced by this generator are in the DSIF -format, which allows for nonlinear terms. For the purpose of the benchmark -collection, we have precomputed four systems and converted them to the Matrix -market format which is easier to import in standard computer algebra packages. - -All examples are made for a steel beam with the following properties: Property -Value Beam length (l) 0.1 m Material density (rho) 8000 kg/m3 -Cross-sectional area (A) 7.854e-7 m2 Moment of inertia (I) 4.909e-14 m4 -Polar moment of inertia (J) 9.817e-14 Modulus of elasticity (E) 2e11 Pa -Poisson ratio (nu) 0.29 Contribution of M to damping 1e2 Contribution of K -to damping 1e-2 Support Simple, both sides - -The following examples are available (all files are compressed .zip archives, -Units: SI): - -File Degrees of freedom Number of nodes Number of equations -File size/Compressed size - -LF10.zip flexural (y and thetaz) 10 18 -LF10000.zip flexural (y and thetaz) 10000 19998 -LFAT5.zip flexural (y and thetaz), axial, torsional 5 14 -LFAT5000.zip flexural (y and thetaz), axial, torsional 50000 19994 - -The zip files contain matrices M, E, K, B and E for the following system of -equations: M x"" + E x' + K x = B u y = C x - -where B is a n×1 matrix and C is a 1×n matrix with the only nonzero entry at -the y DOF of the middle node. - -Details of the implementation are available in a separate report (PDF, 88 kB). -A typical input to this system is a step response; periodic on/off switching is -also possible. The reduced model should thus both represent the step response -as well as the possible influence of higher order harmonics. - -[1] William Weaver, Jr., Stephen P. Timoshenko, and Donovan H. Young, Vibration -problems in engineering, 5th ed., Wiley (1990) Last modified: Wed Sep 8 -16:53:01 CEST 2004 - -================================================================================ -==> piston/README.txt <== -================================================================================ - --------------------------------------------------------------------------------- -Axi symmetric infinite element model for circular piston (piston problem) --------------------------------------------------------------------------------- - -Karl Meerbergen - -Free Field Technologies, 16, place de l'Université, 1348 Louvain-la-Neuve, -Belgium - -This example is a model of the form - -\\begin{displaymath} M \\ddot{x} + E \\dot{x} + K x = f \\end{displaymath} - -with $M$, $C$, and $K$ non-symmetric matrices and $M$ singular. This is thus a -differential algebraic equation. It is shown that it has index one [1]. The -input of the system is $f$, the output is the state vector $x$. The motivation -for using model reduction for this type of problems is the reduction of the -computation time of a simulation. - -This is an example from an acoustic radiation problem discussed in [3]. -Consider a circular piston subtending a polar angle $0 < \\theta < \\theta_p$ on -a submerged massless and rigid sphere of radius $\\delta$. The piston vibrates -harmonically with a uniform radial acceleration. The surrounding acoustic -domain is unbounded and is characterized by its density $\\rho$ and sound speed -$c$. - -We denote by $p$ and $a_r$ the prescribed pressure and normal acceleration -respectively. In order to have a steady state solution $\\tilde p(r,\\theta,t)$ -verifying - -\\begin{displaymath} \\tilde p(r,\\theta,t) = \\mathcal R \\mbox{e} \\left( -p(r,\\theta) e^{i\\omega t} \\right), \\end{displaymath} - -the transient boundary condition is chosen as: - -\\begin{displaymath} a_r = \\left. \\frac{-1}{\\rho} \\frac{\\partial -p(r,\\theta)}{\\pa... ...e \\theta_p, \\\\ 0, & \\theta > \\theta_p.\\\\ -\\end{array}\\right. \\end{displaymath} - -The axisymmetric discrete finite-infinite element model relies on a mesh of -linear quadrangle finite elements for the inner domain (region between -spherical surfaces $r=\\delta$ and $r = 1.5 \\delta$). The numbers of divisions -along radial and circumferential directions are $5$ and $80$, respectively. The -outer domain relies on conjugated infinite elements of order $5$. For this -example we used $\\delta=1 \\mathrm{(m)}$, $\\rho = 1.225 \\mathrm{(kg/m^3)}$, -$c=340 \\mathrm{(m/s)}$, $a_0 = 0.001 \\mathrm{(m/s^2)}$ and $\\omega = 1000 -\\mathrm{(rad/s)}$. - -The matrices $K$, $C$, $M$ and the right-hand side $f$ are computed by -MSC.Actran [2]. The dimension of the second-order system is $N = 2025$. - - - -================================================================================ -==> rail_79841/README.txt <== -================================================================================ - --------------------------------------------------------------------------------- -A Semi-discretized Heat Transfer Problem for Optimal Cooling of Steel Profiles -(rail* problems) --------------------------------------------------------------------------------- - -Summary - -Several generalized state-space models arising from a semi-discretization of a -controlled heat transfer process for optimal cooling of steel profiles are -presented. The models oder differs due to different refinements applied to the -computational mesh. The model equations - -We consider the problem of optimal cooling of steel profiles. This problem -arises in a rolling mill when different steps in the production process require -different temperatures of the raw material. To achieve a high production rate, -economical interests suggest to reduce the temperature as fast as possible to -the required level before entering the next production phase. At the same time, -the cooling process, which is realized by spraying cooling fluids on the -surface, has to be controlled so that material properties, such as durability -or porosity, achieve given quality standards. Large gradients in the -temperature distributions of the steel profile may lead to unwanted -deformations, brittleness, loss of rigidity, and other undesirable material -properties. It is therefore the engineers goal to have a preferably even -temperature distribution. - -The scientific challenge here is to give the engineers a tool to pre-calculate -different control laws yielding different temperature distributions in order to -decide which cooling strategy to choose. - -We can only briefly introduce the model here for details we refer to [Saa03] or -[BS04]. We assume an infinitely long steel profile so that we may restrict -ourselves to a 2D model. Exploiting the symmetry of the workpiece, the -computational domain Ω⊂R2 is chosen as the half of a cross section of the rail -profile. The heat distribution is modeled by the instationary linear heat -equation on Ω - -(1) - -cρ ∂t x(t,ξ) - λΔx(t,ξ) = 0 in R>0 × Ω, -x(0,ξ) = x0(ξ) in Ω, -λ ∂νx(t,ξ) = gi on R>0× Γi, ∂Ω = ∪i Γi - -where x is the temperature distribution (∈H1([0,∞],X) with X:=H1(Ω) the state -space), c the specific heat capacity, λ the heat conductivity and ρ the density -of the rail profile. We split the boundary into several parts Γi on which we -have different boundary functions gi, allowing us to vary controls on different -parts of the surface. By ν we denote the outer normal on the boundary. - -Figure 1: Initial mesh, partitioning of the boundary, and a picture of a -cooling plant. Grobgitter Kühlbett -see http://www.imtek.de/simulation/benchmark/ - -We want to establish the control by a feedback law, i.e. we define the boundary -functions gi to be functions of the state x and the control ui, where -(ui)i=:u=Fy for a linear operator F which is chosen such that the cost -functional - - J(x_0,u):=∫0∞ (Qy,y)Y +(Ru,u)U dt with y=Cx (2) - -is minimized. Here, Q and R are linear selfadjoint operators on the output -space Y and the control space U with Q≥0, R>0 and C∈ L(X,Y). The variational -formulation of (1) with gi(t,ξ) = qi(ui- x(ξ,t)) leads to: - -(∂tx,v)=-∫Ω α∇x ∇v dξ + ∑k { qk uk ∫Γk (cρ)-1 v dσ -∫Γk qk(cρ)-1 xv dσ} (3) - -for all v∈C0∞(Ω). Here the uk are the exterior (cooling fluid) temperatures -used as the controls, qk are constant heat transfer coefficients (i.e. -parameters for the spraying intensity of the cooling nozzles)[2] and α:=λ/(cρ). -Note that q0=0 gives the Neumann isolation boundary condition on the artificial -inner boundary on the symmetry axis. In view of (3) we can apply a standrrd -Galerkin approach for discretizing the heat transfer model in space, resulting -a first-order ordinary differential equation. This idescribed in the following -section. The discretized mathematical model - -For the discretization we use the ALBERTA-1.2 fem-toolbox (see [SS00] for -details). We applied linear Lagrange elements and used a projection method for -the curved boundaries. The initial mesh (see Fig. 1. on the left) was produced -by MATLABs pdetool which implements a Delaunay triangulation algorithm. The -finer discretizations were produce by global mesh refinement using a bisection -refinement method. The discrete LQR problem is then: minimize (2) with respect -to - - (4) - - E d/dt x(t) = A x(t) + B u(t), with t>0, - x(0) = x0, - y(t) = C x(t). - -This benchmark includes four different mesh resolutions. The best -fem-approximation error that one can expect (under suitable smoothness -assumptions on the solution) is of order O(h2) where h is the maximum edge size -in the corresponding mesh. This order should be matched in a model reduction -approach. The following table lists some relevant quantities for the provided -models. - -matrix dimension num. nonzero in A num. nonzero in E max. mesh width -1357 8985 8997 5.5280e-2 -5177 35185 35241 2.7640e-2 -20209 139233 139473 1.3820e-2 -79841 553921 554913 6.9100e-3 - -Note that A is negative definite while E is positive definite, so that the -resulting linear time-invariant system is stable. - -The data sets are named rail_(problem dimension)_C60.(matrix name). Here C60 is -a specific output matrix which is defined to minimize the temperature in the -node numbered 60 (refer to the numbers given in Fig 1.) and keep temperature -gradients small. The latter task is taken into account by the inclusion of -temperature differences between specific points in the interior and reference -points on the boundary, e.g. temperature difference between nodes 83 and 34. -Again refer to Figure 1. for the nodes used. the definitions of other output -matrices that we tested can be found in [Saa03]. The problem resides at -temperatures of approximately 1000 degrees centigrade down to about 500-700 -degrees depending on calculation time. The state values are scaled to 1000 -being equivalent to 1.000. This results in a scaling of the time line with -factor 100, meaning that calculated times have to be divided by 100 to get the -real time in seconds. Acknowledgments - -This benchmark example serves as a model problem for the project A15: Efficient -numerical solution of optimal control problems for instationary -convection-diffusion-reaction-equations of the Sonderforschungsbereich SFB393 -Parallel Numerical Simulation for Physics and Continuum Mechanics, supported by -the Deutsche Forschungsgemeinschaft. It was motivated by the model described in -TU01. A very similar problem is used as model problem in the LYAPACK software -package Pen00b. Bibliography - -BS04 P. Benner and J. Saak. Efficient numerical solution of the LQR-problem -for the heat equation. Proc. Appl. Math. Mech., 2004. submitted. - -Pen00 T. Penzl. LYAPACK Users Guide. Technical Report SFB393/00-33, -Sonderforschungsbereich 393 Numerische Simulation auf massiv parallelen -Rechnern, TU Chemnitz, 09107 Chemnitz, FRG, 2000. Available from -http://www.tu-chemnitz.de/sfb393/sfb00pr.html . - -Saa03 J. Saak. Effiziente numerische Lösung eines Optimalsteuerungsproblems -für die Abkühlung von Stahlprofilen. Diplomarbeit, Fachbereich 3/Mathematik -und Informatik, Universität Bremen, D-28334 Bremen, September 2003. Available -from http://www-user.tu-chemnitz.de/∼saak/Data/index.html . - -SS00 A. Schmidt and K. Siebert. ALBERT: An adaptive hierarchical finite -element toolbox. Preprint 06/2000 / Institut für Angewandte Mathematik, -Albert-Ludwigs-Universität Freiburg, edition: albert-1.0 edition, 2000. -Available from http://www.mathematik.uni-freiburg.de/IAM/ALBERT/doc.html. - -TU01 F. Tröltzsch and A. Unger. Fast solution of optimal control problems in -the selective cooling of steel. Z. Angew. Math. Mech., 81:447-456, 2001. - - -================================================================================ -==> spiral/README.txt <== -================================================================================ - --------------------------------------------------------------------------------- -Spiral Inductor PEEC Model (spiral models) --------------------------------------------------------------------------------- - -Figure: Spiral inductor with part of overhanging copper plane; see -http://www.imtek.de/simulation/benchmark/ - -Excerpt from report ""PEEC model of a spiral inductor generated by -Fasthenry"" at http://www.imtek.de/simulation/benchmark/ : - -This inductor which first appeared in [KWW00] is intended as an integrated RF -passive inductor. To make it also a proximity sensor, a 0.1 um plane of copper -is added 45 um above the spiral. The spiral is also copper with turns 40 um -wide, 15um thick, with a separation of 40um. The spiral is suspended 55um over -the substrate by posts at the corners and centers of the turns in order to -reduce the capacitance to the substrate. (Note that neither the substrate nor -the capacitance is modeled in this example.) The overall extent of the -suspended turns is 1.58mm x 1.58mm. - -... - -The frequency dependence of the resistance shows two effects, first a rise due -to currents induced in the copper plane and then a much sharper rise due to the -skin effect. Capturing the rise due to the skin effect while also maintaining -the low frequency response is a challenge for many model reduction algorithms. - - -Authors: - -Jing-Rebecca Li, INRIA-Rocquencourt, Projet Ondes, Domaine de Voluceau - -Rocquencourt - B.P. 105, 78153 Le Chesnay Cedex, France jingrebecca.li at -inria.fr - -Mattan Kamon, Coventor, Inc. 625 Mt. Auburn St, Cambridge, Ma 02138, USA matt -at coventor.com. - -""E"" dx/dt = ""A""x+""B""u, y = ""B""^Tx. - -The complex impedence is - -Z(w) = Resis(w)+i*w*Induc(w) = (G(i*w))^(-1)=(B^T(-A+i*w*E)^(-1)B)^(-1) - -Download short matlab files from www.imtek.de to 1. plot Resis(w) and Induc(w) -2. perform a PRIMA reduction of order 50 3. produce symmetrized standard -state-space system: dx/dt = ""A_symm""x+""B_symm""u, y = ""B_symm""^Tx, where A_symm -is symmetric. - -Last modified: Wed Jul 6 17:38:31 CEST 2005 - -[KWW00] Mattan Kamon, Frank Wang, and Jacob White. Generating nearly optimally -compact models from krylov-subspace based reduced order models. IEEE -Transactions On Circuits and Systems-II:Analog and Digital Signal Processing, -47(4):239-248, April 2000. - -================================================================================ -==> t2dah/README.txt <== -================================================================================ - -Oberwolfach model reduction benchmark collection ------------------------------------------------- - -Contributions by Evgenii Rudnyi and Dag Billger. - -See also: - -http://www.imtek.de/simulation/benchmark -http://www.imtek.de/simulation/benchmark/wb/38847 -http://www.imtek.de/simulation/benchmark/wb/35889 - --------------------------------------------------------------------------------- -Micropyros Thruster --------------------------------------------------------------------------------- - -E. B. Rudnyi, rudnyi AT imtek dot uni-freiburg -dot de - -The goal of the European project Micropyros -(http://www.laas.fr/Micropyros/) -was to develop a microthruster array -shown in fig. 1. It is based on the co-integration of solid fuel with a -silicon micromachined system [1]. In addition to space applications, -the device can be also used for gas generation or as a highly-energetic -actuator. When the production of a bit-impulse is required, the fuel is -ignited by heating a resistor at the top of a particular microthruster. -Design requirements and modeling alternatives are described in Ref [2]. -The discussion of electro-thermal modeling related to the benchmark can -be found in Ref [3]. - -Figure 1: see http://www.imtek.de/simulation/benchmark/wb/38847/fig1.jpg - -Fig. 1. Firing a micro thruster in an 4x4 array. Illustration -courtesy of C. Rossi, LAAS-CNRS. - -The benchmark contains a simplified thermal model of a single -microthruster to help with a design problem to reach the ignition -temperature within the fuel and at the same time not to reach the -critical temperature at neighboring microthrusters, that is, at the -border of the computational domain. At the same time, the resistor -temperature during the heating pulse should not become too high as this -leads to the destruction of the membrane. -The benchmark suite has been made with the Micropyros software -developed by IMTEK -(http://www.imtek.uni-freiburg.de/simulation/pyros/). -There are four -different test cases described in Table 1 with the goal to cover -different cases of different computational complexity. Note that the -results -from different models -cannot be compared directly with each other as the output nodes are -located in slightly different geometrical positions and there is some -difference in modeling for the 3D and 2D-axisymmetric cases. - -Table 1. Microthruster benchmarks. - - Code comment dimension nnz(A) nnz(E) - T2DAL 2D-axisymmetric, linear elements 4257 20861 4257 - T2DAH 2D-axisymmetric, quadratic elements 11445 93781 93781 - T3DL 3D, linear elements 20360 265113 20360 - T3DH 3D, quadratic elements 79171 2215638 2215638 - -The device solid model has been made and meshed in ANSYS. The material -properties assumed to be constant. The system matrices have been -converted to the (http://math.nist.gov/MatrixMarket/) Matrix -Market format by means of mor4ansys. - -Temperature is assumed to be in Celsius with the initial state of 0 C. -The output nodes are described in Table 2. Nodes 2 to 5 show the fuel -temperature distribution and nodes 6 and 7 characterize temperature in -the wafer, nodes 5 and 7 being the most faraway from the resistor. - -Table 2. Outputs for the microthruster models. - - # Code Comment - 1 aHeater within the heater - 2 FuelTop fuel just below the heater - 3 FT-100 fuel 0.1 mm below the heater - 4 FT-200 fuel 0.2 mm below the heater - 5 FuelBot fuel bottom - 6 WafTop1 wafer top (touching fuel) - 7 WafTop2 wafer top (end of computational domain) - -The benchmark files contain a constant load vector, corresponding to -the constant power input of 150 mW. In order to insert a weak -nonlinearity related to the dependence of the resistivity on -temperature, one has to multiply the load vector by a function - -1 + 0.0009 T + 3E-07 T^2 - -assuming the constant current. It is necessary to replace the -temperature in the equation above by the temperature at the node 1. - -The linear ordinary differential equations of the first order are -written as - -E dT/dt = A T + B -y = C T - -where E and A are the system -matrices (both are symmetric), B is the load vector, C is the output -matrix, and T is the vector of unknown temperatures. - -(Note: the original matrices, in Matrix Market format, are at -http://www.imtek.de/simulation/benchmark). - -The ANSYS results for the original models as well as the reduced models -obtained by mor4ansys can be found at the -(http:///www.imtek/de/simulation/pyros/) Micropyros software page: -choose EleThermo for T2DAL and T2DAH or EleThermo3D for T3DL and T3DH. - -The model reduction of the microthruster model by means of -(http://www.imtek.ed/simulation/mor4ansys/) mor4ansys is described in Ref [4]. - -[1] C. Rossi, B. Larangot, T. Camps, M. Dumonteil, D. Lagrange, P. Q. -Pham, D. Briand, N. F. de Rooij, M. Puig-Vidal, P. Miribel, E. Montane, E. -Lopez, J. Samitier, E. B. Rudnyi, T. Bechtold, J. G. Korvink, Review of solid -propellant microthrusters on silicon, Journal Propulsion and Power -(2004), to be published. - -[2] E. B. Rudnyi, T. Bechtold, J. G. Korvink, C. Rossi, Solid -Propellant Microthruster: Theory of Operation and Modelling Strategy, -Nanotech 2002 - At the Edge of Revolution, September 9-12, 2002, -Houston, USA, AIAA Paper 2002-5755. - -[3] G. Korvink, E. B. Rudnyi, Keynote: Computer-aided engineering of -electro-thermal MST devices: moving from device to system simulation, -EUROSIME'03, 4th international conference on thermal and mechanical -simulation and experiments in micro-electronics and micro-systems -Aix-en-Provence, France, March 30 - April 2, 2003. - -[4] T. Bechtold, E. B. Rudnyi, J. G. Korvink and C. Rossi, Efficient -Modelling and Simulation of 3D Electro-Thermal Model for a -Pyrotechnical Microthruster. International Workshop on Micro and -Nanotechnology for Power Generation and Energy Conversion Applications -PowerMEMS 2003, Makuhari,Japan, 4-5 December 2003. - -Preprints are at http://www.imtek.uni-freiburg.de/simulation/mor4ansys/ - - -==> t2dah/T2DAH.C.names <== -aHeater -FuelTop -FT-100 -FT-200 -FuelBot -WafTop1 -WafTop2 - -==> t2dal/T2DAL.C.names <== -aHeater -FuelTop -FT-100 -FT-200 -FuelBot -WafTop1 -WafTop2 - -==> t3dh/T3DH.C.names <== -aHeater -FuelTop -FT-100 -FT-200 -FuelBot -WafTop1 -WafTop2 - -==> t3dl/T3DL.C.names <== -aHeater -FuelTop -FT-100 -FT-200 -FuelBot -WafTop1 -WafTop2 - -================================================================================ -==> t2dal_bci/README.txt <== -================================================================================ - --------------------------------------------------------------------------------- -Boundary Condition Independent Thermal Model (t2dal_bci_* problems) --------------------------------------------------------------------------------- - -E. B. Rudnyi, rudnyi@imtek.uni-freiburg.de J. G. Korvink, -korvink@imtek.uni-freiburg.de - -One of important requirements for a compact thermal model is that it should be -boundary condition independent. This means that a chip producer does not know -conditions under which the chip will be used and hence the chip compact thermal -model must allow an engineer to research on how the change in the environment -influences the chip temperature. The chip benchmarks representing boundary -condition independent requirements are described in [1]. - -Mathematically, the problem is that the thermal problem is modeled by the heat -transfer partial differential equation when the heat exchange through device -interfaces is modeled by convection boundary conditions. The latter contains -the film coefficient, h_i, to describe the heat exchange for the i-th -interface. After the discretization of both equations one obtains a system of -ordinary differential equations as follows - -E dT/dt = (A + Sum_i h_i A_i)T + B - -where E and A are the device system matrices, A_i is the diagonal matrix due to -the discretization of convection boudnary condition for the i-th interface, T -is the vector with unknown temperatures. - -In terms of the equation above, the engineering requirements read as follows. A -chip producer specifies the system matrices but the film coefficient, h_i, is -controlled later on by another engineer. As such, any reduced model to be -useful should preserve h_i in the symbolic form. This problem can be -mathematically expressed as parametric model reduction [2,3,4]. - -Unfortunately, the benchmark from [1] is not available in the computer readable -format. For research purposes, we have modified a microthruster benchmark [5] -(see Fig 1). In the context of the present work, the model is as a generic -example of a device with a single heat source when the generated heat -dissipates through the device to the surroundings. The exchange between -surrounding and the device is modeled by convection boundary conditions with -different film coefficients at the top, h_top, bottom, h_bottom, and the side, -h_side. From this vewpoint, it is quite similar to a chip model used as a -benchmark in [1]. The goal of parametric model reduction in this case is to -preserve h_top, h_bottom, and h_side in the reduced model in the symbolic form. - -Fig. 1. see http://www.imtek.de/simulation/benchmark - -Figure 1. A 2D-axisymmetrical model of the micro-thruster unit (not scaled). -The axis of the symmetry on the left side. A heater is shown by a red spot. - -We have used a 2D-axisymmetric microthruster model (T2DAL in [5]). The model -has been made in ANSYS and system matrices have been extracted by means of -mor4ansys [6]. The benchmark contains a constant load vector. The input -function equal to one corresponds to the constant input power of 15 mW. - -The linear ordinary differential equations of the first order are written as: - -E T=(A - h_top A_top - h_bottom A_bottom - h_side A_side) T + B u y=Cx - -where E and A are the symmetric sparse system matrices (heat capacity and heat -conductivity matrix), B is the load vector, C is the output matrix, A_top, -A_bottom, and A_side are the diagonal matrices from the discretization of the -convection boundary conditions and T is the vector of unknown temperatures. - -The numerical values of film coefficients, h_top, h_bottom, and h_side can be -from 1 to 1e9. Typical important sets film coefficients can be found in [1]. -The allowable approximation error is 5 % [1]. - -The benchmark has been used in [7,8] where the problem is also described in -more detail. - -Download matrices in the Matrix Market format: T2DAL_BCI.tar.gz, 223938 bytes. -The matrix name is used as an extension of the matrix file. File -T2DAL_BCI.C.names contains a list of ouput names written consecutively. -Bibliography - -1. C. J. M. Lasance, Two benchmarks to facilitate the study of compact thermal -modeling phenomena, IEEE Transactions on Components and Packaging Technologies, -24, 559-565 (2001). - -2. D. S. Weile, E. Michielssen, E. Grimme, K. Gallivan, A method for generating -rational interpolant reduced order models of two-parameter linear systems, -Applied Mathematics Letters, 12, 93-102 (1999). - -3. P. K. Gunupudi, R. Khazaka, M. S. Nakhla, T. Smy, and D. Celo, Passive -parameterized time-domain macromodels for high-speed transmission-line -networks, IEEE Transactions on Microwave Theory and Techniques, 51, 2347-2354 -(2003). - -4. L. Daniel, O. C. Siong, L. S. Chay, K. H. Lee, and J. White, A -Multiparameter Moment-Matching Model-Reduction Approach for Generating -Geometrically Parameterized Interconnect Performance Models, IIEEE Transactions -on Computer-Aided Design of Integrated Circuits and Systems, 23, 678-693 -(2004). - -5. E. B. Rudnyi, Micropyros Thruster, -http://www.imtek.uni-freiburg.de/simulation/benchmark/. - -6. E. B. Rudnyi and J. G. Korvink, Model Order Reduction of MEMS for Efficient -Computer Aided Design and System Simulation, MTNS2004, Sixteenth International -Symposium on Mathematical Theory of Networks and Systems, Katholieke -Universiteit Leuven, Belgium, July 5-9, 2004. -http://www.imtek.uni-freiburg.de/simulation/mor4ansys/ - -7. L. Feng, E. B. Rudnyi, J. G. Korvink, Parametric Model Reduction to Generate -Boundary Condition Independent Compact Thermal Model, THERMINIC 2004, 10th -International Workshop on Thermal Investigations of ICs and Systems, 29 -September - 1 October 2004, Sophia Antipolis, Cªte d'Azur, France. Preprint is -at http://www.imtek.uni-freiburg.de/simulation/mor4ansys/ - -8. L. Feng, E. B. Rudnyi, J. G. Korvink, Preserving the film coefficient as a -parameter in the compact thermal model for fast electro-thermal simulation, -2004, to be submitted. - -==> t2dal_bci/T2DAL_BCI.C.names <== -aHeater -FuelTop -FT-100 -FT-200 -FuelBot -WafTop1 -WafTop2 - -================================================================================ -==> windscreen/README.txt <== -================================================================================ - --------------------------------------------------------------------------------- -Structural model of a car windscreen (windscreen problem) --------------------------------------------------------------------------------- - -Karl Meerbergen Free Field Technologies, Louvain-la-Neuve, Belgium. -Karl.Meerbergen@fft.be - -This is an example for a model in the frequency domain of the form - -\\begin{eqnarray*} K_d x - \\omega^2 M x & = & f \\\\ y & = & f^* x \\end{eqnarray*} - -where $f$ represents a unit point load in one unknown of the state vector. $M$ -is a symmetric positive-definite matrix and $K_d = (1+i\\gamma) K$ where $K$ is -symmetric positive semi-definite. - -The test problem is a structural model of a car windscreen. This is a 3D -problem discretized with 7564 nodes and 5400 linear hexahedral elements (3 -layers of $60 \\times 30$ elements). The mesh is shown in Figure 1. The material -is glass with the following properties: the Young modulus is $7\\,10^{10} -\\mathrm{N}/\\mathrm{m}^2$, the density is $2490 \\mathrm{kg}/\\mathrm{m}^3$, and -the Poisson ratio is $0.23$. The natural damping is $10\\%$, i.e. $\\gamma=0.1$. -The structural boundaries are free (free-free boundary conditions). The -windscreen is subjected to a point force applied on a corner. The goal of the -model reduction is the fast evaluation of y. Modelreduction is used as a fast -linear solver for a sequence of parametrized linear systems. - -The discretized problem has dimension $n=22,692$. The goal is to estimate -$x(\\omega)$ for $\\omega\\in[0.5,200]$. In order to generate the plots the -frequency range was discretized as $\\{\\omega_1,\\ldots,\\omega_m\\} = -\\{0.5j,j=1,\\ldots,m\\}$ with $m=400$. - -Figure 1: Mesh of the car windscreen and frequency response function -see http://www.imtek.de/simulation/benchmark/ - -The archive contains files windscreen.K, windscreen.M and windscreen.B -representing Kd, M and f accordingly. - -Karl Meerbergen 2004-11-17" -LiuWenzhuo,1,"Power system simulation matrix from Wenzhou Liu, EPRI, China.",https://sparse.tamu.edu/LiuWenzhuo,electrical/circuit/device,electrical/circuit/device,1.0,1,power network problem (1),1,real (1),1,unsymmetric (1),15838,15838,1.0,one size,64424,64424,square only,1,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,0,single matrix,"Power system simulation matrix from Wenzhou Liu, EPRI, China." -Lin,1,"Large sparse eigenvalue problem from Chun-hung (Martin) Lin. NCHC, Taiwan. MartinLin AT nchc dot org dot tw. National Center for High-Performance",https://sparse.tamu.edu/Lin,structures/materials,structures/materials,1.0,1,structural problem (1),1,real (1),1,symmetric (1),256000,256000,1.0,one size,1766400,1766400,square only,1,0,all square matrices symmetric/Hermitian,1,1,0,real only,0,1,single matrix,"Large sparse eigenvalue problem from Chun-hung (Martin) Lin. NCHC, Taiwan. -MartinLin AT nchc dot org dot tw. National Center for High-Performance -Computing." -Cannizzo,1,Structural engineering matrix (finite-element) from Fabio Cannizzo.,https://sparse.tamu.edu/Cannizzo,structures/materials,structures/materials,1.0,1,structural problem (1),1,real (1),1,symmetric (1),4098,4098,1.0,one size,72356,72356,square only,1,0,all square matrices symmetric/Hermitian,1,1,1,real only,0,1,single matrix,Structural engineering matrix (finite-element) from Fabio Cannizzo. -GHS_indef,60,"Indefinite matrices collected by Nick Gould, Yifan Hu, and Jennifer Scott. The GHS_indef and GHS_psdef collection includes matrices from the Matrix",https://sparse.tamu.edu/GHS_indef,fluids/transport; generic spatial PDE/thermal; mathematics/statistics; optimization/control; structures/materials,optimization/control,0.5,6,optimization problem (30); 2D/3D problem (17); computational fluid dynamics problem (4); materials problem (4); structural problem (3); statistical/mathematical problem (2),2,real (57); integer (3),1,symmetric (60),3002,466316,155.33510992671552,wide (>100×),9000,11288630,square only,60,0,all square matrices symmetric/Hermitian,60,60,1,real only,0,28,multi-domain/mixed,"Indefinite matrices collected by Nick Gould, Yifan Hu, and Jennifer Scott. - -The GHS_indef and GHS_psdef collection includes matrices from the Matrix -Market (math.nist.gov/MatrixMarket), Rutherford-Boeing collection (Duff, -Grimes, and Lewis), the Parasol project (see below), Kumfert and Pothen, -CUTE/CUTEr optimization test set, the UF sparse matrix collection -(those are excluded here), Andy Wathen, Mario Arioli, Christian Damhaug, -Alison Ramage, Anshul Gupta (Gupta's matrices are in the UF collection -under the Gupta directory), Olaf Schenk (matrices from Olaf Schenk are -located here, as well as in the existing Schenk_* directories), -Miroslav Tuma, Maros and Meszaros, and the Dept. of Mathematical -Modelling in DIAMO (Czech Republic). - -See ftp://ftp.numerical.rl.ac.uk/pub/matrices/symmetric/ for the -GHS collection. - -NOTE: matrices can have upper-case or lower case matrix-type fields -(rsa or RSA, for example). - --------------------------------------------------------------------------------- - -bms3_2 matrix: -Source: www.parallab.uib.no/parasol/data.html (matrix BMW3_2) -Linear static analysis of a car body. Indefinite matrix. - (MSC-CARBODY-220K) -Order 227362 -No. entries 5757996 - --------------------------------------------------------------------------------- - -Matrices form Olaf Schenk: see the description in the Schenk_IBMNA -directory, for the c-55 to c-72 matrices. - --------------------------------------------------------------------------------- - -SIT100, K1_SAN, OLESNIKO, D_PRETOK, and TURON_M are from the Dept. of -Mathematical Modelling in DIAMO, s.e., Straz pod Ralskem, Czech Republic. -They represent fluid flow in porous media." -GHS_psdef,32,"Positive definite matrices collected by Nick Gould, Yifan Hu, & Jennifer Scott. some in unassembled finite-element form (and some in both forms).",https://sparse.tamu.edu/GHS_psdef,fluids/transport; generic spatial PDE/thermal; optimization/control; structures/materials,structures/materials,0.59375,4,structural problem (19); optimization problem (8); computational fluid dynamics problem (3); random 2D/3D problem (2),2,real (22); binary (10),1,symmetric (32),15449,952203,61.63525147258722,moderate (4–100×),101576,77651847,square only,32,0,all square matrices symmetric/Hermitian,32,32,22,real only,10,24,multi-domain/mixed,"Positive definite matrices collected by Nick Gould, Yifan Hu, & Jennifer Scott. -some in unassembled finite-element form (and some in both forms). - -The GHS_indef and GHS_psdef collection includes matrices from the Matrix -Market (math.nist.gov/MatrixMarket), Rutherford-Boeing collection (Duff, -Grimes, and Lewis), the Parasol project (see below), Kumfert and Pothen, -CUTE/CUTEr optimization test set, the UF sparse matrix collection -(those are excluded here), Andy Wathen, Mario Arioli, Christian Damhaug, -Alison Ramage, Anshul Gupta (Gupta's matrices are in the UF collection -under the Gupta directory), Olaf Schenk (matrices from Olaf Schenk are -located here, as well as in the existing Schenk_* directories), -Miroslav Tuma, Maros and Meszaros, and the Dept. of Mathematical -Modelling in DIAMO (Czech Republic). - -See ftp://ftp.numerical.rl.ac.uk/pub/matrices/symmetric/ for the -GHS collection. - -NOTE: some of the *.rse and *.pse finite-element matrices contain -unsorted indices. - -NOTE: matrices can have upper-case or lower case matrix-type fields -(rsa or RSA, for example). - --------------------------------------------------------------------------------- -==> audi.readme <== --------------------------------------------------------------------------------- -Source: www.parallab.uib.no/parasol/data.html (matrix AUDI) - -Automotive crankshaft model with over 900,000 TETRA - elements (MSC-CRANKSHAFT-900K) - -symmetric rb matrix rb.mtx 18129441 117962 4912222 13099257 -rsa 943695 943695 39297771 0 - -Order 943695 -No. entries 39297771 - --------------------------------------------------------------------------------- -==> bmw7st_1.readme <== --------------------------------------------------------------------------------- -Source: www.parallab.uib.no/parasol/data.html (matrix BMW7st_1) - -stiffness matrix BMW7ST_1 2540788 47116 1246836 1246836 -rsa 141347 141347 3740507 0 - - -Linear static analysis of a car body. - (MSC-CARBODY-140K) -Order 141347 -No. entries 3740507 - --------------------------------------------------------------------------------- -==> bmwcra_1.readme <== --------------------------------------------------------------------------------- -Source: www.parallab.uib.no/parasol/data.html (matrix BMWCRA_1) - -Automotive crankshaft model with nearly 150,000 TETRA - elements (MSC-CRANKSHAFT-150K) - -stiffness matrix BMWCRA_1 - 3647183 49591 1798796 1798796 -rsa 148770 148770 5396386 0 -(3I14) (3I14) (1P,3E25.16E3) - -Order 148770 -No. entries 5396386 - --------------------------------------------------------------------------------- -==> copter1.readme <== --------------------------------------------------------------------------------- -Source: Kumfert and Pothen - -require files -copter1.kp (which is obtained from copter1.ptr and copter1.chac) - -N = 17222 -NZ = 96921 (= off diag entries in upper triangle) -Off diag. pattern only supplied. - --------------------------------------------------------------------------------- -==> copter2.readme <== --------------------------------------------------------------------------------- -Source: Kumfert and Pothen - -require files -copter2.kp (which is obtained from copter2.ptr and copter2.chac) - -N = 55476 -NZ = 352238 (= off diag entries in upper triangle) -Off diag. pattern only supplied. - --------------------------------------------------------------------------------- -==> crankseg_1.readme <== --------------------------------------------------------------------------------- -Source: www.parallab.uib.no/parasol/data.html (matrix CRANKSG1) - -Linear static analysis of a crankshaft detail - (MSC-CRANKSHAFT-SEGMENT-1) - -Order 52,804 -No. entries 5,333,507 - --------------------------------------------------------------------------------- -==> crankseg_2.readme <== --------------------------------------------------------------------------------- -Source: www.parallab.uib.no/parasol/data.html (matrix CRANKSG2 - -Linear static analysis of a crankshaft detail - (MSC-CRANKSHAFT-SEGMENT-2) - -Order 52,804 -No. entries 7,106,348 - - --------------------------------------------------------------------------------- -==> Fcondp2.readme <== --------------------------------------------------------------------------------- - -Source: Christain Damahug (Det Norske Veritas, Oslo, Norway) - - - Matrix Format Converter - ----------------------- - - Total number of nodes ................................... : 33913 - Number of active nodes .................................. : 33913 - Number of external nodes ................................ : 0 - Number of nodes with at least one boundary condition .... : 552 - Number of nodes with at least one load intensity ........ : 0 - Number of free variables (equations) .................... : 201822 - - Number of elements ...................................... : 35836 - Number of material types ................................ : 1 - Maximum number of nodes connected to an element ......... : 4 - - Size of complete element node connectivity .............. : 143344 - Size of stripped element node connectivity .............. : 143344 - Size of active adjacency set representation ............. : 282912 - TIME FOR WRITING MATRIX FORMAT = 7.7391695976E+00 - - - = = = = = = = = = = = = = = = = = = = = = = = = = - = = = Storage Requirement Matrix Convertion = = = - = = = = = = = = = = = = = = = = = = = = = = = = = - - Total INTEGER workspace need (runtime measure) .......... : 1173274 - Total REAL workspace need (runtime measure) ............. : 0 - - Total CPU-time consumption to convert the matrix ........ : 8.89181E+00 - - Wall-Clock Time Consumption ............................. : 1.70000E+01 - --------------------------------------------------------------------------------- -==> finance256.readme <== --------------------------------------------------------------------------------- -Source: Kumfert and Pothen - -require files -finance256.kp (which is obtained from finance256.ptr and finance256.chac) - -N = 37376 -NZ = 130560 (= off diag entries in upper triangle) -Off diag. pattern only supplied. - --------------------------------------------------------------------------------- -==> ford1.readme <== --------------------------------------------------------------------------------- -Source: Kumfert and Pothen - -require files -ford1.kp (which is obtained from ford1.ptr and ford1.chac) - -N = 18728 -NZ = 41424 (= off diag entries in upper triangle) -Off diag. pattern only supplied. - --------------------------------------------------------------------------------- -==> ford2.readme <== --------------------------------------------------------------------------------- -Source: Kumfert and Pothen - -require files -ford2.kp (which is obtained from ford2.ptr and ford2.chac) - -N = 100196 -NZ = 222246 (= off diag entries in upper triangle) -Off diag. pattern only supplied. - --------------------------------------------------------------------------------- -==> Fullb.readme <== --------------------------------------------------------------------------------- - -Source: Christain Damahug (Det Norske Veritas, Oslo, Norway) - - - Matrix Format Converter - ----------------------- - - Total number of nodes ................................... : 33442 - Number of active nodes .................................. : 33442 - Number of external nodes ................................ : 0 - Number of nodes with at least one boundary condition .... : 586 - Number of nodes with at least one load intensity ........ : 0 - Number of free variables (equations) .................... : 199187 - - Number of elements ...................................... : 59738 - Number of material types ................................ : 3 - Maximum number of nodes connected to an element ......... : 4 - - Size of complete element node connectivity .............. : 195585 - Size of stripped element node connectivity .............. : 195585 - Size of active adjacency set representation ............. : 294498 - TIME FOR WRITING MATRIX FORMAT = 1.0402039528E+01 - - - = = = = = = = = = = = = = = = = = = = = = = = = = - = = = Storage Requirement Matrix Convertion = = = - = = = = = = = = = = = = = = = = = = = = = = = = = - - Total INTEGER workspace need (runtime measure) .......... : 1584759 - Total REAL workspace need (runtime measure) ............. : 0 - - Total CPU-time consumption to convert the matrix ........ : 1.18173E+01 - - Wall-Clock Time Consumption ............................. : 2.90000E+01 - --------------------------------------------------------------------------------- -==> Halfb.readme <== --------------------------------------------------------------------------------- - - -Source: Christain Damahug (Det Norske Veritas, Oslo, Norway) - - - Matrix Format Converter - ----------------------- - - Total number of nodes ................................... : 38556 - Number of active nodes .................................. : 38556 - Number of external nodes ................................ : 0 - Number of nodes with at least one boundary condition .... : 2470 - Number of nodes with at least one load intensity ........ : 0 - Number of free variables (equations) .................... : 224617 - - Number of elements ...................................... : 70211 - Number of material types ................................ : 3 - Maximum number of nodes connected to an element ......... : 4 - - Size of complete element node connectivity .............. : 221764 - Size of stripped element node connectivity .............. : 221764 - Size of active adjacency set representation ............. : 321406 - TIME FOR WRITING MATRIX FORMAT = 1.1791002274E+01 - - - = = = = = = = = = = = = = = = = = = = = = = = = = - = = = Storage Requirement Matrix Convertion = = = - = = = = = = = = = = = = = = = = = = = = = = = = = - - Total INTEGER workspace need (runtime measure) .......... : 1774320 - Total REAL workspace need (runtime measure) ............. : 0 - - Total CPU-time consumption to convert the matrix ........ : 1.33733E+01 - - Wall-Clock Time Consumption ............................. : 4.20000E+01 - --------------------------------------------------------------------------------- -==> hood.readme <== --------------------------------------------------------------------------------- - -Source: www.parallab.uib.no/parasol/data.html (matrix HOOD) - - INDEED Test Matrix (DC-mh) DCMH 1945127 22055 549449 1373623 -RSA 220542 220542 5494489 0 - - -Order 220542 -No. entries 5494489 - --------------------------------------------------------------------------------- -==> inline_1.readme <== --------------------------------------------------------------------------------- -Source: www.parallab.uib.no/parasol/data.html (INLINE_1) - -Inline skater (MSC-INLINE) - -stiffness matrix INLINE_1 12607923 167905 6220009 6220009 -rsa 503712 503712 18660027 0 -(3I14) (3I14) (1P,3E25.16E3) - -Order 503,712 -No. entries 18,660,027 - --------------------------------------------------------------------------------- -==> ldoor.readme <== --------------------------------------------------------------------------------- -Source: www.parallab.uib.no/parasol/data.html (LDOOR) - -Large door - - INDEED Test Matrix TUER 8403290 95221 2373734 5934335 -RSA 952203 952203 23737339 0 - -Order 952,203 -No. entries 23,737,339 - --------------------------------------------------------------------------------- -==> msdoor.readme <== --------------------------------------------------------------------------------- -Source: www.parallab.uib.no/parasol/data.html (MSDOOR) - - INDEED Test Matrix smltuer smtuer 3656527 41587 1032840 2582100 -rsa 415863 415863 10328399 0 - - -Order 415,863 -No. entries 10,328,399 - --------------------------------------------------------------------------------- -==> M_T1.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Tubular joint - -Order 97,578 -when assembled, entries = 4,925,574 -unassembled entries = 6,882,780 - - DNV-Ex 1 : Tubular joint-1999-01-17 M_T1 - 1747955 533 26727 1720695 0 -RSE 97578 5328 267270 6882780 -(10I8) (10I8) (3e26.16) - - --------------------------------------------------------------------------------- -==> MT1.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug - -Element Repr of - 27260 533 26727 0 0 -PSE 97578 5328 267270 0 -(10I8) (10I8) - --------------------------------------------------------------------------------- -==> OILPAN.readme <== --------------------------------------------------------------------------------- -Source: www.parallab.uib.no/parasol/data.html - - 1. TEST MATRIX FROM INPRO FOR PARASOL (OIL-PAN) - 649791 7376 183547 458868 0 -rsa 73752 73752 1835470 0 - - -Order 73752 -No. entries 1835470 - --------------------------------------------------------------------------------- -==> onera_dual.readme <== --------------------------------------------------------------------------------- -Source: Kumfert and Pothen - -require files -onera_dual.kp (which is obtained from onera_dual.ptr and onera_dual.chac) - -N = 85567 -NZ = 166817 (= off diag entries in upper triangle) -Off diag. pattern only supplied. - --------------------------------------------------------------------------------- -==> opt1.readme <== --------------------------------------------------------------------------------- -Source: Christain Damhaug - -Matrix Representation of OPT1, part of condeep cylinder OPT1 - 5367 98 5269 0 0 -PSE 15449 977 52686 0 -(10I8) (10I8) - --------------------------------------------------------------------------------- -==> pds10.readme <== --------------------------------------------------------------------------------- -Source: Kumfert and Pothen - -require files -pds10.kp (which is obtained from pds10.ptr and pds10.chac) - -N = 16558 -NZ = 66550 (= off diag entries in upper triangle) -Off diag. pattern only supplied. - --------------------------------------------------------------------------------- -==> ramage02.readme <== --------------------------------------------------------------------------------- - -Source: Alison Ramage (Strathclyde) - -Pattern only element problem - -1FINITE ELEMENT PROBLEM. NAVIER-STOKES AND CONTINUITY EQUATIONS. RAMAGE02 - 6495 141 6354 0 0 -PSE 16830 1400 82598 0 -(10I8) (13I6) (3(1PE22.14)) (3(1PE22.14)) - --------------------------------------------------------------------------------- -==> s3dkq4m2.readme <== --------------------------------------------------------------------------------- -Source: Matrix Market -BUT had to edit file as in RB format so RSA needs to be rsa - - * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * - * cyl shell R/t=1000 unif 100 x 150 quad mesh DK-el with drill rotat * - * Key = s3dkq4m2 , Type = RSA * - * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * - * Dimension N = 90449 * - * Number of nonzero elements = 2455670 * - * Average number of nonzero elements/Column = 27.1498 * - * Standard deviation for above average = 2.6724 * - * Nonzero elements in strict lower part = 2365221 * - * Nonzero elements in strict upper part = 2365221 * - * Nonzero elements in main diagonal = 90449 * - * Weight of longest column = 59 * - * Weight of shortest column = 15 * - * Matching elements in symmetry = 0 * - * Relative Symmetry Match (symmetry=1) = NaN * - * Average distance of a(i,j) from diag. = 0.395E+03 * - * Standard deviation for above average = 0.285E+03 * - *-----------------------------------------------------------------* - * Frobenius norm of A = 0.403E+06 * - * Frobenius norm of symmetric part = 0.403E+06 * - * Frobenius norm of nonsymmetric part = 0.000E+00 * - * Maximum element in A = 0.215E+04 * - * Percentage of weakly diagonally dominant rows = 0.329E-02 * - * Percentage of weakly diagonally dominant columns = 0.329E-02 * - *-----------------------------------------------------------------* - * Lower bandwidth (max: i-j, a(i,j) .ne. 0) = 0 * - * Upper bandwidth (max: j-i, a(i,j) .ne. 0) = 614 * - * Maximum Bandwidth = 615 * - * Average Bandwidth = 0.609E+03 * - * Number of nonzeros in skyline storage = 55071893 * - * 90% of matrix is in the band of width = 1217 * - * 80% of matrix is in the band of width = 1211 * - * The total number of nonvoid diagonals is = 331 * - * The 10 most important diagonals are (offsets) : * - * 0 1 2 3 4 5 603 602 604 601 * - * The accumulated percentages they represent are : * - * 3.7 7.4 11.0 14.7 18.3 22.0 25.6 29.2 32.9 36.5 * - *-----------------------------------------------------------------* - * The matrix does not have a block structure * - *-----------------------------------------------------------------* - --------------------------------------------------------------------------------- -==> s3dkt3m2.readme <== --------------------------------------------------------------------------------- -Source: Matrix Market -But had to edit file as in RB format RSA needs to be rsa - - * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * - * cyl shell 100 x 150 unif triang mesh R/t = 1000 * - * Key = s3dkt3m2 , Type = RSA * - * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * - * Dimension N = 90449 * - * Number of nonzero elements = 1921955 * - * Average number of nonzero elements/Column = 21.2490 * - * Standard deviation for above average = 2.3992 * - * Nonzero elements in strict lower part = 1831506 * - * Nonzero elements in strict upper part = 1831506 * - * Nonzero elements in main diagonal = 90449 * - * Weight of longest column = 47 * - * Weight of shortest column = 9 * - * Matching elements in symmetry = 0 * - * Relative Symmetry Match (symmetry=1) = NaN * - * Average distance of a(i,j) from diag. = 0.339E+03 * - * Standard deviation for above average = 0.299E+03 * - *-----------------------------------------------------------------* - * Frobenius norm of A = 0.641E+06 * - * Frobenius norm of symmetric part = 0.641E+06 * - * Frobenius norm of nonsymmetric part = 0.000E+00 * - * Maximum element in A = 0.322E+04 * - * Percentage of weakly diagonally dominant rows = 0.000E+00 * - * Percentage of weakly diagonally dominant columns = 0.000E+00 * - *-----------------------------------------------------------------* - * Lower bandwidth (max: i-j, a(i,j) .ne. 0) = 0 * - * Upper bandwidth (max: j-i, a(i,j) .ne. 0) = 614 * - * Maximum Bandwidth = 615 * - * Average Bandwidth = 0.609E+03 * - * Number of nonzeros in skyline storage = 55071893 * - * 90% of matrix is in the band of width = 1219 * - * 80% of matrix is in the band of width = 1215 * - * The total number of nonvoid diagonals is = 328 * - * The 10 most important diagonals are (offsets) : * - * 0 1 2 3 4 5 603 604 605 6 * - * The accumulated percentages they represent are : * - * 4.7 9.4 14.1 18.8 23.4 28.1 32.7 37.4 42.0 46.6 * - *-----------------------------------------------------------------* - * The matrix does not have a block structure * - *-----------------------------------------------------------------* - --------------------------------------------------------------------------------- -==> SHIP_001.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Ship structure, predesign model -Order 34,920 -when assembled, entries = 2,339,575 -unassembled entries = 3,686,133 - -DNV-Ex 2 : Ship structure, predesign model-1999-01-17 SHIP_001 - 937389 344 15511 921534 0 -RSE 34920 3431 155106 3686133 -(10I8) (10I8) (3e26.16) - - --------------------------------------------------------------------------------- -==> SHIP_003.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Ship structure from production run -Order 121,728 -when assembled, entries = 4,103,881 -unassembled entries =9,728,631 - - -DNV-Ex 3 : Ship structure from production run-1999-01-17 SHIP_003 - 2525272 4547 88317 2432408 0 -RSE 121728 45464 883167 9729631 -(10I8) (10I8) (3e26.16) - --------------------------------------------------------------------------------- -==> SHIPSEC1.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Ship section/detail from production run -Order 140,874 -when assembled, entries = 3,977,139 -unassembled entries = 6,882,780 - -DNV-Ex 4 : Ship section/detail from production run-1999-01-17 SHIPSEC1 - 2237188 4104 78502 2154582 0 -RSE 140874 41037 785016 8618328 -(10I8) (10I8) (3e26.16) - - --------------------------------------------------------------------------------- -==> SHIPSEC5.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Ship section/detail from production run -Order 179,86 -when assembled, entries = 6,146,478 -unassembled entries = 11,118,602 - -DNV-PARASOL Ship section 5-1999-01-15 Shipsec5 - 2885670 5228 100791 2779651 0 -RSE 179860 52272 1007907 11118602 -(10I8) (10I8) (4e22.14) - - --------------------------------------------------------------------------------- -==> SHIPSEC8.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Ship section/detail from production run -Order 114,919 -when assembled, entries = 3,384,159 -unassembled entries = 7,431,867 - -DNV-PARASOL Ship section 5-1999-01-15 Shipsec5 - 2885670 5228 100791 2779651 0 -RSE 179860 52272 1007907 11118602 -(10I8) (10I8) (4e22.14) - - --------------------------------------------------------------------------------- -==> Srb1.readme <== --------------------------------------------------------------------------------- - -Source: Christain Damahug (Det Norske Veritas, Oslo, Norway) - - Matrix Format Converter - ----------------------- - - Total number of nodes ................................... : 9198 - Number of active nodes .................................. : 9154 - Number of external nodes ................................ : 0 - Number of nodes with at least one boundary condition .... : 44 - Number of nodes with at least one load intensity ........ : 0 - Number of free variables (equations) .................... : 54924 - - Number of elements ...................................... : 9240 - Number of material types ................................ : 0 - Maximum number of nodes connected to an element ......... : 4 - - Size of complete element node connectivity .............. : 36872 - Size of stripped element node connectivity .............. : 36784 - Size of active adjacency set representation ............. : 73128 - TIME FOR WRITING MATRIX FORMAT = 1.9468524456E+00 - - - = = = = = = = = = = = = = = = = = = = = = = = = = - = = = Storage Requirement Matrix Convertion = = = - = = = = = = = = = = = = = = = = = = = = = = = = = - - Total INTEGER workspace need (runtime measure) .......... : 303533 - Total REAL workspace need (runtime measure) ............. : 0 - - Total CPU-time consumption to convert the matrix ........ : 2.20886E+00 - - Wall-Clock Time Consumption ............................. : 6.00000E+00 - --------------------------------------------------------------------------------- -==> Thread.readme <== --------------------------------------------------------------------------------- - -Source: Christain Damahug (Det Norske Veritas, Oslo, Norway) - - - Matrix Format Converter - ----------------------- - - Total number of nodes ................................... : 10008 - Number of active nodes .................................. : 9912 - Number of external nodes ................................ : 0 - Number of nodes with at least one boundary condition .... : 96 - Number of nodes with at least one load intensity ........ : 0 - Number of free variables (equations) .................... : 29736 - - Number of elements ...................................... : 2176 - Number of material types ................................ : 1 - Maximum number of nodes connected to an element ......... : 20 - - Size of complete element node connectivity .............. : 42080 - Size of stripped element node connectivity .............. : 41888 - Size of active adjacency set representation ............. : 486760 - TIME FOR WRITING MATRIX FORMAT = 1.0886445045E+00 - - - = = = = = = = = = = = = = = = = = = = = = = = = = - = = = Storage Requirement Matrix Convertion = = = - = = = = = = = = = = = = = = = = = = = = = = = = = - - Total INTEGER workspace need (runtime measure) .......... : 201743 - Total REAL workspace need (runtime measure) ............. : 0 - - Total CPU-time consumption to convert the matrix ........ : 1.33817E+00 - - Wall-Clock Time Consumption ............................. : 4.00000E+00 - --------------------------------------------------------------------------------- -==> THREAD.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Threaded connector/contact problem -Order 29,736 -when assembled, entries = 2,249,892 -unassembled entries = 3,718,704 - -DNV-Ex 7 : Threaded connector/contact problem-1999-01-17 THREAD - 942461 218 12567 929676 0 -RSE 29736 2176 125664 3718704 -(10I8) (10I8) (3e26.16) - - --------------------------------------------------------------------------------- -==> trdheim.readme <== --------------------------------------------------------------------------------- -Source: Christian Damhaug (DNR) - -Matrix Representation of TRDHEIM, Mesh of the Trondheim Fjord TRDHEIM - 4473 82 4391 0 0 -PSE 22098 813 43902 0 -(10I8) (10I8) - --------------------------------------------------------------------------------- -==> Troll.readme <== --------------------------------------------------------------------------------- - -Source: Christain Damahug (Det Norske Veritas, Oslo, Norway) - - Matrix Format Converter - ----------------------- - - Total number of nodes ................................... : 72545 - Number of active nodes .................................. : 71151 - Number of external nodes ................................ : 0 - Number of nodes with at least one boundary condition .... : 1394 - Number of nodes with at least one load intensity ........ : 0 - Number of free variables (equations) .................... : 213453 - - Number of elements ...................................... : 41084 - Number of material types ................................ : 2 - Maximum number of nodes connected to an element ......... : 8 - - Size of complete element node connectivity .............. : 321888 - Size of stripped element node connectivity .............. : 318624 - Size of active adjacency set representation ............. : 1260528 - TIME FOR WRITING MATRIX FORMAT = 8.8573474884E+00 - - - = = = = = = = = = = = = = = = = = = = = = = = = = - = = = Storage Requirement Matrix Convertion = = = - = = = = = = = = = = = = = = = = = = = = = = = = = - - Total INTEGER workspace need (runtime measure) .......... : 1570220 - Total REAL workspace need (runtime measure) ............. : 0 - - Total CPU-time consumption to convert the matrix ........ : 1.10190E+01 - - Wall-Clock Time Consumption ............................. : 2.90000E+01 - --------------------------------------------------------------------------------- -==> tsyl201.readme <== --------------------------------------------------------------------------------- -Source: Christian Damhaug (DNR) - -Matrix Representation of TSYL201, part of condeep cylinder TSYL201 - 5857 97 5760 0 0 -PSE 20685 960 57600 0 -(10I8) (10I8) - --------------------------------------------------------------------------------- -==> vanbody.readme <== --------------------------------------------------------------------------------- -Source: www.parallab.uib.no/parasol/data.html (LDOOR) - -Van body model (MSC-VANBODY) - -stiffness matrix XCARBO_1 810349 15691 397329 397329 -rsa 47072 47072 1191985 0 - -Order 47072 -entries 1191985 - --------------------------------------------------------------------------------- -==> X104.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Beam joint (SESAM test example no. 104) -Order 108,384 -when assembled, entries = 5,138,004 -unassembled entries = 7,065,546 - -DNV-Ex 8 : SESAM test example no 104-1999-01-17 X104 - 1795848 602 28859 1766387 0 -RSE 108384 6019 288588 7065546 -(10I8) (10I8) (3e26.16) - - -################################################################################ -These are matrices in unassembled finite-element form: -################################################################################ - - --------------------------------------------------------------------------------- -==> Fcondp2.readme <== --------------------------------------------------------------------------------- - -Source: Christain Damahug (Det Norske Veritas, Oslo, Norway) - - - Matrix Format Converter - ----------------------- - - Total number of nodes ................................... : 33913 - Number of active nodes .................................. : 33913 - Number of external nodes ................................ : 0 - Number of nodes with at least one boundary condition .... : 552 - Number of nodes with at least one load intensity ........ : 0 - Number of free variables (equations) .................... : 201822 - - Number of elements ...................................... : 35836 - Number of material types ................................ : 1 - Maximum number of nodes connected to an element ......... : 4 - - Size of complete element node connectivity .............. : 143344 - Size of stripped element node connectivity .............. : 143344 - Size of active adjacency set representation ............. : 282912 - TIME FOR WRITING MATRIX FORMAT = 7.7391695976E+00 - - - = = = = = = = = = = = = = = = = = = = = = = = = = - = = = Storage Requirement Matrix Convertion = = = - = = = = = = = = = = = = = = = = = = = = = = = = = - - Total INTEGER workspace need (runtime measure) .......... : 1173274 - Total REAL workspace need (runtime measure) ............. : 0 - - Total CPU-time consumption to convert the matrix ........ : 8.89181E+00 - - Wall-Clock Time Consumption ............................. : 1.70000E+01 - --------------------------------------------------------------------------------- -==> Fullb.readme <== --------------------------------------------------------------------------------- - -Source: Christain Damahug (Det Norske Veritas, Oslo, Norway) - - - Matrix Format Converter - ----------------------- - - Total number of nodes ................................... : 33442 - Number of active nodes .................................. : 33442 - Number of external nodes ................................ : 0 - Number of nodes with at least one boundary condition .... : 586 - Number of nodes with at least one load intensity ........ : 0 - Number of free variables (equations) .................... : 199187 - - Number of elements ...................................... : 59738 - Number of material types ................................ : 3 - Maximum number of nodes connected to an element ......... : 4 - - Size of complete element node connectivity .............. : 195585 - Size of stripped element node connectivity .............. : 195585 - Size of active adjacency set representation ............. : 294498 - TIME FOR WRITING MATRIX FORMAT = 1.0402039528E+01 - - - = = = = = = = = = = = = = = = = = = = = = = = = = - = = = Storage Requirement Matrix Convertion = = = - = = = = = = = = = = = = = = = = = = = = = = = = = - - Total INTEGER workspace need (runtime measure) .......... : 1584759 - Total REAL workspace need (runtime measure) ............. : 0 - - Total CPU-time consumption to convert the matrix ........ : 1.18173E+01 - - Wall-Clock Time Consumption ............................. : 2.90000E+01 - --------------------------------------------------------------------------------- -==> Halfb.readme <== --------------------------------------------------------------------------------- - - -Source: Christain Damahug (Det Norske Veritas, Oslo, Norway) - - - Matrix Format Converter - ----------------------- - - Total number of nodes ................................... : 38556 - Number of active nodes .................................. : 38556 - Number of external nodes ................................ : 0 - Number of nodes with at least one boundary condition .... : 2470 - Number of nodes with at least one load intensity ........ : 0 - Number of free variables (equations) .................... : 224617 - - Number of elements ...................................... : 70211 - Number of material types ................................ : 3 - Maximum number of nodes connected to an element ......... : 4 - - Size of complete element node connectivity .............. : 221764 - Size of stripped element node connectivity .............. : 221764 - Size of active adjacency set representation ............. : 321406 - TIME FOR WRITING MATRIX FORMAT = 1.1791002274E+01 - - - = = = = = = = = = = = = = = = = = = = = = = = = = - = = = Storage Requirement Matrix Convertion = = = - = = = = = = = = = = = = = = = = = = = = = = = = = - - Total INTEGER workspace need (runtime measure) .......... : 1774320 - Total REAL workspace need (runtime measure) ............. : 0 - - Total CPU-time consumption to convert the matrix ........ : 1.33733E+01 - - Wall-Clock Time Consumption ............................. : 4.20000E+01 - --------------------------------------------------------------------------------- -==> M_T1.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Tubular joint - -Order 97,578 -when assembled, entries = 4,925,574 -unassembled entries = 6,882,780 - - DNV-Ex 1 : Tubular joint-1999-01-17 M_T1 - 1747955 533 26727 1720695 0 -RSE 97578 5328 267270 6882780 -(10I8) (10I8) (3e26.16) - - --------------------------------------------------------------------------------- -==> MT1.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug - -Element Repr of - 27260 533 26727 0 0 -PSE 97578 5328 267270 0 -(10I8) (10I8) - --------------------------------------------------------------------------------- -==> opt1.readme <== --------------------------------------------------------------------------------- -Source: Christain Damhaug - -Matrix Representation of OPT1, part of condeep cylinder OPT1 - 5367 98 5269 0 0 -PSE 15449 977 52686 0 -(10I8) (10I8) - --------------------------------------------------------------------------------- -==> ramage02.readme <== --------------------------------------------------------------------------------- - -Source: Alison Ramage (Strathclyde) - -Pattern only element problem - -1FINITE ELEMENT PROBLEM. NAVIER-STOKES AND CONTINUITY EQUATIONS. RAMAGE02 - 6495 141 6354 0 0 -PSE 16830 1400 82598 0 -(10I8) (13I6) (3(1PE22.14)) (3(1PE22.14)) - --------------------------------------------------------------------------------- -==> SHIP_001.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Ship structure, predesign model -Order 34,920 -when assembled, entries = 2,339,575 -unassembled entries = 3,686,133 - -DNV-Ex 2 : Ship structure, predesign model-1999-01-17 SHIP_001 - 937389 344 15511 921534 0 -RSE 34920 3431 155106 3686133 -(10I8) (10I8) (3e26.16) - - --------------------------------------------------------------------------------- -==> SHIP_003.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Ship structure from production run -Order 121,728 -when assembled, entries = 4,103,881 -unassembled entries =9,728,631 - - -DNV-Ex 3 : Ship structure from production run-1999-01-17 SHIP_003 - 2525272 4547 88317 2432408 0 -RSE 121728 45464 883167 9729631 -(10I8) (10I8) (3e26.16) - --------------------------------------------------------------------------------- -==> SHIPSEC1.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Ship section/detail from production run -Order 140,874 -when assembled, entries = 3,977,139 -unassembled entries = 6,882,780 - -DNV-Ex 4 : Ship section/detail from production run-1999-01-17 SHIPSEC1 - 2237188 4104 78502 2154582 0 -RSE 140874 41037 785016 8618328 -(10I8) (10I8) (3e26.16) - - --------------------------------------------------------------------------------- -==> SHIPSEC5.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Ship section/detail from production run -Order 179,86 -when assembled, entries = 6,146,478 -unassembled entries = 11,118,602 - -DNV-PARASOL Ship section 5-1999-01-15 Shipsec5 - 2885670 5228 100791 2779651 0 -RSE 179860 52272 1007907 11118602 -(10I8) (10I8) (4e22.14) - - --------------------------------------------------------------------------------- -==> SHIPSEC8.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Ship section/detail from production run -Order 114,919 -when assembled, entries = 3,384,159 -unassembled entries = 7,431,867 - -DNV-PARASOL Ship section 5-1999-01-15 Shipsec5 - 2885670 5228 100791 2779651 0 -RSE 179860 52272 1007907 11118602 -(10I8) (10I8) (4e22.14) - - --------------------------------------------------------------------------------- -==> Srb1.readme <== --------------------------------------------------------------------------------- - -Source: Christain Damahug (Det Norske Veritas, Oslo, Norway) - - Matrix Format Converter - ----------------------- - - Total number of nodes ................................... : 9198 - Number of active nodes .................................. : 9154 - Number of external nodes ................................ : 0 - Number of nodes with at least one boundary condition .... : 44 - Number of nodes with at least one load intensity ........ : 0 - Number of free variables (equations) .................... : 54924 - - Number of elements ...................................... : 9240 - Number of material types ................................ : 0 - Maximum number of nodes connected to an element ......... : 4 - - Size of complete element node connectivity .............. : 36872 - Size of stripped element node connectivity .............. : 36784 - Size of active adjacency set representation ............. : 73128 - TIME FOR WRITING MATRIX FORMAT = 1.9468524456E+00 - - - = = = = = = = = = = = = = = = = = = = = = = = = = - = = = Storage Requirement Matrix Convertion = = = - = = = = = = = = = = = = = = = = = = = = = = = = = - - Total INTEGER workspace need (runtime measure) .......... : 303533 - Total REAL workspace need (runtime measure) ............. : 0 - - Total CPU-time consumption to convert the matrix ........ : 2.20886E+00 - - Wall-Clock Time Consumption ............................. : 6.00000E+00 - --------------------------------------------------------------------------------- -==> Thread.readme <== --------------------------------------------------------------------------------- - -Source: Christain Damahug (Det Norske Veritas, Oslo, Norway) - - - Matrix Format Converter - ----------------------- - - Total number of nodes ................................... : 10008 - Number of active nodes .................................. : 9912 - Number of external nodes ................................ : 0 - Number of nodes with at least one boundary condition .... : 96 - Number of nodes with at least one load intensity ........ : 0 - Number of free variables (equations) .................... : 29736 - - Number of elements ...................................... : 2176 - Number of material types ................................ : 1 - Maximum number of nodes connected to an element ......... : 20 - - Size of complete element node connectivity .............. : 42080 - Size of stripped element node connectivity .............. : 41888 - Size of active adjacency set representation ............. : 486760 - TIME FOR WRITING MATRIX FORMAT = 1.0886445045E+00 - - - = = = = = = = = = = = = = = = = = = = = = = = = = - = = = Storage Requirement Matrix Convertion = = = - = = = = = = = = = = = = = = = = = = = = = = = = = - - Total INTEGER workspace need (runtime measure) .......... : 201743 - Total REAL workspace need (runtime measure) ............. : 0 - - Total CPU-time consumption to convert the matrix ........ : 1.33817E+00 - - Wall-Clock Time Consumption ............................. : 4.00000E+00 - --------------------------------------------------------------------------------- -==> THREAD.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Threaded connector/contact problem -Order 29,736 -when assembled, entries = 2,249,892 -unassembled entries = 3,718,704 - -DNV-Ex 7 : Threaded connector/contact problem-1999-01-17 THREAD - 942461 218 12567 929676 0 -RSE 29736 2176 125664 3718704 -(10I8) (10I8) (3e26.16) - - --------------------------------------------------------------------------------- -==> trdheim.readme <== --------------------------------------------------------------------------------- -Source: Christian Damhaug (DNR) - -Matrix Representation of TRDHEIM, Mesh of the Trondheim Fjord TRDHEIM - 4473 82 4391 0 0 -PSE 22098 813 43902 0 -(10I8) (10I8) - --------------------------------------------------------------------------------- -==> Troll.readme <== --------------------------------------------------------------------------------- - -Source: Christain Damahug (Det Norske Veritas, Oslo, Norway) - - Matrix Format Converter - ----------------------- - - Total number of nodes ................................... : 72545 - Number of active nodes .................................. : 71151 - Number of external nodes ................................ : 0 - Number of nodes with at least one boundary condition .... : 1394 - Number of nodes with at least one load intensity ........ : 0 - Number of free variables (equations) .................... : 213453 - - Number of elements ...................................... : 41084 - Number of material types ................................ : 2 - Maximum number of nodes connected to an element ......... : 8 - - Size of complete element node connectivity .............. : 321888 - Size of stripped element node connectivity .............. : 318624 - Size of active adjacency set representation ............. : 1260528 - TIME FOR WRITING MATRIX FORMAT = 8.8573474884E+00 - - - = = = = = = = = = = = = = = = = = = = = = = = = = - = = = Storage Requirement Matrix Convertion = = = - = = = = = = = = = = = = = = = = = = = = = = = = = - - Total INTEGER workspace need (runtime measure) .......... : 1570220 - Total REAL workspace need (runtime measure) ............. : 0 - - Total CPU-time consumption to convert the matrix ........ : 1.10190E+01 - - Wall-Clock Time Consumption ............................. : 2.90000E+01 - --------------------------------------------------------------------------------- -==> tsyl201.readme <== --------------------------------------------------------------------------------- -Source: Christian Damhaug (DNR) - -Matrix Representation of TSYL201, part of condeep cylinder TSYL201 - 5857 97 5760 0 0 -PSE 20685 960 57600 0 -(10I8) (10I8) - --------------------------------------------------------------------------------- -==> X104.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Beam joint (SESAM test example no. 104) -Order 108,384 -when assembled, entries = 5,138,004 -unassembled entries = 7,065,546 - -DNV-Ex 8 : SESAM test example no 104-1999-01-17 X104 - 1795848 602 28859 1766387 0 -RSE 108384 6019 288588 7065546 -(10I8) (10I8) (3e26.16)" -DNVS,15,"Positive definite matrices from Christian Damhaug, DNV Software. Originally collected by Nick Gould, Yifan Hu, & Jennifer Scott,",https://sparse.tamu.edu/DNVS,structures/materials,structures/materials,1.0,1,structural problem (15),2,real (8); binary (7),1,symmetric (15),18010,224617,12.471793448084398,moderate (4–100×),960946,12387821,square only,15,0,all square matrices symmetric/Hermitian,15,15,8,real only,7,15,one broad domain; mixed matrix forms,"Positive definite matrices from Christian Damhaug, DNV Software. - -Originally collected by Nick Gould, Yifan Hu, & Jennifer Scott, -some in unassembled finite-element form (and some in both forms). - -(Note: these matrices were previously in the GHS_psdef directory). - -NOTE: matrices can have upper-case or lower case matrix-type fields -(rsa or RSA, for example). - --------------------------------------------------------------------------------- -==> Fcondp2.readme <== --------------------------------------------------------------------------------- - -Source: Christain Damahug (Det Norske Veritas, Oslo, Norway) - - - Matrix Format Converter - ----------------------- - - Total number of nodes ................................... : 33913 - Number of active nodes .................................. : 33913 - Number of external nodes ................................ : 0 - Number of nodes with at least one boundary condition .... : 552 - Number of nodes with at least one load intensity ........ : 0 - Number of free variables (equations) .................... : 201822 - - Number of elements ...................................... : 35836 - Number of material types ................................ : 1 - Maximum number of nodes connected to an element ......... : 4 - - Size of complete element node connectivity .............. : 143344 - Size of stripped element node connectivity .............. : 143344 - Size of active adjacency set representation ............. : 282912 - TIME FOR WRITING MATRIX FORMAT = 7.7391695976E+00 - - - = = = = = = = = = = = = = = = = = = = = = = = = = - = = = Storage Requirement Matrix Convertion = = = - = = = = = = = = = = = = = = = = = = = = = = = = = - - Total INTEGER workspace need (runtime measure) .......... : 1173274 - Total REAL workspace need (runtime measure) ............. : 0 - - Total CPU-time consumption to convert the matrix ........ : 8.89181E+00 - - Wall-Clock Time Consumption ............................. : 1.70000E+01 - --------------------------------------------------------------------------------- -==> Fullb.readme <== --------------------------------------------------------------------------------- - -Source: Christain Damahug (Det Norske Veritas, Oslo, Norway) - - - Matrix Format Converter - ----------------------- - - Total number of nodes ................................... : 33442 - Number of active nodes .................................. : 33442 - Number of external nodes ................................ : 0 - Number of nodes with at least one boundary condition .... : 586 - Number of nodes with at least one load intensity ........ : 0 - Number of free variables (equations) .................... : 199187 - - Number of elements ...................................... : 59738 - Number of material types ................................ : 3 - Maximum number of nodes connected to an element ......... : 4 - - Size of complete element node connectivity .............. : 195585 - Size of stripped element node connectivity .............. : 195585 - Size of active adjacency set representation ............. : 294498 - TIME FOR WRITING MATRIX FORMAT = 1.0402039528E+01 - - - = = = = = = = = = = = = = = = = = = = = = = = = = - = = = Storage Requirement Matrix Convertion = = = - = = = = = = = = = = = = = = = = = = = = = = = = = - - Total INTEGER workspace need (runtime measure) .......... : 1584759 - Total REAL workspace need (runtime measure) ............. : 0 - - Total CPU-time consumption to convert the matrix ........ : 1.18173E+01 - - Wall-Clock Time Consumption ............................. : 2.90000E+01 - --------------------------------------------------------------------------------- -==> Halfb.readme <== --------------------------------------------------------------------------------- - - -Source: Christain Damahug (Det Norske Veritas, Oslo, Norway) - - - Matrix Format Converter - ----------------------- - - Total number of nodes ................................... : 38556 - Number of active nodes .................................. : 38556 - Number of external nodes ................................ : 0 - Number of nodes with at least one boundary condition .... : 2470 - Number of nodes with at least one load intensity ........ : 0 - Number of free variables (equations) .................... : 224617 - - Number of elements ...................................... : 70211 - Number of material types ................................ : 3 - Maximum number of nodes connected to an element ......... : 4 - - Size of complete element node connectivity .............. : 221764 - Size of stripped element node connectivity .............. : 221764 - Size of active adjacency set representation ............. : 321406 - TIME FOR WRITING MATRIX FORMAT = 1.1791002274E+01 - - - = = = = = = = = = = = = = = = = = = = = = = = = = - = = = Storage Requirement Matrix Convertion = = = - = = = = = = = = = = = = = = = = = = = = = = = = = - - Total INTEGER workspace need (runtime measure) .......... : 1774320 - Total REAL workspace need (runtime measure) ............. : 0 - - Total CPU-time consumption to convert the matrix ........ : 1.33733E+01 - - Wall-Clock Time Consumption ............................. : 4.20000E+01 - --------------------------------------------------------------------------------- -==> M_T1.readme <== --------------------------------------------------------------------------------- -Source: Christian Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Tubular joint - -Order 97,578 -when assembled, entries = 4,925,574 -unassembled entries = 6,882,780 - - DNV-Ex 1 : Tubular joint-1999-01-17 M_T1 - 1747955 533 26727 1720695 0 -RSE 97578 5328 267270 6882780 -(10I8) (10I8) (3e26.16) - - --------------------------------------------------------------------------------- -==> MT1.readme <== --------------------------------------------------------------------------------- -Source: Christian Damhaug - -Element Repr of - 27260 533 26727 0 0 -PSE 97578 5328 267270 0 -(10I8) (10I8) - --------------------------------------------------------------------------------- -==> SHIP_001.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Ship structure, predesign model -Order 34,920 -when assembled, entries = 2,339,575 -unassembled entries = 3,686,133 - -DNV-Ex 2 : Ship structure, predesign model-1999-01-17 SHIP_001 - 937389 344 15511 921534 0 -RSE 34920 3431 155106 3686133 -(10I8) (10I8) (3e26.16) - - --------------------------------------------------------------------------------- -==> SHIP_003.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Ship structure from production run -Order 121,728 -when assembled, entries = 4,103,881 -unassembled entries =9,728,631 - - -DNV-Ex 3 : Ship structure from production run-1999-01-17 SHIP_003 - 2525272 4547 88317 2432408 0 -RSE 121728 45464 883167 9729631 -(10I8) (10I8) (3e26.16) - --------------------------------------------------------------------------------- -==> SHIPSEC1.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Ship section/detail from production run -Order 140,874 -when assembled, entries = 3,977,139 -unassembled entries = 6,882,780 - -DNV-Ex 4 : Ship section/detail from production run-1999-01-17 SHIPSEC1 - 2237188 4104 78502 2154582 0 -RSE 140874 41037 785016 8618328 -(10I8) (10I8) (3e26.16) - - --------------------------------------------------------------------------------- -==> SHIPSEC5.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Ship section/detail from production run -Order 179,86 -when assembled, entries = 6,146,478 -unassembled entries = 11,118,602 - -DNV-PARASOL Ship section 5-1999-01-15 Shipsec5 - 2885670 5228 100791 2779651 0 -RSE 179860 52272 1007907 11118602 -(10I8) (10I8) (4e22.14) - - --------------------------------------------------------------------------------- -==> SHIPSEC8.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Ship section/detail from production run -Order 114,919 -when assembled, entries = 3,384,159 -unassembled entries = 7,431,867 - -DNV-PARASOL Ship section 5-1999-01-15 Shipsec5 - 2885670 5228 100791 2779651 0 -RSE 179860 52272 1007907 11118602 -(10I8) (10I8) (4e22.14) - - --------------------------------------------------------------------------------- -==> Thread.readme <== --------------------------------------------------------------------------------- - -Source: Christain Damahug (Det Norske Veritas, Oslo, Norway) - - - Matrix Format Converter - ----------------------- - - Total number of nodes ................................... : 10008 - Number of active nodes .................................. : 9912 - Number of external nodes ................................ : 0 - Number of nodes with at least one boundary condition .... : 96 - Number of nodes with at least one load intensity ........ : 0 - Number of free variables (equations) .................... : 29736 - - Number of elements ...................................... : 2176 - Number of material types ................................ : 1 - Maximum number of nodes connected to an element ......... : 20 - - Size of complete element node connectivity .............. : 42080 - Size of stripped element node connectivity .............. : 41888 - Size of active adjacency set representation ............. : 486760 - TIME FOR WRITING MATRIX FORMAT = 1.0886445045E+00 - - - = = = = = = = = = = = = = = = = = = = = = = = = = - = = = Storage Requirement Matrix Convertion = = = - = = = = = = = = = = = = = = = = = = = = = = = = = - - Total INTEGER workspace need (runtime measure) .......... : 201743 - Total REAL workspace need (runtime measure) ............. : 0 - - Total CPU-time consumption to convert the matrix ........ : 1.33817E+00 - - Wall-Clock Time Consumption ............................. : 4.00000E+00 - --------------------------------------------------------------------------------- -==> THREAD.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Threaded connector/contact problem -Order 29,736 -when assembled, entries = 2,249,892 -unassembled entries = 3,718,704 - -DNV-Ex 7 : Threaded connector/contact problem-1999-01-17 THREAD - 942461 218 12567 929676 0 -RSE 29736 2176 125664 3718704 -(10I8) (10I8) (3e26.16) - - --------------------------------------------------------------------------------- -==> trdheim.readme <== --------------------------------------------------------------------------------- -Source: Christian Damhaug (DNR) - -Matrix Representation of TRDHEIM, Mesh of the Trondheim Fjord TRDHEIM - 4473 82 4391 0 0 -PSE 22098 813 43902 0 -(10I8) (10I8) - --------------------------------------------------------------------------------- -==> Troll.readme <== --------------------------------------------------------------------------------- - -Source: Christain Damahug (Det Norske Veritas, Oslo, Norway) - - Matrix Format Converter - ----------------------- - - Total number of nodes ................................... : 72545 - Number of active nodes .................................. : 71151 - Number of external nodes ................................ : 0 - Number of nodes with at least one boundary condition .... : 1394 - Number of nodes with at least one load intensity ........ : 0 - Number of free variables (equations) .................... : 213453 - - Number of elements ...................................... : 41084 - Number of material types ................................ : 2 - Maximum number of nodes connected to an element ......... : 8 - - Size of complete element node connectivity .............. : 321888 - Size of stripped element node connectivity .............. : 318624 - Size of active adjacency set representation ............. : 1260528 - TIME FOR WRITING MATRIX FORMAT = 8.8573474884E+00 - - - = = = = = = = = = = = = = = = = = = = = = = = = = - = = = Storage Requirement Matrix Convertion = = = - = = = = = = = = = = = = = = = = = = = = = = = = = - - Total INTEGER workspace need (runtime measure) .......... : 1570220 - Total REAL workspace need (runtime measure) ............. : 0 - - Total CPU-time consumption to convert the matrix ........ : 1.10190E+01 - - Wall-Clock Time Consumption ............................. : 2.90000E+01 - --------------------------------------------------------------------------------- -==> tsyl201.readme <== --------------------------------------------------------------------------------- -Source: Christian Damhaug (DNR) - -Matrix Representation of TSYL201, part of condeep cylinder TSYL201 - 5857 97 5760 0 0 -PSE 20685 960 57600 0 -(10I8) (10I8) - --------------------------------------------------------------------------------- -==> X104.readme <== --------------------------------------------------------------------------------- -Source: Chrisitan Damhaug -www.parallab.uib.no/parasol/data.html - -We have this matrix available in assembled and element form - -Beam joint (SESAM test example no. 104) -Order 108,384 -when assembled, entries = 5,138,004 -unassembled entries = 7,065,546 - -DNV-Ex 8 : SESAM test example no 104-1999-01-17 X104 - 1795848 602 28859 1766387 0 -RSE 108384 6019 288588 7065546 -(10I8) (10I8) (3e26.16)" -Bydder,2,"MRI reconstruction matrices from Mark Bydder, UCSD. Note that that A is sparse and rectangular, and b is complex.",https://sparse.tamu.edu/Bydder,imaging/graphics/robotics,imaging/graphics/robotics,1.0,1,computer graphics/vision problem (2),1,real (2),1,rectangular (2),147456,147456,1.0,one size,569160,589824,rectangular only,0,2,N/A (rectangular only),0,0,0,real only,0,2,coherent family/series,"MRI reconstruction matrices from Mark Bydder, UCSD. -Note that that A is sparse and rectangular, and b is complex. -Added March, 2006. - --------------------------------------------------------------------------------- -Excerpts of description, from email: --------------------------------------------------------------------------------- - -Inside there is a matrix A and a RHS vector b. Usually we want to -solve Ax=b, for example x = lsqr(A,b,0,10) uses conjugate gradient -on the normal equations to give an approximate answer. The data b are -Fourier coefficients at arbitrary locations (in this case, in a spiral -trajectory) and the matrix interpolates these data onto -a regular grid suitable for FFT. So it's like non-uniform FFT. - -Please let me know if you find this matrix helpful or want to try some -other (similar) matrices, eg. with radial trajectories. - --------------------------------------------------------------------------------- - -The matrix I sent derives from a technique called regridding - a process -of interpolating data at arbitrary locations onto an equally spaced grid. - -Eg. we sample a function f at two points 0 contains the whole Jacobian matrix -Jac_db only contains the diagonal blocks (lower block triangularisation) - - -For both models, UMFPACK performs relativly good for the second iteration. -As a result of the initial guess. The sparsity pattern changes slightly during -the first few iterations. - -At the third iteration however UMFPACK is significally slower than an -LU-decomposition without ordering. -As an indication I present a number of test results (note UMFPACK is approx a -factor 1.5 faster when using blas of K. Goto, but these results using -imsl-BLAS were the most easy to produce and the exact timings are not -relevent): - -Times in seconds, computing lu-decomposition of diagonal blocks -(symbolic+numeric step for UMFPACK), using Win XP, Pentium IV, 3.4 GHz, -1Gb Ram. - - - normal LU UMFPACK -model iteration cpu wall cpu wall - -std1 2, 2.219, 2.219, 2.500, 2.516 -std1 3, 2.266, 2.266, 34.547, 34.594 -Zd2 2, 3.859, 3.860, 3.594, 3.593 -Zd2 3, 3.969, 3.985, 32.766, 32.828 -Zd2 6, 3.359, 3.359, 29.797, 29.797 - - -Nils van Velzen -VORtech Computing -The Netherlands - -Marco van Goethem -Technip Benulux b.v. -The Netherlands -mvangoethem at technip -dot com" -Lourakis,1,"3D vision matrices, M. Lourakis, Foundation for Research & Technology - Hellas. Inst. of Computer Science",https://sparse.tamu.edu/Lourakis,imaging/graphics/robotics,imaging/graphics/robotics,1.0,1,computer graphics/vision problem (1),1,real (1),1,symmetric (1),10581,10581,1.0,one size,770811,770811,square only,1,0,all square matrices symmetric/Hermitian,1,1,1,real only,0,1,single matrix,"3D vision matrices, M. Lourakis, Foundation for Research & Technology - Hellas. - -Inst. of Computer Science -Foundation for Research & Technology - Hellas -PO Box 1385 -Heraklion, Crete, GR-711 10 GREECE - -http://www.ics.forth.gr/~lourakis -http://www.ics.forth.gr/~lourakis/sba - -SBA: A Generic Sparse Bundle Adjustment C/C++ Package based on the -Levenberg-Marquardt Algorithm." -PARSEC,21,"PARSEC matrices, density functional theory. Zhou, Saad, Tiago, Chelikowsky, UMN. ------------------------------------------------------------------",https://sparse.tamu.edu/PARSEC,chemistry/biology,chemistry/biology,1.0,1,theoretical/quantum chemistry problem (21),1,real (21),1,symmetric (21),769,268096,348.629388816645,wide (>100×),17801,18488476,square only,21,0,all square matrices symmetric/Hermitian,21,21,0,real only,0,0,coherent family/series,"PARSEC matrices, density functional theory. Zhou, Saad, Tiago, Chelikowsky, UMN. - ------------------------------------------------------------------- - Yunkai Zhou - Dept. of Computer Science & Eng. http://www.cs.umn.edu/~yunkai - University of Minnesota fax: 612-625-0572 - 200 Union St. SE. phone: 612-203-1816 (cell) - Minneapolis, MN 55455 email: (first name) at the domain cs.umn.edu ------------------------------------------------------------------- - -The matrices I am trying to submit are from symmetric eigenvalue -problems in density functional theory calculations. - -The matrices are sparse, indefinite, with multiple and clustered -eigenvalues---typical character of Hamiltonian matrices from the -Kohn-Sham equations. - -Sparsity structures of the matrices may be viewed at -http://www-users.cs.umn.edu/~yunkai/matrices/ -One can see very nice, kind of self-similar (fractal) sparsity -structures if using higher resolution for the plots. - ------------------------------------------------------------------- - -Contributors: -Yunkai Zhou, Yousef Saad, Murilo L. Tiago and James R. Chelikowsky. ------------------------------------------------------------------- - -The matrices are obtained using the PARSEC package. - -PARSEC is a FORTRAN90 package in density functional theory (DFT) -calculations, it implements the real-space pseudopotential method -(e.g. [1,2]). High order centered finite difference schemes are used -for the discretization of the Laplacian in the Kohn-Sham equations. -PARSEC is developed by a research group lead by Prof. J. R. -Chelikowsky and Prof. Y. Saad. - -The Hamiltonian matrices are constructed when self-consistency -in the self-consistent loop is reached. - -Some of the matrices have been used in [3, 4]. - -[1] @Article{cts:94, - author = {J. R. Chelikowsky and N. Troullier and Y. Saad}, - title = {Finite-difference-pseudopotential method: Electronic - structure calculations without a basis}, - journal = {Phys. Rev. Lett.}, - year = 1994, - volume = 72, - pages = {1240-1243} -} - -[2] @Article{che-PDFM00, - author = {J.R. Chelikowsky}, - title = {The Pseudopotential-Density Functional Method - Applied to Nanostructures}, - journal = {J. Phys. D: Appl. Phys.}, - year = 2000, - volume = 33, - pages = {R33--R50} -} - -[3] @TechReport{chebdav, - author = {Y. Zhou and Y. Saad}, - title = {A {Chebyshev-Davidson} Algorithm for Large - Symmetric Eigenvalue Problems}, - institution = {Minnesota Supercomputing Institute, Univ. of - Minnesota}, - year = {2005}, -} - -[4] @TechReport{blkchebdav, - author = {Y. Zhou}, - title = {Block-wise Polynomial Filtered {Davidson}-type - Subspace Iteration}, - institution = {Minnesota Supercomputing Institute, Univ. of - Minnesota}, - year = {2005}, -} - - -======================================================================" -Andrianov,16,"Matrices from Alexander Andrianov, SAS Institute Inc.",https://sparse.tamu.edu/Andrianov,optimization/control,optimization/control,1.0,1,optimization problem (16),1,binary (16),1,symmetric (16),5026,534388,106.32471150019896,wide (>100×),94026,10352819,square only,16,0,all square matrices symmetric/Hermitian,16,16,0,real only,16,0,coherent family/series,"Matrices from Alexander Andrianov, SAS Institute Inc." -MKS,1,"2-D Fokker Planck equations, electron dynamics. Dan Smith, MKS Inst.",https://sparse.tamu.edu/MKS,electromagnetics/acoustics,electromagnetics/acoustics,1.0,1,electromagnetics problem (1),1,real (1),1,unsymmetric (1),7548,7548,1.0,one size,834222,834222,square only,1,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,1,single matrix,"2-D Fokker Planck equations, electron dynamics. Dan Smith, MKS Inst." -Raju,1,"3D laminar flow, M. Raju, Case Western Univ. A finite element discretization (u,v,w,p) of 3D laminar",https://sparse.tamu.edu/Raju,fluids/transport,fluids/transport,1.0,1,computational fluid dynamics problem (1),1,real (1),1,unsymmetric (1),67173,67173,1.0,one size,3788857,3788857,square only,1,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,1,single matrix,"3D laminar flow, M. Raju, Case Western Univ. - -A finite element discretization (u,v,w,p) of 3D laminar -flow in a duct. Mesh size is 50x30x10." -Bates,3,"Statistical matrices, D. Bates, Univ. Wisconsin",https://sparse.tamu.edu/Bates,mathematics/statistics; other,mathematics/statistics,0.6666666666666666,2,statistical/mathematical problem (2); least squares problem (1),2,real (2); binary (1),2,rectangular (2); symmetric (1),2541,1748122,687.9661550570642,wide (>100×),7361,6804304,mixed,1,2,all square matrices symmetric/Hermitian,1,1,1,real only,1,0,multi-domain/mixed,"Statistical matrices, D. Bates, Univ. Wisconsin -http://www.stat.wisc.edu/~bates - ---------------- -Chem* matrices: ---------------- - -There will be no fill-in regardless of the ordering and it is even the case -that the inverse has the same pattern of nonzeros as the original L factor. - -To explain why this happens it may be best if I explain what ""nested grouping -factors"" means. In the Chem97 example in that writeup I sent the matrix Zt is -generated according to the school and the lea (local education authority - the -British equivalent of a school district) that the student was in when they took -the exam. The rows of Zt correspond to schools and to lea's. The rows -correspond to students. The ""grouping factors"" are school and lea. We say -that school is nested within lea in the sense that each school occurs in one -and only one lea. This is in contrast to the other example using the ""star"" -data where the grouping factors are student, teacher and school. Multiple -observations on the same student are often with different teachers so student -is not nested within teacher. - -Returning to the Chem97 example, the Zt matrix for this model is an indicator -matrix of the school for each student vertically concatenated with the -indicator matrix of the lea for each student. The structure of Zt (Zt)' has -a diagonal block corresponding to schools, a diagonal block corresponding to -lea's and the off-diagonal block. The point is that there is exactly one -nonzero in each column of the off-diagonal block in the lower-left. - -The way I would reorder these columns and rows to show the structure would be -to put all the schools associated with the first lea followed by the first lea -followed by all the schools associated with the second lea followed by the -second lea ... Then the L matrix will be block diagonal with each block -corresponding to an lea. Within each block the structure is a diagonal matrix -in the first k-1 rows and columns and a dense row underneath it. - -By the way, don't try to decompose ZtZ as it is only positive semidefinite. -The matrix that is decomposed is ZtZ + Omega where Omega is diagonal with the -diagonal consisting of 2410 elements of 4.419696 (corresponding to schools) and -131 elements of 349.0238 (corresponding to lea's). - ---------------- -sls matrix: ---------------- - -The data are 10 years of grade point scores at a large state -university (not mine). The covariates that are recorded with the -scores are the student id, the instructor id and the department. - -Number of obs: 1685394, groups: id, 54711; instr, 7915; dept, 102 - -Even though these scores are from different semesters and one of the -questions of interest is whether grade inflation has taken place, -initially I fit a simple model that has an effect for the student, an -effect for the instructor and an effect for the department. There is -also an overall average gradepoint. The overall average is what we -call a fixed effect. In computations terms this means it is estimated -without a penalty. The other effects are what we call random effects. -In terms of parameters we estimate a variance for each group of random -effects (student, instructor and department). We assume that the -random effects come from a normal (or Gaussian) distribution, which -has the effect of shrinking the estimates of the individual effects -towards the origin. If the variance of that distribution is large, -there is little penalty and the estimates for each of the students or -each of the instructors or each of the departments is close to the -least squares estimate. If the variance is small then they are much -closer to zero. - -Overall we will estimate 3 variances, 1 fixed-effect and 62728 = 54711 -+ 7915 + 102 random effects. The matrix we will decompose will be -62729 columns (the last column comes from the fixed-effect) and -1685394 + 62728 rows. The 62728 rows come from the penalty part and -consist of an identity matrix of size 62728 with a column of zeros -appended on the right. The rows determined by the data (I think of -these as being the top part of the matrix but it doesn't matter if -they are on the top or the bottom) consist of 54711 columns of -indicators for the student followed by 7915 columns of indicators for -the instructor followed by 102 columns of indicators for the -department followed by a column of 1's. In general we will have -columns for random effects followed by columns for fixed effects. -Here the fixed-effects column is trivial but in general we may have -more than 1 and they don't have to be as trivial as this. - -At least as far as I have been able to analyze the computation, I can -allow for permutations of the columns for the random effects but I -don't want to mix up the columns for the random effects and the -columns for the fixed effects. I always want to keep the columns for -the fixed effects as a block on the right. (Usually these columns are -dense or close to it so it isn't a problem to force them to be on the -right.) The reason is that I need the logarithm of the square of the -determinant of the triangular factor from the random effects columns -only. - -During the optimization phase we fix values of the three variances, -update the numeric values in the matrix, decompose, calculate the -determinant of the leading part of the triangular factor, and evaluate -the penalized residual sum of squares. The logarithm of the -determinant and the penalized residual sum of squares are combined to -create a criterion called the profiled log-likelihood which is to be -maximized - -The update operation changes the numerical values of the -data-determined part according to three multipliers. The first -multiplier is applied to the first 54711 columns, the next multiplier -is applied to the next 7915 columns and the last multiplier is applied -to the next 102 columns. The last column stays fixed. - -We start from the factors for the student, instructor and department -then generate the indicators to form Z. The factors can be -represented as integers and the response is a grade point score -(allowed to be half integers)." -Schmid,2,"Unstructured FEM, steady state thermal problem. D. Schmid, Univ. Oslo See for more details. See also:",https://sparse.tamu.edu/Schmid,generic spatial PDE/thermal,generic spatial PDE/thermal,1.0,1,thermal problem (2),1,real (2),1,symmetric (2),82654,1228045,14.857659641396666,moderate (4–100×),574458,8580313,square only,2,0,all square matrices symmetric/Hermitian,2,2,2,real only,0,2,coherent family/series,"Unstructured FEM, steady state thermal problem. D. Schmid, Univ. Oslo - -See http://www.milamin.org for more details. See also: - -Dabrowski, M., M. Krotkiewski, and D. W. Schmid (2008), MILAMIN: MATLAB-based -finite element method solver for large problems, Geochem. Geophys. Geosyst., 9, -Q04030, doi:10.1029/2007GC001719." -Bindel,6,"Coupled linear thermoeslasticity equations. D Bindel, UC Berkeley",https://sparse.tamu.edu/Bindel,generic spatial PDE/thermal,generic spatial PDE/thermal,1.0,1,thermal problem (6),2,real (4); complex (2),2,unsymmetric (4); symmetric (2),10605,10605,1.0,one size,144579,522387,square only,6,0,mixed among square matrices,2,2,2,real + complex,0,6,one broad domain; mixed matrix forms,"Coupled linear thermoeslasticity equations. D Bindel, UC Berkeley" -Koutsovasilis,2,"Symmetric indefinite matrix from P. Koutsovasilis, Tech. Univ. Dresden",https://sparse.tamu.edu/Koutsovasilis,structures/materials,structures/materials,1.0,1,structural problem (2),1,real (2),1,symmetric (2),71505,343791,4.807929515418502,moderate (4–100×),5294285,26837113,square only,2,0,all square matrices symmetric/Hermitian,2,2,0,real only,0,2,coherent family/series,"Symmetric indefinite matrix from P. Koutsovasilis, Tech. Univ. Dresden" -AMD,2,"Circuit simulation matrices from Ufuk Okuyucu, AMD, Inc.",https://sparse.tamu.edu/AMD,electrical/circuit/device,electrical/circuit/device,1.0,1,circuit simulation problem (2),1,real (2),1,symmetric (2),150102,1585478,10.562670717245606,moderate (4–100×),726674,7660826,square only,2,0,all square matrices symmetric/Hermitian,2,2,2,real only,0,0,coherent family/series,"Circuit simulation matrices from Ufuk Okuyucu, AMD, Inc." -Pajek,76,"Pajek data sets, V. Batagelj and A. Mrvar. Pajek data sets, from",https://sparse.tamu.edu/Pajek,graph/network data,graph/network data,1.0,8,directed graph (28); undirected graph (13); directed weighted graph (11); directed multigraph (11); undirected weighted graph (7); bipartite graph (3); weighted bipartite graph (2); undirected multigraph (1),3,binary (44); integer (28); real (4),3,unsymmetric (49); symmetric (22); rectangular (5),10,3774768,377476.8,wide (>100×),29,14970767,mixed,71,5,mixed among square matrices,22,22,1,real only,44,0,one broad domain; mixed matrix forms,"Pajek data sets, V. Batagelj and A. Mrvar. - -Pajek data sets, from http://vlado.fmf.uni-lj.si/pub/networks/data/, -Vladimir Batagelj and Andrej Mrvar (2006): Pajek datasets. If the source of -the data set is not specified otherwise, these data sets are licensed under a -Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License. - -Converted to sparse adjacency matrix format by Tim Davis, October 2006. -A(i,j) is the edge from node i to node j in the graph. If a graph is not -listed as weighted, and yet has non-binary entries, then the entry a(i,j) -reflects the number of edges (i,j) in the original data (the graph is a -multigraph). In some cases, it was clear that the extra entries were -duplicates, and the graph should not be a multigraph (problems GD97, EAT_RS, -and patents). - -All data was converted without loss of information, except where intentional. -Details are given below. - -The largest graph is ""patents"", with over 3.7 million nodes and nearly -15 million entries. - -""Pajek"" is Slovenian for ""spider"" and is roughly pronounced ""Pie yeck"". - -The Pajek networks are all 1-based (nodes are numbered starting at node 1). -Some networks when included in the Pajek Dataset were converted from 0-based -to 1-based. This was done by renaming node 0 as a new node n, and leaving -all other nodes unchanged. This differs from the MATLAB convention, which -is to add 1 to all row/column indices. This conversion was left as-is -(networks: USpowerGrid, EPA, and Kleinberg) when included in the UF -Sparse Matrix Collection. - -Remember that in MATLAB, A(i,:) is slow to compute; A(:,i) is much faster. If -you want row i of a sparse matrix, access the ith column of the transpose -instead. - - -================================================================================ -Summary: -================================================================================ - -CSphd: PhD's in computer science - 1882-by-1882 with 1740 nonzeros - kind: directed graph with auxiliary node data - -California: Kleinberg's web search of ""California"" - 9664-by-9664 with 16150 nonzeros - kind: directed graph - -Cities: www.lboro.ac.uk/gawc, data set 6 - 55-by-46 with 1342 nonzeros - kind: weighted bipartite graph with auxiliary node data - -EAT_RS: Edinburgh Associative Thesaurus (response-stimulus) - 23219-by-23219 with 325592 nonzeros - kind: directed weighted graph - -EAT_SR: Edinburgh Associative Thesaurus (stimulus-response) - 23219-by-23219 with 325589 nonzeros - kind: directed weighted graph - -EPA: Kleinberg, pages linking to www.epa.gov - 4772-by-4772 with 8965 nonzeros - kind: directed graph - -EVA: EVA, corporate inter-relationships - 8497-by-8497 with 6726 nonzeros - kind: directed graph - -Erdos02: Erdos collaboration network - 6927-by-6927 with 16944 nonzeros - kind: undirected graph - -Erdos971: Erdos collaboration network - 472-by-472 with 2628 nonzeros - kind: undirected graph - -Erdos972: Erdos collaboration network - 5488-by-5488 with 14170 nonzeros - kind: undirected graph with auxiliary node data - -Erdos981: Erdos collaboration network - 485-by-485 with 2762 nonzeros - kind: undirected graph - -Erdos982: Erdos collaboration network - 5822-by-5822 with 14750 nonzeros - kind: undirected graph with auxiliary node data - -Erdos991: Erdos collaboration network - 492-by-492 with 2834 nonzeros - kind: undirected graph - -Erdos992: Erdos collaboration network - 6100-by-6100 with 15030 nonzeros - kind: undirected graph with auxiliary node data - -FA: USF Free (word) Association Norms - 10617-by-10617 with 72176 nonzeros - kind: directed weighted graph with auxiliary node data - -GD00_a: Graph Drawing contest 2000 - 352-by-352 with 458 nonzeros - kind: directed graph - -GD00_c: Graph Drawing contest 2000 - 638-by-638 with 1041 nonzeros - kind: directed multigraph - -GD01_A: Graph Drawing contest 2001 - 953-by-953 with 645 nonzeros - kind: directed multigraph - -GD01_a: Graph Drawing contest 2000 - 311-by-311 with 645 nonzeros - kind: directed weighted graph - -GD01_b: Graph Drawing contest 2001 - 18-by-18 with 37 nonzeros - kind: directed graph - -GD01_c: Graph Drawing contest 2001 - 33-by-33 with 135 nonzeros - kind: directed multigraph with auxiliary node data - -GD02_a: Graph Drawing contest 2002 - 23-by-23 with 87 nonzeros - kind: directed graph - -GD02_b: Graph Drawing contest 2002 - 80-by-80 with 232 nonzeros - kind: directed graph - -GD06_Java: Graph Drawing contest 2006 - 1538-by-1538 with 8032 nonzeros - kind: directed graph - -GD06_theory: Graph Drawing contest 2006 - 101-by-101 with 380 nonzeros - kind: undirected graph - -GD95_a: Graph Drawing contest 1995 - 36-by-36 with 57 nonzeros - kind: directed graph - -GD95_b: Graph Drawing contest 1995 - 73-by-73 with 96 nonzeros - kind: directed graph - -GD95_c: Graph Drawing contest 1995 - 62-by-62 with 287 nonzeros - kind: directed graph - -GD96_a: Graph Drawing contest 1996 - 1096-by-1096 with 1677 nonzeros - kind: directed multigraph - -GD96_b: Graph Drawing contest 1996 - 111-by-111 with 193 nonzeros - kind: directed graph - -GD96_c: Graph Drawing contest 1996 - 65-by-65 with 250 nonzeros - kind: undirected graph - -GD96_d: Graph Drawing contest 1996 - 180-by-180 with 229 nonzeros - kind: directed graph - -GD97_a: Graph Drawing contest 1997 - 84-by-84 with 332 nonzeros - kind: directed graph - -GD97_b: Graph Drawing contest 1997 - 47-by-47 with 264 nonzeros - kind: undirected weighted graph - -GD97_c: Graph Drawing contest 1997 - 452-by-452 with 460 nonzeros - kind: directed multigraph - -GD98_a: Graph Drawing contest 1998 - 38-by-38 with 50 nonzeros - kind: directed graph - -GD98_b: Graph Drawing contest 1998 - 121-by-121 with 207 nonzeros - kind: directed graph - -GD98_c: Graph Drawing contest 1998 - 112-by-112 with 336 nonzeros - kind: undirected graph - -GD99_b: Graph Drawing contest 1999 - 64-by-64 with 252 nonzeros - kind: undirected multigraph - -GD99_c: Graph Drawing contest 1999 - 105-by-105 with 149 nonzeros - kind: directed graph with auxiliary node data - -GlossGT: graph and digraph glossary - 72-by-72 with 122 nonzeros - kind: directed graph - -HEP-th-new: High Energy Physics literature - 27770-by-27770 with 352807 nonzeros - kind: directed graph with auxiliary node data - -HEP-th: High Energy Physics literature - 27240-by-27240 with 342437 nonzeros - kind: directed graph - -IMDB: IMDB movie/actor network, www.imdb.com - 428440-by-896308 with 3782463 nonzeros - kind: bipartite graph with auxiliary node data - -Journals: Slovenian journals 1999-2000 - 124-by-124 with 12068 nonzeros - kind: undirected weighted graph with auxiliary node data - -Kohonen: Kohonen citation network - 4470-by-4470 with 12731 nonzeros - kind: directed graph with auxiliary node data - -Lederberg: Lederberg citation network - 8843-by-8843 with 41601 nonzeros - kind: directed multigraph with auxiliary node data - -NotreDame_actors: Barabasi's actor network (of www.imdb.com) - 392400-by-127823 with 1470404 nonzeros - kind: bipartite multigraph - -NotreDame_www: Barabasi's web page network of nd.edu - 325729-by-325729 with 929849 nonzeros - kind: directed graph - -NotreDame_yeast: Barabasi's yeast protein interaction - 2114-by-2114 with 4480 nonzeros - kind: undirected graph - -ODLIS: online dictionary of library & inf. sci - 2909-by-2909 with 18246 nonzeros - kind: directed multigraph - -Ragusa16: Ragusa set - 24-by-24 with 81 nonzeros - kind: directed weighted graph - -Ragusa18: Ragusa set - 23-by-23 with 64 nonzeros - kind: directed weighted graph - -Reuters911: Reuters news, Sept 11 to Nov 15, 2001 - 13332-by-13332 with 296076 nonzeros - kind: temporal undirected weighted graph - -Roget: Roget's Thesaurus, 1879 - 1022-by-1022 with 5075 nonzeros - kind: directed graph - -Sandi_authors: Klavzar bibliography - 86-by-86 with 248 nonzeros - kind: undirected weighted graph - -Sandi_sandi: Klavzar bibliography - 314-by-360 with 613 nonzeros - kind: bipartite graph - -SciMet: SciMet citation network - 3084-by-3084 with 10413 nonzeros - kind: directed multigraph with auxiliary node data - -SmaGri: SmaGri citation network - 1059-by-1059 with 4919 nonzeros - kind: directed multigraph with auxiliary node data - -SmallW: SmallW citation network - 396-by-396 with 994 nonzeros - kind: directed multigraph with auxiliary node data - -Stranke94: Slovene Parliamentary Parties 1994 - 10-by-10 with 90 nonzeros - kind: undirected weighted graph - -Tina_AskCal: student govt, Univ. Ljubljana, 1992 (ask opin., recall) - 11-by-11 with 29 nonzeros - kind: directed graph - -Tina_AskCog: student govt, Univ. Ljubljana, 1992 (ask, recognized) - 11-by-11 with 36 nonzeros - kind: directed graph - -Tina_DisCal: student govt, Univ. Ljubljana, 1992 (discuss, recall) - 11-by-11 with 41 nonzeros - kind: directed graph - -Tina_DisCog: student govt, Univ. Ljubljana, 1992 (discuss, recog.) - 11-by-11 with 48 nonzeros - kind: directed graph - -USAir97: US Air flights, 1997 - 332-by-332 with 4252 nonzeros - kind: undirected weighted graph - -USpowerGrid: US power grid - 4941-by-4941 with 13188 nonzeros - kind: undirected multigraph - -Wordnet3: Wordnet3 dictionary network - 82670-by-82670 with 132964 nonzeros - kind: directed weighted graph with auxiliary node data - -WorldCities: world city network - 315-by-100 with 7518 nonzeros - kind: weighted bipartite graph with auxiliary node data - -Zewail: Zewail citation network - 6752-by-6752 with 54233 nonzeros - kind: directed multigraph with auxiliary node data - -dictionary28: dictionary - 52652-by-52652 with 178076 nonzeros - kind: undirected graph - -divorce: divorce laws in the 50 US states - 50-by-9 with 225 nonzeros - kind: bipartite graph - -foldoc: free on-line dictionary of computing - 13356-by-13356 with 120238 nonzeros - kind: directed weighted graph - -football: World Soccer, Paris 1998 - 35-by-35 with 118 nonzeros - kind: directed weighted graph - -geom: collaboration in computational geometry - 7343-by-7343 with 23796 nonzeros - kind: undirected weighted graph - -internet: connectivity of internet routers - 124651-by-124651 with 207214 nonzeros - kind: directed weighted graph - -patents: NBER US Patent Citations, 1963-1999, cites 1975-1999 - 3774768-by-3774768 with 14970767 nonzeros - kind: directed graph with auxiliary node data - -patents_main: main NBER US Patent Citations, 1963-1999, cites 1975-1999 - 240547-by-240547 with 560943 nonzeros - kind: directed weighted graph with auxiliary node data - -yeast: yeast protein interaction network - 2361-by-2361 with 13828 nonzeros - kind: undirected graph with auxiliary node data - - - -================================================================================ -==> Citations/Cite2001.txt <== -================================================================================ - -Citation networks - -Graph Drawing 2001 Contest - Graph A Graph Drawing 2001 - -Contest task description for Graph A. Graph A in Pajek's format. Selected -citation networks from Garfield's collection Citation networks in Pajek's -format obtained from the Garfield's collection of citation network datasets -produced using HistCite software. All of these networks are the result of -searches in the WebofScience and are used with the permission of ISI of -Philadelphia. Please acknowledge this when publishing results based on these -data. - - SmallW: Papers that cite S Milgram's 1967 Psychology Today paper or use - Small World in title, Tue Jul 23 13:35:11 2002 - - SmaGri: Citations to Small & Griffith and Descendants, Thu Nov 8 10:40:55 - 2001 - - SciMet: Articles from or citing Scientometrics, 1978-2000, Wed Jun - 12 16:39:51 2002 - - Kohonen: Articles with topic ""self-organizing maps"" - or references to ""Kohonen T"", Tue Jun 18 10:39:51 2002 - - Zewail: Articles citing and by AH Zewail, 1970-2002, Wed Jul 31 15:46:38 - 2002 - - Lederberg: Articles by and citing J Lederberg, 1945-2002, Wed Jul 31 - 13:40:22 2002 - -Some references - - * Batagelj V.: Some Mathematics of Network Analysis. Network Seminar, - Department of Sociology, University of Pittsburgh, January 21, 1991. - - * Batagelj V., Mrvar A.: Graph Drawing Contest 2001 Layouts. - - * Garfield E, Sher IH, and Torpie RJ.: The Use of Citation Data in Writing - the History of Science. Philadelphia: The Institute for Scientific - Information, December 1964. - - * Garfield E.: From Computational Linguistics to Algorithmic - Historiography, paper presented at the Symposium in Honor of Casimir - Borkowski at the University of Pittsburgh School of Information Sciences, - September 19, 2001. - - * Hummon N.P., Doreian P.: Connectivity in a Citation Network: The - Development of DNA Theory. Social Networks, 11(1989) 39-63. - -Network Data, Pajek, Vlado - - -================================================================================ -==> Cities/Url.txt <== -================================================================================ - -http://www.lboro.ac.uk/gawc/data.html - -World City Relation Data - -This is the raison d'être of GaWC. For an introduction, see A Brief Guide to -Quantitative Data Collection at GaWC, 1997-2001. - -INVITATION - -World city researchers with relational data on world cities are encouraged to -post it here. Depositors may apply their own protocol for use by others. -(Contact p.j.taylor@lboro.ac.uk) PROTOCOL for using GaWC data - -As part of our mission to promote the study of non-state relations we wish to -encourage people to use the data posted here in their research and teaching. -However, we do ask that, as a matter of courtesy bordering on ethics, the -research project and the information gatherers are properly acknowledged along -with GaWC itself. Each data set will have a statement as to whom to acknowledge -and we ask that this be reproduced in every public use of the data. - - - -Data Set 1: US Cities: Surrogate Measures of Relations, 1990 (T.R. Longcore, C. -McWilliams and P.J. Taylor) - -Data Set 2: London and New York: Surrogate Measures of Relations, 1997 - -Data Set 3: Randstad Cities: Surrogate Measures of Relations, 1970-95 (A.M. -Beerda) - -Data Set 4: London's Relations with other Cities Using Producer Service Office -Geographies (J.V. Beaverstock, R.G. Smith and P.J. Taylor) - -Data Set 5: US Cities: Law Firm Office Geographies, 1998 (J.V. Beaverstock, -R.G. Smith and P.J. Taylor) - -Data Set 6: World Cities and Global Firms (P.J. Taylor and D.R.F. Walker) - -Data Set 7: Inter-City Matrices (P.J. Taylor and D.R.F. Walker) - -Data Set 8: World Cities: Regional Dimensions (P.J. Taylor, D.R.F. Walker and -M. Hoyler) - -Data Set 9: Cities Mentioned in Advertisements in The Economist (May 2000 - -January 2001) (P.J. Taylor) - -Data Set 10: The Relative Centrality of Cities Based upon Air Passenger Travel, -1977-1997 (M. Timberlake and D.A. Smith with K.-H. Shin) - -Data Set 11: World City Network: The Basic Data (P.J. Taylor and G. Catalano) - -Data Set 12: Global Network Service Connectivities for 315 Cities in 2000 (P.J. -Taylor) - - -DATA TOOLS - -Data Tool 1: Macro for Calculating Connectivities (max 254 cities x 255 firms) - (E.C. Rossi and C.C.C. Rossi) - -Data Tool 2: Macro for Calculating Connectivities (max 1100 cities x 255 - firms) (R. Aranya) - - - - World Cities and Global Firms - - P.J. Taylor and D.R.F. Walker - -These data consist of the distribution of offices for 46 'global' advanced -producer service firms over 55 world cities. Global firms are defined by having -offices in at least 15 different cities. World cities are from the GaWC -inventory of world cities (see GaWC Research Bulletin 6). Service values for a -firm in a city are given as 3, 2, 1 or 0 as defined in Data Set 4. - -These data are an experimental set of data derived from Data Set 4 (43 of the -firms qualify as global) but with three additional law firms added which do not -have London offices. For publications that make use of these data, see GaWC -Research Bulletin 13 and GaWC Research Bulletin 17. - - -Key to Data Set 6: - -ADVANCED PRODUCER SERVICE FIRMS (SECTOR, TABLE CODE AND FIRM) - -Accountancy - -KP KPMG -CL Coopers & Lybrand -EY Ernst & Young International -AA Arthur Andersen -PW Price Waterhouse - -Advertising - -GR Grey Worldwide -DM DMB&B (MacManus Group) -LH Lowe Howard -Spink -SS Saatchi and Saatchi -TH JWT (Thompson) -OM Ogilvy & Mather Direct Worldwide -DE Dentsu -YR Young & Rubicam -TM TMP -PU Publicis -AM Abbott Mead Vickers (BBDO) - -Banking and Finance - -HS HSBC -BA Barclays -NW NatWest Group -SC Standard Chartered Group -CR Creditanstalt-Bankverein -DR Dresdner Bank Group -MO J P Morgan -PA Compagnie Financière de Paribas SA -CS Credit Suisse -BB BBV Group -BT Banker's Trust -UB UBS -AB ABN-AMRO -CB Citibank - -Law - -BM Baker & McKenzie -WC White & Case -LL Leboeuf, Lamb, Greene & MacRae -CO Coudert Brothers -SK Skadden Arps -BC Bryan Cave -DW Dorsey & Whitney -GJ Graham & James -HH Hogan & Hartson -JD Jones, Day, Reavis & Pogue -MC Miller, Canfield, Paddock & Stone -SQ Squire, Sanders & Dempsey -WE Wilson, Elser, Moskowitz, Edelman & Dicker -AO Allen & Overy -CC Clifford Chance -FF Freshfields - -As per our data protocol, the following acknowledgement should accompany any -public use of the data: - -ACKNOWLEDGEMENT: The data used is from Data Set 6 from the GaWC Study Group and -Network (http://www.lboro.ac.uk/gawc/). It was created by P.J. Taylor and -D.R.F. Walker as part of their project ""World City Network: Data Matrix -Construction and Analysis"" and is based on primary data collected by J.V. -Beaverstock, R.G. Smith and P.J. Taylor (ESRC project ""The Geographical Scope -of London as a World City"" (R000222050)). - - -================================================================================ -==> Days/Days.txt <== -================================================================================ - -Pajek datasets - -Reuters terror news network (NOTE: renamed Reuters911 for UF sparse collection) - -Dataset days - -Description - -days.net undirected temporal network with 13332 vertices and 243447 edges. - - - -Days, DaysAll. Background - -Reuters terror news network Days.net in Pajek's format obtained from the CRA -networks produced by Steve Corman and Kevin Dooley at Arizona State University. -Please acknowledge this when publishing results based on these data. - -The Reuters terror news network is based on all stories released during 66 -consecutive days by the news agency Reuters concerning the September 11 attack -on the U.S., beginning at 9:00 AM EST 9/11/01. The vertices of a network are -words (terms); there is an edge between two words iff they appear in the same -text unit (sentence). The weight of an edge is its frequency. The network has n -= 13332 vertices (different words in the news) and m = 243447 edges, 50859 with -value larger than 1. There are no loops in the network. - -The network DaysAll.net contains the main connected component of the network -obtained by transforming the Reuters terror news network into a combined -network for all 66 days (union of all time points). It has 13308 vertices. - -The Reuters terror news network was used as a case network for the Viszards -visualization session on the Sunbelt XXII International Sunbelt Social Network -Conference, New Orleans, USA, 13-17. February 2002. - -History - - 1. 5-12. Dec 2001, networks constructed by S. Corman and his group - - 2. December 2001: CRA data transformed in Pajek format by V. Batagelj. - -References - - 1. Steven R. Corman, Timothy Kuhn, Robert D. Mcphee and Kevin J. Dooley - (2002): Studying Complex Discursive Systems: Centering Resonance Analysis of - Communication. (PDF) - - 2. Crawdad Technologies - - 3. Batagelj, V., & Mrvar, A. (2003): A density based approaches to network - analysis: Analysis of Reuters terror news network, Ninth Annual ACM SIGKDD, - Washington, D.C. (PDF); ( SVG) - - 4. Jeffrey C. Johnson and Lothar Krempel (2004): Network Visualization: The - ""Bush Team"" in Reuters News Ticker 9/11-11/15/01. The Journal of Social - Structure's, Vol. 5, No. 1. ( HTML) - -Pajek Data; 20. April 2006 / 27. January 2004 - -------------------------------------------------------------------------------- -When converted to a sparse adjacency matrix for the UF Sparse Matrix -Collection, Day{i} is the graph of the ith day. The diagonal entry -Day{i}(k,k) is 1 if word k appears in any news on the ith day. Note -that it may not appear in conjunction with other words in the same -sentence on that day. The sum of nnz(tril(Day{i})) for i=1:66 is 243,447. -The overall matrix A is the sum of the Day{i} matrices. A(i,j) is the number -of times words i and j appear in same sentence (for i not equal to j). A(k,k) -is the number of days the word k appears in any news report. -Note that this network has been renamed to Reuters911 here. - - -================================================================================ -==> EAT/EAT.txt <== -================================================================================ - - -Pajek datasets EAT The Edinburgh Associative Thesaurus - -Dataset eat - -Description - -eatRS.net directed network with 23219 vertices and 325624 arcs (564 loops); -stimulus X is associated with response Y N times. eatSR.net directed network -with 23219 vertices and 325589 arcs (564 loops); response X is associated with -stimulus Y N times. - -It seems that the SR network is incomplete and that it should be the inverse of -RS network. - - - -EAT response-stimulus (ZIP, 1321K) EAT stimulus-response (ZIP, 1306K) -Background - -The Edinburgh Associative Thesaurus (EAT) is a set of word association norms -showing the counts of word association as collected from subjects. This is not -a developed semantic network such as WordNet (3), but empirical association -data. - -The traditional way to collect word association norms is to show or say a word -to several people and ask them to say the word which first comes to their minds -upon receiving the stimulus. The link established between the stimulus and the -response is not semantically labelled (e.g. as synonym, antonym or by a case -relation) and can only be regarded as an association. - -The Edinburgh association norms were collected by growing the network from a -nucleus set of words. Responses were collected to words in this nucleus set, -then these responses were used to obtain further responses, and so on. In fact -the cycle was repeated about three times since by then the number of different -responses was so large that they could not be re-used as stimuli. Data -collection stopped when 8400 stimulus words had been used. Each stimulus word -was presented to 100 different subjects, each of whom received 100 words. This -gave rise to a total of 55732 nodes in the Thesaurus network. - -The subjects were mostly undergraduates from a wide variety of British -universities. The age range of the subjects was from 17 to 22 with a mode of -19. The sex distribution was 64 per cent male and 36 per cent female. The data -was collected between June 1968 and May 1971. - -The database consists of two files. The SR (stimulus-response) file, and the RS -(response-stimulus) file. Where words have been truncated to 19 characters to -save space the per cent character (%) has been placed as the 20th. - -The EAT here is that included in the MRC Psycholinguistic Database (4), for use -with the other measures available there. EAT Data Collection Procedure (1) -Stimulus words - -Since the objective was to obtain a reasonably large complete mapping of the -associative network for a large set of words, a systematic procedure of -'growing' the network from a small nucleus was followed. At first responses -were obtained from this nucleus set, then these responses were used as stimuli -to obtain further responses, and so on. In fact, this cycle was repeated about -three times, since by then the number of different responses was so large that -they could not all be re-used as stimuli. - -The nucleus set was derived from (a) the 200 stimuli used in the Palermo and -Jenkins (1964) normq (b) the 1,000 most frequent words of the Thorndike and -Lorge (1944) word frequency count and (c) the basic English vocabulary of Ogden -(1954). - -Data collection was stopped when 8,400 stimulus words had been used. Only a -minimal amount of selection of stimuli was applied in each cycle of the data -collection. Effectively all responses which were English words or meaningful -verbal units were included, including some phrasal forms and numerals. The data -cover a wide range of grammatical form classes and inflexional forms. -Procedure - -Each stimulus word was presented to 100 different subjects. Each subject -recieved a computer-printed sheet with 100 stimuli in randomised arrangement -(to minimize priming effects). The total contribution of each subject was thus -100 responses. The verbal environment of each word for each subject was -different. The instructions asked the subject to write down against each -stimulus the first word it made him think of, working as quickly as possible. -the total time spent on this task was measured, and most subjects completed the -sheet in five to ten minutes. - -Most of the data was collected in a classroom setting under supervision. Sheets -which had more than 25 percent blank responses were rejected and fresh data was -collected. - -History - - 1. Original EAT: George Kiss, Christine Armstrong, Robert Milroy and J.R.I. - Piper (collected between June 1968 and May 1971). - - 2. MRC Psycholinguistic Database Version modified by: Max Coltheart, S. - James, J. Ramshaw, B.M. Philip, B. Reid, J. Benyon-Tinker and E. Doctor; - made available by: Philip Quinlan. - - 3. The present version was re-structured and documented by Michael Wilson at - the Rutherford Appleton Laboratory in 1988 (2). - - 4. transformed in Pajek format: V. Batagelj, 31. July 2003. - - -References - - 1. Kiss, G.R., Armstrong, C., Milroy, R., and Piper, J. (1973) An - associative thesaurus of English and its computer analysis. In Aitken, A.J., - Bailey, R.W. and Hamilton-Smith, N. (Eds.), The Computer and Literary - Studies. Edinburgh: University Press. - - 2. The present version of The Edinburgh Associative Thesaurus (ZIP, 2.7M) - - 3. WordNet - - 4. MRC Psycholinguistic Database - - 5. Coltheart, M. (1981) MRC Psycholinguistic Database. Quarterly Journal of - Experimental Psychology, 3A, 497-505. - - 6. MRC Psycholinguistic Database 2 - -Pajek Data; 31. July 2003 - -------------------------------------------------------------------------------- -NOTE regarding conversion for UF sparse matrix collection: in the original data -there are 325,624 weighted edges. Of those only 32 edges are duplicates, and -all of them have identical edge weights as the edges they are duplicates of -These extraneous edges have been removed, since this this appears to be a -graph, not a multigraph. - - -================================================================================ -==> EVA/EVA.txt <== -================================================================================ - -Pajek datasets - -EVA Extraction, Visualization & Analysis of corporate -inter-relationships - -Dataset EVA - -Description - -EVA.net directed network with 8343 vertices and 6726 arcs. - - - -EVA.net (ZIP, 204K); included also original files names.txt and ownership.txt. -Background - -EVA / Denali is a multidisciplinary research project combining information -extraction, information visualization, and social network analysis techniques -to bring greater transparency to the public disclosure of inter-relationships -between corporations. The project is described in the paper [1]. - -Abstract: We present EVA, a prototype system for extracting, visualizing, and -analyzing corporate ownership information as a social network. Using -probabilistic information retrieval and extraction techniques, we automatically -extract ownership relationships from heterogeneous sources of online text, -including corporate annual reports (10-Ks) filed with the U.S. Securities and -Exchange Commission (SEC). A browser-based visualization interface allows users -to query the relationship database and explore large networks of companies. -Applying the system and methodology to the telecommunications and media -industries, we construct an ownership network with 6,726 relationships among -8,343 companies. Analysis reveals a highly clustered network, with over 50% of -all companies connected to one another in a single component. Furthermore, -ownership activity is highly skewed: 90% of companies are involved in no more -than one relationship, but the top ten companies are parents for over 24% of -all relationships. We are also able to identify the most influential companies -in the network using social network analysis metrics such as degree, -betweenness, cutpoints, and cliques. We believe this methodology and tool can -aid government regulators, policy researchers, and the general public to -interpret complex corporate ownership structures, thereby bringing greater -transparency to the public disclosure of corporate inter-relationships. - -Note that we do not have ownership relationships for all companies, so there -will be companies without links. - -An arc (X,Y) from company X to company Y exists in the network if in the -company X is an owner of company Y. - -Copyright 2002 by Denali Project. If you use this dataset in your research, -please use the citation to paper [1] as the source of the data. - -""Denali"" is the Native American name for the tallest peak in North America. It -means ""the Great One."" - -If you have any questions, please contact: John Chuang, Mike Gebbie, Gabe -Lucas, Kim Norlen. - -History - - 1. 2002 collection of original data by the EVA group; 2. March 6, 2004: - original data transformed into Pajek format EVA.net by V. Batagelj. - -References - - 1. Kim Norlen, Gabriel Lucas, Mike Gebbie, and John Chuang. EVA: Extraction, - Visualization and Analysis of the Telecommunications and Media Ownership - Network. Proceedings of International Telecommunications Society 14th - Biennial Conference, Seoul Korea, August 2002. (paper berkeley / local; - slides berkeley / local) - -Pajek Data; 6. March 2004 - - -================================================================================ -==> Foldoc/Foldoc.txt <== -================================================================================ - -Pajek datasets FOLDOC Free On-line Dictionary of Computing - -Dataset Foldoc - -Description - -foldoc.net valued directed network with 13380 vertices and 120700 arcs, value -is the multiplicity of arc. - - - -foldoc.net (ZIP, 517K) Background - -FOLDOC is a searchable dictionary of acronyms, jargon, programming languages, -tools, architecture, operating systems, networking, theory, conventions, -standards, mathematics, telecoms, electronics, institutions, companies, -projects, products, history, in fact anything to do with computing. - -The dictionary has been growing since 1985 and now contains over 13000 -definitions totalling nearly five megabytes of text. Entries are -cross-referenced to each other and to related resources elsewhere on the net. - -An arc (X,Y) from term X to term Y exists in the network iff in the FOLDOC -dictionary the term Y is used to describe the meaning of term X. - -Copyright 1993 by Denis Howe. Permission is granted to copy, distribute and/or -modify the FOLDOC dictionary under the terms of the GNU Free Documentation -License, Version 1.1 or any later version published by the Free Software -Foundation. - -Please refer to the dictionary as ""The Free On-line Dictionary of Computing, -http://www.foldoc.org/, Editor Denis Howe"" or similar. - -History - - 1. FOLDOC started in 1985 by Denis Howe; - - 2. in 1990 put on FTP, other sources included; - - 3. from 1994 available on the WWW, hundreds of contributors; - - 4. February/June 2002: Foldoc.net transformed in Pajek format and 'cleaned' -by V. Batagelj and A. Mrvar. - -References - - 1. Denis Howe, Editor: FOLDOC (2002): Free on-line dictionary of computing. - - 2. V. Batagelj, A. Mrvar, M. Zavešnik: Network analysis of texts. Language -Technologies 2002, Ljubljana, 14 - 15th October 2002, p. 143-148. - - 3. V. Batagelj, A. Mrvar, M. Zavešnik: Network analysis of dictionaries. -Language Technologies 2002, Ljubljana, 14 - 15th October 2002, p. 135-142. - - -Pajek Data; 30. January 2004 - - -================================================================================ -==> Football/Football.txt <== -================================================================================ - -Pajek datasets World Soccer Data Paris 1998 - -Dataset Football - -Description - -football.net - valued network with 35 vertices - - - -complete dataset - -Background - -Our network example describes the 22 soccer teams which participated in the -World Championship in Paris, 1998. - -Players of the national team often have contracts in other countries. This -constitutes a players market where national teams export players to other -countries. Members of the 22 teams had contracts in altogether 35 countries. - -Counting which team exports how many players to which country can be described -with a valued, asymmetric graph. The graph is highly unsymmetric: some -countries only export players, some countries are only importers. - -History - - 1. Data collected by Lothar Krempel, October 5, 1999 - - 2. Transformed in Pajek format by V. Batagelj, February 9, 2001. - -References - - 1. Dagstuhl seminar: Link Analysis and Visualization, Dagstuhl 1-6. July -2001 - -Pajek Data; 21. September 2005 - - -================================================================================ -==> Geom/Geom.txt <== -================================================================================ - - -Pajek datasets Geom Collaboration network in computational geometry - -Dataset Geom - -Description - -Geom.net valued undirected network with 7343 vertices and 11898 edges; author X -wrote a joint work with author Y; value is the number of joint works. - - - -Geom.net (ZIP, 139K) - -Background - -The network Geom.net is based on the file geombib.bib that contains -Computational Geometry Database, version February 2002. - -The authors collaboration network in computational geometry was produced from -the BibTeX bibliography [Beebe, 2002] obtained from the Computational Geometry -Database geombib, version February 2002 [Jones, 2002]. - -Two authors are linked with an edge, iff they wrote a common work (paper, book, -...). The value of an edge is the number of common works. Using a simple -program written in programming language Python, the BibTeX data were -transformed into the corresponding network, and output to the file in Pajek -format. - -The obtained network has 9072 vertices (authors) and 22577 edges (common papers -or books) / 13567 edges as a simple network - multiple edges between a pair of -authors are replaced with a single edge. - -The problem with the obtained network is that, because of non standardized -writing of the author's name, it contains several vertices corresponding to the -same author. For example: - - R.S. Drysdale, Robert L. Drysdale, Robert L. Scot Drysdale, R.L. Drysdale, -S. Drysdale, R. Drysdale, and R.L.S. Drysdale; - -or: - - Pankaj K. Agarwal, P. Agarwal, Pankaj Agarwal, and P.K. Agarwal - -that are easy to guess; but an 'insider' information is needed to know that -Otfried Schwarzkopf and Otfried Cheong are the same person. Also, no provision -is made in the database to discern two persons with the same name. We manually -produced the name equivalence partition and then shrank (in Pajek) the network -according to it. - -The reduced simple network contains 7343 vertices and 11898 edges. It is a -sparse network - its average degree is 2m/n = 3.24. - -History - - 1. Computational Geometry Database started in 1986 by merging two lists of -references - one compiled by Edelsbrunner and van Leeuwen and the other by -Guibas and Stolfi; - - 2. Computational Geometry Database, February 2002 Edition; - - 3. March-April 2002: Geom.bib transformed in Pajek format and 'cleaned' by -V. Batagelj and M. Zaveršnik. - -References - - 1. Beebe, N.H.F. (2002): Nelson H.F. Beebe's Bibliographies Page. - - 2. Jones, B., Computational Geometry Database, February 2002; FTP / HTTP - -Pajek Data; 27. January 2004 - - -================================================================================ -==> GlossGT/GlossGT.txt <== -================================================================================ - -Pajek datasets Graph and Digraph Glossary - -Dataset glossGT - -Description - -glossGT.net mixed network with 72 vertices and 114 arcs and 4 edges; word X is -related to word Y. - - - -glossTG.paj (4K) - -Background - -The network GlossGT.net is based on the file glossary.html containing Bill -Cherowitzo's Graph and Digraph Glossary. An arc (X,Y) from term X to term Y -exists in the network iff in the Graph and Digraph Glossary the term Y is used -to describe the meaning of term X. - -History - - 1. 1998-2001, Bill Cherowitzo prepared the glossary. 2. Graph and Digraph - Glossary transformed in Pajek format: Barbara Zemlji""c, 2. nov 2003. - -References - - 1. Bill Cherowitzo: Graph and Digraph Glossary, version 03-Feb-2001; (Course - page) - -Pajek Data; 25. January 2004 - - -================================================================================ -==> HEP-th/HEP.txt <== -================================================================================ - - -Pajek datasets KDD Cup 2003 High Energy Particle Physics (HEP) literature - -Dataset hep-th - -Description - -hep-th.net directed network with 27240 vertices and 342437 arcs (39 loops). -hep-th-new.net directed network with 27770 vertices and 352807 arcs (39 loops). -date-new.vec integer vector on 27770 vertices. year-new.vec integer vector on -27770 vertices. - - - -complete dataset (ZIP, 2607K) - -Background Citation data from KDD Cup 2003, a knowledge discovery and data -mining competition held in conjunction with the Ninth Annual ACM SIGKDD -Conference. - -The Stanford Linear Accelerator Center SPIRES-HEP database has been -comprehensively cataloguing the High Energy Particle Physics (HEP) literature -online since 1974, and indexes more than 500,000 high-energy physics related -articles including their full citation tree. - -The network contains a citation graph of the hep-th portion of the arXiv. The -units names are the arXiv IDs of papers; the relation is X cites Y . Note that -revised papers may have updated citations. As such, citations may refer to -future papers, i.e. a paper may cite another paper that was published after the -first paper. - -The SLAC/SPIRES dates for all hep-th papers are given. Some older papers were -uploaded years after their intial publication and the arXiv submission date -from the abstracts may not correspond to the publication date. An alternative -date has been provided from SLAC/SPIRES that may be a better estimate for the -initial publication of these old papers. - -The first version of data was updated on May 12, 2003. - -hep-th.net X cites Y relation, first version. hep-th-new.net X cites Y -relation, updated version. date-new.vec SLAC date of paper was transformed to -the number of days since August 1, 1991, updated version. year-new.vec year -from the SLAC date of paper, updated version. - -References - - 1. KDD Cup 2003 - - 2. arXiv - -Transformed in Pajek format by V. Batagelj, 26. July 2003 - - - -================================================================================ -==> IMDB/IMDB.txt <== -================================================================================ - -Pajek data set: IMDB, the Internet Movie Database, http://www.imdb.com. - --------------------------------------------------------------------------------' -Pajek network converted to sparse adjacency matrix for inclusion in UF sparse -matrix collection, Tim Davis. For Pajek datasets, See V. Batagelj & A. Mrvar, -http://vlado.fmf.uni-lj.si/pub/networks/data/. -------------------------------------------------------------------------------- -A(i,j)=1 if actor j played in movie i. colname(j,:) is the name of the actor. -Column j = 362,181 is Kevin Bacon. Year of movie i is year(i). -category(i) gives the category of movie i, use code(category(i),:). -Note that movie names are not provided. - -1: Drama -2: Short -3: Documentary -4: Comedy -5: Western -6: Family -7: Mystery -8: Thriller -9: - -10: Music -11: Crime -12: Sci-Fi -13: Horror -14: War -15: Fantasy -16: Romance -17: Adventure -18: Animation -19: Action -20: Musical -21: Film-Noir -99: Unknown. - -Remember that in MATLAB, A(i,:) is slow to compute; A(:,i) is faster. If you -want row i of a sparse matrix, access the ith column of the transpose instead. -------------------------------------------------------------------------------- - -A ""Bacon number"" is a measure of separation in the graph. Kevin Bacon has -a Bacon number of zero. Any actor who played in a movie with Kevin Bacon -has a Bacon number of 1. In general, an actor has a Bacon number equal to -1 + the minimum Bacon number of any actor he or she has been in a movie with. - -A similar definition can be extended to movies; a movie in which Kevin Bacon -appears has a Bacon number of 0. In general, the Movie-Bacon number is -the smallest Bacon number of any actor in that movie. - -The following code was used to compute the Bacon numbers: - - Bacon = Problem.aux.KevinBacon ; - A = Problem.A ; - [m n] = size (A) ; - C = [speye(m) A ; A' speye(n)] ; - x = zeros (m+n,1) ; - B = inf * ones (m+n,1) ; - x (m + Bacon) = 1 ; - B (m + Bacon) = 0 ; - tlen = 1 ; - for k = 1:m+n - x = x + C*x ; - t = find (x) ; - if (tlen == length (t)) - break - end - tlen = length (t) ; - B (t) = min (B (t), k) ; - end - MovieBacon = (B (1:m) - 1) / 2 ; - ActorBacon = B (m+1:end) / 2 ; - -Note that the movie names have been intentionally omitted from this version of -the data in the UF Sparse Matrix Collection, as has the name of movie code 9. - -The above MATLAB code is not terribly efficient; it makes k passes over the -matrix, each taking O(nnz(A)) time. Fortunately, k is a small constant (8). -A proper breadth-first search would take O(nnz(A)), regardless of k. - -================================================================================ -==> Journals/Journals.txt <== -================================================================================ - -Pajek datasets Slovenian magazines and journals 1999 and 2000 - -Dataset Journals - -Description - -Revije.net - valued network with 124 vertices Revije.clu - partition with 124 -vertices Revije.paj - Pajek project file with complete dataset. - - - -complete dataset (ZIP, 3K) - -Background - -Over 100.000 people have been asked which magazines and journals they read -(survey conducted in 1999 and 2000, source CATI Center Ljubljana). They listed -124 different magazines and journals. The collected data can be represented as -2-mode network: - - Delo Dnevnik Sl.novice ... -Reader1 X X ... -Reader2 X ... -Reader3 X ... -............ ..... ....... ......... ... - -Obtaining 1-mode network - -From 2-mode network reader/journal we generated ordinary network, where the -vertices are journals - - * undirected edge with value a between journals means the number of readers - * of both journals. loop on selected journal means the number of all - * readers that read this journal. - -Obtained matrix (A): - - Delo Dnevnik Sl.novice ... -Delo 20714 3219 4214 ... -Dnevnik 15992 3642 ... -Sl. novice 31997 ... -........ ...... ..... ..... ... - -The second ordinary network on readers would be huge (more than 100.000 -vertices) containing large cliques (readers of particular journal). - -History - - 1. Transformed in journal X journal matrix, 26. December 2000. - - 2. Transformed in Pajek format by V. Batagelj, 21. December 2003. - -References - - 1. Dagstuhl seminar: Link Analysis and Visualization, Dagstuhl 1-6. July 2001 - -Pajek Data; 21. December 2003 - - -================================================================================ -==> KEDS/KEDS.txt <== -================================================================================ - -NOTE: the KEDS data has not been included. - -Pajek datasets KEDS The Kansas Event Data System - -Dataset KEDS - -Description - -GulfLDays.net directed multirelational temporal network with 174 vertices and -57131 arcs. From 'leads' Gulf event data, granularity is 1 day. - -GulfLMonths.net directed multirelational temporal network with 174 vertices and -57131 arcs. From 'leads' Gulf event data, granularity is 1 month. - -GulfLDow.net directed multirelational temporal network with 174 vertices and -57131 arcs. From 'leads' Gulf event data, in day of the week classes. - -GulfADays.net directed multirelational temporal network with 202 vertices and -304401 arcs. From Gulf event data, granularity is 1 day. - -GulfAMonths.net directed multirelational temporal network with 202 vertices and -304401 arcs. From Gulf event data, granularity is 1 month. - -LevantDays.net directed multirelational temporal network with 485 vertices and -196364 arcs. From Levant event data, granularity is 1 day. - -LevantMonths.net directed multirelational temporal network with 485 vertices -and 196364 arcs. From Levant event data, granularity is 1 month. - -BalkanDays.net directed multirelational temporal network with 325 vertices and -78667 arcs. From Balkan event data, granularity is 1 day. - -BalkanMonths.net directed multirelational temporal network with 325 vertices -and 78667 arcs. From Balkan event data, granularity is 1 month. - - - -GulfLDays.net (ZIP, 239K) - -GulfLMonths.net (ZIP, 197K) - -GulfLDow.net (ZIP, 213K) - -GulfADays.net (ZIP, 1078K) - -GulfAMonths.net (ZIP, 941K) - -LevantDays.net (ZIP, 855K) - -LevantMonths.net (ZIP, 735K) - -BalkanDays.net (ZIP, 335K) - -BalkanMonths.net (ZIP, 288K) - -Background - -KEDS - The Kansas Event Data System uses automated coding of English-language -news reports to generate political event data focusing on the Middle East, -Balkans, and West Africa. These data are used in statistical early warning -models to predict political change. The ten-year project is based in the -Department of Political Science at the University of Kansas; it has been funded -primarily by the U.S. National Science Foundation. KEDS data sets from KEDS -data collection. - -Gulf data set: This data set covers the states of the Gulf region and the -Arabian peninsula for the period 15 April 1979 to 31 March 1999. The source -texts prior to 10 June 97 were located using a NEXIS search command -specifically designed to return relevant data. There are two versions of the -data: a set coded from the lead sentences only (57,000 events), and a set coded -from full stories (304,000 events). There are some errors in the GulfAll data. -Events 118196 and 118197 have REUT-0 in place of the date; and in event 173526 -the first actor is missing. In Gulf99All.dat the wrong dates are replaced with -890319, and the incomplete event is skiped. - -Levant data set: Folder containing WEIS-coded events (N=196,337) for dyadic -interactions within the following set of countries: Egypt, Israel, Jordan, -Lebanon, Palestinians, Syria, USA, and USSR/Russia. Coverage is April 1979 to -June 2004. TABARI coding dictionaries are also included. There are some errors -(333) in data set - relation codes 012], O24, O53, 213] are replaced with 012, -024, 053, 213 in Levant.dat. Some events don't have description codes - they -are marked with *** in relation labels in *.net files. - -Balkans data set, 1989-2003: Folder containing WEIS-coded events (N = 78,667) -for the major actors (including ethnic groups) involved in the conflicts in the -former Yugoslavia. Coverage is April 1989 through July 2003. TABARI coding -dictionaries are included in the folder. There are some errors (197) in data -set - relation codes ---], O24, O53 are replaced with 000, 024, 053 in -Balkan.dat. Some events don't have description codes - they are marked with *** -in relation labels in *.net files. - -The original data sets are on MAC files. They should be saved as PC files -before processing. - -History - - 1. Program Recode (in Delphi) by Vladimir Batagelj, Ljubljana, July 25, 2003 - - 2. Program KEDSrec adapted for KEDS from Recode; Gulf (leads) data recoded -into Pajek's format, by Vladimir Batagelj, Ljubljana, November 3, 2003 - - 3. Support for multiple relations networks added to program KEDSrec by -Vladimir Batagelj, Ljubljana, November 22, 2004 - - 4. KedsR - functionality of KEDSrec implemented in R; Gulf (leads) data -recoded into Pajek's format (days, months, day of the week), by Vladimir -Batagelj, Ljubljana, November 27, 2004 - - 5. WeisR - commands from KedsR adapted for WEIS format (similar to KEDS but -TAB delimited); Balkan and Levant data recoded into Pajek's format (days, -months), by Vladimir Batagelj, Ljubljana, November 28, 2004 - - 6. Gulf99All is a large data set - sapply commands in KedsR had to be -replaced by while loops; Gulf (all) data recoded into Pajek's format (days, -months), by Vladimir Batagelj, Ljubljana, November 29, 2004 - - -References - - 1. StuffIt - uncompress program for SIT files - -Pajek Data; 29. November 2004 / 24. November 2004 - - -================================================================================ -==> Mixed/Mixed.txt <== -================================================================================ - - -Pajek datasets from different sources - -US power grid - -Dataset: USpowerGrid - -US power grid - unweighted network from Panayiotis Tsaparas' page. Adapted for -Pajek by V. Batagelj, March 19, 2006 - -File: USpowerGrid.net - undirected network with 4941 vertices and 6594 edges - - - -Kleinberg's web graphs - -Dataset: California - Pages matching the query ""California"". - -This graph was constructed by expanding a 200-page response set to a search -engine query 'California', as in the hub/authority algorithm. Obtained from Jon -Kleinberg's page. Adapted for Pajek by V. Batagelj, March 19, 2006 - -File: California.net - directed network with 9664 vertices and 16150 arcs. - - -References: Amy N. Langville and Carl D. Meyer: A Reordering for the PageRank -problem (March 2004) PDF J. Kleinberg. Authoritative sources in a hyperlinked -environment. Proc. 9th ACM-SIAM Symposium on Discrete Algorithms, 1998. -Extended version in Journal of the ACM 46(1999). PDF - -Dataset: Epa - Pages linking to www.epa.gov. - -This graph was constructed by expanding a 200-page response set to a search -engine query, as in the hub/authority algorithm. Obtained from Jon Kleinberg's -page. Adapted for Pajek by V. Batagelj, March 19, 2006 - -File: Epa.net - directed network with 4772 vertices and 8965 arcs. - - - Stanford web graphs - -Dataset: StanfordWeb - Stanford Web Matrix. - -This graph was obtained from a September 2002 crawl (281903 pages, 2382912 -links). The matrix rows represent the inlinks of a page, and the columns -represent the outlinks. Downloaded from Sepandar D. Kamvar's page. Adapted for -Pajek by V. Batagelj, March 19, 2006 - -File: StanfordWeb.net - directed network with 281903 vertices and 2382912 arcs. - - -References: Sepandar D. Kamvar, Taher H. Haveliwala, Christopher D. Manning, -and Gene H. Golub, ""Exploiting the Block Structure of the Web for Computing -PageRank."" Preprint (March, 2003). PDF - -Dataset: StanfordBerkeleyWeb - Stanford-Berkeley Web Matrix. - -This graph was obtained from a December 2002 crawl (685230 pages, 8006115 -links). The matrix rows represent the inlinks of a page, and the columns -represent the outlinks. Downloaded from Sepandar D. Kamvar's page. Adapted for -Pajek by V. Batagelj, March 19, 2006 - -NOTE by Tim Davis: nodes 683,447 to 685,230 in the Stanford Berkeley web data, -discussed above, are not part of the true results. Kamvar's MATLAB script for -processing the data deletes those nodes. This graph is already in the UF -Sparse Matrix Collection, in the correct size. Also note that Kamvar doesn't -consider multiple links from page i to j to be significant. Thus, duplicate -edges (i,j) in this graph should be ignored. The graph in the UF collection is -thus binary. - -File: StanfordBerkeleyWeb.net - directed network with 685230 vertices and -8006115 arcs. - - -References: Sepandar D. Kamvar, Taher H. Haveliwala, Christopher D. Manning, -and Gene H. Golub, ""Exploiting the Block Structure of the Web for Computing -PageRank."" Preprint (March, 2003). PDF - -World City Network - -Dataset: WorldCities - -These data constitute the empirical basis for measuring the world city network -as described in P.J. Taylor (2004) World City Network: A Global Urban Analysis -(London: Routledge). The data were produced by P.J. Taylor and G. Catalano. -These data were collected in 2000. They are the service values (indicating the -importance of a city in the office network of a firm) of 100 global service -firms distributed across 315 cities worldwide. All firms supply advanced -producer services (accountancy, advertising, banking/finance, insurance, law, -and management consultancy) through offices in at least 15 cities (including at -least one in each of Pacific Asia, western Europe and northern America). The -following coding information is given to help understand and evaluate the data. -Downloaded from GaWC Data Set 11 page. Transformed in Pajek format by V. -Batagelj, March 20, 2006 - -Files: - -WorldCities.net - two-mode network with 415=315+100 vertices and 7518 arcs. - -WorldCities.mat - same network in Pajek matrix format. - -WorldCities.net - type of service partition: 0 - city; 1 - accountancy; 2 - -advertising; 3 - banking/finance; 4 - insurance; 5 - law; 6 - management -consultancy. - - - -References: Taylor, P.J. (2004): World city network: a global urban analysis. -London and New York: Routledge. - -Pajek Data; 19. March 2006 - - -================================================================================ -==> ND/ND.txt <== -================================================================================ - - -Pajek datasets Notre Dame Self-Organized Networks Database - -Datasets NDwww, NDactors, NDyeast - -Description - -Notre Dame Self-Organized Networks: - - 1. NDwww.net directed network with 325729 vertices and 1497135 arcs (27455 -loops); page X is linked to page Y. - - 2. NDactors.net undirected two-mode network with 520223 vertices (392400 -players, 127823 movies) and 1470418 edges; player X plays in movie Y. - - 3. NDyeast.net undirected network with 2114 vertices and 2277 edges (74 -loops); protein X interacts with protein Y. - - - -NDwww.net (ZIP, 2050K) NDactors.net (ZIP, 4150K) NDyeast.net (ZIP, 7K) - -Background - -The networks ND*.net are based on the files from Notre Dame Self-Organized -Networks Database. To transform the data into Pajek format: vertex 0 was -replaced by the vertex number equal to the number of vertices in a network; -Pajek keywords were inserted; and the network was saved in the short (as lists -of neighbors) format. - - 1. World-Wide-Web:: Each number represents webpage within nd.edu domain. -Arcs: From page -> To page Réka Albert, Hawoong Jeong and Albert-László -Barabási: Diameter of the World Wide Web, Nature 401, 130 (1999) [ PDF ] See -also a decompostion of this network in V. Batgelj, A. Mrvar: How to analyze -large networks with Pajek? - - 2. Actor: Actor network data: (based on www.imdb.com) In the original ND -network file: each line corresponds to one movie, each number represents actor: -number_1 number_2 ... number_k (k actors who play in the same movie). -Albert-László Barabási, Réka Albert: Emergence of scaling in random networks, -Science 286, 509 (1999) [ PDF ] - - 3. Protein Interaction Network for Yeast: Each number represents protein in -protein interaction network of yeast. Edges: From protein -> To protein. For -other datasets used in supplementary material, please refer indicated -references. Hawoong Jeong, Sean Mason, Albert-László Barabási and Zoltán N. -Oltvai: Centrality and lethality of protein networks, Nature 411, 41 (2001) [ -PDF ] See also Yeast data - -History - - 1. Notre Dame Networks Database put on WWW by the Notre Dame team, 2001; - - 2. 23-25. July 2001: ND nets transformed in Pajek format by V. Batagelj. - - 3. 23. May 2004: ND nets in Pajek format transformed in short (lists of -neighbors) Pajek format by V. Batagelj. - -References - - 1. Self-Organized Networks Database, University of Notre Dame. - -Copyright Extract from the Notre Dame Networks Database page: ""... Feel free to -use these data in your research."" Mail to Hawoong Jeong (author of original ND -networks). - -Pajek Data; 23. May 2004 - - -================================================================================ -==> ODLIS/ODLIS.txt <== -================================================================================ - - -Pajek datasets - - ODLIS Online Dictionary of Library and Information Science - -Dataset odlis - -Description - -odlis.net directed network with 2909 vertices and 18419 arcs (5 loops). - - - -odlis.net (ZIP, 62K) Background - -The network Odlis.net is based on the ODLIS: Online Dictionary of Library and -Information Science. version December 2000. - -ODLIS is designed to be a hypertext reference resource for library and -information science professionals, university students and faculty, and users -of all types of libraries. The primary criterion for including a new term is -whether a librarian or other information professional might reasonably be -expected to encounter it at some point in his (or her) career, or be required -to know its meaning in the course of executing his or her responsibilities as a -librarian. The vocabulary of publishing, printing, book history, literature, -and computer science has been included when, in the author's judgment, a -definition might prove helpful, not only to library and information -professionals, but also to laypersons. - -An arc (X,Y) from term X to term Y exists in the network iff in the ODLIS -dictionary the term Y is used to describe the meaning of term X. - -ODLIS is the work of Joan M. Reitz, Assistant Professor/Instruction Librarian -at the Ruth A. Haas Library, Western Connecticut State University (WCSU) in -Danbury, CT. Ms. Reitz holds an M.L.I.S. degree (1991) from the University of -Washington in Seattle and an M.A. degree (1998) in European History from -Western Connecticut State University. Her primary research interests are the -history of the book and history of political and social revolutions. - -History - - 1. ODLIS began at the Haas Library in 1994 as a five-page photocopied -handout intended for undergraduates not fluent in English, and students with -limited exposure to library terminology. - - 2. In 1996, it was expanded and converted to HTML format for installation on -the WCSU Libraries HomePage under the title Hypertext Library Lingo: A Glossary -of Library Terminology. - - 3. In 1997, many more hypertext links were added and the format improved in -response to suggestions from users. - - 4. During the summer of 1999, several hundred terms and definitions were -added, and a generic version created which omitted all references to the -specific conditions and practices at the Haas Library. - - 5. In the fall of 1999, the glossary was expanded to 1,800 terms, renamed to -reflect its extended scope, and copyrighted. - - 6. In February, 2000, ODLIS was indexed in Yahoo! under ""Reference - -Dictionaries - Subject."" It is also indexed in the WorldCat database in OCLC -FirstSearch. - - 7. During the year 2000, the dictionary was expanded to 2,600 terms. On -average, it has received over 6,200 visits per month since January 2, 2000. - - 8. December 2000: ODLIS transformed in Pajek format and 'cleaned' by A. -Mrvar and V. Batagelj. - -References - - 1. Joan M. Reitz (2002): ODLIS: Online Dictionary of Library and Information -Science. - -Pajek Data; 27. January 2004 - - -================================================================================ -==> Patents/Patents.txt <== -================================================================================ - - -Pajek datasets Patents The NBER U.S. Patent Citations Data File - -Dataset patents - -Description - -patents.net directed network with 3,774,768 vertices and 16,522,438 arcs (1 -loop). - - - -NET, NAM, Year, Date, Cat, Class, Country, Subcat, AppYear Patents NET / main -subnetwork Background - -The network Patents is based on the The NBER U.S. Patent Citations Data File, -version 2001. - -These data comprise detail information on almost 3 million U.S. patents granted -between January 1963 and December 1999, all citations made to these patents -between 1975 and 1999 (over 16 million), and a reasonably broad match of -patents to Compustat (the data set of all firms traded in the U.S. stock -market). - -These data are described in detail in Hall, B. H., A. B. Jaffe, and M. -Tratjenberg (2001). ""The NBER Patent Citation Data File: Lessons, Insights and -Methodological Tools."" NBER Working Paper 8498. ALL USERS OF THESE DATA SHOULD -READ THIS PAPER, AND SHOULD CITE IT AS THE SOURCE OF THE DATA - -Further documentation on uses of the patent citation data, including the -methodology paper and a CD containing the complete dataset itself, is available -in the book Patents, Citations and Innovations: A Window on the Knowledge -Economy by Adam Jaffe and Manuel Trajtenberg, MIT Press, Cambridge (2002). The -book may be ordered from MIT Press. ISBN 0-262-10095-9. - -History - - 1. Data produced by United States Patent and Trademark Office - - 2. Hall, B. H., A. B. Jaffe, and M. Tratjenberg prepared the NBER dataset, - 2001 - - 3. July 10, 2003: NBER data transformed in Pajek format by V. Batagelj. - - -References - - 1. Hall, B. H., A. B. Jaffe, and M. Tratjenberg (2001): The NBER U.S. Patent - Citations Data File. - - 2. United States Patent and Trademark Office, Patent Number Search - - 3. Batagelj V. (2003): Efficient Algorithms for Citation Network Analysis. - -Pajek Data; 27. January 2004 - -------------------------------------------------------------------------------- -NOTE regarding conversion for UF sparse matrix collection: in original the data -there are 14,973,817 edges (unweighted). Of this, 3050 are duplicates -This graph is binary; the duplicates have been removed. -Also, the original data has auxiliary data for all 6,009,554 US Patents in this -time period. This patent network has only 3,774,768 patents, and the auxiliary -data (appyear, class, etc.) is matched here to the nodes of the graph. - -================================================================================ -==> Roget/Roget.txt <== -================================================================================ - - -Pajek datasets Roget Roget's Thesaurus, 1879 - -Dataset Roget - -Description - -roget.net directed network with 1022 vertices and 5075 arcs (1 loop); word X is -related to word Y. - - - -Roget.net (ZIP, 17K) - -Background - -The network Roget.net is based on the file roget.dat from the Stanford -GraphBase that contains cross-references in Roget's Thesaurus, 1879. - -Dr. Peter Mark Roget (1779-1869) philologist, scientist, physician. The name -Roget could soon become a virtual synonym for the word ""synonym"". For those who -use Roget's Thesaurus it is one of the three most important books ever -printed...along with The Bible and Webster's Dictionary. In order to -communicate one's exact intention...or one's precise meaning, the Thesaurus, -being a list of synonyms or verbal equivalents, is a necessary tool. The first -draft of the Thesaurus was written in 1805, two years before Webster started on -his dictionary. However for a period of 47 years Dr. Roget used his manuscript -as his personal, secret, treasure trove. Not until he was 73 years old did he -decide to reveal and publish this great manuscript. - -Since 1852, Roget's Thesaurus has never been out of print. In fact, each -succeeding edition has increased the popularity of the work. The original -15,000 words included in the 1805 manuscript has increased to over a quarter of -a million in the 1992 edition (the tenth printing). With such an increase in -size, it is encouraging to notice that the basic content still remains -intact..... for example, where the 1805 Thesaurus traces the word: existence: -""Ens, entity, being, existence, essence..."", the 1992 Thesaurus contains -existence: ""existence, being, entity, ens,...essence..."" - -Each vertex of the graph corresponds to one of the 1022 categories in the 1879 -edition of Peter Mark Roget's Thesaurus of English Words and Phrases, edited by -John Lewis Roget. An arc goes from one category to another if Roget gave a -reference to the latter among the words and phrases of the former, or if the -two categories were directly related to each other by their positions in -Roget's book. For example, the vertex for category 312 (`ascent') has arcs to -the vertices for categories 224 (`obliquity'), 313 (`descent'), and 316 -(`leap'), because Roget gave explicit cross-references from 312 to 224 and 316, -and because category 312 was implicitly paired with 313 in his scheme. - -History - - 1. Original Roget's Thesaurus was published in 1852. - - 2. Peter's son John Luis Roget published the second, improved edition in -1879. - - 3. Project Gutenberg Roget's Thesaurus (1911 edition) put into electronic -format in 1991. - - 4. Graph Roget.dat of cross-references based on the second edition was -produced for Stanford GraphBase (SGB) in 1992/3. - - 5. MICRA (Pat Cassidy) prepared the electronic version of the 1911 Roget's -Thesaurus that is widely available on the internet. - - 6. SGB Roget.dat transformed in Pajek format: A. Mrvar, 5. December 1996. - -References - - 1. Peter Mark Roget: Roget's Thesaurus of English Words and Phrases - - 2. Project Gutenberg: Roget's Thesaurus - - 3. Donald E. Knuth: The Stanford GraphBase: A Platform for Combinatorial -Computing . New York: ACM Press, 1993 - - 4. The Stanford GraphBase: roget.dat, version 15.6.1993 - - 5. Pat Cassidy: MICRA / Factotum - - 6. CIDE (Collaborative International Dictionary of English), GNU 1996-2002 - -Pajek Data; 23. January 2004 - - -================================================================================ -==> Sandi/Sandi.txt <== -================================================================================ - - REFERENCES - -DATASET SANDI - -DESCRIPTION 2-mode 674×314 network. - -BACKGROUND These data were obtained from the bibliography of the book Imrich W, -Klavžar S. (1999) Graph products. The result is a author-by-paper 2-mode -network: arc (i,j) - author i is the (co)author of the paper j. - -DERIVED DATA AUTHORS authors network - -REFERENCES - - * Imrich W, Klavžar S. (1999). Graph products. References. - - - -================================================================================ -==> Stranke94/Slovene_Parties.txt <== -================================================================================ - -Pajek datasets Slovene Parliamentary Parties 1994 - -Dataset Stranke94 - -Description - -Stranke94.net - valued signed network with 10 vertices - - - -Stranke94.net Background - -Relations between Slovene parliamentary political parties: - - * SKD - Slovene Christian Democrats; - - * ZLSD - Associated List of Social Democrats; - - * SDSS - Social Democratic Party of Slovenia; - - * LDS - Liberal Democratic Party; - - * ZSESS - first of two Green Parties, separated after 1992 elections; - - * ZS - second Green Party; - - * DS - Democratic Party; - - * SLS - Slovene People's Party; - - * SNS - Slovene National Party; - - * SPS SNS - a group of deputies, former members of SNS, separated after - * 1992 elections - -were estimated by the members of the Slovene National Parliament. So the -respondents were well informed and competent to give such estimations. In the -questionnaire designed by a group of experts on Parliament activities, some -questions about the political space and its dimensions were included and the -following question about relations between parliamentary political parties: - - If various criteria (or various dimensions of the political space) are - taken into account, some parties are by average closer than others. How - would you personally estimate distances between pairs of parties in the - political space? - - Please, estimate the distance between each pair of parties on the scale - from -3 to +3, where: - - -3 means that parties are very dissimilar; - - -2 means that parties are quite dissimilar; - - -1 means that parties are dissimilar; - - 0 means that parties are neither dissimilar nor similar (somewhere in - between); - - +1 means that parties are similar; - - +2 means that parties are quite similar; - - +3 means that parties are very similar. - -To collect estimations each respondent was given a 10-party by 10-party table -with empty cells in the upper triangle. The diagonal and the lower triangle -were coloured in black. Each respondent had to estimate relations between 45 -pairs of parties. - -The measures of central tendency were computed on the basis of the estimations -given by 72 out of 90 members of the Parliament. 17 members of the Parliament -were not available at the time of interviewing and one refused respond. 64 -respondents out of the 72 estimated all 45 requested party relations. Only two -out of 8 respondents with missing values have a large number of missing values -(namely 40), the rest of them have 5 to 10 missing values. As far as the -parties (variables) are considered, there are from 0 to 8 missing values and a -recognisable pattern: relations involving ZS (which had no representatives in -the Parliament at the time of interviewing) include from 6 to 8 missing values, -others from 0 to 3 missing values. - -The weights of arcs in the network are averages of values multiplied by 100 and -rounded. The missing values were excluded. - -History - - 1. Stran format by V. Batagelj, 19. October 1994. - - 2. Transformed in Pajek format by V. Batagelj, 15. February 2004. - -References - - 1. Samo Kropivnik and Andrej Mrvar: An Analysis of the Slovene Parliamentary - Parties Network. Developments in Statistics and Methodology. (A. Ferligoj, - A. Kramberger, editors) Metodološki zvezki 12, FDV, Ljubljana, 1996, p. - 209-216. - - 2. Patrick Doreian and Andrej Mrvar(1996): A Partitioning Approach to - Structural Balance. Social Networks, 18, p. 149-168. - -Pajek Data; 15. February 2004 - - -================================================================================ -==> Tina/Tina.txt <== -================================================================================ - -Pajek datasets -Student Government of the University of Ljubljana / 1992 - -Dataset Tina - -Description - -DisCal.net - network with 11 vertices and 41 arcs -DisCog.net - network with 11 vertices and 48 arcs -AskCal.net - network with 11 vertices and 29 arcs -AskCog.net - network with 11 vertices and 36 arcs -AnsCalT.net - network with 11 vertices and 41 arcs -AnsCogT.net - network with 11 vertices and 42 arcs -DisCalSn.net - network with 11 vertices and 41 arcs -Tina.paj - Pajek project file with complete dataset. - - - -complete dataset (ZIP, 3K) -DisCalSn.net -Background -In the experiment two alternative methods were used for collection of network data: - - Recall: Members of the group were asked to identify the members of their - egocentric networks by memory. The criteria for enumeration was frequency - of the recalled communications. - - Recognition: The list of all members of the group was given to each - member. They were first asked to identify who they communicate with and - than to select the persons they communicate with most often. - -The number of listed persons was not limited in any method. - -The analyzed network consisted of communication interactions among twelve -members and advisors of the Student Government at the University in Ljubljana. -The results of the measurement are not real interactions among actors but -cognition about communication interactions. Data were collected with face to -face interviews which lasted from 20 to 30 minutes. Interviews were conducted -in May 1992. - -Communication flow among actors was identified by three questions: - - Who of the members and advisors of the Student government do you (most - often) informally discuss with? - - Which members and advisors of the Student Government do you (most often) - ask for an opinion? - - Which of the members and advisors of the Student Government (most often) - ask you for an opinion? - -The content of the communication flow was limited to the matters of the Student -Government. The time frame was also defined: questions were referred to the six -months period (from the formation of the government to the day of the -interview). - -Only one respondent listed all the others under the recognition method for two -relations (discussion, asking for an opinion). For that respondent the first -group was defined arbitrary. The cut point was determined by the average number -of selected persons at the recognition method (3.5 for the first relation and -4.5 for the second). - -One respondent refused to cooperate in the experiment. As he was not considered -in the analysis, the network consists of eleven actors. - - -History - - - 1. Transformed in Stran format by V. Batagelj, 28. July 1993. - 2. Transformed in Pajek format by V. Batagelj, 8. August 2003. - -Files - -DisCal.net - discussion, recall -DisCog.net - discussion, recognition -AskCal.net - asking for an opinion, recall -AskCog.net - asking for an opinion, recognition -AnsCalT.net - being asked for an opinion, recall (transposed) -AnsCogT.net - being asked for an opinion, recognition (transposed) -DisCalSn.net - discussion, recall / short names -Tina.paj - Pajek project file with complete dataset. -References - - 1. Valentina Hlebec: Recall Versus Recognition: Comparison of the Two - Alternative Procedures for Collecting Social Network Data. Developments in - Statistics and Methodology. (A. Ferligoj, A. Kramberger, editors) - Metodološki zvezki 9, FDV, Ljubljana, 1993, p. 121-129. - -Pajek Data; 8. August 2003 - - -================================================================================ -==> WassermanFaust/WassermanFaust.txt <== -================================================================================ - -NOTE: the Wasserman/Faust networks have not been included. - - -Pajek datasets -from the book -Social Network Analysis: Methods and Applications -Wasserman and Faust, 1994 -Wasserman and Faust datasets - -Dataset WaFa - -There are five network examples: Krackhardt's high-tech managers, Padgett's -Florentine families, Freeman's EIES network, Countries trade network, and -Galaskiewicz's CEOs and clubs network. - -Description - -HighTech.paj - multirelational network with 21 vertices and 190+102+20 arcs -Padgett.paj - multirelational network with 16 vertices and 30+40 arcs -EIES.paj - multirelational network with 48/32 vertices and 695+830/460 arcs -Trade.paj - multirelational network with 24 vertices and 307+307+310+135+369 - arcs -CEOs.net - two-mode network with 41 vertices and 98 edges. - - -Background - -Complete descriptions of these data, including references for the original -sources of the data, can be found in Chapter 2 (pages 59- 66) and Appendix B -(pages 738-755) of Wasserman and Faust. - -The original data in ASCII, UCINET and KrackPlot formats are available at -INSNA. - -History - - 1. INSNA page. - 2. Transformed in Pajek format by V. Batagelj, 15. January 2005. - -Files - -HighTech.paj - Krackhardt's High-tech managers -Padgett.paj - Padgett's Florentine Families -EIES.paj - Freeman's EIES network -Trade.paj - Countries trade data -CEOs.net - Galaskiewicz's CEOs and clubs. -References - - 1. Stanley Wasserman, Katherine Faust: Social Network Analysis: Methods and - Applications. CUP, 1994. - -Pajek Data; 15. January 2005 - - -================================================================================ -==> Wordnet/Wordnet.txt <== -================================================================================ - - -NOTE: this is a binary graph in the Pajek dataset, but where each edge has a -label (not a weight) in the range 1 to 9. The following labels are used: - -1 hypernym pointer -2 entailment pointer -3 similar pointer -4 member meronym pointer -5 substance meronym pointer -6 part meronym pointer -7 cause pointer -8 grouped pointer -9 attribute pointer - -This is not a multigraph. There are no edges (i,j) between the same nodes -with the same label. Thus, in the sparse matrix, the edge weight A(i,j) -represents the label 1 through 9 of edge (i,j). No loss of information -occurs in this translation. The above table is in aux.edgecode(1:9,:). -Each node is a word in a dictionary. aux.category(i) gives the category -of the word: - 1: n (noun?) 63099 words - 2: v (verb?) 4496 words - 3: a (adjective?) 5501 words - 4: r (?) 2846 words - 5: s (?) 6728 words. - - -================================================================================ -==> yeast/yeast.txt <== -================================================================================ - -Pajek datasets -Protein-protein interaction network in budding yeast - -Dataset Yeast - -Description - -yeastS.net network with 2361 vertices and 7182 edges (536 loops). -yeastL.net network with 2361 vertices and 7182 edges (536 loops). -yeast.clu partition of vertices. -yeast.paj Pajek project file with complete dataset. - - - -complete dataset (ZIP, 134K) - -Background Interaction detection methods have led to the discovery of thousands -of interactions between proteins, and discerning relevance within large-scale -data sets is important to present-day biology. The dataset consists of -protein-protein interaction network described and analyzed in (1) and available -as an example in the software package - PIN (2). - -PIN class encoding: -1 - T, 2 - M, 3 - U, 4 - C, 5 - F, 6 - P, 7 - G, 8 - D, 9 - O, -10 - E, 11 - R, 12 - B, 13 - A. - -yeastS.net X interacts with Y relation, short names. yeastL.net X interacts -with Y relation, long labels. yeast.clu PIN class partition of vertices, see -encoding. yeast.paj Pajek project file with complete dataset. - -References - - 1. Shiwei Sun, Lunjiang Ling, Nan Zhang, Guojie Li and Runsheng Chen: - Topological structure analysis of the protein-protein interaction network in - budding yeast. Nucleic Acids Research, 2003, Vol. 31, No. 9 2443-2450 (PDF). - - 2. Software package Protein Interaction Network PIN - -Transformed in Pajek format by V. Batagelj, 25. July 2003 - - -================================================================================ -==> GD problems <== -================================================================================ - -These graphs are from the Graph Drawing 1995-2006 Contests. - -GD97_b: -NOTE regarding conversion for UF sparse matrix collection: in original the data -every edge appears exactly twice, with the same edge weight. It could be a -multigraph, but it looks more like a graph. The duplicate edges are removed in -this version. You can always add them back in yourself; just look at 2*A." -Barabasi,3,"Notre Dame networks, A. Barabasi. ================================================================================",https://sparse.tamu.edu/Barabasi,graph/network data,graph/network data,1.0,3,bipartite graph (1); directed graph (1); undirected graph (1),1,binary (3),3,rectangular (1); unsymmetric (1); symmetric (1),2114,392400,185.61967833491013,wide (>100×),4480,1470404,mixed,2,1,mixed among square matrices,1,1,0,real only,3,0,one broad domain; mixed matrix forms,"Notre Dame networks, A. Barabasi. - -================================================================================ - -The NotreDame_actors, _www, and _yeast networks were derived from -the Pajek versions of the networks. Details below. - -================================================================================ - -Pajek data sets, from http://vlado.fmf.uni-lj.si/pub/networks/data/, -Vladimir Batagelj and Andrej Mrvar (2006): Pajek datasets. If the source of -the data set is not specified otherwise, these data sets are licensed under a -Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License. - -Converted to sparse adjacency matrix format by Tim Davis, October 2006. -A(i,j) is the edge from node i to node j in the graph. If a graph is not -listed as weighted, and yet has non-binary entries, then the entry a(i,j) -reflects the number of edges (i,j) in the original data (the graph is a -multigraph). - -All data was converted without loss of information, except where intentional. -Details are given below. - -""Pajek"" is Slovenian for ""spider"" and is roughly pronounced ""Pie yeck"". - -The Pajek networks are all 1-based (nodes are numbered starting at node 1). - - -================================================================================ -Summary: -================================================================================ - -NotreDame_actors: Barabasi's actor network (of www.imdb.com) - 392400-by-127823 with 1470404 nonzeros - kind: bipartite multigraph - -NotreDame_www: Barabasi's web page network of nd.edu - 325729-by-325729 with 929849 nonzeros - kind: directed graph - -NotreDame_yeast: Barabasi's yeast protein interaction - 2114-by-2114 with 4480 nonzeros - kind: undirected graph - -================================================================================ - -Pajek datasets Notre Dame Self-Organized Networks Database - -Datasets NDwww, NDactors, NDyeast - -Description - -Notre Dame Self-Organized Networks: - - 1. NDwww.net directed network with 325729 vertices and 1497135 arcs (27455 -loops); page X is linked to page Y. - - 2. NDactors.net undirected two-mode network with 520223 vertices (392400 -players, 127823 movies) and 1470418 edges; player X plays in movie Y. - - 3. NDyeast.net undirected network with 2114 vertices and 2277 edges (74 -loops); protein X interacts with protein Y. - - - -NDwww.net (ZIP, 2050K) NDactors.net (ZIP, 4150K) NDyeast.net (ZIP, 7K) - -Background - -The networks ND*.net are based on the files from Notre Dame Self-Organized -Networks Database. To transform the data into Pajek format: vertex 0 was -replaced by the vertex number equal to the number of vertices in a network; -Pajek keywords were inserted; and the network was saved in the short (as lists -of neighbors) format. - - 1. World-Wide-Web:: Each number represents webpage within nd.edu domain. -Arcs: From page -> To page Réka Albert, Hawoong Jeong and Albert-László -Barabási: Diameter of the World Wide Web, Nature 401, 130 (1999) [ PDF ] See -also a decompostion of this network in V. Batgelj, A. Mrvar: How to analyze -large networks with Pajek? - - 2. Actor: Actor network data: (based on www.imdb.com) In the original ND -network file: each line corresponds to one movie, each number represents actor: -number_1 number_2 ... number_k (k actors who play in the same movie). -Albert-László Barabási, Réka Albert: Emergence of scaling in random networks, -Science 286, 509 (1999) [ PDF ] - - 3. Protein Interaction Network for Yeast: Each number represents protein in -protein interaction network of yeast. Edges: From protein -> To protein. For -other datasets used in supplementary material, please refer indicated -references. Hawoong Jeong, Sean Mason, Albert-László Barabási and Zoltán N. -Oltvai: Centrality and lethality of protein networks, Nature 411, 41 (2001) [ -PDF ] See also Yeast data - -History - - 1. Notre Dame Networks Database put on WWW by the Notre Dame team, 2001; - - 2. 23-25. July 2001: ND nets transformed in Pajek format by V. Batagelj. - - 3. 23. May 2004: ND nets in Pajek format transformed in short (lists of -neighbors) Pajek format by V. Batagelj. - -References - - 1. Self-Organized Networks Database, University of Notre Dame. - -Copyright Extract from the Notre Dame Networks Database page: ""... Feel free to -use these data in your research."" Mail to Hawoong Jeong (author of original ND -networks). - -Pajek Data; 23. May 2004 - -================================================================================ - - -Network Databases - -The aim of this page is to give access to the network topologies we have -studied. We are in the process of expanding this database. Feel free to use -these data in your research. - - World-Wide-Web: [README] [DATA] - Réka Albert, Hawoong Jeong and Albert-László Barabási: - Diameter of the World Wide Web Nature 401, 130 (1999) [ PDF ] - - Actor: [README] [DATA] - Albert-László Barabási, Réka Albert: - Emergence of scaling in random networks Science 286, 509 (1999) [ PDF ] - - Celluar Network: [README] [Whole Cellular Network] - [Metabolic Network Only] - Hawoong Jeong, Bálint Tombor, Réka Albert, - Zoltán N. Oltvai and Albert-László Barabási: - The large-scale organization of metabolic networks - Nature 407, 651 (2000) [ PDF ] - - Protein Interaction Network: [README] [DATA for yeast] - Hawoong Jeong, Sean Mason, Albert-László Barabási and Zoltán N. Oltvai: - Centrality and lethality of protein networks Nature 411, 41 (2001) [ PDF ] - [ Supplementary Material 1 & 2 ] - - Enron Email Dataset - [ Website ] - - Network Dynamic Data will be available soon!" -QCD,14,"Quantum Chromodynamics, B. Medeke, Univ. Wuppertal, Germany Source: Bjoern Medeke, Department of Mathematics, Institute of Applied",https://sparse.tamu.edu/QCD,chemistry/biology,chemistry/biology,1.0,1,theoretical/quantum chemistry problem (14),1,complex (14),1,unsymmetric (14),3072,49152,16.0,moderate (4–100×),119808,1916928,square only,14,0,no square matrix exactly symmetric/Hermitian,0,0,0,complex only,0,0,coherent family/series,"Quantum Chromodynamics, B. Medeke, Univ. Wuppertal, Germany - - -Source: Bjoern Medeke, Department of Mathematics, Institute of Applied -Computer Science, University of Wuppertal, 42097 Wuppertal, Germany. Phone: +49 -202 439-3776. Email: medeke at math.uni-wuppertal.de -Discipline: Physics -Accession (in Matrix Market collection): December 2000 - -Background. Lattice gauge theory is a discretization of quantum chromodynamics -which is generally accepted to be the fundamental physical theory of strong -interactions among the quarks as constituents of matter. The most -time-consuming part of a numerical simulation in lattice gauge theory with -Wilson fermions on the lattice is the computation of quark propagators within a -chromodynamic background gauge field. These computations use up a major part of -the world's high performance computing power. - -Quark propagators are obtained by solving the inhomogeneous lattice Dirac -equation Ax = b, where A = I - kD with 0 <= k < kc is a large but sparse -complex non-Hermitian matrix representing a periodic nearest-neighbour coupling -on a four-dimensional Euclidean space-time lattice. - -From the physical theory it is clear that the matrix A should be positive real -(all eigenvalues lie in the right half plane) for 0 <= k < kc. Here, kc -represents a critical parameter which depends on the given matrix D. Denoting - -\\gamma_5 = \\left( \\begin{array}{cccc} - 0 & 0 & 1 & 0 \\\\ - 0 & 0 & 0 & 1 \\\\ - 1 & 0 & 0 & 0 \\\\ - 0 & 1 & 0 & 0 - \\end{array}\\right) - - the Wilson fermion matrix A is Gamma-5 symmetric, - -\\Gamma_5 A = A^H \\Gamma_5,\\;\\;\\;\\;\\Gamma_5 = I \\otimes ( \\gamma_5 \\otimes I_3 ) - -Due to the nearest neighbour coupling, the matrix A has 'property A'. This -means that with a red-black (or odd-even) ordering of the grid points the -matrix becomes - -A = I - kD - -with - -D = \\left( \\begin{array}{cc} - 0 & D_{\\rm oe} \\\\D_{\\rm eo} & 0 - \\end{array}\\right) - -Set of QCD Matrices. The QCD matrices provided in the set QCD consist of -realistic matrices D generated at different physical temperatures b. - -matrix D b order nonzeros kc -conf5.0-00l4x4-1000.mtx 5.0 3072 119808 0.20611 -conf5.0-00l4x4-1400.mtx 5.0 3072 119808 0.20328 -conf5.0-00l4x4-1800.mtx 5.0 3072 119808 0.20265 -conf5.0-00l4x4-2200.mtx 5.0 3072 119808 0.20235 -conf5.0-00l4x4-2600.mtx 5.0 3072 119808 0.21070 -conf6.0-00l4x4-2000.mtx 6.0 3072 119808 0.15968 -conf6.0-00l4x4-3000.mtx 6.0 3072 119808 0.16453 -conf5.4-00l8x8-0500.mtx 5.4 49152 1916928 0.17865 -conf5.4-00l8x8-1000.mtx 5.4 49152 1916928 0.17843 -conf5.4-00l8x8-1500.mtx 5.4 49152 1916928 0.17689 -conf5.4-00l8x8-2000.mtx 5.4 49152 1916928 0.17835 -conf6.0-00l8x8-2000.mtx 6.0 49152 1916928 0.15717 -conf6.0-00l8x8-3000.mtx 6.0 49152 1916928 0.15649 -conf6.0-00l8x8-8000.mtx 6.0 49152 1916928 0.15623 - -References. A survey of lattice gauge theory is given in - - 1. M. Creutz: Quarks, Gluons, and Lattices, - Cambridge University Press, (1986) - - 2. I. Montvay and G. Munster: Quantum Fields on the Lattice, - Cambridge University Press (1994) - -More background information can be found at various locations on the Web. -Search for ""High Energy Physics"" (HEP). A list of HEP Web sites is available at -CERN (European Laboratory for Particle Physics). - -A PostScript version of this information is also available." -Cylshell,7,"Finite element analysis of cylindrical shells, R. Kouhia, Helsinki. Set CYLSHELL",https://sparse.tamu.edu/Cylshell,structures/materials,structures/materials,1.0,1,structural problem (7),1,real (7),1,symmetric (7),5357,5489,1.0246406570841888,narrow (≤4×),207123,263351,square only,7,0,all square matrices symmetric/Hermitian,7,7,7,real only,0,7,coherent family/series,"Finite element analysis of cylindrical shells, R. Kouhia, Helsinki. - -Set CYLSHELL - -Source: Reijo Kouhia, Helsinki University of Technology, reijo.kouhia at hut.fi - -Discipline: Structural mechanics -Accession (in Matrix Market collection): Summer 1997 - -These matrices result from finite element discretization of an octant of a -cylindrical shell. The ends of the cylinder are free. A summary of the problem -is given below; further details, including plots of the finite element meshes -can be found in a separate description. - -Notation: T indicates the Radius to thickness ratio, R/t = 10T; MO indicates -the model type: DK = Discrete-Kirchhoff, RM = Reissner-Mindlin; EL indicates -element type: T3 = 3-node triangular, Q4 = 4-node quadrilateral, etc.; MX -indicates the mesh type: M1 = 30x30 uniform, M2 = 100x150 uniform, M3 = graded -mesh of 1666 triangles (grading near one boundary and one vertex). The naming -convention, cTMOELMX, is based on these. - -The finite element is a facet type shell element (3/4 nodes) with drilling -rotation incorporated by the Hughes-Brezzi technique and using the penalty -parameter value of G/1000 (regularization parameter) , where G is the shear -modulus. In order to improve the coarse mesh accuracy the membrane -interpolation is amended by the Allman type quadratic modes linked to the -drilling rotation. The bending formulation utilizes the stabilized MITC -technique with the stabilization parameter equal to 0.4. - -For iterative conjugate gradient type solvers, the problem gets harder when the -radius to thickness ratio R/t increases. For quadrilateral meshes using the -IC(0) preconditioner, iteration counts are about 100 (for R/t=10) and 180 (for -R/t=1000) in reaching the relative residual norm of 10-9 depending slightly on -the right-hand side vector. For matrices corresponding to triangular meshes the -number of iterations doubles in comparison to quadrilateral ones. - -The matrices are all symmetric positive definite. - -The following estimates of largest and smallest eigenvalues and spectral -condition numbers were provided by the author. These were computed using the -Lanczos algorithm from QMRPACK (subroutine DSLAL) with 100 steps. - -Matrix Max eigenvalue Min eigenvalue Condition No. -S1RMQ4M1 6.8743215039276076E+05 3.7969627252353000E-01 1.810479E+06 -S2RMQ4M1 6.8743398166879822E+04 3.8746327712024738E-04 1.774191E+08 -S3RMQ4M1 6.8745248885112433E+03 3.8936812501755643E-07 1.765559E+10 -S1RMT3M1 9.6684220885668334E+05 3.7976650205204426E-01 2.545886E+06 -S2RMT3M1 9.6681138121311058E+04 3.8747124280659988E-04 2.495182E+08 -S3RMT3M1 9.6688590706791765E+03 3.8956266582265278E-07 2.481978E+10 -S3DKQ4M2 4.6016534362482462E+03 2.4268618877322254E-08 1.896133E+11 -S3DKT3M2 8.7984363691286471E+03 2.4269393992599454E-08 3.625322E+11 -S3RMT3M3 9.5986080894852857E+03 3.9983547883056739E-07 2.400639E+10" -POLYFLOW,2,"Parasol matrices, collected by Jacko Koster",https://sparse.tamu.edu/POLYFLOW,fluids/transport,fluids/transport,1.0,1,computational fluid dynamics problem (2),1,real (2),1,unsymmetric (2),29957,30412,1.015188436759355,narrow (≤4×),1793881,1990919,square only,2,0,no square matrix exactly symmetric/Hermitian,0,1,0,real only,0,2,coherent family/series,"Parasol matrices, collected by Jacko Koster -http://www.parallab.uib.no/projects/parasol/data - - -Provider: Polyflow, Louvain La Neuve, Belgium (home page) -Contact: Jean-Marie Marchal - -name description format order #entries - -INVEXTR1 Extrusion. Unsymmetric matrix. RUA - order 30,412 #entries 1,793,881 - -MIXTANK Mixing tank. Unsymmetric matrix. RUA - order 29,957 #entries 1,995,041 - -INVEXTR1_NEW Extrusion. Unsymmetric matrix. Modified version of INVEXTR1 to -avoid underflow errors on some machines. - -MIXTANK_NEW Mixing tank. Unsymmetric matrix. Modified version of MIXTANK to -avoid underflow errors on some machines. - -Last update: Mon Feb 07 2005" -INPRO,1,"Parasol matrices, collected by Jacko Koster",https://sparse.tamu.edu/INPRO,structures/materials,structures/materials,1.0,1,structural problem (1),1,real (1),1,symmetric (1),415863,415863,1.0,one size,19173163,19173163,square only,1,0,all square matrices symmetric/Hermitian,1,1,1,real only,0,1,single matrix,"Parasol matrices, collected by Jacko Koster - -http://www.parallab.uib.no/projects/parasol/data - -Provider: INPRO, Berlin, Germany (home page) -Contact: Jochen Weiher - -name description format order #entries -HOOD Hood RSA 220,542 5,494,489 -OILPAN Oilpan RSA 73,752 1,835,470 -QUER . RSA 59,122 1,462,811 -SMDOOR Small door RSA 162,610 4,036,144 -MSDOOR Medium size door RSA 415,863 10,328,399 -LDOOR Large door RSA 952,203 23,737,339" -Mittelmann,30,Linear programming problems from H. Mittelmann 12 Jul 2006,https://sparse.tamu.edu/Mittelmann,optimization/control,optimization/control,1.0,1,linear programming problem (30),2,integer (20); real (10),1,rectangular (30),24460,1961394,80.18781684382665,moderate (4–100×),71264,46168124,rectangular only,0,30,N/A (rectangular only),0,0,0,real only,0,0,one broad domain; mixed matrix forms,"Linear programming problems from H. Mittelmann - -12 Jul 2006 - - ==================================== - Benchmark of commercial LP solvers - ==================================== - -H. Mittelmann (mittelmann@asu.edu) - -Logfiles of these runs at: http://plato.asu.edu/ftp/lp_logs/ - -This benchmark was run on a Linux-PC (3.2 GHz P4, 4 GB RDRAM, Linux-2.6). -The MPS-datafiles for all testcases are in one of (see column ""s"") - - http://miplib.zib.de/ [1] - http://plato.asu.edu/ftp/lptestset/ [2] - http://www.sztaki.hu/~meszaros/public_ftp/lptestset/ -(NETLIB[3], MISC[4], PROBLEMATIC[5], STOCHLP[6], KENNINGTON[7], INFEAS[8]) - -NOTE: files in [2-8] need to be expanded with emps in same directory! - -B/D/P: barrier/dual/primal simplex - -The following codes were tested: - - XP-B/D/P http://www.dashoptimization.com/ (XPRESS-MP-15.20) - CPLEX-B/D/P http://www.cplex.com/ (ILOG-CPLEX-10.0) - MOSEK-4.0.0.32 http://www.mosek.com (barrier) - LOQO-6.06 http://www.princeton.edu/~rvdb/ - LIPSOL linprog in Matlab 7.0 - -Times are user times in secs including input and crossover to a feasible basis -for all codes except LOQO and LIPSOL. ""$"" without crossover. - -===================================================== -s problem CPLEX-B CPLEX-D/P MOSEK LOQO LIPSOL -===================================================== -2 cont1 952 2174/1530 5855 - - -2 cont11 1983 / 45727 - - -2 cont4 3505 2929/736 686 - - -2 cont1_l$ 3171 7102 -2 cont11_l$ f 2081 -1 dano3mip 16 36/23 14 85 14 -4 dbic1 112 116/23 129 156 104 -3 dfl001 14 20/46 12 152 13 -2 fome12 169 141/407 57 485 88 -2 fome13 87 669/1170 88 963 85 -5 gen4 31 2/71 24 33 233 -7 ken-18 11 12/68 21 274 25 -5 l30 f 28/80 3 3 1691 -4 lp22 6 40/72 7 62 9 -4 mod2 10 82/192 20 61 25 -2 neos 109 27/154 167 754 321 -2 neos1 30 808/16 35 153 37 -2 neos2 21 492/29 30 92 29 -2 neos3 201 3208/5915 562 1505 502 -4 nsct2 69 2/2 50 840 131 -4 nug15 77 2912/1278 111 1855 118 -2 nug20 1276 1490 26402 3092 -2 nug08-3rd 1279 2367/ 1925 2004 -2 pds-40 142 37/166 183 15908 252 -2 pds-100 740 160/1586 1951 1673 -3 qap12 10 203/112 15 201 17 -3 qap15 74 2871/1230 107 1978 145 -2 rail4284 239 5235/6476 386 4123 534 -4 rlfprim 4 1/6 5 64 7 -8 self 64 113/100 203 85 3194 -2 sgpf5y6 11 3/2 18 fail 23 -2 spal_004$ 4669 8020 m -4 stat96v1 390 188/641 47 -4 stat96v2 >125000 29364/fail 390 -4 stat96v4 8 396/553 30 -6 storm-125 18 15/26 62 35 62 -2 storm_1000 319 632/1482 916 1269 802 -1 stp3d 168 1067/11715 196 2347 218 -2 watson_2 49 192/346 66 313 70 -4 world 10 94/237 25 44 28 -===================================================== - - - -Problem sizes - - -problem rows columns nonzeros -=============================================== - cont1 160793 40398 399991 - cont11 160793 80396 439989 - cont4 160793 40398 398399 - cont1_l 1918399 641598 5752001 - cont11_l 1468599 981396 4403001 - dano3mip_lp 3203 13873 79656 - dbic1 43200 183235 1038761 - dfl001 6072 12230 41873 - fome12 24285 48920 167492 - fome13 48569 97840 334984 - gen4 1538 4297 110174 - ken-18 105128 154699 512719 - l30 2702 15380 64790 - lp22 2959 13434 78994 - mod2 35665 31728 220116 - neos 479120 36786 1084461 - neos1 131582 1892 468094 - neos2 132569 1560 552596 - neos3 512209 6624 1542816 - nsct2 23004 14981 686396 - nug15 6331 22275 110700 - nug20 15240 72600 304800 - nug08-3rd 19728 20448 139008 - pds-40 66845 212859 605678 - pds-100 156244 505360 1390539 - qap12 3193 8856 44244 - qap15 6331 22275 110700 - rail4284 4284 1092610 12372358 - rlfprim 57422 8052 264483 - self 960 7364 1148845 - sgpf5y6 246078 308634 902275 - spal_004 10203 321696 46167908 - stat96v1 5995 197472 588798 - stat96v2 29089 957432 2852184 - stat96v4 3173 62212 490472 - stormG2-125 66186 157496 529317 - stormG2_1000 528186 1259121 4228817 - stp3d 159488 204880 662128 - watson_2 352014 671861 1843716 - world 35511 32734 220748 -===============================================" -Meszaros,166,Linear programming problems from C. Me'sza'ros email: mcsaba at oplab.sztaki.hu,https://sparse.tamu.edu/Meszaros,optimization/control,optimization/control,1.0,1,linear programming problem (166),3,real (107); integer (41); binary (18),2,rectangular (165); unsymmetric (1),16,1113780,69611.25,wide (>100×),41,11537419,mixed,1,165,no square matrix exactly symmetric/Hermitian,0,1,0,real only,18,0,one broad domain; mixed matrix forms,"Linear programming problems from C. Me'sza'ros - -email: mcsaba at oplab.sztaki.hu -http://www.sztaki.hu/~meszaros/ -http://www.sztaki.hu/~meszaros/public_ftp/lptestset - -http://www.sztaki.hu/~meszaros/bpmpd/ - -Linear Programming Test Problems - -I'm collecting linear programming problems (in MPS format, formulated as -minimization) for testing. You can find them in the following subdirectories of -http://www.sztaki.hu/~meszaros/public_ftp/lptestset: - - * NETLIB, contains the ""usual"" NETLIB LP problems. - - * KENNINGTON, contains the ""Kennington"" collection, also available from - NETLIB. - - * INFEAS, infeasible LP problems (most of them are available from NETLIB). - - * STOCHLP, deterministic equvivalent of stochastic linear programming - problems (mostly 2-stage models). - - * MISC, in which you can find a number of different LP problems not - available from NETLIB. - - * PROBLEMATIC, in which you can find ""problematical"" problems. These - problems are, usually the results of wrong modelling. In the README file - you can find a short explanation about the modelling mistakes. - - * QPDATA, where you can find sets of convex quadratic testproblems. See the - README for more details. NETLIB. - -The problems are compressed in two levels. To uncompress them, you have to -gunzip the files and then use the empc utility to create the MPS file. Please -let you submit me your problems (if you have an interesting one) by sending me -a mail message. - -I would like to emphasize my sincerely thanks to those people who helped me -to extend the MISC collection, namely to - - * Prof. G. Mitra at the Brunel University , - - * Prof. T. Terlaky at the University of Technology Delft, - - * E.D. Andersen at the Univeristy Odensee." -Gleich,10,"Web matrices and wikipedia matrices from David Gleich, Stanford. dgleich at standford dot edu.",https://sparse.tamu.edu/Gleich,graph/network data,graph/network data,1.0,2,directed graph (7); undirected graph (3),1,binary (10),2,unsymmetric (7); symmetric (3),2642,9845725,3726.618092354277,wide (>100×),6606,57156537,square only,10,0,mixed among square matrices,3,3,0,real only,10,0,one broad domain; mixed matrix forms,"Web matrices and wikipedia matrices from David Gleich, Stanford. -dgleich at standford dot edu. - -A(i,j) = 1 if page i links to page j. Node names are provided in separate -files at http://www.cise.ufl.edu/reseach/sparse/aux/Gleich." -UTEP,3,"Matrices from Lenka Dubcova, Jakub Cerveny, and Pavel Solin Univ. of Texas at El Paso. From a PDE solver. The fill-in when working on",https://sparse.tamu.edu/UTEP,generic spatial PDE/thermal,generic spatial PDE/thermal,1.0,1,2D/3D problem (3),1,real (3),1,symmetric (3),16129,146689,9.094736189472378,moderate (4–100×),253009,3636643,square only,3,0,all square matrices symmetric/Hermitian,3,3,3,real only,0,3,coherent family/series,"Matrices from Lenka Dubcova, Jakub Cerveny, and Pavel Solin -Univ. of Texas at El Paso. From a PDE solver. The fill-in when working on -a leading submatrix can be higher than for the whole matrix. - -Subsets of interest are the first k rows and columns where: - - matrix Dubcova1 k = 12033 - matrix Dubcova2 k = 48641 - matrix Dubcova3 k = 81153 - - -Details below: - - -1 Dubcova1 ========================================================== - ----------------- complete matrix: - name: 'UTEP/Dubcova1' - title: 'Univ. Texas at El Paso, from a PDE solver' - id: 1847 - A: [16129x16129 double] - date: '2007' - author: 'L. Dubcova, J. Cerveny, and P. Solin' - ed: 'T. Davis' - kind: '2D/3D problem' - -UMFPACK resid 1.23002e-10, tot time 0.374201 with AMD -nnz L: 576080 (with AMD), ordering time: 0.035546 -nnz L: 608040 with METIS, ordering time: 0.264172 -UMFPACK resid 1.22938e-10, tot time 0.625973 with METIS - ----------------- small subset: -UMFPACK resid 1.20162e-10, tot time 0.406472 with AMD -nnz L: 814164 (with AMD), ordering time: 0.024683 -nnz L: 571918 with METIS, ordering time: 0.169995 -UMFPACK resid 1.19592e-10, tot time 0.458673 with METIS - - -2 Dubcova2 ========================================================== - ----------------- complete matrix: - name: 'UTEP/Dubcova2' - title: 'Univ. Texas at El Paso, from a PDE solver' - id: 1848 - A: [65025x65025 double] - date: '2007' - author: 'L. Dubcova, J. Cerveny, and P. Solin' - ed: 'T. Davis' - kind: '2D/3D problem' - -UMFPACK resid 1.91486e-09, tot time 1.991 with AMD -nnz L: 2956869 (with AMD), ordering time: 0.168376 -nnz L: 3018738 with METIS, ordering time: 1.42383 -UMFPACK resid 1.9355e-09, tot time 3.30308 with METIS - ----------------- small subset: -UMFPACK resid 1.89023e-09, tot time 2.54658 with AMD -nnz L: 4357099 (with AMD), ordering time: 0.121351 -nnz L: 2835807 with METIS, ordering time: 0.92858 -UMFPACK resid 1.87189e-09, tot time 2.48607 with METIS - - -3 Dubcova3 ========================================================== - ----------------- complete matrix: - name: 'UTEP/Dubcova3' - title: 'Univ. Texas at El Paso, from a PDE solver' - id: 1849 - A: [146689x146689 double] - date: '2007' - author: 'L. Dubcova, J. Cerveny, and P. Solin' - ed: 'T. Davis' - kind: '2D/3D problem' - Zeros: [146689x146689 double] - -UMFPACK resid 2.04934e-09, tot time 5.38897 with AMD -nnz L: 7252162 (with AMD), ordering time: 0.331024 -nnz L: 7539100 with METIS, ordering time: 2.49977 -UMFPACK resid 2.05082e-09, tot time 7.39527 with METIS - ----------------- small subset: -UMFPACK resid 2.04213e-09, tot time 5.87802 with AMD -nnz L: 10085274 (with AMD), ordering time: 0.195067 -nnz L: 6536588 with METIS, ordering time: 1.11139 -UMFPACK resid 2.03453e-09, tot time 4.6961 with METIS" -BenElechi,1,"Matrix from S. Ben Elechi, Arcelor, Inc. with many sparse right-hand sides.",https://sparse.tamu.edu/BenElechi,generic spatial PDE/thermal,generic spatial PDE/thermal,1.0,1,2D/3D problem (1),1,real (1),1,symmetric (1),245874,245874,1.0,one size,13150496,13150496,square only,1,0,all square matrices symmetric/Hermitian,1,1,1,real only,0,1,single matrix,"Matrix from S. Ben Elechi, Arcelor, Inc. with many sparse right-hand sides." -Botonakis,6,"FEM thermal/mechanical problems from Ioannis Botonakis, Germany. FEM_3D_thermal1 and FEM_3D_thermal2 are",https://sparse.tamu.edu/Botonakis,generic spatial PDE/thermal,generic spatial PDE/thermal,1.0,1,thermal problem (6),1,real (6),2,unsymmetric (3); symmetric (3),17880,204316,11.427069351230426,moderate (4–100×),430740,3489300,square only,6,0,mixed among square matrices,3,6,3,real only,0,6,one broad domain; mixed matrix forms,"FEM thermal/mechanical problems from Ioannis Botonakis, Germany. - -FEM_3D_thermal1 and FEM_3D_thermal2 are -FEM 3D nonlinear thermal problems, wth 8-node bricks as volume elements. - -The thermomech_* are the temperature and deformation of a steel cylinder." -Wissgott,1,"Parabolic FEM problem from Phillip Wissgott, TU Vienna. A diffusion-convection reaction. The parabolic_fem matrix uses constant",https://sparse.tamu.edu/Wissgott,fluids/transport,fluids/transport,1.0,1,computational fluid dynamics problem (1),1,real (1),1,symmetric (1),525825,525825,1.0,one size,3674625,3674625,square only,1,0,all square matrices symmetric/Hermitian,1,1,1,real only,0,1,single matrix,"Parabolic FEM problem from Phillip Wissgott, TU Vienna. -A diffusion-convection reaction. The parabolic_fem matrix uses constant -homogeneous diffusion which leads to a symmetric positive definite matrix." -Watson,2,"Chemical reaction simulation matrices from Layne Watson and Jingwei Zhang. Virginia Tech, ltw at cs dot vtu doe yes, and jwzhang at the same domain.",https://sparse.tamu.edu/Watson,generic spatial PDE/thermal,generic spatial PDE/thermal,1.0,1,2D/3D problem (2),1,real (2),1,unsymmetric (2),40401,112211,2.7774312517016906,narrow (≤4×),201201,748331,square only,2,0,no square matrix exactly symmetric/Hermitian,0,2,0,real only,0,2,coherent family/series,"Chemical reaction simulation matrices from Layne Watson and Jingwei Zhang. -Virginia Tech, ltw at cs dot vtu doe yes, and jwzhang at the same domain. - -The ODE system \\frac{dp}{dt}=Qp is what we call a chemical master equation (a -Kolmogorov's backward/forward equation). Q is a sparse asymmetric matrix, -whose off-diagonal entries are non-negative and row sum to zero. On each row, -q_{ij}h gives the probability the system makes a transition from current state -i to some other state j, in small time interval h. By ""state"", we mean a -possible combination of number of molecules in each chemical species. Now, h -is small enough so that only one reaction happens. In this way q_{ij} is -nonzero only if there exists a chemical reaction that connects state i and j, -i.e. j=i+s_k, s_k's are constant state vectors that correspond to every -reaction. Say we have M reactions, then there are at most M+1 nonzero -entries on each row of Q. On the other hand, the number of possible -combination of molecules is huge, which means the dimension of Q is huge. -The linear system we want to solve is (I - Q/lambda)x=b, and we have -to solve it several times. (Here lambda is a constant). Problem.A is the Q -matrix." -Sinclair,2,"3-D spectral-element elastic wave modelling in freq. domain, C. Sinclair, Univ. Adelaide, Australia.",https://sparse.tamu.edu/Sinclair,structures/materials,structures/materials,1.0,1,materials problem (2),1,complex (2),1,Hermitian (2),292008,680943,2.331932686775705,narrow (≤4×),12935272,30290827,square only,2,0,all square matrices symmetric/Hermitian,2,2,0,complex only,0,2,coherent family/series,"3-D spectral-element elastic wave modelling in freq. domain, C. Sinclair, -Univ. Adelaide, Australia. - -catherine dot sinclair at adelaide dot edu dot au. - -3Dspectralwave: - -The A matrix is produced using 3-D spectral-element elastic wave modelling in -the frequency domain. The medium is homogeneous and isotropic with elastic -coefficients: c11 = 6.30, c44 = 1.00 The B matrix represents a real -y-directed source, placed approximately in the centre. The model size in -elements is 20x20x20. Each element is 1m x1m x 1m. Each element is a 4x4x4 -Gauss-Lobbato-Legendre mesh, so the height, width and depth of the system is -61 nodes. There are 3 unknown components at each node - the x, y and z -displacements. The A matrix therefore has dimension 680943 x 680943, where -((20 x 4) - (20 - 1))^3 * 3 = 680943. The problem domain is earth sciences. -Note that A is complex and b is sparse and real (b has a single nonzero). - -The A matrix was provided with a nonzero imaginary part, but was otherwise -complex Hermitian. To save space in the Matrix Market and Rutherford/Boeing -formats, the A matrix here has had this imaginary diagonal removed. The -shift can be found in the aux.shift auxiliary matrix. To reproduce the -original A matrix, use A = Problem.A + Problem.aux.shift ; - -Added to the collection in May, 2007, by Tim Davis - --------------------------------------------------------------------------------- -3Dspectralwave2 is a smaller version: - -The A matrix is produced using 3-D spectral-element elastic wave modelling in -the frequency domain. The medium is homogeneous and isotropic with -elastic coefficients: c11 = 6.30, c44 = 1.00 The B matrix -represents a real y-directed source, placed approximately in the centre. - -The model size in elements is 20x20x20. Each element is 1m x1m x 1m. Each -element is a 4x4x4 Gauss-Lobbato-Legendre mesh, so the height, width and -depth of the system is 61 nodes. There are 3 unknown components at each node -- the x, y and z displacements. The A matrix therefore has dimension -680943 x 680943. - -((20 x 4) - (20 - 1))^3 * 3 = 680943 - -Added to the collection in June, 2007, by Tim Davis" -QLi,3,"Mixed complementarity optimization problems from Qaun Li and Michael Ferris. QLi/crashbasis and QLi/majorbasis have the same nonzero pattern. However,",https://sparse.tamu.edu/QLi,optimization/control,optimization/control,1.0,1,optimization problem (3),1,real (3),1,unsymmetric (3),160000,440020,2.750125,narrow (≤4×),1750416,5240084,square only,3,0,no square matrix exactly symmetric/Hermitian,0,1,0,real only,0,0,coherent family/series,"Mixed complementarity optimization problems from Qaun Li and Michael Ferris. - -QLi/crashbasis and QLi/majorbasis have the same nonzero pattern. However, -UMFPACK 5.1 is much slower for majorbasis than for crashbasis, because of -the extensive amount of denormal arithmetic that occurs during the factorization -of majorbasis. This does not occur in the crashbasis matrix. - -QLi/largebasis added in 2008. The matrix is unsymmetric." -Springer,1,"Tim Springer, Solenix (www.solenix.ch), Navigation Support Office, OPS-GN (www.nng.esoc.de), European Space Operations Centre, Germany.",https://sparse.tamu.edu/Springer,other,other,1.0,1,least squares problem (1),1,real (1),1,rectangular (1),327062,327062,1.0,one size,6019939,6019939,rectangular only,0,1,N/A (rectangular only),0,0,0,real only,0,0,single matrix,"Tim Springer, Solenix (www.solenix.ch), Navigation Support Office, -OPS-GN (www.nng.esoc.de), European Space Operations Centre, Germany. - -Orbit estimates (at the 10mm level) and reference fram (station coordinates) -at the few mm level. Speed of factorization is important, since this matrix -is one part of the reprocessing of 15 years of data." -Szczerba,1,"Ill-conditioned CFD matrix from D. Szczerba, ETH Computer Vision Lab, Zurich.",https://sparse.tamu.edu/Szczerba,fluids/transport,fluids/transport,1.0,1,computational fluid dynamics problem (1),1,real (1),1,unsymmetric (1),20896,20896,1.0,one size,191368,191368,square only,1,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,1,single matrix,"Ill-conditioned CFD matrix from D. Szczerba, ETH Computer Vision Lab, Zurich." -Muite,4,"Ill-conditioned Chebyshev integration matrices from Benson Muite, Oxford.",https://sparse.tamu.edu/Muite,structures/materials,structures/materials,1.0,1,structural problem (4),1,real (4),1,unsymmetric (4),261,68121,261.0,wide (>100×),2319,5377761,square only,4,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,4,coherent family/series,"Ill-conditioned Chebyshev integration matrices from Benson Muite, Oxford. -http://www.maths.ox.ac.uk/~muite" -Quaglino,2,"FEM discretization of a viscoplastic collision problem, Alessio Quaglino.",https://sparse.tamu.edu/Quaglino,structures/materials,structures/materials,1.0,1,materials problem (2),1,real (2),1,unsymmetric (2),4326,32769,7.574895977808599,moderate (4–100×),61166,381326,square only,2,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,2,coherent family/series,"FEM discretization of a viscoplastic collision problem, Alessio Quaglino." -YCheng,3,"Power system state simulation matrices Yunzhi Cheng, UT Arlington. In MATLAB, the solution to x=A\\b is desired, but this can be slow in",https://sparse.tamu.edu/YCheng,electrical/circuit/device,electrical/circuit/device,1.0,1,power network problem (3),1,real (3),1,rectangular (3),14318,28634,1.9998603156865484,narrow (≤4×),57376,115262,rectangular only,0,3,N/A (rectangular only),0,0,0,real only,0,0,coherent family/series,"Power system state simulation matrices Yunzhi Cheng, UT Arlington. -In MATLAB, the solution to x=A\\b is desired, but this can be slow in -MATLAB 7.3 because of the speed of sparse QR as compared to sparse -Cholesky. Using x=(A'*A)\\(A'*b) is faster, but of course yields -slightly less accurate (but still acceptable) results. Note that an -initial guess to the solution is provided, for use by an iterative -method. However, sparse Cholesky with an AMD ordering is very fast -for this matrix and thus iterative methods are unlikely to be -competitive." -Dehghani,1,"Light transport in soft tissue, Hamid Dehghani, Univ. of Exeter, UK",https://sparse.tamu.edu/Dehghani,electromagnetics/acoustics,electromagnetics/acoustics,1.0,1,electromagnetics problem (1),1,complex (1),1,unsymmetric (1),29282,29282,1.0,one size,406084,406084,square only,1,0,no square matrix exactly symmetric/Hermitian,0,1,0,complex only,0,1,single matrix,"Light transport in soft tissue, Hamid Dehghani, Univ. of Exeter, UK" -HVDC,2,"Power simulation matrices from Alex Wang, Manitoba HVDC, Canada.",https://sparse.tamu.edu/HVDC,electrical/circuit/device,electrical/circuit/device,1.0,1,power network problem (2),1,real (2),1,unsymmetric (2),24842,189860,7.642701875855406,moderate (4–100×),158426,1339638,square only,2,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,0,coherent family/series,"Power simulation matrices from Alex Wang, Manitoba HVDC, Canada." -Zaoui,1,"Optimal power flow, nonlinear optimization (KKT), Fabrice Zaoui, RTE, France",https://sparse.tamu.edu/Zaoui,optimization/control,optimization/control,1.0,1,optimization problem (1),1,real (1),1,symmetric (1),2063494,2063494,1.0,one size,12771361,12771361,square only,1,0,all square matrices symmetric/Hermitian,1,1,0,real only,0,0,single matrix,"Optimal power flow, nonlinear optimization (KKT), Fabrice Zaoui, RTE, France" -Rost,1,"semiconductor device simulation, Johann Rost, Politechnia Univ., Bucharest. Numerical Methods Laboratory, Politehnica Univ., Bucharest, Romania.",https://sparse.tamu.edu/Rost,electrical/circuit/device,electrical/circuit/device,1.0,1,semiconductor device problem (1),1,complex (1),1,unsymmetric (1),74104,74104,1.0,one size,365580,365580,square only,1,0,no square matrix exactly symmetric/Hermitian,0,0,0,complex only,0,1,single matrix,"semiconductor device simulation, Johann Rost, Politechnia Univ., Bucharest. - -Numerical Methods Laboratory, Politehnica Univ., Bucharest, Romania. -Johann Rost, et al. RF semiconductors and electronic device simulation -in the frequency domain. UMFPACK (x=A\\b in MATLAB) experiences high -fill-in for this matrix. KLU is worse." -Lee,2,"FEM, Electromagnetics, Center for Computational Electromagnetics, UIUC From the Univ of Illinois at Urbana-Champaign, Center for Computational",https://sparse.tamu.edu/Lee,electromagnetics/acoustics,electromagnetics/acoustics,1.0,1,electromagnetics problem (2),1,complex (2),1,unsymmetric (2),74062,491100,6.630930841727202,moderate (4–100×),1731206,20239237,square only,2,0,no square matrix exactly symmetric/Hermitian,0,1,0,complex only,0,2,coherent family/series,"FEM, Electromagnetics, Center for Computational Electromagnetics, UIUC - -From the Univ of Illinois at Urbana-Champaign, Center for Computational -Electromagnetics (development and application of the finite element -method for analyzing antennas, high-frequency circuits, high-speed -circuits, and so on). The governing equations are Maxwell's equations. -The matrix results from the finite-element discretization of a bandpass -microwave filter at 500 MHz. The first-order vector element is employed. -The absorbing boundary condition is applied on the outer boundary of the -structure for emulating the open space. The port boundary condition is -applied on each port of the circuit for the truncating the computational -domain and exciting the circuit. Due to these boundary conditions, the -finite-element system matrix is complex." -Mancktelow,1,"FEM viscoelastic behavior of rocks, Neil Mancktelow, ETH-Zentrum",https://sparse.tamu.edu/Mancktelow,structures/materials,structures/materials,1.0,1,materials problem (1),1,real (1),1,unsymmetric (1),37762,37762,1.0,one size,1133641,1133641,square only,1,0,no square matrix exactly symmetric/Hermitian,0,1,0,real only,0,1,single matrix,"FEM viscoelastic behavior of rocks, Neil Mancktelow, ETH-Zentrum" -Rudnyi,1,"3D fluid flow: water in a tank. Evgenii Rudnyi, IMTEK",https://sparse.tamu.edu/Rudnyi,fluids/transport,fluids/transport,1.0,1,computational fluid dynamics problem (1),1,real (1),1,unsymmetric (1),60740,60740,1.0,one size,2035281,2035281,square only,1,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,1,single matrix,"3D fluid flow: water in a tank. Evgenii Rudnyi, IMTEK" -Rucci,1,"ill-conditioned least-squares problem, A. Rucci",https://sparse.tamu.edu/Rucci,other,other,1.0,1,least squares problem (1),1,real (1),1,rectangular (1),1977885,1977885,1.0,one size,7791168,7791168,rectangular only,0,1,N/A (rectangular only),0,0,0,real only,0,0,single matrix,"ill-conditioned least-squares problem, A. Rucci" -McRae,2,"circuitscape: circuit theory applied to animal/gene flow. B. McRae, UCSB Landscape ecology problem, using electrical network theory to model",https://sparse.tamu.edu/McRae,generic spatial PDE/thermal,generic spatial PDE/thermal,1.0,1,2D/3D problem (2),1,real (2),1,symmetric (2),999999,1000000,1.000001000001,narrow (≤4×),4995991,4996000,square only,2,0,all square matrices symmetric/Hermitian,2,2,1,real only,0,2,coherent family/series,"circuitscape: circuit theory applied to animal/gene flow. B. McRae, UCSB - -Landscape ecology problem, using electrical network theory to model -animal movement an gene flow. - - Brad McRae - National Center for Ecological Analysis and Synthesis - 735 State Street, Suite 300 - Santa Barbara, CA 93101-5504 - Tel: (541) 223-1170 - FAX: (805) 892-2510 - email: mcrae at nceas.ucsb.edu - http://www.nceas.ucsb.edu/~mcrae/ - --------------------------------------------------------------------------------- - -NOTE (added Feb 9, 2012): The McRae/ecology1 matrix is tagged in the UF Sparse -Matrix Collection as 'not positive definite', but it appears to be very close -to being positive definite. See below for more details. - --------------------------------------------------------------------------------- - -On 01/09/2012 02:20 PM, Alexander Andrianov wrote: -Dear Professor Davis, - -I am trying to test an eigensolver on your sparse matrix collection but am -having difficulty figuring out the ""indefiniteness"" of the following instance: -ecology1. It looks like the smallest eigenvalue is so close to the machine -precision that running the eigensolver with different settings results in a -range of the estimates from 9.2271971e-15 to -4.8936273e-14. Running the dense -eigensolver is not an option due to the problems size (1000000). To make things -more interesting, both LDL and (somewhat modified for semi-definite case) -Cholesky factorizations seem to factorize that matrix fine even though Cholesky -results in a normalized residual which may vary between 1.0e-11 and 1.e-16 -while LDL factorization's normalized residual is around the machine precision. - -So I was wondering if you have some eigenvalue info on that matrix. And more -generally would it be possible for you to include more information about each -matrix in your collection such as the eigenvalue range (smallest and largest) -as well as some conditioning info? That would most certainly help in instance -selection for test sets for various solvers. - -Thank you for nay help with this. - -Best regards, -Alexander Andrianov SAS Institute Inc. - --------------------------------------------------------------------------------- - -From: Tim Davis -Sent: Monday, January 09, 2012 2:26 PM -To: Alexander Andrianov -Cc: Leslie Foster -Subject: Re: UF collection matrices info - -I've begun to collect the singular values, but so far I have only matrices of -dimension 25,000 or less (or so). I don't have eigenvalue info for any matrix, -but it's something I've considered. I just haven't done it yet. - -The difficulty I would have for this matrix is the same that you have, though. -I might report -1e-14, or 1e-16, depending on how I compute it. - -Les Foster may have more info. You could try our recent spqr_rank pagckage -to find the rank. It's pretty accurate and doesn't require an SVD. - -Thanks, -Tim" -NYPA,8,"Rank-deficient least squares problems. D. Maragal, New York Power Authority These matrices are both structurally and numerically singular. The numerical",https://sparse.tamu.edu/NYPA,other,other,1.0,1,least squares problem (8),1,real (8),1,rectangular (8),32,75077,2346.15625,wide (>100×),234,1308415,rectangular only,0,8,N/A (rectangular only),0,0,0,real only,0,0,coherent family/series,"Rank-deficient least squares problems. D. Maragal, New York Power Authority - -These matrices are both structurally and numerically singular. The numerical -rank is less than the structural rank (rank(A) < sprank(A) in MATLAB). - -Deepak Maragal -New York Power Authority -White Plains, NY -email: Deepak.Maragal at nypa.gov" -Marini,1,"Economic Time Series, T. Di Fonzo (Univ Padua) and M. Marini, (ISTAT, Italy) Description of the Sparse Linear System arising from the",https://sparse.tamu.edu/Marini,other,other,1.0,1,economic problem (1),1,real (1),1,symmetric (1),7245,7245,1.0,one size,46142,46142,square only,1,0,all square matrices symmetric/Hermitian,1,1,0,real only,0,0,single matrix,"Economic Time Series, T. Di Fonzo (Univ Padua) and M. Marini, (ISTAT, Italy) - -Description of the Sparse Linear System arising from the -Reconciliation Problem of a System of Economic Time Series - -Notes for Professor Tim Davis - -by Tommaso Di Fonzo and Marco Marini -July 3, 2008 - -Dear Prof. Davis, -we have read your message Sparse QR mexFunction on the Matlab Central -website, in which you invite the community to provide sparse problems -arising from real applications for testing a new sparse QR function. In -particular, you seem particularly interested in rank deficient problems. We -have a problem with such characteristics, so we have decided to upload in the -CISE website the elements of this linear system (coefficient matrix and right- -hand side). This problem is generally known as the reconciliation problem -in the statistics and economics literature, which we intend to describe you -very briefly in this note for your information. - -Economic statistics are often published in the form of time series, as a collec -tion of observations sampled at equally-spaced time periods (months, quar -ters). Economic concepts behind such statistics are often linked by a system -of linear relationships, deriving from the economic theory. However, these -restrictions are rarely met by the original time series for various reasons. -Then, data sets of real-world variables generally show discrepancies with -respect to prior restrictions on their values. Such discrepancies are rarely -accepted because their existence generally causes confusion among users and -embarrassment to data producers. The adjustment of a set of data in order -to satisfy a number of accounting restrictions -and thus to remove any dis -crepancy -is generally known as the reconciliation problem. - -The reconciliation problem can be seen as the solution of a linear system. -The dimension of the system increases rapidly as the number of time pe -riods, the number of variables and the number of constraints grow. Since -it is convenient to solve the system simultaneously (that is, considering all -periods, all variables, all constraints), the use of sparse matrices and algo -rithms is strongly suggested for solving practical applications. However, as -far as we know, such problems are generally solved by data producers by -using dense matrices and/or iterative methods (i.e. the conjugate gradi -ent method). Our intention is to encourage the use of direct methods with -sparse matrices for solving this kind of problems. - -A formalization of the problem is helpful to understand the features of this -linear system. Let p be an n-dimensional vector containing all the obser- -vations of the system to be reconciled (where n is the number of variables -multiplied by the number of periods in which such variables are observed). -Let H be the matrix defining the relationships between such observations. -Supposing there are k constraints, the matrix H has dimension k × n: for -each constraint we have a known total that must be satisfied by the obser -vations, collected together in the k-dimensional vector w. Notice that (i) -k 5.9e7). - -So in my problem, I consider -K = span is the kernel of A, and -L = span = orthogonal(K) is isomorph to Im(A). - -The rank deficient is not quite clear since of A is a Jacobian of -certain non-linear operator. One can prove this non-linear operator has -a same value on a manifold of dimension 7. This manifold has some sort -of curvature. - -Best regards, - -Bruno" -YZhou,1,"Circuit simulation problem, Y. Zhou, Carleton Univ, Canada",https://sparse.tamu.edu/YZhou,electrical/circuit/device,electrical/circuit/device,1.0,1,circuit simulation problem (1),1,real (1),1,unsymmetric (1),1020,1020,1.0,one size,5883,5883,square only,1,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,0,single matrix,"Circuit simulation problem, Y. Zhou, Carleton Univ, Canada" -CEMW,4,"Electromagnetic problems from D. Isaak, Computational_EM_Works - cemw_vfem is a vector finite element complex symmetric matrix with 93,476",https://sparse.tamu.edu/CEMW,electromagnetics/acoustics,electromagnetics/acoustics,1.0,1,electromagnetics problem (4),3,real (2); integer (1); complex (1),2,unsymmetric (3); symmetric (1),93476,921632,9.85955753348453,moderate (4–100×),1434636,5080961,square only,4,0,mixed among square matrices,1,3,1,real + complex,0,4,one broad domain; mixed matrix forms,"Electromagnetic problems from D. Isaak, Computational_EM_Works - - - cemw_vfem is a vector finite element complex symmetric matrix with 93,476 - unknowns and 1,434,636 nonzeros. - - - cemw_t2em.mat is a T2EM real symmetric matrix with 921,632 unknowns and - 6,442,778 nonzeros. - - - cemw_tmt_sym.mat is a TmT real symmetric matrix with 726,713 unknowns and - 5,080,961 nonzeros. - - - cemw_tmt_nosym.mat is a TmT real non-symmetric matrix with 917,825 unknowns - and 4,584,801 nonzeros." -Schenk,5,"Symmetric indefinite KKT matrices, O. Schenk, Univ. of Basel, Switzerland Nonlinear programming problems for a 3D PDE-constrained optimization",https://sparse.tamu.edu/Schenk,optimization/control,optimization/control,1.0,1,optimization problem (5),1,real (5),1,symmetric (5),1062400,27993600,26.349397590361445,moderate (4–100×),28192672,760648352,square only,5,0,all square matrices symmetric/Hermitian,5,5,0,real only,0,0,coherent family/series,"Symmetric indefinite KKT matrices, O. Schenk, Univ. of Basel, Switzerland -Nonlinear programming problems for a 3D PDE-constrained optimization -problem with boundary control as a function of the discretization -parameter N using 2nd-order finite difference approximations. - -O. Schenk, A. W\\""achter, and M. Weiser, Inertia Revealing -Preconditioning For Large-Scale Nonconvex Constrained Optimization, -Technical Report, Unversity of Basel, 2008, submitted. - -Abstract: Fast nonlinear programming methods following the -all-at-once approach usually employ Newton's method for solving -linearized Karush-Kuhn-Tucker (KKT) systems. In nonconvex problems, -the Newton direction is only guaranteed to be a descent direction if -the Hessian of the Lagrange function is positive definite on the -nullspace of the active constraints, otherwise some modifications to -Newton's method are necessary. This condition can be verified using -the signs of the KKT's eigenvalues (inertia), which are usually -available from direct solvers for the arising linear saddle point -problems. Iterative solvers are mandatory for very large-scale -problems, but in general do not provide the inertia. Here we present -a preconditioner based on a multilevel incomplete LBL^T -factorization, from which an approximation of the inertia can be -obtained. The suitability of the heuristics for application in -optimization methods is verified on an interior point method applied -to the CUTE and COPS test problems, on large-scale 3D PDE-constrained -optimal control problems, as well as 3D PDE-constrained optimization -in biomedical cancer hyperthermia treatment planning. The efficiency -of the preconditioner is demonstrated on convex and nonconvex -problems with 1503 state variables and 1502 control variables, both -subject to bound constraints. - -Note that the 3 largest matrices (nlpkkt160, nlpkkt200, and nlpkkt240) -require MATLAB 7.3 or later to read them." -TKK,12,"Matrices from Reijo Kouhia, Structural Mechanics, Helsinki Univ. of Technology (Teknillinen Korkeakoulo)",https://sparse.tamu.edu/TKK,structures/materials,structures/materials,1.0,1,structural problem (12),3,real (5); integer (4); binary (3),2,symmetric (10); unsymmetric (2),1282,143571,111.98985959438377,wide (>100×),30644,4706073,square only,12,0,mixed among square matrices,10,12,3,real only,3,12,one broad domain; mixed matrix forms,"Matrices from Reijo Kouhia, Structural Mechanics, Helsinki Univ. of Technology -(Teknillinen Korkeakoulo) - -http://users.tkk.fi/~kouhia/sparse.html - -Sparse matrix test problems -These matrices are stored either in the compressed column format (Harwell-Boeing format, see I. Duff et al.: Sparse Matrix Test Problems, ACM Transactions on Mathematical Software, Vol. 15 No. 1, March 1989, 1-14) or in the MatrixMarket coordinate format (R.F. Boisvert et al.: The Matrix Market Exchange Formats: Initial Design, NISTIR 5935, 1996). Description of both formats can be found from MatrixMarket. - - * Cylindrical shell problems: Short description (html), and slightly more - informative documentation (gzipped postcript-file cylshell.ps.gz, size - 205 k). - - The following matrices can be found from the MatrixMarket in the set CYLSHELL - - o Cyl shell, R/t = 10, unif 30 x 30 quadrilateral mesh, N = 5489, - NZ = 143300, triangular mesh NZ = 112505, - o Cyl shell, R/t = 100, unif 30 x 30 quadrilateral mesh, N = 5489, - NZ = 143300, triangular mesh NZ = 112505, - o Cyl shell, R/t = 1000, unif 30 x 30 quadrilateral mesh, N = 5489, - NZ = 143300, triangular mesh NZ = 112505 - o Cyl shell, R/t = 1000, unif 100 x 150 quadrilateral mesh, N = 90449, - NZ = 2455670, triangular mesh NZ = 1921955 - o Cyl shell graded mesh with 1666 triangles R/t = 1000, N = 5357, - NZ = 106526, - -Matrices in this group: - - Kouhia/s4dkt3m2 - Kouhia/g3rmt3m3 - Kouhia/t520 - Kouhia/smt - Kouhia/engine - Kouhia/plbuckle - Kouhia/cbuckle - Kouhia/cyl6 - Kouhia/tube1 - Kouhia/tube2 - Kouhia/t2d_q4 - Kouhia/t2d_q9 - -Matrices from Reijo Kouhia in other matrix groups: - - Cylshell/s1rmq4m1 - Cylshell/s2rmq4m1 - Cylshell/s3rmq4m1 - Cylshell/s1rmt3m1 - Cylshell/s2rmt3m1 - Cylshell/s3rmt3m1 - Cylshell/s3rmt3m3 - GHS_psdef/s3dkq4m2 - GHS_psdef/s3dkt3m2 - - --------------------------------------------------------------------------------- -s4dkt3m2 --------------------------------------------------------------------------------- - - Some additional cases not in MatrixMarket: - o Cyl shell R/t = 10000, unif 100 x 150 triangular mesh, - s4dkt3m2.dat.gz , N = 90449, NZ = 1921955 - --------------------------------------------------------------------------------- -g3rmt3m3 --------------------------------------------------------------------------------- - - o g3rmt3m3.dat.gz - --------------------------------------------------------------------------------- -t520.hb.gz, t520.load.gz --------------------------------------------------------------------------------- - - * T-beam: t520.hb.gz , load vector, t520.load.gz , - --------------------------------------------------------------------------------- -smt.hb.gz, smt.mtx.gz --------------------------------------------------------------------------------- - - * 3-D solid model from a thermal stress analysis of a surface mounted - transistor, N = 25710, NZ = 1889447, - 1704 triquadratic Serendipity 20 node brick elements, four different - materials. HB-format file smt.hb.gz, MM-format file smt.mtx.gz, - figure of the FE-model: smt.ps.gz - --------------------------------------------------------------------------------- -engine.hb.gz, engine.rhs.gz --------------------------------------------------------------------------------- - - * 3-D solid model from a thermal stress analysis of an engine head: matrix - in HB-format: engine.hb.gz, rhs-vector: engine.rhs.gz - --------------------------------------------------------------------------------- -eigen/plb_k, eigen/plb_g -> renamed plbuckle --------------------------------------------------------------------------------- - - * Double eigenvalue plate buckling problem: stiffness matrix and initial - stress matrix . Symmetric matrices, upper triangle is stored in coordinate - format. - --------------------------------------------------------------------------------- -ck.dat, cg.dat -> renamed cbuckle --------------------------------------------------------------------------------- - - * Compressed cylindrical shell -- buckling problem: stiffness matrix and - initial stress matrix . Symmetric matrices, lower triangle is stored in - coordinate format N = 13681. - --------------------------------------------------------------------------------- -cyl6, v_6_[0,1,2] --------------------------------------------------------------------------------- - - * Cylindrical shell, from non-linear analysis, indefinite: HB-format file - cyl6.dat.gz Symmetric matrix N = 13681, NZ = 363961. - Some rhs vectors at step 6 v_6_0.rhs, v_6_1.rhs, v_6_2.rhs - --------------------------------------------------------------------------------- -tube* --------------------------------------------------------------------------------- - - * Tube, SPD matrix: HB-format file tube1.dat.gz Symmetric matrix N = 21498, - NZ = 459277. HB-format file tube1-2.dat.gz Symmetric matrix N = 21498, - NZ = 459277, load vector tube1-2.load.gz - --------------------------------------------------------------------------------- -t2d_q4 --------------------------------------------------------------------------------- - - * Non-linear diffusion problem, sequence of matrices and rhs-vectors. These - matrices are in the compressed sparse row format (CSR). 100x100 bilinear - mesh (41 MB) - --------------------------------------------------------------------------------- -t2d_q9 --------------------------------------------------------------------------------- - - * Non-linear diffusion problem, sequence of matrices and rhs-vectors. These - matrices are in the compressed sparse row format (CSR) - 50x50 biquadratic mesh (87 MB). Temperature field. - - -The following data was not imported into the UF Sparse Matrix Collection: - --------------------------------------------------------------------------------- -mesh2 --------------------------------------------------------------------------------- - - Node-unknown data for mesh 2 (100 x 150 element mesh)" -Um,2,"FEM, electromagnetics, 2 cubes in a sphere. Evan Um, Geophysics, Stanford A matrix from Evan Um, Geophysics, Stanford. Studying finite-element",https://sparse.tamu.edu/Um,electromagnetics/acoustics,electromagnetics/acoustics,1.0,1,electromagnetics problem (2),1,real (2),1,symmetric (2),101492,259789,2.559699286643282,narrow (≤4×),1647264,4242673,square only,2,0,all square matrices symmetric/Hermitian,2,2,2,real only,0,2,coherent family/series,"FEM, electromagnetics, 2 cubes in a sphere. Evan Um, Geophysics, Stanford - -A matrix from Evan Um, Geophysics, Stanford. Studying finite-element -time domain solvers for electromagnetic diffusion equations. The 3-D -computational domain consists of 88,213 tetrahedral elements. The -computational domain consists of the two parts. First, there are two -300m x 300m x 150m boxes where a fine mesh is used. Second, the two -boxes are enclosed by a large sphere whose radius is 10 km. An element -growth factor is used to increase the mesh size gradually inside the -sphere. This is because absorbing boundary conditions are not very -good choices for these problems. The finite element technique is -edge-based rather than node-based. Therefore, the unknowns are -amplitudes of electromagnetic fields on an edge of each element." -JGD_BIBD,21,"Balanced Incomplete Block Design from Mark Giesbrecht, Univ. of Waterloo From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,",https://sparse.tamu.edu/JGD_BIBD,optimization/control,optimization/control,1.0,1,combinatorial problem (21),1,binary (21),2,rectangular (20); symmetric (1),84,319770,3806.785714285714,wide (>100×),252,8953560,mixed,1,20,all square matrices symmetric/Hermitian,1,1,1,real only,21,0,one broad domain; mixed matrix forms,"Balanced Incomplete Block Design from Mark Giesbrecht, Univ. of Waterloo -From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, -http://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html" -JGD_CAG,3,"CAG matrix set from Michael Monagan, Simon Fraser Univ., Canada From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,",https://sparse.tamu.edu/JGD_CAG,optimization/control,optimization/control,1.0,1,combinatorial problem (3),1,integer (3),1,unsymmetric (3),72,1916,26.61111111111111,moderate (4–100×),1012,195985,square only,3,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,0,coherent family/series,"CAG matrix set from Michael Monagan, Simon Fraser Univ., Canada -From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, -http://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html - -Strongly Connected Graph Components and Computing -Characteristic Polynomials of Integer Matrices in Maple, -Simon Lo, Michael Monagan, Allan Wittkopf -{sclo,mmonagan,wittkopf} at cecm.sfu.ca -Centre for Experimental and Constructive Mathematics, -Department of Mathematics, Simon Fraser University, -Burnaby, B.C., V5A 1S6, Canada. - -abstract: -Let A be an n x n matrix of integers. We present details of our Maple -implementation of a simple modular method for computing the -characteristic polynomial of A. We consider several different -representations for the computation modulo primes, in particular, the -use of double precision floats. The algorithm used in Maple releases -7-10 is the Berkowitz algorithm. We present some timings comparing the -two algorithms on a sequence of matrices arising from an application in -combinatorics of Jocelyn Quaintance. These matrices have a hidden block -structure. Once identified, we can further reduce the computing time -dramatically. This work has been incorporated into Maple 11's -LinearAlgebra package. - -http://www.cecm.sfu.ca/~monaganm/papers/CP8.pdf" -JGD_Forest,10,"Forests and Trees from Nicolas Thiery From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,",https://sparse.tamu.edu/JGD_Forest,optimization/control,optimization/control,1.0,1,combinatorial problem (10),1,integer (10),1,rectangular (10),107,317955,2971.542056074766,wide (>100×),622,4370721,rectangular only,0,10,N/A (rectangular only),0,0,0,real only,0,0,coherent family/series,"Forests and Trees from Nicolas Thiery -From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, -http://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html - -TF10 : rank = 99 , 0.0263529 s, Gauss: 0.00382595 -TF11 : rank = 216 , 0.136469 s, Gauss: 0.0256469 -TF12 : rank = 488 , 0.83511 s, Gauss: 0.295295 -TF13 : rank = 1121 , 6.06873 s, Gauss: 4.97585 -TF14 : rank = 2644 , 36.9781 s, Gauss: 81.5788 -TF15 : rank = 6334 , 229.029 s, Gauss: 1309.48 -TF16 : rank = 15437 , 1442.08 s, Gauss: 36000 -TF17 : rank = 38132 , 9706.03 s, Gauss: - -http://www.lapcs.univ-lyon1.fr/~nthiery" -JGD_Franz,11,"Cohomology of various rings, from Matthias Franz, Univ. Konstanz, Germany From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,",https://sparse.tamu.edu/JGD_Franz,optimization/control,optimization/control,1.0,1,combinatorial problem (11),1,integer (11),1,rectangular (11),2240,47104,21.02857142857143,moderate (4–100×),5120,329728,rectangular only,0,11,N/A (rectangular only),0,0,0,real only,0,0,coherent family/series,"Cohomology of various rings, from Matthias Franz, Univ. Konstanz, Germany -From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, -http://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html" -JGD_G5,13,"Decomposable subspaces at degree d of the invariant ring of G5, Nicolas Thiery. Univ. Paris Sud.",https://sparse.tamu.edu/JGD_G5,optimization/control,optimization/control,1.0,1,combinatorial problem (13),1,integer (13),1,rectangular (13),77,47894,622.0,wide (>100×),251,1790490,rectangular only,0,13,N/A (rectangular only),0,0,0,real only,0,0,coherent family/series,"Decomposable subspaces at degree d of the invariant ring of G5, Nicolas Thiery. -Univ. Paris Sud. - -From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, -http://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html - -http://www.lapcs.univ-lyon1.fr/~nthiery/LinearAlgebra/ - -Linear Algebra for combinatorics - -Abstract: Computations in algebraic combinatorics often boils down to -sparse linear algebra over some exact field. Such computations are -usually done in high level computer algebra systems like MuPAD or -Maple, which are reasonnably efficient when the ground field requires -symbolic computations. However, when the ground field is, say Q or -Z/pZ, the use of external specialized libraries becomes necessary. This -document, geared toward developpers of such libraries, present a brief -overview of my needs, which seems to be fairly typical in the -community. - -IG5-6: 30 x 77 : rang = 30 (Iteratif: 0.01 s, Gauss: 0.01 s) -IG5-7: 62 x 150 : rang = 62 (Iteratif: 0.02 s, Gauss: 0.01 s) -IG5-8: 156 x 292 : rang = 154 (Iteratif: 0.08 s, Gauss: 0.01 s) -IG5-9: 342 x 540 : rang = 308 (Iteratif: 0.46 s, Gauss: 0.02 s) -IG5-10: 652 x 976 : rang = 527 (Iteratif: 2.1 s, Gauss: 0.07 s) -IG5-11: 1227 x 1692 : rang = 902 (Iteratif: 7.5 s, Gauss: 0.22 s) -IG5-12: 2296 x 2875 : rang = 1578 (Iteratif: 26 s, Gauss: 0.93 s) -IG5-13: 3994 x 4731 : rang = 2532 (Iteratif: 80 s, Gauss: 3.35 s) -IG5-14: 6727 x 7621 : rang = 3906 (Iteratif: 244 s, Gauss: 10.06 s) -IG5-15: 11358 x 11987 : rang = 6146 (Iteratif: s, Gauss: 29.74 s) -IG5-16: 18485 x 18829 : rang = 9519 (Iteratif: s, Gauss: 621.97 s) -IG5-17: 27944 x 30131 : rang = 14060 (Iteratif: s, Gauss: 1973.8 s)" -JGD_GL6,5,"Differentials of the Voronoi complex of perfect forms of rank 6 mod GL_6(Z), from Philippe Elbaz-Vincent, Institut Fourier, Grenoble, France.",https://sparse.tamu.edu/JGD_GL6,optimization/control,optimization/control,1.0,1,combinatorial problem (5),1,integer (5),1,rectangular (5),341,637,1.8680351906158357,narrow (≤4×),2053,6153,rectangular only,0,5,N/A (rectangular only),0,0,0,real only,0,0,coherent family/series,"Differentials of the Voronoi complex of perfect forms of rank 6 mod GL_6(Z), -from Philippe Elbaz-Vincent, Institut Fourier, Grenoble, France. - -From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, -http://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html - -http://www-fourier.ujf-grenoble.fr/-Informations-personnelles-.html?P=pev - -D_6 Smith Invariants = [ 1:156 ] -D_7 Smith Invariants = [ 1:307 2:3 60:2 ] -D_8 Smith Invariants = [ 1:320 2:1 6:2 12:1 ] -D_9 Smith Invariants = [ 1:217 2:3 ] -D_10 Smith Invariants = [ 1:120 ]" -JGD_GL7d,17,"Differentials of the Voronoi complex of perfect forms of rank 7 mod GL_7(Z) equivalences, (related to the cohomology of GL_7(Z) and the K-theory of Z).",https://sparse.tamu.edu/JGD_GL7d,optimization/control,optimization/control,1.0,1,combinatorial problem (17),1,integer (17),1,rectangular (17),60,1955309,32588.483333333334,wide (>100×),8,37322725,rectangular only,0,17,N/A (rectangular only),0,0,0,real only,0,0,coherent family/series,"Differentials of the Voronoi complex of perfect forms of rank 7 mod GL_7(Z) -equivalences, (related to the cohomology of GL_7(Z) and the K-theory of Z). -from Philippe Elbaz-Vincent, Institut Fourier, Grenoble, France. - -From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, -http://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html - -http://www-fourier.ujf-grenoble.fr/-Informations-personnelles-.html?P=pev - -mtx rank n m ker rank/min(n,m) homology -10 1 60 1 59 -11 59 1019 60 960 0,98333 0 -12 960 8899 1019 7939 0,94210 1 -13 7938 47271 8899 39333 0,89201 1 -14 39332 171375 47271 132043 0,83205 0 -15 132043 460261 171375 328218 0,77049 0 -16 328218 955128 460261 626910 0,71311 0 -17 626910 1548650 955128 921740 0,65636 0* -18 921740* 1955309 1548650 1033569* 0,60* 1/0* -19 103356(8/9)* 1911130 1955309 87756(2/1)* 0,54* 0/1* -20 877562 1437547 1911130 559985 0,61 0 -21 559985 822922 1437547 262937 0,68048 0 -22 262937 349443 822922 86506 0,75245 0 -23 86505 105054 349443 18549 0,82343 1 -24 18549 21074 105054 2525 0,88018 0 -25 2525 2798 21074 273 0,90243 0 -26 273 305 2798 32 0,89508 0 - -file size elements rank SF -GL7d10 1 x 60 8 1 1 (1) -GL7d11 60 x 1019 1513 59 1 (59) -GL7d12 1019 x 8899 37519 960 1 (958), 2 (2) -GL7d13 8899 x 47271 356232 7938 1 (7937), 2 (1) -GL7d14 47271 x 171375 1831183 39332 1 (39300),2 (29),4 (3) -GL7d15 171375 x 460261 6080381 132043 1 (131993), 2*??? (46), 6*??? (4) -GL7d16 955128 x 460261 14488881 328218 -GL7d17 1548650 x 955128 25978098 -GL7d18 1955309 x 1548650 35590540 -GL7d19 1911130 x 1955309 37322725 -GL7d20 1437547 x 1911130 29893084 877562 -GL7d21 822922 x 1437547 18174775 559985 -GL7d22 349443 x 822922 8251000 262937 -GL7d23 105054 x 349443 2695430 86505 1 (86488), 2*??? (12), 6*??? (5) -GL7d24 21074 x 105054 593892 18549 1 (18544),2 (4),4 (1) -GL7d25 21074 x 2798 81671 2525 1 (2507), 2 (18) -GL7d26 2798 x 305 7412 273 1 (258), 2 (7), 6 (7), 36 (1)" -JGD_Groebner,8,"Gro""bner basis from Jean-Charles Fauge`res, From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,",https://sparse.tamu.edu/JGD_Groebner,optimization/control,optimization/control,1.0,1,combinatorial problem (8),2,integer (7); binary (1),1,rectangular (8),404,5761,14.259900990099009,moderate (4–100×),15118,2462970,rectangular only,0,8,N/A (rectangular only),0,0,0,real only,1,0,one broad domain; mixed matrix forms,"Gro""bner basis from Jean-Charles Fauge`res, -From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, -http://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html - -http://www-calfor.lip6.fr/~jcf/" -JGD_Homology,128,"Simplicial complexes from Homology from Volkmar Welker. From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,",https://sparse.tamu.edu/JGD_Homology,optimization/control,optimization/control,1.0,1,combinatorial problem (128),1,integer (128),2,rectangular (122); unsymmetric (6),15,564480,37632.0,wide (>100×),18,3386880,mixed,6,122,no square matrix exactly symmetric/Hermitian,0,1,0,real only,0,0,one broad domain; mixed matrix forms,"Simplicial complexes from Homology from Volkmar Welker. -From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, -http://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html - -http://www.mathematik.uni-marburg.de/~welker/" -QY,2,"Transient stabilty constrained interior pt. optimal power flow, J. Quanyuan Two problem sets from Dr. Jiang Quanyuan from Zhejiang University,",https://sparse.tamu.edu/QY,electrical/circuit/device,electrical/circuit/device,1.0,1,power network problem (2),1,real (2),1,symmetric (2),14454,40216,2.78234398782344,narrow (≤4×),147972,1042160,square only,2,0,all square matrices symmetric/Hermitian,2,2,0,real only,0,0,coherent family/series,"Transient stabilty constrained interior pt. optimal power flow, J. Quanyuan -Two problem sets from Dr. Jiang Quanyuan from Zhejiang University, -Hangzhou, China, March, 2008, used in an electrical power system. -Each matrix A is solved sequentially with two right-hand-sides, b1 and -b2, one at a time. In the UF collection, the sequence of all first -and second right-hand-sides is in Problem.aux.b2 and Problem.aux.b1. -These matrices are symmetric indefinite (x=A\\b uses MA57). -Note that the last matrices in the sequence are ill-conditioned. - -Transient Stability Constrained Interior Point Optimal Power Flow Program - Version 1.0 -- Developed by Dr. Jiang Quanyuan, March 2008 - -case9.m - TSOPF converges after 12 iterations - object = 3.945939E+03 - max_equ = 3.287326E-11 - low_inequ = None - up_inequ = None - -case39.m - TSOPF converges after 13 iterations - object = 2.696268E+04 - max_equ = 5.684342E-14 - low_inequ = -1.110223E-16 - up_inequ = None" -JGD_Kocay,12,"Brute force disjoint product matrices in tree algebra on n nodes, Nicolas Thiery From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,",https://sparse.tamu.edu/JGD_Kocay,optimization/control,optimization/control,1.0,1,combinatorial problem (12),2,integer (11); binary (1),1,rectangular (12),2,15905,7952.5,wide (>100×),1,2872265,rectangular only,0,12,N/A (rectangular only),0,0,0,real only,1,0,one broad domain; mixed matrix forms,"Brute force disjoint product matrices in tree algebra on n nodes, Nicolas Thiery -From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, -http://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html - -http://www.lapcs.univ-lyon1.fr/~nthiery/LinearAlgebra - -Linear algebra for combinatorics - -Abstract: Computations in algebraic combinatorics often boils down to -sparse linear algebra over some exact field. Such computations are -usually done in high level computer algebra systems like MuPAD or -Maple, which are reasonnably efficient when the ground field requires -symbolic computations. However, when the ground field is, say Q or -Z/pZ, the use of external specialized libraries becomes necessary. This -document, geared toward developpers of such libraries, present a brief -overview of my needs, which seems to be fairly typical in the -community." -JGD_Margulies,23,"Combinatorial optimization as polynomial eqns, Susan Margulies, UC Davis From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,",https://sparse.tamu.edu/JGD_Margulies,optimization/control,optimization/control,1.0,1,combinatorial problem (23),1,binary (23),2,rectangular (22); unsymmetric (1),25,902103,36084.12,wide (>100×),74,2170814,mixed,1,22,no square matrix exactly symmetric/Hermitian,0,0,0,real only,23,0,one broad domain; mixed matrix forms,"Combinatorial optimization as polynomial eqns, Susan Margulies, UC Davis -From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, -http://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html - -http://arxiv.org/abs/0706.0578 - -Expressing Combinatorial Optimization Problems by Systems of Polynomial -Equations and the Nullstellensatz - -Authors: J.A. De Loera, J. Lee, Susan Margulies, S. Onn - -(Submitted on 5 Jun 2007) - -Abstract: Systems of polynomial equations over the complex or real -numbers can be used to model combinatorial problems. In this way, a -combinatorial problem is feasible (e.g. a graph is 3-colorable, -hamiltonian, etc.) if and only if a related system of polynomial -equations has a solution. In the first part of this paper, we construct -new polynomial encodings for the problems of finding in a graph its -longest cycle, the largest planar subgraph, the edge-chromatic number, -or the largest k-colorable subgraph. For an infeasible polynomial -system, the (complex) Hilbert Nullstellensatz gives a certificate that -the associated combinatorial problem is infeasible. Thus, unless P = -NP, there must exist an infinite sequence of infeasible instances of -each hard combinatorial problem for which the minimum degree of a -Hilbert Nullstellensatz certificate of the associated polynomial system -grows. We show that the minimum-degree of a Nullstellensatz -certificate for the non-existence of a stable set of size greater than -the stability number of the graph is the stability number of the graph. -Moreover, such a certificate contains at least one term per stable set -of G. In contrast, for non-3- colorability, we found only graphs with -Nullstellensatz certificates of degree four." -JGD_Relat,15,"Relations from Florent Hiver, Univ. Paris-Est Marne-La-Valle'e From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,",https://sparse.tamu.edu/JGD_Relat,optimization/control,optimization/control,1.0,1,combinatorial problem (15),1,integer (15),1,rectangular (15),12,12360060,1030005.0,wide (>100×),18,38955420,rectangular only,0,15,N/A (rectangular only),0,0,0,real only,0,0,coherent family/series,"Relations from Florent Hiver, Univ. Paris-Est Marne-La-Valle'e -From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, -http://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html - -http://www-igm.univ-mlv.fr/~hivert/" -JGD_SL6,7,"Differentials of the Voronoi complex of perfect forms from Philippe Elbaz-Vincent, Institut Fourier, Grenoble, France.",https://sparse.tamu.edu/JGD_SL6,optimization/control,optimization/control,1.0,1,combinatorial problem (7),1,integer (7),1,rectangular (7),434,1271,2.9285714285714284,narrow (≤4×),1832,14966,rectangular only,0,7,N/A (rectangular only),0,0,0,real only,0,0,coherent family/series,"Differentials of the Voronoi complex of perfect forms -from Philippe Elbaz-Vincent, Institut Fourier, Grenoble, France. - -From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, -http://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html - -http://www-fourier.ujf-grenoble.fr/-Informations-personnelles-.html?P=pev - -D_5 Smith Invariants = [ 1:92 3:2 18:1 ] -D_6 Smith Invariants = [ 1:338 2:1 ] -D_7 Smith Invariants = [ 1:621 2:5 6:1 60:2 ] -D_8 Smith Invariants = [ 1:637 3:3 12:1 ] -D_9 Smith Invariants = [ 1:491 ] -D_10 Smith Invariants = [ 1:318 2:3 4:2 ] -D_11 Smith Invariants = [ 1:129 2:6 6:1 ]" -JGD_SPG,7,"Symmetric powers of graphs from Gordon Royle, Univ Western Australia From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,",https://sparse.tamu.edu/JGD_SPG,optimization/control,optimization/control,1.0,1,combinatorial problem (7),2,binary (6); integer (1),2,symmetric (6); unsymmetric (1),300,6545,21.816666666666666,moderate (4–100×),592,295680,square only,7,0,mixed among square matrices,6,6,0,real only,6,0,one broad domain; mixed matrix forms,"Symmetric powers of graphs from Gordon Royle, Univ Western Australia -From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, -http://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html - -http://www.csse.uwa.edu.au/~gordon/sympower.html" -JGD_Taha,2,"Taha matrix set from Abdul Salam Jarrah, New Mexico State Univ. From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,",https://sparse.tamu.edu/JGD_Taha,optimization/control,optimization/control,1.0,1,combinatorial problem (2),1,integer (2),1,rectangular (2),14596,37932,2.5987941901890927,narrow (≤4×),51307,137228,rectangular only,0,2,N/A (rectangular only),0,0,0,real only,0,0,coherent family/series,"Taha matrix set from Abdul Salam Jarrah, New Mexico State Univ. -From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, -http://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html - -http://www.math.nmsu.edu/~ajarrah" -JGD_Trefethen,11,"Diagonal matrices with primes, Nick Trefethen, Oxford Univ. From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,",https://sparse.tamu.edu/JGD_Trefethen,optimization/control,optimization/control,1.0,1,combinatorial problem (11),1,integer (11),1,symmetric (11),19,20000,1052.6315789473683,wide (>100×),147,554466,square only,11,0,all square matrices symmetric/Hermitian,11,11,11,real only,0,0,coherent family/series,"Diagonal matrices with primes, Nick Trefethen, Oxford Univ. -From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, -http://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html - -Problem 7 of the Hundred-dollar, Hundred-digit Challenge Problems, -SIAM News, vol 35, no. 1. - -7. Let A be the 20,000 x 20,000 matrix whose entries are zero -everywhere except for the primes 2, 3, 5, 7, . . . , 224737 along the -main diagonal and the number 1 in all the positions A(i,j) with -|i-j| = 1,2,4,8, . . . ,16384. What is the (1,1) entry of inv(A)? - -http://www.siam.org/news/news.php?id=388" -FreeFieldTechnologies,1,"3D vibro-acoustic problem, aircraft engine nacelle, from M. Gontier Free Field Technologies, Mont-Saint Guibert, Belguim (",https://sparse.tamu.edu/FreeFieldTechnologies,electromagnetics/acoustics,electromagnetics/acoustics,1.0,1,acoustics problem (1),1,complex (1),1,unsymmetric (1),169410,169410,1.0,one size,5033796,5033796,square only,1,0,no square matrix exactly symmetric/Hermitian,0,1,0,complex only,0,1,single matrix,"3D vibro-acoustic problem, aircraft engine nacelle, from M. Gontier -Free Field Technologies, Mont-Saint Guibert, Belguim (http://www.fft.be)" -Freescale,8,"Circuit simulation matrices K. Gullapalli, Freescale Semiconductor",https://sparse.tamu.edu/Freescale,electrical/circuit/device,electrical/circuit/device,1.0,2,circuit simulation problem (7); circuit simulation matrix (1),1,real (8),1,unsymmetric (8),178866,5558326,31.07536368007335,moderate (4–100×),961368,59524291,square only,8,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,0,coherent family/series,"Circuit simulation matrices K. Gullapalli, Freescale Semiconductor" -TSOPF,27,"Transient stability-constrained optimal power flow, G. Geng, Zhejiang Univ. comments on the TSOPF_RS_b2303 matrix:",https://sparse.tamu.edu/TSOPF,electrical/circuit/device,electrical/circuit/device,1.0,1,power network problem (27),1,real (27),2,unsymmetric (15); symmetric (12),2454,120216,48.987775061124694,moderate (4–100×),25032,16171169,square only,27,0,mixed among square matrices,12,12,0,real only,0,0,one broad domain; mixed matrix forms,"Transient stability-constrained optimal power flow, G. Geng, Zhejiang Univ. - -comments on the TSOPF_RS_b2303 matrix: -We are now trying UMFPACK in MATLAB to do some research work -power system optimization. UMFPACK LU function (5 outputs) performs much -faster than normal LU function (3 outputs) of MATLAB for most test cases in -our experiment. However, in one of our test, UMFPACK runs out of memory when -performing LU decomposition to an unsymmetric, non-positive definite matrix -Ck (uploaded on the website). But the normal LU function with COLAMD -successfully completes LU decomposition without any warning and error. All -tests are done in MATLAB 2008b x64 on a PC with 4Gb RAM. We have tried both -UMFPACK 5.0 and 5.2 but neither works. We would very appreciate it if you -could give some suggestions on our implementation. - -comments from Guangchao Geng regarding the TSOPF_FS_300 matrix: -KLU (which you recommend us in your last letter) does a perfect -a job in our application of power system optimization. It's faster than -UMFPACK and SuperLU about 10 times in LU factorization phase and 5 times in -backward substitution phase. It greatly speeds up our research work. However, -when we try UMFPACK and KLU to solve a symmetric sparse linear system in -another program, both of the two fails to exceed backslash in MATLAB. -The left-hand side matrix A and right-hand side vector b1 are uploaded in a -single .MAT file at the website. We wonder if MATLAB backslash is the -performance limit for matrices of this kind, or we have other external -solvers worth to try. - -Thank you for your consideration. Guangchao Geng - --------------- Guangchao Geng E-mail: genggc at gmail dot com -Institute of Power System, College of Electrical Engineering, -Zhejiang University, Hangzhou, 310027, China. - - -remainder: -Here is a matrix collection of our power system optimization application, -This .zip file contains matrices collection of Transient Stability -constrained Optimal Power Flow (TSOPF). All matrices are stored in MAT format -and divided in two groups: Full-Space (FS) group and Reduced-Space (RS) -group. - -FS group: - - Left-hand side: A (symmetric & indefinite) - - Right-hand side: b1 - - Solve fastest with Matlab backslash '\\' (actually MA57) - -RS group: - - Left-hand side: Ck (asymmetric & indefinite) - - Right-hand side: Nk (sparse with 300+ columns for large systems) - - Solve fastest with KLU - -Guangchao Geng (genggc at gmail dot com) -2009.01.08 - -(Update: the titles of the matrices were corrected on March 6, 2009)." -Buss,2,"Website recommendation system, Steven Buss, Univ. Florida connectus:",https://sparse.tamu.edu/Buss,graph/network data,graph/network data,1.0,1,undirected bipartite graph (2),1,binary (2),1,rectangular (2),394792,872622,2.2103335427262962,narrow (≤4×),1127525,22624727,rectangular only,0,2,N/A (rectangular only),0,0,0,real only,2,0,coherent family/series,"Website recommendation system, Steven Buss, Univ. Florida - -connectus: -This is a matrix from Steven Buss, for a website recommendation system (to -be called www.connectious.com). Each row of the matrix is a unique user. -Each column of the matrix is a web page. A(i,j)=1 if user i has tagged -web page j as favorable, and a 0 represents no opinion. The data was -obtained from digg.com. Note that the matrix A*A' contains one -connected component of size 342, and 170 singletons (nodes with no edges) -This means that a group of 341 users share some interests, at least -indirectly, and that there are 171 other users that share no interests -at all with any other users (they have no pages in common with anyone). - -12month1: another matrix from the same problem, with 12,646 users -and 394,792 web pages, from July 2008 to July 2009." -Yoshiyasu,2,"Image editting and mesh deformation problem from Y. Yoshiyasu, Keio Univ., Japan I uploaded 5 matrices A1,b1,A2,b2 and D (for x=(A1'*A1)/(A1'*b1) and",https://sparse.tamu.edu/Yoshiyasu,imaging/graphics/robotics,imaging/graphics/robotics,1.0,1,computer graphics/vision problem (2),1,real (2),1,rectangular (2),234023,240000,1.0255402246787708,narrow (≤4×),711683,853829,rectangular only,0,2,N/A (rectangular only),0,0,0,real only,0,2,coherent family/series,"Image editting and mesh deformation problem from Y. Yoshiyasu, Keio Univ., Japan - -I uploaded 5 matrices A1,b1,A2,b2 and D (for x=(A1'*A1)/(A1'*b1) and -v=(A2'*D*A2)/(A2'*D*b2)). What I'm doing is template-mesh deformation to match -with silhouettes. In this process, there are two kinds of linear systems that -I have to solve. - -First one is smooth vector field construction from images, which is harmonic -interpolation (minimizing laplacian: Lx=0) of intensity gradient field p. I'm -solving this by normal equation and cholesky factorization. - -x=(A1'*A1)/(A1'*b1) - -where A1=[L;C] and b1=[zeros(size(length(L),1);1);C*p]. C is a square diagonal -matrix containing weights. I'm doing this on a 400x300 image, so -Ix=reshape(x,400,300) must be done to get the vector field. After solving y -direction for Iy, the result is visualized with quiver(Ix,Iy). At each -iteration the both C submatrix and the right-hand-side change but L remains -unchanged. - -Second one is template deformation using above vector field. I'm solving this -by: - -v=(A2'*D*A2)/(A2'*D*b2) - -where D is diagonal matrix containing weights. Structures of A2 and b2 are a -little bit more complex than A1 and b1, but they are similar. - -Both systems are not quite time-consuming for themselves, but I iterate this -process more than 10 times for 10 views. So I would like them to be faster and -also I'd like to reuse and update the factored matrix (if it's possible). - -[Reply: if all of C changes at each step, this is a nearly n-rank change, -so an update/downdate process would be much more expensive than refactorizing -the matrix. Update/downdate is effective for low-rank changes. - -In the collection,]" -Priebel,6,"Quadratic sieve matrices from David Priebel, Tenn. Tech. Univ. Each column in the matrix corresponds to a number in the factor base",https://sparse.tamu.edu/Priebel,optimization/control,optimization/control,1.0,1,combinatorial problem (6),1,binary (6),1,rectangular (6),584,24430,41.832191780821915,moderate (4–100×),6120,299756,rectangular only,0,6,N/A (rectangular only),0,0,0,real only,6,0,coherent family/series,"Quadratic sieve matrices from David Priebel, Tenn. Tech. Univ. - -Each column in the matrix corresponds to a number in the factor base -less than some bound B. Each row corresponds to a smooth number (able -to be completely factored over the factor base). Each value in a row -binary vector corresponds to the exponent of the factor base mod 2. -For example: - - factor base: 2 7 23 - smooth numbers: 46, 28, 322 - 2^1 * 23^1 = 46 - 2^2 * 7^1 = 28 - 2^1 * 7^1 * 23^1 = 322 - Matrix: - 101 - 010 - 111 - -A solution to the matrix is considered to be a set of rows which when -combined in GF2 produce a null vector. Thus, if you multiply each of -the smooth numbers which correspond to that particular set of rows you -will get a number with only even exponents, making it a perfect -square. In the above example you can see that combining the 3 vectors -results in a null vector and, indeed, it is a perfect square: 644^2. - -Problem.A: A GF(2) matrix constructed from the exponents of the -factorization of the smooth numbers over the factor base. A solution of -this matrix is a kernel (nullspace). Such a solution has a 1/2 chance of -being a factorization of N. - -Problem.aux.factor_base: The factor base used. factor_base(j) corresponds -to column j of the matrix. Note that a given column may or may not have -nonzero elements in the matrix. - -Problem.aux.smooth_number: The smooth numbers, smooth over the factor -base. smooth_number(i) corresponds to row i of the matrix. - -Problem.aux.solution: A sample solution to the matrix. Combine, in GF(2) -the rows with these indicies to produce a solution to the matrix with the -additional property that it factors N (a matrix solution only has 1/2 -probability of factoring N). - -Problem specific information: - -n = 787911249838484617926390474950774839909 (130-bits) -Divisor: 23850290715477455017 (probably prime) -Divisor: 33035708421246870877 (probably prime) - -n = 27393004579711727757848513391018843988362569 (145-bits) -Divisor: 4762476283061573160587 (probably prime) -Divisor: 5751840629031342254587 (probably prime) - -n = 3408489886335277144344023699527218196631767672957 (162-bits) -Divisor: 1816046478796474796528999 (probably prime) -Divisor: 1876873706775468629074043 (probably prime) - -n = 73363722971930954428433124842779099222294372095286387 (176-bits) -Divisor: 236037985789994529800050193 (probably prime) -Divisor: 310813205452462332837525059 (probably prime) - -n = 4232562527578032866150921497850842593296760823796443077101 (192-bits) -Divisor: 83135929635332984850508004533 (probably prime) -Divisor: 50911351399373573113182167897 (probably prime) - -n = 239380926372595066574100671394554319947805305453767699448870971 (208-bits) -Divisor: 12216681953629826483019726942851 (probably prime) -Divisor: 19594594283554225566102143686121 (probably prime)" -MaxPlanck,2,"Weather shallow water equations, Max-Planck Institute of Meteoroloy The two shallow_water* matrices arise from weather shallow water equations",https://sparse.tamu.edu/MaxPlanck,fluids/transport,fluids/transport,1.0,1,computational fluid dynamics problem (2),1,real (2),1,symmetric (2),81920,81920,1.0,one size,327680,327680,square only,2,0,all square matrices symmetric/Hermitian,2,2,2,real only,0,2,coherent family/series,"Weather shallow water equations, Max-Planck Institute of Meteoroloy - -The two shallow_water* matrices arise from weather shallow water equations -(see http://www.icon.enes.org), from the Max-Plank Institute of Meteorology. -The problem gives rise to an automatic differentiation problem. An iterative -solver is used for the larger problem, but a direct sovler is used for -finding the adjoints of a linear problem. The velocity field is integrated -over a time loop with a semi-implicit method. The implicit part leads to -a linear problem A*x=b, whose entries vary with time. Two of these matrices -A are in this collection, with shallow_water1 at dtime=100 and shallow_water2 -at dtime=200. The nonzero patterns of the two matrices are the same, but -shallow_water1 is much slower. The reason is that many denormals appear -during factorization, which greatly slows down the BLAS. This can be solved -by compiling with gcc -ffast-math, to flush denormals to zero." -Clark,1,"Tomographic reconstruction of a particle field in fluid flow, T. Clark See",https://sparse.tamu.edu/Clark,imaging/graphics/robotics,imaging/graphics/robotics,1.0,1,computer graphics/vision problem (1),1,real (1),1,rectangular (1),73159,73159,1.0,one size,647495,647495,rectangular only,0,1,N/A (rectangular only),0,0,0,real only,0,1,single matrix,"Tomographic reconstruction of a particle field in fluid flow, T. Clark - -See http://www.mathworks.com/matlabcentral/newsreader/view_thread/254747 -for a discussion. - -The problem can be solved via lsqlin in MATLAB: -lb = zeros (n,1) ; -x = lsqlin (A, b, [ ], [ ], [ ], [ ], lb) ;" -Bourchtein,4,Atmospheric modeling problems from Andrei Bourchtein These matrices arise in the numerical weather prediction and atmospheric,https://sparse.tamu.edu/Bourchtein,fluids/transport,fluids/transport,1.0,1,computational fluid dynamics problem (4),2,real (3); integer (1),1,unsymmetric (4),1270432,1489752,1.1726341905745448,narrow (≤4×),8814880,10319760,square only,4,0,no square matrix exactly symmetric/Hermitian,0,4,0,real only,0,4,one broad domain; mixed matrix forms,"Atmospheric modeling problems from Andrei Bourchtein - -These matrices arise in the numerical weather prediction and atmospheric -modeling. Such matrices usually appear in semi-implicit schemes applied to -three-dimensional Euler or Navier-Stokes equations (called nonhydrostatic -models in the atmospheric sciences) or to their simplified form with -hydrostatic balance equation instead of vertical momentum equation (called -hydrostatic or primitive equation models in the atmospheric sciences). - -Such linear systems represent discretization of three-dimensional elliptic -problems (frequently Dirichlet or Neumann problems for Helmholtz or -quasi-Helmholtz equations), arising at each time step of semi-implicit -algorithms due to implicit time approximation of some linear terms in the -governing equations. If spectral spatial approximation is applied, then the -elliptic problem is usually transformed to the linear system with a diagonal -matrix solved trivially. If finite-difference or finite-element approximation -is used, then the linear systems with the sparse matrices of the coefficients -similar to the four submitted matrices arise. However, the semi-implicit -schemes usually do not require explicit construction of the matrix of -coefficients, neither do iterative methods used to solve these systems in the -atmospheric models. Besides, avoiding construction of the matrix of -coefficients allows reducing the required computer memory. Due to these -reasons, as far as I know, the explicit form of matrices of coefficients -usually is not described, except for the local structure of the respective -difference equations. - -The two right-hand sides b(:,1) and b(:,2) refer to the long wave or -short wave perturbation of atmospheric fields, respectively. - -The description of such semi-implicit algorithms together with arising elliptic -problems can be found, for example, in the following recent papers (and -references therein): - -1. Steppeler J., Hess R., Schattler U., Bonaventura L.: Review of numerical -methods for nonhydrostatic weather prediction models. Met. Atm Phys. 82 (2003) -287-301. - - This is a review paper on nonhydrostatic models, including particularly, - semi-implicit time differencing. Some description of arising elliptic - problems and their solvers used in atmospheric models can be found on - pp.294-296. - -2. Cote J., Gravel S., Methot A., Patoine A., Roch M., Staniforth A.: The CMC-MRB -global environmental multiscale (GEM) model. Part I: Design considerations and -formulation. Mon. Wea. Rev. 126 (1998) 1373-1395. - -3. Yeh K.S., Cote J., Gravel S., Methot A., Patoine A., Roch M., Staniforth A.: The -CMC-MRB global environmental multiscale (GEM) model. Part III: Nonhydrostatic -formulation. Mon. Wea. Rev. 130 (2002) 339-356. - - This pair of papers is about hydrostatic and nonhydrostatic versions of the - modern semi-implicit Canadian model. Some description of elliptic problems - and their solution can be found on p.1389 of the first paper and p.343 of - the second paper. - -4. Davies T., Cullen M.J.P., Malcolm A.J., Mawson M.H., Staniforths A., White A.A., -Wood N.: A new dynamical core for the Met Office’s global and regional modeling. -Q. J. Roy. Met. Soc. 131 (2005) 1759-1782. - - This is a brief report on United Kingdom modern semi-implicit model. Some - description of elliptic problem can be found on p.1778 and its solution - on pp.1771-1772." -Puri,4,ABAQUS benchmarks: pt.loaded fluid-filled shell. Srinivasan Puri ROM benchmark test matrices.,https://sparse.tamu.edu/Puri,eigenvalue/model reduction,eigenvalue/model reduction,1.0,1,model reduction problem (4),2,complex (3); real (1),1,unsymmetric (4),23412,23412,1.0,one size,218484,218484,square only,4,0,no square matrix exactly symmetric/Hermitian,0,4,0,real + complex,0,4,one broad domain; mixed matrix forms,"ABAQUS benchmarks: pt.loaded fluid-filled shell. Srinivasan Puri -ROM benchmark test matrices. - -http://sites.google.com/site/srinivaspuri/Home/rom-test-matrices - -The problem can be found in any ABAQUS Benchmark manual: Section -1.10.2, and is often known as the ""acid-test"" within the -structural-acoustic community. - -A small description of the problem follows. - -The Abaqus/Standard Model: - -This problem involves the steady-state vibration of a point-loaded -spherical shell coupled to an acoustic fluid that fills its interior. -It is modeled using axisymmetric shell and acoustic elements. The -closed form solution of Stepanishen and Cox (2000) is used for -validation of the analysis. The basis of the coupled -acoustic-structural vibration capability in Abaqus is described in -""Coupled acoustic-structural medium analysis,"" Section 2.9.1 of the -Abaqus Theory Manual, and ""Acoustic, shock, and coupled -acoustic-structural analysis,"" Section 6.9.1 of the Abaqus Analysis -User's Manual. - -Problem description - -The model is a semicircular shell and fluid mesh of radius 2.286 m. A -point load on the symmetry axis of magnitude 1.0 N is applied to the -shell. The shells are 0.0254 m in thickness and have a Young's modulus -of 206.8 GPa, a Poisson's ratio of 0.3, and a mass density of 7800.0 -kg/m^{3}. The acoustic fluid has a density, of 1000 kg/m^{3} and a bulk -modulus, of 2.25 GPa. The response of the coupled system is calculated -for frequencies ranging from 100 to 1000 Hz in 5 Hz increments. There -are two different finite element meshes used: one with explicitly -defined acoustic-structural interaction elements and one that uses the -*TIE option. The former model consists of 220 SAX1 elements surrounding -a mesh of 15848 ACAX4 elements. Coupling is effected using 220 ASI2A -elements. The latter model uses 80 SAX2 elements surrounding a mesh of -965 ACAX8 elements. For this mesh, coupling is effected using the *TIE -option to generate the acoustic-structural interaction elements -internally. - -A dummy part is included in the models to ensure that the analytical -solution appears in the output database. This part consists of a single -point mass, uncoupled from the model described above, with a -displacement boundary condition on degree of freedom 1. This imposed -displacement uses an amplitude table consisting of the Stepanishen/Cox -analytical solution for the drive point admittance. - -The ANSYS Model & the Higher Dimensional Coupled System Matrices: - -We generate the required matrices in ANSYS by reading through the -ABAQUS documentation and preparing the relevant finite element mesh in -ANSYS. This particular model was done with ANSYS V10/V11. A detailed -description can be found in and R. S. Puri, D Morrey, J. L. Cipolla -(2007) and R. S. Puri (2008). In the ANSYS model, we sweep from -100-1000Hz in 1Hz increments as opposed to 5Hz increments in the ABAQUS -model. This is simply for ease of comparison with analytical solutions -- since we have closed form solutions at every 1Hz. increments for the -undamped case. - -A diagram of the coupled Finite Element mesh along with the point -loading condition/location & the sparsity plots of the coupled -stiffness and mass matrices are given at -http://sites.google.com/site/srinivaspuri/Home/rom-test-matrices - -For the undamped model, the matrices can be found in BM_Uda.zip in the -Attachments section. - -For damped models, we have three sub test cases: Low Damping, Moderate -Damping, High Damping. The higher dimensional matrices can be found in -BM_Ld.zip ; BM_Md.zip ; BM_Hd.zip in the Attachments sections (see -""contd.."" sub page also). - -General file description are as follows: - -full* represents higher dimensional system. - -full.M: Coupled Mass matrix: Unsymm. (explicit zeros in Mzeros matrix) -full.K: Coupled Stiffness matrix: Unsymm. -full.E: Structural-Acoustic damping matrix - (In this case, we vary only structural damping). -full.B: Structural-Acoustic input vector - (i.e forcing function for the freq domain simulation) -full.C: Output measurement matrix. -full.C.names: Description of the output measurement: - Not required for model reduction. -frequency.txt: Frequencies for frequency domain simulation. - (100-1000Hz.) -N10945_.txt: Results from direct inversion of the - higher dimensional system: Coupled nodal displacements. -TSSOAR_N10945_.txt: Results from Two Sided Arnoldi - projection: Coupled nodal displacements. -closedform.txt: Velocity Results from analytical solution - [modal expansion] for the problem. - -Key: : undamped; - : low damping; - : moderate damping; - : high damping. - -Coupled System Matrix Properties: - -Type: Second order, SISO. -Format: Matrix Market. -Dimension: 23412 x 23412. -Mass Matrix: Coupled, Unsymmetric. -Stiffness Matrix: Coupled, Unsymmetric. -Damping Matrix: Undamped & Structurally Damped Symmetric. - -Approximation Properties: - -State Variable to be approximated: Coupled Displacement at driving point location. -Eigen Solution: - 50 coupled vectors for say Coupled Lanczos projection procedure ; - Uncoupled approach: 50 structural and uncoupled acoustic modes - till 4000Hz for AMLS projection. - -Arnoldi Vectors: - 100 for undamped via One Sided or Two Sided Arnoldi Process [OSA-TSA]; - 110 for damped via Two-Sided Second Order Arnoldi Process [SOAR]. - -Matrix Extraction Information: - -[1.0] Undamped Matrices: - -BM_Uda.zip : No issues to report. You should be able to generate the -plots in Figure: 3 and Figure: 4 with minimal effort. - -[1.1] Damped Matrices: - -BM_.zip : No issues with Mass and Stiffness Matrices. The -damping matrices for the weakly non-linear test cases are assembled -from the total Dynamic matrix in ANSYS. That is, ANSYS writes the -dynamic, coupled structural-acoustic ""supermatrix"" as follows: - -[A] {x} = F ; -- Equation (1) - -where, [A] is the dynamic ""super matrix"" which is computed as follows: - -[A] = -Omega^{2} [M] + i*Omega [E] + [K] ; -- Equation (2) - -which is directly written to the ANSYS FULL file. It follows from -above that the damping matrix can be ""back calculated"" by using the -following relationship: - -[E] = [Ai] / Omega ; -- Equation (3) - -where, [Ai] represents the imaginary part of the dynamic coupled -structural-acoustic ""super matrix"". - -Unfortunately, at this moment, there is no other direct way to extract -the damping matrix including linearly varying damping. - -Shortly speaking, the damping matrix [E] for the coupled -structural-acoustic system can be extracted by dividing the imaginary -part of the ""super matrix"" with a high enough frequency. - -Remarks on Matrix Extraction for Damped Test Cases: - - -For the damped cases, the [E] matrices for the benchmark models were -extracted at around 1000Hz, which is close to the end frequency of the -analysis range. - -This above extraction procedure sometimes results in a ""non-symmetric"" -damping matrix rather than a symmetric one, which can be attributed to -the very small ""round-off"" differences at different frequencies. Note -also that this ""unsymmetricity"" is not always the case. In many cases -(e.g. ones with lower dimension), the damping matrices are indeed -symmetric. - -Saying that, we note that this should not yield any issues/differences -in the dimension reduction process. - -Please let me know if you do encounter any difficulties or questions -on the matrices/reduction process. Results for the linearly damped -cases can be found in R. S. Puri (2008). - -References: - -Stepanishen, P., and D. L. Cox, ""Structural-Acoustic Analysis of an -Internally Fluid-Loaded Spherical Shell: Comparison of Analytical and -Finite Element Modeling Results,"" NUWC Technical Memorandum 00-118, -Newport, Rhode Island, 2000. - -Puri, R. S., D. Morrey, and J. L. Cipolla (2007), A comparison between -One-sided and Two-sided Arnoldi based model order reduction techniques -for fully coupled structural acoustic analysis. In 153rd Meeting of -the Acoustical Society of America, The Journal of Acoustical Society -of America, Volume 121. - -R.S. Puri (2008), Krylov Subspace Based Direct Projection Techniques -For Low Frequency, Fully Coupled, Structural Acoustic Analysis and -Optimization, Ph.D. Thesis, School of Technology, Department of -Mechanical Engineering and Mathematical Sciences, Oxford Brookes -University, Oxford, UK. - -E. B. Rudnyi and J. G. Korvink (2006), Model Order Reduction for Large -Scale Engineering Models Developed in ANSYS. Lecture Notes in Computer -Science, v. 3732, pp. 349-356. - -If you run into any trouble, do ping me: -srinivaspuri[at]gmail[dot].com" -Stevenson,1,"Circuit optimization for digital circuits. John Peter Stevenson, Stanford. LargeRegFile is a matrix representing an optimization problem.",https://sparse.tamu.edu/Stevenson,electrical/circuit/device,electrical/circuit/device,1.0,1,circuit simulation problem (1),1,real (1),1,rectangular (1),2111154,2111154,1.0,one size,4944201,4944201,rectangular only,0,1,N/A (rectangular only),0,0,0,real only,0,0,single matrix,"Circuit optimization for digital circuits. John Peter Stevenson, Stanford. - -LargeRegFile is a matrix representing an optimization problem. -Specifically, it is a geometric program, a kind of convex optimization -problem. The matrix entries are the exponents found in the geometric -program constraints; specifically, this matrix is a portion of the -input required by mskexpopt: -http://docs.mosek.com/6.0/capi/node008.html - -This matrix represents a digital circuit. The circuit is a large -register file: 256 registers, 64 bits each, 2 read ports, 1 write port. -The optimization problem is to minimize circuit delay, subject to -constraints on energy cost and area cost. The optimization variables -are the transistor size, the supply voltage, and the threshold voltage. -The circuit optimization method is described by Patil, et. al.: Robust -energy-efficient adder topologies. In ARITH07: Proceedings of the 18th -IEEE Symposium on Computer Arithmetic, pages 16-28, Washington, DC, -USA, 2007. IEEE Computer Society." -Belcastro,3,"Gene networks from V. Belcastro, TIGEM, Naples, Italy Currently working on gene regulatory network inference. Obtained",https://sparse.tamu.edu/Belcastro,graph/network data,graph/network data,1.0,1,undirected weighted graph (3),1,real (3),1,symmetric (3),14340,45101,3.1451185495118548,narrow (≤4×),18068388,28967291,square only,3,0,all square matrices symmetric/Hermitian,3,3,0,real only,0,0,coherent family/series,"Gene networks from V. Belcastro, TIGEM, Naples, Italy - -Currently working on gene regulatory network inference. Obtained -human as well as mouse gene regulatory networks by analyzing gene -expression profiles from both the species. - -Vincenzo Belcastro, M.Sc. -Research Associate -Telethon Institute of Genetics and Medicine -(TIGEM) - Systems Biology Group -Via P. Castellino 111 -80131, Napoli -Italy" -SNAP,68,"Networks from SNAP (Stanford Network Analysis Platform) Network Data Sets, Jure Leskovec",https://sparse.tamu.edu/SNAP,graph/network data,graph/network data,1.0,8,directed graph (30); undirected graph (14); directed temporal multigraph (8); undirected graph with communities (6); directed weighted graph (5); directed graph with communities (2); directed weighted temporal graph (2); directed weighted temporal multigraph (1),2,binary (53); integer (15),2,unsymmetric (47); symmetric (21),1005,65608366,65281.95621890547,wide (>100×),20296,3612134270,square only,68,0,mixed among square matrices,21,22,0,real only,53,0,one broad domain; mixed matrix forms,"Networks from SNAP (Stanford Network Analysis Platform) Network Data Sets, -Jure Leskovec http://snap.stanford.edu/data/index.html -email jure at cs.stanford.edu - -Citation for the SNAP collection: - -@misc{snapnets, - author = {Jure Leskovec and Andrej Krevl}, - title = {{SNAP Datasets}: {Stanford} Large Network Dataset Collection}, - howpublished = {\\url{http://snap.stanford.edu/data}}, - month = jun, - year = 2014 - } - -The following matrices/graphs were added to the collection in June 2010 -by Tim Davis (problem id and name): --------------------------------------------------------------------------------- - -2284 SNAP/soc-Epinions1 who-trusts-whom network of Epinions.com -2285 SNAP/soc-LiveJournal1 LiveJournal social network -2286 SNAP/soc-Slashdot0811 Slashdot social network, Nov 2008 -2287 SNAP/soc-Slashdot0902 Slashdot social network, Feb 2009 -2288 SNAP/wiki-Vote Wikipedia who-votes-on-whom network -2289 SNAP/email-EuAll Email network from a EU research institution -2290 SNAP/email-Enron Email communication network from Enron -2291 SNAP/wiki-Talk Wikipedia talk (communication) network -2292 SNAP/cit-HepPh Arxiv High Energy Physics paper citation network -2293 SNAP/cit-HepTh Arxiv High Energy Physics paper citation network -2294 SNAP/cit-Patents Citation network among US Patents -2295 SNAP/ca-AstroPh Collaboration network of Arxiv Astro Physics -2296 SNAP/ca-CondMat Collaboration network of Arxiv Condensed Matter -2297 SNAP/ca-GrQc Collaboration network of Arxiv General Relativity -2298 SNAP/ca-HepPh Collaboration network of Arxiv High Energy Physics -2299 SNAP/ca-HepTh Collaboration network of Arxiv High Energy Physics Theory -2300 SNAP/web-BerkStan Web graph of Berkeley and Stanford -2301 SNAP/web-Google Web graph from Google -2302 SNAP/web-NotreDame Web graph of Notre Dame -2303 SNAP/web-Stanford Web graph of Stanford.edu -2304 SNAP/amazon0302 Amazon product co-purchasing network from March 2 2003 -2305 SNAP/amazon0312 Amazon product co-purchasing network from March 12 2003 -2306 SNAP/amazon0505 Amazon product co-purchasing network from May 5 2003 -2307 SNAP/amazon0601 Amazon product co-purchasing network from June 1 2003 -2308 SNAP/p2p-Gnutella04 Gnutella peer to peer network from August 4 2002 -2309 SNAP/p2p-Gnutella05 Gnutella peer to peer network from August 5 2002 -2310 SNAP/p2p-Gnutella06 Gnutella peer to peer network from August 6 2002 -2311 SNAP/p2p-Gnutella08 Gnutella peer to peer network from August 8 2002 -2312 SNAP/p2p-Gnutella09 Gnutella peer to peer network from August 9 2002 -2313 SNAP/p2p-Gnutella24 Gnutella peer to peer network from August 24 2002 -2314 SNAP/p2p-Gnutella25 Gnutella peer to peer network from August 25 2002 -2315 SNAP/p2p-Gnutella30 Gnutella peer to peer network from August 30 2002 -2316 SNAP/p2p-Gnutella31 Gnutella peer to peer network from August 31 2002 -2317 SNAP/roadNet-CA Road network of California -2318 SNAP/roadNet-PA Road network of Pennsylvania -2319 SNAP/roadNet-TX Road network of Texas -2320 SNAP/as-735 733 daily instances(graphs) from November 8 1997 to January 2 2000 -2321 SNAP/as-Skitter Internet topology graph, from traceroutes run daily in 2005 -2322 SNAP/as-caida The CAIDA AS Relationships Datasets, from January 2004 to November 2007 -2323 SNAP/Oregon-1 AS peering information inferred from Oregon route-views between March 31 and May 26 2001 -2324 SNAP/Oregon-2 AS peering information inferred from Oregon route-views between March 31 and May 26 2001 -2325 SNAP/soc-sign-epinions Epinions signed social network -2326 SNAP/soc-sign-Slashdot081106 Slashdot Zoo signed social network from November 6 2008 -2327 SNAP/soc-sign-Slashdot090216 Slashdot Zoo signed social network from February 16 2009 -2328 SNAP/soc-sign-Slashdot090221 Slashdot Zoo signed social network from February 21 2009 - -Then the following problems were added in July 2018. All data and -metadata from the SNAP data set was imported into the SuiteSparse -Matrix Collection. - -2777 SNAP/CollegeMsg Messages on a Facebook-like platform at UC-Irvine -2778 SNAP/com-Amazon Amazon product network -2779 SNAP/com-DBLP DBLP collaboration network -2780 SNAP/com-Friendster Friendster online social network -2781 SNAP/com-LiveJournal LiveJournal online social network -2782 SNAP/com-Orkut Orkut online social network -2783 SNAP/com-Youtube Youtube online social network -2784 SNAP/email-Eu-core E-mail network -2785 SNAP/email-Eu-core-temporal E-mails between users at a research institution -2786 SNAP/higgs-twitter twitter messages re: Higgs boson on 4th July 2012. -2787 SNAP/loc-Brightkite Brightkite location based online social network -2788 SNAP/loc-Gowalla Gowalla location based online social network -2789 SNAP/soc-Pokec Pokec online social network -2790 SNAP/soc-sign-bitcoin-alpha Bitcoin Alpha web of trust network -2791 SNAP/soc-sign-bitcoin-otc Bitcoin OTC web of trust network -2792 SNAP/sx-askubuntu Comments, questions, and answers on Ask Ubuntu -2793 SNAP/sx-mathoverflow Comments, questions, and answers on Math Overflow -2794 SNAP/sx-stackoverflow Comments, questions, and answers on Stack Overflow -2795 SNAP/sx-superuser Comments, questions, and answers on Super User -2796 SNAP/twitter7 A collection of 476 million tweets collected between June-Dec 2009 -2797 SNAP/wiki-RfA Wikipedia Requests for Adminship (with text) -2798 SNAP/wiki-talk-temporal Users editing talk pages on Wikipedia -2799 SNAP/wiki-topcats Wikipedia hyperlinks (with communities) - -The following 13 graphs/networks were in the SNAP data set in July 2018 but -have not yet been imported into the SuiteSparse Matrix Collection. They may be -added in the future: - - amazon-meta - ego-Facebook - ego-Gplus - ego-Twitter - gemsec-Deezer - gemsec-Facebook - ksc-time-series - memetracker9 - web-flickr - web-Reddit - web-RedditPizzaRequests - wiki-Elec - wiki-meta - wikispeedia - -The 2010 description of the SNAP data set gave these categories: - - * Social networks: online social networks, edges represent interactions - between people - - * Communication networks: email communication networks with edges - representing communication - - * Citation networks: nodes represent papers, edges represent citations - - * Collaboration networks: nodes represent scientists, edges represent - collaborations (co-authoring a paper) - - * Web graphs: nodes represent webpages and edges are hyperlinks - - * Blog and Memetracker graphs: nodes represent time stamped blog posts, - edges are hyperlinks [revised below] - - * Amazon networks : nodes represent products and edges link commonly - co-purchased products - - * Internet networks : nodes represent computers and edges communication - - * Road networks : nodes represent intersections and edges roads connecting - the intersections - - * Autonomous systems : graphs of the internet - - * Signed networks : networks with positive and negative edges (friend/foe, - trust/distrust) - -By July 2018, the following categories had been added: - - * Networks with ground-truth communities : ground-truth network communities - in social and information networks - - * Location-based online social networks : Social networks with geographic - check-ins - - * Wikipedia networks, articles, and metadata : Talk, editing, voting, and - article data from Wikipedia - - * Temporal networks : networks where edges have timestamps - - * Twitter and Memetracker : Memetracker phrases, links and 467 million - Tweets - - * Online communities : Data from online communities such as Reddit and Flickr - - * Online reviews : Data from online review systems such as BeerAdvocate and - Amazon - -================================================================================ -Note that some versions of these graphs already appear in the SuiteSparse -collection. Some have similar names: - -web-BerkStan Kamvar/Stanford_Berkeley - in SNAP/: n: 685,230 nz: 7,600,595 - in Kamvar/ n: 683,446 nz: 7,583,376 - - I obtained the Kamvar/Stanford_Berkeley directly - from Sep Kamvar. It is slightly smaller than the - version in SNAP. It is thus likely that Sep created - multiple versions of the graph. - -web-Google appears only in SNAP. - -web-NotreDame Barabasi/NotreDame_www - in SNAP/: n: 325,729 nz: 1,497,134 - in Barabasi/: n: same nz: 929,849 - - The Barabasi/NotreDame_www is an exact copy of - the graph of that name in the Pajek data set. - The SNAP collection has a different version of this - graph, of which SNAP/web-NotreDame is an exact copy. - It is possible that Barabasi's version of the graph is yet - a 3rd version of this graph. - -web-Stanford Kamvar/Stanford (same size and nnz) - n: 281,903 nz: 2,312,497 - - The SNAP/web-Stanford graph and the Kamvar/Stanford - graphs have the same number of nodes and edges. - However, they differ in nonzero pattern. - -cit-HepTh Pajek/HEP-th-new is identical to the - SNAP/cit-HeptTh graph. Since it's small, I have - decided to include both in the collection, to - keep the SNAP/ collection complete. - n: 27,770 nz: 352,807 - -cit-HepPh appears only in SNAP -ca-HepPh appears only in SNAP -ca-HepTh appears only in SNAP - -Pajek/HEP-th appears only in the Pajek collection - -cit-Patents in SNAP n: 3,774,768 nz: 16,518,948 - Pajek/patents n: same nz: 14,970,767 - - Both of these come from the NBER data. However, - the edges are not the same. The SNAP/cit-Patents - data is a strict superset of the Pajek/patents graph. - If A0 = Pajek/patents and A1 = SNAP/cit-Patents, - then nnz(A1-A0) = nnz(A1)-nnz(A0) = 1,548,181. - All edges in A0 appear in A1. - - The aux data is not the same. Pajek/patents contains - more auxiliary data for each node. This data can be - used to interpret the SNAP/cit-Patents graph as well, - since the nodes match up from one graph to the other. - -NOTE: the 2018 GraphChallenge data sets include many of these matrices, -at https://graphchallenge.mit.edu. In the GraphChallenge data set, -the graphs have all been made symmetric, and the diagonal entries -(self edges) have been removed. The meta data has been preserved -in the SuiteSparse Matrix Collection, but does not appear in the -2018 GraphChallenge data sets. Finally, the node ordering differs -between the two; the SuiteSparse ordering either matches the SNAP -node ids 1:n or 0:n-1, or when the graph is a subset of node ids, -the node number is provided here in a Problem.aux.nodeid, or .userid -component of the problem. This information is not provided in the -GraphChallenge data sets." -Dziekonski,5,High order vector finite element method in electromagnetics The matrices came from analysis of a 9-th order microwave,https://sparse.tamu.edu/Dziekonski,electromagnetics/acoustics,electromagnetics/acoustics,1.0,1,electromagnetics problem (5),2,real (3); complex (2),1,symmetric (5),420408,1157456,2.7531731080283914,narrow (≤4×),21758924,89306020,square only,5,0,mixed among square matrices,3,5,0,real + complex,0,5,one broad domain; mixed matrix forms,"High order vector finite element method in electromagnetics - -The matrices came from analysis of a 9-th order microwave -combline filter with second order (LT\\QN) vector finite elements -with different mesh quality. The matrices were used as an -example in our paper [1]. - -- gsm_106857 - real symmetric matrix (589446 x 589446) and - 21758924 nonzero elements. First 98577 unknowns corresponds - to lowest level (CT\\LN) base functions. - -All matrices are sparse and come with right-hand-sides. - -[1] GPU Acceleration of Multilevel Solvers for Analysis of -Microwave Components with Finite Element Method, A. Dziekonski, -A. Lamecki, A., and M. Mrozowski, M., IEEE Microwave and -Wireless Components Letters, vol 20, number 12, -Dec 2010. http://dx.doi.org/10.1109/LMWC.2010.2089974 - -------- - -The dielFilter* matrices came from analysis of a 4th-pole -dielectric resonator [4] generated with Finite Element Method. -The tetrahedral mesh of the structure was generated with the -Netgen mesher [2]. The matrices were used as an example in -our paper [3]. - -dielFilterV2clx - complex symmetric matrix (607,232 x 607,232), - 25,309,272 nonzero (real) and 728,900 nonzero (imag) elements. - First 109,108 unknowns correspond to lowest level base - functions. - -dielFilterV2real - real symmetric matrix (1,157,456 x 1,157,456) - and 48,538,952 nonzero elements. First 209,432 unknowns - correspond to lowest level base functions. - -dielFilterV3clx - complex symmetric matrix (420,408 x 420,408), - 32,886,208 nonzero (real) and 3,706,513 (imag) elements. First - 24,716 unknowns correspond to lowest level base functions, - next 116,152 unknowns correspond to the second level. - -dielFilterV3real - real symmetric matrix (1,102,824 x 1,102,824) - and 89,306,020 nonzero elements. First 66,353 unknowns - correspond to lowest level base functions, next 305,729 - unknowns correspond to the second level. - -All matrices are sparse and come with right-hand-sides. - -[2] J. Schoberl, ""NETGEN An advancing front 2D/3D-mesh -generator based on abstract rules,"" Computing and Visualization -in Science, vol. 1, No. 1, pp. 41-52, July 1997 - -[3] A. Dziekonski, A. Lamecki, M. Mrozowski, Tuning A -Hybrid GPU-CPU V-cycle Multilevel Preconditioner for Solving -Large Real and Complex Systems of FEM Equations. - -[4] F. Alessandri, M. Chiodetti, A. Giugliarelli; D. Maiarelli, -G. Martirano, D. Schmitt, L. Vanni and F. Vitulli. The -electric-field Integral-equation method for the analysis and -design of a class of rectangular cavity filters loaded by -dielectric and metallic cylindrical pucks, Microwave Theory -and Techniques, IEEE Transactions on, vol. 52, no 8, pp. -1790-1797, Aug. 2004." -Fluorem,5,"CFD matrices from Francois Pacull, FLUOREM, in Lyon, France We are dealing with CFD and more precisely steady flow",https://sparse.tamu.edu/Fluorem,fluids/transport,fluids/transport,1.0,1,computational fluid dynamics problem (5),1,real (5),1,unsymmetric (5),903,2017169,2233.8527131782944,wide (>100×),11766,283073458,square only,5,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,5,coherent family/series,"CFD matrices from Francois Pacull, FLUOREM, in Lyon, France - -We are dealing with CFD and more precisely steady flow -parametrization. The equations involved are the compressible -Navier-Stokes ones (RANS). These matrices are real, square and -indefinite, they correspond to the Jacobian with respect the -conservative fluid variables of the discretized governing -equations (finite-volume discretization). Thus they have a -block structure (corresponding to the mesh nodes: the block -size is the number of variables per mesh node), they are not -symmetric (however, their blockwise structure has a high level -of symmetry) and they often show some kind of hyperbolic -behavior. They have not been scaled or reordered. - -They are generated through automatic differentiation of the -flow solver around a steady state. A right hand-side is also -given for each matrix: this represents the derivative of the -equations with respect to a parameter (of operation or shape). -Since they are generated automatically, they may have ""silent"" -variables: these are variables corresponding to an identity -submatrix associated with a null right hand-side, for example -one of the three velocity components in a 2D case, or the -turbulent variables in a ""frozen"" turbulence case. - -We believe that these matrices are good test cases when -studying preconditioning methods for iterative methods, such as -block incomplete factorization, or when studying domain -decomposition methods or deflation. They are actually being -studied by a few researchers in France regarding numerical -methods, through the LIBRAERO research project of the ANR (national -research agency): ANR-07-TLOG-011. - -Francois Pacull, Lyon, France. fpacull at fluorem.com - - Reference: ""A Study of ILU Factorization for Schwartz - Preconditioners with Application to Computational Fluid - Dynamics"", F. Pacull, S. Aubert, M. Buisson, Proceedings - of the 2nd Intl Conf on Parallel, Distributed, Grid, and - Cloud Computing for Engineering, B.H.V Topping and P. - Iva'nyi, Editors. Civil-Comp Press, Stirlingshire, - Scotland, 2011. - - -Specific problem descriptions: - - DK01R: 1D turbulent case - number of mesh nodes: 129 - block size: 7 - variables: [rho,rho*u,rho*v,rho*w,rho*E,rho*k,rho*omega] - (rho v and rho w are ""silent"", the third and fourth rows - and columns - in each block can be removed) - matrix order: 903 - nnz: 11766 - comments: The DK01R matrix corresponds to a small 1D turbulent - case. The grid has 129 nodes, non-uniformly spaced - (geometrical distribution). The number of unknowns per node is - 7, leading to a linear system of 903 real algebraic equations. - The 1D discretization of the partial differential equations - uses a 5 points stencil, leading to a block penta-diagonal - matrix, each block having size 7 by 7. Each diagonal block is - related to two up- and two down-stream neighboring nodes, - corresponding respectively to the 14 upper and 14 lower matrix - rows, the node ordering being coherent with the 1D spatial - node distribution. The stationary flow on which the matrix is - based on is dominated by advection, characterized by a Mach - number around 0.3. - - GT01R: 2D inviscid case - number of mesh nodes: 1596 - block size: 5 - variables: [rho,rho*u,rho*v,rho*w,rho*E] - (rho w is ""silent"", the fourth row and column in each - block can be removed) - matrix order: 7980 - nnz: 430909 - comments: This is a 2D linear cascade turbine case. The grid - corresponds to one inter-blade channel. The stencil involved - by the convective scheme uses 9 nodes. Thus, there are 9 - non-zero blocks for each node in the matrix. The specificity - is that the computational domain is periodic, which introduces - some non-zeros elements far away from the diagonal. - - PR02R: 2D turbulent case - number of mesh nodes: 23010 - block size: 7 - variables: [rho,rho*u,rho*v,rho*w,rho*E,rho*k,rho*omega] - (rho v is ""silent"", the third row and column in each block - can be removed) - matrix order: 161070 - nnz: 8185136 - comments: This is a 2D turbulent case, dominated by - convection. The geometry is a RAE wing profile and the mesh - is C-shaped. - - RM07R: 3D viscous case with ""frozen"" turbulence - number of mesh nodes: 54527 - block size: 7 - variables: [rho,rho*u,rho*v,rho*w,rho*E,rho*k,rho*omega] - (rho k and rho omega are ""silent"", the sixth and seventh rows - and columns in each block can be removed) - matrix order: 381689 - nnz: 37464962 - comments: The geometry is a jet engine compressor. - - HV15R: 3D engine fan. The flow has a low Mach number. - This is a 3D Reynolds-Averaged-Navier-Stokes case. - Number of mesh nodes: 288,167 - block size: 7 - variables: [rho, rho*u, rho*v, rho*w, rho*E, rho*k, rho*omega] - matrix order: 2,017,169 - nnz: 283,073,458" -Rommes,35,"Power system models from Joost Rommes, Nelson Martins, Francisco Freitas This collection of power system models originates from real power systems,",https://sparse.tamu.edu/Rommes,eigenvalue/model reduction,eigenvalue/model reduction,1.0,1,eigenvalue/model reduction problem (35),1,real (35),1,unsymmetric (35),66,40366,611.6060606060606,wide (>100×),1194,145019,square only,35,0,no square matrix exactly symmetric/Hermitian,0,16,0,real only,0,35,coherent family/series,"Power system models from Joost Rommes, Nelson Martins, Francisco Freitas - -This collection of power system models originates from real power systems, -mostly based on Brazilian interconection power systems (BIPS) models (the file -names refer to the actual power system related to a given year electric load -scenario). These systems [E dx/dt = Ax + Bu ; y=Cx + Du] are interesting -benchmarks for several numerical algorithms, including eigenvalue algorithms -(dominant modes/poles/zeros, stability analysis, computing rightmost -eigenvalues and/or with smallest damping ratio, eigenvalue parameter -sensitivity) and model order reduction (large-scale DAEs ). Refer to the -corresponding publications for more details on the systems and numerical -results of several eigenvalue/model order reduction algorithms. For -corresponding software, see http://sites.google.com/site/rommes/software - -If E is not present in the problem, then E=I should be assumed. -If D is not present, D=0 should be assumed. (Note that as of Jan 2011, -no problem has a nonzero D). - -The iv vector in some of the files is a vector with nonzeros (ones) at indices -that represent state-variables (the zeros are algebraic variables). One can -construct the descriptor matrix E by E=spdiags(iv,0,n,n). This iv vector is -generated by the Brazilian power system simulation software, and can be more -efficient to compute with. - - -Test systems: - -All power system models originate from CEPEL ( http://www.cepel.br/ ) - -power system file n #inputs #outputs references ------------- ---- ------ ------- -------- -- -New England ww_36_pmec_36 66 1 1 [1] -BIPS/97 ww_vref_6405 13251 1 1 [1] -BIPS/2007 xingo_afonso_itaipu 13250 1 1 [2] -BIPS/97 mimo8x8_system 13309 8 8 [3] -BIPS/97 mimo28x28_system 13251 28 28 [3] -BIPS/97 mimo46x46_system 13250 46 46 [4] -Juba5723 juba40k 40337 2 1 [5] -Bauru5727 bauru5727 40366 2 2 [5] -zeros_nopss zeros_nopss_13k 13296 46 46 [5] -xingo6u descriptor_xingo6u 20738 1 6 [5] -nopss nopss_11k 11685 1 1 [5] -xingo3012 xingo3012 20944 2 2 [5] -bips98_606 bips98_606 7135 4 4 [6] -bips98_1142 bips98_1142 9735 4 4 [6] -bips98_1450 bips98_1450 11305 4 4 [6] -bips07_1693 bips07_1693 13275 4 4 [6] -bips07_1998 bips07_1998 15066 4 4 [6] -bips07_2476 bips07_2476 16861 4 4 [6] -bips07_3078 bips07_3078 21128 4 4 [6] - -Several SISO/MIMO test systems, whose main components are transmission lines -(TL) are available. TLs are modeled by ladder networks, comprised of cascaded -RLC PI-circuits, having fixed parameters. - - Transmission lines with 10--80 PI Sections are considered. - PIsections10to80.zip [Submitted] - - There are two kinds of files for representing a same system: the file with - termination '_n' refers to an index-2 system DAE model, while '_n1' means - a model of the same system, but for an index-1 DAE representation. The - representation of each test system has the form [E dx/dt = Ax + Bu ; y=Cx] - - MIMO_PI_n: - M10PI_n - M20PI_n - M40PI_n - M80PI_n - MIMO_PI_n1: - M10PI_n1 - M20PI_n1 - M40PI_n1 - M80PI_n1 - SISO_PI_n: - S10PI_n - S20PI_n - S40PI_n - S80PI_n - SISO_PI_n1: - S10PI_n1 - S20PI_n1 - S40PI_n1 - S80PI_n1 - -References: - -[1] ROMMES, J., MARTINS, N., Efficient computation of transfer function - dominant poles using subspace acceleration. IEEE Trans. on Power Systems, - Vol. 21, Issue 3, Aug. 2006, pp. 1218-1226 - -[2] ROMMES, J., MARTINS, N., Computing large-scale system eigenvalues most - sensitive to parameter changes, with applications to power system - small-signal stability , IEEE Transactions on Power Systems, Vol. 23, Issue - 2, May 2008, pp. 434-442 - -[3] ROMMES, J., MARTINS, N., Efficient computation of multivariable transfer - function dominant poles using subspace acceleration. 2006, IEEE Trans. on - Power Systems, Vol. 21, Issue 4, Nov. 2006, pp. 1471-1483. - -[4] MARTINS, N., PELLANDA, P.C.,ROMMES, J., Computation of transfer function - dominant zeros with applications to oscillation damping control of large - power systems, IEEE Trans. on Power Systems, Vol. 22, Issue 4, Nov. 2007, - pp. 1657-1664 - -[5] ROMMES, J., MARTINS, N., FREITAS, F., Computing Rightmost Eigenvalues for - Small-Signal Stability Assessment of Large-Scale Power Systems, IEEE - Transactions on Power Systems, Vol. 25, Issue 2, May 2010, pp.929-938 - -[6] FREITAS, F., ROMMES, J., MARTINS, N., Gramian-Based Reduction Method - Applied to Large Sparse Power System Descriptor Models, IEEE Transactions - on Power Systems, Vol. 23, Issue 3, August 2008, pp. 1258-1270" -Williams,7,Matrices used by S. Williams et al for sparse matrix multiplication on GPUs. 14 matrices were used in the following paper:,https://sparse.tamu.edu/Williams,generic spatial PDE/thermal; graph/network data; other,generic spatial PDE/thermal,0.5714285714285714,4,2D/3D problem (4); weighted undirected graph (1); economic problem (1); weighted directed graph (1),2,real (6); integer (1),2,symmetric (4); unsymmetric (3),36417,1000005,27.459840184529202,moderate (4–100×),1273389,6010480,square only,7,0,mixed among square matrices,4,4,3,real only,0,4,multi-domain/mixed,"Matrices used by S. Williams et al for sparse matrix multiplication on GPUs. - -14 matrices were used in the following paper: - - S. Williams, L. Oliker, R. Vuduc, J. Shalf, K. Yelick, J. Demmel, - ""Optimization of Sparse Matrix-Vector Multiplication on Emerging Multicore - Platforms"", Parallel Computing Volume 35, Issue 3, March 2009, Pages - 178-194. Special issue on Revolutionary Technologies for Acceleration of - Emerging Petascale Applications. - - https://hpcrd.lbl.gov/~swwilliams/research/papers/parco08_spmv.pdf - http://dx.doi.org/10.1016/j.parco.2008.12.006 - -This same set of 14 matrices was also used in a subsequent technical report by -NVIDIA: - - http://www.nvidia.com/object/nvidia_research_pub_001.html ""Efficient Sparse - Matrix-Vector Multiplication on CUDA"" Nathan Bell and Michael Garland, in, - ""NVIDIA Technical Report NVR-2008-004"", December 2008 - -file Name dim* nnz description - -dense2 Dense 2K 4.0M dense matrix in sparse format -pdb1HYS Protein 36K 4.3M protein data bank 1HYS -consph FEM/Spheres 83K 6.0M FEM concentric spheres -cant FEM/Cantilever 62K 4.0M FEM cantilever -pwtk Wind Tunnel 218K 11.6M pressurized wind tunnel -rma10 FEM/Harbor 47K 2.37M 3D CFD of Charleston Harbor -qcd5_4 QCD 49K 1.90M quark propagators (QCD/LGT) -shipsec1 FEM/Ship 141K 3.98M FEM Ship section / detail -mac_econ_fwd500 Economics 207K 1.27M Macroeconomic model -mc2depi Epidemiology 526K 2.1M 2D Markov model of epidemic -cop20k_A FEM/Accelerator 121K 2.62M Accelerator cavity design -scircuit Circuit 171K 959K Motorola circuit simulation -webbase-1M webbase 1M 3.1M Web connectivity matrix -rail4284 LP 4Kx1.1M 11.3M Railways set cover, - constraint matrix - -(*) the matrix is square if only one dimension listed. - -Six of the matrices are nearly identical to the matrices already in the -UF Collection. They are thus not included in the UF Collection: - - pwtk Boeing/pwtk. The matrix here differs in only 2 entries. - The pattern is the same. The norm of the difference between - the two is round-off error (5e-26). Tim Davis obtained the - matrix directly from Roger Grimes, and was careful to preserve - each significant bit. The Boeing/pwtk matrix in the UF - Collection is thus the correct one. - - qcd5_4 this is the binary pattern of QCD/conf5_4-* and QCD/conf6_0-*, - ids: 1598:1604 in the UF Collection. These are from B. - Medeke, and were orginally collected by R. Boisvert et al. - for the Matrix Market. - - rma10 Bova/rma10. The matrix here is binary; Bova/rma10 is not. - The rma10 matrix here is binary pattern of Problem.A + - Problem.Zeros in the UF Collection. Tim Davis obtained - Bova/rma10 directly from the matrix creator, Steve Bova. - - scircuit Hamm/scircuit. The pattern is the same. The 1-norm of the - difference is 5e-10, which is round-off error. This matrix was - obtained by Tim Davis from Steve Hamm (at Motorola) directly, - and thus the Hamm/scircuit matrix already in the UF Collection - is the correct version. - - shipsec1 similar to DNVS/shipsec1, but different patterns. It appears - that the shipsec1 matrix here is missing many entries in - the lower right quadrant. The pattern differs by 0.9M - entries. The matrix is from C. Damhaug, collected by - J. Koster. - - rail4284 an exact submatrix of Mittelmann/rail4284. The UF matrix has - exactly 4284 more columns than the rail4284 matrix in this - collection. Let A1 be the Mittelman/rail4284 matrix, and let - A2 be the rail4284 matrix in this collection. Then A2 is - identical to A1(:,m+1:end). The submatrix A1(:,1:4284) of the - matrix in the UF collection is exactly equal to -speye(m). - Tim Davis obtained the matrix directly from Mittelmann's - test set. It's possible that multiple versions exist, or - that the identity was added as the initial basis. In any - case, the rail4284 matrix from Williams' test set is not - added to the UF Collection. - -The remaining eight matrices are unique to this set of matrices, but one is of -no interest (dense2). Thus, there are seven matrices in the Williams/ group in -the UF Collection: - - pdb1HYS - consph - cant - mac_econ_fwd500 - mc2depi - cop20k_A - webbase-1M - -I presume the pdb1HYS matrix comes from this source: - - http://www.rcsb.org/pdb/explore.do?structureId=1HYS - http://dx.doi.org/10.2210/pdb1hys/pdb - Crystal structure of HIV-1 reverse transcriptase in complex with a - polypurine tract RNA:DNA. - Sarafianos, S.G., Das, K., Tantillo, C., Clark Jr., A.D., Ding, - J., Whitcomb, J.M., Boyer, P.L., Hughes, S.H., Arnold, E. - Journal: (2001) EMBO J. 20: 1449-1461 - PubMed: 11250910 - PubMedCentral: PMC145536 - DOI: 10.1093/emboj/20.6.1449 - Search Related Articles in PubMed - PubMed Abstract: - We have determined the 3.0 A resolution structure of wild-type HIV-1 - reverse transcriptase in complex with an RNA:DNA oligonucleotide whose - sequence includes a purine-rich segment from the HIV-1 genome called the - polypurine tract (PPT). The PPT is resistant to ribonuclease... [ Read - More & Search PubMed Abstracts ] We have determined the 3.0 A resolution - structure of wild-type HIV-1 reverse transcriptase in complex with an - RNA:DNA oligonucleotide whose sequence includes a purine-rich segment from - the HIV-1 genome called the polypurine tract (PPT). The PPT is resistant - to ribonuclease H (RNase H) cleavage and is used as a primer for second - DNA strand synthesis. The 'RNase H primer grip', consisting of amino - acids that interact with the DNA primer strand, may contribute to RNase H - catalysis and cleavage specificity. Cleavage specificity is also - controlled by the width of the minor groove and the trajectory of the - RNA:DNA, both of which are sequence dependent. An unusual 'unzipping' of 7 - bp occurs in the adenine stretch of the PPT: an unpaired base on the - template strand takes the base pairing out of register and then, following - two offset base pairs, an unpaired base on the primer strand - re-establishes the normal register. The structural aberration extends to - the RNase H active site and may play a role in the resistance of PPT to - RNase H cleavage." -Dattorro,3,"Dattorro Convex Optimization of Eternity II, Jon Dattoro",https://sparse.tamu.edu/Dattorro,optimization/control,optimization/control,1.0,1,optimization problem (3),1,integer (3),1,rectangular (3),150638,262144,1.740224910049257,narrow (≤4×),782087,1503732,rectangular only,0,3,N/A (rectangular only),0,0,0,real only,0,0,coherent family/series,"Dattorro Convex Optimization of Eternity II, Jon Dattoro - -http://www.convexoptimization.com/wikimization/index.php/Dattorro_Convex_Optimization_of_Eternity_II - -An Eternity II puzzle (http://www.eternityii.com/) problem -formulation A*x=b is discussed thoroughly in section 4.6.0.0.15 of -the book Convex Optimization & Euclidean Distance Geometry which -is freely available. That A matrix is obtained by presolving a -sparse 864,593-by-1,048,576 system. The 3 problems in this set -contains three successive reductions, each equivalent to that -larger system: - - * EternityII_E: a 11077-by-262144 system E*x=tau, where tau is - 11077-by-1. This is the million column Eternity II matrix - having redundant rows and columns removed analytically. - In the UF Collection, E is the Problem.A matrix, and tau - is Problem.b. All entries in E are from the set {-1,0,1,2}. - tau is binary. - - * EternityII_Etilde: a 10054-by-204304 system Etilde*x=tautilde - with tautilde of size 10054-by-1. The system has columns - removed corresponding to some known zero variables - (removal produced dependent rows). In the UF Collection, - Etilde is the Problem.A matrix, and tautilde is Problem.b. - All entries in Etilde are from the set {-1,0,1}. - tautilde is binary. - - * EternityII_A: a 7362-by-150638 system A*x=b, where b is - 7362-by-1. This system has columns removed not in - smallest face (containing tautilde) of polyhedral cone - K = { Etilde*x | x >= 0 }. - -The following linear program is a very difficult problem that -remains unsolved: - - maximize_x z'*x, subject to A*x=b and x >= 0 - -The matrix A in the EternityII_A problem is sparse, having only -782,087 nonzeros. All entries of A are integers from the set -{-1,0,1}. The vector b is binary, with only 358 nonzeros. - -Direction vector z is determined by Convex Iteration: - - maximize_z z'*x^{star} - subject to 0 <= z <= 1 and z'*1 = 256 - -(for a vector x, x >= 0 means all(x>=0) in MATLAB notation). - -These two problems are iterated to find a minimal cardinality -solution x. Constraint A*x=b bounds the variable from above by 1. -Any minimal cardinality solution is binary and solves the Eternity -II puzzle. The Eternity II puzzle is solved when -z^{star}'*x^{star} = 256. - -Minimal cardinality of this Eternity II problem is equal to number -of puzzle pieces, 256. - -Comment: The technique, convex iteration, requires no modification -(and works very well) when applied instead to mixed integer -programming (MIP, not discussed in book). There is no modification -to the linear program statement here except 256 variables, -corresponding to the largest entries of iterate x^{star}, are -declared binary." -Newman,16,"Network graph collection from Mark Newman, University of Michigan",https://sparse.tamu.edu/Newman,graph/network data,graph/network data,1.0,5,undirected weighted graph (7); undirected graph (6); directed weighted graph (1); undirected multigraph (1); directed multigraph (1),3,binary (6); real (6); integer (4),2,symmetric (14); unsymmetric (2),34,40421,1188.8529411764705,wide (>100×),156,351382,square only,16,0,mixed among square matrices,14,14,0,real only,6,0,one broad domain; mixed matrix forms,"Network graph collection from Mark Newman, University of Michigan -http://www-personal.umich.edu/~mejn/netdata/ - -Problems in this data set: - -adjnoun Common adjective and nouns in ""David Copperfield"" -as-22july06 (symmetrized) structure of internet routers as of July 22, 2006 -astro-ph collaboration network, preprints in astrophysics archive, www.arxiv.org -celegansneural Neural network of the nematode C. Elegans -cond-mat collaboration network, preprints in condensed matter archive, www.arxiv.org -cond-mat-2003 collaboration network, preprints in condensed matter archive, www.arxiv.org -cond-mat-2005 collaboration network, preprints in condensed matter archive, www.arxiv.org -dolphins social network of dolphins, Doubtful Sound, New Zealand -football American football games between Div IA colleges, Fall 2000 -hep-th collaboration network, preprints in high-energy physics, www.arxiv.org -karate Karate club, from Wayne Zachary, 1977 -lesmis co-appearences of characters in Les Miserables by Victor Hugo -netscience co-authoship of scientists in network theory & experiments -polblogs Political blogosphere Feb. 2005 -polbooks Books about US politics, compiled by V. Krebs -power Western States Power Grid, Watts & Strogatz" -Arenas,4,"Networks from Alex Arenas, Univeristy Rovira i Virgili, Tarragona, Spain",https://sparse.tamu.edu/Arenas,graph/network data,graph/network data,1.0,2,undirected multigraph (3); undirected graph (1),2,integer (3); binary (1),1,symmetric (4),198,10680,53.93939393939394,moderate (4–100×),4065,48632,square only,4,0,all square matrices symmetric/Hermitian,4,4,0,real only,1,0,one broad domain; mixed matrix forms,"Networks from Alex Arenas, Univeristy Rovira i Virgili, Tarragona, Spain -http://deim.urv.cat/~aarenas/data/welcome.htm - -PGPgiantcompo: - - Graph of the largest component of the network of users of the - Pretty-Good-Privacy algorithm for secure information interchange. - Reference: M. Boguna, R. Pastor-Satorras, A. Diaz-Guilera and - A. Arenas, Physical Review E, vol. 70, 056122 (2004). - -jazz: - - Network of Jazz musicians. - Reference: P.Gleiser and L. Danon , Adv. Complex Syst.6, 565 (2003). - -celegans_metabolic: - - Graph of the metabolic network of C.elegans. - Reference: Community identification using Extremal Optimization, - J. Duch and A. Arenas, Physical Review E , vol. 72, 027104, (2005). - -email: - - Network of e-mail interchanges between members of - the Univeristy Rovira i Virgili (Tarragona). - Reference: R. Guimera, L. Danon, A. Diaz-Guilera, F. Giralt and A. Arenas - Physical Review E , vol. 68, 065103(R), (2003). - -All data compiled by members of Alex Arenas' group." -AG-Monien,32,"AG-Monien Graph Collection, Ralf Diekmann and Robert Preis",https://sparse.tamu.edu/AG-Monien,generic spatial PDE/thermal; graph/network data,generic spatial PDE/thermal,0.8125,2,2D/3D problem (26); undirected graph (6),1,binary (32),1,symmetric (32),224,1048576,4681.142857142857,wide (>100×),840,4194298,square only,32,0,all square matrices symmetric/Hermitian,32,32,0,real only,32,26,multi-domain/mixed,"AG-Monien Graph Collection, Ralf Diekmann and Robert Preis -http://www2.cs.uni-paderborn.de/fachbereich/AG/monien/RESEARCH/PART/graphs.html - -A collection of test graphs from various sources. Many of the graphs -include XY or XYZ coordinates. This set also includes some graphs from -the Harwell-Boeing collection, the NASA matrices, and some random matrices -which are not included here in the AG-Monien/ group of the UF Collection. -In addition, two graphs already appear in other groups: - - AG-Monien/big : same as Nasa/barth5, Pothen/barth5 (not included here) - AG-Monien/cage_3_11 : same as Pajek/GD98_c (included here) - -The AG-Monien/GRID subset is not included. It contains square grids that -are already well-represented in the UF Collection. - -These graphs appear in this set, as individual graphs, all with XY or XYZ -coordinates: - - AG-Monien/3elt - AG-Monien/3elt_dual - AG-Monien/airfoil1 - AG-Monien/airfoil1_dual - AG-Monien/big_dual - AG-Monien/crack - AG-Monien/crack_dual - AG-Monien/grid1 - AG-Monien/grid1_dual - AG-Monien/grid2 - AG-Monien/grid2_dual - AG-Monien/netz4504 - AG-Monien/netz4504_dual - AG-Monien/ukerbe1 - AG-Monien/ukerbe1_dual - AG-Monien/whitaker3 - AG-Monien/whitaker3_dual - AG-Monien/brack2 - AG-Monien/wave - AG-Monien/diag - AG-Monien/L - AG-Monien/L-9 - AG-Monien/stufe - AG-Monien/stufe-10 - AG-Monien/biplane-9 - AG-Monien/shock-9 - -Note that L-9, stufe-10, biplane-9 and shock-9 were L.9, stufe.10, -etc, in the AG-Monien set. The UF Collection does not permit ""."" in -the matrix name. - -Six more problem sets are included as sequences, each sequence being -a single problem instance in the UF Collection: - - AG-Monien/bfly: 10 butterfly graphs 3..12 - AG-Monien/cage: 45 cage graphs 3..12 - AG-Monien/cca: 10 cube-connected cycle graphs, no wrap - AG-Monien/ccc: 10 cube-connected cycle graphs, with wrap - AG-Monien/debr: 18 De Bruijn graphs - AG-Monien/se: 13 shuffle-exchange graphs - -The primary graph (Problem.A) in each sequence is the last graph -in the sequence. In the Matrix Market and Rutherford-Boeing -formats, the filenames will differ from the names given below, -because in the UF Collection, the file name gives the place of -a graph in its sequence. The correspondence with the original -graph names is given below. - -Graphs in the bfly sequence: - - 1 : BFLY3 : 24 nodes 48 edges 96 nonzeros - 2 : BFLY4 : 64 nodes 128 edges 256 nonzeros - 3 : BFLY5 : 160 nodes 320 edges 640 nonzeros - 4 : BFLY6 : 384 nodes 768 edges 1536 nonzeros - 5 : BFLY7 : 896 nodes 1792 edges 3584 nonzeros - 6 : BFLY8 : 2048 nodes 4096 edges 8192 nonzeros - 7 : BFLY9 : 4608 nodes 9216 edges 18432 nonzeros - 8 : BFLY10 : 10240 nodes 20480 edges 40960 nonzeros - 9 : BFLY11 : 22528 nodes 45056 edges 90112 nonzeros - 10 : BFLY12 : 49152 nodes 98304 edges 196608 nonzeros - -Graphs in the cage sequence: - - 1 : cage_3_5 : 10 nodes 15 edges 30 nonzeros - 2 : cage_3_6 : 14 nodes 21 edges 42 nonzeros - 3 : cage_3_7 : 24 nodes 36 edges 72 nonzeros - 4 : cage_3_8 : 30 nodes 45 edges 90 nonzeros - 5 : cage_3_9.1 : 58 nodes 87 edges 174 nonzeros - 6 : cage_3_9.2 : 58 nodes 87 edges 174 nonzeros - 7 : cage_3_9.3 : 58 nodes 87 edges 174 nonzeros - 8 : cage_3_9.4 : 58 nodes 87 edges 174 nonzeros - 9 : cage_3_9.5 : 58 nodes 87 edges 174 nonzeros - 10 : cage_3_9.6 : 58 nodes 87 edges 174 nonzeros - 11 : cage_3_9.7 : 58 nodes 87 edges 174 nonzeros - 12 : cage_3_9.8 : 58 nodes 87 edges 174 nonzeros - 13 : cage_3_9.9 : 58 nodes 87 edges 174 nonzeros - 14 : cage_3_9.10 : 58 nodes 87 edges 174 nonzeros - 15 : cage_3_9.11 : 58 nodes 87 edges 174 nonzeros - 16 : cage_3_9.12 : 58 nodes 87 edges 174 nonzeros - 17 : cage_3_9.13 : 58 nodes 87 edges 174 nonzeros - 18 : cage_3_9.14 : 58 nodes 87 edges 174 nonzeros - 19 : cage_3_9.15 : 58 nodes 87 edges 174 nonzeros - 20 : cage_3_9.16 : 58 nodes 87 edges 174 nonzeros - 21 : cage_3_9.17 : 58 nodes 87 edges 174 nonzeros - 22 : cage_3_9.18 : 58 nodes 87 edges 174 nonzeros - 23 : cage_3_10.1 : 70 nodes 105 edges 210 nonzeros - 24 : cage_3_10.2 : 70 nodes 105 edges 210 nonzeros - 25 : cage_3_10.3 : 70 nodes 105 edges 210 nonzeros - 26 : cage_3_11 : 112 nodes 168 edges 336 nonzeros - 27 : cage_3_12 : 126 nodes 189 edges 378 nonzeros - 28 : cage_3_13 : 272 nodes 408 edges 816 nonzeros - 29 : cage_3_14 : 406 nodes 609 edges 1218 nonzeros - 30 : cage_3_15 : 620 nodes 930 edges 1860 nonzeros - 31 : cage_4_5 : 19 nodes 38 edges 76 nonzeros - 32 : cage_4_6 : 26 nodes 52 edges 104 nonzeros - 33 : cage_4_7 : 76 nodes 152 edges 304 nonzeros - 34 : cage_4_8 : 80 nodes 160 edges 320 nonzeros - 35 : cage_5_5 : 30 nodes 75 edges 150 nonzeros - 36 : cage_5_6 : 42 nodes 105 edges 210 nonzeros - 37 : cage_6_6 : 62 nodes 186 edges 372 nonzeros - 38 : cage_7_5 : 50 nodes 175 edges 350 nonzeros - 39 : cage_8_5 : 94 nodes 376 edges 752 nonzeros - 40 : cage_8_6 : 114 nodes 456 edges 912 nonzeros - 41 : cage_9_5 : 118 nodes 531 edges 1062 nonzeros - 42 : cage_9_6 : 146 nodes 657 edges 1314 nonzeros - 43 : cage_10_6 : 182 nodes 910 edges 1820 nonzeros - 44 : cage_12_6 : 266 nodes 1596 edges 3192 nonzeros - 45 : cage_14_6 : 366 nodes 2562 edges 5124 nonzeros - -Graphs in the cca sequence: - - 1 : CCA3 : 24 nodes 28 edges 56 nonzeros - 2 : CCA4 : 64 nodes 80 edges 160 nonzeros - 3 : CCA5 : 160 nodes 208 edges 416 nonzeros - 4 : CCA6 : 384 nodes 512 edges 1024 nonzeros - 5 : CCA7 : 896 nodes 1216 edges 2432 nonzeros - 6 : CCA8 : 2048 nodes 2816 edges 5632 nonzeros - 7 : CCA9 : 4608 nodes 6400 edges 12800 nonzeros - 8 : CCA10 : 10240 nodes 14336 edges 28672 nonzeros - 9 : CCA11 : 22528 nodes 31744 edges 63488 nonzeros - 10 : CCA12 : 49152 nodes 69632 edges 139264 nonzeros - -Graphs in the ccc sequence: - - 1 : CCC3 : 24 nodes 36 edges 72 nonzeros - 2 : CCC4 : 64 nodes 96 edges 192 nonzeros - 3 : CCC5 : 160 nodes 240 edges 480 nonzeros - 4 : CCC6 : 384 nodes 576 edges 1152 nonzeros - 5 : CCC7 : 896 nodes 1344 edges 2688 nonzeros - 6 : CCC8 : 2048 nodes 3072 edges 6144 nonzeros - 7 : CCC9 : 4608 nodes 6912 edges 13824 nonzeros - 8 : CCC10 : 10240 nodes 15360 edges 30720 nonzeros - 9 : CCC11 : 22528 nodes 33792 edges 67584 nonzeros - 10 : CCC12 : 49152 nodes 73728 edges 147456 nonzeros - -Graphs in the debr sequence: - - 1 : DEBR3 : 8 nodes 13 edges 26 nonzeros - 2 : DEBR4 : 16 nodes 29 edges 58 nonzeros - 3 : DEBR5 : 32 nodes 61 edges 122 nonzeros - 4 : DEBR6 : 64 nodes 125 edges 250 nonzeros - 5 : DEBR7 : 128 nodes 253 edges 506 nonzeros - 6 : DEBR8 : 256 nodes 509 edges 1018 nonzeros - 7 : DEBR9 : 512 nodes 1021 edges 2042 nonzeros - 8 : DEBR10 : 1024 nodes 2045 edges 4090 nonzeros - 9 : DEBR11 : 2048 nodes 4093 edges 8186 nonzeros - 10 : DEBR12 : 4096 nodes 8189 edges 16378 nonzeros - 11 : DEBR13 : 8192 nodes 16381 edges 32762 nonzeros - 12 : DEBR14 : 16384 nodes 32765 edges 65530 nonzeros - 13 : DEBR15 : 32768 nodes 65533 edges 131066 nonzeros - 14 : DEBR16 : 65536 nodes 131069 edges 262138 nonzeros - 15 : DEBR17 : 131072 nodes 262141 edges 524282 nonzeros - 16 : DEBR18 : 262144 nodes 524285 edges 1048570 nonzeros - 17 : DEBR19 : 524288 nodes 1048573 edges 2097146 nonzeros - 18 : DEBR20 : 1048576 nodes 2097149 edges 4194298 nonzeros - -Graphs in the se sequence: - - 1 : SE3 : 8 nodes 10 edges 20 nonzeros - 2 : SE4 : 16 nodes 21 edges 42 nonzeros - 3 : SE5 : 32 nodes 46 edges 92 nonzeros - 4 : SE6 : 64 nodes 93 edges 186 nonzeros - 5 : SE7 : 128 nodes 190 edges 380 nonzeros - 6 : SE8 : 256 nodes 381 edges 762 nonzeros - 7 : SE9 : 512 nodes 766 edges 1532 nonzeros - 8 : SE10 : 1024 nodes 1533 edges 3066 nonzeros - 9 : SE11 : 2048 nodes 3070 edges 6140 nonzeros - 10 : SE12 : 4096 nodes 6141 edges 12282 nonzeros - 11 : SE13 : 8192 nodes 12286 edges 24572 nonzeros - 12 : SE14 : 16384 nodes 24573 edges 49146 nonzeros - 13 : SE15 : 32768 nodes 49150 edges 98300 nonzeros" -LAW,15,"Laboratory for Web Algorithmics (LAW), Università degli Studi di Milano",https://sparse.tamu.edu/LAW,graph/network data,graph/network data,1.0,2,directed graph (13); undirected graph (2),1,binary (15),2,unsymmetric (13); symmetric (2),69244,118142155,1706.171726070129,wide (>100×),276143,1949412601,square only,15,0,mixed among square matrices,2,2,0,real only,15,0,one broad domain; mixed matrix forms,"Laboratory for Web Algorithmics (LAW), Università degli Studi di Milano -http://law.di.unimi.it/index.php - -When using matrices in the LAW/ group in the collection, please follow the -citation instructions at http://law.di.unimi.it/datasets.php - -If you publish results based on these graphs, please acknowledge the usage of -WebGraph and LLP by quoting the following papers: - -@inproceedings{BoVWFI, - author =""Paolo Boldi and Sebastiano Vigna"", - title = ""The {W}eb{G}raph Framework {I}: {C}ompression Techniques"", - year = 2004, - booktitle=""Proc. of the Thirteenth International World Wide Web Conference (WWW 2004)"", - address=""Manhattan, USA"", - pages=""595--601"", - publisher=""ACM Press"" -} - -@inproceedings{BRSLLP, - author = ""Paolo Boldi and Marco Rosa and Massimo Santini and Sebastiano Vigna"", - title = ""Layered Label Propagation: A MultiResolution Coordinate-Free Ordering - for Compressing Social Networks"", - booktitle=""Proceedings of the 20th international conference on World Wide Web"", - year = 2011, - publisher=""ACM Press"" -} - -If the graphs you are using were gathered by UbiCrawler, please acknowledge the -usage of UbiCrawler by quoting the following paper: - -@article{BCSU3, - author=""Paolo Boldi and Bruno Codenotti and Massimo Santini and Sebastiano Vigna"", - title=""UbiCrawler: A Scalable Fully Distributed Web Crawler"", - journal=""Software: Practice and Experience"", - year=2004, - volume=34, - number=8, - pages=""711--726"" -} - -Notes on the graphs in the UF Collection: - - The node labels have not been imported into the UF Collection, - since they are not yet compatible with the UF format. - They are available at the http://law.di.unimi.it/index.php site - as separate files. - - The MATLAB Problem.A matrix is stored in double precision, but - these files are all binary. These graphs are also quite huge. - If you have problems with memory usage, you can convert the - graphs to the logical format. For example: - - Problem = UFget ('LAW/uk-2002') ; - A = logical (Problem.A) ; - clear Problem - - The above transformation will temporarily take additional space, - but once the transformation is made, A will be about half as - big as the original Problem.A. This will free up memory for - other algorithms to operate on the graph (assuming that they - can operate on logical sparse matrices). - -For a complete list of the graphs in the LAW collection, and -for a wealth of statistical data on the graphs, please -refer to http://law.di.unimi.it/datasets.php for details." -DIMACS10,151,10th DIMACS Implementation Challenge Updated July 2012,https://sparse.tamu.edu/DIMACS10,graph/network data,graph/network data,1.0,6,undirected graph (67); undirected weighted graph (50); random unweighted graph (15); undirected random graph (10); undirected multigraph (6); random undirected graph (3),2,binary (95); integer (56),1,symmetric (151),39,50912018,1305436.358974359,wide (>100×),340,265114400,square only,151,0,all square matrices symmetric/Hermitian,151,151,0,real only,95,0,one broad domain; mixed matrix forms,"10th DIMACS Implementation Challenge - -Updated July 2012 - -http://www.cc.gatech.edu/dimacs10/index.shtml -http://www.cise.ufl.edu/research/sparse/dimacs10 - -As stated on their main website ( -http://dimacs.rutgers.edu/Challenges/ ), the ""DIMACS Implementation -Challenges address questions of determining realistic algorithm -performance where worst case analysis is overly pessimistic and -probabilistic models are too unrealistic: experimentation can provide -guides to realistic algorithm performance where analysis fails."" - -For the 10th DIMACS Implementation Challenge, the two related -problems of graph partitioning and graph clustering were chosen. -Graph partitioning and graph clustering are among the aforementioned -questions or problem areas where theoretical and practical results -deviate significantly from each other, so that experimental outcomes -are of particular interest. - -Problem Motivation - -Graph partitioning and graph clustering are ubiquitous subtasks in -many application areas. Generally speaking, both techniques aim at -the identification of vertex subsets with many internal and few -external edges. To name only a few, problems addressed by graph -partitioning and graph clustering algorithms are: - - * What are the communities within an (online) social network? - * How do I speed up a numerical simulation by mapping it - efficiently onto a parallel computer? - * How must components be organized on a computer chip such that - they can communicate efficiently with each other? - * What are the segments of a digital image? - * Which functions are certain genes (most likely) responsible - for? - -Challenge Goals - - * One goal of this Challenge is to create a reproducible picture - of the state-of-the-art in the area of graph partitioning - (GP) and graph clustering (GC) algorithms. To this end we - are identifying a standard set of benchmark instances and - generators. - - * Moreover, after initiating a discussion with the community, we - would like to establish the most appropriate problem - formulations and objective functions for a variety of - applications. - - * Another goal is to enable current researchers to compare their - codes with each other, in hopes of identifying the most - effective algorithmic innovations that have been proposed. - - * The final goal is to publish proceedings containing results - presented at the Challenge workshop, and a book containing - the best of the proceedings papers. - -Problems Addressed - -The precise problem formulations need to be established in the course -of the Challenge. The descriptions below serve as a starting point. - - * Graph partitioning: - - The most common formulation of the graph partitioning problem - for an undirected graph G = (V,E) asks for a division of V into - k pairwise disjoint subsets (partitions) such that all - partitions are of approximately equal size and the edge-cut, - i.e., the total number of edges having their incident nodes in - different subdomains, is minimized. The problem is known to be - NP-hard. - - * Graph clustering: - - Clustering is an important tool for investigating the - structural properties of data. Generally speaking, clustering - refers to the grouping of objects such that objects in the same - cluster are more similar to each other than to objects of - different clusters. The similarity measure depends on the - underlying application. Clustering graphs usually refers to the - identification of vertex subsets (clusters) that have - significantly more internal edges (to vertices of the same - cluster) than external ones (to vertices of another cluster). - -There are 12 data sets in the DIMACS10 collection: - -clustering: real-world graphs commonly used as benchmarks -coauthor: citation and co-author networks -Delaunay: Delaunay triangulations of random points in the plane -dyn-frames: frames from a 2D dynamic simulation -Kronecker: synthetic graphs from the Graph500 benchmark -numerical: graphs from numerical simulation -random: random geometric graphs (random points in the unit square) -streets: real-world street networks -Walshaw: Chris Walshaw's graph partitioning archive -matrix: graphs from the UF collection (not added here) -redistrict: census networks -star-mixtures : artificially generated from sets of real graphs - -Some of the graphs already exist in the UF Collection. In some cases, -the original graph is unsymmetric, with values, whereas the DIMACS -graph is the symmetrized pattern of A+A'. Rather than add duplicate -patterns to the UF Collection, a MATLAB script is provided at -http://www.cise.ufl.edu/research/sparse/dimacs10 which downloads -each matrix from the UF Collection via UFget, and then performs whatever -operation is required to convert the matrix to the DIMACS graph problem. -Also posted at that page is a MATLAB code (metis_graph) for reading the -DIMACS *.graph files into MATLAB. - --------------------------------------------------------------------------------- -clustering: Clustering Benchmarks --------------------------------------------------------------------------------- - - These real-world graphs are often used as benchmarks in the graph - clustering and community detection communities. All but 4 of the 27 - graphs already appear in the UF collection in other groups. The - DIMACS10 version is always symmetric, binary, and with zero-free - diagonal. The version in the UF collection may not have those - properties, but in those cases, if the pattern of the UF matrix - is symmetrized and the diagonal removed, the result is the DIMACS10 - graph. - - DIMACS10 graph: new? UF matrix: - --------------- ---- ------------- - clustering/adjnoun Newman/adjoun - clustering/as-22july06 Newman/as-22july06 - clustering/astro-ph Newman/astro-ph - clustering/caidaRouterLevel * DIMACS10/caidaRouterLevel - clustering/celegans_metabolic Arenas/celegans_metabolic - clustering/celegansneural Newman/celegansneural - clustering/chesapeake * DIMACS10/chesapeake - clustering/cnr-2000 LAW/cnr-2000 - clustering/cond-mat-2003 Newman/cond-mat-2003 - clustering/cond-mat-2005 Newman/cond-mat-2005 - clustering/cond-mat Newman/cond-mat - clustering/dolphins Newman/dolphins - clustering/email Arenas/email - clustering/eu-2005 LAW/eu-2005 - clustering/football Newman/football - clustering/hep-th Newman/hep-th - clustering/in-2004 LAW/in-2004 - clustering/jazz Arenas/jazz - clustering/karate Arenas/karate - clustering/lesmis Newman/lesmis - clustering/netscience Newman/netscience - clustering/PGPgiantcompo Arenas/PGPgiantcompo - clustering/polblogs Newman/polblogs - clustering/polbooks Newman/polbooks - clustering/power Newman/power - clustering/road_central * DIMACS10/road_central - clustering/road_usa * DIMACS10/road_usa - - the following graphs were added on July 2012: - - G_n_pin_pout - preferentialAttachment - smallworld - - uk-2002 was 'added' on July 2012 to the dimacs10 MATLAB interface, - but it already appears as the LAW/uk-2002 matrix. - - uk-2007-05 is in the DIMACS10 collection but is not yet added here, - because it's too large for the file format of the UF collection. - --------------------------------------------------------------------------------- -coauthor: Citation Networks --------------------------------------------------------------------------------- - - These graphs are examples of social networks used in R. Geisberger, P. - Sanders, and D. Schultes. Better approximation of betweenness - centrality. In 10th Workshop on Algorithm Engineering and - Experimentation, pages 90-108, San Francisco, 2008. SIAM. - --------------------------------------------------------------------------------- -Delaunay: Delaunay Graphs --------------------------------------------------------------------------------- - - These files have been generated as Delaunay triangulations of random - points in the unit square. - - Engineering a scalable high quality graph partitioner, - M. Holtgrewe, P. Sanders, C. Schulz, IPDPS 2010 - http://dx.doi.org/10.1109/IPDPS.2010.5470485 - --------------------------------------------------------------------------------- -dyn-frames: Frames from 2D Dynamic Simulations --------------------------------------------------------------------------------- - - These files have been created with the generator described in Oliver - Marquardt, Stefan Schamberger: Open Benchmarks for Load Balancing - Heuristics in Parallel Adaptive Finite Element Computations. In Proc. - International Conference on Parallel and Distributed Processing - Techniques and Applications (PDPTA 2005), Volume 2, pp. 685-691. CSREA - Press 2005, ISBN 1-932415-59-9685-691. - - The graphs are meshes taken from indivudual frames of a dynamic - sequence that resembles two-dimensional adaptive numerical simulations. - Smaller versions of these files (and their dynamic sequences as videos) - can be found on Stefan Schamberger's website ( - http://www.upb.de/cs/schaum/benchmark.html ) dedicated to these - benchmarks. The files presented here are the frames 0, 10, and 20 of - the sequences, respectively. - --------------------------------------------------------------------------------- -Kronecker: Kronecker Generator Graphs --------------------------------------------------------------------------------- - - The original Kronecker files contain self-loops and multiple edges. - These properties are also present in real-world data sets. However, - some tools cannot handle these ""artifacts"" at the moment. That is why - we present ""cleansed"" versions of the data sets as well. For the - Challenge you should expect to be confronted with the original data - with self-loops and multiple edges. However, the final decision on - this issue will be made based on participant feedback. - - All files have been generated with the R-MAT parameters A=0.57, B=0.19, - C=0.19, and D=1-(A+B+C)=0.05 and edgefactor=48, i.e., the number of - edges equals 48*n, where n is the number of vertices. Details about the - generator and the parameter meanings can be found on the Graph500 - website. ( http://www.graph500.org/Specifications.html ) - - There are 12 graphs in the DIMACS10 test set at - http://www.cc.gatech.edu/dimacs10/index.shtml . Them come in 6 pairs. - One graph in each pair is a multigraph, with self-edges. The other - graph is the nonzero pattern of the first (binary), with self-edges - removed. MATLAB cannot directly represent multigraph, so in the UF - Collection the unweighted multigraph G is represented as a matrix A - where A(i,j) is an integer equal to the number edges (i,j) in G. - - The binary graphs include the word 'simple' in their - name In the UF Collection, only the multigraph is included, - since the simple graph can be constructed from the multigraph. - If A is the multigraph, the simple graph S can be computed as: - - S = spones (tril (A,-1)) + spones (triu (A,1)) ; - - DIMACS10 graph: UF matrix: - --------------- ------------- - kronecker/kron_g500-logn16 DIMACS10/kron_g500-logn16 - kronecker/kron_g500-simple-logn16 - - kronecker/kron_g500-logn17 DIMACS10/kron_g500-logn17 - kronecker/kron_g500-simple-logn17 - - kronecker/kron_g500-logn18 DIMACS10/kron_g500-logn18 - kronecker/kron_g500-simple-logn18 - - kronecker/kron_g500-logn19 DIMACS10/kron_g500-logn19 - kronecker/kron_g500-simple-logn19 - - kronecker/kron_g500-logn20 DIMACS10/kron_g500-logn20 - kronecker/kron_g500-simple-logn20 - - kronecker/kron_g500-logn21 DIMACS10/kron_g500-logn21 - kronecker/kron_g500-simple-logn21 - - - References: ""Introducing the Graph 500,"" Richard C. Murphy, Kyle B. - Wheeler, Brian W. Barrett, James A. Ang, Cray User's Group (CUG), May - 5, 2010. - - D.A. Bader, J. Feo, J. Gilbert, J. Kepner, D. Koester, E. Loh, K. - Madduri, W. Mann, Theresa Meuse, HPCS Scalable Synthetic Compact - Applications #2 Graph Analysis (SSCA#2 v2.2 Specification), 5 - September 2007. - - D. Chakrabarti, Y. Zhan, and C. Faloutsos, R-MAT: A recursive model - for graph mining, SIAM Data Mining 2004. - - Section 17.6, Algorithms in C (third edition). Part 5 Graph - Algorithms, Robert Sedgewick (Programs 17.7 and 17.8) - - P. Sanders, Random Permutations on Distributed, External and - Hierarchical Memory, Information Processing Letters 67 (1988) pp - 305-309. - - ""DFS: A Simple to Write Yet Difficult to Execute Benchmark,"" Richard C. - Murphy, Jonathan Berry, William McLendon, Bruce Hendrickson, Douglas - Gregor, Andrew Lumsdaine, IEEE International Symposium on Workload - Characterizations 2006 (IISWC06), San Jose, CA, 25-27 October 2006. - - ---- sample code for generating these matrices: - - function ij = kronecker_generator (SCALE, edgefactor) - %% Generate an edgelist according to the Graph500 - %% parameters. In this sample, the edge list is - %% returned in an array with two rows, where StartVertex - %% is first row and EndVertex is the second. The vertex - %% labels start at zero. - %% - %% Example, creating a sparse matrix for viewing: - %% ij = kronecker_generator (10, 16); - %% G = sparse (ij(1,:)+1, ij(2,:)+1, ones (1, size (ij, 2))); - %% spy (G); - %% The spy plot should appear fairly dense. Any locality - %% is removed by the final permutations. - - %% Set number of vertices. - N = 2^SCALE; - - %% Set number of edges. - M = edgefactor * N; - - %% Set initiator probabilities. - [A, B, C] = deal (0.57, 0.19, 0.19); - - %% Create index arrays. - ij = ones (2, M); - %% Loop over each order of bit. - ab = A + B; - c_norm = C/(1 - (A + B)); - a_norm = A/(A + B); - - for ib = 1:SCALE, - %% Compare with probabilities and set bits of indices. - ii_bit = rand (1, M) > ab; - jj_bit = rand (1, M) > ( c_norm * ii_bit + a_norm * not (ii_bit) ); - ij = ij + 2^(ib-1) * [ii_bit; jj_bit]; - end - - %% Permute vertex labels - p = randperm (N); - ij = p(ij); - - %% Permute the edge list - p = randperm (M); - ij = ij(:, p); - - %% Adjust to zero-based labels. - ij = ij - 1; - - function G = kernel_1 (ij) - %% Compute a sparse adjacency matrix representation - %% of the graph with edges from ij. - - %% Remove self-edges. - ij(:, ij(1,:) == ij(2,:)) = []; - %% Adjust away from zero labels. - ij = ij + 1; - %% Find the maximum label for sizing. - N = max (max (ij)); - %% Create the matrix, ensuring it is square. - G = sparse (ij(1,:), ij(2,:), ones (1, size (ij, 2)), N, N); - %% Symmetrize to model an undirected graph. - G = spones (G + G.'); - --------------------------------------------------------------------------------- -numerical: graphs from numerical simulations --------------------------------------------------------------------------------- - - For the graphs adaptive and venturiLevel3, please refer to the preprint: - - Hartwig Anzt, Werner Augustin, Martin Baumann, Hendryk Bockelmann, - Thomas Gengenbach, Tobias Hahn, Vincent Heuveline, Eva Ketelaer, - Dimitar Lukarski, Andrea Otzen, Sebastian Ritterbusch, Bjo""rn Rocker, - Staffan Ronnås, Michael Schick, Chandramowli Subramanian, Jan-Philipp - Weiss, and Florian Wilhelm. Hiflow3 - a flexible and hardware-aware - parallel Finite element package. In Parallel/High-Performance Object- - Oriented Scientific Computing (POOSC'10). - - For the graphs channel-500x100x100-b050 and packing-500x100x100-b050, - please refer to: - - Markus Wittmann, Thomas Zeiser. Technical Note: Data Structures of - ILBDC Lattice Boltzmann Solver. - http://www.cc.gatech.edu/dimacs10/archive/numerical-overview-Erlangen.pdf - - The instances NACA0015, M6, 333SP, AS365, and NLR are 2-dimensional FE - triangular meshes with coordinate information. 333SP and AS365 are actually - converted from existing 3-dimensional models to 2D places, while the rest - are created from geometry. The corresponding coordinate files have been - assembled in one archive. They have been created and contributed by Chan - Siew Yin with the help of Jian Tao Zhang, Department of Mechanical - Engineering, University of New Brunswick, Fredericton, Canada. - --------------------------------------------------------------------------------- -random: Random Geometric Graphs --------------------------------------------------------------------------------- - - rggX is a random geometric graph with 2^X vertices. Each vertex is a - random point in the unit square and edges connect vertices whose - Euclidean distance is below 0.55 (ln n)/n. This threshold was choosen - in order to ensure that the graph is almost connected. - - Note: the UF Collection is a collection of matrices primarily from - real applications. The only random matrices I add to the collection - are those used in established benchmarks (such as DIMACS10). - - Engineering a scalable high quality graph partitioner, - M. Holtgrewe, P. Sanders, C. Schulz, IPDPS 2010. - http://dx.doi.org/10.1109/IPDPS.2010.5470485 - --------------------------------------------------------------------------------- -steets: Street Networks --------------------------------------------------------------------------------- - - The graphs Asia, Belgium, Europe, Germany, Great-Britain, Italy, - Luxemburg and Netherlands are (roughly speaking) undirected and - unweighted versions of the largest strongly connected component - of the corresponding Open Street Map road networks. The Open - Street Map road networks have been taken from - http://download.geofabrik.de and have been converted for DIMACS10 - by Moritz Kobitzsch (kobitzsch at kit.edu) as follows: - - First, we took the corresponding graph and extracted all routeable - streets. Routable streets are objects in this file that are tagged - using one of the following tags: motorway, motorway_link, trunk - trunk_link, primary, primary_link, secondary, secondary_link, - tertiary, tertiary_link, residential, unclassified, road, - living_street, and service. Next, we now compute the largest - strongly connected component of this extracted open street map graph. - Self-edges and parallel edges are removed afterwards. - The DIMACS 10 graph is now the undirected and unweighted version - of the constructed graph, i.e. if there is an edge (u,v) but the - reverse edge (v,u) is missing, we insert the reverse edge into the - graph. The XY coordinates of each node in the graph are preserved. - --------------------------------------------------------------------------------- -Walshaw: Chris Walshaw's graph partitioning archive --------------------------------------------------------------------------------- - - Chris Walshaw's graph partitioning archive contains 34 graphs that - have been very popular as benchmarks for graph partitioning algorithms - ( http://staffweb.cms.gre.ac.uk/~wc06/partition/ ). - - 17 of them are already in the UF Collection. Only the 17 new graphs - not yet in the collection are added here in the DIMACS10 set. - - DIMACS10 graph: new? UF matrix: - --------------- ---- ------------- - walshaw/144 * DIMACS10/144 - walshaw/3elt AG-Monien/3elt - walshaw/4elt Pothen/barth5 - walshaw/598a * DIMACS10/598a - walshaw/add20 Hamm/add20 - walshaw/add32 Hamm/add32 - walshaw/auto * DIMACS10/auto - walshaw/bcsstk29 HB/bcsstk29 - walshaw/bcsstk30 HB/bcsstk30 - walshaw/bcsstk31 HB/bcsstk31 - walshaw/bcsstk32 HB/bcsstk32 - walshaw/bcsstk33 HB/bcsstk33 - walshaw/brack2 AG-Monien/brack2 - walshaw/crack AG-Monient/crack - walshaw/cs4 * DIMACS10/cs4 - walshaw/cti * DIMACS10/cti - walshaw/data * DIMACS10/data - walshaw/fe_4elt2 * DIMACS10/fe_4elt2 - walshaw/fe_body * DIMACS10/fe_body - walshaw/fe_ocean * DIMACS10/fe_ocean - walshaw/fe_pwt Pothen/pwt - walshaw/fe_rotor * DIMACS10/fe_rotor - walshaw/fe_sphere * DIMACS10/fe_sphere - walshaw/fe_tooth * DIMACS10/fe_tooth - walshaw/finan512 Mulvey/finan512 - walshaw/m14b * DIMACS10/m14b - walshaw/memplus Hamm/memplus - walshaw/t60k * DIMACS10/t60k - walshaw/uk * DIMACS10/uk - walshaw/vibrobox Cote/vibrobox - walshaw/wave AG-Monien/wave - walshaw/whitaker3 AG-Monien/whitaker3 - walshaw/wing * DIMACS10/wing - walshaw/wing_nodal * DIMACS10/wing_nodal - --------------------------------------------------------------------------------- -redistrict: census networks --------------------------------------------------------------------------------- - - These graphs represent US states. They are used for solving the - redistricting problem. All data have been provided by Will Zhao. As stated - on the project website, The nodes are Census2010 blocks. Two nodes have an - edge linking them if they share a line segment on their borders, i.e. - rook-style neighboring. The nodes weights are the POP100 variable of - Census2010-PL94, and the area of eache district. - --------------------------------------------------------------------------------- -star-mixtures : artificially generated from sets of real graphs --------------------------------------------------------------------------------- - - Each graph in this benchmark represents a star-like structure of different - graphs S0 , . . . , St. Graphs S1 , . . . , St are weakly connected to the - center S0 by random edges. The total number of edges between each Si and S0 - was less than 3% out of the total number of edges in Si . The graphs are - mixtures of the following structures: social networks, finite-element - graphs, VLSI chips, peer-to-peer networks, and matrices from optimization - solvers. - - More info can be found in the paper I. Safro, P. Sanders, C. Schulz: - Advanced Coarsening Schemes for Graph Partitioning, SEA 2012. - - Communicated by Christian Schulz, uploaded on March 30, 2012." -IPSO,7,"Power simulation matrices, Li Peijie, Inst. of Power Systems, Guangxi Univ. Three different kinds of optimization methods (OPF, TSCOPF, HTC)",https://sparse.tamu.edu/IPSO,electrical/circuit/device,electrical/circuit/device,1.0,1,power network problem (7),1,real (7),1,symmetric (7),8140,226340,27.805896805896804,moderate (4–100×),141478,2012833,square only,7,0,all square matrices symmetric/Hermitian,7,7,0,real only,0,0,coherent family/series,"Power simulation matrices, Li Peijie, Inst. of Power Systems, Guangxi Univ. - -Three different kinds of optimization methods (OPF, TSCOPF, HTC) -applied to power system simulation. All matrices are symmetric -indefinite. From Li Peijie, Institute of Power Systems (IPSO), -Guangxi University (beyondpeijie at gxu.edu.cn). Collected by -Jonathan Hogg, Rutherford Appleton Laboratory, Oxfordshire, UK." -Janna,18,"Finite element simulations: gas reservoir and structural problems. Univ Padova. C. Janna, Univ. of Padova. Authors: Carlo Janna and Massimiliano Ferronato",https://sparse.tamu.edu/Janna,fluids/transport; generic spatial PDE/thermal; structures/materials,structures/materials,0.7777777777777778,3,structural problem (14); 2D/3D problem (3); computational fluid dynamics problem (1),1,real (18),2,symmetric (14); unsymmetric (4),377002,4147110,11.000233420512359,moderate (4–100×),17277420,316548962,square only,18,0,mixed among square matrices,14,18,10,real only,0,18,multi-domain/mixed,"Finite element simulations: gas reservoir and structural problems. Univ Padova. -C. Janna, Univ. of Padova. Authors: Carlo Janna and Massimiliano Ferronato -All matrices in the set are symmetric and positive definite. - -Serena: - - Gas reservoir simulation for CO2 sequestration. - - # equations: 1,391,349 - # non-zeroes: 64,531,701 - Problem description: structural problem - - The matrix Serena is obtained from a structural problem discretizing a gas - reservoir with tetrahedral Finite Elements. The medium is strongly - heterogeneous and characterized by a complex geometry consisting of - alternating sequences of thin clay and sand layers. References: [1] - -Emilia_923: - - # equations: 923136 - # non-zeroes: 41005206 - Problem description: Geomechanical problem - - The matrix Emilia_923 is obtained from a structural problem discretizing a - gas reservoir with tetrahedral Finite Elements. Due to the complex - geometry of the geological formation it was not possible to obtain a - computational grid characterized by regularly shaped elements. The - problem arises from a 3D discretization with three displacement unknowns - associated to each node of the grid. References: [1], [2] - -Fault_639: - - # equations: 638802 - # non-zeroes: 28614564 - Problem description: contact mechanics - - The matrix Fault_639 is obtained from a structural problem discretizing a - faulted gas reservoir with tetrahedral Finite Elements and triangular - Interface Elements. The Interface Elements are used with a Penalty - formulation to simulate the faults behaviour. The problem arises from a - 3D discretization with three displacement unknowns associated to each node - of the grid. References [3,4,5,6] - -Flan_1565: - - # equations: 1564794 - # non-zeroes: 117406044 - Problem description: Structural problem - - The matrix Flan_1565 is obtained from a 3D mechanical problem discretizing - a steel flange with hexahedral Finite Elements. Due to the regular shape - of the mechanical piece, the computational grid is a structured mesh with - regularly shaped elements. Three displacement unknowns are associated to - each node of the grid. References [6,7] - -Geo_1438: - - # equations: 1437960 - # non-zeroes: 63156690 - Problem description: Geomechanical problem - - The matrix Geo_1438 is obtained from a geomechanical problem discretizing - a region of the earth crust subject to underground deformation. The - computational domain is a box with an areal extent of 50 x 50 km and 10 km - deep consisting of regularly shaped tetrahedral Finite Elements. The - problem arises from a 3D discretization with three displacement unknowns - associated to each node of the grid. Reference: [6] - -Hook_1498: - - # equations: 1498023 - # non-zeroes: 60917445 - Problem description: Structural problem - - The matrix Hook_1498 is obtained from a 3D mechanical problem discretizing - a steel hook with tetrahedral Finite Elements. The computational grid - consists of regularly shaped elements with three displacement unknowns - associated to each node. - -StocF-1465: - - # equations: 1465137 - # non-zeroes: 21005389 - Problem description: Flow in porous medium with a stochastic permeabilies - - The matrix StocF_1465 is obtained from a fluid-dynamical problem of flow - in porous medium. The computational grid consists of tetrahedral Finite - Elements discretizing an underground aquifer with stochastic - permeabilties. References: [2,8] - -References: - -[1] M. Ferronato, G. Gambolati, C. Janna, P. Teatini. ""Geomechanical - issues of anthropogenic CO2 sequestration in exploited gas fields"", Energy - Conversion and Management, 51, pp. 1918-1928, 2010. - -[2] C. Janna, M. Ferronato. ""Adaptive pattern research for block FSAI - preconditionig"". SIAM Journal on Scientific Computing, to appear. - -[3] M. Ferronato, G. Gambolati, C. Janna, P. Teatini. ""Numerical modelling - of regional faults in land subsidence prediction above gas/oil - reservoirs"", International Journal for Numerical and Analytical Methods in - Geomechanics, 32, pp. 633-657, 2008. - -[4] M. Ferronato, C. Janna, G. Gambolati. ""Mixed constraint preconditioning - in computational contact mechanics"", Computer Methods in Applied Mechanics - and Engineering, 197, pp. 3922-3931, 2008. - -[5] C. Janna, M. Ferronato, G. Gambolati. ""Multilevel incomplete - factorizations for the iterative solution of non-linear FE problems"". - International Journal for Numerical Methods in Engineering, 80, pp. - 651-670, 2009. - -[6] C. Janna, M. Ferronato, G. Gambolati. ""A Block FSAI-ILU parallel - preconditioner for symmetric positive definite linear systems"". SIAM - Journal on Scientific Computing, 32, pp. 2468-2484, 2010. - -[7] C. Janna, A. Comerlati, G. Gambolati. ""A comparison of projective and - direct solvers for finite elements in elastostatics"". Advances in - Engineering Software, 40, pp. 675-685, 2009. - -[8] M. Ferronato, C. Janna, G. Pini. ""Shifted FSAI preconditioners for the - efficient parallel solution of non-linear groundwater flow models"". - International Journal for Numerical Methods in Engineering, to appear. - - --------------------------------------------------------------------------------- -Cube_Coup_* and Long_Coup_* matrices: --------------------------------------------------------------------------------- - -Authors: Carlo Janna and Massimiliano Ferronato - -Cube_Coup_*: Symmetric Indefinite Matrix -# equations: 2,164,760 -# non-zeroes: 127,206,144 -Problem description: Coupled consolidation problem - -The matrix Cube_Coup is obtained from a 3D coupled consolidation -problem of a cube discretized with tetrahedral Finite Elements. The -computational grid is characterized by regularly shaped elements. The -copuled consolidation problem gives rise to a matrix having 4 unknowns -associated to each node: the first three are displacement unknowns, the -fourth is a pressure. Coupled consolidation is a transient problem with -the matrix ill-conditioning strongly depending on the time step size. -We provide a relatively simple problem, ""dt0"" with a time step size of -10^0 seconds, and a more difficult one, ""dt6"" with a time step of 10^6 -seconds. The two Cube_Coup_* matrices are symmetric indefinite. - -Long_Coup_*: Symmetric Indefinite Matrix -# equations: 1,470,152 -# non-zeroes: 87,088,992 -Problem description: Coupled consolidation problem - -The matrix Long_Coup is obtained from a 3D coupled consolidation -problem of a geological formation discretized with tetrahedral Finite -Elements. Due its complex geometry it was not possible to obtain a -computational grid characterized by regularly shaped elements. The -copuled consolidation problem gives rise to a matrix having 4 unknowns -associated to each node: the first three are displacement unknowns, the -fourth is a pressure. Coupled consolidation is a transient problem with -the matrix ill-conditioning strongly depending on the time step size. -We provide a relatively simple problem, ""dt0"" with a time step size of -10^0 seconds, and a more difficult one, ""dt6"" with a time step of 10^6 -seconds. The two Long_Coup_* matrices are symmetric indefinite. - -Further information on the 4 matrices may be found in the following papers: - -1) C. Janna, M. Ferronato, G. Gambolati. ""Parallel inexact constraint -preconditioning for ill-conditioned consolidation problems"". -Computational Geosciences, submitted. - -2) M. Ferronato, L. Bergamaschi, G. Gambolati. ""Performance and -robustness of block constraint preconditioners in FE coupled -consolidation problems"". International Journal for Numerical Methods -in Engineering, 81, pp. 381-402, 2010. - -3) L. Bergamaschi, M. Ferronato, G. Gambolati. ""Mixed constraint -preconditioners for the iterative solution to FE coupled consolidation -equations"". Journal of Computational Physics, 227, pp. 9885-9897, 2008. - -4) L. Bergamaschi, M. Ferronato, G. Gambolati. ""Novel preconditioners -for the iterative solution to FE-discretized coupled consolidation -equations"". Computer Methods in Applied Mechanics and Engineering, 196, -pp. 2647-2656, 2007. - --------------------------------------------------------------------------------- -Janna/CoupCons3D --------------------------------------------------------------------------------- - - Authors: Carlo Janna, Massimiliano Ferronato Giorgio Pini - Matrix type: Unsymmetric - # equations: 416,800 - # non-zeroes: 22,322,336 - - Problem description: Fully coupled poroelastic problem - (structural problem) - - The matrix CoupCons3D has been obtained through a Finite Element - transient simulation of a fully coupled consolidation problem on - a three-dimensional domain using Finite Differences for the - discretization in time. - - Further information can be found in the following papers: - - 1) M. Ferronato, G. Pini, and G. Gambolati. The role of - preconditioning in the solution to FE coupled consolidation - equations by Krylov subspace methods. International Journal for - Numerical and Analytical Methods in Geomechanics 33 (2009), pp. - 405-423. - - 2) M. Ferronato, C. Janna, and G. Pini. Parallel solution to - ill-conditioned FE geomechanical problems. International Journal - for Numerical and Analytical Methods in Geomechanics 36 (2012), - pp. 422-437. - - 3) M. Ferronato, C. Janna and G. Pini. A generalized Block FSAI - preconditioner for unsymmetric indefinite matrices. Journal of - Computational and Applied Mathematics (2012), submitted. - Authors: Carlo Janna, Massimiliano Ferronato, Giorgio Pini - --------------------------------------------------------------------------------- -Janna/ML_Laplace --------------------------------------------------------------------------------- - - Authors: Carlo Janna, Massimiliano Ferronato Giorgio Pini - Matrix type: Unsymmetric - # equations: 377,002 - # non-zeroes: 27,689,972 - - Problem description: Poisson problem - - The matrix ML_Laplace has been obtained by discretizing a 2D - Poisson equation with a Meshless Local Petrov-Galerkin method. - - Further information can be found in the following papers: - - 1) G. Pini, A. Mazzia, and F. Sartoretto. Accurate MLPG solution - of 3D potential problems. CMES - Computer Modeling in Engineering - & Sciences 36 (2008), pp. 43-64. - - 2) M. Ferronato, C. Janna and G. Pini. A generalized Block FSAI - preconditioner for unsymmetric indefinite matrices. Journal of - Computational and Applied Mathematics (2012), submitted. - Authors: Carlo Janna, Massimiliano Ferronato, Giorgio Pini - --------------------------------------------------------------------------------- -Janna/Transport --------------------------------------------------------------------------------- - - Authors: Carlo Janna, Massimiliano Ferronato Giorgio Pini - Matrix type: Unsymmetric - # equations: 1,602,111 - # non-zeroes: 23,500,731 - - Problem description: 3D Finite Element flow and transport - - The matrix Transport has been obtained by a FE tetrahedral - discretization of a density driven coupled flow and transport. - - Further information can be found in the following papers: - - 1) A. Mazzia, and M. Putti. High order Godunov mixed methods on - tetrahedral meshes for density driven flow simulations in porous - media. Journal of Computational Physics 208 (2005), pp. 154-174. - - 2) M. Ferronato, C. Janna and G. Pini. A generalized Block FSAI - preconditioner for unsymmetric indefinite matrices. Journal of - Computational and Applied Mathematics (2012), submitted. - --------------------------------------------------------------------------------- -Janna/ML_Geer --------------------------------------------------------------------------------- - - Authors: Carlo Janna, Massimiliano Ferronato, Giorgio Pini - Matrix type: Unsymmetric - # equations: 1,504,002 - # non-zeroes: 110,879,972 - - Problem description: Poroelastic problem (structural problem) - - The matrix ML_Geer has been obtained to find through a Meshless - Petrov-Galerkin discretization the deformed configuration of an - axial-symmetric porous medium subject to a pore-pressure drawdown. - - Further information can be found in the following papers: - - 1) M. Ferronato, A. Mazzia, G. Pini, and G. Gambolati. A meshless - method for axi-symmetric poroelastic simulations: numerical - study. International Journal for Numerical Methods in Engineering - 70 (2007), pp. 1346-1365. - - 2) M. Ferronato, C. Janna and G. Pini. A generalized Block FSAI - preconditioner for unsymmetric indefinite matrices. Journal of - Computational and Applied Mathematics (2012), submitted. - --------------------------------------------------------------------------------- -Bump_2991 --------------------------------------------------------------------------------- - - Matrix Name: Bump_2911 - - Authors: Carlo Janna and Massimiliano Ferronato - Symmetric Positive Definite Matrix - # equations: 2,911,419 - # non-zeroes: 130,378,257 - Problem description: 3D geomechanical reservoir simulation - - The matrix Bump_2911 is obtained from the 3D geomechanical - simulation of a gas-reservoir discretized by linear tetrahedral - Finite Elements. The mechanical properties of the medium vary - with the depth and the geological formation. Zero displacement - are applied on bottom and lateral boundary, while a traction-free - top boundary is assumed. - - Further information may be found in the following papers: - - 1) C. Janna, M. Ferronato, G. Gambolati. ""Enhanced Block FSAI - preconditioning using Domain Decomposition techniques"". SIAM - Journal on Scientific Computing, 35, pp. S229-S249, 2013. - - 2) C. Janna, M. Ferronato, G. Gambolati. ""The use of supernodes - in factored sparse approximate inverse preconditioning"". SIAM - Journal on Scientific Computing, submitted. - --------------------------------------------------------------------------------- -Queen_4147 --------------------------------------------------------------------------------- - - Matrix Name: Queen_4147 - - Authors: Carlo Janna and Massimiliano Ferronato - Symmetric Positive Definite Matrix - # equations: 4,147,110 - # non-zeroes: 329,499,288 - Problem description: 3D structural problem - - The matrix Queen_4147 is obtained from the 3D discretizaion - of a structural problem by isoparametric hexahedral Finite - Elements. The solid material is strongly heterogeneous and - several elements exhibit shape distortion thus producing an - ill-conditioned stiffness matrix. - - Further information may be found in the following paper: - - 1) C. Janna, M. Ferronato, G. Gambolati. ""The use of supernodes - in factored sparse approximate inverse preconditioning"". SIAM - Journal on Scientific Computing, submitted. - --------------------------------------------------------------------------------- -PFlow_742 --------------------------------------------------------------------------------- - - Matrix Name: PFlow_742 - - Authors: Carlo Janna and Massimiliano Ferronato - Symmetric Positive Definite Matrix - # equations: 742,793 - # non-zeroes: 37,138,461 - Problem description: 3D pressure-temperature evolution - in porous media - - The matrix PFlow_742 is obtained from a 3D simulation of the - pressure-temperature field in a multilayered porous media - discretized by hexahedral Finite Elements. The ill-conditioning - of the matrix is due to the strong contrasts in the material - properties fo different layers. - - Further information may be found in the following paper: - - 1) C. Janna, M. Ferronato, G. Gambolati. ""The use of supernodes - in factored sparse approximate inverse preconditioning"". SIAM - Journal on Scientific Computing, submitted." -Schulthess,3,"Biochemical networks from Pascal Schulthess, Inst. Pathology, Berlin Matrices from Pascal Schulthess, Institute for Pathology,",https://sparse.tamu.edu/Schulthess,chemistry/biology,chemistry/biology,1.0,1,biochemical network (3),1,integer (3),1,rectangular (3),1996,16559,8.296092184368737,moderate (4–100×),4335,43816,rectangular only,0,3,N/A (rectangular only),0,0,0,real only,0,0,coherent family/series,"Biochemical networks from Pascal Schulthess, Inst. Pathology, Berlin - -Matrices from Pascal Schulthess, Institute for Pathology, -Chariteplatz 1, 10117 Berlin, Germany. - -Three large biochemical networks (N_biocarta, N_pid, and N_reactome). -These are stoichiometric matrices extracted from three biochemical -databases (BioCarta, PID, and REACTOME) describing cell signaling -pathways and protein-protein interaction networks. The goal is to -find the left nullspace of the matrix; in MATLAB notation: - -N = null (Problem.A') ; - -The matrix (Problem.A')*N will thus be essentially zero. -This can be done much more efficiently with the spqr_rank toolbox by -Leslie Foster and Tim Davis, as: - -N = spqr_null (Problem.A') ; - -Results: -The matrix A is transposed, then N = null (A) or N = spqr_null (A) -is computed. The size statistic is the memory taken by N. -spqr_null can compute either an explicit matrix N, or an implicit -Householder-based representation. The latter takes less memory. - -Matrix: N_biocarta size: 1996 by 1922 (transposed) - -spqr_null stats: - flag: 0 - rank: 1023 - tol: 3.5456e-12 - est_sval_upper_bounds: [0.1689 3.4534e-15] - est_sval_lower_bounds: [0.1203 0] - sval_numbers_for_bounds: [1023 1024] - est_norm_A_times_N: 2.4349e-15 - -spqr_null, implicit: 0.03 sec, norm(A*N) 9e-15 size: 0.08 MB -spqr_null, explicit: 0.10 sec, norm(A*N) 9e-15 size: 0.11 MB -MATLAB null: 3.31 sec, norm(A*N) 2e-13 size: 13.82 MB -all report dim(N) of 899. - -Matrix: N_pid size: 3923 by 3625 (transposed) - -spqr_null stats: - flag: 0 - rank: 2048 - tol: 1.3937e-11 - est_sval_upper_bounds: [0.0922 5.1310e-15] - est_sval_lower_bounds: [0.0585 0] - sval_numbers_for_bounds: [2048 2049] - est_norm_A_times_N: 1.6751e-15 - -spqr_null, implicit: 0.05 sec, norm(A*N) 4e-14 size: 0.21 MB -spqr_null, explicit: 0.34 sec, norm(A*N) 4e-14 size: 1.32 MB -MATLAB null: 24.86 sec, norm(A*N) 9e-13 size: 45.73 MB -all report dim(N) of 1577 - -Matrix: N_reactome size: 16559 by 10204 (transposed) - -spqr_null stats: - flag: 0 - rank: 9025 - tol: 1.1766e-10 - est_sval_upper_bounds: [0.6722 1.3042e-14] - est_sval_lower_bounds: [0.0106 0] - sval_numbers_for_bounds: [9025 9026] - est_norm_A_times_N: 9.4695e-15 - -spqr_null, implicit: 0.95 sec, norm(A*N) 2e-13 size: 7.5 MB -spqr_null, explicit: 3.53 sec, norm(A*N) 2e-13 size: 25.2 MB -MATLAB null: 904.54 sec, norm(A*N) 2e-10 size: 96.2 MB -all report dim(N) of 1179." -Chevron,4,"2D Helmholtz; Temporal freq. domain seismic modeling. J. Washbourne, Chevron Linearized for Gauss-Newton method.",https://sparse.tamu.edu/Chevron,generic spatial PDE/thermal,generic spatial PDE/thermal,1.0,1,2D/3D problem (4),1,complex (4),1,unsymmetric (4),37365,711450,19.04054596547571,moderate (4–100×),330633,6376412,square only,4,0,no square matrix exactly symmetric/Hermitian,0,0,0,complex only,0,0,coherent family/series,"2D Helmholtz; Temporal freq. domain seismic modeling. J. Washbourne, Chevron -Linearized for Gauss-Newton method." -CPM,9,"Closest Point Method, Yujia Chen, Andy Wathen, Shengxin Zhu A method for computing on surfaces. For more information see:",https://sparse.tamu.edu/CPM,generic spatial PDE/thermal,generic spatial PDE/thermal,1.0,1,2D/3D problem (9),1,real (9),1,unsymmetric (9),148,40948,276.6756756756757,wide (>100×),1527,412148,square only,9,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,9,coherent family/series,"Closest Point Method, Yujia Chen, Andy Wathen, Shengxin Zhu - -A method for computing on surfaces. For more information see: -http://people.maths.ox.ac.uk/~macdonald/closestpoint/ - -One of the matrices in this collection (cz10228) is solved poorly -by x=A\\b in MATLAB R2012a. The solution is to use this setting: - - spparms ('piv_tol', 0.5) - x = A\\b - -which changes the pivot tolerance. MATLAB has a test which -checks the accuracy of x=A\\b when using UMFPACK, and if the -accuracy is poor, it refactorizes the matrix with a piv_tol -of 1.0 (standard partial pivoting). For the cz10228 matrix, -the test is nearly, but not quite, triggered. The other matrices -do not cause this problem. - -Further details from Shengxin Zhu: - -Generally speaking, the matrix comes from the numerical solution of -Poisson equation on a unit circle by solving an embedding PDE posed -in a narrow band around the circle. Of course the easiest way to -solve PDE on a unit circle is to parametrize it and then solve a 1D -problem; but here we just want to test the effectiveness of the -embedding method for solving PDEs on general curves or surfaces. -The method extends the original surface PDE to a band around the -surface, and then solve the extended PDE on a Cartesian grid by a -finite difference scheme. In order to define such a PDE, one has to -both define proper embedding differential operator and extend the -solution from the surface to the embedding space. One natural way -is to enforce the solution being constant along the normal -direction of the surface so that the surface differential operator -could be replaced by standard Cartesian differential operator. Here -in the case of Poisson equation on a unit circle, we solve a -standard Poisson equation on a 2D cartesian grid around the circle -with suitable right hand side and Neumann boundary condition on the -boundary of the band. We enforce the Neumann boundary condition by -taking the value of each boundary point to be the value of its -closest point on the circle; and since the closest point is usually -not a grid point, its value is obtained by interpolation of values -of the surrounding points. Putting the process into a sparse -matrix, and change one row of the matrix to fix the value of one -point of the solution and ensure the matrix is nonsingular. - -I tried different bandwidth, different interpolation order for the -value of the closest points, 2nd order and 4-th order finite -difference scheme to the Laplace operator. In most cases, MATLAB -backslash works pretty well in solving the resulting linear system. -The size of the matrix which makes MATLAB backslash not work is not -the largest among all, and its condition number is not largest -among all. By the way, the AMG solver by Notay and a geometric -multigrid solver written by myself works pretty well for this case." -Davis,1,"Analysis of the MATLAB FileExchange, March 2010 For the 8th Edition of the MATLAB Primer (published by CRC Press)",https://sparse.tamu.edu/Davis,graph/network data,graph/network data,1.0,1,term/document graph (1),1,integer (1),1,rectangular (1),9498,9498,1.0,one size,301899,301899,rectangular only,0,1,N/A (rectangular only),0,0,0,real only,0,0,single matrix,"Analysis of the MATLAB FileExchange, March 2010 - -For the 8th Edition of the MATLAB Primer (published by CRC Press) -I wrote a script that downloaded and parsed all 9489 submissions -to the MATLAB File Exchange (as of March 12, 2010, 8am), at: -http://www.mathworks.com/matlabcentral/fileexchange/ . -Each submission was parsed to determine how many times each of -1319 keywords appears. The list of keywords to search for was -obtained by parsing the MATLAB Help documentation. In the primary -matrix (Problem.A), the entry A(i,j) is the nubmer of times that -the ith keyword appears in the jth ""file"" (each of the submissions -to the MATLAB File Exchange is called a ""file"", although each -submission often consists of more than one actual file). -Problem.aux.key{i} is the ith keyword, and Problem.aux.id(j) is -the MATLAB File ID corresponding to the jth column of the matrix A. -The first 454 rows of the A matrix correspond to the 454 functions -or keywords that appear in the ""MATLAB Top 500"" list of most- -frequently used functions, keywords, and operators in the 8th -Edition of the MATLAB Primer. The remaining are operators -(+ - / * . etc) to round out the ""Top 500"" (these do not appear -in the Problem.A matrix since parsing them proved difficult). - -Note that some editorial adjustments were made to the Top 500. -In particular, deprecated functions were removed. Some of the -trigonometric functions are rarely used, but all of them were -included in the ""Top 500"" to make a complete set. You can -visualize this with semilogy(sum(A,2)). The rows of A are -mostly sorted in decreasing row-sum, except for rows 430 to -470 or so. - -This matrix was constructed in March 2010 by Tim Davis and added -to the collection in July 2012; Tim Davis is solely responsible -for this matrix. It was not submitted by or authorized by -The MathWorks, Inc, but since the File Exchange is now under -a BSD license, this is included in the collection as a legitimate -derivative work." -Bodendiek,5,"Curl-Curl operator of 2nd order Maxwell's equations, A. Bodendiek From Andre' Bodendiek, Institut Computational Mathematics,",https://sparse.tamu.edu/Bodendiek,eigenvalue/model reduction,eigenvalue/model reduction,1.0,1,model reduction problem (5),1,real (5),1,symmetric (5),11083,2380515,214.78976811332672,wide (>100×),113343,26515867,square only,5,0,all square matrices symmetric/Hermitian,5,5,0,real only,0,5,coherent family/series,"Curl-Curl operator of 2nd order Maxwell's equations, A. Bodendiek - -From Andre' Bodendiek, Institut Computational Mathematics, -TU Braunschweig - -The following matrix collection consists of the curl-curl-operator -of a second-order Maxwell's equations with PEC boundary conditions, -i.e. E x n = 0, where E and n denote the electric field strength -and the unit outer normal of the computational domain. The -curl-curl-operator has been discretized using the Finite Element -Method with first-order Nedelec elements resulting in the weak -formulation - - 1/mu0 ( curl E, curl v ), - -where v resembles a test function of H(curl) and -mu0 = 1.25 1e-9 H / mm denotes the magnetic permeability of vacuum, -see [Hipt02]. - -In general, the underlying model problem of Maxwell's equations -results from a Coplanar Waveguide, which will be considered for -the analysis of parasitic effects in the development of new -semiconductors. Since the corresponding dynamical systems are often -high-dimensional, model order reduction techniques have become an -appealing approach for the efficient simulation and accurate analysis -of the parasitic effects. However, different kinds of model order -techniques require the repeated solution of high-dimensional linear -systems of the original model problem, see [Bai02,An09]. Therefore, -the development of efficient solvers resembles an important task -in model order reduction. - -Each matrix CurlCurl_ consists of a different number of degrees -of freedom, given in the following table: - - = 0: 11083 - = 1: 226451 - = 2: 806529 - = 3: 1219574 - = 4: 2380515 - -References. - -@ARTICLE{Bai02, - author = {Z. Bai}, - title = {Krylov subspace techniques for reduced-order modeling - of large-scale dynamical systems}, - journal = {Applied Numerical Mathematics}, - year = {2002}, - volume = {43}, - pages = {9--44}, - number = {1--2} -} - -@ARTICLE{Hipt02, - author = {R. Hiptmair}, - title = {Finite elements in computational electromagnetism}, - journal = {Acta Numerica, Cambridge University Press}, - year = {2002}, - pages = {237-339} -} - -@BOOK{An09, - title = {Approximation of {L}arge-{S}cale {D}ynamical {S}ystems}, - publisher = {Society for Industrial Mathematics}, - year = {2009}, - author = {Athanasios C. Antoulas} -}" -ANSYS,3,Underdetermined systems needing well-conditioned bases to be found. The goal is to find a permutation or factorization that places A in upper,https://sparse.tamu.edu/ANSYS,other,other,1.0,1,least squares problem (3),1,real (3),1,rectangular (3),887058,1823928,2.056154163538348,narrow (≤4×),652140,4211599,rectangular only,0,3,N/A (rectangular only),0,0,0,real only,0,0,coherent family/series,"Underdetermined systems needing well-conditioned bases to be found. - -The goal is to find a permutation or factorization that places A in upper -trapezoidal form, [R1 R2] where R1 is well-conditioned, square, and upper -triangular, and where R1\\R2 is as sparse as possible. Submitted to the -UF collection by Emmannuel Delor, ANSYS. - - opts.tol = 0.01 ; - [m n] = size(A) ; - x = ones (n,1) ; - y = A*x ; - [c R P info] = spqr (A, y, opts) ; - info - Rs = R (:, 1:m) ; % must be well conditioned - fprintf ('condest(Rs) %g\\n', condest (Rs)) ; - xs = x (1:m) ; - xm = x (m+1:n) ; - A2 = -Rs \\ R (:, m+1:n) ; - y2 = Rs \\ c ; - norm(A2*xm + y2 - xs) % should be very small - nnz (A2) % should also be as small as possible" -Sorensen,1,Call graph of the Linux kernel. The sparse matrix represents a call-graph made from the source code of,https://sparse.tamu.edu/Sorensen,graph/network data,graph/network data,1.0,1,directed weighted graph (1),1,integer (1),1,unsymmetric (1),324085,324085,1.0,one size,1208908,1208908,square only,1,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,0,single matrix,"Call graph of the Linux kernel. - -The sparse matrix represents a call-graph made from the source code of -the Linux kernel (version 3.7.10) - -Svend Haugaard Sørensen -May 6 2013" -LeGresley,3,"Power flow analyses of electrical grids. Submitted by Patrick LeGresley, Stanford, July 2013",https://sparse.tamu.edu/LeGresley,electrical/circuit/device,electrical/circuit/device,1.0,1,power network problem (3),1,real (3),1,unsymmetric (3),2508,87936,35.0622009569378,moderate (4–100×),16727,593276,square only,3,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,0,coherent family/series,"Power flow analyses of electrical grids. -Submitted by Patrick LeGresley, Stanford, July 2013" -Brogan,1,"specular surface reconstruction problem, Submitted by Joel Brogan, Notre Dame, July 2014",https://sparse.tamu.edu/Brogan,imaging/graphics/robotics,imaging/graphics/robotics,1.0,1,computer vision problem (1),1,real (1),1,rectangular (1),477976,477976,1.0,one size,7647040,7647040,rectangular only,0,1,N/A (rectangular only),0,0,0,real only,0,1,single matrix,"specular surface reconstruction problem, -Submitted by Joel Brogan, Notre Dame, July 2014 -Requires a sparse QR factorization, x=A\\b" -Moqri,1,"Document similarity graph for MIS articles, including metadata KnowledgeMap -- Visualizing Invisible Networks of Knowledge",https://sparse.tamu.edu/Moqri,graph/network data,graph/network data,1.0,1,undirected weighted graph (1),1,real (1),1,symmetric (1),2427,2427,1.0,one size,57022,57022,square only,1,0,all square matrices symmetric/Hermitian,1,1,0,real only,0,0,single matrix,"Document similarity graph for MIS articles, including metadata - -KnowledgeMap -- Visualizing Invisible Networks of Knowledge -in a Scientific Discipline, by M. Moqri, Univ of Florida, -Warrington College of Business Administration. -http://misknowledgemap.warrington.ufl.edu/ -http://www.moqri.com/ - -This undirected wieghted graph describes the similarities -between a set of 2427 articles. A(i,j) is 0 if articles -i and j are not similar at all, 1 if they are extremely -similar. The similarity metric is based on LSA, co-citation, -and bibliographic coupling of the articles. Information -about each node is as follows: - -label: the title of the document (text) -year: year published (ranges from 2005 to 2014) (number) -source: journal (text) -authors: list of authors (text) -keywords: list of keywords separated by semicolons (text) -topics: list of keywords separated by semicolons (text) -abstract: (text) -times cited: (number)" -Mazaheri,1,"Sparse bundle adjustment problem from Mehdi Mazaheri, University of Calgary A is symmetric positive definite, coming from bundle adjustment of 1700 images",https://sparse.tamu.edu/Mazaheri,imaging/graphics/robotics,imaging/graphics/robotics,1.0,1,computer vision problem (1),1,real (1),1,symmetric (1),513351,513351,1.0,one size,20207907,20207907,square only,1,0,all square matrices symmetric/Hermitian,1,1,1,real only,0,1,single matrix,"Sparse bundle adjustment problem from Mehdi Mazaheri, University of Calgary - -A is symmetric positive definite, coming from bundle adjustment of 1700 images -to optimize the trajectory of a multi-camera data acqusition system. The -trajectory is a loop inside a building at the University of Calgary. - -Mehdi Mazaheri -Dept of Geomatics, University of Calgary" -VDOL,91,"Optimal control problems, Vehicle Dynamics & Optimization Lab, UF Anil Rao and Begum Senses, University of Florida",https://sparse.tamu.edu/VDOL,optimization/control,optimization/control,1.0,1,optimal control problem (91),1,real (91),1,symmetric (91),99,18476,186.62626262626262,wide (>100×),927,224897,square only,91,0,all square matrices symmetric/Hermitian,91,91,0,real only,0,0,coherent family/series,"Optimal control problems, Vehicle Dynamics & Optimization Lab, UF -Anil Rao and Begum Senses, University of Florida -http://vdol.mae.ufl.edu - -Each optimal control problem is described below. Each of these -problems gives rise to a sequence of matrices of different sizes -when they are being solved inside GPOPS, an optimal control -solver created by Anil Rao, Begum Senses, and others at in VDOL -lab at the University of Florida. The matrices are all -symmetric indefinite. - -Rao, Senses, and Davis have created a graph coarsening strategy -that matches pairs of nodes. The mapping is given for each matrix, -where map(i)=k means that node i in the original graph is mapped to -node k in the smaller graph. map(i)=map(j)=k means that both nodes -i and j are mapped to the same node k, and thus nodes i and j have -been merged. - -Each matrix consists of a set of nodes (rows/columns) and the -names of these rows/cols are given for each matrix. - -Anil Rao, Begum Sense, and Tim Davis, 2015. - ----------------------------------------------------------------------- -VDOL/dynamicSoaring ----------------------------------------------------------------------- - -Dynamic soaring optimal control problem is taken from -Ref.~\\cite{zhao2004optimal} where the dynamics of a glider is -derived using a point mass model under the assumption of a flat -Earth and stationary winds. The goal of the dynamic soaring -problem is to determine the state and the control that minimize -the average wind gradient slope that can sustain a powerless -dynamic soaring flight. The state of the system is defined by the -air speed, heading angle, air-realtive flight path angle, -altitude, and the position of the glider and the control of the -system is the lift coefficient. The specified accuracy tolerance -of $10^{-7}$ were satisfied after eight mesh iterations. As the -mesh refinement proceeds, the size of the KKT matrices increases -from 647 to 3543. - -@article{zhao2004optimal, - title={Optimal Patterns of Glider Dynamic Soaring}, - author={Zhao, Yiyuan J}, - journal={Optimal Control applications and methods}, - volume={25}, - number={2}, - pages={67--89}, - year={2004}, - publisher={Wiley Online Library} -} - - ----------------------------------------------------------------------- -VDOL/freeFlyingRobot ----------------------------------------------------------------------- - -Free flying robot optimal control problem is taken from -Ref.~\\cite{sakawa1999trajectory}. Free flying robot technology is -expected to play an important role in unmanned space missions. -Although NASA currently has free flying robots, called spheres, -inside the International Space Station (ISS), these free flying -robots have neither the technology nor the hardware to complete -inside and outside inspection and maintanance. NASA's new plan is to -send new free flying robots to ISS that are capable of completing -housekeeping of ISS during off hours and working in extreme -environments for the external maintanance of ISS. As a result, the -crew in ISS can have more time for science experiments. The current -free flying robots in ISS works are equipped with a propulsion -system. The goal of the free flying robot optimal control problem is -to determine the state and the control that minimize the magnitude of -thrust during a mission. The state of the system is defined by the -inertial coordinates of the center of gravity, the corresponding -velocity, thrust direction, and the anglular velocity and the control -is the thrust from two engines. The specified accuracy tolerance of -$10^{-6}$ were satisfied after eight mesh iterations. As the mesh -refinement proceeds, the size of the KKT matrices increases from 798 -to 6078. - - ----------------------------------------------------------------------- -VDOL/goddardRocketProblem ----------------------------------------------------------------------- - -Goddard rocket maximum ascent optimal control problem is taken from -Ref.~\\cite{goddard1920method}. The goal of the Goddard rocket maximum -ascent problem is to determine the state and the control that -maximize the final altitude of an ascending rocket. The state of the -system is defined by the altitude, velocity, and the mass of the -rocket and the control of the system is the thrust. The Goddard -rocket problem contains a singular arc where the continuous-time -optimality conditions are indeterminate, thereby the nonlinear -programming problem solver will have difficulty determining the -optimal control during the singular arc. In order to prevent this -difficulty and obtain more accurate solutions the Goddard rocket -problem is posed as a three-phase optimal control problem. Phase one -and phase three contains the same dynamics and the path constraints -as the original problem, while phase two contains an additional path -constraint and an event constraint. The specified accuracy tolerance -of $10^{-8}$ were satisfied after two mesh iterations. As the mesh -refinement proceeds, the size of the KKT matrices increases from 831 -to 867. - -@article{goddard1920method, - title={A Method of Reaching Extreme Altitudes.}, - author={Goddard, Robert H}, - journal={Nature}, - volume={105}, - pages={809--811}, - year={1920} -} - ----------------------------------------------------------------------- -VDOL/hangGlider ----------------------------------------------------------------------- - -Range maximization of a hang glider optimal control problem is taken -from Ref.~\\cite{bulirsch1993combining}. The goal of the optimal -control problem is to determine the state and the control that -maximize the range of the hang glider in the presence of a thermal -updraft. The state of the system is defined by horizontal distance, -altitude, horizontal velocity, and the vertical velocity and the -control is the lift coefficient. The specified accuracy tolerance of -$10^{-8}$ were satisfied after five mesh iterations. As the mesh -refinement proceeds, the size of the KKT matrices increases from 360 -to 16011. This problem is sensitive to accuracy of the mesh and it -requires excessive number of collocation points to be able to satisfy -the accuracy tolerance. Thus, the size of the KKT matrices changes -drastically. - -@book{bulirsch1993combining, - title={Combining Direct and Indirect Methods in Optimal Control: - Range Maximization of a Hang Glider}, - author={Bulirsch, Roland and Nerz, Edda and Pesch, Hans Josef and - von Stryk, Oskar}, - year={1993}, - publisher={Springer} -} - ----------------------------------------------------------------------- -VDOL/kineticBatchReactor ----------------------------------------------------------------------- - - ----------------------------------------------------------------------- -VDOL/lowThrust ----------------------------------------------------------------------- - -Low-thrust orbit transfer optimal control problem is taken from -Ref.~\\cite{betts2010practical}. The goal of the low-thrust orbit -transfer problem is to determine the state and the control that -minimize the fuel consumption during the orbit transfer of a -spacecraft that starts from a low-earth orbit and terminates at the -geostationary orbit via low-thrust propulsion systems. The highly -nonlinear dynamics of the low-thrust orbit transfer problem is given -in modified equinoctial elements (state of the system) and the thrust -direction (control of the system). Furthermore, the low-thrust -optimal control problem is a badly scaled problem because of the -small thrust-to-initial-mass ratio, that is typically on the order of -$O(10^{-4})$, and the long orbit transfer duration. Badly scaling of -the problem leads to a lot of delayed pivots. The specified accuracy -tolerance of $10^{-8}$ were satisfied after thirteen mesh iterations. -As the mesh refinement proceeds, the size of the KKT matrices -increases from 584 to 18476. - -@book{betts2010practical, - title={Practical Methods for Optimal Control and Estimation Using - Nonlinear Programming}, - author={Betts, John T}, - volume={19}, - year={2010}, - publisher={SIAM Press}, - address = {Philadelphia, Pennsylvania}, -} - - - ----------------------------------------------------------------------- -VDOL/orbitRaising ----------------------------------------------------------------------- - -Orbit raising problem that is taken from -Ref.~\\cite{bryson1975applied}. The goal of the optimal control -problem is to determine the state and the control that maximize the -radius of an orbit transfer in a given time. The state of the system -is defined by radial distance of the spacecraft from the attracting -center (e.g Earth, Mars, etc.) and velocity of the spacecraft and the -control is the thrust direction. The specified accuracy tolerance of -$10^{-8}$ were satisfied after four mesh iterations. As the mesh -refinement proceeds, the size of the KKT matrices increases from 442 -to 915. - -@book{bryson1975applied, - title={Applied Optimal Control: Optimization, Estimation, and - Control}, - author={Bryson, Arthur Earl}, - year={1975}, - publisher={CRC Press} -} - - - ----------------------------------------------------------------------- -VDOL/reorientation ----------------------------------------------------------------------- - -Minimum-time reorientation of an asymmetric rigid body optimal -control problem is taken from Ref.~\\cite{betts2010practical}. The -goal of the problem is to determine the state and the control that -minimize the time that is required to reorient a rigid body. The -state of the system is defined by quaternians that gives the -orientation of the spacecraft and the angular velocity of the -spacecraft and the control of the system is torque. The vehicle data -that is used to model the dynamics are taken from NASA X-ray Timing -Explorer spacecraft. The specified accuracy tolerance of $10^{-8}$ -were satisfied after eight mesh iterations. As the mesh refinement -proceeds, the size of the KKT matrices increases from 677 to 3108. - -@book{betts2010practical, - title={Practical Methods for Optimal Control and Estimation - Using Nonlinear Programming}, - author={Betts, John T}, - volume={19}, - year={2010}, - publisher={SIAM Press}, - address = {Philadelphia, Pennsylvania}, -} - ----------------------------------------------------------------------- -VDOL/spaceShuttleEntry ----------------------------------------------------------------------- - -Space shuttle launch vehicle reentry optimal control problem is taken -from Ref.~\\cite{betts2010practical}. The goal of the optimal control -problem is to determine the state and the control that maximize the -cross range (maximize the final latitude) during the atmospheric -entry of a reusable launch vehicle. State of the system is defined by -the position, velocity, and the orientation of the space shuttle and -the control of the system is the angle of attack and the bank angle -of the space shuttle. The specified accuracy tolerance of $10^{-8}$ -were satisfied after two mesh iterations. As the mesh refinement -proceeds, the size of the KKT matrices increases from 560 to 2450. - - ----------------------------------------------------------------------- -VDOL/spaceStation ----------------------------------------------------------------------- - -Space station attitude optimal control problem is taken from -Ref.~\\cite{betts2010practical}. The goal of the space station -attitude control problem is to determine the state and the control -that minimize the magnitude of the final momentum while the space -statition reaches an orientation at the final time that can be -maintained without utilizing additional control torque. The state of -the system is defined by the angular velocity of the spacecraft with -respect to an inertial reference frame, Euler-Rodriguez parameters -used to defined the vehicle attitude, and the angular momentum of the -control moment gyroscope and the control of the system is the torque. -The specified accuracy tolerance of $10^{-7}$ were satisfied after -thirteen mesh iterations. As the mesh refinement proceeds, the size -of the KKT matrices increases from 99 to 1640. - - ----------------------------------------------------------------------- -VDOL/tumorAntiogenesis ----------------------------------------------------------------------- - -Tumor anti-angiogenesis optimal control problem is taken from -Ref.~\\cite{ledzewicz2008analysis}. A tumor first uses the blood -vessels of its host but as the tumor grows oxygen that is carried by -the blood vessels of its host cannot defuse very far into the tumor. -Therefore, the tumor grows its own blood vessels by producing -vascular endothelial growth factor (VEGF). This process is called -angiogenesis. But blood vessels have a defense mechanism, called -endostatin, that tries to impede the development of new blood cells -by targeting VEGF. In addition, new pharmacological therapies that is -developed for tumor-type cancers also targets VEGF. The goal of the -tumor anti-angiogenesis problem is to determine the state and control -that minimizing the size of the tumor at the final time. The state of -the system is defined by the tumor volume, carrying capacity of a -vessel, and the total anti-angiogenic treatment administered and the -control of the system is the angiogenic dose rate. The specified -accuracy tolerance of $10^{-7}$ were satisfied after eight mesh -iterations. As the mesh refinement proceeds, the size of the KKT -matrices increases from 205 to 490. - -@article{ledzewicz2008analysis, - title={Analysis of Optimal Controls for a Mathematical Model of - Tumour Anti-Angiogenesis}, - author={Ledzewicz, Urszula and Sch{\\""a}ttler, Heinz}, - journal={Optimal Control Applications and Methods}, - volume=29, - number=1, - pages={41--57}, - year=2008, - publisher={Wiley Online Library} -}" -Meng,1,"Jacobian matrix from a computational chemistry problem, iChem, China' Lingyi Meng, Collaborative Innovation Center of Chemistry for",https://sparse.tamu.edu/Meng,chemistry/biology,chemistry/biology,1.0,1,computational chemistry problem (1),1,complex (1),1,unsymmetric (1),274087,274087,1.0,one size,4137369,4137369,square only,1,0,no square matrix exactly symmetric/Hermitian,0,0,0,complex only,0,0,single matrix,"Jacobian matrix from a computational chemistry problem, iChem, China' -Lingyi Meng, Collaborative Innovation Center of Chemistry for -Energy Materials (iChem), Xiamen University, Fujian, China, -www.2011-ichem.org. The matrix is complex, and has a sparse -and real right-hand-side. - -References: -Lingyi Meng, ChiYung Yam, SiuKong Koo, Quan Chen, Ngai Wong, and -GuanHua Chen, Dynamic Multiscale Quantum Mechanics/Electromagnetics -Simulation Method, J. of Chemical Theory and Computation, 2012, -vol 8, pp 1190-1199, dx.doi.org/10.1021/ct200859h - -Lingyi Meng, Zhenyu Yin, ChiYung Yam, SiuKong Koo, Quan Chen, -Ngai Wong, and GuanHua Chen, Frequency-domain multiscale quantum -mechanics/electromagnetics simulation method, J. of Chemical -Physics 139, 244111 (2013); doi: 10.1063/1.4853635" -Harvard_Seismology,1,"Earth's crust underneath Japan, Harvard Seismology Group. This matrix represents the forward operator of a linearized seismic",https://sparse.tamu.edu/Harvard_Seismology,imaging/graphics/robotics,imaging/graphics/robotics,1.0,1,tomography problem (1),1,real (1),1,rectangular (1),87616,87616,1.0,one size,13734559,13734559,rectangular only,0,1,N/A (rectangular only),0,0,0,real only,0,0,single matrix,"Earth's crust underneath Japan, Harvard Seismology Group. - -This matrix represents the forward operator of a linearized seismic -tomography problem for imaging Japan's crust and upper mantle. The -rows of the matrix correspond to 87,616 P-wave ray paths, from 125 -moderate size earthquakes (stars), deeper that ~150 km that occurred -the last decade, and recorded from seismic stations of the Hi-net -array (triangles). The 3-Dimensional space is parameterized using a -grid of 67,320 parameters that correspond to the columns of the matrix. - -The matrix is structurally and numerically rank deficient. This is -because some parts of the Earth are not imaged well from the data, -due to the lack of signals (earthquakes) and receivers (seismic stations -in those areas. This leads to an under-determined part of the system. -Other parts of the problem are overdetermined, because deep earthquakes -repeat in the same region of the Earth, over time, and the seismic -stations do not move. Repeated earthquakes from the same source -are recorded by the same seismic stations, leading to an over- -determined problem for those regions of the Earth. - -In MATLAB, cs_dmspy(Problem.A) displays these two parts of the matrix, -using a function from CSparse, available at http://suitesparse.com. - -Petros Bogiatzis, Miaki Ishii, Harvard Seismology Group, 2015 -http://www.seismology.harvard.edu - -References: - -Bogiatzis, P., & Ishii, M., 2014. Resolution Analysis and Jointly Inverted P- -and S-Wave Models of Japan's Crust and Upper Mantle. American Geophysical -Union, Fall Meeting 2014, S33A-4501." -Mycielski,19,"Mycielski graph adjacency matrices matrices/Mycielski/README. Scott Kolodziej, June 27, 2018.",https://sparse.tamu.edu/Mycielski,graph/network data,graph/network data,1.0,1,undirected graph (19),1,binary (19),1,symmetric (19),2,786431,393215.5,wide (>100×),2,2710370560,square only,19,0,all square matrices symmetric/Hermitian,19,19,0,real only,19,0,coherent family/series,"Mycielski graph adjacency matrices - -matrices/Mycielski/README. Scott Kolodziej, June 27, 2018. - -The Mycielskian graph sequence generates graphs that are triangle-free -and with a known chromatic number (i.e. the minimum number of colors -required to color the vertices of the graph). The sequence starts with a -2 vertex, 1 edge graph (denoted M2) and repeatedly generates subsequent -graphs by applying the Mycielskian operation. - -In MATLAB, the (sparse) Mycielskian operation can be written as follows: - -function M_new = mycielski(M) - [n,~] = size(M); - m = nnz(M); - M_new = sparse([], [], [], 2*n+1, 2*n+1, 3*m+n); - M_new(1:n, 1:n) = M; - M_new((n+1):2*n, :) = sparse([M sparse(n,n) ones(n,1)]); - M_new(:, (n+1):2*n) = sparse([M; sparse(n,n); ones(1,n)]); -end - -More abstractly, the structure of the Mycielskian operation can be represented -by the block matrix below: - n n 1 - _____ _____ _ - | | | | - n | M | M |0| - |_____|_____|_| - M_new = | | | | - n | M | 0 |1| - |_____|_____|_| - 1 | 0 | 1 |0| - ------------- - -Known properties of the Mycielskian operation include the following: - - * If a graph has a minimum chromatic number k, the Mycielskian of that graph - has a minimum chromatic number of k + 1. - * If a graph is triangle-free (i.e. no cycles of length 3 exist), so is the - Mycielskian of that graph. - * If a graph has a Hamiltonian cycle, so does the Mycielskian of that graph. - * If a graph is factor-critical, so is the Mycielskian of that graph, meaning - that every subgraph of |V|-1 vertices has a perfect matching. - -Mycielski graphs were first described by Jan Mycielski in the following -publication: - - Mycielski, J., 1955. Sur le coloriage des graphes. Colloq. Math., - 3: 161-162. - ---------------------------------------------------------------------------------" -MAWI,5,MAWI Working Group Traffic Archive ( The MAWI (Measurement and Analysis on the WIDE Internet) Working,https://sparse.tamu.edu/MAWI,graph/network data,graph/network data,1.0,1,undirected weighted graph (5),1,integer (5),1,symmetric (5),18571154,226196185,12.179974653163718,moderate (4–100×),38040320,480047894,square only,5,0,all square matrices symmetric/Hermitian,5,5,0,real only,0,0,coherent family/series,"MAWI Working Group Traffic Archive (http://mawi.wide.ad.jp/mawi/): -The MAWI (Measurement and Analysis on the WIDE Internet) Working -Group is a working group that has carried out network traffic -measurement, analysis, evaluation, and verification from the -beginning of the WIDE Project. The graphs provided here were -generated from packet trace data from the WIDE backbone maintained by -the MAWI Working Group. - -MAWI Datasets: Name, link, and Description - -Graph 1 Num. vertices : 18571154, Edge count : 38040320 - http://mawi.wide.ad.jp/mawi/ditl/ditl2015/201512012345.html - in 2018 GraphChallenge: 201512012345.v18571154_e38040320.tsv.gz - in the SuiteSparse Matrix Collection: MAWI/mawi_201512012345 - -Graph 2 Num. vertices : 35991342, Edge count : 74485420 - http://mawi.wide.ad.jp/mawi/ditl/ditl2015/201512020000.html - in 2018 GraphChallenge: 201512020000.v35991342_e74485420.tsv.gz - in the SuiteSparse Matrix Collection: MAWI/mawi_201512020000 - -Graph 3 Num. vertices : 68863315, Edge count : 143414960 - http://mawi.wide.ad.jp/mawi/ditl/ditl2015/201512020030.html - in 2018 GraphChallenge: 201512020030.v68863315_e143414960.tsv.gz - in the SuiteSparse Matrix Collection: MAWI/mawi_201512020030 - -Graph 4 Num. vertices : 128568730, Edge count : 270234840 - http://mawi.wide.ad.jp/mawi/ditl/ditl2015/201512020130.html - in 2018 GraphChallenge: 201512020130.v128568730_e270234840.tsv.gz - in the SuiteSparse Matrix Collection: MAWI/mawi_201512020130 - -Graph 5 Num. vertices : 226196185, Edge count : 480047894 - http://mawi.wide.ad.jp/mawi/ditl/ditl2015/201512020330.html - in 2018 GraphChallenge: 201512020330.v226196185_e480047894.tsv.gz - in the SuiteSparse Matrix Collection: MAWI/mawi_201512020330 - -These matrices appear in the 2018 GraphChallenge: -see https://graphchallenge.mit.edu/data-sets - -MAWILab is a database that assists researchers to evaluate their -traffic anomaly detection methods. It consists of a set of labels -locating traffic anomalies in the MAWI archive (samplepoints B and -F). The labels are obtained using an advanced graph-based methodology -that compares and combines different and independent anomaly -detectors. The data set is daily updated to include new traffic from -upcoming applications and anomalies. - -You may use WIDE traffic data for only research purposes. Actions -that trespass upon users'' privacy are prohibited. - -Guidelines for protecting user privacy in WIDE traffic archive: -http://mawi.wide.ad.jp/mawi/guideline.txt - -In the 2018 GraphChallenge data set (and thus also in the SuiteSparse -Matrix Collection), only the connectivity of the graph is provided, -with no user information at all on the nodes or edges. Privacy -guidelines are thus met. - -Related Papers: - -Kenjiro Cho. Recursive Lattice Search: Hierarchical Heavy Hitters -Revisited. ACM IMC 2017, London, UK, November 2017. - -Romain Fontugne, Patrice Abry, Kensuke Fukuda, Darryl Veitch, Kenjiro -Cho, Pierre Borgnat, Herwig Wendt. Scaling in Internet Traffic: a 14 -year and 3 day longitudinal study, with multiscale analyses and -random projections. IEEE/ACM Transactions on Networking, vol.25(4). -pp2152--2165. August 2017. - -Midori Kato, Kenjiro Cho, Michio Honda, Hideyuki Tokuda. Monitoring -the Dynamics of Network Traffic by Recursive Multi-dimensional -Aggregation. OSDI2012 MAD Workshop. Hollywood, CA. October 2012. - -R. Fontugne, P. Borgnat, P. Abry, K. Fukuda. ""MAWILab: Combining -diverse anomaly detectors for automated anomaly labeling and -performance benchmarking"". ACM CoNEXT 2010. Philadelphia, PA. -December 2010. - -Pierre Borgnat, Guillaume Dewaele, Kensuke Fukuda, Patrice Abry, -Kenjiro Cho. ""Seven Years and One Day: Sketching the Evolution of -Internet Traffic."" INFOCOM2009. Rio de Janeiro, Brazil. April 2009. - -Guilaume Dewaele, Kensuke Fukuda, Pierre Borgnat, Patrice Abry, -Kenjiro Cho. ""Extracting Hidden Anomalies using Sketch and Non -Gaussian Multiresolution Statistical Detection Procedures"" -SIGCOMM2007 LSAD Workshop, Kyoto Japan. August 2007.. - -Kenjiro Cho, Ryo Kaizaki and Akira Kato. ""Aguri: An -Aggregation-based Traffic Profiler"" In Proceedings of QofIS2001 -(published by Springer-Verlag in the LCNS series). September 2001. - -Kenjiro Cho, Koushirou Mitsuya and Akira Kato. ""Traffic Data -Repository at the WIDE Project"" USENIX 2000 FREENIX Track, San Diego, -CA, June 2000. - -Akira KATO, Jun MURAI, Satoshi KATSUNO and Tohru ASAMI. ""An Internet -Traffic Data Repository: The Architecture and the Design Policy"" -INET99, San Jose, CA, June 1999. - -MAWI data set imported into the SuiteSparse Matrix Collection on July -2018, from the 2018 GraphChallenge data set" -GenBank,5,Protein k-mer graphs generated using data from GenBank are available below.,https://sparse.tamu.edu/GenBank,graph/network data,graph/network data,1.0,1,undirected graph (5),1,binary (5),1,symmetric (5),55042369,214005017,3.888005201956333,narrow (≤4×),117217600,465410904,square only,5,0,all square matrices symmetric/Hermitian,5,5,0,real only,5,0,coherent family/series,"Protein k-mer graphs generated using data from GenBank -https://www.ncbi.nlm.nih.gov/genbank/ are available below. -Nodes of the graph represent segments of amino acids. - -Protein k-mer graphs -Name Description -A2a.tsv Graph 1 Num. vertices : 170728175 Edge count : 360585172 -P1a.tsv Graph 2 Num. vertices : 139353211 Edge count : 297829984 -U1a.tsv Graph 3 Nun. vertices : 67716231 Edge count : 138778562 -V1r.tsv Graph 4 Num. vertices : 214005017 Edge count : 465410904 -V2a.tsv Graph 5 Num. vertices : 55042369 Edge count : 117217600 - -From the 2018 GraphChallenge, https://graphchallenge.mit.edu. -Imported into the SuiteSparse Matrix Collection as ""GenBank/kmer_*"" -where ""*"" is the 3-letter name above, on July 2018." -Sybrandt,2,Sybrandt: machine learning graphs from document analysis ----------------------------------------------------------------------,https://sparse.tamu.edu/Sybrandt,graph/network data,graph/network data,1.0,2,undirected weighted graph (1); undirected graph (1),2,real (1); binary (1),1,symmetric (2),30239687,183964077,6.083531122527822,moderate (4–100×),6669254694,11588725964,square only,2,0,all square matrices symmetric/Hermitian,2,2,0,real only,1,0,one broad domain; mixed matrix forms,"Sybrandt: machine learning graphs from document analysis - ----------------------------------------------------------------------- -MOLIERE Hypothesis Generation Network, J. Sybrandt, Clemson Univ. ----------------------------------------------------------------------- - -Justin Sybrandt, jsybran at clemson.edu -Matrix Name: MOLIERE_2016 Hypothesis Generation Network -Kind/Problem Domain: Term/Document Graph - -This network was presented in ""MOLIERE: Automatic Biomedical Hypothesis -Generation System (KDD''17)."" Nodes in this network are either MEDLINE -documents, UMLS terms, or n-grams we extracted through ToPMine. - -citation: -Justin Sybrandt (Clemson University, Clemson, SC, USA), -Michael Shtutman (University of South Carolina, Columbia, SC, USA) -Ilya Safro (Clemson University, Clemson, SC, USA), -""MOLIERE: Automatic Biomedical Hypothesis Generation System"", -23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining -August 13-17, 2017, Halifax, Nova Scotia - Canada -https://doi.org/10.1145/3097983.3098057 - -The original data was 0-based with nodes numbered 0 to n-1. Converted -to 1-based for the SuiteSparse Matrix Collection, July 2018. - -The graph has n=30,239,687 nodes. Node labels are held in the char array -Problem.aux.labels. Nodes 1 to 22,281,874 all have labels starting with -the capital letter ""P"", and are PubMed indentification numbers of the -MEDLINE documents. Nodes 27,683,534 to n all start with the letter ""C"", -and refer to UMLS terms. Nodes between these two sets of nodes refer to -n-grams; none of them start with P or C (or any capital letter). The -label of the kth node is also the kth line in the labels text file, and -to the kth row and column of the matrix. - -The graph has 4,023,336 explicit zero edges, which are very important -to the problem. They link automatically mined n-grams to UMLS terms -representing the same concept. The goal is to find shortest-paths, -so an edge of zero-length is important, and not the same as no edge -at all. The pattern of the explicit zeros is held in Problem.Zeros -in the MATLAB representation, and are part of the files for the Matrix -Market and Rutherford-Boeing formats. To operate on the graph G in -MATLAB, use G = Problem.A + 1e-100 * Problem.Zeros, or some other -suitable tiny value. The nonzero edge weights in the graph range in -value between 5e-15 and 3.0. There are no negative edge weights. - -The graph also has d=3,106,164 duplicate edges; it could be considered -a multigraph. However, since the problem is to find shortest paths, the -duplicate edges are not needed. The matrix in the SuiteSparse Matrix -Collection holds the smallest edge weight for any duplicate. The -duplicates not in the matrix are held in Problem.aux.duplicate_edges, -as a d-by-3 dense matrix, where each row holds [i j eij] for the edge -(i,j) with weight eij. - ----------------------------------------------------------------------- -Sybrandt/AGATHA_2015: deep-learning graph ----------------------------------------------------------------------- - -AGATHA: Automatic Graph-mining And Transformer based Hypothesis -Generation Approach - -Justin Sybrandt, Ilya Tyagin, Michael Shtutman, Ilya Safro -Clemson University -paper: https://arxiv.org/abs/2002.05635. - -abstract: Medical research is risky and expensive. Drug discovery, as -an example, requires that researchers efficiently winnow thousands of -potential targets to a small candidate set for more thorough -evaluation. However, research groups spend significant time and money -to perform the experiments necessary to determine this candidate set -long before seeing intermediate results. Hypothesis generation systems -address this challenge by mining the wealth of publicly available -scientific information to predict plausible research directions. We -present AGATHA, a deep-learning hypothesis generation system that can -introduce data-driven insights earlier in the discovery process. -Through a learned ranking criteria, this system quickly prioritizes -plausible term-pairs among entity sets, allowing us to recommend new -research directions. We massively validate our system with a temporal -holdout wherein we predict connections first introduced after 2015 -using data published beforehand. We additionally explore biomedical -sub-domains, and demonstrate AGATHA's predictive capacity across the -twenty most popular relationship types. This system achieves -best-in-class performance on an established benchmark, and demonstrates -high recommendation scores across subdomains. Reproducibility: All -code, experimental data, and pre-trained models are available online: -http://sybrandt.com/2020/agatha . - -Appears in the 29TH ACM Intl. Conf. on Information and Knowledge -Management, Oct 2020. https://www.cikm2020.org/ - -Details of this matrix: - -This matrix represents the semantic graph associated with the Agatha -hypothesis generation system: https://arxiv.org/abs/2002.05635. - -The matrix was created by selecting all biomedical papers published -prior to January 1st 2015. We extracted terms, phrases, entities, and -author-supplied metadata keywords for each. In addition we identify all -SemRep predicate arguments. Each sentence, term, phrase, entity, and -predicate represents a node. Edges follow a particular schema, -described in detail in the paper. Most edges indicate that two nodes -co-occur. For instance, if a sentence contains a term, then an edge -exists between the two nodes. We introduce a set of edges between -sentences based on the nearest-neighbors network of sentence -embeddings. - -The graph is really big, consisting of 183,964,077 nodes and -11,588,725,964 entries (half that many edges since the matrix is -symmetric). - -Each node has a label, consisting of a -character string, with a length ranging from 0 to 782 characters. -The mean string lenth is 17.4. A single node (171,649,101 in 1-based -notation) has a label of length zero, and is an artifact from how the -original data was processed. All other node labels range in length -from 3 to 782. The longest string comes from the following paper: -https://pubmed.ncbi.nlm.nih.gov/183954/ which states: ""The primary -structure of the enzyme was determined: Ac-Met-Glu-...--Ala-Leu-Lys."" - -To save space in the MATLAB representation, the node labels are held -in a single character array, Problem.aux.names, where each label is -terminated with a newline character. To extract the label of any -given node, do the following: - - names = Problem.aux.names ; - p = [1 find(names==10)+1] ; - label = names (p(i):p(i+1)-2) ; - -For example, to list all nodes with labels of length 0 to 4: - - p = [1 find(names==10)+1] ; - d = diff (p) ; - for len = 0:4 - fprintf ('\\nnodes with labels of length %d:\\n', len) ; - nodes = find (d == len+1) ; - for i = nodes - fprintf ('%12d: [%s]\\n', i, names (p(i):p(i+1)-2)) ; - end - end" -Guettel,3,"3D Transient Electromagnetic Modelleing, Stefan Guettel, Univ Manchester The TEM problem relates to the time-dependent modelling of a transient",https://sparse.tamu.edu/Guettel,electromagnetics/acoustics,electromagnetics/acoustics,1.0,1,electromagnetics problem (3),1,real (3),1,symmetric (3),27623,181302,6.563443507222242,moderate (4–100×),439469,7839010,square only,3,0,all square matrices symmetric/Hermitian,3,3,0,real only,0,3,coherent family/series,"3D Transient Electromagnetic Modelleing, Stefan Guettel, Univ Manchester - -The TEM problem relates to the time-dependent modelling of a transient -electromagnetic field in geophysical exploration. The set contains a -matrix pencil (C,M) and an initial value vector q, corresponding to a -Nedelec finite element discretization of Maxwell's equations for the -electric field density e(t). The curl-curl matrix C is symmetric -positive semi-definite and the mass matrix M is symmetric positive -definite. The problem to be solved is a linear initial value problem - - M*e'(t) = C*e(t), M*e(0) = q, - -for logarithmically distributed time points t in the interval -[1e-6,1e-3]. - -There are three test sets which are described in the following -publication: - -@article{BEG2015, - title={Three-dimensional transient electromagnetic modelling using - rational {K}rylov methods}, - author={B{\\""o}rner, Ralph-Uwe and Ernst, Oliver G and G{\\""u}ttel, - Stefan}, - journal={Geophysical Journal International}, - volume={202}, - number={3}, - pages={2025--2043}, - year={2015}, - publisher={Oxford University Press} -} - -The problem details are - -TEM27623: section 5.1 in the above paper; layered half-space problem -discretized by 24582 tetrahedral elements of order 1 giving rise to -27623 degrees of freedom. - -TEM152078: section 5.1 in the above paper; layered half-space problem -discretized by 24582 tetrahedral elements of order 2 giving rise to -152078 degrees of freedom. - -TEM181302: section 5.2 in the above paper; homogeneous subsurface with -topography discretized by 28849 tetrahedral elements of order 2 giving -rise to 181302 degrees of freedom." -Grueninger,2,"Wind tunnel evaporation, Christoph Grueninger, Univ. Stuttgart Source: Christoph Grueninger, pr at grueninger.de",https://sparse.tamu.edu/Grueninger,fluids/transport,fluids/transport,1.0,1,computational fluid dynamics problem (2),1,real (2),1,unsymmetric (2),8256,40816,4.9437984496124034,moderate (4–100×),109368,803978,square only,2,0,no square matrix exactly symmetric/Hermitian,0,2,0,real only,0,2,coherent family/series,"Wind tunnel evaporation, Christoph Grueninger, Univ. Stuttgart - -Source: Christoph Grueninger, pr at grueninger.de -Stuttgart Research Centre for Simulation Technology, -Univ. of Stuttgart, Germany - -Matrix files: windtunnel-evaporation-(2|3)d-time4-newton1.mat -Right-hand sides: windtunnel-evaporation-(2|3)d-time4-newton1-rhs.mat - -== Background == -The matrix origins from a simulation of soil-water evaporation from a -water-filled sand box to air in a pipe. It is based on a coupled -Navier-Stokes/Darcy model. The Navier-Stokes model features one fluid -phase, the Darcy model two fluid phases. Each fluid phase may be composed -of two components, in addition, non-isothermal processes are considered. -A cell-centered finite volume method (FVM) is combined with a marker and -cell (MAC) scheme. The coupled problem is compiled in one monolithic -system and solved using Newton's method. The given matrix occurs at the -4th time-step in the 1st Newton step. - -== Publication and software== -The related model and the software is described in: - Christoph Gr\\""uninger, Thomas Fetzer, Bernd Flemisch, Rainer Helmig: - ""Coupling DuMuX and DUNE-PDELab to investigate evaporation - at the interface between Darcy and Navier-Stokes flow"" - SimTech Technical Report 2017 - 1, 2017. - doi: 10.18419/opus-9360 - https://elib.uni-stuttgart.de/handle/11682/9377?mode=simple - -abstract: -An implementation of a coupled Navier-Stokes/Darcy model based on -different Dune discretization modules is presented. The Darcy model is -taken from DuMuX, the Navier-Stokes model is implemented on top of -Dune-PDELab, and the coupling is done with help of Dune-MultiDomain -together with some project-specific auxiliary code. The Navier-Stokes -model features one fluid phase, the Darcy model two fluid phases. Each -fluid phase may be composed of two components, in addition, -non-isothermal processes are considered. The coupling between free and -porous-medium flow uses a sharp interface between both subdomains and -conserves mass, momentum, and energy by accounting for the corresponding -fluxes across the interface. A cell-centered finite volume method (FVM) -is combined with a marker and cell (MAC) scheme. It solves the coupled -problem in one monolithic system using a Newton method and a direct -linear solver. Numerical results demonstrate the basic functioning and a -lab-scale reference application. - -The software to reproduce the matrices is available as open source: -https://git.iws.uni-stuttgart.de/dumux-pub/Grueninger2017b" -PowerSystem,1,Power system matrix,https://sparse.tamu.edu/PowerSystem,electrical/circuit/device,electrical/circuit/device,1.0,1,power network problem (1),1,real (1),1,unsymmetric (1),197156,197156,1.0,one size,805839,805839,square only,1,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,0,single matrix,Power system matrix -TAMU_SmartGridCenter,3,Texas A&M Smart Grid Center: synthetic (yet realistic) power system grids Group name: TAMU_SmartGridCenter,https://sparse.tamu.edu/TAMU_SmartGridCenter,electrical/circuit/device,electrical/circuit/device,1.0,1,power network problem (3),1,real (3),2,unsymmetric (2); symmetric (1),4000,69999,17.49975,moderate (4–100×),28505,238627,square only,3,0,mixed among square matrices,1,3,0,real only,0,0,one broad domain; mixed matrix forms,"Texas A&M Smart Grid Center: synthetic (yet realistic) power system grids - -Group name: TAMU_SmartGridCenter - -Synthetic electric grid models are fictitious representations that are -designed to be statistically and functionally similar to actual electric -grids while containing no confidential critical energy infrastructure -information (CEII). Some of these cases were developed with the support -of the U.S. DOE ARPA-E Grid Data program; their support is gratefully -acknowledged. - -For more information and full datasets, see: -https://electricgrids.engr.tamu.edu/electric-grid-test-cases/ - -A. B. Birchfield; T. Xu; K. M. Gegner; K. S. Shetye; T. J. Overbye, ""Grid -Structural Characteristics as Validation Criteria for Synthetic Networks,"" -in IEEE Transactions on Power Systems, vol. 32, no. 4, pp. 3258-3265, July -2017. - -A. B. Birchfield, E. Schweitzer, H. Athari, T. Xu, T. J. Overbye, A. -Scaglione, and Z.Wang, ""Validation metrics to assess the realism of -synthetic power grids,"" Energies, vol. 10, no. 8, p. 1233, Aug. 2017. - -A. B. Birchfield, T. Xu, and T. J. Overbye, ""Power flow convergence and -reactive power planning in the creation of large synthetic grids,"" in IEEE -Transactions on Power Systems, 2018. - ----------------------------------------------------------------------- -Matrix: ACTIVSg2000 - -Title: Power flow Jacobian for a synthetic 2000-bus power grid - -Description: -ACTIVSg2000 is a 2000-bus power system dataset that is entirely synthetic, -built from public information and a statistical analysis of real power -systems. Its geographic footprint is Texas, but it bears no relation to -any actual grid, except that generation and load profiles are similar, -based on public data. This matrix is the power flow Jacobian for the peak -planning operating point. - -The full dataset can be downloaded at: -https://electricgrids.engr.tamu.edu/electric-grid-test-cases/activsg2000/ - ----------------------------------------------------------------------- -Matrix: ACTIVSg10K -Title: Power flow Jacobian for a synthetic 10,000-bus power grid -Description: - -ACTIVSg10K is a 10,000-bus power system dataset that is entirely -synthetic, built from public information and a statistical analysis of -real power systems. Its geographic footprint is the Western United -States, but it bears no relation to any actual grid, except that -generation and load profiles are similar, based on public data. This -matrix is the power flow Jacobian for the peak planning operating point. - -The full dataset can be downloaded at: -https://electricgrids.engr.tamu.edu/electric-grid-test-cases/activsg10K/ - ----------------------------------------------------------------------- -Matrix: ACTIVSg70K -Title: Linearized or ""dc"" power flow susceptance matrix for a synthetic -70,000-bus power grid - -Description: -ACTIVSg70K is a 70,000-bus power system dataset that is entirely -synthetic, built from public information and a statistical analysis of -real power systems. Its geographic footprint is the Eastern United -States, but it bears no relation to any actual grid, except that -generation and load profiles are similar, based on public data. This -matrix is the linearized or ""dc"" power flow susceptance matrix. - -The full dataset can be downloaded at: -https://electricgrids.engr.tamu.edu/electric-grid-test-cases/activsg70K/" -Embree,1,"Fluid stability problem from IFISS package, Mark Embree, Virginia Tech The matrix comes from a shift-invert eigenvalue computation that arises",https://sparse.tamu.edu/Embree,fluids/transport,fluids/transport,1.0,1,computational fluid dynamics (1),1,real (1),1,unsymmetric (1),96307,96307,1.0,one size,3599932,3599932,square only,1,0,no square matrix exactly symmetric/Hermitian,0,1,0,real only,0,1,single matrix,"Fluid stability problem from IFISS package, Mark Embree, Virginia Tech - -The matrix comes from a shift-invert eigenvalue computation that arises -during linear stability analysis of a 2D backward facing step. The -discretizations were created with IFISS, which we also use to compute the -steady-state flow whose stability we are assessing. - -Created by the IFISS package, by David Silvester (School of Mathematics, -Univ. of Manchester), Howard Elman (Computer Science, Univ. of Maryland), -and Alison Ramage (Dept. of Mathematics and Statistics, Univ. of -Strathclyde). http://www.maths.manchester.ac.uk/~djs/ifiss/ - -This matrix requires one step of iterative refinement after LU -factorization. x=A\\b in MATLAB (using UMFPACK) does iterative refinement -by default but with just lu, no iterative refinement is done: - - In MATLAB R2014a: - x = randn(96307,1); - b = A*x; - x1 = A\\b; - norm(x-x1) is 1.1045984e-12 - norm(b-A*x1) is 9.3538018e-15 - - [L,U,P,Q,R] = lu(A); - x2 = Q*(U\\(L\\(P*(R\\b)))); - norm(x-x2) is 4.9150874e-05 - norm(b-A*x2) is 4.0055911e-07 - -The matrix is well conditioned, with singular values ranging from -1.003 to 2e-5. The singluar values themselves are in -Problem.aux.singular_values. The need for iterative refinement -comes from the threshold partial pivoting in UMFPACK, which tries -to balance reduction in fill with finding good numerical pivots. -Thus UMFPACK uses iterative refinement with sparse backward error using -the method described in Arioli, Demmel, and Duff, ""Sovling sparse linear -systems with sparse backward error"", SIAM J. Matrix Analysis & Applic, -vol 10, no 2, pp 165-190, Apr 1989). - -The matrix itself is described in this paper: - -https://arxiv.org/abs/1601.00044 - -Pseudospectra of Matrix Pencils for Transient Analysis of -Differential-Algebraic Equations - -Mark Embree, Blake Keeler -(Submitted on 1 Jan 2016 (v1), -last revised 27 Jun 2017 (this version, v3)) - -To understand the solution of a linear, time-invariant -differential-algebraic equation, one must analyze a matrix pencil (A,E) -with singular E. Even when this pencil is stable (all its finite -eigenvalues fall in the left-half plane), the solution can exhibit -transient growth before its inevitable decay. When the equation results -from the linearization of a nonlinear system, this transient growth gives -a mechanism that can promote nonlinear instability. One might hope to -enrich the conventional large-scale eigenvalue calculation used for -linear stability analysis to signal the potential for such transient -growth. Toward this end, we introduce a new definition of the -pseudospectrum of a matrix pencil, use it to bound transient growth, -explain how to incorporate a physically-relevant norm, and derive -approximate pseudospectra using the invariant subspace computed in -conventional linear stability analysis. We apply these tools to several -canonical test problems in fluid mechanics, an important source of -differential-algebraic equations." -Hardesty,3,"Surface fitting problem for visualization, Sean Hardesty Visualization of 3D structures in the earth",https://sparse.tamu.edu/Hardesty,imaging/graphics/robotics,imaging/graphics/robotics,1.0,1,computer graphics/vision problem (3),2,real (2); integer (1),2,rectangular (2); symmetric (1),929901,8217820,8.837306336911134,moderate (4–100×),4020731,40451632,mixed,1,2,all square matrices symmetric/Hermitian,1,1,0,real only,0,3,one broad domain; mixed matrix forms,"Surface fitting problem for visualization, Sean Hardesty - -Visualization of 3D structures in the earth - -The Hardesty3 matrix is an interpolation matrix stacked above a -weighted Laplacian, to to fit a surface z(x,y) to a set of points -in R^3 subject to a smoothness constraint enforced via regularization. -Hardesty2 is a smaller version of this problem. - -For the big matrix (Hardesty/Hardesty3), sparse QR (via SuiteSparseQR, -or SPQR) finds an R factor and a set of Householder vectors (Q.H) with -about 150 million nonzeros. Sparse LU factorization (with UMFPACK -v5.7.1) sees very high fillin (about 2.5 billion nonzeros in L+U). - -The Hardesty1 matrix is a simple discretization of a 2D biharmonic -operator with some Lagrange multiplier constraints used for smoothing." -VLSI,14,VLSI: semiconductor device and process simulation Process and Device Simulation matrices: nv1 and nv2 are very ill-conditioned.,https://sparse.tamu.edu/VLSI,electrical/circuit/device,electrical/circuit/device,1.0,2,semiconductor device problem (8); semiconductor process problem (6),1,real (14),1,unsymmetric (14),46994,11449533,243.63818785376856,wide (>100×),845089,349321980,square only,14,0,no square matrix exactly symmetric/Hermitian,0,8,0,real only,0,14,coherent family/series,"VLSI: semiconductor device and process simulation - -Process and Device Simulation matrices: nv1 and nv2 are very ill-conditioned. -Device simulation matrices in this set: - nv1, nv2, imagesensor, mosfet2, power9, radiation, test1, dgreen -Process simulation matrices in this set: - vas_stokes_1M, vas_stokes_2M, vas_stokes_4M, stokes, ss1, ss - -Source: these are real matrices from industry. The submittor -requested that their name and company be withheld. - -Added to the collection in January 2019." -Martin,1,"Martin/marine1: chemical oceanography; a marine nitrogen cycle inverse model A matrix submitted by Taylor Martin, Stanford, discussed the following paper:",https://sparse.tamu.edu/Martin,fluids/transport,fluids/transport,1.0,1,chemical oceanography problem (1),1,real (1),1,unsymmetric (1),400320,400320,1.0,one size,6226538,6226538,square only,1,0,no square matrix exactly symmetric/Hermitian,0,0,0,real only,0,0,single matrix,"Martin/marine1: chemical oceanography; a marine nitrogen cycle inverse model - -A matrix submitted by Taylor Martin, Stanford, discussed the following paper: - -Title: Modeling oceanic nitrite concentrations and isotopes using a 3D -inverse N cycle model -Authors: Taylor S. Martin(1), Francois Primeau(2), and Karen L. Casciotti(1) -(1) Stanford University, Department of Earth System Science -(2) University of California, Irvine, Department of Earth System Science -Received: 05 Sep 2018 -Abstract. Nitrite (NO2-) is a key intermediate in the marine nitrogen (N) cycle -and a substrate in nitrification, which produces nitrate (NO3-), as well as -water column N loss processes, denitrification and anammox. In models of the -marine N cycle, NO2- is often not considered as a separate state variable, -since NO3- occurs in much higher concentrations in the ocean. In oxygen -deficient zones (ODZs), however, NO2- represents a substantial fraction of the -bioavailable N, and modeling its production and consumption is important to -understanding the N cycle processes occurring there, especially those where -bioavailable N is lost from or retained within the water column. Here we -present the expansion of a global 3D inverse N cycle model to include NO2- as a -reactive intermediate as well as the processes that produce and consume NO2- in -marine ODZs. NO2- accumulation in ODZs is accurately represented by the model -involving NO3- reduction, NO2- reduction, NO2- oxidation, and anammox. We model -both 14N and 15N and use a compilation of oceanographic measurements of NO3- -and NO2- concentrations and isotopes to place a better constraint on the N -cycle processes occurring. The model is optimized using a range of isotope -effects for denitrification and NO2- oxidation, and we find that the larger -(more negative) inverse isotope effects for NO2- oxidation along with -relatively high rates of NO2- oxidation give a better simulation of NO3- and -NO2- concentrations and isotopes in marine ODZs. - -How to cite: Martin, T. S., Primeau, F., and Casciotti, K. L.: Modeling -oceanic nitrite concentrations and isotopes using a 3D inverse N cycle model, -Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-397, in review, 2018." -Negre,1,"Negre: phenyl dendrimer density matrix, for graph partitioning The phenyl dendrimer structure is composed of 22 covalently bonded",https://sparse.tamu.edu/Negre,chemistry/biology,chemistry/biology,1.0,1,computational chemistry problem (1),1,real (1),1,symmetric (1),730,730,1.0,one size,63024,63024,square only,1,0,all square matrices symmetric/Hermitian,1,1,0,real only,0,0,single matrix,"Negre: phenyl dendrimer density matrix, for graph partitioning - -The phenyl dendrimer structure is composed of 22 covalently bonded -phenyl groups. The density matrix hereby uploaded accounts for the -connectivity of 730 orbitals. This matrix has been used as a substrate -for implementing a graph-partitioning method using the D-wave quantum -annealer. See: https://dl.acm.org/citation.cfm?id=3149531 - -Christian F. A. Negre, Los Alamos National Lab, cnegre@lanl.gov - -Graph Partitioning Using Quantum Annealing on the D-Wave System, -H. Ushijima-Mwesigwa, C. F. A. Negre, and S. M. Mniszewski, -Proc. of 2nd Intl. Workshop on Post Moore's Era Supercomputing, -(PMES17) 2017, Denver, CO, USA, 22--29, ACM. - -Abstract: -Graph partitioning (GP) applications are ubiquitous throughout -mathematics, computer science, chemistry, physics, bio-science, machine -learning, and complex systems. Post Moore's era supercomputing has -provided us an opportunity to explore new approaches for traditional -graph algorithms on quantum computing architectures. In this work, we -explore graph partitioning using quantum annealing on the D-Wave 2X -machine. Motivated by a recently proposed graph-based electronic -structure theory applied to quantum molecular dynamics (QMD) simulations, -graph partitioning is used for reducing the calculation of the density -matrix into smaller subsystems rendering the calculation more -computationally efficient. Unconstrained graph partitioning as community -clustering based on the modularity metric can be naturally mapped into -the Hamiltonian of the quantum annealer. On the other hand, when -constraints are imposed for partitioning into equal parts and minimizing -the number of cut edges between parts, a quadratic unconstrained binary -optimization (QUBO) reformulation is required. This reformulation may -employ the graph complement to fit the problem in the Chimera graph of -the quantum annealer. Partitioning into 2 parts and k parts concurrently -for arbitrary k are demonstrated with benchmark graphs, random graphs, -and small material system density matrix based graphs. Results for graph -partitioning using quantum and hybrid classical-quantum approaches are -shown to be comparable to current ""state of the art"" methods and -sometimes better." -Precima,1,"Precima: data analytics problem Submitted by Carl Ugaz, Precima, Inc. CUgaz@precima.com",https://sparse.tamu.edu/Precima,data analytics,data analytics,1.0,1,data analytics problem (1),1,real (1),1,symmetric (1),303813,303813,1.0,one size,2006126,2006126,square only,1,0,all square matrices symmetric/Hermitian,1,1,0,real only,0,0,single matrix,"Precima: data analytics problem - -Submitted by Carl Ugaz, Precima, Inc. CUgaz@precima.com - -The matrix is nonsingular, ill-conditioned,, and symmetric indefinite. -If factorized with KLU, best results are obtained if the permutation to -block triangular form (BTF) is not used, because it breaks the symmetry -of the pattern. - -The matrix is nonsingular, and symmetric indefinite. If factorized with -KLU, best results are obtained if the permutation to block triangular -form (BTF) is not used, because it breaks the symmetry of the pattern. - -Ignoring the diagonal (which has some zero entries and some nonzero) -the undirected graph of A divides into 41,422 connected components. -Of those, 40,966 are single nodes, and 451 components have between -4 and 90 nodes. The remaining 5 components have 93738, 89686, 40319, -15884, and 4289 nodes, respectively." -GAP,5,"GAP Benchmark matrices: Scott Beamer, Krste Asanovic', and David Patterson. as described in ""The GAP Benchmark Suite"", .",https://sparse.tamu.edu/GAP,graph/network data,graph/network data,1.0,2,directed weighted graph (3); random undirected weighted graph (2),1,integer (5),2,symmetric (3); unsymmetric (2),23947347,134217728,5.604701347502084,moderate (4–100×),57708624,4294966740,square only,5,0,mixed among square matrices,3,3,0,real only,0,0,one broad domain; mixed matrix forms,"GAP Benchmark matrices: Scott Beamer, Krste Asanovic', and David Patterson. -as described in ""The GAP Benchmark Suite"", https://arxiv.org/abs/1508.03619 . - -(1) GAP-twitter (|V|=61.6M, |E|=1,468.4M, directed) is an example of a - social network topology [18]. This particular crawl of Twitter - has been commonly used by researchers and thus eases comparisons - with prior work. By virtue of it coming from real-world data, it - has interesting irregularities and the skew in its degree - distribution can be a challenge for some implementations. - - [18] Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. - What is Twitter, a social network or a news media? International - World Wide Web Conference (WWW), 2010. - - A permuted version of this matrix appears as SNAP/twitter7 in - the SuiteSparse Matrix Collection. - -(2) GAP-web (|V|=50.6M, |E|=1,949.4M, directed) is a web-crawl of the - .sk domain (sk-2005) [9]. Despite its large size, it exhibits - substantial locality due to its topology and high average - degree. - - The matrix comes from the Laboratory for Web Algorithmics (LAW), Universita - degli Studi di Milano, http://law.di.unimi.it/index.php. - - The pattern of this GAP-web matrix also appears as LAW/sk-2005, in the - SuiteSparse Matrix Collection. - -(3) GAP-road (|V|=23.9M, |E|=58.3M, directed) is the distances of all of - the roads in the USA [10]. Although it is substantially smaller - than the rest of the graphs, it has a high diameter which can - cause some synchronous implementations to have long runtimes. - - [10] 9th DIMACS implementation challenge -- shortest paths. - http://www.dis.uniroma1.it/challenge9/, 2006. - - The pattern of the GAP-road matrix also appears as DIMACS10/road_usa - in the SuiteSparse Matrix Collection. - -(4) GAP-kron (|V|=134.2M, |E|=2,111.6M, undirected) uses the Kronecker - synthetic graph generator [19] with the same parameters as - Graph 500 (A=0.57, B=C=0.19, D=0.05) [14]. It has been used - frequently in research due to Graph 500, so it also provides - continuity with prior work. - - [19] Jurij Leskovec, Deepayan Chakrabarti, Jon Kleinberg, and - Christos Faloutsos. Realistic, mathematically tractable graph - generation and evolution, using Kronecker multiplication. - European Conference on Principles and Practice of Knowledge - Discovery in Databases, 2005. - - [14] Graph500 benchmark. www.graph500.org. - -(5) GAP-urand (|V|=134.2M, |E|=2,147.4M, undirected) is synthetically - generated by the Erdos– Reyni model (Uniform Random) [11]. With - respect to locality, it represents the worst case as every - vertex has equal probability of being a neighbor of every other - vertex. When contrasted with the similarly sized kron graph, it - demonstrates the impact of kron’s scale-free property. - - [11] Paul Erdos and Alfred Reyni. On random graphs. I. - Publicationes Mathematicae, 6:290–297, 1959." -ML_Graph,36,"ML_Graph: adjacency matrices from machine learning datasets, Olaf Schenk. The following adjacency matrices were used in the numerical experiments in:",https://sparse.tamu.edu/ML_Graph,graph/network data,graph/network data,1.0,1,undirected weighted graph (36),1,real (36),1,symmetric (36),106,38547,363.6509433962264,wide (>100×),1412,618158,square only,36,0,all square matrices symmetric/Hermitian,36,36,0,real only,0,0,coherent family/series,"ML_Graph: adjacency matrices from machine learning datasets, Olaf Schenk. - -The following adjacency matrices were used in the numerical experiments in: - -D. Pasadakis, C.L. Alappat, O. Schenk, and G. Wellein, ""Multiway p-spectral -graph cuts on Grassmann manifolds,"" Machine Learning 111, 791–829, 2022, -https://doi.org/10.1007/s10994-021-06108-1 - -------------------------------------------------------------------------------- -Graph Creation -------------------------------------------------------------------------------- - -For all the graphs in the folder, for $n$ data points, the connectivity matrix -$G \\in \\mathbb{R}^{n\\times n}$ is created from a k nearest neighbors routine, -with k set such that the resulting graph is connected. The similarity matrix -$S \\in \\mathbb{R}^{n\\times n}$ between the data points is defined as -\\begin{equation} - s_{ij} = \\max\\{s_i(j), s_j(i)\\} \\;\\; \\text{with}\\; - s_i(j) = \\exp (-4 \\frac{\\|x_i - x_j \\|^2}{\\sigma_i^2} ) -\\end{equation} -with $\\sigma_i$ standing for the Euclidean distance between the $i$th data point -and its nearest k-nearest neighbor. The adjacency matrix $W$ is then created -as - -\\begin{equation} - W = G \\odot S. -\\end{equation} - -Besides the adjacency matrices $W$, the node labels for each graph are part of -the submission. If the graph has c classes, the node labels are integers in -the range 0 to c-1. - -------------------------------------------------------------------------------- -Description of matrices -------------------------------------------------------------------------------- - -For a more detailed description of the datasets, and references of the sources -the datasets were obtained from, see the paper cited in the beginning of this -description. - - Graphs from various machine learning datasets - - Name Neighbours Nodes Edges Classes -har_10NN 10 10299 75868 6 -indianpines_10NN 10 9144 62328 8 -JapaneseVowels_10NN 10 9961 65572 9 -worms20_10NN 10 20055 120413 20 -optdigits_10NN 10 5620 39825 10 -Vehicle_10NN 10 846 5447 4 -mfeatkarhunen_10NN 10 2000 13834 10 -mfeatfactors_10NN 10 2000 13721 10 -mfeatmorphological_10NN 10 2000 11416 10 -mfeatpixel_10NN 10 2000 13966 10 -mfeatzernike_10NN 10 2000 13707 10 -semeion_10NN 10 1593 11113 10 -mice_10NN 10 1077 6742 8 -yeast_30NN 30 1484 31175 12 -cnae9_10NN 10 1080 9139 9 -dermatology_5NN 5 366 1220 6 -iris_30NN 30 150 2759 3 -Ecoli_10NN 10 336 2280 8 -Binaryalphadigs_10NN 10 1404 9696 36 -Glass_10NN 10 214 1493 6 -collins_15NN 15 1000 8246 30 -micromass_10NN 10 571 4834 20 -breasttissue_10NN 10 106 706 6 -Plants_10NN 10 1600 10965 100 -plantsmargin_12NN 12 1600 12741 100 -plantstexture_10NN 10 1599 10602 100 -Spectro_10NN 10 531 3711 48 - - Graphs from image classification problems. The RGB values are normalised - in the interval [0,1]. - - Name Neighbours Nodes Edges Classes -usps_norm_5NN 5 11000 40556 10 -Fashion_MNIST_norm_10NN 10 10000 79152 10 -kmnist_norm_10NN 10 10000 78466 10 -k49_norm_10NN 10 38547 309079 49 -mnist_test_norm_10NN 10 10000 72800 10 -YaleB_10NN 10 2414 8568 10 -umistfacesnorm_10NN 10 575 3495 20 -YaleA_10NN 10 165 1134 10 -Olivetti_norm_10NN 10 400 2828 40" -FlowIPM22,11,FlowIPM22: Laplacians from Newton-Steps of Maxflow Short-step IPM on Chimera Graphs A collection of matrices arising from solving maximum flow problems on a,https://sparse.tamu.edu/FlowIPM22,graph/network data,graph/network data,1.0,1,undirected weighted graph (11),1,real (11),1,symmetric (11),100000,72180402,721.80402,wide (>100×),499696,216902404,square only,11,0,all square matrices symmetric/Hermitian,11,11,4,real only,0,0,coherent family/series,"FlowIPM22: Laplacians from Newton-Steps of Maxflow Short-step IPM on Chimera Graphs - -A collection of matrices arising from solving maximum flow problems on a -diverse collection of graphs (known as Chimeras) using a short-step -interior point method. The Laplacian matrices arise when computing Newton -steps of the interior point method. - -The matrices are symmetric graph Laplacians, meaning they are symmetric -diagonally dominant matrices with non-positive off-diagonals and row sums -equal to zero. - -Generated by Yuan Gao, Rasmus Kyng, and Daniel Spielman. 2022. - -References: - -data set: https://www.spe.org/web/csp/datasets/set02.htm - -An Algebraic Sparsified Nested Dissection Algorithm Using Low-Rank -Approximations Léopold Cambier, Chao Chen, Erik G. Boman, Sivasankaran -Rajamanickam, Raymond S. Tuminaro, and Eric Darve -SIAM Journal on Matrix Analysis and Applications, vol 41, no 2, -pp 715-746, 2020. https://epubs.siam.org/doi/10.1137/19M123806X" diff --git a/report_notes/group_taxonomy/group_metrics.json b/report_notes/group_taxonomy/group_metrics.json deleted file mode 100644 index 0aaa36f..0000000 --- a/report_notes/group_taxonomy/group_metrics.json +++ /dev/null @@ -1,6705 +0,0 @@ -{ - "revision": "31-Oct-2023 18:12:37", - "matrix_count": 2904, - "groups": [ - { - "group": "HB", - "count": 292, - "documented_meaning": "The original Harwell-Boeing collection. Iain Duff, Roger Grimes, and John Lewis. Duff, I. S. and R. G. Grimes and J. G. Lewis,", - "source_url": "https://sparse.tamu.edu/HB", - "coarse_kind_families_interpretive": "chemistry/biology; electrical/circuit/device; electromagnetics/acoustics; fluids/transport; generic spatial PDE/thermal; graph/network data; mathematics/statistics; optimization/control; other; structures/materials", - "dominant_coarse_family_interpretive": "structures/materials", - "dominant_coarse_family_share": 0.4178082191780822, - "normalized_kind_count": 16, - "normalized_kinds": "structural problem (121); 2D/3D problem (36); computational fluid dynamics problem (27); power network problem (18); optimization problem (17); chemical process simulation problem (16); economic problem (14); thermal problem (12); least squares problem (11); directed weighted graph (8); acoustics problem (4); counter-example problem (3); semiconductor device problem (2); materials problem (1); statistical/mathematical problem (1); directed graph (1)", - "curator_value_type_count": 4, - "curator_value_types": "real (170); binary (112); integer (7); complex (3)", - "curator_structure_count": 4, - "curator_structures": "symmetric (169); unsymmetric (105); rectangular (16); skew-symmetric (2)", - "min_dimension": 9, - "max_dimension": 44609, - "dimension_ratio": 4956.555555555556, - "size_span": "wide (>100\u00d7)", - "min_nnz": 24, - "max_nnz": 2043492, - "shape_profile": "mixed", - "square": 276, - "rectangular": 16, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 169, - "structurally_symmetric_square": 184, - "known_spd": 60, - "value_profile": "real + complex", - "binary": 112, - "geometric_2d3d": 203, - "cohesion": "multi-domain/mixed", - "official_notes": "The original Harwell-Boeing collection. Iain Duff, Roger Grimes, and John Lewis.\n\nDuff, I. S. and R. G. Grimes and J. G. Lewis,\nSparse Matrix Problems, ACM Trans. on Mathematical Software,\nvol 14, no. 1, pp 1-14, 1989." - }, - { - "group": "ATandT", - "count": 4, - "documented_meaning": "Frequency domain analysis of large nonlinear analog circuits, AT&T. See ATT.ps for a paper describing these matrices, in the Other directory:", - "source_url": "https://sparse.tamu.edu/ATandT", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "frequency-domain circuit simulation problem (4)", - "curator_value_type_count": 1, - "curator_value_types": "real (4)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (4)", - "min_dimension": 36057, - "max_dimension": 659033, - "dimension_ratio": 18.277532795296338, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 222596, - "max_nnz": 5834044, - "shape_profile": "square only", - "square": 4, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Frequency domain analysis of large nonlinear analog circuits, AT&T.\n\nSee ATT.ps for a paper describing these matrices, in the Other directory: \n\n@inproceedings{FeldmannMelvilleLong96,\n\tauthor={Feldmann, P. and Melville, R. and Long, D.},\n\ttitle={Efficient frequency domain analysis of large nonlinear analog circuits},\n\tbooktitle={Proceedings of the IEEE Custom Integrated Circuits Conference},\n\tyear={1996}\n\t,address={Santa Clara, CA}\n\t,annote={online, in matrices/ATandT/Documentation/ATT.ps}\n\t}\n\n\n--------------------------------------------------------------------------------\n\nrich.m converted to onetone1.rua, and sparse.m converted to onetone2.rua:\n\n\tDate: Wed, 8 May 96 07:30:09 EDT\n\tFrom: David Long \n\tTo: davis at cise.ufl.edu\n\tSubject: matrices\n\n\tI put two matrices in incoming/rich.m.gz and incoming/sparse.m.gz.\n\tBoth are for the same medium-sized example. The first one has a\n\trelatively rich collection of off-diagonal entries and is typical of\n\twhat we might use for an SSOR-like preconditioner. The second is\n\tsparser, but probably more typical of what would be used with direct\n\tfactorization. Both of these are fairly typical of the matrices that\n\tarise in \"one-tone\" problems, in that the off-diagonal entries are\n\tgenerally confined in some band around the diagonal. I'll also try to\n\tfind an appropriate matrix from a multi-tone problem. Those tend to\n\thave one set of off-diagonal entries close to the diagonal and another\n\tset relatively far away.\n\n\tDavid\n\n--------------------------------------------------------------------------------\n\nThe tt3.m (multitone) matrix was converted to twotone.rua:\n\n\tDate: Thu, 9 May 96 07:12:46 EDT\n\tFrom: David Long \n\tTo: davis at cise.ufl.edu\n\tSubject: Re: matrices\n\n\tA matrix from a two-tone problem is in incoming/tt3.m.gz.\n\n--------------------------------------------------------------------------------\nRegarding a question about the paper (ATT.ps):\n--------------------------------------------------------------------------------\n\t\n\tDate: Fri, 10 May 96 14:07:22 EDT\n\tFrom: David Long \n\tTo: davis at cise.ufl.edu\n\tSubject: Re: matrices\n\t\n\tYes, figure 2 is supposed to be a diagram of the Jacobian, but let me\n\tadd a few comments that may help clarify things. First, you can just\n\tignore the Y part. It's included in the formulation for completeness,\n\tbut is not generally needed. None of the matrices that I sent have a\n\tY part. Second, the matrices can be naturally structured in one of\n\ttwo ways, which we call nN format and Nn format. In nN format, you\n\tview the matrix as an n by n block matrix, with each block having size\n\tN by N. Similarly, Nn format indicates an N by N block matrix, with\n\teach block having size n by n. The two formats are related by the\n\tobvious permutations. The picture of the Jacobian in figure 2 is for\n\tthe matrix in nN format. However, to confuse matters, our program\n\tactually builds the preconditioner matrix in Nn format, and that's the\n\tformat of the matrices that I sent. (Also to confuse matters, the\n\tpaper is a bit inconsistent. For example, the statement before\n\tequation 15 about the Jacobian being block-diagonal for a linear\n\tcircuit assumes that Jacobian is in Nn format.) The almost-circulant\n\tstructure in figure 2 is in the dense N by N blocks, i.e., each N by N\n\tblock is basically a circulant matrix. I say almost-circulant because\n\tthe multiplication of Gamma C Gamma^-1 by Omega breaks the circulant\n\tstructure. For simplicity, in the paper, the discussion of the\n\tJacobian structure is carried out assuming that the problem is\n\tformulated in the complex domain. In the complex case though, half of\n\tthe coefficients that define the soluation are just conjugates of the\n\tother half. Thus, if you translate everything into the real domain,\n\tyou can save some memory. The program uses this observation, and\n\tthat's why the matrices that I sent are real. The Jacobian in the\n\treal case is\n\t\n\tOmega T^-1 Gamma C Gamma^-1 T + T^-1 Gamma G Gamma^-1 T\n\t\n\twhere T is a matrix that translates from the real domain to the\n\tcomplex domain. It turns out that when you apply T^-1 and T to the\n\tcomplex circulant matrix, each pair of conjugate stripes in the\n\tcirculant maps into one of those tilted boxes that we discussed. You\n\tcan see these clearly in the tt3.m preconditioner matrix.\n\t\n\tThe paper appeared in the 1996 Custom Integrated Circuits Conference\n\t(CICC'96). The conference just ended Wednesday, and I haven't\n\tactually seen the proceedings yet, so I can't give you page numbers\n\tand such.\n\t\n\tDavid\n\n--------------------------------------------------------------------------------\nMy response to the message just above:\n--------------------------------------------------------------------------------\n\nTo: David Long \ncc: davis at cise.ufl.edu, rcm at allegra.att.com\nSubject: Re: matrices \nDate: Fri, 10 May 1996 17:22:19 -0400\nFrom: Tim Davis \n\n\nIn a previous message, David Long wrote\n> Yes, figure 2 is supposed to be a diagram of the Jacobian, but let me\n> add a few comments that may help clarify things...\n\nYes - that clarifies things a lot. I'll have to think it through.\nIn the nN form, the N-by-N blocks are dense?\n\nSo in the Nn format, the n-by-n blocks have a sparse structure like the\nleft hand side of Figure 2 (the \"MNA\" matrix)? And the n-by-n matrix\nhas a rather irregular structure, right?\n\nThe dense blocks, by the way, are why UMFPACK gets good performance on these\nmatrices. You may have observed this ... as you increase N, the mflop\nrate should increase nicely.\n\nThe dense blocks are still there, in nN or Nn format, at least in the\nsense that UMFPACK will find them no matter how you contort the input\nmatrix. In one sense this is an ideal matrix for UMFPACK ... unsymmetric\nin the n-by-n structure (UMFPACK handles asymmetry and irregular structure\nfairly well, but there are better methods when the structure is regular),\nwith each element being a dense N-by-N matrix (UMFPACK likes dense blocks,\nsince it gets good performance on them in the dense BLAS kernels).\n\nHere are some results so far, on my UltraSparc (128 MB memory, two level\ncache (Level 1: 16K data and 16K instruction, Level 2: 512K), and 2GB swap\nspace). Peak performance of the BLAS is about 80 mflops in double precision,\n160 mflops in single. Theoretical peak is 333 mflops.\n\nI've renamed the matrices (rich.m = onetone1, sparse.m = onetone2,\nand tt3 = twotone).\n\nMethods used:\n\tUMFPACK2-defaults: with default parameters\n\tUMFPACK2-nondef.: no BTF, prefer symmetric pivots\n\tUMFPACK2 is the same as MA38 (to appear in Harwell Subr. Library).\n\tSuperLU: a sparse partial pivoting code, from Berkeley. Also uses\n\t\tthe BLAS.\n\tMA48:\ta successor to MA28. Lots faster than MA28. Does not make as\n\t\textensive use of the BLAS as does SuperLU and UMFPACK. Better\n\t\tsuited to more irregular problems (I've seen it much faster\n\t\tthan UMFPACK for some matrices).\n\nThe following is the factorization time (including ordering,\nsymbolic factorization, etc.):\n\n\t\t\ttime(cpu)\tnz in LU\tflops\t\tMflops\n\t\t\tin seconds\n\nonetone1:\n\nUMFPACK2-defaults\t60.8 \t\t4693767\t\t0.2148D+10\t35.3\nUMFPACK2-nondef.\t61.0\t\t4907410\t\t0.2337D+10\t38.2\nSuperLU\t\t 108.2\t\t4674848\t\t0.2525D+10\t23.3\nMA48\t\t 331.0\t\t5109483\t\t0.4564D+10\t13.8\n\n\nonetone2:\n\nUMFPACK2-defaults\t10.5 \t\t1269790\t\t0.1588D+09\t15.1\nUMFPACK2-nondef.\t 9.6\t\t1439817\t\t0.2200D+09\t22.9\nSuperLU\t\t\t 8.6\t\t1319194\t\t0.1306D+09\t15.2\nMA48\t\t\t38.3\t\t1260893\t\t0.2229D+09\t 5.8\n\ntwotone:\n\nUMFPACK2-defaults 217.3 \t\t9746806\t\t0.6988D+10\t32.2\nUMFPACK2-nondef. 238.9\t 15730302\t\t0.9601D+10\t40.2\nSuperLU\t\t\tfailed**\nMA48\t\t 734.1\t 10860368 1.4675D+10\t20.0\n\nFor twotone, SuperLU ran out of memory in a preordering part that I wrote,\nnot in SuperLU proper (I need to try it again, and give it more memory in\nthat part).\n\nI haven't included pure factorization time - all of these methods can use\nprior orderings, for example (UMD2FA vs. UMD2RF, for example).\nDo you use UMD2RF? It would be used if the values of the\nJacobian changes, but the pattern remains the same (and the pivot order\nremains the same, and stays numerical acceptable). In that case, UMFPACK2,\nSuperLU, and MA48 would all be faster in the 2nd factorization than they would\nin the first.\n\nDo these numbers compare with the results you have? Actually, your BLAS on\nthe RS/6000 should get closer to the theoretical peak than does the\nUltraSparc.\n\nOh - and thanks for the matrices. Nice to have larger ones in which UMFPACK2\ndoes so nicely.\n\nThanks,\nTim\n\n\n--------------------------------------------------------------------------------\nMatrix pre2.m, converted to pre2.rua" - }, - { - "group": "Averous", - "count": 4, - "documented_meaning": "Matrices from David Averous, Institut National Polytechnique de Toulouse (INPT). --------------------------------------------------------------------------------", - "source_url": "https://sparse.tamu.edu/Averous", - "coarse_kind_families_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "thermal problem (4)", - "curator_value_type_count": 1, - "curator_value_types": "real (4)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (4)", - "min_dimension": 1794, - "max_dimension": 84617, - "dimension_ratio": 47.166666666666664, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 7764, - "max_nnz": 463625, - "shape_profile": "square only", - "square": 4, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 4, - "cohesion": "coherent family/series", - "official_notes": "Matrices from David Averous, Institut National Polytechnique de Toulouse (INPT).\n\n--------------------------------------------------------------------------------\n\nDate: Thu, 19 Feb 1998 15:13:41 +0100\nTo: davis at cise.ufl.edu\nFrom: David Averous \nSubject: UMFPACK\n\nDear Prof. T. DAVIS,\nWe work at ENSIGCT (in Toulouse (France) which is in the same university as\nENSEEIHT)in Laboratory of Chemical Engineering. Ours fields are simulation,\ndynamic and static, and modeling of chemical processes. So we deal with\nmatrices that can be small and quiet full (N=100) up to very large, very\nsparse and unsymmetric (N=100000, NNZ=500000).\nWe have tested various codes and according to literature and also workshops\n(ILAY 95 CERFACS) we use UMF (2.*). For most of ours cases we have very\ngood results, fast integration and acceptable fill in.\n\nWe use it with a general DAE (differential Algebraic Equations) systems\nsolver using Gear, write in Fortran 77. Due to its general aspect we don't\nknow a priori the structure of matrix. So for static allocation of\nintegrator work vector, does a method (or a function) exist to evaluate an\nupper limit of LVALUE and LINDEX with N(nb of equations) and NNZ(nb of non\nzero) ? (Do you intend to make a f90 version of UMF in the future.. ?)\n\nWe have another problem due to fill in. For static simulation of plate fin\nheat exchanger, we deal with matrices\nfrom N~10000 and NNZ~50000 to N~100000 and NNZ~500000. But we can't reach\nthe upper limit of N due to fill in, the factor range from 5 to 50 ,\ndepending of cases. It depends on matrix structure. But perhaps there is a\nmatrix structure (large or small bandwidth, with or without border ...)\nthat reduce fill-in in UMF ?(we tried to change the number of columns\nexamined during pivot search but the behavior was unpredictable.)\n\nThanking you by advance for your courtesy,\n\n\tBest regards.\t\t\t\t\tProf. J.M. LE LANN\n\t\t\t\t\t\t\tD. AVEROUS\n\t\t\t\t\t\t\tK. HAMMADI\n\t\t\t\t\t\t\tA. SARGOUSSE\n\n\n--------------------------------------------------------------------------\nDavid AVEROUS\nInstitut National Polytechnique de Toulouse (INPT)\nEcole Nationale Sup=E9rieure d'Ing=E9nieurs de G=E9nie Chimique (ENSIGC)\nLaboratoire de G=E9nie Chimique (UMR 5503 CNRS)\nEquipe Analyse Fonctionnelle des Proc=E9d=E9s\n18, Chemin de la Loge - F31078 TOULOUSE CEDEX (France)=09\n\tT=E9l.: 33 (0)5 62 25 23 00 Poste 2585\tFax : 33 (0)5 62 25 23 18\n\tE-mail : David.Averous at ensigct.fr\n--------------------------------------------------------------------------\n\nDate: Mon, 02 Mar 1998 10:47:35 +0100\nTo: davis at cise.ufl.edu\nFrom: David Averous \nSubject: Matrices\n\nI have sent you matrices (epb0.mat, epb1.mat, epb2.mat, epb3.mat). I use\ngunzip for compression.=20\n\nepb0 : N = 1794 ; NE = 8960 Plate-Fin Heat Exchanger with Simple Model\nepb1 : N = 14734 ; NE = 95053 PFHE Model (simple case)\nepb2 : N = 25228 ; NE = 175027 PFHE model ( case with flow redistributio=\nn\nrepresent by the extra diagonal blocs)\nepb3 : N = 84617 ; NE = 463625 PFHE model (large case)\n\nI have made some tests on memory requirements as function of the amalgation\nparameter. Normalized memory requirements increases faster than in your\ntests (TR-97-016) unfortunately for me I have an extremun at default value\n(2.0), I reach 1.6 in some case. A value of 1.1 for amalgation seems better\nfor my kind of matrices, of course it means a little increase in\nfactorization time.\n\nBest wishes\n\nDavid Averous" - }, - { - "group": "Bai", - "count": 78, - "documented_meaning": "Matrices from Z. Bai, D. Day, J. Demmel, and J. Dongarra. located at ftp.ms.uky.edu : pub/misc/bai/Collection.", - "source_url": "https://sparse.tamu.edu/Bai", - "coarse_kind_families_interpretive": "chemistry/biology; electrical/circuit/device; electromagnetics/acoustics; fluids/transport; generic spatial PDE/thermal; imaging/graphics/robotics; mathematics/statistics; structures/materials", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 0.3974358974358974, - "normalized_kind_count": 8, - "normalized_kinds": "computational fluid dynamics problem (31); electromagnetics problem (26); 2D/3D problem (8); statistical/mathematical problem (4); power network problem (3); chemical process simulation problem (2); robotics problem (2); materials problem (2)", - "curator_value_type_count": 3, - "curator_value_types": "real (70); complex (6); integer (2)", - "curator_structure_count": 3, - "curator_structures": "unsymmetric (66); symmetric (11); Hermitian (1)", - "min_dimension": 62, - "max_dimension": 23560, - "dimension_ratio": 380.0, - "size_span": "wide (>100\u00d7)", - "min_nnz": 342, - "max_nnz": 463360, - "shape_profile": "square only", - "square": 78, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 8, - "structurally_symmetric_square": 39, - "known_spd": 4, - "value_profile": "real + complex", - "binary": 0, - "geometric_2d3d": 69, - "cohesion": "multi-domain/mixed", - "official_notes": "Matrices from Z. Bai, D. Day, J. Demmel, and J. Dongarra.\nlocated at ftp.ms.uky.edu : pub/misc/bai/Collection.\n\nModifications:\nHeader line (line 1) of AF23560 modified - 11 null characters removed.\nNames changed to reflect the naming conventions at this site\n(lower case, followed by a 3 character extension with the matrix type - .rua,\n.rsa, etc.).\n\nJan, 2003: Matrices with unsorted columns were sorted (originals in\nJumbled directory).\nApr 2003: Matrices with \"(5E16)\" format changed to \"(5E16.8)\". Originals in\n5e16 directory.\n\n--------------------------------------------------------------------------------\nOriginal README file:\n--------------------------------------------------------------------------------\n\nThis directory contains the data files for the test matrices. \n\n\nTitle Key \n-----------------------------------------------------------------------+--------\n\nsquare dielectric waveguid, H. Dong DW256A\n DW245B\n DW1024\n DW4096\n\nbounded finline waveguide eigenmodes, B. Shultz and S. Gedney BFWA62\n BFWB62\n BFWA398\n BFWB398\n BFWA782\n BFWB782\n\nMagneto-hydro-dynamics Alfven spectral problem, MHDA416\n MHDB416\n\nAirfoil (A. Mahajan) AF23560 \n\nQuebec Hydro Power system, Deo QH882 \n\nChuck matrix (multiple eigenvalues) J. Cullum CK104\n CK400\n CK656\n\nLOPSI Stochastic Test Matrix LOP163\n\n\nModel ordinary differential eigenvalue problem ODEPA400\n ODEPB400\n\t\t\t\t\t\t\t\nModel convection-diffusion differential equations CDDE1 \n CDDE2 \n CDDE3 \n CDDE4 \n CDDE5 \n CDDE6 \n\nBrusselator wave model in transport interaction of chemical solutions BWM200 \n BWM2000 \n \n\nMarkov Chain Modeling, Random Walk (m = 15) G. W. Stewart RW136\n (m = 30) RW496\n (m = 100) RW5151\n\nmodel PDE problem, H. Elman nx = ny = 15 PDE225\n nx = ny = 30 PDE900\n nx=47, ny=63 PDE2961\n\n\nQuantum chemistry (complex symmetric) QC324\n QC2534 \n\n---------------- to be included ------------------------------------------------ \nOrr-Sommerfeld (complex matrix) \n\nIsing mode (size? and parameters?) \n\nRobotic control (CAD in C program) ROB480\n\nTaylor-Couette flow \n\nNASA AMES arc2d problems (16,256 by 16,256)" - }, - { - "group": "Boeing", - "count": 32, - "documented_meaning": "Structural engineering matrices from Roger Grimes, Boeing. The files have been renamed to fit my naming scheme (matrixname.type),", - "source_url": "https://sparse.tamu.edu/Boeing", - "coarse_kind_families_interpretive": "structures/materials", - "dominant_coarse_family_interpretive": "structures/materials", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 2, - "normalized_kinds": "structural problem (24); materials problem (8)", - "curator_value_type_count": 2, - "curator_value_types": "real (27); binary (5)", - "curator_structure_count": 1, - "curator_structures": "symmetric (32)", - "min_dimension": 588, - "max_dimension": 217918, - "dimension_ratio": 370.60884353741494, - "size_span": "wide (>100\u00d7)", - "min_nnz": 10485, - "max_nnz": 11524432, - "shape_profile": "square only", - "square": 32, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 32, - "structurally_symmetric_square": 32, - "known_spd": 15, - "value_profile": "real only", - "binary": 5, - "geometric_2d3d": 32, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Structural engineering matrices from Roger Grimes, Boeing.\nThe files have been renamed to fit my naming scheme (matrixname.type),\nwhere type = rsa, rua, psa, etc. (via the script \"fix\" in this directory).\n\nNOTE: the BCSSTK39 matrix has been recently added (Dec 2002).\n\nThe NASA* matrices are almost duplicates of ../Nasa/*:\n\nnasa1824.rsa: Boeing version has different numerical values (same pattern).\n\tI think the Nasa/nasa1824.rsa copy is the true version (do a diff\n\tbetween the two, and you'll see what I mean).\n\nnasa2910.?sa and nasa4704.?sa: Boeing versions are just the pattern\n\tThe Nasa/ versions include numerical values, and right-hand side.\n\tThe patterns are the same.\n\n--------------------------------------------------------------------------------\nThe following matrices appear to be corrupted:\n\n\tct20mass.rsa\n\tmn12k.rsa\n\tmn12m.rsa\n\tpwtm.rsa\n\n--------------------------------------------------------------------------------\nDate: Fri, 2 Jun 95 09:23:48 PDT\nFrom: Roger Grimes 206-865-3585 \nMessage-Id: <9506021623.AA02721 :at: espresso.rt.cs.boeing.com>\nTo: davis :at: cis.ufl.edu\nSubject: Re: sparse matrices\n\nTim,\n\nI am sending you (u.s. snail mail) today an 8 mm tape.\nIt has a copy of 2 tar tapes.\n+\nThe first tar tape has the following matrices.\n\nBCSSTK34\nBCSSTK35\nBCSSTK36\nBCSSTK37\nBCSSTK38\nBCSSTK39\nBCSSTM34\nBCSSTM35\nBCSSTM36\nBCSSTM37\nBCSSTM38\nBCSSTM39\nCRYSTK01\nCRYSTK02\nCRYSTK03\nCRYSTM01\nCRYSTM02\nCRYSTM03\nCT20MASS\nCT20STIF\nMSC00726\nMSC01050\nMSC01440\nMSC04515\nMSC10848\nMSC23052\nNASA1824\nNASA2910\nNASA4704\n\nThe second tar tape has\n\nmn12.K\nmn12.M\npwt.K\npwt.M\n\nI believe I already have sent you the headers for all of these matrices.\nIf not let me know and I will do so.\n\nTo extract the 2 different tar files from the 8 mm tape you will need\nto know some about the 'dd' and 'mt' command on unix. I had to learn\nthis recently so, in case you need it, here are commands that will let\nyou get the table of contents from the two tar files. Note that our\n8 mm tape drive is referenced by 'rst1'. 'nrst1' says look at tape\ndevice rst1 but do not rewind the tape. \n\nmt -f /dev/rst1 rewind \"positions tape to beginning\" \ntar tvf /dev/rst1 \"gets table of contents of first tar file\"\nmt -f /dev/rst1 rewind \"positions tape to beginning\"\nmt -f /dev/nrst1 fsf 1 \"skips past first tar file\"\ntar tvf /dev/rst1 \"gets table of contents of second tar file\"\n\nIf you are interested only working on ordering/symbolic factorization \nfor these problems you may find the following program useful. It\nreads the H-B matrix and puts out a H-B representation of only\nthe pattern.\n\n program pattern\nc\nc---------------------------------------------------------------------\nc\nc this program creates a pattern only representation of a sparse\nc matrix in the harwell/boeing sparse matrix format. \nc\nc---------------------------------------------------------------------\nc\n character* 3 mtxtyp, rhstyp\n character* 8 key\n character*16 ptrfmt, indfmt\n character*20 valfmt, rhsfmt\n character*72 title\n character*80 line\nc\n integer lintot, linptr, linind, linval, linrhs,\n 1 nrow, ncol, nnzero, nelmnt, nrhs, nrhsi\nc\nc---------------------------------------------------------------------\nc\n read ( 5, 1000, end = 900 ) title, key,\n 1 lintot, linptr, linind, linval, linrhs,\n 2 mtxtyp, nrow, ncol, nnzero, nelmnt,\n 3 ptrfmt, indfmt, valfmt, rhsfmt\n 1000 format ( a72, a8 / 5i14 / a3, 11x, 4i14 / 2a16, 2a20 )\nc\n if ( linrhs .gt. 0 ) read ( 5, 1100 ) rhstyp, nrhs, nrhsi\n 1100 format ( a3, 11x, 2i14 )\nc\n\t if ( mtxtyp(1:1) .ne. 'P' ) then\n\t\t\tlintot = linptr + linind\n\t\t\tlinval = 0\n\t\t\tlinrhs = 0\n\t\t\tmtxtyp(1:1) = 'P'\n\t\t\tvalfmt(1:20) = ' '\n\t\t\trhsfmt(1:20) = ' '\n end if\nc\n write ( 6, 2000) title, key,\n 1 lintot, linptr, linind, linval, linrhs,\n 2 mtxtyp, nrow, ncol, nnzero, nelmnt,\n 3 ptrfmt, indfmt, valfmt, rhsfmt\n 2000 format ( a72, a8 / 5i14 / a3, 11x, 4i14 / 2a16, 2a20 )\nc\n do 200 i = 1, lintot\n read ( 5, '(a80)' ) line\n write ( 6, '(a80)' ) line\n 200 continue\nc\nc---------------------------------------------------------------------\nc\n 900 stop\n end\n\nJust a reminder. You will need some significant disk space to deal with\nmn12.K and pwt.K. As a compress file, mn12.K requires 28M and pwt.K\nrequires 54M. Uncompressed files will need 2.5 to 3 times as much. \nThey are definitely not files for the weak of heart or for those short\non disk space.\n\nRoger Grimes\n\n--\n------------------------------+-------------------------------------\nRoger G. Grimes | E-mail: rgrimes :at: espresso.rt.cs.boeing.com\nMath and Eng. Analysis | Tel.: (206) 865-3585 \nBoeing Information and | FAX: (206) 865-2966 \n Support Services |\n------------------------------+-------------------------------------\nThe comments in this message are mine and not necessarily those of \nThe Boeing Company.\n---------------------------+----------------------------------------" - }, - { - "group": "Bomhof", - "count": 4, - "documented_meaning": "Matrices from circuit simulation problems. Wim Bomhof, Univ. Utrecht. Modelled as a system of differential algebraic equations.", - "source_url": "https://sparse.tamu.edu/Bomhof", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "circuit simulation problem (4)", - "curator_value_type_count": 1, - "curator_value_types": "real (4)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (4)", - "min_dimension": 2624, - "max_dimension": 80209, - "dimension_ratio": 30.567454268292682, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 21199, - "max_nnz": 307604, - "shape_profile": "square only", - "square": 4, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Matrices from circuit simulation problems. Wim Bomhof, Univ. Utrecht.\n\nModelled as a system of differential algebraic equations.\nNormally solved with a BDF method, and Newton's method, which\ngive rise to linear systems that need to be solved. A, x, and b\nare provided.\n\nWim Bomhof, Univ. Utrecht (now at PLAXIS (www.plaxis.nl), a small company\nthat develops a finite element code for geomechanical problems.\nemail: (first name in lower case).(current directory name in lower case) at hetnet.nl.\ncircuit matrices posted at http://www.math.uu.nl/people/bomhof/.\n\nAdded to the UF sparse matrix collection in December 2001.\n\n\nCitations:\n\nWim Bomhof and Henk van der Vorst, A parallel linear system solver\nfor circuit simulation problems. Numer. Linear Algebra Appl. 7 (2000), pp.\n649-665. \n\nWim Bomhof and Henk van der Vorst, A parallelizable GMRES-type method\nfor p-cyclic matrices, with applications in circuit simulation. To appear\nin the proceedings of the SCEE-2000 Workshop, August 20-23, 2000, Warnemunde. \n\nWim Bomhof, Iterative and parallel methods for linear systems, with\napplications in circuit simulation. PhD thesis, Mathematical Institute, Utrecht\nUniversity, 2001." - }, - { - "group": "Bova", - "count": 1, - "documented_meaning": "Model of Charleston Harbor. Steve Bova, Nichols Research Corporation. 2524 S. I-20 Frontage Road, Suite A, P.0. Box 820186,", - "source_url": "https://sparse.tamu.edu/Bova", - "coarse_kind_families_interpretive": "fluids/transport", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computational fluid dynamics problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 46835, - "max_dimension": 46835, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 2329092, - "max_nnz": 2329092, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 1, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "Model of Charleston Harbor. Steve Bova, Nichols Research Corporation.\n2524 S. I-20 Frontage Road, Suite A, P.0. Box 820186,\nVicksburg, MS 39182-0186. email: swb :at the domain: nrcpet3.wes.army.mil.\nLocated at the U.S. Army Engineer Waterways Experiment\nStation, Vicksburg, MS.\n\nRMA10: A 3D finite-element element matrix from a CFD code.\n\nFrom Steve Bova: This is a discretization of Charleston\nHarbor, which has an island in the middle of it." - }, - { - "group": "Brethour", - "count": 2, - "documented_meaning": "Numerical simulation of coating flows, from Jim Brethour. Original data was sent with 12 decimal digits in the mantissa, thus", - "source_url": "https://sparse.tamu.edu/Brethour", - "coarse_kind_families_interpretive": "fluids/transport", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computational fluid dynamics problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (2)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (2)", - "min_dimension": 1348, - "max_dimension": 9540, - "dimension_ratio": 7.077151335311573, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 19457, - "max_nnz": 207308, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 2, - "cohesion": "coherent family/series", - "official_notes": "Numerical simulation of coating flows, from Jim Brethour.\n\nOriginal data was sent with 12 decimal digits in the mantissa, thus\nthe 4E20.12 format for the numerical values.\n\nJim Brethour writes:\n\n\"Problems such as ours, incompressible \nfree-surface flows, with (in my case) deformable boundaries, represent \npractical problems where direct solvers are typically the best way to go.\nThe large system is for the tail end of a converging slot \ncoater (418 elements) and the smaller is for a 64 element lid-drvien cavity.\"\n\n--------------------------------------------------\nJim Brethour, brethour :at the domain: cems.umn.edu\nBox #13, 151 Amundson Hall, \nDept. of Chemical Engineering & Materials Science,\nUniversity of Minnesota, \n421 Washington Avenue S.E.\nMinneapolis, MN 55455\n(612)-625-3083\n--------------------------------------------------\n\n(PhD student of Prof. Scriven).\n\nAdded November 3, 1997." - }, - { - "group": "Brunetiere", - "count": 1, - "documented_meaning": "A matrix from Noel Brunetiere. Finite-element discretization of thermal problem. (Noel.Brunetiere :at the domain: lms.univ-poitiers.fr),", - "source_url": "https://sparse.tamu.edu/Brunetiere", - "coarse_kind_families_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "thermal problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 3456, - "max_dimension": 3456, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 66528, - "max_nnz": 66528, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 1, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "A matrix from Noel Brunetiere. Finite-element discretization of thermal problem.\n(Noel.Brunetiere :at the domain: lms.univ-poitiers.fr),\na PhD student (as of Spring 2001) in University of Poitiers (France).\n\nFinite-element discretization of a thermal problem.\nThe original matrix in triplet form had 100,512 duplicate entries\n(not present in the RUA form of the matrix).\n\nThe mesh is regular." - }, - { - "group": "Cote", - "count": 2, - "documented_meaning": "Vibroacoustic matrices from Andre Cote, Univ. Sherbrooke, Quebec. Andre Cote", - "source_url": "https://sparse.tamu.edu/Cote", - "coarse_kind_families_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "acoustics problem (2)", - "curator_value_type_count": 2, - "curator_value_types": "complex (1); real (1)", - "curator_structure_count": 1, - "curator_structures": "symmetric (2)", - "min_dimension": 5962, - "max_dimension": 12328, - "dimension_ratio": 2.067762495806776, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 142190, - "max_nnz": 301700, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 2, - "known_spd": 0, - "value_profile": "real + complex", - "binary": 0, - "geometric_2d3d": 2, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Vibroacoustic matrices from Andre Cote, Univ. Sherbrooke, Quebec.\n\n Andre Cote\n GAUS, genie mecanique\n Universite de Sherbrooke | E-mail :\n Andre.Cote :at the domain: gme.usherb.ca\n 2500, boul. Universite | Tel : (819) 821-8000 ext. 3166\n Sherbrooke ( Quebec ) | Fax : (819) 821-7163\n J1K 2R1 | Web : http://www-gaus.gme.usherb.ca/\n\n\nAndre Cote writes:\n\nDepending on the type of problems we solve ( structure only, fluid only,\nfluid-structure, etc.), and the type of solutions we are interested in\n(eigenvalue or forced problems), we use different calculations schemes.\nBut, basically, the main bottleneck for every path comes down to two\nsituations:\n\n1. Solving Ax = b for A symmetric and real (double precision)\n\n\tfile name:\tvibrobox.rsa\n\n\tHow large ? 12328 DOFS \n\tWhat is the structure like ? symmetric with large bandwidth\n\tWhat structural problem ? flexible box (structure only)\n\n\n2. Solving Ax = b for A symmetric and complex (double precision)\n\n\tfile name:\tmplate.csa\n\n\tHow large ? 5962 DOFS \n\tWhat is the structure like ? symmetric \n\tWhat structural problem ? multi-layer plate (that is\n\ta plate-air-poroelastic-air-plate system)\n\nMany solves are required (a few to 5000)." - }, - { - "group": "DRIVCAV", - "count": 26, - "documented_meaning": "Driven cavity, finite element matrices from Yousef Saad (SPARSKIT2 collection). Matrix types appended to file names by Tim Davis.", - "source_url": "https://sparse.tamu.edu/DRIVCAV", - "coarse_kind_families_interpretive": "fluids/transport", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computational fluid dynamics problem (26)", - "curator_value_type_count": 1, - "curator_value_types": "real (26)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (26)", - "min_dimension": 317, - "max_dimension": 4562, - "dimension_ratio": 14.391167192429021, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 5923, - "max_nnz": 138040, - "shape_profile": "square only", - "square": 26, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 26, - "cohesion": "coherent family/series", - "official_notes": "Driven cavity, finite element matrices from Yousef Saad (SPARSKIT2 collection).\nMatrix types appended to file names by Tim Davis.\n\nc----------------------------------------------------------------------------\nc Code for Driven Cavity (Finite Element Method)\nc\nc Author: Andrew Chapman (chapman :at the domain: msi.umn.edu)\nc\nc Matrices extracted by: Abdelkader Baggag (baggag :at the domain: cs.umn.edu)\nc\nc For more information, contact Prof.\nc Yousef Saad (saad :at the domain: cs.umn.edu)\nc\nc----------------------------------------------------------------------------\nc Directories\nc----------------------------------------------------------------------------\nc src : source code\nc Block_fmt : Matrices in Block format\nc NoBlock_fmt : Matrices in Non Block format\nc\nc----------------------------------------------------------------------------" - }, - { - "group": "FIDAP", - "count": 35, - "documented_meaning": "Fluid dynamics, finite element matrices from Yousef Saad (SPARSKIT2 collection). Names converted to Univ. of Florida naming convention (.mat changed to", - "source_url": "https://sparse.tamu.edu/FIDAP", - "coarse_kind_families_interpretive": "fluids/transport", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computational fluid dynamics problem (35)", - "curator_value_type_count": 1, - "curator_value_types": "real (35)", - "curator_structure_count": 2, - "curator_structures": "unsymmetric (22); symmetric (13)", - "min_dimension": 27, - "max_dimension": 19716, - "dimension_ratio": 730.2222222222222, - "size_span": "wide (>100\u00d7)", - "min_nnz": 279, - "max_nnz": 1096948, - "shape_profile": "square only", - "square": 35, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 13, - "structurally_symmetric_square": 35, - "known_spd": 8, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 35, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Fluid dynamics, finite element matrices from Yousef Saad (SPARSKIT2 collection).\nNames converted to Univ. of Florida naming convention (.mat changed to\n.rua).\n\nc------------------------------------------------------------------------c\nc FIDAP MATRICES: c\nc------------------------------------------------------------------------c\nc These matrices are extracted from the examples provided by Fidap c\nc The matrices are stored in Harwell-Boeing format c\nc For the physic of the problems, see detailed description in Fidap c\nc c\nc Matrices extracted by: Abdelkader Baggag (baggag at cs.umn.edu)\nc with the help of Barry Rackner (bnr at msc.edu)\nc\t\t\t\t\t\t\t\t\t c\nc For help or more informations, contact:\t\t\t\t c\nc Prof. Yousef Saad (saad at cs.umn.edu)\nc c\nc------------------------------------------------------------------------c\nc GENERAL INFORMATION ON THE PROBLEMS c\nc------------------------------------------------------------------------c\nc The elements used are Q2 for the velocity and P1 for the pressure c\nc for 2D problems and the so-called Brick elements (8 nodes) for 3D c\nc problems (unless specified)\t\t\t\t\t \t c\nc\t\t\t\t\t\t\t\t\t c\nc We are solving the fully coupled Navier-Stokes Equations (unless c\nc specified), including the Temperature Equation for the Heat Transfer c\nc Problems and the Chemical Convection-Diffusion Equations for different c\nc species c\nc The Mesh is regular structured but not uniform c\nc\t\t\t\t\t\t\t\t\t c\nc | Kmax \t\t\t\t c\nc\t\t |\t\t\t\t\t\t\t c\nc |\t\t\t\t\t\t\t c\nc \t \t | \t\t\t\t\t\t\t c\nc\t\t |__________________ Jmax \t\t c\nc / \t\t\t\t\t\t\t c\nc / \t\t\t\t\t\t\t c\nc\t /\t\t\t\t\t\t\t c\nc /\t\t\t\t\t\t\t\t c\nc / Imax\t\t\t\t\t\t\t c\t\nc------------------------------------------------------------------------c\nc This version is dated: June 09, 1994 c\nc------------------------------------------------------------------------c\nc ex1.mat : 2D Transient Poiseuille Flow in a Pipe\nc Reynolds = 1.0\nc Imax = 9, Jmax = 5\nc\nc ex2.mat : 3D Steady Couette Flow\nc Reynolds = 1.0\nc Imax = 9, Jmax = 3\nc\nc ex3.mat : 2D Flow Past a Cylinder in Freestream\nc Reynolds = 40.0\nc Imax = 19, Jmax = 9\nc\nc ex4.mat : 2D Hamel Flow\nc Reynolds = 0.0\nc Imax = 9, Jmax = 5\nc\nc ex5.mat : Non-newtonian Flow in a Channel (Power Law Poiseuille Flow)\nc Reynolds = Power Law\nc \nc ex6.mat : 2D Die Swell Problem\nc Capillary number = 1.0\nc Imax = 43, Jmax = 15\nc\nc ex7.mat : 2D natural Convection in a Square Enclosure\nc Rayleigh = 1000\nc Imax = 9, Jmax = 9\nc\nc ex8.mat : Developing Flow, Vertical Channel, Angle = 0.0\nc Rayleigh = 1000\nc Imax = 5, Jmax = 13\nc\nc ex9.mat : 2D Jet Impingement Cooling\nc Reynolds = 0.0 ; (Jet) = 300\nc Imax = 21, Jmax = 89\nc\nc ex10.mat : 2D Flow over Multisteps in a Channel\nc Reynolds = 100 ; Prandtl = 0.6896\nc Imax = 13, Jmax = 5\nc\nc ex11.mat : 3D Steady Flow; Cylinder and Flat Plate Heat Exchanger\nc Imax = 21; Jmax = 7; Kmax = 5 \nc Density = 1.0; Viscosity = 0.025; SpecifiHeat = 1.0; \nc Conductivity = 3.760e-3\nc\nc ex12.mat : 2D Flow Stokes Flow in Lid-Driven Wedge (28.5 Degree Angle)\nc Imax = 7; Jmax = 5\nc Density = 1.0; Viscosity = 5.0 \nc\nc ex13.mat : Axisymmetric Flow through a Poppet Valve\nc Imax = 12; Jmax = 13\nc Steady, Newtonian, Axi-Symmetric, Isothermal, Nonlinear\nc Density = 3.122e-2; Viscosity = 6.77e-4\nc\nc ex14.mat : 2D Isothermal Seepage Flow\nc Imax = 23; Jmax = 23\nc Density = 1000.0; Permeability = 100; Porosity = 0.5\nc\nc ex15.mat : 2D Spin up of a Liquid in an Annulus \nc Imax = 7; Jmax = 7\nc Transient, Nonlinear, Newtonian, Cylindrical\nc Density = 1.0; Viscosity = 0.02\nc\nc ex16.mat : 3D Flow Past a Heated Obstacle\nc Imax = 13; Jmax = 13; Kmax = 9\nc Nonlinear\nc Density = 1.0; Viscosity = 0.1; Conductivity = 0.1\nc\nc ex17.mat : 3D Flow in a Rectangular Duct Around a 120 Degree Bend\nc Imax = 5; Jmax = 3; Kmax = 3\nc Nonlinear, Turbulent\nc Density = 1.74e-3; Viscosity = 1.84e-4 1.0 (Mixing)\nc\nc ex18.mat : 2D Turbulent Flow Over a backward-Facing Step\nc\t Imax = 5; Jmax = 9\nc Nonlinear, Turbulent (k-epsilon)\nc Density = 1.0; Viscosity = 2.2222e-5\nc \nc ex19.mat : 2D Developing Pipe Flow, Turbulent k-epsilon\nc Imax = 5; Jmax = 5\nc Nonlinear, Axi-Symmetric, Turbulent\nc Density = 1.0; Viscosity = 1.0e-5\nc\nc ex20.mat : 2D Attenuation of a Surface Disturbance\nc Imax = 7; Jmax = 5\nc Transient, Nonlinear, free\nc Density = 1.0; Viscosity = 0.1\nc\nc ex21.mat : 2D Growth of a Drop from a Nozzle\nc Imax = 5; Jmax = 3\nc Transient, Nonlinear, free, axi-symmetric\nc Density = 1.0; Viscosity = 1.0\nc\nc ex22.mat : 2D Slot Coater\nc Imax = 6; Jmax = 3\nc Steady, Nonlinear, free\nc Density = 1.0; Viscosity = 0.1; SurfaceTension = 1.0\nc\nc ex23.mat : 2D Fountain Flow in Injection Molding\nc Imax = 4; Jmax = 3\nc Nonlinear, Free\nc Density = 1.0; Viscosity = 2706.0\nc\nc ex24.mat : 2D Unsymmetric Forward Roll Coating\nc Imax = 5; Jmax = 3\nc Nonlinear, Free\nc Viscosity = 1.0; Density = 1.0; SurfaceTension = 10\nc\nc ex25.mat : 2D Drop Growth Resulting from a Moving Piston\nc Imax = 3; Jmax = 3\nc Transient, Nonlinear, Free, AxiSymmetric\nc Density = 1.0; Viscosity = 1.0; SurfaceTension = 10.0\nc\nc ex26.mat : 2D surface Tension Gradient Driven Thermal Convection\nc Imax = 3; Jmax = 3\nc Steady, Nonlinear, Free, Weakly\nc Density = 1.0; Viscosity = 1.0; Surface Tension = variable;\nc Conductivity = 0.565\nc\nc ex27.mat : 2D Crystal Growth Simulation\nc Imax = 3; Jmax = 3\nc Axisymmetric, Transient, Nonlinear, Free, Strongly \nc Density = 1.0; Viscosity = 1.0; Conductivity = 0.051282\nc\nc ex28.mat : 2D Two Merging Liquids with one External and an interior Interface\nc Imax = 7; Jmax = 6\nc Nonlinear, Steady, Free\nc Viscosity = 0.50; density = \nc\nc ex29.mat : 2D Turbulent flow in AxiSymmetric U-Bend: k-epsilon\nc Imax = 5; Jmax = 3\nc Nonlinear, Turbulent, Axisymmetric\nc Density = 1.0; Viscosity = 1.0e-5\nc\nc ex30.mat : 3D Crystal Growth: Bridgman Technique\nc Imax = 11; Jmax = 7; Kmax = 3; MaxPoints = 999\nc Nonlinear, Strongly\nc Density = 1.0; Conductivity = 0.6; SpecifiHeat = 1.0 (Solid region)\nc density = 306; Viscosity = 4; Enthalpy = 4; Conductivity = 4 (Fluid Region)\nc\nc ex31.mat : 2D Dilute Species Deposition on a Titled heated Plate\nc Imax = 7; Jmax = 3\nc Nonlinear, Weakly\nc Density = 1.0; Viscosity = 0.16; Conductivity = variable; Diffusivity = 0.15\nc\nc ex32.mat : 2D radiation Heat Transfer in an Open Channel\nc Imax = 5; Jmax = 7\nc Nonlinear, weakly\nc Density = 1.3698; Conductivity = variable; Emissivity = variable\nc\nc ex33.mat : 2D Radiation Heat Transfer in a Square Cavity\nc Imax = 7; Jmax = 7\nc Ra = 1.6e7; Rad = 5.88; Density = 1.3698; SpecifiHeat = 0.7300\nc Emissivity = 0.45--0.88\nc Nonlinear, Strongly \nc\nc ex34.mat : 2D Conduction Melting\nc Imax = 3; Jmax = 3\nc Transient, Nonlinear, Strongly\nc Density = 1.0; Conductivity = 0.1; Diffusivity = 1.0 (NonIsotropic)\nc Viscosity and Enthalpy definec on curves\nc\nc ex35.mat : 2D Turbulent Flow in a Heated Channel\nc Imax = 41; Jmax = 7\nc Reynolds = 25960; k-epsilon\nc Nonlinear, Turbulent\nc\nc ex36.mat : 2D Chemical Vapor Deposition in a Horizontal Reactor\nc Imax = 7; Jmax = 3\nc Nonlinear, Strongly \nc Density = variable; Viscosity = variable defined on a curve\nc All the other properties are variable and defined on curves\nc\nc ex37.mat : 3D Flow of Plastic in a profile extrusion Die\nc Imax = 7; Jmax = 9; Kmax = 5\nc Nonlinear, NonNewtonian, Weakly\nc Viscosity = variable; Conductivity = 1.0 \nc\nc ex38.mat : Multiple Species Reaction A + 2B -----> 2C + D\nc Imax = 5; Jmax = 5\nc axisymmetric, nonlinear, weakly\nc all the properties are variable\nc\nc ex39.mat : 3D Mixing Tank 2 Blades\nc Imax = 9; Jmax = 5; Kmax =7\nc Nonlinear\nc Density = 1.0; Viscosity = 1.0\nc\nc ex40.mat : 3D Die Swell Problem on a Square Die\nc Imax = 5; Jmax = 11; Kmax = 5\nc Re = 1.0; Ca = inf\nc Viscosity = 1.0; Density = 0.5\nc\nc-----------------------------------------------------------------------\nc-----------------------------------------------------------------------" - }, - { - "group": "Gaertner", - "count": 3, - "documented_meaning": "Matrices from Dr. Klaus Gaertner, Weierstrass Institute Berlin.", - "source_url": "https://sparse.tamu.edu/Gaertner", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 2, - "normalized_kinds": "directed weighted graph (2); undirected weighted graph (1)", - "curator_value_type_count": 1, - "curator_value_types": "integer (3)", - "curator_structure_count": 2, - "curator_structures": "unsymmetric (2); symmetric (1)", - "min_dimension": 10774, - "max_dimension": 13209, - "dimension_ratio": 1.2260070540189345, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 70842, - "max_nnz": 91465, - "shape_profile": "square only", - "square": 3, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 3, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Matrices from Dr. Klaus Gaertner, Weierstrass Institute Berlin." - }, - { - "group": "Garon", - "count": 2, - "documented_meaning": "Matrices from Andre Garon, Univ. of Montreal. 2D Navier-Stokes. (andreg :at the domain: CERCA.UMontreal.CA).", - "source_url": "https://sparse.tamu.edu/Garon", - "coarse_kind_families_interpretive": "fluids/transport", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computational fluid dynamics problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (2)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (2)", - "min_dimension": 3175, - "max_dimension": 13535, - "dimension_ratio": 4.262992125984252, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 84723, - "max_nnz": 373235, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 2, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 2, - "cohesion": "coherent family/series", - "official_notes": "Matrices from Andre Garon, Univ. of Montreal. 2D Navier-Stokes.\n\n(andreg :at the domain: CERCA.UMontreal.CA).\n2D Finite-Element discretisation of the Navier-Stokes\nEquations. The geometry is a simply a square, with inlet and outlet on \nopposing sides.\n\nThe \"matrix_big\" file got truncated in transmission...\n\nPerformance of various solvers:\n\n--------------------------------------------------------------------------------\nTo: andreg :at the domain: CERCA.UMontreal.CA\ncc: bramley :at the domain: cs.indiana.edu, davis :at the domain: cise.ufl.edu\nDate: Mon, 13 May 1996 13:48:24 -0400\nFrom: Tim Davis \n\nAndre,\n\nHere are some initial results with your matrices.\nRun time is the complete factorization time (ordering,\nsymbolic factorization, and numerical factorization).\n\nThis is on a Sun UltraSparc, with 128MB of memory,\n2GB of swap space. Peak performance in the BLAS is about\n80 mflops in double precision, 160 mflops in single\n(I'm using double prec.).\n\nThe methods used:\n\n\tUMFPACK2: default parameters, version 2.1 (at my ftp site,\n\t\tbut very similar to version 2.0 in netlib).\n\tSuperLU-mmd: SuperLU with MMD preordering on A'*A\n\tSuperLU-camd: SuperLU with COLAMD preodering on A'*A\n\t\t(COLAMD is a code I'm working on).\n\tMA48-def: from Harwell Subr. Library. default parameters,\n\t\tsuccessor to MA28.\n\tMA48-sym: MA48 but strictly with symmetric pivoting only.\n\tMA42: from Harwell Subr. Library. A unifrontal code.\n\nBoth matrix_small and matrix_medium are easily factorizable.\nPerhaps not as fast as an iterative method might work, but\nthey can be factorized.\n\n\"matrix_small\" n=3175, nz=88927.\n\n\t\ttime(sec)\tnz in LU\tflop count\nUMFPACK2\t3.95\t\t754158\t\t0.1318D+09\nSuperLU-mmd\t4.62\t\t700626\t\t0.1061D+09\nSuperLU-camd\t3.94\t\t625623\t\t0.0788D+09\nMA48-def 17.8\t\t646982\t\t0.1371D+09\nMA48-sym 11.9\t\t485733\t\t0.0693D+09\nMA42\t 10.9\t 1044187\t\t0.0772D+09\n\n\"matrix_medium\" n=13535, nz=390607\n\n\t\ttime(sec)\tnz in LU\tflop count\nUMFPACK2\t116.9\t\t8298907\t\t0.5235D+10\nSuperLU-mmd\t 48.1\t\t4725883\t\t0.1251D+10\nSuperLU-camd\t 54.5\t\t4673004\t\t0.1280D+10\nMA48-def\t472.1\t\t6297647\t\t0.4531D+10\nMA48-sym\t293.9\t\t3826925\t\t0.1520D+10\nMA42\t\t128.1\t\t9020565\t\t1.3587D+10\n\nIt looks like UMFPACK2 is getting unacceptable fill-in. The\ndiagonal is good - MA48-sym works better than MA48-def.\nSuperLU seems to work the best ... HOWEVER ... you can't use\njust SuperLU alone. It needs a column preordering. I shudder to\nthink what would happen if you didn't preordering the columns\n(maybe I should try it).\n\nThis is from a fluid flow problem, right? UMFPACK seems to have\ntrouble with those. Can you email me the details?\n\n\n\n\n\n\n--------------------------------------------------------------------------------\nSubject: more results\nDate: Fri, 17 May 1996 16:23:22 -0400\nFrom: Tim Davis \n\n\nAndre,\n\nI ran MA41, the \"symmetric-pattern\" multifrontal method (a new version,\nto appear in the next release of the Harwell Subroutine Library), on\nyour matrices.\n\nHere are the results. Basically, MA41 is quite a bit faster for these\nmatrices than UMFPACK (=MA38). I doubt there's much I can do to beat\nthese run times. It would be possible to improve UMFPACK, I think,\nso it wouldn't be as slow as it is. MA41 is also faster than\nSuperLU, MA42, and MA48 for these matrices. MA41 is also more\naccurate than UMFPACK2, probably because of the smaller flop count.\n\nThese matrices have symmetric nonzero pattern.\n\nDo you have problems that lead to matrices with unsymmetric nonzero\npattern?\n\nThanks,\nTim\n\np.s., you'll need a wide screen to read these results.\n--------------------------------------------------------------------------------\n\nThis is on a lightly loaded UltraSparc, May 15-17, 1996,\n128MB memory, 2GB swap space. Large differences between CPU and\nWALL CLOCK time indicate swap-space thrashing of the method.\n\nMethod.A is the method using DEFAULT parameters, except UMFPACK uses u=0.01.\nMethod.B uses non-default parameters.\n\n MA41.A: no max transversal, u=0.01 (defaults)\n MA41.B: max transversal, u=0.01\n UMF*.A: BTF and no symmetric preference (defaults), u=0.01\n UMF*.B: no BTF, and with symmetric preference, u=0.01\n\n\n\"total time\" is analysis+factorize, not including solve time.\nAll times in seconds.\n\nmethod total time | num. factorize | solve time | nz in | flop | error\n cpu wall | cpu wall | cpu wall | LU | count | (max norm)\n\n\nmatrices/Garon/garon1.rua\nMA41.A 0.835 0.938 0.729 0.810 0.036 0.078 357037 2.6470e+07 5.15D-12\nMA41.B 0.869 0.941 0.730 0.790 0.036 0.037 357037 2.6470e+07 5.15D-12\nUMF2.A 3.844 3.951 3.304 3.332 0.068 0.082 728318 1.3160e+08 0.6468E-04\nUMF2.B 2.501 2.504 2.103 2.110 0.057 0.057 606371 8.2460e+07 0.3974E-07\nUMF1.A 4.077 4.087 3.863 3.889 0.083 0.083 821459 1.1397e+08 0.1419E-03\nUMF1.B 2.102 2.109 1.641 1.643 0.058 0.058 558734 5.5743e+07 0.7491E-06\n\n\nmatrices/Garon/garon2.rua\nMA41.A 7.645 8.046 7.133 7.492 0.215 0.217 2396585 3.4220e+08 2.76D-11\nMA41.B 7.785 8.187 7.141 7.499 0.216 0.224 2396585 3.4220e+08 2.76D-11\nUMF2.A 76.076 81.396 82.050 167.636 1.013 27.110 7322867 3.2870e+09 0.6202E+02\nUMF2.B 31.693 31.801 30.761 31.005 0.386 0.386 4556143 1.2800e+09 0.5692E-04\nUMF1.A 73.480 82.058 86.705 239.346 1.098 28.960 7100507 2.9960e+09 0.7279E-01\nUMF1.B 25.236 25.665 25.874 26.828 0.408 0.408 4099241 9.2035e+08 0.1846E-04" - }, - { - "group": "Goodwin", - "count": 12, - "documented_meaning": "Fluid mechanics matrices from Ralph Goodwin Matrices from 2018:", - "source_url": "https://sparse.tamu.edu/Goodwin", - "coarse_kind_families_interpretive": "fluids/transport", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 2, - "normalized_kinds": "computational fluid dynamics (10); computational fluid dynamics problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (12)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (12)", - "min_dimension": 1182, - "max_dimension": 178437, - "dimension_ratio": 150.96192893401016, - "size_span": "wide (>100\u00d7)", - "min_nnz": 32282, - "max_nnz": 5778545, - "shape_profile": "square only", - "square": 12, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 12, - "cohesion": "coherent family/series", - "official_notes": "Fluid mechanics matrices from Ralph Goodwin\n\nMatrices from 2018:\n\n Finite element discretization of the Navier-Stokes and similar transport\n equations on various geometries:\n\n Goodwin_010\n Goodwin_013\n Goodwin_017\n Goodwin_023\n Goodwin_030\n Goodwin_040\n Goodwin_054\n Goodwin_071\n Goodwin_095\n Goodwin_127\n\nMatrices from 1995:\n\nThe goodwin.rua matrix:\n\n\tA finite-element matrix from Ralph Goodwin, Chemical Engineering Dept.,\n\tUniv. of Illinois at Urbana-Champaign. A fluid mechanics problem.\n\tFrom a nonlinear solver (Newton iterations. Uses several back\n\tsubstitutions for the Sherman-Morrison formula to deal with a dense\n\trow (not given in the matrix).\n\n\tThe matrix was originally obtained in triplet (i,j,a_ij) format, with\n\tlots of duplicate entries (from unassembled finite elements). \n\tConverted (and duplicates assembled) into RUA format by Tim Davis.\n\n\temail: ralph :at the domain: wsnext.scs.uiuc.edu \n\nThe rim.rua matrix: n=22560, nz=1014951. Originally in triplet format with\n\tduplicate entries. See comments below:\n\n\n\tFrom ralph :at the domain: wsnext.scs.uiuc.edu Mon May 22 16:31:26 1995\n\n\tTim,\n\n\tI have another matrix that may interest you. It is similar \n\tto the matrix that you added to the Boeing-Harwell library.\n\tTHere are 22560 equations. The frontwidth is about 144.\n\tHowever pivoting consideration cause the frontwidth to grow\n\tto 2174. I have not tried using UMFPACK with this matrix\n\tbecause my code requires 287 Mbytes to store the LU factors,\n\tand therefore goes out-of-core. Still if you are\n\tinterested in a matrix that appears to have severe pivoting\n\tproblems and is big then I have one for you.\n\n\tRalph Goodwin\n\n\tFrom ralph :at the domain: wsnext Fri May 26 08:03:45 1995\n\n\tTim,\n\n\tI solved my problem of excessive front width growth by rescaling\n\tthe rows of the matrix, so I guess I sounded a false alarm.\n\tPreviously, the front would grow from 155 to 2300, now it stays\n\tat 144. I rescaled by the 1 norm of each row.\n\n\tRalph\n\n\tFrom ralph :at the domain: wsnext.scs.uiuc.edu Fri May 26 14:27:21 1995\n\n\tTim,\n\n\tI ftped the matrix to you. It is in the\n\tdirectory incoming and is named rim.mat.gz. It is\n\ta 15Mb file that uncompresses to about 42Mb. The matrix\n\tis stored in triple format with the first two lines\n\tbeing the order of the matrix and the number of nonzeroes.\n\tThere are duplicate nonzeroes that must be summed.\n\n\tEarlier I said that I scaled the rows by their 1 norm.\n\tThis is not exactly correct. I scaled by the 1 norm \n\tof the *unsummed* nonzeroes in the row, which is of\n\tcourse different than the 1 norm of the row.\n\n\tRalph\n\n\n\tFrom ralph :at the domain: wsnext.scs.uiuc.edu Fri May 26 14:53:18 1995\n\n\n\tYes, it is from the same application. The physical properties\n\tare different (viscosity, density, ...) but otherwise it is\n\tthe same problem. One more thing about this problem is that\n\tit involves not only solving the steady Navier-Stokes (N-S) equations\n\tbut also solving a pair of elliptic mesh generation (EMG) equations.\n\tThe EMG equations are coupled to the N-S equations by\n\tboundary conditions. So the system of equations represented\n\tby the matrix I sent you (and also goodwin.rua) is both\n\tN-S equations and EMG equations all discretized using quadrilateral\n\tbiquadratic finite elements. The main point is that the matrix\n\tis not just a discretization of the N-S equations.\n\n\tRalph\n\n\nMinor change, 3/31/03: \"rua\" changed to \"RUA\", in rim.rua header." - }, - { - "group": "Graham", - "count": 1, - "documented_meaning": "Finite element matrix from Dave Graham, Univ. of Illinois. GRAHAM1 Obtained Feb. 18, 1998.", - "source_url": "https://sparse.tamu.edu/Graham", - "coarse_kind_families_interpretive": "fluids/transport", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computational fluid dynamics problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 9035, - "max_dimension": 9035, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 335472, - "max_nnz": 335472, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "Finite element matrix from Dave Graham, Univ. of Illinois.\n\nGRAHAM1\t\tObtained Feb. 18, 1998.\n\t\tJacobian from a Galerkin finite element discretization of\n\t\tthe Navier-Stokes equations applied to a two-phase fluid flow\n\t\tproblem.\n\n\t\tOriginal problem was in finite-element (unassembled) form.\n\t\tWhen converted to RUA type, 93877 entries with duplicate\n\t\trow & column indices were summed." - }, - { - "group": "Grund", - "count": 22, - "documented_meaning": "Matrices from Friedrich Grund, WIAS Berlin. Chemical process simulation. --------------------------------------------------------------------------------", - "source_url": "https://sparse.tamu.edu/Grund", - "coarse_kind_families_interpretive": "chemistry/biology; electrical/circuit/device; other", - "dominant_coarse_family_interpretive": "chemistry/biology", - "dominant_coarse_family_share": 0.7272727272727273, - "normalized_kind_count": 3, - "normalized_kinds": "chemical process simulation problem (16); economic problem (4); circuit simulation problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (22)", - "curator_structure_count": 2, - "curator_structures": "unsymmetric (21); symmetric (1)", - "min_dimension": 7, - "max_dimension": 57735, - "dimension_ratio": 8247.857142857143, - "size_span": "wide (>100\u00d7)", - "min_nnz": 15, - "max_nnz": 275094, - "shape_profile": "square only", - "square": 22, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 1, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "multi-domain/mixed", - "official_notes": "Matrices from Friedrich Grund, WIAS Berlin. Chemical process simulation.\n\n--------------------------------------------------------------------------------\n\nTo: davis :at the domain: cise.ufl.edu\nSubject: Matrices to your subdirectory\nDate: Tue, 07 Oct 97 10:53:02 +0200\nFrom: \"F. Grund, WIAS Berlin\" \n\n\nI put the files\n\nbayer.tar.gz \nchem.tar.gz \npoli.tar.gz\n\nin your subdirectory pub/faculty/davis/incoming\n\nThe matrices in bayer.tar.gz are of the german chemical industry\n (chemical process simulation). \n\nIn chem.tar.gz you find matrices of demonstration examples of the\n chemical process simulator SPEEDUP.\n\nThe two matrices in poli.tar.gz are of account of capital links. The\n problem is:\n\nDetermine the inverse matrix of I-A. I is the unity matrix and A a matrix\n with elements a(i,j) and\n\n\t0 <= a(i,j) < 1\n\nThe elements b(i,j) of B = (I-A)**(-1) are >= 0. These matrices are\n very sparse and the fill in is very small.\n\n\n\nBest Regards,\nFriedrich Grund" - }, - { - "group": "Gset", - "count": 67, - "documented_meaning": "Random graphs from Yinyu Ye, Univ. of Iowa. Obtained from ftp://dollar.biz.uiowa.edu/pub/yyye/Gset/", - "source_url": "https://sparse.tamu.edu/Gset", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 2, - "normalized_kinds": "undirected random graph (34); undirected weighted random graph (33)", - "curator_value_type_count": 2, - "curator_value_types": "binary (34); integer (33)", - "curator_structure_count": 1, - "curator_structures": "symmetric (67)", - "min_dimension": 800, - "max_dimension": 10000, - "dimension_ratio": 12.5, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 3200, - "max_nnz": 82918, - "shape_profile": "square only", - "square": 67, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 67, - "structurally_symmetric_square": 67, - "known_spd": 0, - "value_profile": "real only", - "binary": 34, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Random graphs from Yinyu Ye, Univ. of Iowa.\n\nObtained from ftp://dollar.biz.uiowa.edu/pub/yyye/Gset/\nSee also http://dollar.biz.uiowa.edu/col/ye/\n\nFrom 'rudy', an automatic graph generator.\n\n Three types of patterns:\n \teven:\tevenly, randomly distributed entries\n \ttor:\t2D torus. All nodes have degree 4. Wrapped in both directions\n \tskew:\trandomly distributed entries, but the avg degree of nodes 1:n\n \t\tappears to be a decaying function, from high (node 1) to low\n \t\t(node n)\n\n Matrices are binary or have values 0,1,-1. The distribution of +/-1 are\n random. Entries are in the strictly lower triangular part only.\n The original G* files in ftp://dollar.biz.uiowa.edu/pub/yyye/Gset/\n contain no duplicate entries, and are strictly upper triangular.\n\n mat\tn\tnz\ttype\tbinary\tcomments\n -------------------------------------------------------------------------------\n Matrices G1-G10: exactly 3% of the upper tri. entries are nonzero\n G1\t800\t19176\teven\tyes\t= spones (G6)\n G2\t800\t19176\teven\tyes\t= spones (G7)\n G3\t800\t19176\teven\tyes\t= spones (G8)\n G4\t800\t19176\teven\tyes\t= spones (G9)\n G5\t800\t19176\teven\tyes\t= spones (G10)\n G6\t800\t19176\teven\tno\n G7\t800\t19176\teven\tno\n G8\t800\t19176\teven\tno\n G9\t800\t19176\teven\tno\n G10\t800\t19176\teven\tno\n\n -------------------------------------------------------------------------------\n G11\t800\t1600\ttor\tno\t8-by-100\n G12\t800\t1600\ttor\tno\t16-by-50\n G13\t800\t1600\ttor\tno\t32-by-25\n\n -------------------------------------------------------------------------------\n G14\t800\t4694\tskew\tyes\t= spones (G18)\n G15\t800\t4661\tskew\tyes\t= spones (G19)\n G16\t800\t4672\tskew\tyes\t= spones (G20)\n G17\t800\t4667\tskew\tyes\t= spones (G21)\n G18\t800\t4694\tskew\tno\n G19\t800\t4661\tskew\tno\n G20\t800\t4672\tskew\tno\n G21\t800\t4667\tskew\tno\n\n -------------------------------------------------------------------------------\n Matrices G22-G31: exactly 1% of the upper tri. entries are nonzero\n G22\t2000\t19990\teven\tyes\t= spones (27)\n G23\t2000\t19990\teven\tyes\tNOT = spones (28), a unique pattern\n G24\t2000\t19990\teven\tyes\t= spones (29)\n G25\t2000\t19990\teven\tyes\t= spones (30)\n G26\t2000\t19990\teven\tyes\t= spones (31)\n G27\t2000\t19990\teven\tno\n G28\t2000\t19990\teven\tno\n G29\t2000\t19990\teven\tno\n G30\t2000\t19990\teven\tno\n G31\t2000\t19990\teven\tno\n\n -------------------------------------------------------------------------------\n G32\t2000\t4000\ttor\tno\t20-by-100\n G33\t2000\t4000\ttor\tno\t25-by-80\n G34\t2000\t4000\ttor\tno\t40-by-50\n\n -------------------------------------------------------------------------------\n G35\t2000\t11778\tskew\tyes\t= spones (G39)\n G36\t2000\t11766\tskew\tyes\t= spones (G40)\n G37\t2000\t11785\tskew\tyes\t= spones (G41)\n G38\t2000\t11779\tskew\tyes\t= spones (G42)\n G39\t2000\t11778\tskew\tno\n G40\t2000\t11766\tskew\tno\n G41\t2000\t11785\tskew\tno\n G42\t2000\t11779\tskew\tno\n\n -------------------------------------------------------------------------------\n Matrices G43-G47: exactly 1% of the upper tri. entries are nonzero\n G43\t1000\t9990\teven\tyes\n G44\t1000\t9990\teven\tyes\n G45\t1000\t9990\teven\tyes\n G46\t1000\t9990\teven\tyes\n G47\t1000\t9990\teven\tyes\n\n -------------------------------------------------------------------------------\n G48\t3000\t6000\ttor\tyes\t60-by-50\n G49\t3000\t6000\ttor\tyes\t100-by-30\n G50\t3000\t6000\ttor\tyes\t120-by-25\n\n -------------------------------------------------------------------------------\n G51\t1000\t5909\tskew\tyes\n G52\t1000\t5916\tskew\tyes\n G53\t1000\t5914\tskew\tyes\n G54\t1000\t5916\tskew\tyes\n\n -------------------------------------------------------------------------------\n Matrices G55-G56: approximately 0.05% of the upper tri. entries are nonzero\n (0.05% is 12497.5, which rounds up to 12498)\n G55\t5000\t12498\teven\tyes\t= spones (56)\n G56\t5000\t12498\teven\tno\n\n -------------------------------------------------------------------------------\n G57\t5000\t10000\ttor\tno\t50-by-100\n\n -------------------------------------------------------------------------------\n G58\t5000\t29570\tskew\tyes\t= spones (58)\n G59\t5000\t29570\tskew\tno\n\n -------------------------------------------------------------------------------\n Matrices G60-G61: approximately 0.035% of the upper tri. entries are nonzero\n (0.035% is 17147.55, which rounds up to 17148)\n G60\t7000\t17148\teven\tyes\t= spones (61)\n G61\t7000\t17148\teven\tno\n\n -------------------------------------------------------------------------------\n G62\t7000\t14000\ttor\tno\t70-by-100\n\n -------------------------------------------------------------------------------\n G63\t7000\t41459\tskew\tyes\t= spones (64)\n G64\t7000\t41459\tskew\n\n -------------------------------------------------------------------------------\n G65\t8000\t16000\ttor\tno\t80-by-100\n\n -------------------------------------------------------------------------------\n G66\t9000\t18000\ttor\tno\t100-by-90\n\n -------------------------------------------------------------------------------\n G67\t10000\t20000\ttor\tno\t100-by-100\n\n -------------------------------------------------------------------------------" - }, - { - "group": "Gupta", - "count": 3, - "documented_meaning": "Linear programming matrices from Anshul Gupta, IBM T. J. Watson Research Center. Discussed in \"Fast and Effective Algorithms for Graph Partitioning and Sparse", - "source_url": "https://sparse.tamu.edu/Gupta", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "optimization problem (3)", - "curator_value_type_count": 1, - "curator_value_types": "binary (3)", - "curator_structure_count": 1, - "curator_structures": "symmetric (3)", - "min_dimension": 16783, - "max_dimension": 62064, - "dimension_ratio": 3.69802776619198, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 2164210, - "max_nnz": 9323427, - "shape_profile": "square only", - "square": 3, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 3, - "structurally_symmetric_square": 3, - "known_spd": 0, - "value_profile": "real only", - "binary": 3, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Linear programming matrices from Anshul Gupta, IBM T. J. Watson Research Center.\n\nDiscussed in \"Fast and Effective Algorithms for Graph Partitioning and Sparse\nMatrix Ordering,\" Ansul Gupta, IBM Research Division, T.J. Watson Research\nCenter, PO Box 218, Yorktown Heights, New York, 10598, USA,\nanshul :at the domain: watson.ibm.com. Tech Report RC 20496 (90799)\nJuly 10, 1996\nComputer Science / Mathematics. The report discusses the WGPP graph\npartitioner.\n\nAll three matrices come from linear programming problems. They\nare matrices of the form A times A transpose, where A is a rectangular\nmatrix with N rows (not provided). All three matrices are positive definite,\nbut values are not provided.\n\n\t N\t NZ (in all of matrix, not just lower triangular part)\n\nGupta1.psa 31,802 2,164,210\n\nGupta2.psa 62,064 4,248,286\n\nGupta3.psa 16,783 9,323,427\n\n\nIf you \"spy\" these matrices in Matlab, you can see that\nthe Gupta1 and Gupta2 matrices have a structure roughly of the form:\n\n\tA D\n\tD A D\n\t D A D\n\t D ...\n\nwhere D is banded, and A is an arrowhead matrix of the form\n\n\tx x x x x x x x x \n\tx x\n\tx x\n\tx x\n\tx x\n\tx x\n\tx x\n\tx x\n\tx x\n\nwhere the \"x\" above, is a dense block. Gupta1 has 3 of these arrowheads,\nand Gupta2 has 6 of them. The Gupta1 matrix is not used in the tech. report,\nbut it is similar to Gupta2, which is used in the experiments reported there.\n\nThe Gupta3 matrix has a form\n\n\t x x\n\t x x\n\t x x\n\t x x\n\t x x\n\t x x\n\t x x\n\t x x\n\t x x x x x x x x x\t<=== the border, with nearly all the nonzeros\n\nwhere the \"x\"'s are block submatrices (dense, or with some sparse pattern).\n\nThe leading 15343-by-15343 submatrix of Gupta3 has only 596,765 nonzeros.\nThis leading submatrix appears to be block diagonal, with dense blocks of\nsize 39, 40, or 41. If a natural ordering is used, L has the same number of\nnonzeros in its leading 15343-by-15343 submatrix as does the lower triangular\npart of the leading 15343-by-15343 submatrix of Gupta3. That is, no fill-in\noccurs in this part of the matrix.\n\nThe remaining 8.72 million nonzeros are in the border (which is very dense).\nCholesky factorization of Gupta3, using the natural ordering, results in a\nfactor L with 5.4 million nonzeros, requiring 2.262 billion floating-point\noperations to compute. This compares with 2.181 billion flops when using an\nordering from WGPP. Essentially, the matrix does not need any permutation to\nreduce fill-in.\n\nThese dense rows cause minimum degree ordering algorithms (AMD, for example)\nto take a long time to run. A simple preprocessing can remove them from\nthe matrix prior to calling AMD. The dense rows/columns can then be\nordered last. This technique can greatly improve AMD's run time." - }, - { - "group": "Hamm", - "count": 6, - "documented_meaning": "Semiconductor simulation matrices from Steve Hamm, Motorola, Inc. Semiconductor Systems Design Technology", - "source_url": "https://sparse.tamu.edu/Hamm", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "circuit simulation problem (6)", - "curator_value_type_count": 1, - "curator_value_types": "real (6)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (6)", - "min_dimension": 2395, - "max_dimension": 170998, - "dimension_ratio": 71.39791231732777, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 13151, - "max_nnz": 958936, - "shape_profile": "square only", - "square": 6, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 4, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Semiconductor simulation matrices from Steve Hamm, Motorola, Inc.\nSemiconductor Systems Design Technology\n3501 Ed Bluestein Blvd., MD-M2, Austin TX 78762 USA\nInternet: hamm :at the domain: ssdt-bluestein.sps.mot.com \nPh: 512-928-6612 \nFax: 512-928-7662 \n\nIn Harwell/Boeing format. One dense right-hand side with each matrix.\n\nadd20.rua\t20-bit adder\nadd32.rua\t32-bit adder\nmemplus.rua\tmemory circuit\n\n\nMatrices added July 2001. New address:\n\n email Steve.Hamm :at the domain: motorola.com\n Motorola Inc. Circuit Simulation Group\n TX32 PL29, 7700 W. Parmer Lane, Austin TX 78729 USA\n\nbcircuit.rua\twith parasitics\nhcircuit.rua\tno parasitics\nscircuit.rua\tmany parasitics" - }, - { - "group": "Hollinger", - "count": 49, - "documented_meaning": "Economic models from Peter Holinger, Intex Solutions, Inc. from MULTIMOD, a multi-country forward-looking macroeconometric model", - "source_url": "https://sparse.tamu.edu/Hollinger", - "coarse_kind_families_interpretive": "other", - "dominant_coarse_family_interpretive": "other", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "economic problem (49)", - "curator_value_type_count": 1, - "curator_value_types": "real (49)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (49)", - "min_dimension": 2880, - "max_dimension": 64089, - "dimension_ratio": 22.253125, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 18229, - "max_nnz": 717620, - "shape_profile": "square only", - "square": 49, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Economic models from Peter Holinger, Intex Solutions, Inc.\n\nfrom MULTIMOD, a multi-country forward-looking macroeconometric model\n(see http://www.imf.org/external/np/res/mmod/mark3/index.htm).\n\nmark3jacTTT[sc]:\n\n These matrices are the large block of a Jacobian matrix from the IMF's\n MULTIMOD Mark3 model stacked for 'TTT' periods. In each 'TTT' pair,\n the 'sc' version is the same matrix after row and column scaling.\n\njan99jac*:\n\n Jacobians from a stacked forward-looking macroeconomic model, \n in this case from the Bank of Canada. \n\ng7jac*:\n\n This set of matrices is from an \"Overlapping Generations Model\"\n used to study the social security systems of the G7 nations.\n It's from CEPII, a research institute in Paris.\n\n\n-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n Peter Hollinger Tel:781-449-6222 Fax:781-444-2318\n peterh :at the domain: intex.com\n Intex Solutions, Inc. 110 A Street Needham, MA 02494-2807\n-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-" - }, - { - "group": "LPnetlib", - "count": 138, - "documented_meaning": "NETLIB Linear Programming problems, www.netlib.org. matrices/LPnetlib/README. Tim Davis, May 15, 1997. (davis :at the domain:", - "source_url": "https://sparse.tamu.edu/LPnetlib", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "linear programming problem (138)", - "curator_value_type_count": 2, - "curator_value_types": "real (108); integer (30)", - "curator_structure_count": 1, - "curator_structures": "rectangular (138)", - "min_dimension": 13, - "max_dimension": 243246, - "dimension_ratio": 18711.23076923077, - "size_span": "wide (>100\u00d7)", - "min_nnz": 22, - "max_nnz": 1408073, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 138, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "NETLIB Linear Programming problems, www.netlib.org.\n\nmatrices/LPnetlib/README. Tim Davis, May 15, 1997. (davis :at the domain:\ncise.ufl.edu)\n\nThe files in this directory are a translation of the linear programming\nproblems in Netlib. You can obtain more information about the Netlib LP\nproblems by sending email to netlib :at the domain:\nornl.gov with the message\n\"send index from lp\".\n\n\n++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\nNOTICE! The Netlib LP problems, in MPS format, should be considered the\n\"canonical\" problems. If you find any problems with different optimal\nobjectives than what is reported in Netlib, then please let me know.\n++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n\nThe Netlib LP problems are in MPS or compressed MPS format. Any compressed\nMPS files were uncompressed using emps.c in the Netlib lp/data directory.\nThe MPS files were then converted to the KAR format using the mpsrd program\nby M.G.C. Resende and G. Veiga. The mpsrd program does not process RANGES,\nso five problems (boeing1, boeing2, forplan, nesm, and seba) do not appear\nhere. The KAR format is written out by mpsrd as follows:\n\nc name - problem name (same as mps name card)\n write(out,'(a8)') name\nc m - number or rows (input)\nc n - number of columns (input)\n write(out,*) m,n\nc ia - pointers to the begining of storage of column\n write(out,*) (ia(i),i=1,n+1)\nc ja - row indices for each non zero entry (input)\n write(out,*) (ja(i),i=1,ia(n+1)-1)\nc a - non zero entries (input)\n write(out,*) (a(i),i=1,ia(n+1)-1)\nc b - right hand side (input)\n write(out,*) (b(i),i=1,m)\nc c - objective vector (minimize)\n write(out,*) (c(i),i=1,n)\nc z0 - initial fixed value for objective\n write(out,*) z0\nc lobnd - lower bounds on variables\n write(out,*) (lobnd(i),i=1,n)\nc upbnd - upper bounds on variables\n write(out,*) (upbnd(i),i=1,n)\n\nMpsrd converts the MPS problem into a single form:\n\n\tminimize c'*x, subject to Ax=b and l<=x<=u.\n\t(and given an initial value of the objective, z0).\n\nNext, the KAR formatted version of the LP problem was converted into\ntwo formats by the readkar.f program, written by Tim Davis. It generates\na single Matlab script, such as lp_afiro.m for the AFIRO problem.\nFor infeasible (lp/infeas) problems, the prefix is lpi_ (lpi_galenet.m for\nthe problem GALENET, for example). This prefix was chosen so that you\nare sure to notice that the problem is infeasible, and also because there\nare two versions of the same problem in Netlib (GREENBEA), one feasible\nand the other not.\n\n\n\n--------------------------------------------------------------------------------\n\n\nThe *.clu files contain the c, l, and u vectors,\nand the scalar z0, for the LP problems held in\nRRA format in the LPnetlib directory. The dense vectors\nappear in order: c, l, u, and then z0. One entry is held\non each line. \n\nThe RRA files contain A and b, in Harwell/Boeing format.\n\nThe LP problems are all of the form\n\n\tminimize c'*x, subject to Ax=b and l<=x<=u\n\tand where z0 is the initial value of the\n\tobjective." - }, - { - "group": "Li", - "count": 2, - "documented_meaning": "3D magnetohydrodynamics finite element matrix from Ben Li, Washington State Univ. A magnetohydrodynamics problem with 5 variables. Collected and converted", - "source_url": "https://sparse.tamu.edu/Li", - "coarse_kind_families_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "electromagnetics problem (2)", - "curator_value_type_count": 2, - "curator_value_types": "real (1); binary (1)", - "curator_structure_count": 2, - "curator_structures": "unsymmetric (1); symmetric (1)", - "min_dimension": 22695, - "max_dimension": 22695, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 1215181, - "max_nnz": 1350309, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 2, - "known_spd": 0, - "value_profile": "real only", - "binary": 1, - "geometric_2d3d": 2, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "3D magnetohydrodynamics finite element matrix from Ben Li, Washington State Univ.\nA magnetohydrodynamics problem with 5 variables. Collected and converted\nto Harwell/Boeing format by Tim Davis.\n\nEmail: li :at the domain:\nmme.wsu.edu. http://www.mme.wsu.edu/people/bql.htm\n\nOrder: 22,695 with 1,350,309 entries. 1,215,181 nonzero entries.\nIf the specified zero entries (135,128 of them)\nin li.rua are included, the pattern is symmetric.\n\nDoing a Cholesky factorization (if it were possible) would lead\nto 19,896,926 entries in L if *no* reordering is done, and about 2e10\nfloating-point operations. A symmetric-pattern LU code, such as MA41,\ncould get close to that (with about 4e10 ops), I would think, assuming \nthat the diagonal entries are reasonably large in value. The smallest\nnonzero entry on the diagonal is 0.09, which is not too bad.\n\nFor comparison, a skyline solver got 40,262,417 entries\nin L and U. That's almost the same as a non-skyline code\n(L+L' for Cholesky would have about 39.7 million entries).\nSo a skyline solver is doing very well for this matrix.\n\nOn an 167 Mhz UltraSPARC-I, AMDBAR takes only 2.06 seconds to get a\npermutation P such that the L (for Cholesky factorization) has 30,408,134\nentries ... much worse than when using the given ordering, which is unusual.\n\nDoing a nested dissection ordering using Chaco (from Sandia) as the\ngraph partitioner gives an ordering such that the Cholesky factorization\nhas 13,142,711 nonzeros in L. It took 81.7 seconds to get this\nordering on an UltraSPARC-1. I used John Gilbert's mesh partitioner code at\nftp://parcftp.xerox.com in /pub/gilbert/meshpart.uu which as a Matlab\ninterface to Chaco, and P = ndperm ('chaco', 8, B) in Matlab,\nwhere B is the pattern that includes the explicit zeros.\nThis ordering results in only 1.4e10 flops for Cholesky, or about\ntwice that for a symmetric-pattern LU factorization, assuming no \"delayed\npivoting\" becaose of small entries on the diagonal.\n\nli.psa is the symmetric pattern of li.rua, where all specified entries of\nli.rua are included, including entries with zero value.\n\nUsing the li.psa version, Bruce Hendrickson reports the following.\nUsing BEND on an SGI Onyx, in 8 seconds and ordering was obtained\nin which L has 13,052,539 nonzeros and about 1.37e10 flops for a\nCholesky factorization.\n\nTim Davis, June 23, 1999" - }, - { - "group": "Mallya", - "count": 18, - "documented_meaning": "Chemical process simulation matrices, J. Mallya, S. Zitney, M. Stadtherr. Reference:", - "source_url": "https://sparse.tamu.edu/Mallya", - "coarse_kind_families_interpretive": "chemistry/biology", - "dominant_coarse_family_interpretive": "chemistry/biology", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "chemical process simulation problem (18)", - "curator_value_type_count": 1, - "curator_value_types": "real (18)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (18)", - "min_dimension": 1477, - "max_dimension": 70304, - "dimension_ratio": 47.599187542315505, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 18427, - "max_nnz": 1528092, - "shape_profile": "square only", - "square": 18, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Chemical process simulation matrices, J. Mallya, S. Zitney, M. Stadtherr.\n\nReference:\nZitney, S. E. and Mallya, J. and Davis, T. A. and Stadtherr, M. A.,\nMultifrontal Techniques for Chemical Process Simulation on Supercomputers,\nFifth International Symposium on Process Systems Engineering, May 1994,\nKyongju, Korea (available via anonymous ftp to ftp.cis.ufl.edu as the\nfile cis/tech-reports/tr94/tr94-002.ps.Z).\n\nLight hydrocarbon recovery problems - large-scale chemical process\nsimulation using rigorous equation-based models. Note that these matrices\nseem to be well-suited to unifrontal methods.\n\n\t ---Mallya, Jayarama U\n\t Dept of Chemical Engg\n\t U of Illinois.\n\t mally :at the domain: turing.scs.uiuc.edu\n\n\t NOTE: The 1k problem is the same as the old lhr (light hydrocarbon)\n\t recovery problem. The other problems are constructed from the 1k\n\t problem. Some of the matrices are ill-conditioned, but this is OK,\n\t since even with a poor residual the nonlinear solver rapidly\n\t converges. Subsequent linear systems (not provided - these are\n\t the first in the sequences) can be solved with a much lower residual.\n\nThese matrices occur in steady-state chemical process simulation problems.\nThe problems involve a light hydrocarbon recovery process. The larger\nproblems are extensions of the smallest, involving more chemical species\nwith larger and/or more chemical processing units. These problems were\ndeveloped by Jayarama Mallya and Mark Stadtherr at the University of\nIllinois (Department of Chemical Engineering). The matrices are taken\nfrom an early iteration in a Newton-based solution procedure starting \nfrom a poor initial guess. Automatic scaling options in the simulation\ncode were turned off. The matrices are ill-conditioned and contain hard\nzeros. Please relate experiences with these matrices to Mark Stadtherr\n(m-stadtherr :at the domain: uiuc.edu).\n\nThe rank of lhr71 was estimated at 70301 (it is 70304-by-70304) by MA48.\n\n================================================================================\n\nMay 16, 1997:\n\nSome of the ill-conditioned behaviour of these matrices is due to a modelling\nerror. Better-conditioned versions have been derived (lhr04c, lhr07c, etc.),\nwith permission of the creators of these matrices. The two smallest\nmatrices (lhr01 and lhr02) are already reasonably well-conditioned and do not\nrequire any modifications.\n\nThe corrections were made as follows (see the FIX subdirectory for the\ncode to do this):\n\n\t1) the matrices were converted to triplet format, with explicit zeros\n\t\treplaced by the value 1e-300. The modified matrices were\n\t\tthen loaded into Matlab. Zeros were changed to 1e-300 so\n\t\tthat Matlab would not drop the explicit zeros.\n\n\t2) the matrices were fixed, in Matlab. First, the matrix A is\n\t\tpermuted to block triangular form (PAQ), with a zero-free\n\t\tdiagonoal. Next, 0.001 is\n\t\tadded to each entry (PAQ)_ii for which (PAQ)_ii => 0,\n\t\tand -0.001 if added to each (PAQ)_ii for which (PAQ)_ii is\n\t\t< 0. The correction to A is then P'*F*Q', where F is\n\t\tdiagonal, and F_ii is the correction made to (PAQ)_ii.\n\n\t3) the corrected matrix C = A+(P'*F*Q') was then written in the\n\t\tHarwell/Boeing format. It has the same pattern of its\n\t\tentries as A." - }, - { - "group": "Mulvey", - "count": 2, - "documented_meaning": "Multistage stochastic financial modelling. J. Mulvey, Princeton. Matrices provided by Ed Rothberg, Silicon Graphics, Inc.", - "source_url": "https://sparse.tamu.edu/Mulvey", - "coarse_kind_families_interpretive": "other", - "dominant_coarse_family_interpretive": "other", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "economic problem (2)", - "curator_value_type_count": 2, - "curator_value_types": "real (1); binary (1)", - "curator_structure_count": 1, - "curator_structures": "symmetric (2)", - "min_dimension": 74752, - "max_dimension": 74752, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 596992, - "max_nnz": 596992, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 2, - "structurally_symmetric_square": 2, - "known_spd": 1, - "value_profile": "real only", - "binary": 1, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Multistage stochastic financial modelling. J. Mulvey, Princeton.\nMatrices provided by Ed Rothberg, Silicon Graphics, Inc.\nFrom the paper:\n\n\tA. Berger, J. Mulvey, E. Rothberg, and R. Vanderbei,\n\t\"Solving multistage stochastic programs using tree dissection,\"\n\ttech. report SOR-97-07, Program in Statistics and Operations\n\tResearch\n\nEd Rothberg, email: rothberg :at the domain: multnomah.engr.sgi.com\nJohn Mulvey, email: mulvey :at the domain: macbeth.Princeton.edu \n\nA stochastic programming matrix in an interior-point algorithm\nfor financial portfolio optimization.\n\nRandom nonzero values added (finan512.rsa) by Tim Davis. The values were\nthen modified to ensure that the matrix was symmetric positive definite.\n\nThis matrix is quite strange, and appears to be highly sensitive to\ntie-breaking issues, or some other phenomenon... See the paper, above,\nfor a description of the phenomenon.\n\nThe following ordering algorithms were run\non a Sparc-10. AMDperm is the Approximate Minimum Degree method, with\nvarious \"elbow room\" sizes (see TR-94-038),\nbased on upper bounds on the external degree.\nAMDtry3 is AMD with a different tie-breaking strategy. oldAMDt is the same\nas MA27, except that exact degree computations are replaced with an upper\nbound on the true degree. initMD is MA27, and origMD is a modification in MUPS\nthat ignores the computation of degrees above a certain threshold. MMD is\nJoseph Liu's multiple minimum degree algorithm (with delta = 0, 1, and 2).\nND is the nested dissection algorithm in Sparspak, and QMD is the minimum\ndegree ordering algorithm in Sparspak. The columns in the table are\nthe number of nonzeros in L, the Mflops needed to compute L, the ordering\ntime, the number of garbage collections, the memory usage (excluding O(n)-size\narrays), and the number of size-n work arrays.\n\nNOTE: Lnz is the number of nonzeros in the strictly lower diagonal part.\nThe A+AT number is the number of off-diagonal nonzeros in A+A^T.\nSince this is a symmetric matrix, that's just the nonzeros in A minus n.\n\nMatrix Portfolio/finan512.psa.gz n 74752 Anz 596992 A+AT 522240 -\nMethod:delta/elbow Lnz LL^Tmflops time comp memory O(n)usage\nAMDperm: 3361993 1035.385920 5.53 0 833747 8\nAMDperm:4 3361993 1035.385920 5.47 0 833747 8\nAMDperm:3 3361993 1035.385920 5.63 1 776090 8\nAMDperm:2 3361993 1035.385920 5.66 1 671745 8\nAMDperm:1 3361993 1035.385920 5.64 1 671745 8\nAMDtry3: 2522769 536.937792 5.18 0 797305 9\noldAMDt: 9285069 14968.698880 5.90 0 996344 8\n initMD: 9285069 14968.315904 10.07 0 1000263 7\n origMD: 9237356 14654.392320 6.56 0 1036206 7\n MMD:0 6432433 5933.023232 292.65 0 522240 7\n MMD:1 6432433 5933.025280 291.97 0 522240 7\n MMD:2 6432433 5933.025280 278.82 0 522240 7\n ND: 8530875 3412.739840 6.20 0 522240 5\n QMD: 4345895 2121.354368 970.03 0 522240 9\n\n\nThese minimum degree algorithms do show large variations in ordering times for\nother matrices, but they usually produce comparable results. This matrix\n(finan512) is a very unusual exception. The bcsstk30 matrix shows\nmore typical relative results, for example:\n\n\nMatrix HB/bcsstk30.psa n 28924 Anz 2043492 A+AT 2014568 -\nMethod:delta/elbow Lnz LL^Tmflops time comp memory O(n)usage\nAMDperm: 3768625 899.876992 2.30 0 2088703 8\nAMDperm:4 3768625 899.876992 2.31 0 2088703 8\nAMDperm:3 3768625 899.876992 2.29 0 2088703 8\nAMDperm:2 3768625 899.876992 2.31 0 2088703 8\nAMDperm:1 3768625 899.876992 2.76 1 2072417 8\nAMDtry3: 3764679 916.359040 2.32 0 2088942 9\noldAMDt: 4518625 1363.998720 2.23 0 2107723 8\n initMD: 4583033 1409.133568 2.66 0 2107538 7\n origMD: 4487456 1336.230912 2.73 0 2135673 7\n MMD:0 3849328 938.700288 4.63 0 2014568 7\n MMD:1 3863974 955.433984 4.53 0 2014568 7\n MMD:2 3886867 963.582848 4.29 0 2014568 7\n ND: 5743266 2204.590848 12.11 0 2014568 5\n QMD: 4689554 1586.877184 59.55 0 2014568 9" - }, - { - "group": "Nasa", - "count": 11, - "documented_meaning": "NASA matrices obtained from Alex Pothen, ICASE and Old Dominion Univ. Converted to HB format by Tim Davis. The following is the original", - "source_url": "https://sparse.tamu.edu/Nasa", - "coarse_kind_families_interpretive": "structures/materials", - "dominant_coarse_family_interpretive": "structures/materials", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "structural problem (11)", - "curator_value_type_count": 2, - "curator_value_types": "binary (6); real (5)", - "curator_structure_count": 2, - "curator_structures": "symmetric (8); unsymmetric (3)", - "min_dimension": 1824, - "max_dimension": 54870, - "dimension_ratio": 30.082236842105264, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 23492, - "max_nnz": 2677324, - "shape_profile": "square only", - "square": 11, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 8, - "structurally_symmetric_square": 8, - "known_spd": 5, - "value_profile": "real only", - "binary": 6, - "geometric_2d3d": 11, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "NASA matrices obtained from Alex Pothen, ICASE and Old Dominion Univ.\nConverted to HB format by Tim Davis. The following is the original\nREADME file, and email discussing the *.nas format.\n\nThe original *.nas files have coordinate information for the nodes of the\ngraph. They are located in the Conversion/ subdirectory, if you're interested.\n\n--------------------------------------------------------------------------------\n\nftp://ftp.cs.odu.edu/pub/incoming/kumfert/README\n\nWARNING:\nThe contents of this ftp site are temporary and subject\nto change without notice.\n\nDISCLAIMER:\nNothing free is guaranteed.\n\nCONTENTS:\nThe current contents are matrices in various formats.\nAll are compressed and should be ftp'd using binary mode.\nThe two major formats are .nas and .hb for NASA and \nHarwell-Boeing formats respectively. The current list includes:\n\n\tbarth.nas.Z\n\tbarth4.nas.Z\n\tbarth5.nas.Z\n\tnasa1824.hb.Z\n\tnasa2146.hb.Z\n\tnasa2910.hb.Z\n\tnasa4704.hb.Z\n\tnasasrb.hb.Z\n\tpwt.nas.Z\n\tshuttle_eddy.nas.Z\n\tskirt.nas.Z\n\nAlso included are some files for reading in these problems into\nthe Matlab(R) environment. \n\n hbo2mat.f\n hbo.m\n readnasa.m\n\nNasa files can be read in using the readnasa.m utility. Loading\nin Harwell-Boeing files is a two step process. First one must\nconvert the .hb file to a .mat file using hbo2mat. Compilation\ninstructions are included in the file. Then within Matlab, \nuse hbo.m to properly load the .mat file. The latter two\nfiles were written by Cleve Moler, The MathWorks. The readnasa.m\nfile was writtem by Gary Kumfert, Old Dominion University.\n\nComments/suggestions can be sent to Gary Kumfert .\n\nMatlab is a registered trademark of The MathWorks Inc.\n\n--------------------------------------------------------------------------------\n\n\nDate: Thu, 1 Jun 1995 12:59:00 -0400\nFrom: Pothen Alex \nTo: davis :at the domain: cis.ufl.edu\nSubject: Re: problems\n\nproblem.nas is an inhouse format used by us. \nIt lists n coordinates of the nodes \nx_i y_i (z_i) \nand then tuples or triples\ni j a_{ij} \nThe first line lists n (no. of nodes) and \nnnz (the no. of nonzeros).\n\n--Alex \n\n\nFrom: Alex Pothen \nDate: Thu, 1 Jun 1995 18:25:15 -0400\nTo: davis :at the domain: cis.ufl.edu\nSubject: Re: matrices\n\nTim, \nI am working at Icase and don't have access to barth\nI will check and let you know tomorrow.\n--Alex \nP.S. If it is a nas file, I trust you are using \nreadnasa.m file to read it. \nIt is an airfoil problem, so you should be able to \nvisualize it using gplot. \n--Alex \n\nFrom: Alex Pothen \nDate: Fri, 2 Jun 1995 10:06:46 -0400\nTo: davis :at the domain: cis.ufl.edu\nSubject: Re: matrices\n\nIt is possible that the original numbering of barth makes \nit look random in a spy plot.\n--Alex" - }, - { - "group": "Nemeth", - "count": 26, - "documented_meaning": "A set of matrices from Karoly Nemeth, theoretical chemistry. currently postdoctoral fellow at Rice University with Prof. G.E. Scuseria", - "source_url": "https://sparse.tamu.edu/Nemeth", - "coarse_kind_families_interpretive": "chemistry/biology", - "dominant_coarse_family_interpretive": "chemistry/biology", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "theoretical/quantum chemistry problem (26)", - "curator_value_type_count": 1, - "curator_value_types": "real (26)", - "curator_structure_count": 1, - "curator_structures": "symmetric (26)", - "min_dimension": 9506, - "max_dimension": 9506, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 394808, - "max_nnz": 1511760, - "shape_profile": "square only", - "square": 26, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 26, - "structurally_symmetric_square": 26, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "A set of matrices from Karoly Nemeth, theoretical chemistry.\ncurrently postdoctoral fellow at Rice University with Prof. G.E. Scuseria\n(as of Summer 1999).\n\nSymmetric-positive semidefinite matrices.\n\n\n--------------------------------------------------------------------------------\n\nDear Prof Davis,\n\nThe problem, where my matrices come from, is the Newton-Schultz iteration \n(NSI), which is used for the calculation of the matrix sign function. \nIf we denote the \"sign\" of a hermitian matrix \"F\" by \"sign(F)\",\nsign(F) can be calculated by the NSI, like:\n\n Z_{k+1} = Z_{k} + 1/2 * (I - Z_{k}^2) * Z_{k} .\n\n\"Z\" is a general symmetric matrix, \"k\" is the iteration step, \"I\" is the\nidentity, Z_{0} = F/||F||, where ||F|| is the absolute value of the\nlargest eigenvalue of F. In my problems NSI converged in most cases in\nless than 20 iterations. \n\nCurrently I am very much interested in the Cholesky-decomposition of\n(I - Z_{k}^2)^2: (I - Z_{k}^2)^2 = T^{t}T (T is upper triangular) . This \nwould help me to construct some orthogonal vectors, which are important \nfor some applications, the vectors are generated by :\n(I - Z_{k}^2)*T^{-1}.\n\nAs NSI converges, the eigenvalues in Z_{k} tend to +1 or -1. (I - Z_{k}^2) \ngets more and more rank deficient (Z_{k}^2 tend to I). Thus normal Cholesky\ncannot be used for the decomposition of (I - Z_{k}^2)^2. This is why I \nneed Cholesky factorization for positive semidefinite matrices. Thus I \nexpect to be able to generate \"r\" orthonormal vectors from (I - Z_{k}^2), \nwhere \"r\" is the rank of (I - Z_{k}^2). \n\nHere I used a somewhat \nmodified version of NSI, where the trace of the matrices is kept fixed. \nHowever the matrices needed to be Cholesky decomposed are the corresponding \n(1-Z_{k}^2)^2 matrices. \n\nBest regards:\n\nKaroly\n\n\n(The Nemeth{K}.rsa file holds the Z_{k} matrix.)\n\n\n\n***********************************\nKaroly Nemeth, Ph.D.\n----------------------------------\nRice University\nDepartment of Chemistry - MS 60\n6100 Main Street\nHouston, TX 77005-1892\n\ne-mail: karoly :at the domain: celaeno.rice.edu\n\noffice phone: before 5.30pm : +713-527-8101/2826 \n after 5.30pm : +713-527-8750/2826 \n fax: +713-285-5155\n***********************************" - }, - { - "group": "Okunbor", - "count": 2, - "documented_meaning": "Acoustic radiation around aft duct fan. Walter Eversman and Daniel Okunbor. These matrices are generated by a finite element code.", - "source_url": "https://sparse.tamu.edu/Okunbor", - "coarse_kind_families_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "acoustics problem (2)", - "curator_value_type_count": 2, - "curator_value_types": "real (1); complex (1)", - "curator_structure_count": 2, - "curator_structures": "symmetric (1); unsymmetric (1)", - "min_dimension": 8184, - "max_dimension": 8205, - "dimension_ratio": 1.0025659824046922, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 125567, - "max_nnz": 127762, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 2, - "known_spd": 1, - "value_profile": "real + complex", - "binary": 0, - "geometric_2d3d": 2, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Acoustic radiation around aft duct fan. Walter Eversman and Daniel Okunbor.\nThese matrices are generated by a finite element code.\n\n(C)1997 Copyright by\nWalter Eversman and Daniel Okunbor\nUniversity of Missouri-Rolla\n\nokunbor :at the domain: cs.umr.edu\n\n--------------------------------------------------------------------------------\n\nNOTE (added Feb 9, 2012): The aft01 matrix is numerically rank deficient. It\nis tagged in the UF Sparse Matrix Collection as 'positive definite' because\nchol works on it, but some eigensolvers report negative eigenvalues. See below\nfor details.\n\n--------------------------------------------------------------------------------\n\nOn 01/26/2012 11:37 AM, Alexander Andrianov wrote:\nJust a quick note somewhat related to the previous discussion. It looks like\naft01 instance is not PD: its smallest eigenvalue is about -2.0312E-03. Just\nconfirmed that by running dense eigensolvers including one from MKL library\n(LAPACK\u2019s DSYEV with 'N' option).\n\nA modified Cholesky factorization however does not choke and factorizes it\nnicely, with normalized residual of about 1.0e-17.\n\nNot sure about its numerical rank though.\n\nBest regards, Alex\n\n--------------------------------------------------------------------------------\n\nFrom: Tim Davis\nSent: Friday, January 27, 2012 5:50 PM\nTo: Alexander Andrianov\nCc: Leslie Foster\nSubject: Re: UF collection matrices info\n\nThis is a wierd matrix. Its numerical rank is one.\n\nchol in MATLAB succeeds, unmodified, with a low residual\n\n[L,p,s]=chol(A,'lower','vector');\nnorm (L*L'-A(s,s),1)\n\nthe diagonal of L has elements in the range 0.1325 to 3.16e7\n\nMaybe the eigenvalue solver is the one giving the 'wrong' answer.\nThis problem might be close to PD, and chol and eig are giving\ndifferent results. I tried this:\n\ne = eig (full(A)) ;\n\nand got a largest eigenvalue of 1e15, and a smallest one as 1.493e-4.\nI don't see the -2e-3 that you got.\n\nSo eig in MATLAB (also uses LAPACK), says the matrix (barely) PD.\n\nNumerically, the positive definiteness of this matrix looks like a\ntoss up.\n\nThanks,\nTim\n\n\n--------------------------------------------------------------------------------\n\nFrom: Alexander Andrianov at SAS.com\nSubject: RE: UF collection matrices info\nDate: January 30, 2012 9:52:55 AM EST\nTo: Davis Tim\nCc: Leslie Foster\n\nYou may be right. I just ran it under various dense MKL solvers and got the\nfollowing:\n\n Smallest\nDSYEV -2.0311917e-3 eigenvalues only ('N' option)\nDYSEV -2.3789104e-1 complete eigendecomposition ('V' option)\nDSYEVR 1.5329287e-4 complete eigendecomposition\nDSYEVD -2.2634604e-3 complete eigendecomposition\n\nAlso ran with my version of DSYEVD and got:\n1.5259751e-4 for eigenvalues only\n9.0656595e-3 for complete eigendecomposition\n\nThe normalized maximum residual was 6.423e-4 (excellent) but the normalized\northogonality loss was 1.347e11 (very bad). This is probably due to the fact\nthat it is rank-deficient.\n\nI don't know if this helps: the LAPACK team labels DSYEV and DSYEVD as being in\ngeneral more accurate (see LAPACK working note 183, page 16) but it doesn't\nimply that they will beat DSYEVR all the time.\n\nI guess this instance can be labeled as 'numerically PD' due to the rounding\nerror during Cholesky factorization which helps make it PD after all.\n\nThank you for running MATLAB on it,\nAlex" - }, - { - "group": "Qaplib", - "count": 8, - "documented_meaning": "Linear programming, lower bounds for quadratic assignment problem, M. Resende. In the paper \"Computing lower bounds for the quadratic assignment problem", - "source_url": "https://sparse.tamu.edu/Qaplib", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "linear programming problem (8)", - "curator_value_type_count": 1, - "curator_value_types": "integer (8)", - "curator_structure_count": 1, - "curator_structures": "rectangular (8)", - "min_dimension": 225, - "max_dimension": 379350, - "dimension_ratio": 1686.0, - "size_span": "wide (>100\u00d7)", - "min_nnz": 1050, - "max_nnz": 1567800, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 8, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Linear programming, lower bounds for quadratic assignment problem, M. Resende.\n\nIn the paper \"Computing lower bounds for the quadratic assignment problem\nwith an interior point algorithm for linear programming,\" (Resende,\nRamakrishnan, and Drezner, O.R. 43 (781-791) 1995), compute an LP-based lower \nbound for the QAP using a interior point algorithm on numerous \nproblems from the QAPLIB. These linear programs are very difficult \nfor direct factorization based LP solvers.\n\nSee http://www.research.att.com/~mgcr/ for more information.\nThe above paper is at http://www.research.att.com/~mgcr/doc/qaplp.ps. \n\n--------------------------------------------------------------------------------\n\nSee ../LPnetlib/README.txt for a description of the *.clu files." - }, - { - "group": "Ronis", - "count": 2, - "documented_meaning": "Crystalline compounds (zeolites,sodalites), D. Ronis, Univ. of McGill. Two matrices from David Ronis, Chemistry Dept, Univ of McGill, Canada.", - "source_url": "https://sparse.tamu.edu/Ronis", - "coarse_kind_families_interpretive": "structures/materials", - "dominant_coarse_family_interpretive": "structures/materials", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "materials problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (2)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (2)", - "min_dimension": 48600, - "max_dimension": 157464, - "dimension_ratio": 3.24, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 1181120, - "max_nnz": 3866688, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 2, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 2, - "cohesion": "coherent family/series", - "official_notes": "Crystalline compounds (zeolites,sodalites), D. Ronis, Univ. of McGill.\n\nTwo matrices from David Ronis, Chemistry Dept, Univ of McGill, Canada.\nEmail: ronis :at the domain: onsager.chem.mcgill.ca.\n\nAdded to the UF sparse matrix collection on March 19, 2001.\n\nHis comments:\n\n The matrix is generated on the fly in a large code that analyses the\n elastic properties (and many other things) in a class of complicated\n crystalline compounds called zeolites or sodalites.\n\n Note that in the actual inversions, I add a positive number times the\n unit matrix to this matrix. The typical value of this scalar is\n anywhere from 1e24 to 1e28--it really doesn't matter from the point of\n view of the calculation I want to do--the matrix is nonsingular in all\n cases.\n\n(in the two matrices, xenon1.rua and xenon2.rua, 1e24 has already been\nadded to the diagonal)." - }, - { - "group": "Rothberg", - "count": 6, - "documented_meaning": "Structural engineering matrices from Ed Rothberg, Silicon Graphics, Inc. Some of the matrix names and file names don't match.", - "source_url": "https://sparse.tamu.edu/Rothberg", - "coarse_kind_families_interpretive": "fluids/transport; structures/materials", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 0.5, - "normalized_kind_count": 2, - "normalized_kinds": "computational fluid dynamics problem (3); structural problem (3)", - "curator_value_type_count": 2, - "curator_value_types": "binary (4); real (2)", - "curator_structure_count": 1, - "curator_structures": "symmetric (6)", - "min_dimension": 4350, - "max_dimension": 153746, - "dimension_ratio": 35.34390804597701, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 237798, - "max_nnz": 9080404, - "shape_profile": "square only", - "square": 6, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 6, - "structurally_symmetric_square": 6, - "known_spd": 2, - "value_profile": "real only", - "binary": 4, - "geometric_2d3d": 6, - "cohesion": "multi-domain/mixed", - "official_notes": "Structural engineering matrices from Ed Rothberg, Silicon Graphics, Inc.\n\nSome of the matrix names and file names don't match.\nThis might be changed soon." - }, - { - "group": "Shyy", - "count": 2, - "documented_meaning": "Computational fluid dynamics, viscous flow, Wei Shyy, Univ. Florida. Matrices: shyy161.rua, shyy41.rua", - "source_url": "https://sparse.tamu.edu/Shyy", - "coarse_kind_families_interpretive": "fluids/transport", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computational fluid dynamics problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (2)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (2)", - "min_dimension": 4720, - "max_dimension": 76480, - "dimension_ratio": 16.203389830508474, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 20042, - "max_nnz": 329762, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 2, - "cohesion": "coherent family/series", - "official_notes": "Computational fluid dynamics, viscous flow, Wei Shyy, Univ. Florida.\n\nMatrices: shyy161.rua, shyy41.rua\n\nFrom:\n\nProf. Wei Shyy\nAerospace Engineering, Mechanics & Engineering Science\n231 Aerospace Building\nP.O. Box 116250\nUniversity of Florida\nGainesville, FL 32611-6250\nUSA\nemail: wss :at the domain: tiger.aero.ufl.edu\nphone: (904) 392-6416\n\nMatrices are derived from a direct, fully-coupled method for solving\nthe Navier-Stokes equations for viscous flow calculations (in which the\npressure and velocity fields are coupled within the same linear system).\nA body-fitted co-ordinate system is used. This method is less sensitive\nto variations in parameters (such as the Reynolds number) than decoupled\nmethods, in which the pressure equation is artificially derived by combining\nthe momentum and continuity equations.\n\nReference:\nBraaten, M.E. and Shyy, W., \"Comparison of Iterative and Direct Solution\nMethods for Viscous Flow Calculations in Body-Fitted Coordinates,\"\nInter. J. Numer. Meths. in Fluids, vol 6, pp. 325-349, 1986.\n\n\nNOTE: these matrices give large errors and residuals when factorized.\nThey are linear systems from early on in the nonlinear iteration. A seemingly\npoor solution to these linear systems is OK, since the solution still moves the\nnonlinear iteration in the right direction. For example, Mups (multifrontal\ncode) gives the following for shyy41.rua (with a right-hand-side of b(i)=n/i):\n\n\tmaxnorm (Ax-b)\t\t\t2.2e+68\n\tmaxnorm (A)\t\t\t1.9e+02\n\tmaxnorm (x)\t\t\t2.9e+67\n\tmaxnorm(b-Ax)/maxnorm(Ax)\t3.9E-02\n\n\n\nMinor change 3/31/03: \"rua\" changed to \"RUA\" in both files." - }, - { - "group": "Simon", - "count": 12, - "documented_meaning": "Structural engineering, computational fluid dynamics. Horst Simon. Matrices from Horst Simon,", - "source_url": "https://sparse.tamu.edu/Simon", - "coarse_kind_families_interpretive": "fluids/transport; graph/network data; structures/materials", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 0.5833333333333334, - "normalized_kind_count": 3, - "normalized_kinds": "computational fluid dynamics problem (7); structural problem (4); directed weighted random graph (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (12)", - "curator_structure_count": 2, - "curator_structures": "unsymmetric (10); symmetric (2)", - "min_dimension": 3242, - "max_dimension": 62424, - "dimension_ratio": 19.254780999383097, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 130371, - "max_nnz": 1853104, - "shape_profile": "square only", - "square": 12, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 2, - "structurally_symmetric_square": 9, - "known_spd": 2, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 11, - "cohesion": "multi-domain/mixed", - "official_notes": "Structural engineering, computational fluid dynamics. Horst Simon.\n\nMatrices from Horst Simon,\nNAS Systems Division\nNASA Ames Research Center\nMoffett Field, CA 94035\nUSA\n\nemail: simon :at the domain: nas.nasa.gov\nphone: (415) 604-4322\n\n\nThe following matrices have the same nonzero pattern:\n\t1) VENKAT01, VENKAT25, and VENKAT50\n\t2) RAEFSKY1 and RAEFSKY2\n\nNames changed (see \"fix\" script).\n\nMinor change 3/31/03: \"rua\" changed to \"RUA\" in bbmat.rua header." - }, - { - "group": "TOKAMAK", - "count": 4, - "documented_meaning": "TOKAMAK test matrices from the SPARSKIT2 collection, Yousef Saad, Univ. MN. (saad :at the domain: cs.umn.edu)", - "source_url": "https://sparse.tamu.edu/TOKAMAK", - "coarse_kind_families_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "electromagnetics problem (4)", - "curator_value_type_count": 1, - "curator_value_types": "real (4)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (4)", - "min_dimension": 300, - "max_dimension": 5940, - "dimension_ratio": 19.8, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 3155, - "max_nnz": 83842, - "shape_profile": "square only", - "square": 4, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 4, - "cohesion": "coherent family/series", - "official_notes": "TOKAMAK test matrices from the SPARSKIT2 collection, Yousef Saad, Univ. MN.\n\n (saad :at the domain: cs.umn.edu)\n\nNames changed (see \"fix\" script). Keys in first lines of\nheader fixed, also." - }, - { - "group": "Vavasis", - "count": 1, - "documented_meaning": "Irregular finite-element matrix from Steve Vavasis, Cornell. (copy provided by Sherry Li).", - "source_url": "https://sparse.tamu.edu/Vavasis", - "coarse_kind_families_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "2D/3D problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 41092, - "max_dimension": 41092, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 1683902, - "max_nnz": 1683902, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "Irregular finite-element matrix from Steve Vavasis, Cornell.\n\n(copy provided by Sherry Li).\nConverted to Harwell/Boeing format by Tim Davis.\n\nThis matrix has also been refered to as \"Vavasis3\"." - }, - { - "group": "Wang", - "count": 6, - "documented_meaning": "3D semiconductor device, 2D incompressible flow. S. Wang, Univ. New South Wales. The simulators solve the corresponding coupled nonlinear PDE", - "source_url": "https://sparse.tamu.edu/Wang", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "semiconductor device problem (6)", - "curator_value_type_count": 1, - "curator_value_types": "real (6)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (6)", - "min_dimension": 2903, - "max_dimension": 26068, - "dimension_ratio": 8.979676197037547, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 19093, - "max_nnz": 177196, - "shape_profile": "square only", - "square": 6, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 6, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 6, - "cohesion": "coherent family/series", - "official_notes": "3D semiconductor device, 2D incompressible flow. S. Wang, Univ. New South Wales.\n\nThe simulators solve the corresponding coupled nonlinear PDE\nsystem (i.e. the semiconductor device equations or the Navier-Stokes equations)\niteratively and in each nonlinear iteration unsymmetric sparse matrices \nwith the same sparse pattern are formed.\n\nThe size of the matrices depends on the mesh used. Typical size (3d problems)\nis 60,000. Presently the CGS is used. These represent smaller test matrices.\n\nThe swang1.rua and swang2.rua matrices:\n---------------------------------------\nThey are from the discretisation of the vorticity equation based on\na arbitrary triangular mesh with 3489 mesh nodes. The problem\nis a square driven cavity problem and the Reynolds number is 1000.\nThe discretisation used is an exponentially fitted finite volume\nmethod. Reference: ``An exponentially fitted finite volume method\nfor the solution of incomopressible flow problems'' by Miller J.J.H\nand Wang S. in Proceedings of the 4th international symposium\non computational fluid dynamics, vol.3, Davis, California, 1991 pp49-54.\n\nswang1.rua and swang2.rua have the same nonzero pattern.\n\nThe wang1.rua and wang2.rua matrices:\n-------------------------------------\nThese two files are from the discretisation of the electron continuity\nequations of a 3d diode with piecewise doping function N = 10e16 in\nn-region and -10e16 in p-region. The mesh is a non-uniform mesh with\n14x14x16 (=3136) mesh nodes, and the forward bias is 1 volt. \nReference: ``An exponentially fitted finite element method for\na stationary convection-diffusion problem'' by Miller J.J.H. and Wang S.\nin Computatioal methods for boundary and interior layers in\nseveral dimensions (ed. Miller J.J.H.), Boole Press, Dublin, 1991\npp 120-137.\n\nwang1.rua and wang2.rua have the same nonzero pattern.\n\nThe wang3.rua matrix:\n---------------------\nThe same 3d diode semiconductor device problem as the wang1.rua and\nwang2.rua matrices, but at a higher resolution.\n\nThe wang4.rua matrix:\n---------------------\nA MOSFET (metal on semiconductor field-effect device). The doping profile \nis 1.0e19 in the two n-regions and 1.0e15 in the p-region (it is an\nn-channel device). The applied biases are 0 on substrate and source\nterminals, 0.2 on drain terminal and 5.0 on gate terminal.\n\nMeshes for the wang3.rua and wang4.rua problems: 30 X 30 X 30 uniform mesh is\nused for both. The orders of the two linear systems are about 26,000 \n(due to the Dirichlet boundaries).\n\nReference for the discretisation scheme (wang3 and wang4):\neither the previous reference or `An analysis of the Scharfetter-Gummel box\nmethod for the stationary semiconductor device equations' by J.J.H. Miller\nand S. Wang, RIORO Model. Math. Nal. Numer., vol. 28, No.2, 1994, pp123-140\n\n\nDr. Song Wang\nSchool of Mathematics\nUniversity of New South Wales\nSydney, 2052\nAustralia\nemail: S.Wang :at the domain: unsw.edu.au\n\n\nMinor change, 3/31/03: \"rua\" changed to \"RUA\" 4 of the matrices." - }, - { - "group": "Zhao", - "count": 2, - "documented_meaning": "Reduced-order modeling, electromagnetics. A. Cangellaris and L. Zhao. Matrices from A. C. Cangellaris and Li Zhao, \"Reduced-order", - "source_url": "https://sparse.tamu.edu/Zhao", - "coarse_kind_families_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "electromagnetics problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (2)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (2)", - "min_dimension": 33861, - "max_dimension": 33861, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 166453, - "max_nnz": 166453, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 2, - "cohesion": "coherent family/series", - "official_notes": "Reduced-order modeling, electromagnetics. A. Cangellaris and L. Zhao.\n\nMatrices from A. C. Cangellaris and Li Zhao, \"Reduced-order\nmodeling of electromagnetic systems with Pade' via Lanczos\nApproximations,\" Applied Computational Electromagnetics\nSymposium, 1997 (ACES '97)." - }, - { - "group": "Zitney", - "count": 8, - "documented_meaning": "Chemical engineering applications. S. Zitney, Cray Research. Matrices from Dr. Stephen E. Zitney", - "source_url": "https://sparse.tamu.edu/Zitney", - "coarse_kind_families_interpretive": "chemistry/biology", - "dominant_coarse_family_interpretive": "chemistry/biology", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "chemical process simulation problem (8)", - "curator_value_type_count": 1, - "curator_value_types": "real (8)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (8)", - "min_dimension": 1048, - "max_dimension": 5308, - "dimension_ratio": 5.064885496183206, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 10965, - "max_nnz": 94408, - "shape_profile": "square only", - "square": 8, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Chemical engineering applications. S. Zitney, Cray Research.\n\n\nMatrices from Dr. Stephen E. Zitney\nCray Research, Inc.\n655-E Lone Oak Drive\nEagan, MN 55121-1560 USA\nEmail: sez :at the domain: cray.com\nphone:(612) 683-3690\nfax:(612) 683-3099\n\nChemical engineering applications. The rdist* matrices are discussed in:\n\nZitney, S. E., \"Sparse matrix methods for chemical process separation\ncalculations on supercomputers,\" in Proc. Supercomputing '92, pp. 414-423,\n(Minneapolis, MN, November 16-20, 1992), IEEE Computer Society Press, Los\nAlamitos, CA, 1992.\n\n\nThese matrices come from a nonlinear solver, so they represent a selection\nfrom a sequence of matrices with similar values and pattern.\n\nMinor change: 3/31/03. \"rua\" changed to \"RUA\" in the headers of each matrix." - }, - { - "group": "Cunningham", - "count": 4, - "documented_meaning": "Finite element matrices from Andrew Cunningham, Vibro-Acoustic Sciences, Inc.", - "source_url": "https://sparse.tamu.edu/Cunningham", - "coarse_kind_families_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "acoustics problem (4)", - "curator_value_type_count": 1, - "curator_value_types": "real (4)", - "curator_structure_count": 2, - "curator_structures": "symmetric (3); unsymmetric (1)", - "min_dimension": 11107, - "max_dimension": 66127, - "dimension_ratio": 5.953632844152336, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 6639, - "max_nnz": 1660579, - "shape_profile": "square only", - "square": 4, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 3, - "structurally_symmetric_square": 4, - "known_spd": 1, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 4, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Finite element matrices from Andrew Cunningham, Vibro-Acoustic Sciences, Inc.\nhttp://www.vasci.com\n\nqa8fk: Finite element stiffness matrix for a 3D acoustic problem.\nqa8fm: Mass matrix for the above problem.\nBoth matrices are symmetric.\n\nThe MATLAB version of qa8fk and qa8fm includes the 3D coordinates of\nthe nodes of the matrix as Problem.coord. They appear here as *.xyz.gz\nfiles. See ../Pothen/README.txt for a description of the xyz file format.\n\nk3plates: Finite element mass matrix, 3 plates meeting at a line\nm3plates: mass matrix for the above problem.\n\nSubmitted to the UF sparse matrix collection in January, 2002." - }, - { - "group": "Chen", - "count": 14, - "documented_meaning": "Finite element matrices from Pu Chen, Peking University. 14 symmetric positive-definite matrices (patterns only).", - "source_url": "https://sparse.tamu.edu/Chen", - "coarse_kind_families_interpretive": "structures/materials", - "dominant_coarse_family_interpretive": "structures/materials", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "structural problem (14)", - "curator_value_type_count": 1, - "curator_value_types": "binary (14)", - "curator_structure_count": 1, - "curator_structures": "symmetric (14)", - "min_dimension": 10800, - "max_dimension": 151926, - "dimension_ratio": 14.067222222222222, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 810000, - "max_nnz": 14836504, - "shape_profile": "square only", - "square": 14, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 14, - "structurally_symmetric_square": 14, - "known_spd": 0, - "value_profile": "real only", - "binary": 14, - "geometric_2d3d": 14, - "cohesion": "coherent family/series", - "official_notes": "Finite element matrices from Pu Chen, Peking University.\n\n14 symmetric positive-definite matrices (patterns only).\n\nExcept for pkustk07, pkustk08, and pkustk13, all problems\narose in civil engineering. There is more than 1 degree\nof freedom at each node in the finite-element mesh.\n\npkustk07 and pkustk08 are regular 3D meshes, with one side fixed\nin the original FE models.\n\nProblem Description\t\t\t n\t\tnnz in lower triangular\n\t\t\t\t\t\t\tpart (incl. diagonal)\nPKUSTK01 Beijing botanical exhibition hall 22,044 500,712\nPKUSTK02 Feiyue twin tower building\t 10,800 410,400\nPKUSTK03 Dalian group silo\t\t 63,336 1,596,876\nPKUSTK04 Yunsan Plaza\t\t\t 55,590 2,137,125\nPKUSTK05 Cofferdam (reduced model)\t 37,164 1,121,154\nPKUSTK06 Cofferdam (reduced model)\t 43,164 1,307,466\nPKUSTK07 Cubic 21 nodes solid, 10x10x10 mesh 16,860 1,217,832\nPKUSTK08 Cubic 21 nodes solid, 11x11x11 mesh 22,209 1,624,440\nPKUSTK09 Group silo\t\t\t 33,960 808,800\nPKUSTK10 4 tower silo\t\t\t 80,676 2,194,830\nPKUSTK11 Cofferdam (full size)\t\t 87,804 2,652,858\nPKUSTK12 Jijian Plaza, tall building\t 94,653 3,803,485\nPKUSTK13 Machine element, 21 nodes solid 94,893 3,355,860\nPKUSTK14 Tall building\t\t\t 151,926 7,494,215\n\nPermanent address:\n\nPu Chen, Dr.-Ing.\nDept of Mechanics and Engineering\nPeking University\nBeijing 100871, China\ne-mail: chenpu :at the domain: pku.edu.cn\n\nCurrently address:\n\nPu Chen, Dr.-Ing.\nDept of Civil Engineering\nThe University of Akron\nAkron, OH 44325-3905\nUSA\ne-mail: chenpu :at the domain: pku.edu.cn" - }, - { - "group": "MathWorks", - "count": 11, - "documented_meaning": "Matrices contributed by The Mathworks, Inc. Some matrices come from other sources.", - "source_url": "https://sparse.tamu.edu/MathWorks", - "coarse_kind_families_interpretive": "graph/network data; imaging/graphics/robotics; mathematics/statistics; structures/materials", - "dominant_coarse_family_interpretive": "mathematics/statistics", - "dominant_coarse_family_share": 0.6363636363636364, - "normalized_kind_count": 5, - "normalized_kinds": "counter-example problem (6); structural problem (2); statistical/mathematical problem (1); directed graph (1); computer graphics/vision problem (1)", - "curator_value_type_count": 2, - "curator_value_types": "real (10); binary (1)", - "curator_structure_count": 3, - "curator_structures": "unsymmetric (7); rectangular (2); symmetric (2)", - "min_dimension": 102, - "max_dimension": 12406, - "dimension_ratio": 121.62745098039215, - "size_span": "wide (>100\u00d7)", - "min_nnz": 306, - "max_nnz": 340200, - "shape_profile": "mixed", - "square": 9, - "rectangular": 2, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 2, - "structurally_symmetric_square": 4, - "known_spd": 2, - "value_profile": "real only", - "binary": 1, - "geometric_2d3d": 3, - "cohesion": "multi-domain/mixed", - "official_notes": "Matrices contributed by The Mathworks, Inc.\nSome matrices come from other sources.\n\npivtol: A small matrix from the spline toolbox that illustrates the dramatic\n growth factor that can occur with a pivot tolerance of 0.1 (in\n UMFPACK v4.0). Needs a tolerance of 0.26, for off-diagonal pivoting.\n\nPd: An unsymmetric matrix (x=P\\\\d) with many sparse right-hand-sides. The Pd.A\n matrix is 8081-by-8081. Pd.b and Pd_rhs.A are the same 8081-by-12406 matrix\n (the right-hand-sides). It is highly reducible to block triangular form,\n with 7877 diagonal blocks, the largest of which is 30-by-30. Only 9 of\n the blocks are bigger than 1-by-1. This is a good candidate for a solver\n based on dmperm.\n\nHarvard500: Web connectivity matrix from Cleve Moler\n (Numerical Computing with MATLAB, 2004). A(i,j) is nonzero if there\n is a link from page j to page i. The URL of page j is in Problem.url{j}\n in the MATLAB form of this problem. For more details, see Cleve Moler's\n book at http://www.mathworks.com/moler . The matrix is 500-by-500, with\n a structural rank of 233 and numerical rank of 170.\n\ntomography: An earth science problem from David Yang. Contributed by\n Bobby Cheng. Much like a medical tomography problem. \"UMFPACK 4.0\n is consistently slower than MATLAB v5.\" In MATLAB 7.1,\n [L,U,P] = lu (A) takes 0.07 seconds (using GPLU), but UMFPACK v4.3 with\n [L,U,P,Q] = lu (A) takes 0.11 seconds.\n\nKuu, Muu: symmetric positive definite matrices which caused problems with\n UMFPACK v4.0 (in MATLAB 6.5). chol, or UMFPACK v4.3 or later, work fine.\n\nQRpivot is a counter-example problem from The MathWorks, Pat Quillen\n\n This matrix was obtained from a MATLAB user. It illustrates the\n limitations inherent in computing a basic solution to an under-\n determined system without the use of column pivoting.\n\n With column pivoting (which can only be done in MATLAB with full\n matrices), the problem is solved properly.\n\n When finding the min 2-norm solution (ignoring fill-in):\n\n [Q,R] = qr (A') ;\n x = Q*(R'\\\\b) ;\n\n a good solution is found. To reduce fill-in:\n\n p = colamd (A') ;\n [Q,R] = qr (A (p,:)') ;\n x = Q*(R'\\\\b(p)) ;\n \n which also finds a good solution.\n\n However, x=A\\\\b computes a basic solution, using this algorithm:\n\n q = colamd (A) ;\n [Q,R] = qr (A (:,q)) ;\n x = R\\\\(Q'*b) ;\n x (q) = q ;\n\n which finds an error with norm(A*x-b) of 1e-9 in MATLAB 7.6.\n\n With random permutations, and determining the cond(R1) of the leading\n trianglar part (R is \"squeezed\" and the columns can be partitioned into\n [R1 R2] where R1 is square and upper triangular) leads to the following\n results.\n\n Note that the error is high when condest(R1) is high. Note in\n particular the last trial.\n\n So this clinches the question. MATLAB's QR, and my new sparse QR, both\n use a rank-detection method (by Heath) that does not do column pivoting,\n and which is known to fail for some problems - for which Grimes & Lewis'\n method will likely succeed.\n\n The advantage of my QR is that I now always return R as upper\n trapezoidal, so if the user is concerned, he/she can easily check\n condest(R(:,1:m)) if m < n.\n\n err 7.71e-07 condest R1 2.18e+12\n err 1.25e-09 condest R1 9.82e+08\n err 2.47e-09 condest R1 2.46e+11\n err 4.00e-09 condest R1 4.03e+09\n err 9.88e-10 condest R1 4.73e+09\n err 2.25e-08 condest R1 5.34e+09\n err 2.00e-08 condest R1 1.04e+09\n err 1.09e-09 condest R1 6.83e+08\n err 6.18e-08 condest R1 8.13e+10\n err 3.13e-10 condest R1 4.23e+09\n err 6.64e-10 condest R1 2.46e+10\n err 5.76e-09 condest R1 4.31e+09\n err 7.61e-07 condest R1 5.08e+10\n err 2.27e-09 condest R1 4.94e+09\n err 3.99e-10 condest R1 2.80e+09\n err 1.37e-09 condest R1 3.13e+09\n err 6.93e-05 condest R1 1.84e+14\n err 1.35e-08 condest R1 7.18e+09\n err 1.09e-08 condest R1 1.79e+11\n err 1.81e-09 condest R1 2.99e+08\n err 1.55e-01 condest R1 2.45e+18\n\n In summary, this is a \"feature\" not a \"bug\". If you want a reliable\n solution to an underdetermined system, find the min 2norm solution\n via a QR factorization of A'.\n\nTS: a counter-example that triggers a bug in MATLAB R2009a. Fixed in R2009b." - }, - { - "group": "Kim", - "count": 2, - "documented_meaning": "2D complex meshes, from Wonsik Kim, Korea.", - "source_url": "https://sparse.tamu.edu/Kim", - "coarse_kind_families_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "2D/3D problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "complex (2)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (2)", - "min_dimension": 38415, - "max_dimension": 456976, - "dimension_ratio": 11.895769881556683, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 933195, - "max_nnz": 11330020, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "complex only", - "binary": 0, - "geometric_2d3d": 2, - "cohesion": "coherent family/series", - "official_notes": "2D complex meshes, from Wonsik Kim, Korea." - }, - { - "group": "Langemyr", - "count": 1, - "documented_meaning": "FEM structural mechanics matrix from Lars Langemyr, COMSOL. This is one of the few matrices for which a pivot tolerance of 0.1 (in UMFPACK", - "source_url": "https://sparse.tamu.edu/Langemyr", - "coarse_kind_families_interpretive": "structures/materials", - "dominant_coarse_family_interpretive": "structures/materials", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "structural problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 1500, - "max_dimension": 1500, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 97645, - "max_nnz": 97645, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "FEM structural mechanics matrix from Lars Langemyr, COMSOL.\n\nThis is one of the few matrices for which a pivot tolerance of 0.1 (in UMFPACK\nV4.0) is not sufficient. An inaccurate solution is found with that tolerance.\n\nPenny Anderson, of The MathWorks, Inc, solved the problem in MATLAB 6.5 by\ndetecting a poor solution and automatically refactorizing with a pivot\ntolerance of 1.0." - }, - { - "group": "Pothen", - "count": 22, - "documented_meaning": "Structrual engineering matrices from NASA, collected by Alex Pothen. Each matrix has xy or xyz coordinates for each node (row/column) in the matrix.", - "source_url": "https://sparse.tamu.edu/Pothen", - "coarse_kind_families_interpretive": "structures/materials", - "dominant_coarse_family_interpretive": "structures/materials", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "structural problem (22)", - "curator_value_type_count": 2, - "curator_value_types": "binary (12); real (10)", - "curator_structure_count": 1, - "curator_structures": "symmetric (22)", - "min_dimension": 48, - "max_dimension": 94069, - "dimension_ratio": 1959.7708333333333, - "size_span": "wide (>100\u00d7)", - "min_nnz": 306, - "max_nnz": 460493, - "shape_profile": "square only", - "square": 22, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 22, - "structurally_symmetric_square": 22, - "known_spd": 10, - "value_profile": "real only", - "binary": 12, - "geometric_2d3d": 22, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Structrual engineering matrices from NASA, collected by Alex Pothen.\nEach matrix has xy or xyz coordinates for each node (row/column) in the matrix.\n\nThese are all symmetric positive definite matrices. Some have only their\nnonzero patterns supplied. The diagonal entries were not specified, so\nfor non-binary matrices they have been defined so that the diagonal A(i,i)\nis equal to the sum of the absolute values of the off-diagonal entries in\nrow i, plus one.\n\nThe xy or xyz coordinates for each matrix are in seperate files.\nFor example, for the bodyy4.rsa matrix, the coordinates are in bodyy4.xyz.\nRow i of the bodyy4.xyz file lists the xy or xyz coordinates of node i.\n2D problems list just 2 values per line, 3D problems have 3 values per line.\n\nConverted to Harwell/Boeing format by Tim Davis, January 2003." - }, - { - "group": "Norris", - "count": 12, - "documented_meaning": "Finite element modelling of human body parts. Stuart Norris, Univ. Aukland. email: s.norris :at the domain: auckland.ac.nz", - "source_url": "https://sparse.tamu.edu/Norris", - "coarse_kind_families_interpretive": "fluids/transport; generic spatial PDE/thermal", - "dominant_coarse_family_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_share": 0.8333333333333334, - "normalized_kind_count": 2, - "normalized_kinds": "2D/3D problem (10); computational fluid dynamics problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (12)", - "curator_structure_count": 2, - "curator_structures": "unsymmetric (9); symmetric (3)", - "min_dimension": 1650, - "max_dimension": 259156, - "dimension_ratio": 157.06424242424242, - "size_span": "wide (>100\u00d7)", - "min_nnz": 7419, - "max_nnz": 8516500, - "shape_profile": "square only", - "square": 12, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 3, - "structurally_symmetric_square": 6, - "known_spd": 3, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 12, - "cohesion": "multi-domain/mixed", - "official_notes": "Finite element modelling of human body parts. Stuart Norris, Univ. Aukland.\n\nemail: s.norris :at the domain: auckland.ac.nz\nBioengineering Institute, University of Auckland\nhttp://www.esc.auckland.ac.nz/People/Staff/Norris wk: +(64 9) 373 7599 x3055\n\nFinite element matrices from bioengineering problems.\n\n\nMatrices:\nfv1\tFV_100x100_works finite element, Laplace eqn. on a 2D mesh\nfv2\tFV_101x101_fails finite element, Laplace eqn. on a 2D mesh, causes\n\t\t\t UMFPACK V4.0 with default pivot tolerance to fail.\n\t\t\t Requires a larger tolerance.\nfv3\tFV_101x101_works finite element, Laplace eqn. on a 2D mesh\n\nlung1\tLung_1\t\tCoupled temperature and water vapour transport in a lung\nlung2\tLung_2\t\tDitto, with finer mesh resolution\n\nheart1\tHeart_1\t\tQuasi-static finite-element model of a heart\nheart2\tHeart_2\t\tQuasi-static finite-element model of a heart\nheart3\tHeart_3\t\tQuasi-static finite-element model of a heart\n\nstomach\tStomach3D\tElectro-physiological model of a Duodenum\n\ntorso1\tTorso_FDandBE\tCoupled Finite difference/Boundary element\n\t\t\telectro-physiological model of a torso (2D)\n\ntorso2\tTorso_FDin2D\tFinite difference electro-physiological model of\n\t\t\ta torso (2D)\ntorso3\tTorso_FDin3D\tFinite difference electro-physiological model of\n\t\t\ta torso (3D)" - }, - { - "group": "Shen", - "count": 4, - "documented_meaning": "Matrices from Kai Shen, UCSB (now at Univ. of Rochester, NY). Kai Shen", - "source_url": "https://sparse.tamu.edu/Shen", - "coarse_kind_families_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "2D/3D problem (4)", - "curator_value_type_count": 1, - "curator_value_types": "real (4)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (4)", - "min_dimension": 3432, - "max_dimension": 18510, - "dimension_ratio": 5.393356643356643, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 25220, - "max_nnz": 553562, - "shape_profile": "square only", - "square": 4, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 4, - "cohesion": "coherent family/series", - "official_notes": "Matrices from Kai Shen, UCSB (now at Univ. of Rochester, NY).\n\nKai Shen\nAssistant Professor\nDepartment of Computer Science\nUniversity of Rochester, Rochester, NY 14627\nOffice: CSB 714\nEmail: kshen :at the domain: cs.rochester.edu\n585-275-5426(voice), 585-273-4556(fax)\n\n\nAdded to the UF sparse matrix collection Jan. 2003." - }, - { - "group": "Pereyra", - "count": 1, - "documented_meaning": "From Victor Pereyra, Stanford University.", - "source_url": "https://sparse.tamu.edu/Pereyra", - "coarse_kind_families_interpretive": "other", - "dominant_coarse_family_interpretive": "other", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "least squares problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "rectangular (1)", - "min_dimension": 71952, - "max_dimension": 71952, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 1146848, - "max_nnz": 1146848, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 1, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "single matrix", - "official_notes": "From Victor Pereyra, Stanford University." - }, - { - "group": "vanHeukelum", - "count": 13, - "documented_meaning": "DNA electrophoresis model. Alexander van Heukelum, Utrect Univ, The Netherlands. Date: Wed, 5 Jun 2002 14:12:02 +0200 (MET DST)", - "source_url": "https://sparse.tamu.edu/vanHeukelum", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "directed weighted graph (13)", - "curator_value_type_count": 1, - "curator_value_types": "real (13)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (13)", - "min_dimension": 5, - "max_dimension": 5154859, - "dimension_ratio": 1030971.8, - "size_span": "wide (>100\u00d7)", - "min_nnz": 19, - "max_nnz": 99199551, - "shape_profile": "square only", - "square": 13, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 13, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "DNA electrophoresis model. Alexander van Heukelum, Utrect Univ, The Netherlands.\n\nDate: Wed, 5 Jun 2002 14:12:02 +0200 (MET DST)\nFrom: Alexander van Heukelum \nTo: davis :at the domain: cise.ufl.edu\nSubject: Sparse matrix library\n\n\n-- Alexander van Heukelum --\tInstitute for Theoretical Physics\nRoom: Minnaert Building 307\tUtrecht University\nPhone: (+31) (30) 253 4622\tLeuvenlaan 4\nFax: (+31) (30) 253 5937\t3584 CE Utrecht\nEmail: heukelum :at the domain: phys.uu.nl\tThe Netherlands\n\n\n-----------------------------------------------------------------------\n\nThe cage model of DNA electrophoresis\n\nThe cage model of DNA electrophoresis describes the drift, induced by a\nconstant electric field, of homogeneously charged polymers through a\ngel. The gel forms an effective \"tube\", from which the polymers can only\nescape by reptation, i.e., motion of stored length along the chain. The\npolymer chain is modeled as a random walk on a cubic lattice, and stored\nlength is represented by two adjacent segments with opposite directions\n(a kink). The dynamics consists of single-monomer moves: only\nconfigurations which differ by the direction of a kink or the direction\nof an end segment have a finite transition probability.\n\nThe direction of the field is chosen in the direction of the main body\ndiagonal of the lattice, such that many polymer configurations become\nequivalent. The matrices presented here describe the transition\nprobabilities between such equivalence-classes of the configurations,\nfor an applied field of E = (0.1, 0.1, 0.1) for polymers of L = 3 to 15\nmonomers.\n\n name length rows columns nonzeros\n-------- -------- -------- -------- --------\n cage3 3 5 5 19\n cage4 4 9 9 49\n cage5 5 37 37 233\n cage6 6 93 93 785\n cage7 7 340 340 3084\n cage8 8 1015 1015 11003\n cage9 9 3534 3534 41594\n cage10 10 11397 11397 150645\n cage11 11 39082 39082 559722\n cage12 12 130228 130228 2032536\n cage13 13 445315 445315 7479343\n cage14 14 1505785 1505785 27130349\n cage15 15 5154859 5154859 99199551\n\nFor more information on the matrices, see A. van Heukelum, G. T.\nBarkema, and R. H. Bisseling, \"DNA electrophoresis studied with\nthe cage model\", J. Comp. Phys. 180, 313-326 (2002), or\nhttp://arxiv.org/format/cond-mat/0101467." - }, - { - "group": "Hohn", - "count": 6, - "documented_meaning": "Single-material crack problem, Michael Hohn, Univ. of Utah. The included files contain the matrices and rhs vectors arising in", - "source_url": "https://sparse.tamu.edu/Hohn", - "coarse_kind_families_interpretive": "structures/materials", - "dominant_coarse_family_interpretive": "structures/materials", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "materials problem (6)", - "curator_value_type_count": 1, - "curator_value_types": "real (6)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (6)", - "min_dimension": 7500, - "max_dimension": 16428, - "dimension_ratio": 2.1904, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 28462, - "max_nnz": 948696, - "shape_profile": "square only", - "square": 6, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 6, - "cohesion": "coherent family/series", - "official_notes": "Single-material crack problem, Michael Hohn, Univ. of Utah.\n\nThe included files contain the matrices and rhs vectors arising in\ntwo ways of discretizing a single-material crack problem.\n\nThe *coord files are in zero-indexed coordinate format (row, column, value);\nthe *vec files contain \n -- the size N\n -- the entries from first to last\n\nThe fd-* files come from a finite difference approximation;\nthe sinc-* files from a sinc-basis approximation.\n\nBoth use the same logarithmic grid.\n\nThe fd matrices are very sparse, and factor in a few seconds, so\nthey're not terribly interesting, but the sinc matrices are another matter...\n\nLarger matrices available on demand :)\n\n-- Michael Hohn, hohn :at the domain: math.utah.edu" - }, - { - "group": "Alemdar", - "count": 1, - "documented_meaning": "A finite-element matrix from Bulent Alemdar, RAM International.", - "source_url": "https://sparse.tamu.edu/Alemdar", - "coarse_kind_families_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "2D/3D problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "integer (1)", - "curator_structure_count": 1, - "curator_structures": "symmetric (1)", - "min_dimension": 6245, - "max_dimension": 6245, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 42581, - "max_nnz": 42581, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 1, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "A finite-element matrix from Bulent Alemdar, RAM International." - }, - { - "group": "Andrews", - "count": 1, - "documented_meaning": "Large eigenvalue problem from Stuart Andrews, Brown University, Rhode Island. stu at cs.brown.edu", - "source_url": "https://sparse.tamu.edu/Andrews", - "coarse_kind_families_interpretive": "imaging/graphics/robotics", - "dominant_coarse_family_interpretive": "imaging/graphics/robotics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computer graphics/vision problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "integer (1)", - "curator_structure_count": 1, - "curator_structures": "symmetric (1)", - "min_dimension": 60000, - "max_dimension": 60000, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 760154, - "max_nnz": 760154, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 1, - "known_spd": 1, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "Large eigenvalue problem from Stuart Andrews, Brown University, Rhode Island.\nstu at cs.brown.edu\n\nIn MATLAB, the problem statement is:\n\n LL = load('LL.mat');\n L = LL.L;\n opts.tol = 1e-9;\n opts.issym=1;\n opts.disp = 5;\n [E,V] = eigs(L, 5, 'sm', opts);\n\n\n - - - - -\nStuart Andrews\nBrown University, Computer Science, Box 1910, Prov, RI, 02912\nstu at cs.brown.edu\n - - - - -\n\nA large symmetric positive definite matrix, with seemingly random\n sparsity pattern. LU (using UMFPACK v4.6) fails, because it uses AMD.\n AMD obtains an ordering with nnz(L) of 117,039,940. METIS gets 36,595,274.\n CHOLMOD's NESDIS gets 37,089,917. CHOLMOD can solve it with either the\n METIS or NESDIS ordering, but not with AMD (x = cholmod(A,b) works if METIS\n is installed, but not otherwise)." - }, - { - "group": "FEMLAB", - "count": 9, - "documented_meaning": "Test matrices from FEMLAB, a finite-element method toolbox for MATLAB. developed by Comsol, Inc. See for more details.", - "source_url": "https://sparse.tamu.edu/FEMLAB", - "coarse_kind_families_interpretive": "electromagnetics/acoustics; fluids/transport; generic spatial PDE/thermal; structures/materials", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 0.4444444444444444, - "normalized_kind_count": 4, - "normalized_kinds": "computational fluid dynamics problem (4); structural problem (3); 2D/3D problem (1); electromagnetics problem (1)", - "curator_value_type_count": 2, - "curator_value_types": "real (8); complex (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (9)", - "min_dimension": 367, - "max_dimension": 85623, - "dimension_ratio": 233.30517711171663, - "size_span": "wide (>100\u00d7)", - "min_nnz": 2417, - "max_nnz": 3148656, - "shape_profile": "square only", - "square": 9, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 5, - "known_spd": 0, - "value_profile": "real + complex", - "binary": 0, - "geometric_2d3d": 9, - "cohesion": "multi-domain/mixed", - "official_notes": "Test matrices from FEMLAB, a finite-element method toolbox for MATLAB.\ndeveloped by Comsol, Inc. See http://www.femlab.com for more details." - }, - { - "group": "Lucifora", - "count": 2, - "documented_meaning": "GSM cell traffic matrices from Salvatore Lucifora, Telecom Italia Mobile. Ing. Salvatore Lucifora", - "source_url": "https://sparse.tamu.edu/Lucifora", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "directed weighted graph (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (2)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (2)", - "min_dimension": 7055, - "max_dimension": 7055, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 30082, - "max_nnz": 30082, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "GSM cell traffic matrices from Salvatore Lucifora, Telecom Italia Mobile.\n\nIng. Salvatore Lucifora\nTIM R/SR-TI\nvia L. Rizzo, 22\n00136 Roma\n\ntel. +39 06 3900 9241\nfax. +39 06 3900 9315\ncell. +39 335 7534668\nemail: slucifora :at the domain:\nmail.tim.it\n\n\nThese 2 matrices come from a software tool that evaluates the correct\ndimensioning for GSM cell with the coexistence of 2 kinds of traffic channels\n(Full-Rate and Half-Rate: the latter requires only half bandwidth with\nrespect to the FR channel). The tool requires the solution of a sparse linear\nsystem Ax=b.\n\nThe cell1.rua and cell2.rua matrices have the same nonzero pattern, and\nvery similar values. The 1-norm of C1-C2 is only 0.002, compared with\na 1-norm of about 416 for both C1 and C2. MATLAB's condest reports a\ncondition number of 10^12 for C1 and 10^20 for C2." - }, - { - "group": "ND", - "count": 4, - "documented_meaning": "ND problem set. 3D mesh problems. May 7, 2003.", - "source_url": "https://sparse.tamu.edu/ND", - "coarse_kind_families_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "2D/3D problem (4)", - "curator_value_type_count": 1, - "curator_value_types": "real (4)", - "curator_structure_count": 1, - "curator_structures": "symmetric (4)", - "min_dimension": 9000, - "max_dimension": 72000, - "dimension_ratio": 8.0, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 3279690, - "max_nnz": 28715634, - "shape_profile": "square only", - "square": 4, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 4, - "structurally_symmetric_square": 4, - "known_spd": 4, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 4, - "cohesion": "coherent family/series", - "official_notes": "ND problem set. 3D mesh problems.\nMay 7, 2003." - }, - { - "group": "Schenk_AFE", - "count": 16, - "documented_meaning": "Sheet metal forming matrices, Olaf Schenk, Univ. of Basel. Univ. of Basel collection, Olaf Schenk (semiconductors, sheet metal forming", - "source_url": "https://sparse.tamu.edu/Schenk_AFE", - "coarse_kind_families_interpretive": "structures/materials", - "dominant_coarse_family_interpretive": "structures/materials", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "structural problem (16)", - "curator_value_type_count": 1, - "curator_value_types": "real (16)", - "curator_structure_count": 1, - "curator_structures": "symmetric (16)", - "min_dimension": 503625, - "max_dimension": 1508065, - "dimension_ratio": 2.9944204517249937, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 17550675, - "max_nnz": 52259885, - "shape_profile": "square only", - "square": 16, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 16, - "structurally_symmetric_square": 16, - "known_spd": 10, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 16, - "cohesion": "coherent family/series", - "official_notes": "Sheet metal forming matrices, Olaf Schenk, Univ. of Basel.\n\nUniv. of Basel collection, Olaf Schenk (semiconductors, sheet metal forming\nproblems, and non-linear optimization)\n\nhttp://www.computational.unibas.ch/computer_science/scicomp/matrices\n\nThe following restriction applies (see the above web site):\n\n-------------------------------------------------------------------------------\nSparse Matrix Collection University of Basel\n-------------------------------------------------------------------------------\n\nThis page contains links to test data that we used to test our iterative and\ndirect parallel solvers projects. Within these projects, new parallel\nalgorithms and software for the solution of sparse systems of linear equations\nwill be developed. These include algorithms for direct solution, iterative\nsolution based on multilevel and domain decomposition methods, and for\npreconditioning.\n\nTHIS MATERIAL IS PROVIDED AS IS,\nWITH ABSOLUTELY NO WARRANTY EXPRESSED OR IMPLIED.\nANY USE IS AT YOUR OWN RISK\n\nThe user shall acknowledge the providers of this data in any publication of\nmaterial dependent upon the use of the data. The user shall use reasonable\nendeavours to notify the providers of the data of this publication. Data\nmay be used for research purposes only.\n\nDr. Olaf Schenk\nDepartment of Computer Science\nUniversity of Basel\nKlingelbergstrasse 50\nCH-4056 Basel\nSwitzerland\n\nDirect dial: +41 61 2671465\nSecretary: +41 61 2671460\nFax: +41 61 2671461\n\nE-Mail: olaf.schenk :at the domain: unibas.ch\nURL: http://informatik.unibas.ch/personen/schenk_o.html\n\n\nAll matrices come with right-hand-sides. Many of the matrices have\nmany explicit zero entries.\n\nIn the Schenk_IBMSDS directory:\n\n Provider: IBM Thomas Watson Research Center, Yorktown, USA,\n Semiconductor Device Simulation Group\n\n Name Description Order No. entries\n 2D_27628_bjtcai Semiconductor Device 27,628 442,898\n 2D_54019_highK Semiconductor Device 54,019 996,414\n 3D_28984_Tetra Semiconductor Device 28,984 599,170\n 3D_51448_3D Semiconductor Device 51,448 1,056,610\n ibm_matrix_2 Semiconductor Device 51,448 1,056,610\n matrix-new_3 Semiconductor Device 125,329 2,678,750\n matrix_9 Semiconductor Device 103,430 2,121,550\n\nIn the Schenk_ISEI directory:\n\n Provider: Integrated Systems Engineering Inc., San Jose, USA,\n Semiconductor Device Simulation Group\n\n Name Description Order No. entries\n barrier2-1 Semiconductor Device 113,076 3,805,068\n barrier2-2 Semiconductor Device 113,076 3,805,068\n barrier2-3 Semiconductor Device 113,076 3,805,068\n barrier2-4 Semiconductor Device 113,076 3,805,068\n barrier2-9 Semiconductor Device 115,625 3,897,557\n barrier2-10 Semiconductor Device 115,625 3,897,557\n barrier2-11 Semiconductor Device 115,625 3,897,557\n barrier2-12 Semiconductor Device 115,625 3,897,557\n igbt3 Semiconductor Device 10,938 234,006\n nmos3 Semiconductor Device 18,558 386,594\n ohne2 Semiconductor Device 181,343 11,063,545\n para-4 Semiconductor Device 153,226 5,326,228\n para-5 Semiconductor Device 155,924 5,416,358\n para-6 Semiconductor Device 155,924 5,416,358\n para-7 Semiconductor Device 155,924 5,416,358\n para-8 Semiconductor Device 155,924 5,416,358\n para-9 Semiconductor Device 155,924 5,416,358\n para-10 Semiconductor Device 155,924 5,416,358\n\nIn the Schenk_AFE directory:\n\n Provider:AutoForm Engineering GmbH, Zurich, Switzerland, Sheet Metal\n Forming Simulation Group\n\n Name Description Order No. entries\n AF_SHELL1 Indefinite 504,855 9,046,865\n AF_SHELL2 Indefinite 504,855 9,046,865\n AF_SHELL3 Positive Definite 504,855 9,046,865\n AF_SHELL4 Positive Definite 504,855 9,046,865\n AF_SHELL5 Indefinite 504,855 9,046,865\n AF_SHELL6 Indefinite 504,855 9,046,865\n AF_SHELL7 Positive Definite 504,855 9,046,865\n AF_SHELL8 Positive Definite 504,855 9,046,865\n AF_SHELL9 Indefinite 504,855 9,046,865\n\nIn the Schenk_IBMNA directory:\n\n Provider:IBM Thomas Watson Research Center, Yorktown, USA,\n Numerical Analysis Group\n\n Name Description Order No. entries\n C-62 Non-linear Optimization (IP-OPT) 41,731 300,537\n C-64 Non-linear Optimization (IP-OPT) 51,035 384,438\n C-66 Non-linear Optimization (IP-OPT) 49,989 274,498\n C-67 Non-linear Optimization (IP-OPT) 57,975 294,955\n C-73 Non-linear Optimization (IP-OPT) 169,422 724,348" - }, - { - "group": "Schenk_IBMNA", - "count": 52, - "documented_meaning": "Non-linear optimization matrices, Olaf Schenk, Univ. of Basel. Univ. of Basel collection, Olaf Schenk (semiconductors, sheet metal forming", - "source_url": "https://sparse.tamu.edu/Schenk_IBMNA", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "optimization problem (52)", - "curator_value_type_count": 1, - "curator_value_types": "real (52)", - "curator_structure_count": 1, - "curator_structures": "symmetric (52)", - "min_dimension": 2169, - "max_dimension": 345241, - "dimension_ratio": 159.17058552328263, - "size_span": "wide (>100\u00d7)", - "min_nnz": 15145, - "max_nnz": 2340859, - "shape_profile": "square only", - "square": 52, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 52, - "structurally_symmetric_square": 52, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Non-linear optimization matrices, Olaf Schenk, Univ. of Basel.\n\nUniv. of Basel collection, Olaf Schenk (semiconductors, sheet metal forming\nproblems, and non-linear optimization)\n\nhttp://www.computational.unibas.ch/computer_science/scicomp/matrices\n\nThe following restriction applies (see the above web site):\n\n-------------------------------------------------------------------------------\nSparse Matrix Collection University of Basel\n-------------------------------------------------------------------------------\n\nThis page contains links to test data that we used to test our iterative and\ndirect parallel solvers projects. Within these projects, new parallel\nalgorithms and software for the solution of sparse systems of linear equations\nwill be developed. These include algorithms for direct solution, iterative\nsolution based on multilevel and domain decomposition methods, and for\npreconditioning.\n\nTHIS MATERIAL IS PROVIDED AS IS,\nWITH ABSOLUTELY NO WARRANTY EXPRESSED OR IMPLIED.\nANY USE IS AT YOUR OWN RISK\n\nThe user shall acknowledge the providers of this data in any publication of\nmaterial dependent upon the use of the data. The user shall use reasonable\nendeavours to notify the providers of the data of this publication. Data\nmay be used for research purposes only.\n\nDr. Olaf Schenk\nDepartment of Computer Science\nUniversity of Basel\nKlingelbergstrasse 50\nCH-4056 Basel\nSwitzerland\n\nDirect dial: +41 61 2671465\nSecretary: +41 61 2671460\nFax: +41 61 2671461\n\nE-Mail: olaf.schenk :at the domain: unibas.ch\nURL: http://informatik.unibas.ch/personen/schenk_o.html\n\n\nAll matrices come with right-hand-sides. Many of the matrices have\nmany explicit zero entries.\n\nIn the Schenk_IBMSDS directory:\n\n Provider: IBM Thomas Watson Research Center, Yorktown, USA,\n Semiconductor Device Simulation Group\n\n Name Description Order No. entries\n 2D_27628_bjtcai Semiconductor Device 27,628 442,898\n 2D_54019_highK Semiconductor Device 54,019 996,414\n 3D_28984_Tetra Semiconductor Device 28,984 599,170\n 3D_51448_3D Semiconductor Device 51,448 1,056,610\n ibm_matrix_2 Semiconductor Device 51,448 1,056,610\n matrix-new_3 Semiconductor Device 125,329 2,678,750\n matrix_9 Semiconductor Device 103,430 2,121,550\n\nIn the Schenk_ISEI directory:\n\n Provider: Integrated Systems Engineering Inc., San Jose, USA,\n Semiconductor Device Simulation Group\n\n Name Description Order No. entries\n barrier2-1 Semiconductor Device 113,076 3,805,068\n barrier2-2 Semiconductor Device 113,076 3,805,068\n barrier2-3 Semiconductor Device 113,076 3,805,068\n barrier2-4 Semiconductor Device 113,076 3,805,068\n barrier2-9 Semiconductor Device 115,625 3,897,557\n barrier2-10 Semiconductor Device 115,625 3,897,557\n barrier2-11 Semiconductor Device 115,625 3,897,557\n barrier2-12 Semiconductor Device 115,625 3,897,557\n igbt3 Semiconductor Device 10,938 234,006\n nmos3 Semiconductor Device 18,558 386,594\n ohne2 Semiconductor Device 181,343 11,063,545\n para-4 Semiconductor Device 153,226 5,326,228\n para-5 Semiconductor Device 155,924 5,416,358\n para-6 Semiconductor Device 155,924 5,416,358\n para-7 Semiconductor Device 155,924 5,416,358\n para-8 Semiconductor Device 155,924 5,416,358\n para-9 Semiconductor Device 155,924 5,416,358\n para-10 Semiconductor Device 155,924 5,416,358\n\nIn the Schenk_AFE directory:\n\n Provider:AutoForm Engineering GmbH, Zurich, Switzerland, Sheet Metal\n Forming Simulation Group\n\n Name Description Order No. entries\n AF_SHELL1 Indefinite 504,855 9,046,865\n AF_SHELL2 Indefinite 504,855 9,046,865\n AF_SHELL3 Positive Definite 504,855 9,046,865\n AF_SHELL4 Positive Definite 504,855 9,046,865\n AF_SHELL5 Indefinite 504,855 9,046,865\n AF_SHELL6 Indefinite 504,855 9,046,865\n AF_SHELL7 Positive Definite 504,855 9,046,865\n AF_SHELL8 Positive Definite 504,855 9,046,865\n AF_SHELL9 Indefinite 504,855 9,046,865\n\nIn the Schenk_IBMNA directory:\n\n Provider:IBM Thomas Watson Research Center, Yorktown, USA,\n Numerical Analysis Group\n\n Name Description Order No. entries\n C-62 Non-linear Optimization (IP-OPT) 41,731 300,537\n C-64 Non-linear Optimization (IP-OPT) 51,035 384,438\n C-66 Non-linear Optimization (IP-OPT) 49,989 274,498\n C-67 Non-linear Optimization (IP-OPT) 57,975 294,955\n C-73 Non-linear Optimization (IP-OPT) 169,422 724,348" - }, - { - "group": "Schenk_IBMSDS", - "count": 7, - "documented_meaning": "Semiconductor device simulation matrices, Olaf Schenk, Univ. of Basel. Univ. of Basel collection, Olaf Schenk (semiconductors, sheet metal forming", - "source_url": "https://sparse.tamu.edu/Schenk_IBMSDS", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "semiconductor device problem (7)", - "curator_value_type_count": 1, - "curator_value_types": "real (7)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (7)", - "min_dimension": 27628, - "max_dimension": 125329, - "dimension_ratio": 4.536303749819024, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 206670, - "max_nnz": 1205518, - "shape_profile": "square only", - "square": 7, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 7, - "cohesion": "coherent family/series", - "official_notes": "Semiconductor device simulation matrices, Olaf Schenk, Univ. of Basel.\n\nUniv. of Basel collection, Olaf Schenk (semiconductors, sheet metal forming\nproblems, and non-linear optimization)\n\nhttp://www.computational.unibas.ch/computer_science/scicomp/matrices\n\nThe following restriction applies (see the above web site):\n\n-------------------------------------------------------------------------------\nSparse Matrix Collection University of Basel\n-------------------------------------------------------------------------------\n\nThis page contains links to test data that we used to test our iterative and\ndirect parallel solvers projects. Within these projects, new parallel\nalgorithms and software for the solution of sparse systems of linear equations\nwill be developed. These include algorithms for direct solution, iterative\nsolution based on multilevel and domain decomposition methods, and for\npreconditioning.\n\nTHIS MATERIAL IS PROVIDED AS IS,\nWITH ABSOLUTELY NO WARRANTY EXPRESSED OR IMPLIED.\nANY USE IS AT YOUR OWN RISK\n\nThe user shall acknowledge the providers of this data in any publication of\nmaterial dependent upon the use of the data. The user shall use reasonable\nendeavours to notify the providers of the data of this publication. Data\nmay be used for research purposes only.\n\nDr. Olaf Schenk\nDepartment of Computer Science\nUniversity of Basel\nKlingelbergstrasse 50\nCH-4056 Basel\nSwitzerland\n\nDirect dial: +41 61 2671465\nSecretary: +41 61 2671460\nFax: +41 61 2671461\n\nE-Mail: olaf.schenk :at the domain: unibas.ch\nURL: http://informatik.unibas.ch/personen/schenk_o.html\n\n\nAll matrices come with right-hand-sides. Many of the matrices have\nmany explicit zero entries.\n\nIn the Schenk_IBMSDS directory:\n\n Provider: IBM Thomas Watson Research Center, Yorktown, USA,\n Semiconductor Device Simulation Group\n\n Name Description Order No. entries\n 2D_27628_bjtcai Semiconductor Device 27,628 442,898\n 2D_54019_highK Semiconductor Device 54,019 996,414\n 3D_28984_Tetra Semiconductor Device 28,984 599,170\n 3D_51448_3D Semiconductor Device 51,448 1,056,610\n ibm_matrix_2 Semiconductor Device 51,448 1,056,610\n matrix-new_3 Semiconductor Device 125,329 2,678,750\n matrix_9 Semiconductor Device 103,430 2,121,550\n\nIn the Schenk_ISEI directory:\n\n Provider: Integrated Systems Engineering Inc., San Jose, USA,\n Semiconductor Device Simulation Group\n\n Name Description Order No. entries\n barrier2-1 Semiconductor Device 113,076 3,805,068\n barrier2-2 Semiconductor Device 113,076 3,805,068\n barrier2-3 Semiconductor Device 113,076 3,805,068\n barrier2-4 Semiconductor Device 113,076 3,805,068\n barrier2-9 Semiconductor Device 115,625 3,897,557\n barrier2-10 Semiconductor Device 115,625 3,897,557\n barrier2-11 Semiconductor Device 115,625 3,897,557\n barrier2-12 Semiconductor Device 115,625 3,897,557\n igbt3 Semiconductor Device 10,938 234,006\n nmos3 Semiconductor Device 18,558 386,594\n ohne2 Semiconductor Device 181,343 11,063,545\n para-4 Semiconductor Device 153,226 5,326,228\n para-5 Semiconductor Device 155,924 5,416,358\n para-6 Semiconductor Device 155,924 5,416,358\n para-7 Semiconductor Device 155,924 5,416,358\n para-8 Semiconductor Device 155,924 5,416,358\n para-9 Semiconductor Device 155,924 5,416,358\n para-10 Semiconductor Device 155,924 5,416,358\n\nIn the Schenk_AFE directory:\n\n Provider:AutoForm Engineering GmbH, Zurich, Switzerland, Sheet Metal\n Forming Simulation Group\n\n Name Description Order No. entries\n AF_SHELL1 Indefinite 504,855 9,046,865\n AF_SHELL2 Indefinite 504,855 9,046,865\n AF_SHELL3 Positive Definite 504,855 9,046,865\n AF_SHELL4 Positive Definite 504,855 9,046,865\n AF_SHELL5 Indefinite 504,855 9,046,865\n AF_SHELL6 Indefinite 504,855 9,046,865\n AF_SHELL7 Positive Definite 504,855 9,046,865\n AF_SHELL8 Positive Definite 504,855 9,046,865\n AF_SHELL9 Indefinite 504,855 9,046,865\n\nIn the Schenk_IBMNA directory:\n\n Provider:IBM Thomas Watson Research Center, Yorktown, USA,\n Numerical Analysis Group\n\n Name Description Order No. entries\n C-62 Non-linear Optimization (IP-OPT) 41,731 300,537\n C-64 Non-linear Optimization (IP-OPT) 51,035 384,438\n C-66 Non-linear Optimization (IP-OPT) 49,989 274,498\n C-67 Non-linear Optimization (IP-OPT) 57,975 294,955\n C-73 Non-linear Optimization (IP-OPT) 169,422 724,348" - }, - { - "group": "Schenk_ISEI", - "count": 18, - "documented_meaning": "Semiconductor device simulation matrices, Olaf Schenk, Univ. of Basel. Univ. of Basel collection, Olaf Schenk (semiconductors, sheet metal forming", - "source_url": "https://sparse.tamu.edu/Schenk_ISEI", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "semiconductor device problem (18)", - "curator_value_type_count": 1, - "curator_value_types": "real (18)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (18)", - "min_dimension": 10938, - "max_dimension": 181343, - "dimension_ratio": 16.57917352349607, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 130500, - "max_nnz": 6869939, - "shape_profile": "square only", - "square": 18, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 18, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 18, - "cohesion": "coherent family/series", - "official_notes": "Semiconductor device simulation matrices, Olaf Schenk, Univ. of Basel.\n\nUniv. of Basel collection, Olaf Schenk (semiconductors, sheet metal forming\nproblems, and non-linear optimization)\n\nhttp://www.computational.unibas.ch/computer_science/scicomp/matrices\n\nThe following restriction applies (see the above web site):\n\n-------------------------------------------------------------------------------\nSparse Matrix Collection University of Basel\n-------------------------------------------------------------------------------\n\nThis page contains links to test data that we used to test our iterative and\ndirect parallel solvers projects. Within these projects, new parallel\nalgorithms and software for the solution of sparse systems of linear equations\nwill be developed. These include algorithms for direct solution, iterative\nsolution based on multilevel and domain decomposition methods, and for\npreconditioning.\n\nTHIS MATERIAL IS PROVIDED AS IS,\nWITH ABSOLUTELY NO WARRANTY EXPRESSED OR IMPLIED.\nANY USE IS AT YOUR OWN RISK\n\nThe user shall acknowledge the providers of this data in any publication of\nmaterial dependent upon the use of the data. The user shall use reasonable\nendeavours to notify the providers of the data of this publication. Data\nmay be used for research purposes only.\n\nDr. Olaf Schenk\nDepartment of Computer Science\nUniversity of Basel\nKlingelbergstrasse 50\nCH-4056 Basel\nSwitzerland\n\nDirect dial: +41 61 2671465\nSecretary: +41 61 2671460\nFax: +41 61 2671461\n\nE-Mail: olaf.schenk :at the domain: unibas.ch\nURL: http://informatik.unibas.ch/personen/schenk_o.html\n\n\nAll matrices come with right-hand-sides. Many of the matrices have\nmany explicit zero entries.\n\nIn the Schenk_IBMSDS directory:\n\n Provider: IBM Thomas Watson Research Center, Yorktown, USA,\n Semiconductor Device Simulation Group\n\n Name Description Order No. entries\n 2D_27628_bjtcai Semiconductor Device 27,628 442,898\n 2D_54019_highK Semiconductor Device 54,019 996,414\n 3D_28984_Tetra Semiconductor Device 28,984 599,170\n 3D_51448_3D Semiconductor Device 51,448 1,056,610\n ibm_matrix_2 Semiconductor Device 51,448 1,056,610\n matrix-new_3 Semiconductor Device 125,329 2,678,750\n matrix_9 Semiconductor Device 103,430 2,121,550\n\nIn the Schenk_ISEI directory:\n\n Provider: Integrated Systems Engineering Inc., San Jose, USA,\n Semiconductor Device Simulation Group\n\n Name Description Order No. entries\n barrier2-1 Semiconductor Device 113,076 3,805,068\n barrier2-2 Semiconductor Device 113,076 3,805,068\n barrier2-3 Semiconductor Device 113,076 3,805,068\n barrier2-4 Semiconductor Device 113,076 3,805,068\n barrier2-9 Semiconductor Device 115,625 3,897,557\n barrier2-10 Semiconductor Device 115,625 3,897,557\n barrier2-11 Semiconductor Device 115,625 3,897,557\n barrier2-12 Semiconductor Device 115,625 3,897,557\n igbt3 Semiconductor Device 10,938 234,006\n nmos3 Semiconductor Device 18,558 386,594\n ohne2 Semiconductor Device 181,343 11,063,545\n para-4 Semiconductor Device 153,226 5,326,228\n para-5 Semiconductor Device 155,924 5,416,358\n para-6 Semiconductor Device 155,924 5,416,358\n para-7 Semiconductor Device 155,924 5,416,358\n para-8 Semiconductor Device 155,924 5,416,358\n para-9 Semiconductor Device 155,924 5,416,358\n para-10 Semiconductor Device 155,924 5,416,358\n\nIn the Schenk_AFE directory:\n\n Provider:AutoForm Engineering GmbH, Zurich, Switzerland, Sheet Metal\n Forming Simulation Group\n\n Name Description Order No. entries\n AF_SHELL1 Indefinite 504,855 9,046,865\n AF_SHELL2 Indefinite 504,855 9,046,865\n AF_SHELL3 Positive Definite 504,855 9,046,865\n AF_SHELL4 Positive Definite 504,855 9,046,865\n AF_SHELL5 Indefinite 504,855 9,046,865\n AF_SHELL6 Indefinite 504,855 9,046,865\n AF_SHELL7 Positive Definite 504,855 9,046,865\n AF_SHELL8 Positive Definite 504,855 9,046,865\n AF_SHELL9 Indefinite 504,855 9,046,865\n\nIn the Schenk_IBMNA directory:\n\n Provider:IBM Thomas Watson Research Center, Yorktown, USA,\n Numerical Analysis Group\n\n Name Description Order No. entries\n C-62 Non-linear Optimization (IP-OPT) 41,731 300,537\n C-64 Non-linear Optimization (IP-OPT) 51,035 384,438\n C-66 Non-linear Optimization (IP-OPT) 49,989 274,498\n C-67 Non-linear Optimization (IP-OPT) 57,975 294,955\n C-73 Non-linear Optimization (IP-OPT) 169,422 724,348" - }, - { - "group": "Kamvar", - "count": 2, - "documented_meaning": "Web matrices from Sep Kamvar, Stanford University. * Stanford Web Matrix:", - "source_url": "https://sparse.tamu.edu/Kamvar", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "directed graph (2)", - "curator_value_type_count": 1, - "curator_value_types": "binary (2)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (2)", - "min_dimension": 281903, - "max_dimension": 683446, - "dimension_ratio": 2.424401301156781, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 2312497, - "max_nnz": 7583376, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 2, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Web matrices from Sep Kamvar, Stanford University.\n\n* Stanford Web Matrix:\n 281903 pages, ~2.3 million links. From a September 2002 crawl.\n\n* Stanford-Berkeley Web Matrix:\n 683446 pages, ~7.6 million links. From a December 2002 crawl.\n\nThe data comes fro the Stanford WebBase project,\nhttp://www-diglib.stanford.edu/~testbed/doc2/WebBase .\n\nThe original data is posted at http://www.stanford.edu/~sdkamvar/research.html .\nIn the data posted there, one of the matrices is normalized, and the other is\nnot (but includes MATLAB code to produce the normalized matrix). Both have\nsome non-binary values in their connectivity, which represents pages with more\nthan one link to another specific page. The matrices posted here are purely\nbinary, and correspond to the matrix G discussed in Chapter 2 of\n\"Numerical Computing with MATLAB\" by Cleve Moler and Kathryn Moler,\nhttp://www.mathworks.com/moler .\n\nIn these matrices, if G(i,j)=1 then there is a link from page j to page i.\nThus, column j of G reflects the links that you can click on, if you are\nvisiting page j.\n\nA basic statement of the pagerank algorithm is given at\nhttp://www.mathworks.com/moler/ncm/pagerank.m , but that algorithm is not\nsuited for these large problems. The methods posted at\nhttp://www.mathworks.com/moler/ncm/pagerankpow.m or \nhttp://www.stanford.edu/~sdkamvar/research.html \nare more suitable for these problems.\n\nThe related eigenvalue problem is to find the vector x such that x=A*x,\nwhere A = (p*G*D + delta*e*e'), and:\n\n p = a scalar damping factor less than one (typically 0.85)\n G = the binary connectivity matrix (Problem.A in the MATLAB .mat files,\n\tor the .pua files). Don't confuse Problem.A with the matrix\n\tA = (p*G*D + delta*e*e'). Problem.A is the matrix G, not A.\n D = a diagonal matrix with D (i,i) equal to the sum of column i of G\n delta = (1-p)/n\n e = the column vector of all one's\n\nThe urls of the Stanford_Berkeley matrix are given in a cell array,\nProblem.colname. The url associate with column j is Problem.colname {j}.\nOnly a few of these are given, corresponding to root urls.\nNo urls are given for the Stanford matrix." - }, - { - "group": "Sumner", - "count": 1, - "documented_meaning": "A computer graphics problem from Bob Sumner, PhD student at MIT (as of 2003). From sumner :at the domain: graphics.lcs.mit.edu Fri Aug 8 15:28:05 2003", - "source_url": "https://sparse.tamu.edu/Sumner", - "coarse_kind_families_interpretive": "imaging/graphics/robotics", - "dominant_coarse_family_interpretive": "imaging/graphics/robotics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computer graphics/vision problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "rectangular (1)", - "min_dimension": 29493, - "max_dimension": 29493, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 117954, - "max_nnz": 117954, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 1, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "A computer graphics problem from Bob Sumner, PhD student at MIT (as of 2003).\n\nFrom sumner :at the domain: graphics.lcs.mit.edu Fri Aug 8 15:28:05 2003\n\nA computer graphics problem from Bob Sumner, PhD student at MIT. \n\nhttp://graphics.lcs.mit.edu/~sumner" - }, - { - "group": "Tromble", - "count": 1, - "documented_meaning": "Finite-state machine model for natural language processing. Roy Tromble. A matrix from Roy Tromble", - "source_url": "https://sparse.tamu.edu/Tromble", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "directed weighted graph (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 399130, - "max_dimension": 399130, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 1216334, - "max_nnz": 1216334, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "single matrix", - "official_notes": "Finite-state machine model for natural language processing. Roy Tromble.\n\nA matrix from Roy Tromble\n(royt :at the domain: cs.jhu.edu)\na PhD student in Computer Science\nat Johns Hopkins University (in Jan 2003). For this matrix, UMFPACK v4.0 and\nv4.1 perform an unusually large number of symbolic defragmentations in the\nordering / analysis phase.\n\nThe matrix comes from finite-state machines for natural-language processing.\nIt has a zero-free diagonal, but a very unsymmetric nonzero pattern.\nFactorizing A instead of A' may give better results." - }, - { - "group": "Sanghavi", - "count": 1, - "documented_meaning": "ECL32, a semiconductor device simulation matrix. Jagesh Sanghavi, UC Berkeley. Obtained by Christof Voemel, Lawrence Berkeley National Lab.", - "source_url": "https://sparse.tamu.edu/Sanghavi", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "semiconductor device problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 51993, - "max_dimension": 51993, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 380415, - "max_nnz": 380415, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "ECL32, a semiconductor device simulation matrix. Jagesh Sanghavi, UC Berkeley.\nObtained by Christof Voemel, Lawrence Berkeley National Lab." - }, - { - "group": "Sandia", - "count": 192, - "documented_meaning": "Circuit simulation matrices from Sandia National Laboratory.", - "source_url": "https://sparse.tamu.edu/Sandia", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "circuit simulation problem (192)", - "curator_value_type_count": 1, - "curator_value_types": "real (192)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (192)", - "min_dimension": 430, - "max_dimension": 682862, - "dimension_ratio": 1588.0511627906976, - "size_span": "wide (>100\u00d7)", - "min_nnz": 1544, - "max_nnz": 2638997, - "shape_profile": "square only", - "square": 192, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 10, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Circuit simulation matrices from Sandia National Laboratory." - }, - { - "group": "IBM_Austin", - "count": 1, - "documented_meaning": "Circuit simulation matrix from Emrah Acar, IBM Austin. High degree of coupling. From Emrah Acar, IBM Research-Austin", - "source_url": "https://sparse.tamu.edu/IBM_Austin", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "circuit simulation problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 11341, - "max_dimension": 11341, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 97193, - "max_nnz": 97193, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 1, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "single matrix", - "official_notes": "Circuit simulation matrix from Emrah Acar, IBM Austin. High degree of coupling.\n\nFrom Emrah Acar, IBM Research-Austin\n\nthis matrix failed in umd4[umfpack4.0], ma28 takes forever, sparse1.3\nis slower than umd2, haven't tested in 4.3. The matrix is from transient\nsimulation of a timing path. The circuit contains a high level of coupling.\n\nFor more info contact Emrah Acar, emrah (at sign) us dot ibm dotcom\nIBM Austin Research Lab\n\nResults from Tim Davis:\nOn a 3.2Ghz Pentium 4, UMFPACK v4.0 takes 7.4 seconds (backslash in\nMATLAB 6.5). UMFPACK v4.3 takes 0.20 seconds." - }, - { - "group": "ACUSIM", - "count": 1, - "documented_meaning": "Computational fluid dynamics matrices from ACUSIM, Inc. from Farzin Shakib, ACUSIM, Inc.", - "source_url": "https://sparse.tamu.edu/ACUSIM", - "coarse_kind_families_interpretive": "fluids/transport", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computational fluid dynamics problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "symmetric (1)", - "min_dimension": 14822, - "max_dimension": 14822, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 715804, - "max_nnz": 715804, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 1, - "known_spd": 1, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "Computational fluid dynamics matrices from ACUSIM, Inc.\n\nfrom Farzin Shakib, ACUSIM, Inc." - }, - { - "group": "Rajat", - "count": 32, - "documented_meaning": "Circuit simulation matrices from Rajat and Raj. From a company that develops commerical circuit simulation tools.", - "source_url": "https://sparse.tamu.edu/Rajat", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "circuit simulation problem (32)", - "curator_value_type_count": 2, - "curator_value_types": "real (25); binary (7)", - "curator_structure_count": 2, - "curator_structures": "unsymmetric (26); symmetric (6)", - "min_dimension": 135, - "max_dimension": 4690002, - "dimension_ratio": 34740.75555555556, - "size_span": "wide (>100\u00d7)", - "min_nnz": 665, - "max_nnz": 20316253, - "shape_profile": "square only", - "square": 32, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 6, - "structurally_symmetric_square": 13, - "known_spd": 0, - "value_profile": "real only", - "binary": 7, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Circuit simulation matrices from Rajat and Raj.\nFrom a company that develops commerical circuit simulation tools." - }, - { - "group": "Hamrle", - "count": 3, - "documented_meaning": "Circuit simulation matrices from Jaroslav Hamrle, RIKEN, Japan. --------------------------------------------------------------------------------", - "source_url": "https://sparse.tamu.edu/Hamrle", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "circuit simulation problem (3)", - "curator_value_type_count": 1, - "curator_value_types": "real (3)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (3)", - "min_dimension": 32, - "max_dimension": 1447360, - "dimension_ratio": 45230.0, - "size_span": "wide (>100\u00d7)", - "min_nnz": 98, - "max_nnz": 5514242, - "shape_profile": "square only", - "square": 3, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Circuit simulation matrices from Jaroslav Hamrle, RIKEN, Japan.\n\n--------------------------------------------------------------------------------\n\nDate: Tue, 24 Feb 2004 17:33:31 +0900\nFrom: Jaroslav Hamrle <(first initial, then last name) at the domain\n postman dot riken dot go\n dot jp >\nTo: Cleve Moler <(last name) at mathworks dotcom>\nSubject: Re: How to solve A\\\\y with large ( 60MB) sparse matrices ?\n\nHello,\n\nthe matrix is coming from solving of somewhat-like very large electrical\nnetwork. So, it is like a large graph, where matrix elements gives\nconnections between graph points.\n\nI'm sending you 'spy' of three matrices of this type.\n\nMaybe the most clear is the smallest 'spy', named Amatrix-small.pdf.\nIn this matrix, I changed colors depending on values:\nred ..... +1\ngreen ... -1\nblue .... different from 1 and -1, usually number between 0.01 to 100\n\nFirst two upper pars of the matrix given relation between values of nodes\ninside the graph. More precisely, they express what is the relation between\ncurrent and voltage between both sides of one resistor.\n\nThe first part treats resistors, which have special boundary conditions\n(resistors whith connected only one end to the circuity). In this case, the\nsub-matrices goes by on 'diagonal'. I used quotes here, because this\n'diagonal' is not exactly diagonal, by goes in the way that skip of one\nline is followed by skip of two raws.\n\nThe second part treats relationship between both ends of the resistors.\nIn this part, the matrix 4x4 together with four adjective '-1' goes also\non 'diagonal'. Note that for large matrices, this second part is much\nlarger that the first one.\n\nThe division between first two parts and second two part is exactly in the\nhalf of the matrix.\n\nThe thrid part of the matrix provide relationship, that the sum of the current\nin the each electrical node is zero (sum(j_i))=0. Hence, each line of this\npart of the matrix contains severals (in presented matrix 4x) '1' or '-1'.\n\nThe fourth part of the matrix says that the voltage at each nod is the same\nfor all resistors ending/starting at given node. Hence, this part of matrix\ncontains in each line just once '1' and once '-1'. Hence, this part only\nexpress that that some pairs of elements in resulting vector are identical.\nAs this part takes more that 1/4 of total matrix, maybe by some reordiring\nit is possible to reduce significantly number of raws/lines.\n\n\nIn conclusion, the most of the matrix contain only '1' and '-1' elements. And\nhalf of the lines (all third and fourt part) contains only '1' and '-1'\nelements. More that 1/4 of lines contains just one pair of '1' and '-1'.\n\nAlso, it is important to say, that the vector x (when solving A\\\\x) contains\nmainly zeros. Only 1%-5% of the vector x is non-zero. (And these zeros\nappears only in start of the x-vector, in part corresponding to the first\npart of the matrix.)\n\nIf you are interested, I can send you my matrix by ftp.\n\nI thank you very much for any help.\n\nJaroslav Hamrle" - }, - { - "group": "Oberwolfach", - "count": 38, - "documented_meaning": "Oberwolfach model reduction benchmark collection ================================================================================", - "source_url": "https://sparse.tamu.edu/Oberwolfach", - "coarse_kind_families_interpretive": "eigenvalue/model reduction", - "dominant_coarse_family_interpretive": "eigenvalue/model reduction", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "model reduction problem (38)", - "curator_value_type_count": 2, - "curator_value_types": "real (37); complex (1)", - "curator_structure_count": 2, - "curator_structures": "symmetric (33); unsymmetric (5)", - "min_dimension": 14, - "max_dimension": 986703, - "dimension_ratio": 70478.78571428571, - "size_span": "wide (>100\u00d7)", - "min_nnz": 46, - "max_nnz": 47851783, - "shape_profile": "square only", - "square": 38, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 32, - "structurally_symmetric_square": 37, - "known_spd": 13, - "value_profile": "real + complex", - "binary": 0, - "geometric_2d3d": 38, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Oberwolfach model reduction benchmark collection\n\n\n================================================================================\n==> bone010/README.txt <==\n================================================================================\n\n--------------------------------------------------------------------------------\nTrabecular Bone Micro-Finite Element Models (bone* problems)\n--------------------------------------------------------------------------------\n\nB. van Rietbergen, E. B. Rudnyi, J. G. Korvink\n\nThree-dimensional serial reconstruction techniques allow us to develop very\ndetailed micro-finite element (micro-FE) model of bones that can very\naccurately represent the porous bone micro-architecture. Fig. 1 sketches the\nmicro finite element analysis [1]. Micro computed tomography (CT) is employed\nto make 3D high-resolution images (~50 microns) of a bone. Then the 3D\nreconstruction is directly transformed into an equally shaped micro finite\nelement model by simply converting all bone voxels to equally sized 8-node\nbrick elements. This results in finite element (FE) models with a very large\nnumber of elements. Such models can be used, for example to study differences\nin bone tissue loading between healthy and osteoporotic human bones during\nquasi static loading [2].\n\nFig. 1: see http://www.imtek.de/simulation/benchmark/\n\nFig. 1. Micro finite element analysis.\n\nThere is increasing evidence, however, that bone responds in particular to\ndynamic loads [3]. It has been shown that the application of high-frequency,\nvery low magnitude strains to a bone can prevent bone loss due to osteoporosis\nand can even result in increased bone strength in bones that are already\nosteoporotic. In order to better understand this phenomenon, it is necessary to\ndetermine the strain as sensed by the bone cells due to this loading. This\nwould be possible with the micro-FE analysis, but then such an analysis need to\nbe a dynamic one.\n\nThe present benchmark presents 6 bone models varying in dimension from about\n200 thousand to 12 millions equations with the goal to research on scalability\nof model reduction software. Each model represent a second order system in the\nform\n\nM d2x/dt2 + K x = B\n\ny = Cx\n\nwhere the matrices M and K are symmetric and positive definite. The goal of\nmodel reduction is to speed up harmonic response analysis in the frequency\nrange 1-100 Hz.\n\nThe matrix properties are given in Table 1 below.\n\nTable 1.\n\nBone micro-finite\nelement models.\n\nBS01 BS10 B010 B025 B050 B120\nnumber of elements 20098 192539 278259 606253 1378782\t3387547\nnumber of nodes\t 42508 305066 329001 719987 1644848\t3989996\nnumber of DoFs\t 127224 914898 986703 2159661 4934244\t11969688\nnnz in half M\t 1182804 9702186 12437739 27150810 61866069 151251738\nnnz in half K\t 3421188 28191660 36326514 79292769 180663963 441785526\n\nFig. 2 and 3 : see http://www.imtek.de/simulation/benchmark/\n\nFig. 2. Bone models for BS01 and BS10.\n\nIt should be stressed that the first two models have been obtained differently\nand they are much simpler to deal with than the last four. The connectivity in\nthe last four models is about four times higher. This can be seen by comparing\nmodels BS10 and B010. Although models look similar by number of nonzeros in the\nsystem matrices, the model B010 is much harder to solve: the number of nonzero\nelements in the factor for model B010 is about four times more than for BS10.\n\nThe method allows for the compact representation of the models, as the element\nmass and stiffness matrices are the same for all elements. As a result, a file\ndescribing the node indices for each element is enough to assemble the global\nmatrix. Each node has three degrees of freedom (UX, UY, UZ) and it contributes\nthree consecutive entries to the state vector. The node numbering is natural\nfrom the first to the last. The assembly procedure as a pseudo-code is\npresented below (indices start from one). It is assumed that the last 300\ndegrees of freedom are fixed as zero Dirichlet boundary conditions. For\nsimplicity, the pseudo-code does not take into account that the matrix is\nsymmetric.\n\nThe data file for each model contains the number of elements, nel, and the\nnumber of nodes, nnod, in the first line and then nel lines with eight numbers\nfor node indices in each line.\n\nThe input matrix contains a single column with B(1) = 1. The output matrix\ntakes first three components of the state vector, that is, three displacements\nUX, UY and UZ for the first node.\n\nThe archive assemble.tar.gz contains the element mass and stiffness matrices as\nwell as the sample code in C++ to assemble the dynamic system. The code can\nwrite the dynamic system in the Matrix Market format or can be used as a hook\nto transform the global matrices to an appropriate format. The gzipped data\nfiles for element assembly as described above can be downloaded from Table 1.\n\nModel reduction for models BS010 and BS10 was performed in [4]. The\nbenchmarking of the parallel MUMPS direct solver [5] for the stiffness matrices\nis described in [6]. References\n\n[1] B. van Rietbergen, H. Weinans, R. Huiskes, A. Odgaard, A new method to\ndetermine trabecular bone elastic properties and loading using micromechanical\nfinite-elements models. J. Biomechanics, v. 28, N 1, p. 69-81, 1995.\n\n[2] B. van Rietbergen, R. Huiskes, F. Eckstein, P. Rueegsegger, Trabecular Bone\nTissue Strains in the Healthy and Osteoporotic Human Femur, J. Bone Mineral\nResearch, v. 18, N 10, p. 1781-1787, 2003.\n\n[3] L. E. Lanyon, C. T. Rubin, Static versus dynamic loads as an influence on\nbone remodelling, Journal of Biomechanics, v. 17, p. 897-906, 1984.\n\n[4] E. B. Rudnyi, B. van Rietbergen, J. G. Korvink. Efficient Harmonic\nSimulation of a Trabecular Bone Finite Element Model by means of Model\nReduction. 12th Workshop \"The Finite Element Method in Biomedical Engineering,\nBiomechanics and Related Fields\", University of Ulm, 20-21 July 2005.\nProceedings of the 12th FEM Workshop, p. 61-68. ISBN: 3-9806183-8-2.\n\n[5] P. R. Amestoy and A. Guermouche and J.-Y. L'Excellent, S. Pralet, Hybrid\nscheduling for the parallel solution of linear systems. Parallel Computing, v.\n32, N 2, pp. 136-156 (2006). http://graal.ens-lyon.fr/MUMPS/\n\n[6] E. B. Rudnyi, B. van Rietbergen, J. G. Korvink. Model Reduction for High\nDimensional Micro-FE Models. TAM'06, The Third HPC-Europa Transnational Access\nMeeting, Barcelona, 14 - 16 June 2006.\n\n==> bone010/bone010.C.names <==\nNode1_UX\nNode1_UY\nNode1_UZ\n==> boneS01/boneS01.C.names <==\nNode1_UX\nNode1_UY\nNode1_UZ\n==> boneS10/boneS10.C.names <==\nNode1_UX\nNode1_UY\nNode1_UZ\n\n================================================================================\n==> chipcool0/README.txt <==\n================================================================================\n\n--------------------------------------------------------------------------------\nConvective Thermal Flow Problems (chipcool*, flowmeter* problems)\n--------------------------------------------------------------------------------\n\nChristian Moosmann, moosmann@imtek.uni-freiburg.de Andreas Greiner,\ngreiner@imtek.uni-freiburg.de\n\nMany thermal problems require simulation of heat exchange between a solid body\nand a fluid flow. The most elaborate approach to this problem is computational\nfluid dynamics (CFD). However, CFD is computationally expensive. A popular\nsolution is to exclude the flow completely from the computational domain and to\nuse convection boundary conditions for the solid model. However, caution has to\nbe taken to select the film coefficient.\n\nAn intermediate level is to include a flow region with a given velocity\nprofile, that adds convective transport to the model. Compared to convection\nboundary conditions this approach has the advantage that the film coefficient\nhas not to be specified and that information about the heat profile in the flow\ncan be obtained. A drawback of the method is the greatly increased number of\nelements needed to perform a physically valid simulation, because the solution\naccuracy when employing upwind finite element schemes depends on the element\nsize. While this problem still is linear, due to the forced convection, the\nconductivity matrix changes from a symmetric matrix to an un-symmetric one. So\nthis problem type can be used as a benchmark for problems containing\nun-symmetric matrices.\n\nFig. 1a. Fig. 1b. see http://www.imtek.de/simulation/benchmark\n\nFig. 1. Convective heat flow examples: 2D anemometer model (left), 3D cooling\nstructure (right)\n\nTwo different designs are tested: a 2D model of an anemometer-like structure\nmainly consisting of a tube and a small heat source (Fig 1 left) [1]. The solid\nmodel has been generated and meshed in ANSYS. Triangular PLANE55 elements have\nbeen used for meshing and discretizing by the finite element method, resulting\nin 19 282 elements and 9710 nodes. The second design is a 3D model of a chip\ncooled by forced convection (Fig 1 right) [2]. In this case the tetrahedral\nelement type SOLID70 was used, resulting in 107 989 elements and 20542 nodes.\nSince the implementation of the convective term in ANSYS does not allow for\ndefinition of the fluid speed on a per element, but on a per region basis, the\nflow profile has to be approximated by piece-wise step functions. The\napproximation used for this benchmarks is shown in figure 1.\n\nThe Dirichlet boundary conditions are applied to the original system. In both\nmodels the reference temperature is set to 300 K, Dirichlet boundary conditions\nas well as initial conditions are set to 0 with respect to the reference. The\nspecified Dirichlet boundary conditions are in both cases the inlet of the\nfluid and the outer faces of the solids. Matrices are supplied for the\nsymmetric case (fluid speed is zero; no convection), and the unsymmetric case\n(with forced convection). Table 1 shows the output nodes specified for the two\nbenchmarks, table 2 links the filenames according to the different cases.\n\nMatrices are in the Matrix Market format. The matrix name is used as an\nextension of the matrix file. *.C.names contains a list of ouput names written\nconsecutively. The system matrices have been extracted from ANSYS models by\nmeans of mor4ansys.\n\nTable 1: Output nodes for the two models\n\nModel\tNumber\tCode\tComment\nFlow Meter 1\tout1\toutlet position\n\t 2\tout2\toutlet position\n\t 3\tSenL\tleft sensor position\n\t 4\tHeater\twithin the heater\n\t 5\tSenR\tright sensor position\ncooling Structure 1\tout1\toutlet position\n\t 2\tout2\toutlet position\n\t 3\tout3\toutlet position\n\t 4\tout4\toutlet position\n\t 5\tHeater within the heater\n\nTable 2: Provided files\n\nModel\tfluid speed (m/s) name\nFlow Meter \n\t 0\t flow_meter_model_v0.tgz\t renamed flowmeter0\n\t 0.5\t flow_meter_model_v0.5.tgz\t renamed flowmeter5\ncooling Structure\n\t 0\tchip_cooling_model_v0.tgz\t renamed chipcool0\n\t 0.1\t chip_cooling_model_v0.1.tgz\t renamed chipcool1\n\n\nFurther information on the models can be found in [3] where model reduction by\nmeans of the Arnoldi algorithm is also presented. Bibliography\n\n1 H. Ernst : High-Resolution Thermal Measurements in Fluids, PhD thesis,\nUniversity of Freiburg, Germany(2001).\n\n2 C. A. Harper : Electronic packaging and interconnection handbook, New York\nMcGraw- Hill, USA (1997)\n\n3 C. Moosmann, E. B. Rudnyi, A. Greiner, J. G. Korvink: Model Order Reduction\nfor Linear Convective Thermal Flow, Proceedings of 10th International Workshops\non THERMal INvestigations of ICs and Systems, THERMINIC2004, 29 Sept - 1 Oct ,\n2004, Sophia Antipolis, France. Preprint is at\nhttp://www.imtek.uni-freiburg.de/simulation/mor4ansys/\n\n==> chipcool0/chip_cooling_model_v0.C.names <==\nout1\nout2\nout3\nout4\nHeater\n\n==> chipcool1/chip_cooling_model_v0.1.C.names <==\nout1\nout2\nout3\nout4\nHeater\n\n==> flowmeter0/flow_meter_model_v0.C.names <==\nout1\nout2\nSenL\nHeater\nSenR\n\n==> flowmeter5/flow_meter_model_v0.5.C.names <==\nout1\nout2\nSenL\nHeater\nSenR\n\n================================================================================\n==> filter2D/README.txt <==\n================================================================================\n\n--------------------------------------------------------------------------------\nTunable Optical Filter (filter2D, filter3D problems)\n--------------------------------------------------------------------------------\n\nDennis Hohlfeld, hohlfeld@imtek.uni-freiburg.de Tamara Bechtold,\nbechtold@imtek.uni-freiburg.de Hans Zappe, zappe@imtek.uni-freiburg.de\n\nThe DFG project AFON (funded under grant ZA 276/2-1) aimed at the development\nof an optical filter, which is tunable by thermal means. The thin-film filter\nis configured as a membrane in order to improve thermal isolation. Fabrication\nis based on silicon technology. Wavelength tuning is achieved through thermal\nmodulation of resonator optical thickness, using metal resistor deposited onto\nthe membrane. The devices features low power consumption, high tuning speed and\nexcellent optical performance [1].\n\nFig. 1. see http://www.imtek.de/simulation/benchmark\n\nFig. 1. Tunable optical filter.\n\nThe benchmark contains a simplified thermal model of a filter device. It helps\ndesigners to consider important thermal issues, such as what electrical power\nshould be applied in order to reach the critical temperature at the membrane or\nhomogeneous temperature distribution over the membrane. The original model is\nthe heat transfer partial differential equation.\n\nThere are two different benchmarks, 2D model and 3D model (see Table 1). Due to\nmodeling differences, their simulation results cannot be compared with each\nother directly.\n\nTable 1: Tunable optical filter benchmarks\nCode\t comment\t\t\t dimension nnz(A)\tnnz(E)\nfilter2D 2D, linear elements, PLANE55 1668\t6209 1668\nfilter3D 3D, linear elements, SOLID90 108373 1406808\t1406791\n\nThe device solid models have been made, meshed and discretized in ANSYS 6.1 by\nthe finite element method. All material properties are considered as\ntemperature independent. Temperature is assumed to be in Celsius with the\ninitial state of 0 C. The Dirichlet boundary conditions of T = 0 C have been\napplied at the bottom of the chip.\n\nThe output nodes for the models are described in Table 2 and schematically\ndisplayed in Fig 2. Output 1 is located at the very center of the membrane. By\nsimulating it\u00d5s temperature one can prove what input power is needed to reach\nthe critical membrane temperature for each wavelength. Furthermore, the output\n2 to 5 must be very close to output 1 (homogenous temperature distribution) in\norder to provide the same optical properties across the complete diameter of\nthe laser beam.\n\nTable 2: Outputs for the optical filter model\nNumber\tCode\tComment\n1 Memb1 Membrane center\n2 Memb2 Membrane node with radius 25E-6, theta 90\n3 Memb3 Membrane node with radius 50E-6 theta 90\n4 Memb4 Membrane node with radius 25E-6, theta 135\n5 Memb5 Membrane node with radius 50E-6 theta 135\n\nFig. 2. see http://www.imtek.de/simulation/benchmark\n\nFig. 2. Schematical position of the chosen output nodes.\n\nThe benchmark contains a constant load vector. The input function equal to 1\ncorresponds to the constant input power of of 1 mW for 2D model and 10 mW for\n3D model. The linear ordinary differential equations of the first order are\nwritten as:\n\nE dT/dt = A T + B u y = C T\n\nwhere E and A are the symmetric sparse system matrices (heat capacity and heat\nconductivity matrix), B is the load vector, C is the output matrix, and T is\nthe vector of unknown temperatures.\n\nThe output of the transient simulation for node 1 over the rise time of the\ndevice (0.25 s) for 3D model can be find in Filter3DTransResults. The results\ncan be used to compare the solution of a reduced model with the original one.\nThe time integration has been performed in ANSYS with accuracy of about 0.1 %.\nThe results are given as matrices where the first row is made of times, the\nsecond of the temperatures.\n\nDownload matrices in the Matrix Market format: filter2D.tar.gz, 106502 bytes;\nfilter3D.tar.gz, 37417415 bytes. The matrix name is used as an extension of the\nmatrix file. File *.C.names contains a list of ouput names written\nconsecutively. The system matrices have been extracted from ANSYS models by\nmeans of mor4ansys.\n\nThe discussion of electro-thermal modeling related to the benchmark can be\nfound in [2].\n\nBibliography\n\n1. D. Hohlfeld, H. Zappe, All-dielectric tunable optical filter based on the\nthermo-optic effect, Journal of Optics A: Pure and Applied Optics, 6(6), 504-\n511 (2003).\n\n2. T. Bechtold, Model Order Reduction of Electro-Thermal MEMS, PhD thesis,\nUniversity of Freiburg, Germany (In preparation).\n\n==> filter2D/filter2D.C.names <==\nMemb1\nMemb2\nMemb3\nMemb4\nMemb5\n\n==> filter3D/filter3D.C.names <==\nMemb1\nMemb2\nMemb3\nMemb4\nMemb5\n\n================================================================================\n==> gas_sensor/README.txt <==\n================================================================================\n\n--------------------------------------------------------------------------------\nMicrohotplate Gas sensor (gas_sensor problem)\n--------------------------------------------------------------------------------\n\nJ\u00fcrgen Hildenbrand, hildenbr@imtek.uni-freiburg.de Tamara Bechtold,\nbechtold@imtek.uni-freiburg.de J\u00fcrgen W\u00f6llenstein, woellen@ipm.fhg.de\n\nThe goal of European project Glassgas (IST-99-19003) was to develop a novel\nmetal oxide low power microhotplate gas sensor [1]. In order to assure a robust\ndesign and good thermal isolation of the membrane from the surrounding wafer,\nthe silicon microhotplate is supported by glass pillars emanating from a glass\ncap above the silicon wafer, as shown in Fig 1. In present design, four\ndifferent sensitive layers can be deposited on the membrane. The thermal\nmanagement of a microhotplate gas sensor is of crucial importance.\n\nFig. 1. see http://www.imtek.de/simulation/benchmark/\n\nFig. 1. Micromashined metal oxide gas sensor array; Bottom view (left), top\nview (right).\n\nThe benchmark contains a thermal model of a single gas sensor device with three\nmain components: a silicon rim, a silicon hotplate and glass structure [2]. It\nallows us to simulate important thermal issues, such as the homogeneous\ntemperature distribution over gas sensitive regions or thermal decoupling\nbetween the hotplate and the silicon rim. The original model is the heat\ntransfer partial differential equation.\n\nThe device solid model has been made and then meshed and discretized in ANSYS\n6.1 by means of the finite element method (SOLID70 elements were used). It\ncontains 68000 elements and 73955 nodes. Material properties were considered as\ntemperature independent. Temperature is assumed to be in degree Celsius with\nthe initial state of 0 C. The Dirichlet boundary conditions of T = 0 C is\napplied at the top and bottom of the chip (at 7038 nodes).\n\nThe output nodes are described in Table 1. In Fig 2 the red marked nodes are\npositioned on the silicon rim. Their temperature should be close to the initial\ntemperature in the case of good thermal decoupling between the membrane and the\nsilicon rim. The black marked nodes are placed on the sensitive layers above\nthe heater and are numbered from left to right row by row, as schematically\nshown in Fig 2. They allow us to prove whether the temperature distribution\nover the gas sensitive layers is homogeneous (maximum difference of 10C is\nallowed by design).\n\nTable 1: Outputs for the gas sensor model\nNumber Code\t\t Comment\n1\taHeater\t\t within a heater, to be used for nonlinear input\n2-7\tSiRim1 to SiRim7 silicon rim\n8-28\tMemb1 to Memb21\t gas sensitive layer\n\nFig. 2. see http://www.imtek.de/simulation/benchmark/\n\nFig. 2. Masks disposition (left) and the schematical position of the chosen\noutput nodes (right).\n\nThe benchmark contains a constant load vector. The input function equal to 1\ncorresponds to the constant input power of 340 mW. One can insert a weak input\nnonlinearity related to the dependence of heater's resistivity on temperature\ngiven as:\n\nR(T)=R{0}(1 + alpha T)\n\nwhere alpha =1.469e-3 K^-1. To this end, one has to multiply the load vector by\na function:\n\n{U^2 274.94 (1 + alpha T)}/{0.34 (274.94 (1 + alpha T)+148.13)^2}\n\nwhere U is a desired constant voltage. The temperature in equation above should\nbe replaced by the temperature at the output 1.\n\nThe linear ordinary differential equations of the first order are written as:\n\nE dT/dt = A T + B u y = C T\n\nwhere E and A are the symmetric sparse system matrices (heat capacity and heat\nconductivity matrix), B is the load vector, C is the output matrix, and T is\nthe vector of unknown temperatures. The dimension of the system is 66917, the\nnumber of nonzero elements in matrix E is 66917, in matrix A is 885141.\n\nThe outputs of the transient simulation at output 18 (Memb11) over the rise\ntime of the device of 5 s for the original linear (with constant input power of\n340 mW) and nonlinear (with constant voltage of 14 V) model are placed in files\nLinearResults and NonlinearResults respectively. The results can be used to\ncompare the solution of a reduced model with the original one. The time\nintegration has been performed in ANSYS with accuracy of about 0.1 %. The\nresults are given as matrices where the first row is made of times, the second\nof the temperatures.\n\nDownload matrices in the Matrix Market format: GasSensor.tar.gz, 8407057 bytes.\nThe matrix name is used as an extension of the matrix file. File *.C.names\ncontains a list of ouput names written consecutively. The system matrices have\nbeen extracted from ANSYS models by means of mor4ansys.\n\nThe discussion of electro-thermal modeling related to the benchmark including\nthe nonlinear input function can be found in [3]. Bibliography\n\n1. J. W\u00f6llenstein H. B\u00f6ttner, J. A. Pl\u00e1za, Carlos Carn\u00e9, Y. Min, H. L. Tuller :\nA novel single chip thin film metal oxide array, Sensors and Actuators B:\nChemical 93 (1-3) 350-355 (2003).\n\n2. J. Hildenbrand : Simulation and Characterisation of a Gas sensor and\nPreparation for Model Order Reduction, Diploma Thesis, University of Freiburg,\nGermany (2003).\n\n3. T. Bechtold, J. Hildenbrand, J. W\u00f6llenstein, J. G. Korvink : Model Order\nReduction of 3D Electro-Thermal Model for a Novel, Micromachined Hotplate Gas\nSensor, Proceedings of 5th International conference on thermal and mechanical\nsimulation and experiments in microelectronics and microsystems, EUROSIME2004,\nMay 10-12, 2004, Brussels, Belgium, p. 263-267. Preprint is at\nhttp://www.imtek.uni-freiburg.de/simulation/mor4ansys/\n\n==> gas_sensor/GasSensor.C.names <==\naHeater\nSiRim1\nSiRim2\nSiRim3\nSiRim4\nSiRim5\nSiRim6\nMemb1\nMemb2\nMemb3\nMemb4\nMemb5\nMemb6\nMemb7\nMemb8\nMemb9\nMemb10\nMemb11\nMemb12\nMemb13\nMemb14\nMemb15\nMemb16\nMemb17\nMemb18\nMemb19\nMemb20\nMemb21\n\n================================================================================\n==> gyro/README.txt <==\n================================================================================\n\nOberwolfach model reduction benchmark collection\n------------------------------------------------\n\nContributions by Evgenii Rudnyi and Dag Billger.\n\nSee also:\n\nhttp://www.imtek.de/simulation/benchmark\nhttp://www.imtek.de/simulation/benchmark/wb/38847\nhttp://www.imtek.de/simulation/benchmark/wb/35889\n\n\n--------------------------------------------------------------------------------\nIMEGO gyroscope\n--------------------------------------------------------------------------------\n\nThe Butterfly Gyro\nDag Billger, The Imego Institute, Arvid Hedvalls Backe 4, SE-411 33,\nGoteborg, Sweden, Tel. +46 317 501 853, Fax. +46 317 501 801,\nEmail: dag dot billger at imego dotcom\n\nFigure 1: see http://www.imtek.de/simulation/benchmark/wb/35889/fig1.jpg\n\nFig. 1. The Butterfly and micro-SIC mounted together. \n\nThe Butterfly gyro is developed at the Imego Institute in an ongoing\nproject with Saab Bofors Dynamics AB. The Butterfly is a\nvibrating micro-mechanical gyro that has sufficient theoretical\nperformance characteristics to make it a promising candidate for use in\ninertial navigation applications. The goal of the current project is to\ndevelop a micro unit for inertial navigation that can be commercialized\nin the high-end segment of the rate sensor market. This project has\nreached the final stage of a three-year phase where the development and\nresearch efforts have ranged from model based signal processing, via\nelectronics packaging to design and prototype manufacturing of the\nsensor element. The project has also included the manufacturing of an\nASIC, named micro-SIC, that has been especially designed for the\nsensor (see Fig. 1). \n\nThe gyro chip consists of a three-layer silicon wafer stack, in\nwhich the middle layer contains the sensor element. The sensor consists\nof two wing pairs that are connected to a common frame by a set of beam\nelements (see Fig. 2 and 3); this is the reason the gyro is called the\nButterfly. Since the structure is manufactured using an anisotropic\nwet-etch process, the connecting beams are slanted. This makes it\npossible to keep all electrodes, both for capacitive excitation and\ndetection, confined to one layer beneath the two wing pairs. The\nexcitation electrodes are the smaller dashed areas shown in Fig. 2. The\ndetection electrodes correspond to the four larger ones.\n\nFigure 2: see http://www.imtek.de/simulation/benchmark/wb/35889/fig2.jpg\n\nFig. 2. Schematic layout of the Butterfly design. \n\nBy applying DC-biased AC-voltages to the four pairs of small\nelectrodes, the wings are forced to vibrate in anti-phase in the wafer\nplane. This is the excitation mode. As the structure rotates\nabout the axis of sensitivity (see Fig. 2), each of the masses will be\naffected by a Coriolis acceleration. This acceleration can be\nrepresented as an inertial force that is applied at right angles with\nthe external angular velocity and the direction of motion of the mass.\nThe Coriolis force induces an anti-phase motion of the wings out of the\nwafer plane. This is the detection mode. The external angular velocity\ncan be related to the amplitude of the detection mode, which is\nmeasured via the large electrodes. \n\nWhen planning for and making decisions on future improvements of the\nButterfly, it is of importance to improve the efficiency of the gyro\nsimulations. Repeated analyses of the sensor structure have to be\nconducted with respect to a number of important issues. Examples of\nsuch are sensitivity to shock, linear and angular vibration\nsensitivity, reaction to large rates and/or acceleration, different\ntypes of excitation load cases and the effect of force-feedback.\n\nThe use of model order reduction indeed decreases runtimes for\nrepeated simulations. Moreover, the reduction technique enables a\ntransformation of the FE representation of the gyro into a state space\nequivalent formulation. This will prove helpful in testing the model\nbased Kalman signal processing algorithms that are being designed for\nthe Butterfly gyro.\n\nThe structural model of the gyroscope has been done in ANSYS using\nquadratic tetrahedral elements (SOLID187, see Fig. 3). The model shown\nis a simplified one\nwith a coarse mesh as it is designed to test the model reduction\napproaches. It includes the pure structural mechanics problem only. The\nload vector is composed from time-varying nodal forces applied at\nthe centres of the excitation electrodes (see Fig. 2). The amplitude and\nfrequency of each force is equal to 0.055 micro-Newtons and 2384 Hz,\nrespectively. The Dirichlet boundary conditions have been applied to\nall degree of freedom of the nodes\nbelonging to the top and bottom surfaces of the frame. The output nodes\nare listed in Table 2 and correspond to the centres of the detection\nelectrodes (see Fig. 3).\n\nFigure 3: see http://www.imtek.de/simulation/benchmark/wb/35889/fig3.jpg\n\nFig. 3. Finite element mesh of the gyro with a background photograph\nof the gyro wafer pre-bonding.\n\nThe structural model\n\nM d2x/dt2 + E dx/dt + K x = B\ny = C x\n\ncontains the mass M and stifness matrices K. The\ndamping matrix E can be modeled as E = alpha M + beta K, where the typical\nvalues of alpha and beta are 0 and 1e-6 respectively. The nature of the\ndamping matrix is in reality more complex (squeeze film damping, thermo\nelastic damping, etc.) but this simple approach has been chosen with respect\nto the model reduction test. B is the load vector, C is the output matrix.\n\nThe dynamic model has been converted to\n(http://math.nist.gov/MatrixMarket/) Matrix Market format by\nmeans of (http://www.imtek.de/simulation/mor4ansys/) mor4ansys. The\nstatistics for the matrices is shown in Table 1.\n\nTable 1. System matrices for the gyroscope.\n\n matrix m n nnz Is Symmetric? \n M 17361 17361 178896 yes\n K 17361 17361 519260 yes\n B 17361 1 8 no \n C 12 17361 12 no\n\nTable 2. Outputs for the Butterfly Gyro Model \n\n # Code Comment \n\n 1-3 det1m_Ux, det1m_Uy, det1m_Uz\n\t Displacements of detection electrode 1,\n\t (bottom left large electrode of Fig. 2) \n\n 4-6 det1p_Ux, det1p_Uy, det1p_Uz \n\t Displacements of detection electrode 2,\n\t (bottom right large electrode of Fig. 2) \n\n 7-9 det2m_Ux, det2m_Uy, det2m_Uz \n\t Displacements of detection electrode 3,\n\t (top left large electrode of Fig. 2) \n \n \n 10-12 det2p_Ux, det2p_Uy, det2p_Uz \n\t Displacements of detection electrode 4,\n\t (top right large electrode of Fig. 2) \n\n(Note: the original matrices, in Matrix Market format, are at\nhttp://www.imtek.de/simulation/benchmark).\n\nThe model reduction of the gyroscope model by means of \n(http://www.imtek.de/simulation/mor4ansys/) mor4ansys is described in Ref [1].\n\n[1] Jan Lienemann, Dag Billger, Evgenii B. Rudnyi, Andreas Greiner,\nand Jan G. Korvink, MEMS Compact Modeling Meets Model Order Reduction:\nExamples of the Application of Arnoldi Methods to Microsystem Devices,\nthe Technical Proceedings of the 2004 Nanotechnology Conference and\nTrade Show, Nanotech 2004, March 7-11, 2004, Boston, Massachusetts, USA\n\nPreprint is at http://www.imtek.uni-freiburg.de/simulation/mor4ansys/\n\n\n\n\n==> gyro/gyro.C.names <==\ndet1pUX\ndet1pUY\ndet1pUZ\ndet1mUX\ndet1mUY\ndet1mUZ\ndet2pUX\ndet2pUY\ndet2pUZ\ndet2mUX\ndet2mUY\ndet2mUZ\n\n================================================================================\n==> inlet/README.txt <==\n================================================================================\n\n--------------------------------------------------------------------------------\nActive Control of a Supersonic Engine Inlet (Inlet problem)\n--------------------------------------------------------------------------------\n\nKaren Willcox, Guillaume Lassaux, and David Gratton\n\nhttp://web.mit.edu/kwillcox/www/home.htm\n\n================================================================================\n==> LF10/README.txt <==\n================================================================================\n\n--------------------------------------------------------------------------------\nLinear 1D Beam Models (LF* problems)\n--------------------------------------------------------------------------------\n\nJan Lienemann , Andreas Greiner , Jan G. Korvink Introduction\n\nMoving structures are an essential part for many microsystem devices, among\nthem fluidic components like pumps and electrically controllable valves,\nsensing cantilevers, and optical structures.\n\nSeveral actuation principles can be employed on microscopic length scales, the\nmost frequent certainly the electromagnetic forces. While electrostatic\nactuation falls behind at the macro scale, the effect of charged bodies\noutperforms magnetic forces in the micro scale both in terms of performance and\nfabrication expense.\n\nWhile the single component can easily be simulated on a usual desktop computer,\nthe calculation of a system of many coupled devices still presents a challenge.\nThis challenge is raised by the fact that many of these devices show a\nnonlinear behavior. Especially for electrostatic structures, a further\ndifficulty is the large reach of the electrostatic forces, leading to a strong\nspatial coupling of charges.\n\nAccurate modelling of such a system typically leads to high order models. The\ntasks of simulation, analysis and controller design of high order nonlinear\ncontrol systems can be simplified by reducing the order of the original system\nand approximate it by a lower order model.\n\nA application of electrostatic moving structures are e.g. RF switches or\nfilters. Given a simple enough shape, they often can be modelled as\none-dimensional beams embedded in two or three dimensional space. Model\ndescription\n\nSetup of beam\n\nThis model describes a slender beam with four degrees of freedom per node:\n\nx Axial displacement thetax Axial rotation y Flexural displacement thetaz\nFlexural rotation\n\nDegree of Freedom x\n\nDegree of Freedom theta x\n\nDegrees of Freedom y and theta z\n\nThe beam is supported either on the left side or on both sides. For the left\nside (fixed) support, the force is applied on the rightmost node in y\ndirection, whereas for the support on both sides (simply supported), a node in\nthe middle is loaded. The damping matrix is calculated by a linear combination\nof the mass matrix M and the stiffness matrix K. Benchmark examples\n\nBased on the finite element discretization presented in [1], an interactive\nmatrix generator has been created using Wolfram Research's webMathematica. It\nis available online at\nhttp://www.imtek.uni-freiburg.de/simulation/mstkmpkt/models/LinearBeam.html,\n\"Linear beam model\". However, models produced by this generator are in the DSIF\nformat, which allows for nonlinear terms. For the purpose of the benchmark\ncollection, we have precomputed four systems and converted them to the Matrix\nmarket format which is easier to import in standard computer algebra packages.\n\nAll examples are made for a steel beam with the following properties: Property\nValue Beam length (l)\t0.1 m Material density (rho)\t8000 kg/m3\nCross-sectional area (A) 7.854e-7 m2 Moment of inertia (I)\t4.909e-14 m4\nPolar moment of inertia (J) 9.817e-14 Modulus of elasticity (E) 2e11 Pa\nPoisson ratio (nu) 0.29 Contribution of M to damping\t1e2 Contribution of K\nto damping\t1e-2 Support\tSimple, both sides\n\nThe following examples are available (all files are compressed .zip archives,\nUnits: SI):\n\nFile\tDegrees of freedom Number of nodes Number of equations\nFile size/Compressed size\n\nLF10.zip flexural (y and thetaz) 10\t18 \nLF10000.zip flexural (y and thetaz) 10000 19998 \nLFAT5.zip flexural (y and thetaz), axial, torsional\t5 14\nLFAT5000.zip\tflexural (y and thetaz), axial, torsional 50000 19994 \n\nThe zip files contain matrices M, E, K, B and E for the following system of\nequations: M x\" + E x' + K x = B u y = C x\n\nwhere B is a n\u00d71 matrix and C is a 1\u00d7n matrix with the only nonzero entry at\nthe y DOF of the middle node.\n\nDetails of the implementation are available in a separate report (PDF, 88 kB).\nA typical input to this system is a step response; periodic on/off switching is\nalso possible. The reduced model should thus both represent the step response\nas well as the possible influence of higher order harmonics.\n\n[1] William Weaver, Jr., Stephen P. Timoshenko, and Donovan H. Young, Vibration\nproblems in engineering, 5th ed., Wiley (1990) Last modified: Wed Sep 8\n16:53:01 CEST 2004 \n\n================================================================================\n==> piston/README.txt <==\n================================================================================\n\n--------------------------------------------------------------------------------\nAxi symmetric infinite element model for circular piston (piston problem)\n--------------------------------------------------------------------------------\n\nKarl Meerbergen\n\nFree Field Technologies, 16, place de l'Universit\u00e9, 1348 Louvain-la-Neuve,\nBelgium\n\nThis example is a model of the form\n\n\\\\begin{displaymath} M \\\\ddot{x} + E \\\\dot{x} + K x = f \\\\end{displaymath}\n\nwith $M$, $C$, and $K$ non-symmetric matrices and $M$ singular. This is thus a\ndifferential algebraic equation. It is shown that it has index one [1]. The\ninput of the system is $f$, the output is the state vector $x$. The motivation\nfor using model reduction for this type of problems is the reduction of the\ncomputation time of a simulation.\n\nThis is an example from an acoustic radiation problem discussed in [3].\nConsider a circular piston subtending a polar angle $0 < \\\\theta < \\\\theta_p$ on\na submerged massless and rigid sphere of radius $\\\\delta$. The piston vibrates\nharmonically with a uniform radial acceleration. The surrounding acoustic\ndomain is unbounded and is characterized by its density $\\\\rho$ and sound speed\n$c$.\n\nWe denote by $p$ and $a_r$ the prescribed pressure and normal acceleration\nrespectively. In order to have a steady state solution $\\\\tilde p(r,\\\\theta,t)$\nverifying\n\n\\\\begin{displaymath} \\\\tilde p(r,\\\\theta,t) = \\\\mathcal R \\\\mbox{e} \\\\left(\np(r,\\\\theta) e^{i\\\\omega t} \\\\right), \\\\end{displaymath}\n\nthe transient boundary condition is chosen as:\n\n\\\\begin{displaymath} a_r = \\\\left. \\\\frac{-1}{\\\\rho} \\\\frac{\\\\partial\np(r,\\\\theta)}{\\\\pa... ...e \\\\theta_p, \\\\\\\\ 0, & \\\\theta > \\\\theta_p.\\\\\\\\\n\\\\end{array}\\\\right. \\\\end{displaymath}\n\nThe axisymmetric discrete finite-infinite element model relies on a mesh of\nlinear quadrangle finite elements for the inner domain (region between\nspherical surfaces $r=\\\\delta$ and $r = 1.5 \\\\delta$). The numbers of divisions\nalong radial and circumferential directions are $5$ and $80$, respectively. The\nouter domain relies on conjugated infinite elements of order $5$. For this\nexample we used $\\\\delta=1 \\\\mathrm{(m)}$, $\\\\rho = 1.225 \\\\mathrm{(kg/m^3)}$,\n$c=340 \\\\mathrm{(m/s)}$, $a_0 = 0.001 \\\\mathrm{(m/s^2)}$ and $\\\\omega = 1000\n\\\\mathrm{(rad/s)}$.\n\nThe matrices $K$, $C$, $M$ and the right-hand side $f$ are computed by\nMSC.Actran [2]. The dimension of the second-order system is $N = 2025$.\n\n\n\n================================================================================\n==> rail_79841/README.txt <==\n================================================================================\n\n--------------------------------------------------------------------------------\nA Semi-discretized Heat Transfer Problem for Optimal Cooling of Steel Profiles\n(rail* problems)\n--------------------------------------------------------------------------------\n\nSummary\n\nSeveral generalized state-space models arising from a semi-discretization of a\ncontrolled heat transfer process for optimal cooling of steel profiles are\npresented. The models oder differs due to different refinements applied to the\ncomputational mesh. The model equations\n\nWe consider the problem of optimal cooling of steel profiles. This problem\narises in a rolling mill when different steps in the production process require\ndifferent temperatures of the raw material. To achieve a high production rate,\neconomical interests suggest to reduce the temperature as fast as possible to\nthe required level before entering the next production phase. At the same time,\nthe cooling process, which is realized by spraying cooling fluids on the\nsurface, has to be controlled so that material properties, such as durability\nor porosity, achieve given quality standards. Large gradients in the\ntemperature distributions of the steel profile may lead to unwanted\ndeformations, brittleness, loss of rigidity, and other undesirable material\nproperties. It is therefore the engineers goal to have a preferably even\ntemperature distribution.\n\nThe scientific challenge here is to give the engineers a tool to pre-calculate\ndifferent control laws yielding different temperature distributions in order to\ndecide which cooling strategy to choose.\n\nWe can only briefly introduce the model here for details we refer to [Saa03] or\n[BS04]. We assume an infinitely long steel profile so that we may restrict\nourselves to a 2D model. Exploiting the symmetry of the workpiece, the\ncomputational domain \u03a9\u2282R2 is chosen as the half of a cross section of the rail\nprofile. The heat distribution is modeled by the instationary linear heat\nequation on \u03a9\n\n(1)\n\nc\u03c1 \u2202t x(t,\u03be) - \u03bb\u0394x(t,\u03be) = 0 in R>0 \u00d7 \u03a9,\nx(0,\u03be) = x0(\u03be) in \u03a9,\n\u03bb \u2202\u03bdx(t,\u03be) = gi on R>0\u00d7 \u0393i, \u2202\u03a9 = \u222ai \u0393i\t\n\nwhere x is the temperature distribution (\u2208H1([0,\u221e],X) with X:=H1(\u03a9) the state\nspace), c the specific heat capacity, \u03bb the heat conductivity and \u03c1 the density\nof the rail profile. We split the boundary into several parts \u0393i on which we\nhave different boundary functions gi, allowing us to vary controls on different\nparts of the surface. By \u03bd we denote the outer normal on the boundary.\n\nFigure 1: Initial mesh, partitioning of the boundary, and a picture of a\ncooling plant. Grobgitter K\u00fchlbett\nsee http://www.imtek.de/simulation/benchmark/\n\nWe want to establish the control by a feedback law, i.e. we define the boundary\nfunctions gi to be functions of the state x and the control ui, where\n(ui)i=:u=Fy for a linear operator F which is chosen such that the cost\nfunctional\n\n J(x_0,u):=\u222b0\u221e (Qy,y)Y +(Ru,u)U dt with y=Cx (2)\n\nis minimized. Here, Q and R are linear selfadjoint operators on the output\nspace Y and the control space U with Q\u22650, R>0 and C\u2208 L(X,Y). The variational\nformulation of (1) with gi(t,\u03be) = qi(ui- x(\u03be,t)) leads to:\n\n(\u2202tx,v)=-\u222b\u03a9 \u03b1\u2207x \u2207v d\u03be + \u2211k { qk uk \u222b\u0393k (c\u03c1)-1 v d\u03c3 -\u222b\u0393k qk(c\u03c1)-1 xv d\u03c3}\t (3)\n\nfor all v\u2208C0\u221e(\u03a9). Here the uk are the exterior (cooling fluid) temperatures\nused as the controls, qk are constant heat transfer coefficients (i.e.\nparameters for the spraying intensity of the cooling nozzles)[2] and \u03b1:=\u03bb/(c\u03c1).\nNote that q0=0 gives the Neumann isolation boundary condition on the artificial\ninner boundary on the symmetry axis. In view of (3) we can apply a standrrd\nGalerkin approach for discretizing the heat transfer model in space, resulting\na first-order ordinary differential equation. This idescribed in the following\nsection. The discretized mathematical model\n\nFor the discretization we use the ALBERTA-1.2 fem-toolbox (see [SS00] for\ndetails). We applied linear Lagrange elements and used a projection method for\nthe curved boundaries. The initial mesh (see Fig. 1. on the left) was produced\nby MATLABs pdetool which implements a Delaunay triangulation algorithm. The\nfiner discretizations were produce by global mesh refinement using a bisection\nrefinement method. The discrete LQR problem is then: minimize (2) with respect\nto\n\n (4)\n\n E d/dt x(t) = A x(t) + B u(t), with t>0,\n x(0) = x0,\n y(t) = C x(t).\t\n\nThis benchmark includes four different mesh resolutions. The best\nfem-approximation error that one can expect (under suitable smoothness\nassumptions on the solution) is of order O(h2) where h is the maximum edge size\nin the corresponding mesh. This order should be matched in a model reduction\napproach. The following table lists some relevant quantities for the provided\nmodels.\n\nmatrix dimension num. nonzero in A\tnum. nonzero in E max. mesh width\n1357\t\t 8985\t\t8997\t\t 5.5280e-2\n5177\t\t 35185\t\t35241\t\t 2.7640e-2\n20209\t\t 139233\t\t139473\t\t 1.3820e-2\n79841\t\t 553921\t\t554913\t\t 6.9100e-3\n\nNote that A is negative definite while E is positive definite, so that the\nresulting linear time-invariant system is stable.\n\nThe data sets are named rail_(problem dimension)_C60.(matrix name). Here C60 is\na specific output matrix which is defined to minimize the temperature in the\nnode numbered 60 (refer to the numbers given in Fig 1.) and keep temperature\ngradients small. The latter task is taken into account by the inclusion of\ntemperature differences between specific points in the interior and reference\npoints on the boundary, e.g. temperature difference between nodes 83 and 34.\nAgain refer to Figure 1. for the nodes used. the definitions of other output\nmatrices that we tested can be found in [Saa03]. The problem resides at\ntemperatures of approximately 1000 degrees centigrade down to about 500-700\ndegrees depending on calculation time. The state values are scaled to 1000\nbeing equivalent to 1.000. This results in a scaling of the time line with\nfactor 100, meaning that calculated times have to be divided by 100 to get the\nreal time in seconds. Acknowledgments\n\nThis benchmark example serves as a model problem for the project A15: Efficient\nnumerical solution of optimal control problems for instationary\nconvection-diffusion-reaction-equations of the Sonderforschungsbereich SFB393\nParallel Numerical Simulation for Physics and Continuum Mechanics, supported by\nthe Deutsche Forschungsgemeinschaft. It was motivated by the model described in\nTU01. A very similar problem is used as model problem in the LYAPACK software\npackage Pen00b. Bibliography\n\nBS04 P. Benner and J. Saak. Efficient numerical solution of the LQR-problem\nfor the heat equation. Proc. Appl. Math. Mech., 2004. submitted.\n\nPen00 T. Penzl. LYAPACK Users Guide. Technical Report SFB393/00-33,\nSonderforschungsbereich 393 Numerische Simulation auf massiv parallelen\nRechnern, TU Chemnitz, 09107 Chemnitz, FRG, 2000. Available from\nhttp://www.tu-chemnitz.de/sfb393/sfb00pr.html .\n\nSaa03 J. Saak. Effiziente numerische L\u00f6sung eines Optimalsteuerungsproblems\nf\u00fcr die Abk\u00fchlung von Stahlprofilen. Diplomarbeit, Fachbereich 3/Mathematik\nund Informatik, Universit\u00e4t Bremen, D-28334 Bremen, September 2003. Available\nfrom http://www-user.tu-chemnitz.de/\u223csaak/Data/index.html .\n\nSS00 A. Schmidt and K. Siebert. ALBERT: An adaptive hierarchical finite\nelement toolbox. Preprint 06/2000 / Institut f\u00fcr Angewandte Mathematik,\nAlbert-Ludwigs-Universit\u00e4t Freiburg, edition: albert-1.0 edition, 2000.\nAvailable from http://www.mathematik.uni-freiburg.de/IAM/ALBERT/doc.html.\n\nTU01 F. Tr\u00f6ltzsch and A. Unger. Fast solution of optimal control problems in\nthe selective cooling of steel. Z. Angew. Math. Mech., 81:447-456, 2001. \n\n\n================================================================================\n==> spiral/README.txt <==\n================================================================================\n\n--------------------------------------------------------------------------------\nSpiral Inductor PEEC Model (spiral models)\n--------------------------------------------------------------------------------\n\nFigure: Spiral inductor with part of overhanging copper plane; see\nhttp://www.imtek.de/simulation/benchmark/\n\nExcerpt from report \"PEEC model of a spiral inductor generated by\nFasthenry\" at http://www.imtek.de/simulation/benchmark/ :\n\nThis inductor which first appeared in [KWW00] is intended as an integrated RF\npassive inductor. To make it also a proximity sensor, a 0.1 um plane of copper\nis added 45 um above the spiral. The spiral is also copper with turns 40 um\nwide, 15um thick, with a separation of 40um. The spiral is suspended 55um over\nthe substrate by posts at the corners and centers of the turns in order to\nreduce the capacitance to the substrate. (Note that neither the substrate nor\nthe capacitance is modeled in this example.) The overall extent of the\nsuspended turns is 1.58mm x 1.58mm.\n\n...\n\nThe frequency dependence of the resistance shows two effects, first a rise due\nto currents induced in the copper plane and then a much sharper rise due to the\nskin effect. Capturing the rise due to the skin effect while also maintaining\nthe low frequency response is a challenge for many model reduction algorithms.\n\n\nAuthors:\n\nJing-Rebecca Li, INRIA-Rocquencourt, Projet Ondes, Domaine de Voluceau -\nRocquencourt - B.P. 105, 78153 Le Chesnay Cedex, France jingrebecca.li at\ninria.fr\n\nMattan Kamon, Coventor, Inc. 625 Mt. Auburn St, Cambridge, Ma 02138, USA matt\nat coventor.com. \n\n\"E\" dx/dt = \"A\"x+\"B\"u, y = \"B\"^Tx.\n\nThe complex impedence is\n\nZ(w) = Resis(w)+i*w*Induc(w) = (G(i*w))^(-1)=(B^T(-A+i*w*E)^(-1)B)^(-1)\n\nDownload short matlab files from www.imtek.de to 1. plot Resis(w) and Induc(w)\n2. perform a PRIMA reduction of order 50 3. produce symmetrized standard\nstate-space system: dx/dt = \"A_symm\"x+\"B_symm\"u, y = \"B_symm\"^Tx, where A_symm\nis symmetric.\n \nLast modified: Wed Jul 6 17:38:31 CEST 2005 \n\n[KWW00] Mattan Kamon, Frank Wang, and Jacob White. Generating nearly optimally\ncompact models from krylov-subspace based reduced order models. IEEE\nTransactions On Circuits and Systems-II:Analog and Digital Signal Processing,\n47(4):239-248, April 2000.\n\n================================================================================\n==> t2dah/README.txt <==\n================================================================================\n\nOberwolfach model reduction benchmark collection\n------------------------------------------------\n\nContributions by Evgenii Rudnyi and Dag Billger.\n\nSee also:\n\nhttp://www.imtek.de/simulation/benchmark\nhttp://www.imtek.de/simulation/benchmark/wb/38847\nhttp://www.imtek.de/simulation/benchmark/wb/35889\n\n--------------------------------------------------------------------------------\nMicropyros Thruster\n--------------------------------------------------------------------------------\n\nE. B. Rudnyi, rudnyi AT imtek dot uni-freiburg\ndot de\n\nThe goal of the European project Micropyros\n(http://www.laas.fr/Micropyros/)\nwas to develop a microthruster array\nshown in fig. 1. It is based on the co-integration of solid fuel with a\nsilicon micromachined system [1]. In addition to space applications,\nthe device can be also used for gas generation or as a highly-energetic\nactuator. When the production of a bit-impulse is required, the fuel is\nignited by heating a resistor at the top of a particular microthruster.\nDesign requirements and modeling alternatives are described in Ref [2].\nThe discussion of electro-thermal modeling related to the benchmark can\nbe found in Ref [3].\n\nFigure 1: see http://www.imtek.de/simulation/benchmark/wb/38847/fig1.jpg\n\nFig. 1. Firing a micro thruster in an 4x4 array. Illustration\ncourtesy of C. Rossi, LAAS-CNRS.\n\nThe benchmark contains a simplified thermal model of a single\nmicrothruster to help with a design problem to reach the ignition\ntemperature within the fuel and at the same time not to reach the\ncritical temperature at neighboring microthrusters, that is, at the\nborder of the computational domain. At the same time, the resistor\ntemperature during the heating pulse should not become too high as this\nleads to the destruction of the membrane.\nThe benchmark suite has been made with the Micropyros software\ndeveloped by IMTEK\n(http://www.imtek.uni-freiburg.de/simulation/pyros/).\nThere are four\ndifferent test cases described in Table 1 with the goal to cover\ndifferent cases of different computational complexity. Note that the\nresults\nfrom different models\ncannot be compared directly with each other as the output nodes are\nlocated in slightly different geometrical positions and there is some\ndifference in modeling for the 3D and 2D-axisymmetric cases.\n\nTable 1. Microthruster benchmarks.\n\n Code comment dimension nnz(A) nnz(E) \n T2DAL 2D-axisymmetric, linear elements 4257 20861 4257 \n T2DAH 2D-axisymmetric, quadratic elements 11445 93781 93781 \n T3DL 3D, linear elements 20360 265113 20360 \n T3DH 3D, quadratic elements 79171 2215638 2215638 \n\nThe device solid model has been made and meshed in ANSYS. The material\nproperties assumed to be constant. The system matrices have been\nconverted to the (http://math.nist.gov/MatrixMarket/) Matrix\nMarket format by means of mor4ansys.\n\nTemperature is assumed to be in Celsius with the initial state of 0 C.\nThe output nodes are described in Table 2. Nodes 2 to 5 show the fuel\ntemperature distribution and nodes 6 and 7 characterize temperature in\nthe wafer, nodes 5 and 7 being the most faraway from the resistor.\n\nTable 2. Outputs for the microthruster models.\n\n # Code Comment \n 1 aHeater within the heater \n 2 FuelTop fuel just below the heater \n 3 FT-100 fuel 0.1 mm below the heater \n 4 FT-200 fuel 0.2 mm below the heater \n 5 FuelBot fuel bottom \n 6 WafTop1 wafer top (touching fuel) \n 7 WafTop2 wafer top (end of computational domain) \n\nThe benchmark files contain a constant load vector, corresponding to\nthe constant power input of 150 mW. In order to insert a weak\nnonlinearity related to the dependence of the resistivity on\ntemperature, one has to multiply the load vector by a function\n\n1 + 0.0009 T + 3E-07 T^2\n\nassuming the constant current. It is necessary to replace the\ntemperature in the equation above by the temperature at the node 1.\n\nThe linear ordinary differential equations of the first order are\nwritten as\n\nE dT/dt = A T + B\ny = C T\n\nwhere E and A are the system\nmatrices (both are symmetric), B is the load vector, C is the output\nmatrix, and T is the vector of unknown temperatures.\n\n(Note: the original matrices, in Matrix Market format, are at\nhttp://www.imtek.de/simulation/benchmark).\n\nThe ANSYS results for the original models as well as the reduced models\nobtained by mor4ansys can be found at the \n(http:///www.imtek/de/simulation/pyros/) Micropyros software page:\nchoose EleThermo for T2DAL and T2DAH or EleThermo3D for T3DL and T3DH.\n\nThe model reduction of the microthruster model by means of \n(http://www.imtek.ed/simulation/mor4ansys/) mor4ansys is described in Ref [4].\n\n[1] C. Rossi, B. Larangot, T. Camps, M. Dumonteil, D. Lagrange, P. Q.\nPham, D. Briand, N. F. de Rooij, M. Puig-Vidal, P. Miribel, E. Montane, E.\nLopez, J. Samitier, E. B. Rudnyi, T. Bechtold, J. G. Korvink, Review of solid\npropellant microthrusters on silicon, Journal Propulsion and Power\n(2004), to be published.\n\n[2] E. B. Rudnyi, T. Bechtold, J. G. Korvink, C. Rossi, Solid\nPropellant Microthruster: Theory of Operation and Modelling Strategy,\nNanotech 2002 - At the Edge of Revolution, September 9-12, 2002,\nHouston, USA, AIAA Paper 2002-5755.\n\n[3] G. Korvink, E. B. Rudnyi, Keynote: Computer-aided engineering of\nelectro-thermal MST devices: moving from device to system simulation,\nEUROSIME'03, 4th international conference on thermal and mechanical\nsimulation and experiments in micro-electronics and micro-systems\nAix-en-Provence, France, March 30 - April 2, 2003.\n\n[4] T. Bechtold, E. B. Rudnyi, J. G. Korvink and C. Rossi, Efficient\nModelling and Simulation of 3D Electro-Thermal Model for a\nPyrotechnical Microthruster. International Workshop on Micro and\nNanotechnology for Power Generation and Energy Conversion Applications\nPowerMEMS 2003, Makuhari,Japan, 4-5 December 2003.\n\nPreprints are at http://www.imtek.uni-freiburg.de/simulation/mor4ansys/\n\n\n==> t2dah/T2DAH.C.names <==\naHeater\nFuelTop\nFT-100\nFT-200\nFuelBot\nWafTop1\nWafTop2\n\n==> t2dal/T2DAL.C.names <==\naHeater\nFuelTop\nFT-100\nFT-200\nFuelBot\nWafTop1\nWafTop2\n\n==> t3dh/T3DH.C.names <==\naHeater\nFuelTop\nFT-100\nFT-200\nFuelBot\nWafTop1\nWafTop2\n\n==> t3dl/T3DL.C.names <==\naHeater\nFuelTop\nFT-100\nFT-200\nFuelBot\nWafTop1\nWafTop2\n\n================================================================================\n==> t2dal_bci/README.txt <==\n================================================================================\n\n--------------------------------------------------------------------------------\nBoundary Condition Independent Thermal Model (t2dal_bci_* problems)\n--------------------------------------------------------------------------------\n\nE. B. Rudnyi, rudnyi@imtek.uni-freiburg.de J. G. Korvink,\nkorvink@imtek.uni-freiburg.de\n\nOne of important requirements for a compact thermal model is that it should be\nboundary condition independent. This means that a chip producer does not know\nconditions under which the chip will be used and hence the chip compact thermal\nmodel must allow an engineer to research on how the change in the environment\ninfluences the chip temperature. The chip benchmarks representing boundary\ncondition independent requirements are described in [1].\n\nMathematically, the problem is that the thermal problem is modeled by the heat\ntransfer partial differential equation when the heat exchange through device\ninterfaces is modeled by convection boundary conditions. The latter contains\nthe film coefficient, h_i, to describe the heat exchange for the i-th\ninterface. After the discretization of both equations one obtains a system of\nordinary differential equations as follows\n\nE dT/dt = (A + Sum_i h_i A_i)T + B\n\nwhere E and A are the device system matrices, A_i is the diagonal matrix due to\nthe discretization of convection boudnary condition for the i-th interface, T\nis the vector with unknown temperatures.\n\nIn terms of the equation above, the engineering requirements read as follows. A\nchip producer specifies the system matrices but the film coefficient, h_i, is\ncontrolled later on by another engineer. As such, any reduced model to be\nuseful should preserve h_i in the symbolic form. This problem can be\nmathematically expressed as parametric model reduction [2,3,4].\n\nUnfortunately, the benchmark from [1] is not available in the computer readable\nformat. For research purposes, we have modified a microthruster benchmark [5]\n(see Fig 1). In the context of the present work, the model is as a generic\nexample of a device with a single heat source when the generated heat\ndissipates through the device to the surroundings. The exchange between\nsurrounding and the device is modeled by convection boundary conditions with\ndifferent film coefficients at the top, h_top, bottom, h_bottom, and the side,\nh_side. From this vewpoint, it is quite similar to a chip model used as a\nbenchmark in [1]. The goal of parametric model reduction in this case is to\npreserve h_top, h_bottom, and h_side in the reduced model in the symbolic form.\n\nFig. 1. see http://www.imtek.de/simulation/benchmark\n\nFigure 1. A 2D-axisymmetrical model of the micro-thruster unit (not scaled).\nThe axis of the symmetry on the left side. A heater is shown by a red spot.\n\nWe have used a 2D-axisymmetric microthruster model (T2DAL in [5]). The model\nhas been made in ANSYS and system matrices have been extracted by means of\nmor4ansys [6]. The benchmark contains a constant load vector. The input\nfunction equal to one corresponds to the constant input power of 15 mW.\n\nThe linear ordinary differential equations of the first order are written as:\n\nE T=(A - h_top A_top - h_bottom A_bottom - h_side A_side) T + B u y=Cx\n\nwhere E and A are the symmetric sparse system matrices (heat capacity and heat\nconductivity matrix), B is the load vector, C is the output matrix, A_top,\nA_bottom, and A_side are the diagonal matrices from the discretization of the\nconvection boundary conditions and T is the vector of unknown temperatures.\n\nThe numerical values of film coefficients, h_top, h_bottom, and h_side can be\nfrom 1 to 1e9. Typical important sets film coefficients can be found in [1].\nThe allowable approximation error is 5 % [1].\n\nThe benchmark has been used in [7,8] where the problem is also described in\nmore detail.\n\nDownload matrices in the Matrix Market format: T2DAL_BCI.tar.gz, 223938 bytes.\nThe matrix name is used as an extension of the matrix file. File\nT2DAL_BCI.C.names contains a list of ouput names written consecutively.\nBibliography\n\n1. C. J. M. Lasance, Two benchmarks to facilitate the study of compact thermal\nmodeling phenomena, IEEE Transactions on Components and Packaging Technologies,\n24, 559-565 (2001).\n\n2. D. S. Weile, E. Michielssen, E. Grimme, K. Gallivan, A method for generating\nrational interpolant reduced order models of two-parameter linear systems,\nApplied Mathematics Letters, 12, 93-102 (1999).\n\n3. P. K. Gunupudi, R. Khazaka, M. S. Nakhla, T. Smy, and D. Celo, Passive\nparameterized time-domain macromodels for high-speed transmission-line\nnetworks, IEEE Transactions on Microwave Theory and Techniques, 51, 2347-2354\n(2003).\n\n4. L. Daniel, O. C. Siong, L. S. Chay, K. H. Lee, and J. White, A\nMultiparameter Moment-Matching Model-Reduction Approach for Generating\nGeometrically Parameterized Interconnect Performance Models, IIEEE Transactions\non Computer-Aided Design of Integrated Circuits and Systems, 23, 678-693\n(2004).\n\n5. E. B. Rudnyi, Micropyros Thruster,\nhttp://www.imtek.uni-freiburg.de/simulation/benchmark/.\n\n6. E. B. Rudnyi and J. G. Korvink, Model Order Reduction of MEMS for Efficient\nComputer Aided Design and System Simulation, MTNS2004, Sixteenth International\nSymposium on Mathematical Theory of Networks and Systems, Katholieke\nUniversiteit Leuven, Belgium, July 5-9, 2004.\nhttp://www.imtek.uni-freiburg.de/simulation/mor4ansys/\n\n7. L. Feng, E. B. Rudnyi, J. G. Korvink, Parametric Model Reduction to Generate\nBoundary Condition Independent Compact Thermal Model, THERMINIC 2004, 10th\nInternational Workshop on Thermal Investigations of ICs and Systems, 29\nSeptember - 1 October 2004, Sophia Antipolis, C\u00aate d'Azur, France. Preprint is\nat http://www.imtek.uni-freiburg.de/simulation/mor4ansys/\n\n8. L. Feng, E. B. Rudnyi, J. G. Korvink, Preserving the film coefficient as a\nparameter in the compact thermal model for fast electro-thermal simulation,\n2004, to be submitted.\n\n==> t2dal_bci/T2DAL_BCI.C.names <==\naHeater\nFuelTop\nFT-100\nFT-200\nFuelBot\nWafTop1\nWafTop2\n\n================================================================================\n==> windscreen/README.txt <==\n================================================================================\n\n--------------------------------------------------------------------------------\nStructural model of a car windscreen (windscreen problem)\n--------------------------------------------------------------------------------\n\nKarl Meerbergen Free Field Technologies, Louvain-la-Neuve, Belgium.\nKarl.Meerbergen@fft.be\n\nThis is an example for a model in the frequency domain of the form\n\n\\\\begin{eqnarray*} K_d x - \\\\omega^2 M x & = & f \\\\\\\\ y & = & f^* x \\\\end{eqnarray*}\n\nwhere $f$ represents a unit point load in one unknown of the state vector. $M$\nis a symmetric positive-definite matrix and $K_d = (1+i\\\\gamma) K$ where $K$ is\nsymmetric positive semi-definite.\n\nThe test problem is a structural model of a car windscreen. This is a 3D\nproblem discretized with 7564 nodes and 5400 linear hexahedral elements (3\nlayers of $60 \\\\times 30$ elements). The mesh is shown in Figure 1. The material\nis glass with the following properties: the Young modulus is $7\\\\,10^{10}\n\\\\mathrm{N}/\\\\mathrm{m}^2$, the density is $2490 \\\\mathrm{kg}/\\\\mathrm{m}^3$, and\nthe Poisson ratio is $0.23$. The natural damping is $10\\\\%$, i.e. $\\\\gamma=0.1$.\nThe structural boundaries are free (free-free boundary conditions). The\nwindscreen is subjected to a point force applied on a corner. The goal of the\nmodel reduction is the fast evaluation of y. Modelreduction is used as a fast\nlinear solver for a sequence of parametrized linear systems.\n\nThe discretized problem has dimension $n=22,692$. The goal is to estimate\n$x(\\\\omega)$ for $\\\\omega\\\\in[0.5,200]$. In order to generate the plots the\nfrequency range was discretized as $\\\\{\\\\omega_1,\\\\ldots,\\\\omega_m\\\\} =\n\\\\{0.5j,j=1,\\\\ldots,m\\\\}$ with $m=400$.\n\nFigure 1: Mesh of the car windscreen and frequency response function\nsee http://www.imtek.de/simulation/benchmark/\n \nThe archive contains files windscreen.K, windscreen.M and windscreen.B\nrepresenting Kd, M and f accordingly.\n\nKarl Meerbergen 2004-11-17" - }, - { - "group": "LiuWenzhuo", - "count": 1, - "documented_meaning": "Power system simulation matrix from Wenzhou Liu, EPRI, China.", - "source_url": "https://sparse.tamu.edu/LiuWenzhuo", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "power network problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 15838, - "max_dimension": 15838, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 64424, - "max_nnz": 64424, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "single matrix", - "official_notes": "Power system simulation matrix from Wenzhou Liu, EPRI, China." - }, - { - "group": "Lin", - "count": 1, - "documented_meaning": "Large sparse eigenvalue problem from Chun-hung (Martin) Lin. NCHC, Taiwan. MartinLin AT nchc dot org dot tw. National Center for High-Performance", - "source_url": "https://sparse.tamu.edu/Lin", - "coarse_kind_families_interpretive": "structures/materials", - "dominant_coarse_family_interpretive": "structures/materials", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "structural problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "symmetric (1)", - "min_dimension": 256000, - "max_dimension": 256000, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 1766400, - "max_nnz": 1766400, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 1, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "Large sparse eigenvalue problem from Chun-hung (Martin) Lin. NCHC, Taiwan.\nMartinLin AT nchc dot org dot tw. National Center for High-Performance\nComputing." - }, - { - "group": "Cannizzo", - "count": 1, - "documented_meaning": "Structural engineering matrix (finite-element) from Fabio Cannizzo.", - "source_url": "https://sparse.tamu.edu/Cannizzo", - "coarse_kind_families_interpretive": "structures/materials", - "dominant_coarse_family_interpretive": "structures/materials", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "structural problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "symmetric (1)", - "min_dimension": 4098, - "max_dimension": 4098, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 72356, - "max_nnz": 72356, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 1, - "known_spd": 1, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "Structural engineering matrix (finite-element) from Fabio Cannizzo." - }, - { - "group": "GHS_indef", - "count": 60, - "documented_meaning": "Indefinite matrices collected by Nick Gould, Yifan Hu, and Jennifer Scott. The GHS_indef and GHS_psdef collection includes matrices from the Matrix", - "source_url": "https://sparse.tamu.edu/GHS_indef", - "coarse_kind_families_interpretive": "fluids/transport; generic spatial PDE/thermal; mathematics/statistics; optimization/control; structures/materials", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 0.5, - "normalized_kind_count": 6, - "normalized_kinds": "optimization problem (30); 2D/3D problem (17); computational fluid dynamics problem (4); materials problem (4); structural problem (3); statistical/mathematical problem (2)", - "curator_value_type_count": 2, - "curator_value_types": "real (57); integer (3)", - "curator_structure_count": 1, - "curator_structures": "symmetric (60)", - "min_dimension": 3002, - "max_dimension": 466316, - "dimension_ratio": 155.33510992671552, - "size_span": "wide (>100\u00d7)", - "min_nnz": 9000, - "max_nnz": 11288630, - "shape_profile": "square only", - "square": 60, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 60, - "structurally_symmetric_square": 60, - "known_spd": 1, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 28, - "cohesion": "multi-domain/mixed", - "official_notes": "Indefinite matrices collected by Nick Gould, Yifan Hu, and Jennifer Scott.\n\nThe GHS_indef and GHS_psdef collection includes matrices from the Matrix\nMarket (math.nist.gov/MatrixMarket), Rutherford-Boeing collection (Duff,\nGrimes, and Lewis), the Parasol project (see below), Kumfert and Pothen,\nCUTE/CUTEr optimization test set, the UF sparse matrix collection\n(those are excluded here), Andy Wathen, Mario Arioli, Christian Damhaug,\nAlison Ramage, Anshul Gupta (Gupta's matrices are in the UF collection\nunder the Gupta directory), Olaf Schenk (matrices from Olaf Schenk are\nlocated here, as well as in the existing Schenk_* directories),\nMiroslav Tuma, Maros and Meszaros, and the Dept. of Mathematical\nModelling in DIAMO (Czech Republic).\n\nSee ftp://ftp.numerical.rl.ac.uk/pub/matrices/symmetric/ for the\nGHS collection.\n\nNOTE: matrices can have upper-case or lower case matrix-type fields\n(rsa or RSA, for example).\n\n--------------------------------------------------------------------------------\n\nbms3_2 matrix:\nSource: www.parallab.uib.no/parasol/data.html (matrix BMW3_2)\nLinear static analysis of a car body. Indefinite matrix.\n (MSC-CARBODY-220K)\nOrder 227362 \nNo. entries 5757996 \n\n--------------------------------------------------------------------------------\n\nMatrices form Olaf Schenk: see the description in the Schenk_IBMNA\ndirectory, for the c-55 to c-72 matrices.\n\n--------------------------------------------------------------------------------\n\nSIT100, K1_SAN, OLESNIKO, D_PRETOK, and TURON_M are from the Dept. of\nMathematical Modelling in DIAMO, s.e., Straz pod Ralskem, Czech Republic.\nThey represent fluid flow in porous media." - }, - { - "group": "GHS_psdef", - "count": 32, - "documented_meaning": "Positive definite matrices collected by Nick Gould, Yifan Hu, & Jennifer Scott. some in unassembled finite-element form (and some in both forms).", - "source_url": "https://sparse.tamu.edu/GHS_psdef", - "coarse_kind_families_interpretive": "fluids/transport; generic spatial PDE/thermal; optimization/control; structures/materials", - "dominant_coarse_family_interpretive": "structures/materials", - "dominant_coarse_family_share": 0.59375, - "normalized_kind_count": 4, - "normalized_kinds": "structural problem (19); optimization problem (8); computational fluid dynamics problem (3); random 2D/3D problem (2)", - "curator_value_type_count": 2, - "curator_value_types": "real (22); binary (10)", - "curator_structure_count": 1, - "curator_structures": "symmetric (32)", - "min_dimension": 15449, - "max_dimension": 952203, - "dimension_ratio": 61.63525147258722, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 101576, - "max_nnz": 77651847, - "shape_profile": "square only", - "square": 32, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 32, - "structurally_symmetric_square": 32, - "known_spd": 22, - "value_profile": "real only", - "binary": 10, - "geometric_2d3d": 24, - "cohesion": "multi-domain/mixed", - "official_notes": "Positive definite matrices collected by Nick Gould, Yifan Hu, & Jennifer Scott.\nsome in unassembled finite-element form (and some in both forms).\n\nThe GHS_indef and GHS_psdef collection includes matrices from the Matrix\nMarket (math.nist.gov/MatrixMarket), Rutherford-Boeing collection (Duff,\nGrimes, and Lewis), the Parasol project (see below), Kumfert and Pothen,\nCUTE/CUTEr optimization test set, the UF sparse matrix collection\n(those are excluded here), Andy Wathen, Mario Arioli, Christian Damhaug,\nAlison Ramage, Anshul Gupta (Gupta's matrices are in the UF collection\nunder the Gupta directory), Olaf Schenk (matrices from Olaf Schenk are\nlocated here, as well as in the existing Schenk_* directories),\nMiroslav Tuma, Maros and Meszaros, and the Dept. of Mathematical\nModelling in DIAMO (Czech Republic).\n\nSee ftp://ftp.numerical.rl.ac.uk/pub/matrices/symmetric/ for the\nGHS collection.\n\nNOTE: some of the *.rse and *.pse finite-element matrices contain\nunsorted indices.\n\nNOTE: matrices can have upper-case or lower case matrix-type fields\n(rsa or RSA, for example).\n\n--------------------------------------------------------------------------------\n==> audi.readme <==\n--------------------------------------------------------------------------------\nSource: www.parallab.uib.no/parasol/data.html (matrix AUDI)\n\nAutomotive crankshaft model with over 900,000 TETRA\n elements (MSC-CRANKSHAFT-900K)\n\nsymmetric rb matrix rb.mtx 18129441 117962 4912222 13099257\nrsa 943695 943695 39297771 0\n\nOrder 943695 \nNo. entries 39297771 \n\n--------------------------------------------------------------------------------\n==> bmw7st_1.readme <==\n--------------------------------------------------------------------------------\nSource: www.parallab.uib.no/parasol/data.html (matrix BMW7st_1)\n\nstiffness matrix BMW7ST_1 2540788 47116 1246836 1246836\nrsa 141347 141347 3740507 0\n\n\nLinear static analysis of a car body.\n (MSC-CARBODY-140K)\nOrder 141347\nNo. entries 3740507 \n\n--------------------------------------------------------------------------------\n==> bmwcra_1.readme <==\n--------------------------------------------------------------------------------\nSource: www.parallab.uib.no/parasol/data.html (matrix BMWCRA_1)\n\nAutomotive crankshaft model with nearly 150,000 TETRA\n elements (MSC-CRANKSHAFT-150K)\n\nstiffness matrix BMWCRA_1\n 3647183 49591 1798796 1798796\nrsa 148770 148770 5396386 0\n(3I14) (3I14) (1P,3E25.16E3) \n\nOrder 148770 \nNo. entries 5396386 \n\n--------------------------------------------------------------------------------\n==> copter1.readme <==\n--------------------------------------------------------------------------------\nSource: Kumfert and Pothen\n\nrequire files\ncopter1.kp (which is obtained from copter1.ptr and copter1.chac)\n\nN = 17222\nNZ = 96921 (= off diag entries in upper triangle) \nOff diag. pattern only supplied.\n\n--------------------------------------------------------------------------------\n==> copter2.readme <==\n--------------------------------------------------------------------------------\nSource: Kumfert and Pothen\n\nrequire files\ncopter2.kp (which is obtained from copter2.ptr and copter2.chac)\n\nN = 55476\nNZ = 352238 (= off diag entries in upper triangle) \nOff diag. pattern only supplied.\n\n--------------------------------------------------------------------------------\n==> crankseg_1.readme <==\n--------------------------------------------------------------------------------\nSource: www.parallab.uib.no/parasol/data.html (matrix CRANKSG1)\n\nLinear static analysis of a crankshaft detail\n (MSC-CRANKSHAFT-SEGMENT-1)\n\nOrder 52,804 \nNo. entries 5,333,507 \n\n--------------------------------------------------------------------------------\n==> crankseg_2.readme <==\n--------------------------------------------------------------------------------\nSource: www.parallab.uib.no/parasol/data.html (matrix CRANKSG2\n\nLinear static analysis of a crankshaft detail\n (MSC-CRANKSHAFT-SEGMENT-2)\n\nOrder 52,804 \nNo. entries 7,106,348 \n\n\n--------------------------------------------------------------------------------\n==> Fcondp2.readme <==\n--------------------------------------------------------------------------------\n\nSource: Christain Damahug (Det Norske Veritas, Oslo, Norway)\n\n\n Matrix Format Converter\n -----------------------\n\n Total number of nodes ................................... : 33913\n Number of active nodes .................................. : 33913\n Number of external nodes ................................ : 0\n Number of nodes with at least one boundary condition .... : 552\n Number of nodes with at least one load intensity ........ : 0\n Number of free variables (equations) .................... : 201822\n\n Number of elements ...................................... : 35836\n Number of material types ................................ : 1\n Maximum number of nodes connected to an element ......... : 4\n\n Size of complete element node connectivity .............. : 143344\n Size of stripped element node connectivity .............. : 143344\n Size of active adjacency set representation ............. : 282912\n TIME FOR WRITING MATRIX FORMAT = 7.7391695976E+00\n\n\n = = = = = = = = = = = = = = = = = = = = = = = = =\n = = = Storage Requirement Matrix Convertion = = =\n = = = = = = = = = = = = = = = = = = = = = = = = =\n\n Total INTEGER workspace need (runtime measure) .......... : 1173274\n Total REAL workspace need (runtime measure) ............. : 0\n\n Total CPU-time consumption to convert the matrix ........ : 8.89181E+00\n\n Wall-Clock Time Consumption ............................. : 1.70000E+01\n\n--------------------------------------------------------------------------------\n==> finance256.readme <==\n--------------------------------------------------------------------------------\nSource: Kumfert and Pothen\n\nrequire files\nfinance256.kp (which is obtained from finance256.ptr and finance256.chac)\n\nN = 37376\nNZ = 130560 (= off diag entries in upper triangle) \nOff diag. pattern only supplied.\n\n--------------------------------------------------------------------------------\n==> ford1.readme <==\n--------------------------------------------------------------------------------\nSource: Kumfert and Pothen\n\nrequire files\nford1.kp (which is obtained from ford1.ptr and ford1.chac)\n\nN = 18728\nNZ = 41424 (= off diag entries in upper triangle) \nOff diag. pattern only supplied.\n\n--------------------------------------------------------------------------------\n==> ford2.readme <==\n--------------------------------------------------------------------------------\nSource: Kumfert and Pothen\n\nrequire files\nford2.kp (which is obtained from ford2.ptr and ford2.chac)\n\nN = 100196\nNZ = 222246 (= off diag entries in upper triangle) \nOff diag. pattern only supplied.\n\n--------------------------------------------------------------------------------\n==> Fullb.readme <==\n--------------------------------------------------------------------------------\n\nSource: Christain Damahug (Det Norske Veritas, Oslo, Norway)\n\n\n Matrix Format Converter\n -----------------------\n\n Total number of nodes ................................... : 33442\n Number of active nodes .................................. : 33442\n Number of external nodes ................................ : 0\n Number of nodes with at least one boundary condition .... : 586\n Number of nodes with at least one load intensity ........ : 0\n Number of free variables (equations) .................... : 199187\n\n Number of elements ...................................... : 59738\n Number of material types ................................ : 3\n Maximum number of nodes connected to an element ......... : 4\n\n Size of complete element node connectivity .............. : 195585\n Size of stripped element node connectivity .............. : 195585\n Size of active adjacency set representation ............. : 294498\n TIME FOR WRITING MATRIX FORMAT = 1.0402039528E+01\n\n\n = = = = = = = = = = = = = = = = = = = = = = = = =\n = = = Storage Requirement Matrix Convertion = = =\n = = = = = = = = = = = = = = = = = = = = = = = = =\n\n Total INTEGER workspace need (runtime measure) .......... : 1584759\n Total REAL workspace need (runtime measure) ............. : 0\n\n Total CPU-time consumption to convert the matrix ........ : 1.18173E+01\n\n Wall-Clock Time Consumption ............................. : 2.90000E+01\n\n--------------------------------------------------------------------------------\n==> Halfb.readme <==\n--------------------------------------------------------------------------------\n\n\nSource: Christain Damahug (Det Norske Veritas, Oslo, Norway)\n\n\n Matrix Format Converter\n -----------------------\n\n Total number of nodes ................................... : 38556\n Number of active nodes .................................. : 38556\n Number of external nodes ................................ : 0\n Number of nodes with at least one boundary condition .... : 2470\n Number of nodes with at least one load intensity ........ : 0\n Number of free variables (equations) .................... : 224617\n\n Number of elements ...................................... : 70211\n Number of material types ................................ : 3\n Maximum number of nodes connected to an element ......... : 4\n\n Size of complete element node connectivity .............. : 221764\n Size of stripped element node connectivity .............. : 221764\n Size of active adjacency set representation ............. : 321406\n TIME FOR WRITING MATRIX FORMAT = 1.1791002274E+01\n\n\n = = = = = = = = = = = = = = = = = = = = = = = = =\n = = = Storage Requirement Matrix Convertion = = =\n = = = = = = = = = = = = = = = = = = = = = = = = =\n\n Total INTEGER workspace need (runtime measure) .......... : 1774320\n Total REAL workspace need (runtime measure) ............. : 0\n\n Total CPU-time consumption to convert the matrix ........ : 1.33733E+01\n\n Wall-Clock Time Consumption ............................. : 4.20000E+01\n\n--------------------------------------------------------------------------------\n==> hood.readme <==\n--------------------------------------------------------------------------------\n\nSource: www.parallab.uib.no/parasol/data.html (matrix HOOD)\n\n INDEED Test Matrix (DC-mh) DCMH 1945127 22055 549449 1373623\nRSA 220542 220542 5494489 0\n\n\nOrder 220542\nNo. entries 5494489 \n\n--------------------------------------------------------------------------------\n==> inline_1.readme <==\n--------------------------------------------------------------------------------\nSource: www.parallab.uib.no/parasol/data.html (INLINE_1)\n\nInline skater (MSC-INLINE)\n\nstiffness matrix INLINE_1 12607923 167905 6220009 6220009\nrsa 503712 503712 18660027 0\n(3I14) (3I14) (1P,3E25.16E3) \n\nOrder 503,712\nNo. entries 18,660,027 \n\n--------------------------------------------------------------------------------\n==> ldoor.readme <==\n--------------------------------------------------------------------------------\nSource: www.parallab.uib.no/parasol/data.html (LDOOR)\n\nLarge door\n\n INDEED Test Matrix TUER 8403290 95221 2373734 5934335\nRSA 952203 952203 23737339 0\n\nOrder 952,203\nNo. entries 23,737,339 \n\n--------------------------------------------------------------------------------\n==> msdoor.readme <==\n--------------------------------------------------------------------------------\nSource: www.parallab.uib.no/parasol/data.html (MSDOOR)\n\n INDEED Test Matrix smltuer smtuer 3656527 41587 1032840 2582100\nrsa 415863 415863 10328399 0\n\n\nOrder 415,863\nNo. entries 10,328,399 \n\n--------------------------------------------------------------------------------\n==> M_T1.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nTubular joint\n\nOrder 97,578\nwhen assembled, entries = 4,925,574\nunassembled entries = 6,882,780\n\n DNV-Ex 1 : Tubular joint-1999-01-17 M_T1 \n 1747955 533 26727 1720695 0\nRSE 97578 5328 267270 6882780 \n(10I8) (10I8) (3e26.16) \n \n\n--------------------------------------------------------------------------------\n==> MT1.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\n\nElement Repr of \n 27260 533 26727 0 0\nPSE 97578 5328 267270 0\n(10I8) (10I8) \n\n--------------------------------------------------------------------------------\n==> OILPAN.readme <==\n--------------------------------------------------------------------------------\nSource: www.parallab.uib.no/parasol/data.html\n\n 1. TEST MATRIX FROM INPRO FOR PARASOL (OIL-PAN)\n 649791 7376 183547 458868 0\nrsa 73752 73752 1835470 0\n\n\nOrder 73752\nNo. entries 1835470 \n\n--------------------------------------------------------------------------------\n==> onera_dual.readme <==\n--------------------------------------------------------------------------------\nSource: Kumfert and Pothen\n\nrequire files\nonera_dual.kp (which is obtained from onera_dual.ptr and onera_dual.chac)\n\nN = 85567\nNZ = 166817 (= off diag entries in upper triangle) \nOff diag. pattern only supplied.\n\n--------------------------------------------------------------------------------\n==> opt1.readme <==\n--------------------------------------------------------------------------------\nSource: Christain Damhaug\n\nMatrix Representation of OPT1, part of condeep cylinder OPT1 \n 5367 98 5269 0 0\nPSE 15449 977 52686 0\n(10I8) (10I8) \n\n--------------------------------------------------------------------------------\n==> pds10.readme <==\n--------------------------------------------------------------------------------\nSource: Kumfert and Pothen\n\nrequire files\npds10.kp (which is obtained from pds10.ptr and pds10.chac)\n\nN = 16558\nNZ = 66550 (= off diag entries in upper triangle) \nOff diag. pattern only supplied.\n\n--------------------------------------------------------------------------------\n==> ramage02.readme <==\n--------------------------------------------------------------------------------\n\nSource: Alison Ramage (Strathclyde)\n\nPattern only element problem\n\n1FINITE ELEMENT PROBLEM. NAVIER-STOKES AND CONTINUITY EQUATIONS. RAMAGE02\n 6495 141 6354 0 0 \nPSE 16830 1400 82598 0 \n(10I8) (13I6) (3(1PE22.14)) (3(1PE22.14))\n\n--------------------------------------------------------------------------------\n==> s3dkq4m2.readme <==\n--------------------------------------------------------------------------------\nSource: Matrix Market\nBUT had to edit file as in RB format so RSA needs to be rsa\n\n * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\n * cyl shell R/t=1000 unif 100 x 150 quad mesh DK-el with drill rotat *\n * Key = s3dkq4m2 , Type = RSA *\n * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\n * Dimension N = 90449 *\n * Number of nonzero elements = 2455670 *\n * Average number of nonzero elements/Column = 27.1498 *\n * Standard deviation for above average = 2.6724 *\n * Nonzero elements in strict lower part = 2365221 *\n * Nonzero elements in strict upper part = 2365221 *\n * Nonzero elements in main diagonal = 90449 *\n * Weight of longest column = 59 *\n * Weight of shortest column = 15 *\n * Matching elements in symmetry = 0 *\n * Relative Symmetry Match (symmetry=1) = NaN *\n * Average distance of a(i,j) from diag. = 0.395E+03 *\n * Standard deviation for above average = 0.285E+03 *\n *-----------------------------------------------------------------*\n * Frobenius norm of A = 0.403E+06 *\n * Frobenius norm of symmetric part = 0.403E+06 *\n * Frobenius norm of nonsymmetric part = 0.000E+00 *\n * Maximum element in A = 0.215E+04 *\n * Percentage of weakly diagonally dominant rows = 0.329E-02 *\n * Percentage of weakly diagonally dominant columns = 0.329E-02 *\n *-----------------------------------------------------------------*\n * Lower bandwidth (max: i-j, a(i,j) .ne. 0) = 0 *\n * Upper bandwidth (max: j-i, a(i,j) .ne. 0) = 614 *\n * Maximum Bandwidth = 615 *\n * Average Bandwidth = 0.609E+03 *\n * Number of nonzeros in skyline storage = 55071893 *\n * 90% of matrix is in the band of width = 1217 *\n * 80% of matrix is in the band of width = 1211 *\n * The total number of nonvoid diagonals is = 331 *\n * The 10 most important diagonals are (offsets) : *\n * 0 1 2 3 4 5 603 602 604 601 *\n * The accumulated percentages they represent are : *\n * 3.7 7.4 11.0 14.7 18.3 22.0 25.6 29.2 32.9 36.5 *\n *-----------------------------------------------------------------*\n * The matrix does not have a block structure *\n *-----------------------------------------------------------------*\n\n--------------------------------------------------------------------------------\n==> s3dkt3m2.readme <==\n--------------------------------------------------------------------------------\nSource: Matrix Market\nBut had to edit file as in RB format RSA needs to be rsa\n\n * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\n * cyl shell 100 x 150 unif triang mesh R/t = 1000 *\n * Key = s3dkt3m2 , Type = RSA *\n * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\n * Dimension N = 90449 *\n * Number of nonzero elements = 1921955 *\n * Average number of nonzero elements/Column = 21.2490 *\n * Standard deviation for above average = 2.3992 *\n * Nonzero elements in strict lower part = 1831506 *\n * Nonzero elements in strict upper part = 1831506 *\n * Nonzero elements in main diagonal = 90449 *\n * Weight of longest column = 47 *\n * Weight of shortest column = 9 *\n * Matching elements in symmetry = 0 *\n * Relative Symmetry Match (symmetry=1) = NaN *\n * Average distance of a(i,j) from diag. = 0.339E+03 *\n * Standard deviation for above average = 0.299E+03 *\n *-----------------------------------------------------------------*\n * Frobenius norm of A = 0.641E+06 *\n * Frobenius norm of symmetric part = 0.641E+06 *\n * Frobenius norm of nonsymmetric part = 0.000E+00 *\n * Maximum element in A = 0.322E+04 *\n * Percentage of weakly diagonally dominant rows = 0.000E+00 *\n * Percentage of weakly diagonally dominant columns = 0.000E+00 *\n *-----------------------------------------------------------------*\n * Lower bandwidth (max: i-j, a(i,j) .ne. 0) = 0 *\n * Upper bandwidth (max: j-i, a(i,j) .ne. 0) = 614 *\n * Maximum Bandwidth = 615 *\n * Average Bandwidth = 0.609E+03 *\n * Number of nonzeros in skyline storage = 55071893 *\n * 90% of matrix is in the band of width = 1219 *\n * 80% of matrix is in the band of width = 1215 *\n * The total number of nonvoid diagonals is = 328 *\n * The 10 most important diagonals are (offsets) : *\n * 0 1 2 3 4 5 603 604 605 6 *\n * The accumulated percentages they represent are : *\n * 4.7 9.4 14.1 18.8 23.4 28.1 32.7 37.4 42.0 46.6 *\n *-----------------------------------------------------------------*\n * The matrix does not have a block structure *\n *-----------------------------------------------------------------*\n\n--------------------------------------------------------------------------------\n==> SHIP_001.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nShip structure, predesign model\nOrder 34,920\nwhen assembled, entries = 2,339,575\nunassembled entries = 3,686,133\n\nDNV-Ex 2 : Ship structure, predesign model-1999-01-17 SHIP_001\n 937389 344 15511 921534 0\nRSE 34920 3431 155106 3686133 \n(10I8) (10I8) (3e26.16) \n \n\n--------------------------------------------------------------------------------\n==> SHIP_003.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nShip structure from production run\nOrder 121,728\nwhen assembled, entries = 4,103,881\nunassembled entries =9,728,631\n\n \nDNV-Ex 3 : Ship structure from production run-1999-01-17 SHIP_003\n 2525272 4547 88317 2432408 0\nRSE 121728 45464 883167 9729631 \n(10I8) (10I8) (3e26.16) \n\n--------------------------------------------------------------------------------\n==> SHIPSEC1.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nShip section/detail from production run\nOrder 140,874\nwhen assembled, entries = 3,977,139\nunassembled entries = 6,882,780\n\nDNV-Ex 4 : Ship section/detail from production run-1999-01-17 SHIPSEC1\n 2237188 4104 78502 2154582 0\nRSE 140874 41037 785016 8618328 \n(10I8) (10I8) (3e26.16) \n \n\n--------------------------------------------------------------------------------\n==> SHIPSEC5.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nShip section/detail from production run\nOrder 179,86\nwhen assembled, entries = 6,146,478\nunassembled entries = 11,118,602\n\nDNV-PARASOL Ship section 5-1999-01-15 Shipsec5\n 2885670 5228 100791 2779651 0\nRSE 179860 52272 1007907 11118602 \n(10I8) (10I8) (4e22.14) \n \n\n--------------------------------------------------------------------------------\n==> SHIPSEC8.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nShip section/detail from production run\nOrder 114,919\nwhen assembled, entries = 3,384,159\nunassembled entries = 7,431,867\n\nDNV-PARASOL Ship section 5-1999-01-15 Shipsec5\n 2885670 5228 100791 2779651 0\nRSE 179860 52272 1007907 11118602 \n(10I8) (10I8) (4e22.14) \n \n\n--------------------------------------------------------------------------------\n==> Srb1.readme <==\n--------------------------------------------------------------------------------\n\nSource: Christain Damahug (Det Norske Veritas, Oslo, Norway)\n\n Matrix Format Converter\n -----------------------\n\n Total number of nodes ................................... : 9198\n Number of active nodes .................................. : 9154\n Number of external nodes ................................ : 0\n Number of nodes with at least one boundary condition .... : 44\n Number of nodes with at least one load intensity ........ : 0\n Number of free variables (equations) .................... : 54924\n\n Number of elements ...................................... : 9240\n Number of material types ................................ : 0\n Maximum number of nodes connected to an element ......... : 4\n\n Size of complete element node connectivity .............. : 36872\n Size of stripped element node connectivity .............. : 36784\n Size of active adjacency set representation ............. : 73128\n TIME FOR WRITING MATRIX FORMAT = 1.9468524456E+00\n\n\n = = = = = = = = = = = = = = = = = = = = = = = = =\n = = = Storage Requirement Matrix Convertion = = =\n = = = = = = = = = = = = = = = = = = = = = = = = =\n\n Total INTEGER workspace need (runtime measure) .......... : 303533\n Total REAL workspace need (runtime measure) ............. : 0\n\n Total CPU-time consumption to convert the matrix ........ : 2.20886E+00\n\n Wall-Clock Time Consumption ............................. : 6.00000E+00\n\n--------------------------------------------------------------------------------\n==> Thread.readme <==\n--------------------------------------------------------------------------------\n\nSource: Christain Damahug (Det Norske Veritas, Oslo, Norway)\n\n\n Matrix Format Converter\n -----------------------\n\n Total number of nodes ................................... : 10008\n Number of active nodes .................................. : 9912\n Number of external nodes ................................ : 0\n Number of nodes with at least one boundary condition .... : 96\n Number of nodes with at least one load intensity ........ : 0\n Number of free variables (equations) .................... : 29736\n\n Number of elements ...................................... : 2176\n Number of material types ................................ : 1\n Maximum number of nodes connected to an element ......... : 20\n\n Size of complete element node connectivity .............. : 42080\n Size of stripped element node connectivity .............. : 41888\n Size of active adjacency set representation ............. : 486760\n TIME FOR WRITING MATRIX FORMAT = 1.0886445045E+00\n\n\n = = = = = = = = = = = = = = = = = = = = = = = = =\n = = = Storage Requirement Matrix Convertion = = =\n = = = = = = = = = = = = = = = = = = = = = = = = =\n\n Total INTEGER workspace need (runtime measure) .......... : 201743\n Total REAL workspace need (runtime measure) ............. : 0\n\n Total CPU-time consumption to convert the matrix ........ : 1.33817E+00\n\n Wall-Clock Time Consumption ............................. : 4.00000E+00\n\n--------------------------------------------------------------------------------\n==> THREAD.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nThreaded connector/contact problem\nOrder 29,736\nwhen assembled, entries = 2,249,892\nunassembled entries = 3,718,704\n\nDNV-Ex 7 : Threaded connector/contact problem-1999-01-17 THREAD \n 942461 218 12567 929676 0\nRSE 29736 2176 125664 3718704 \n(10I8) (10I8) (3e26.16) \n \n\n--------------------------------------------------------------------------------\n==> trdheim.readme <==\n--------------------------------------------------------------------------------\nSource: Christian Damhaug (DNR)\n\nMatrix Representation of TRDHEIM, Mesh of the Trondheim Fjord TRDHEIM \n 4473 82 4391 0 0\nPSE 22098 813 43902 0\n(10I8) (10I8) \n\n--------------------------------------------------------------------------------\n==> Troll.readme <==\n--------------------------------------------------------------------------------\n\nSource: Christain Damahug (Det Norske Veritas, Oslo, Norway)\n\n Matrix Format Converter\n -----------------------\n\n Total number of nodes ................................... : 72545\n Number of active nodes .................................. : 71151\n Number of external nodes ................................ : 0\n Number of nodes with at least one boundary condition .... : 1394\n Number of nodes with at least one load intensity ........ : 0\n Number of free variables (equations) .................... : 213453\n\n Number of elements ...................................... : 41084\n Number of material types ................................ : 2\n Maximum number of nodes connected to an element ......... : 8\n\n Size of complete element node connectivity .............. : 321888\n Size of stripped element node connectivity .............. : 318624\n Size of active adjacency set representation ............. : 1260528\n TIME FOR WRITING MATRIX FORMAT = 8.8573474884E+00\n\n\n = = = = = = = = = = = = = = = = = = = = = = = = =\n = = = Storage Requirement Matrix Convertion = = =\n = = = = = = = = = = = = = = = = = = = = = = = = =\n\n Total INTEGER workspace need (runtime measure) .......... : 1570220\n Total REAL workspace need (runtime measure) ............. : 0\n\n Total CPU-time consumption to convert the matrix ........ : 1.10190E+01\n\n Wall-Clock Time Consumption ............................. : 2.90000E+01\n\n--------------------------------------------------------------------------------\n==> tsyl201.readme <==\n--------------------------------------------------------------------------------\nSource: Christian Damhaug (DNR)\n\nMatrix Representation of TSYL201, part of condeep cylinder TSYL201 \n 5857 97 5760 0 0\nPSE 20685 960 57600 0\n(10I8) (10I8) \n\n--------------------------------------------------------------------------------\n==> vanbody.readme <==\n--------------------------------------------------------------------------------\nSource: www.parallab.uib.no/parasol/data.html (LDOOR)\n\nVan body model (MSC-VANBODY)\n\nstiffness matrix XCARBO_1 810349 15691 397329 397329\nrsa 47072 47072 1191985 0\n\nOrder 47072\nentries 1191985\n\n--------------------------------------------------------------------------------\n==> X104.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nBeam joint (SESAM test example no. 104)\nOrder 108,384\nwhen assembled, entries = 5,138,004\nunassembled entries = 7,065,546\n\nDNV-Ex 8 : SESAM test example no 104-1999-01-17 X104 \n 1795848 602 28859 1766387 0\nRSE 108384 6019 288588 7065546 \n(10I8) (10I8) (3e26.16) \n\n\n################################################################################\nThese are matrices in unassembled finite-element form:\n################################################################################\n\n\n--------------------------------------------------------------------------------\n==> Fcondp2.readme <==\n--------------------------------------------------------------------------------\n\nSource: Christain Damahug (Det Norske Veritas, Oslo, Norway)\n\n\n Matrix Format Converter\n -----------------------\n\n Total number of nodes ................................... : 33913\n Number of active nodes .................................. : 33913\n Number of external nodes ................................ : 0\n Number of nodes with at least one boundary condition .... : 552\n Number of nodes with at least one load intensity ........ : 0\n Number of free variables (equations) .................... : 201822\n\n Number of elements ...................................... : 35836\n Number of material types ................................ : 1\n Maximum number of nodes connected to an element ......... : 4\n\n Size of complete element node connectivity .............. : 143344\n Size of stripped element node connectivity .............. : 143344\n Size of active adjacency set representation ............. : 282912\n TIME FOR WRITING MATRIX FORMAT = 7.7391695976E+00\n\n\n = = = = = = = = = = = = = = = = = = = = = = = = =\n = = = Storage Requirement Matrix Convertion = = =\n = = = = = = = = = = = = = = = = = = = = = = = = =\n\n Total INTEGER workspace need (runtime measure) .......... : 1173274\n Total REAL workspace need (runtime measure) ............. : 0\n\n Total CPU-time consumption to convert the matrix ........ : 8.89181E+00\n\n Wall-Clock Time Consumption ............................. : 1.70000E+01\n\n--------------------------------------------------------------------------------\n==> Fullb.readme <==\n--------------------------------------------------------------------------------\n\nSource: Christain Damahug (Det Norske Veritas, Oslo, Norway)\n\n\n Matrix Format Converter\n -----------------------\n\n Total number of nodes ................................... : 33442\n Number of active nodes .................................. : 33442\n Number of external nodes ................................ : 0\n Number of nodes with at least one boundary condition .... : 586\n Number of nodes with at least one load intensity ........ : 0\n Number of free variables (equations) .................... : 199187\n\n Number of elements ...................................... : 59738\n Number of material types ................................ : 3\n Maximum number of nodes connected to an element ......... : 4\n\n Size of complete element node connectivity .............. : 195585\n Size of stripped element node connectivity .............. : 195585\n Size of active adjacency set representation ............. : 294498\n TIME FOR WRITING MATRIX FORMAT = 1.0402039528E+01\n\n\n = = = = = = = = = = = = = = = = = = = = = = = = =\n = = = Storage Requirement Matrix Convertion = = =\n = = = = = = = = = = = = = = = = = = = = = = = = =\n\n Total INTEGER workspace need (runtime measure) .......... : 1584759\n Total REAL workspace need (runtime measure) ............. : 0\n\n Total CPU-time consumption to convert the matrix ........ : 1.18173E+01\n\n Wall-Clock Time Consumption ............................. : 2.90000E+01\n\n--------------------------------------------------------------------------------\n==> Halfb.readme <==\n--------------------------------------------------------------------------------\n\n\nSource: Christain Damahug (Det Norske Veritas, Oslo, Norway)\n\n\n Matrix Format Converter\n -----------------------\n\n Total number of nodes ................................... : 38556\n Number of active nodes .................................. : 38556\n Number of external nodes ................................ : 0\n Number of nodes with at least one boundary condition .... : 2470\n Number of nodes with at least one load intensity ........ : 0\n Number of free variables (equations) .................... : 224617\n\n Number of elements ...................................... : 70211\n Number of material types ................................ : 3\n Maximum number of nodes connected to an element ......... : 4\n\n Size of complete element node connectivity .............. : 221764\n Size of stripped element node connectivity .............. : 221764\n Size of active adjacency set representation ............. : 321406\n TIME FOR WRITING MATRIX FORMAT = 1.1791002274E+01\n\n\n = = = = = = = = = = = = = = = = = = = = = = = = =\n = = = Storage Requirement Matrix Convertion = = =\n = = = = = = = = = = = = = = = = = = = = = = = = =\n\n Total INTEGER workspace need (runtime measure) .......... : 1774320\n Total REAL workspace need (runtime measure) ............. : 0\n\n Total CPU-time consumption to convert the matrix ........ : 1.33733E+01\n\n Wall-Clock Time Consumption ............................. : 4.20000E+01\n\n--------------------------------------------------------------------------------\n==> M_T1.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nTubular joint\n\nOrder 97,578\nwhen assembled, entries = 4,925,574\nunassembled entries = 6,882,780\n\n DNV-Ex 1 : Tubular joint-1999-01-17 M_T1 \n 1747955 533 26727 1720695 0\nRSE 97578 5328 267270 6882780 \n(10I8) (10I8) (3e26.16) \n \n\n--------------------------------------------------------------------------------\n==> MT1.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\n\nElement Repr of \n 27260 533 26727 0 0\nPSE 97578 5328 267270 0\n(10I8) (10I8) \n\n--------------------------------------------------------------------------------\n==> opt1.readme <==\n--------------------------------------------------------------------------------\nSource: Christain Damhaug\n\nMatrix Representation of OPT1, part of condeep cylinder OPT1 \n 5367 98 5269 0 0\nPSE 15449 977 52686 0\n(10I8) (10I8) \n\n--------------------------------------------------------------------------------\n==> ramage02.readme <==\n--------------------------------------------------------------------------------\n\nSource: Alison Ramage (Strathclyde)\n\nPattern only element problem\n\n1FINITE ELEMENT PROBLEM. NAVIER-STOKES AND CONTINUITY EQUATIONS. RAMAGE02\n 6495 141 6354 0 0 \nPSE 16830 1400 82598 0 \n(10I8) (13I6) (3(1PE22.14)) (3(1PE22.14))\n\n--------------------------------------------------------------------------------\n==> SHIP_001.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nShip structure, predesign model\nOrder 34,920\nwhen assembled, entries = 2,339,575\nunassembled entries = 3,686,133\n\nDNV-Ex 2 : Ship structure, predesign model-1999-01-17 SHIP_001\n 937389 344 15511 921534 0\nRSE 34920 3431 155106 3686133 \n(10I8) (10I8) (3e26.16) \n \n\n--------------------------------------------------------------------------------\n==> SHIP_003.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nShip structure from production run\nOrder 121,728\nwhen assembled, entries = 4,103,881\nunassembled entries =9,728,631\n\n \nDNV-Ex 3 : Ship structure from production run-1999-01-17 SHIP_003\n 2525272 4547 88317 2432408 0\nRSE 121728 45464 883167 9729631 \n(10I8) (10I8) (3e26.16) \n\n--------------------------------------------------------------------------------\n==> SHIPSEC1.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nShip section/detail from production run\nOrder 140,874\nwhen assembled, entries = 3,977,139\nunassembled entries = 6,882,780\n\nDNV-Ex 4 : Ship section/detail from production run-1999-01-17 SHIPSEC1\n 2237188 4104 78502 2154582 0\nRSE 140874 41037 785016 8618328 \n(10I8) (10I8) (3e26.16) \n \n\n--------------------------------------------------------------------------------\n==> SHIPSEC5.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nShip section/detail from production run\nOrder 179,86\nwhen assembled, entries = 6,146,478\nunassembled entries = 11,118,602\n\nDNV-PARASOL Ship section 5-1999-01-15 Shipsec5\n 2885670 5228 100791 2779651 0\nRSE 179860 52272 1007907 11118602 \n(10I8) (10I8) (4e22.14) \n \n\n--------------------------------------------------------------------------------\n==> SHIPSEC8.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nShip section/detail from production run\nOrder 114,919\nwhen assembled, entries = 3,384,159\nunassembled entries = 7,431,867\n\nDNV-PARASOL Ship section 5-1999-01-15 Shipsec5\n 2885670 5228 100791 2779651 0\nRSE 179860 52272 1007907 11118602 \n(10I8) (10I8) (4e22.14) \n \n\n--------------------------------------------------------------------------------\n==> Srb1.readme <==\n--------------------------------------------------------------------------------\n\nSource: Christain Damahug (Det Norske Veritas, Oslo, Norway)\n\n Matrix Format Converter\n -----------------------\n\n Total number of nodes ................................... : 9198\n Number of active nodes .................................. : 9154\n Number of external nodes ................................ : 0\n Number of nodes with at least one boundary condition .... : 44\n Number of nodes with at least one load intensity ........ : 0\n Number of free variables (equations) .................... : 54924\n\n Number of elements ...................................... : 9240\n Number of material types ................................ : 0\n Maximum number of nodes connected to an element ......... : 4\n\n Size of complete element node connectivity .............. : 36872\n Size of stripped element node connectivity .............. : 36784\n Size of active adjacency set representation ............. : 73128\n TIME FOR WRITING MATRIX FORMAT = 1.9468524456E+00\n\n\n = = = = = = = = = = = = = = = = = = = = = = = = =\n = = = Storage Requirement Matrix Convertion = = =\n = = = = = = = = = = = = = = = = = = = = = = = = =\n\n Total INTEGER workspace need (runtime measure) .......... : 303533\n Total REAL workspace need (runtime measure) ............. : 0\n\n Total CPU-time consumption to convert the matrix ........ : 2.20886E+00\n\n Wall-Clock Time Consumption ............................. : 6.00000E+00\n\n--------------------------------------------------------------------------------\n==> Thread.readme <==\n--------------------------------------------------------------------------------\n\nSource: Christain Damahug (Det Norske Veritas, Oslo, Norway)\n\n\n Matrix Format Converter\n -----------------------\n\n Total number of nodes ................................... : 10008\n Number of active nodes .................................. : 9912\n Number of external nodes ................................ : 0\n Number of nodes with at least one boundary condition .... : 96\n Number of nodes with at least one load intensity ........ : 0\n Number of free variables (equations) .................... : 29736\n\n Number of elements ...................................... : 2176\n Number of material types ................................ : 1\n Maximum number of nodes connected to an element ......... : 20\n\n Size of complete element node connectivity .............. : 42080\n Size of stripped element node connectivity .............. : 41888\n Size of active adjacency set representation ............. : 486760\n TIME FOR WRITING MATRIX FORMAT = 1.0886445045E+00\n\n\n = = = = = = = = = = = = = = = = = = = = = = = = =\n = = = Storage Requirement Matrix Convertion = = =\n = = = = = = = = = = = = = = = = = = = = = = = = =\n\n Total INTEGER workspace need (runtime measure) .......... : 201743\n Total REAL workspace need (runtime measure) ............. : 0\n\n Total CPU-time consumption to convert the matrix ........ : 1.33817E+00\n\n Wall-Clock Time Consumption ............................. : 4.00000E+00\n\n--------------------------------------------------------------------------------\n==> THREAD.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nThreaded connector/contact problem\nOrder 29,736\nwhen assembled, entries = 2,249,892\nunassembled entries = 3,718,704\n\nDNV-Ex 7 : Threaded connector/contact problem-1999-01-17 THREAD \n 942461 218 12567 929676 0\nRSE 29736 2176 125664 3718704 \n(10I8) (10I8) (3e26.16) \n \n\n--------------------------------------------------------------------------------\n==> trdheim.readme <==\n--------------------------------------------------------------------------------\nSource: Christian Damhaug (DNR)\n\nMatrix Representation of TRDHEIM, Mesh of the Trondheim Fjord TRDHEIM \n 4473 82 4391 0 0\nPSE 22098 813 43902 0\n(10I8) (10I8) \n\n--------------------------------------------------------------------------------\n==> Troll.readme <==\n--------------------------------------------------------------------------------\n\nSource: Christain Damahug (Det Norske Veritas, Oslo, Norway)\n\n Matrix Format Converter\n -----------------------\n\n Total number of nodes ................................... : 72545\n Number of active nodes .................................. : 71151\n Number of external nodes ................................ : 0\n Number of nodes with at least one boundary condition .... : 1394\n Number of nodes with at least one load intensity ........ : 0\n Number of free variables (equations) .................... : 213453\n\n Number of elements ...................................... : 41084\n Number of material types ................................ : 2\n Maximum number of nodes connected to an element ......... : 8\n\n Size of complete element node connectivity .............. : 321888\n Size of stripped element node connectivity .............. : 318624\n Size of active adjacency set representation ............. : 1260528\n TIME FOR WRITING MATRIX FORMAT = 8.8573474884E+00\n\n\n = = = = = = = = = = = = = = = = = = = = = = = = =\n = = = Storage Requirement Matrix Convertion = = =\n = = = = = = = = = = = = = = = = = = = = = = = = =\n\n Total INTEGER workspace need (runtime measure) .......... : 1570220\n Total REAL workspace need (runtime measure) ............. : 0\n\n Total CPU-time consumption to convert the matrix ........ : 1.10190E+01\n\n Wall-Clock Time Consumption ............................. : 2.90000E+01\n\n--------------------------------------------------------------------------------\n==> tsyl201.readme <==\n--------------------------------------------------------------------------------\nSource: Christian Damhaug (DNR)\n\nMatrix Representation of TSYL201, part of condeep cylinder TSYL201 \n 5857 97 5760 0 0\nPSE 20685 960 57600 0\n(10I8) (10I8) \n\n--------------------------------------------------------------------------------\n==> X104.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nBeam joint (SESAM test example no. 104)\nOrder 108,384\nwhen assembled, entries = 5,138,004\nunassembled entries = 7,065,546\n\nDNV-Ex 8 : SESAM test example no 104-1999-01-17 X104 \n 1795848 602 28859 1766387 0\nRSE 108384 6019 288588 7065546 \n(10I8) (10I8) (3e26.16)" - }, - { - "group": "DNVS", - "count": 15, - "documented_meaning": "Positive definite matrices from Christian Damhaug, DNV Software. Originally collected by Nick Gould, Yifan Hu, & Jennifer Scott,", - "source_url": "https://sparse.tamu.edu/DNVS", - "coarse_kind_families_interpretive": "structures/materials", - "dominant_coarse_family_interpretive": "structures/materials", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "structural problem (15)", - "curator_value_type_count": 2, - "curator_value_types": "real (8); binary (7)", - "curator_structure_count": 1, - "curator_structures": "symmetric (15)", - "min_dimension": 18010, - "max_dimension": 224617, - "dimension_ratio": 12.471793448084398, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 960946, - "max_nnz": 12387821, - "shape_profile": "square only", - "square": 15, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 15, - "structurally_symmetric_square": 15, - "known_spd": 8, - "value_profile": "real only", - "binary": 7, - "geometric_2d3d": 15, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Positive definite matrices from Christian Damhaug, DNV Software.\n\nOriginally collected by Nick Gould, Yifan Hu, & Jennifer Scott,\nsome in unassembled finite-element form (and some in both forms).\n\n(Note: these matrices were previously in the GHS_psdef directory).\n\nNOTE: matrices can have upper-case or lower case matrix-type fields\n(rsa or RSA, for example).\n\n--------------------------------------------------------------------------------\n==> Fcondp2.readme <==\n--------------------------------------------------------------------------------\n\nSource: Christain Damahug (Det Norske Veritas, Oslo, Norway)\n\n\n Matrix Format Converter\n -----------------------\n\n Total number of nodes ................................... : 33913\n Number of active nodes .................................. : 33913\n Number of external nodes ................................ : 0\n Number of nodes with at least one boundary condition .... : 552\n Number of nodes with at least one load intensity ........ : 0\n Number of free variables (equations) .................... : 201822\n\n Number of elements ...................................... : 35836\n Number of material types ................................ : 1\n Maximum number of nodes connected to an element ......... : 4\n\n Size of complete element node connectivity .............. : 143344\n Size of stripped element node connectivity .............. : 143344\n Size of active adjacency set representation ............. : 282912\n TIME FOR WRITING MATRIX FORMAT = 7.7391695976E+00\n\n\n = = = = = = = = = = = = = = = = = = = = = = = = =\n = = = Storage Requirement Matrix Convertion = = =\n = = = = = = = = = = = = = = = = = = = = = = = = =\n\n Total INTEGER workspace need (runtime measure) .......... : 1173274\n Total REAL workspace need (runtime measure) ............. : 0\n\n Total CPU-time consumption to convert the matrix ........ : 8.89181E+00\n\n Wall-Clock Time Consumption ............................. : 1.70000E+01\n\n--------------------------------------------------------------------------------\n==> Fullb.readme <==\n--------------------------------------------------------------------------------\n\nSource: Christain Damahug (Det Norske Veritas, Oslo, Norway)\n\n\n Matrix Format Converter\n -----------------------\n\n Total number of nodes ................................... : 33442\n Number of active nodes .................................. : 33442\n Number of external nodes ................................ : 0\n Number of nodes with at least one boundary condition .... : 586\n Number of nodes with at least one load intensity ........ : 0\n Number of free variables (equations) .................... : 199187\n\n Number of elements ...................................... : 59738\n Number of material types ................................ : 3\n Maximum number of nodes connected to an element ......... : 4\n\n Size of complete element node connectivity .............. : 195585\n Size of stripped element node connectivity .............. : 195585\n Size of active adjacency set representation ............. : 294498\n TIME FOR WRITING MATRIX FORMAT = 1.0402039528E+01\n\n\n = = = = = = = = = = = = = = = = = = = = = = = = =\n = = = Storage Requirement Matrix Convertion = = =\n = = = = = = = = = = = = = = = = = = = = = = = = =\n\n Total INTEGER workspace need (runtime measure) .......... : 1584759\n Total REAL workspace need (runtime measure) ............. : 0\n\n Total CPU-time consumption to convert the matrix ........ : 1.18173E+01\n\n Wall-Clock Time Consumption ............................. : 2.90000E+01\n\n--------------------------------------------------------------------------------\n==> Halfb.readme <==\n--------------------------------------------------------------------------------\n\n\nSource: Christain Damahug (Det Norske Veritas, Oslo, Norway)\n\n\n Matrix Format Converter\n -----------------------\n\n Total number of nodes ................................... : 38556\n Number of active nodes .................................. : 38556\n Number of external nodes ................................ : 0\n Number of nodes with at least one boundary condition .... : 2470\n Number of nodes with at least one load intensity ........ : 0\n Number of free variables (equations) .................... : 224617\n\n Number of elements ...................................... : 70211\n Number of material types ................................ : 3\n Maximum number of nodes connected to an element ......... : 4\n\n Size of complete element node connectivity .............. : 221764\n Size of stripped element node connectivity .............. : 221764\n Size of active adjacency set representation ............. : 321406\n TIME FOR WRITING MATRIX FORMAT = 1.1791002274E+01\n\n\n = = = = = = = = = = = = = = = = = = = = = = = = =\n = = = Storage Requirement Matrix Convertion = = =\n = = = = = = = = = = = = = = = = = = = = = = = = =\n\n Total INTEGER workspace need (runtime measure) .......... : 1774320\n Total REAL workspace need (runtime measure) ............. : 0\n\n Total CPU-time consumption to convert the matrix ........ : 1.33733E+01\n\n Wall-Clock Time Consumption ............................. : 4.20000E+01\n\n--------------------------------------------------------------------------------\n==> M_T1.readme <==\n--------------------------------------------------------------------------------\nSource: Christian Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nTubular joint\n\nOrder 97,578\nwhen assembled, entries = 4,925,574\nunassembled entries = 6,882,780\n\n DNV-Ex 1 : Tubular joint-1999-01-17 M_T1 \n 1747955 533 26727 1720695 0\nRSE 97578 5328 267270 6882780 \n(10I8) (10I8) (3e26.16) \n \n\n--------------------------------------------------------------------------------\n==> MT1.readme <==\n--------------------------------------------------------------------------------\nSource: Christian Damhaug\n\nElement Repr of \n 27260 533 26727 0 0\nPSE 97578 5328 267270 0\n(10I8) (10I8) \n\n--------------------------------------------------------------------------------\n==> SHIP_001.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nShip structure, predesign model\nOrder 34,920\nwhen assembled, entries = 2,339,575\nunassembled entries = 3,686,133\n\nDNV-Ex 2 : Ship structure, predesign model-1999-01-17 SHIP_001\n 937389 344 15511 921534 0\nRSE 34920 3431 155106 3686133 \n(10I8) (10I8) (3e26.16) \n \n\n--------------------------------------------------------------------------------\n==> SHIP_003.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nShip structure from production run\nOrder 121,728\nwhen assembled, entries = 4,103,881\nunassembled entries =9,728,631\n\n \nDNV-Ex 3 : Ship structure from production run-1999-01-17 SHIP_003\n 2525272 4547 88317 2432408 0\nRSE 121728 45464 883167 9729631 \n(10I8) (10I8) (3e26.16) \n\n--------------------------------------------------------------------------------\n==> SHIPSEC1.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nShip section/detail from production run\nOrder 140,874\nwhen assembled, entries = 3,977,139\nunassembled entries = 6,882,780\n\nDNV-Ex 4 : Ship section/detail from production run-1999-01-17 SHIPSEC1\n 2237188 4104 78502 2154582 0\nRSE 140874 41037 785016 8618328 \n(10I8) (10I8) (3e26.16) \n \n\n--------------------------------------------------------------------------------\n==> SHIPSEC5.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nShip section/detail from production run\nOrder 179,86\nwhen assembled, entries = 6,146,478\nunassembled entries = 11,118,602\n\nDNV-PARASOL Ship section 5-1999-01-15 Shipsec5\n 2885670 5228 100791 2779651 0\nRSE 179860 52272 1007907 11118602 \n(10I8) (10I8) (4e22.14) \n \n\n--------------------------------------------------------------------------------\n==> SHIPSEC8.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nShip section/detail from production run\nOrder 114,919\nwhen assembled, entries = 3,384,159\nunassembled entries = 7,431,867\n\nDNV-PARASOL Ship section 5-1999-01-15 Shipsec5\n 2885670 5228 100791 2779651 0\nRSE 179860 52272 1007907 11118602 \n(10I8) (10I8) (4e22.14) \n \n\n--------------------------------------------------------------------------------\n==> Thread.readme <==\n--------------------------------------------------------------------------------\n\nSource: Christain Damahug (Det Norske Veritas, Oslo, Norway)\n\n\n Matrix Format Converter\n -----------------------\n\n Total number of nodes ................................... : 10008\n Number of active nodes .................................. : 9912\n Number of external nodes ................................ : 0\n Number of nodes with at least one boundary condition .... : 96\n Number of nodes with at least one load intensity ........ : 0\n Number of free variables (equations) .................... : 29736\n\n Number of elements ...................................... : 2176\n Number of material types ................................ : 1\n Maximum number of nodes connected to an element ......... : 20\n\n Size of complete element node connectivity .............. : 42080\n Size of stripped element node connectivity .............. : 41888\n Size of active adjacency set representation ............. : 486760\n TIME FOR WRITING MATRIX FORMAT = 1.0886445045E+00\n\n\n = = = = = = = = = = = = = = = = = = = = = = = = =\n = = = Storage Requirement Matrix Convertion = = =\n = = = = = = = = = = = = = = = = = = = = = = = = =\n\n Total INTEGER workspace need (runtime measure) .......... : 201743\n Total REAL workspace need (runtime measure) ............. : 0\n\n Total CPU-time consumption to convert the matrix ........ : 1.33817E+00\n\n Wall-Clock Time Consumption ............................. : 4.00000E+00\n\n--------------------------------------------------------------------------------\n==> THREAD.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nThreaded connector/contact problem\nOrder 29,736\nwhen assembled, entries = 2,249,892\nunassembled entries = 3,718,704\n\nDNV-Ex 7 : Threaded connector/contact problem-1999-01-17 THREAD \n 942461 218 12567 929676 0\nRSE 29736 2176 125664 3718704 \n(10I8) (10I8) (3e26.16) \n \n\n--------------------------------------------------------------------------------\n==> trdheim.readme <==\n--------------------------------------------------------------------------------\nSource: Christian Damhaug (DNR)\n\nMatrix Representation of TRDHEIM, Mesh of the Trondheim Fjord TRDHEIM \n 4473 82 4391 0 0\nPSE 22098 813 43902 0\n(10I8) (10I8) \n\n--------------------------------------------------------------------------------\n==> Troll.readme <==\n--------------------------------------------------------------------------------\n\nSource: Christain Damahug (Det Norske Veritas, Oslo, Norway)\n\n Matrix Format Converter\n -----------------------\n\n Total number of nodes ................................... : 72545\n Number of active nodes .................................. : 71151\n Number of external nodes ................................ : 0\n Number of nodes with at least one boundary condition .... : 1394\n Number of nodes with at least one load intensity ........ : 0\n Number of free variables (equations) .................... : 213453\n\n Number of elements ...................................... : 41084\n Number of material types ................................ : 2\n Maximum number of nodes connected to an element ......... : 8\n\n Size of complete element node connectivity .............. : 321888\n Size of stripped element node connectivity .............. : 318624\n Size of active adjacency set representation ............. : 1260528\n TIME FOR WRITING MATRIX FORMAT = 8.8573474884E+00\n\n\n = = = = = = = = = = = = = = = = = = = = = = = = =\n = = = Storage Requirement Matrix Convertion = = =\n = = = = = = = = = = = = = = = = = = = = = = = = =\n\n Total INTEGER workspace need (runtime measure) .......... : 1570220\n Total REAL workspace need (runtime measure) ............. : 0\n\n Total CPU-time consumption to convert the matrix ........ : 1.10190E+01\n\n Wall-Clock Time Consumption ............................. : 2.90000E+01\n\n--------------------------------------------------------------------------------\n==> tsyl201.readme <==\n--------------------------------------------------------------------------------\nSource: Christian Damhaug (DNR)\n\nMatrix Representation of TSYL201, part of condeep cylinder TSYL201 \n 5857 97 5760 0 0\nPSE 20685 960 57600 0\n(10I8) (10I8) \n\n--------------------------------------------------------------------------------\n==> X104.readme <==\n--------------------------------------------------------------------------------\nSource: Chrisitan Damhaug\nwww.parallab.uib.no/parasol/data.html\n\nWe have this matrix available in assembled and element form\n\nBeam joint (SESAM test example no. 104)\nOrder 108,384\nwhen assembled, entries = 5,138,004\nunassembled entries = 7,065,546\n\nDNV-Ex 8 : SESAM test example no 104-1999-01-17 X104 \n 1795848 602 28859 1766387 0\nRSE 108384 6019 288588 7065546 \n(10I8) (10I8) (3e26.16)" - }, - { - "group": "Bydder", - "count": 2, - "documented_meaning": "MRI reconstruction matrices from Mark Bydder, UCSD. Note that that A is sparse and rectangular, and b is complex.", - "source_url": "https://sparse.tamu.edu/Bydder", - "coarse_kind_families_interpretive": "imaging/graphics/robotics", - "dominant_coarse_family_interpretive": "imaging/graphics/robotics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computer graphics/vision problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (2)", - "curator_structure_count": 1, - "curator_structures": "rectangular (2)", - "min_dimension": 147456, - "max_dimension": 147456, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 569160, - "max_nnz": 589824, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 2, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 2, - "cohesion": "coherent family/series", - "official_notes": "MRI reconstruction matrices from Mark Bydder, UCSD.\nNote that that A is sparse and rectangular, and b is complex.\nAdded March, 2006.\n\n--------------------------------------------------------------------------------\nExcerpts of description, from email:\n--------------------------------------------------------------------------------\n\nInside there is a matrix A and a RHS vector b. Usually we want to\nsolve Ax=b, for example x = lsqr(A,b,0,10) uses conjugate gradient\non the normal equations to give an approximate answer. The data b are\nFourier coefficients at arbitrary locations (in this case, in a spiral\ntrajectory) and the matrix interpolates these data onto\na regular grid suitable for FFT. So it's like non-uniform FFT.\n\nPlease let me know if you find this matrix helpful or want to try some\nother (similar) matrices, eg. with radial trajectories.\n\n--------------------------------------------------------------------------------\n\nThe matrix I sent derives from a technique called regridding - a process\nof interpolating data at arbitrary locations onto an equally spaced grid.\n\nEg. we sample a function f at two points 0 contains the whole Jacobian matrix\nJac_db only contains the diagonal blocks (lower block triangularisation)\n\n\nFor both models, UMFPACK performs relativly good for the second iteration.\nAs a result of the initial guess. The sparsity pattern changes slightly during\nthe first few iterations.\n\nAt the third iteration however UMFPACK is significally slower than an\nLU-decomposition without ordering.\nAs an indication I present a number of test results (note UMFPACK is approx a\nfactor 1.5 faster when using blas of K. Goto, but these results using\nimsl-BLAS were the most easy to produce and the exact timings are not\nrelevent):\n\nTimes in seconds, computing lu-decomposition of diagonal blocks\n(symbolic+numeric step for UMFPACK), using Win XP, Pentium IV, 3.4 GHz,\n1Gb Ram.\n\n\n normal LU UMFPACK\nmodel iteration cpu wall cpu wall\n\nstd1 2, 2.219, 2.219, 2.500, 2.516\nstd1 3, 2.266, 2.266, 34.547, 34.594\nZd2 2, 3.859, 3.860, 3.594, 3.593\nZd2 3, 3.969, 3.985, 32.766, 32.828\nZd2 6, 3.359, 3.359, 29.797, 29.797\n\n\nNils van Velzen\nVORtech Computing \nThe Netherlands\n\nMarco van Goethem\nTechnip Benulux b.v.\nThe Netherlands\nmvangoethem at technip\ndot com" - }, - { - "group": "Lourakis", - "count": 1, - "documented_meaning": "3D vision matrices, M. Lourakis, Foundation for Research & Technology - Hellas. Inst. of Computer Science", - "source_url": "https://sparse.tamu.edu/Lourakis", - "coarse_kind_families_interpretive": "imaging/graphics/robotics", - "dominant_coarse_family_interpretive": "imaging/graphics/robotics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computer graphics/vision problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "symmetric (1)", - "min_dimension": 10581, - "max_dimension": 10581, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 770811, - "max_nnz": 770811, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 1, - "known_spd": 1, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "3D vision matrices, M. Lourakis, Foundation for Research & Technology - Hellas.\n\nInst. of Computer Science\nFoundation for Research & Technology - Hellas\nPO Box 1385\nHeraklion, Crete, GR-711 10 GREECE\n\nhttp://www.ics.forth.gr/~lourakis\nhttp://www.ics.forth.gr/~lourakis/sba\n\nSBA: A Generic Sparse Bundle Adjustment C/C++ Package based on the\nLevenberg-Marquardt Algorithm." - }, - { - "group": "PARSEC", - "count": 21, - "documented_meaning": "PARSEC matrices, density functional theory. Zhou, Saad, Tiago, Chelikowsky, UMN. ------------------------------------------------------------------", - "source_url": "https://sparse.tamu.edu/PARSEC", - "coarse_kind_families_interpretive": "chemistry/biology", - "dominant_coarse_family_interpretive": "chemistry/biology", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "theoretical/quantum chemistry problem (21)", - "curator_value_type_count": 1, - "curator_value_types": "real (21)", - "curator_structure_count": 1, - "curator_structures": "symmetric (21)", - "min_dimension": 769, - "max_dimension": 268096, - "dimension_ratio": 348.629388816645, - "size_span": "wide (>100\u00d7)", - "min_nnz": 17801, - "max_nnz": 18488476, - "shape_profile": "square only", - "square": 21, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 21, - "structurally_symmetric_square": 21, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "PARSEC matrices, density functional theory. Zhou, Saad, Tiago, Chelikowsky, UMN.\n\n------------------------------------------------------------------\n Yunkai Zhou\n Dept. of Computer Science & Eng. http://www.cs.umn.edu/~yunkai\n University of Minnesota fax: 612-625-0572\n 200 Union St. SE. phone: 612-203-1816 (cell)\n Minneapolis, MN 55455 email: (first name) at the domain cs.umn.edu\n------------------------------------------------------------------\n\nThe matrices I am trying to submit are from symmetric eigenvalue\nproblems in density functional theory calculations.\n\nThe matrices are sparse, indefinite, with multiple and clustered\neigenvalues---typical character of Hamiltonian matrices from the\nKohn-Sham equations.\n\nSparsity structures of the matrices may be viewed at\nhttp://www-users.cs.umn.edu/~yunkai/matrices/\nOne can see very nice, kind of self-similar (fractal) sparsity\nstructures if using higher resolution for the plots.\n\n------------------------------------------------------------------\n\nContributors:\nYunkai Zhou, Yousef Saad, Murilo L. Tiago and James R. Chelikowsky.\n------------------------------------------------------------------\n\nThe matrices are obtained using the PARSEC package.\n\nPARSEC is a FORTRAN90 package in density functional theory (DFT)\ncalculations, it implements the real-space pseudopotential method\n(e.g. [1,2]). High order centered finite difference schemes are used\nfor the discretization of the Laplacian in the Kohn-Sham equations.\nPARSEC is developed by a research group lead by Prof. J. R.\nChelikowsky and Prof. Y. Saad.\n\nThe Hamiltonian matrices are constructed when self-consistency\nin the self-consistent loop is reached.\n\nSome of the matrices have been used in [3, 4].\n\n[1] @Article{cts:94,\n author =\t {J. R. Chelikowsky and N. Troullier and Y. Saad},\n title =\t {Finite-difference-pseudopotential method: Electronic\n structure calculations without a basis},\n journal =\t {Phys. Rev. Lett.},\n year =\t 1994,\n volume =\t 72,\n pages =\t {1240-1243}\n}\n\n[2] @Article{che-PDFM00,\n author =\t {J.R. Chelikowsky},\n title =\t {The Pseudopotential-Density Functional Method\n Applied to Nanostructures},\n journal =\t {J. Phys. D: Appl. Phys.},\n year =\t 2000,\n volume =\t 33,\n pages =\t {R33--R50}\n}\n\n[3] @TechReport{chebdav,\n author =\t {Y. Zhou and Y. Saad},\n title =\t {A {Chebyshev-Davidson} Algorithm for Large\n Symmetric Eigenvalue Problems},\n institution =\t {Minnesota Supercomputing Institute, Univ. of\n Minnesota},\n year =\t {2005},\n}\n\n[4] @TechReport{blkchebdav,\n author =\t {Y. Zhou},\n title =\t {Block-wise Polynomial Filtered {Davidson}-type\n Subspace Iteration},\n institution =\t {Minnesota Supercomputing Institute, Univ. of\n Minnesota},\n year =\t {2005},\n}\n\n\n======================================================================" - }, - { - "group": "Andrianov", - "count": 16, - "documented_meaning": "Matrices from Alexander Andrianov, SAS Institute Inc.", - "source_url": "https://sparse.tamu.edu/Andrianov", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "optimization problem (16)", - "curator_value_type_count": 1, - "curator_value_types": "binary (16)", - "curator_structure_count": 1, - "curator_structures": "symmetric (16)", - "min_dimension": 5026, - "max_dimension": 534388, - "dimension_ratio": 106.32471150019896, - "size_span": "wide (>100\u00d7)", - "min_nnz": 94026, - "max_nnz": 10352819, - "shape_profile": "square only", - "square": 16, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 16, - "structurally_symmetric_square": 16, - "known_spd": 0, - "value_profile": "real only", - "binary": 16, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Matrices from Alexander Andrianov, SAS Institute Inc." - }, - { - "group": "MKS", - "count": 1, - "documented_meaning": "2-D Fokker Planck equations, electron dynamics. Dan Smith, MKS Inst.", - "source_url": "https://sparse.tamu.edu/MKS", - "coarse_kind_families_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "electromagnetics problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 7548, - "max_dimension": 7548, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 834222, - "max_nnz": 834222, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "2-D Fokker Planck equations, electron dynamics. Dan Smith, MKS Inst." - }, - { - "group": "Raju", - "count": 1, - "documented_meaning": "3D laminar flow, M. Raju, Case Western Univ. A finite element discretization (u,v,w,p) of 3D laminar", - "source_url": "https://sparse.tamu.edu/Raju", - "coarse_kind_families_interpretive": "fluids/transport", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computational fluid dynamics problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 67173, - "max_dimension": 67173, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 3788857, - "max_nnz": 3788857, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "3D laminar flow, M. Raju, Case Western Univ.\n\nA finite element discretization (u,v,w,p) of 3D laminar\nflow in a duct. Mesh size is 50x30x10." - }, - { - "group": "Bates", - "count": 3, - "documented_meaning": "Statistical matrices, D. Bates, Univ. Wisconsin", - "source_url": "https://sparse.tamu.edu/Bates", - "coarse_kind_families_interpretive": "mathematics/statistics; other", - "dominant_coarse_family_interpretive": "mathematics/statistics", - "dominant_coarse_family_share": 0.6666666666666666, - "normalized_kind_count": 2, - "normalized_kinds": "statistical/mathematical problem (2); least squares problem (1)", - "curator_value_type_count": 2, - "curator_value_types": "real (2); binary (1)", - "curator_structure_count": 2, - "curator_structures": "rectangular (2); symmetric (1)", - "min_dimension": 2541, - "max_dimension": 1748122, - "dimension_ratio": 687.9661550570642, - "size_span": "wide (>100\u00d7)", - "min_nnz": 7361, - "max_nnz": 6804304, - "shape_profile": "mixed", - "square": 1, - "rectangular": 2, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 1, - "known_spd": 1, - "value_profile": "real only", - "binary": 1, - "geometric_2d3d": 0, - "cohesion": "multi-domain/mixed", - "official_notes": "Statistical matrices, D. Bates, Univ. Wisconsin\nhttp://www.stat.wisc.edu/~bates\n\n---------------\nChem* matrices:\n---------------\n\nThere will be no fill-in regardless of the ordering and it is even the case\nthat the inverse has the same pattern of nonzeros as the original L factor.\n\nTo explain why this happens it may be best if I explain what \"nested grouping\nfactors\" means. In the Chem97 example in that writeup I sent the matrix Zt is\ngenerated according to the school and the lea (local education authority - the\nBritish equivalent of a school district) that the student was in when they took\nthe exam. The rows of Zt correspond to schools and to lea's. The rows\ncorrespond to students. The \"grouping factors\" are school and lea. We say\nthat school is nested within lea in the sense that each school occurs in one\nand only one lea. This is in contrast to the other example using the \"star\"\ndata where the grouping factors are student, teacher and school. Multiple\nobservations on the same student are often with different teachers so student\nis not nested within teacher.\n\nReturning to the Chem97 example, the Zt matrix for this model is an indicator\nmatrix of the school for each student vertically concatenated with the\nindicator matrix of the lea for each student. The structure of Zt (Zt)' has\na diagonal block corresponding to schools, a diagonal block corresponding to\nlea's and the off-diagonal block. The point is that there is exactly one\nnonzero in each column of the off-diagonal block in the lower-left.\n\nThe way I would reorder these columns and rows to show the structure would be\nto put all the schools associated with the first lea followed by the first lea\nfollowed by all the schools associated with the second lea followed by the\nsecond lea ... Then the L matrix will be block diagonal with each block\ncorresponding to an lea. Within each block the structure is a diagonal matrix\nin the first k-1 rows and columns and a dense row underneath it.\n\nBy the way, don't try to decompose ZtZ as it is only positive semidefinite.\nThe matrix that is decomposed is ZtZ + Omega where Omega is diagonal with the\ndiagonal consisting of 2410 elements of 4.419696 (corresponding to schools) and\n131 elements of 349.0238 (corresponding to lea's).\n\n---------------\nsls matrix:\n---------------\n\nThe data are 10 years of grade point scores at a large state\nuniversity (not mine). The covariates that are recorded with the\nscores are the student id, the instructor id and the department.\n\nNumber of obs: 1685394, groups: id, 54711; instr, 7915; dept, 102\n\nEven though these scores are from different semesters and one of the\nquestions of interest is whether grade inflation has taken place,\ninitially I fit a simple model that has an effect for the student, an\neffect for the instructor and an effect for the department. There is\nalso an overall average gradepoint. The overall average is what we\ncall a fixed effect. In computations terms this means it is estimated\nwithout a penalty. The other effects are what we call random effects.\nIn terms of parameters we estimate a variance for each group of random\neffects (student, instructor and department). We assume that the\nrandom effects come from a normal (or Gaussian) distribution, which\nhas the effect of shrinking the estimates of the individual effects\ntowards the origin. If the variance of that distribution is large,\nthere is little penalty and the estimates for each of the students or\neach of the instructors or each of the departments is close to the\nleast squares estimate. If the variance is small then they are much\ncloser to zero.\n\nOverall we will estimate 3 variances, 1 fixed-effect and 62728 = 54711\n+ 7915 + 102 random effects. The matrix we will decompose will be\n62729 columns (the last column comes from the fixed-effect) and\n1685394 + 62728 rows. The 62728 rows come from the penalty part and\nconsist of an identity matrix of size 62728 with a column of zeros\nappended on the right. The rows determined by the data (I think of\nthese as being the top part of the matrix but it doesn't matter if\nthey are on the top or the bottom) consist of 54711 columns of\nindicators for the student followed by 7915 columns of indicators for\nthe instructor followed by 102 columns of indicators for the\ndepartment followed by a column of 1's. In general we will have\ncolumns for random effects followed by columns for fixed effects.\nHere the fixed-effects column is trivial but in general we may have\nmore than 1 and they don't have to be as trivial as this.\n\nAt least as far as I have been able to analyze the computation, I can\nallow for permutations of the columns for the random effects but I\ndon't want to mix up the columns for the random effects and the\ncolumns for the fixed effects. I always want to keep the columns for\nthe fixed effects as a block on the right. (Usually these columns are\ndense or close to it so it isn't a problem to force them to be on the\nright.) The reason is that I need the logarithm of the square of the\ndeterminant of the triangular factor from the random effects columns\nonly.\n\nDuring the optimization phase we fix values of the three variances,\nupdate the numeric values in the matrix, decompose, calculate the\ndeterminant of the leading part of the triangular factor, and evaluate\nthe penalized residual sum of squares. The logarithm of the\ndeterminant and the penalized residual sum of squares are combined to\ncreate a criterion called the profiled log-likelihood which is to be\nmaximized\n\nThe update operation changes the numerical values of the\ndata-determined part according to three multipliers. The first\nmultiplier is applied to the first 54711 columns, the next multiplier\nis applied to the next 7915 columns and the last multiplier is applied\nto the next 102 columns. The last column stays fixed.\n\nWe start from the factors for the student, instructor and department\nthen generate the indicators to form Z. The factors can be\nrepresented as integers and the response is a grade point score\n(allowed to be half integers)." - }, - { - "group": "Schmid", - "count": 2, - "documented_meaning": "Unstructured FEM, steady state thermal problem. D. Schmid, Univ. Oslo See for more details. See also:", - "source_url": "https://sparse.tamu.edu/Schmid", - "coarse_kind_families_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "thermal problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (2)", - "curator_structure_count": 1, - "curator_structures": "symmetric (2)", - "min_dimension": 82654, - "max_dimension": 1228045, - "dimension_ratio": 14.857659641396666, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 574458, - "max_nnz": 8580313, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 2, - "structurally_symmetric_square": 2, - "known_spd": 2, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 2, - "cohesion": "coherent family/series", - "official_notes": "Unstructured FEM, steady state thermal problem. D. Schmid, Univ. Oslo\n\nSee http://www.milamin.org for more details. See also:\n\nDabrowski, M., M. Krotkiewski, and D. W. Schmid (2008), MILAMIN: MATLAB-based\nfinite element method solver for large problems, Geochem. Geophys. Geosyst., 9,\nQ04030, doi:10.1029/2007GC001719." - }, - { - "group": "Bindel", - "count": 6, - "documented_meaning": "Coupled linear thermoeslasticity equations. D Bindel, UC Berkeley", - "source_url": "https://sparse.tamu.edu/Bindel", - "coarse_kind_families_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "thermal problem (6)", - "curator_value_type_count": 2, - "curator_value_types": "real (4); complex (2)", - "curator_structure_count": 2, - "curator_structures": "unsymmetric (4); symmetric (2)", - "min_dimension": 10605, - "max_dimension": 10605, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 144579, - "max_nnz": 522387, - "shape_profile": "square only", - "square": 6, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 2, - "structurally_symmetric_square": 2, - "known_spd": 2, - "value_profile": "real + complex", - "binary": 0, - "geometric_2d3d": 6, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Coupled linear thermoeslasticity equations. D Bindel, UC Berkeley" - }, - { - "group": "Koutsovasilis", - "count": 2, - "documented_meaning": "Symmetric indefinite matrix from P. Koutsovasilis, Tech. Univ. Dresden", - "source_url": "https://sparse.tamu.edu/Koutsovasilis", - "coarse_kind_families_interpretive": "structures/materials", - "dominant_coarse_family_interpretive": "structures/materials", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "structural problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (2)", - "curator_structure_count": 1, - "curator_structures": "symmetric (2)", - "min_dimension": 71505, - "max_dimension": 343791, - "dimension_ratio": 4.807929515418502, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 5294285, - "max_nnz": 26837113, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 2, - "structurally_symmetric_square": 2, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 2, - "cohesion": "coherent family/series", - "official_notes": "Symmetric indefinite matrix from P. Koutsovasilis, Tech. Univ. Dresden" - }, - { - "group": "AMD", - "count": 2, - "documented_meaning": "Circuit simulation matrices from Ufuk Okuyucu, AMD, Inc.", - "source_url": "https://sparse.tamu.edu/AMD", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "circuit simulation problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (2)", - "curator_structure_count": 1, - "curator_structures": "symmetric (2)", - "min_dimension": 150102, - "max_dimension": 1585478, - "dimension_ratio": 10.562670717245606, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 726674, - "max_nnz": 7660826, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 2, - "structurally_symmetric_square": 2, - "known_spd": 2, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Circuit simulation matrices from Ufuk Okuyucu, AMD, Inc." - }, - { - "group": "Pajek", - "count": 76, - "documented_meaning": "Pajek data sets, V. Batagelj and A. Mrvar. Pajek data sets, from", - "source_url": "https://sparse.tamu.edu/Pajek", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 8, - "normalized_kinds": "directed graph (28); undirected graph (13); directed weighted graph (11); directed multigraph (11); undirected weighted graph (7); bipartite graph (3); weighted bipartite graph (2); undirected multigraph (1)", - "curator_value_type_count": 3, - "curator_value_types": "binary (44); integer (28); real (4)", - "curator_structure_count": 3, - "curator_structures": "unsymmetric (49); symmetric (22); rectangular (5)", - "min_dimension": 10, - "max_dimension": 3774768, - "dimension_ratio": 377476.8, - "size_span": "wide (>100\u00d7)", - "min_nnz": 29, - "max_nnz": 14970767, - "shape_profile": "mixed", - "square": 71, - "rectangular": 5, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 22, - "structurally_symmetric_square": 22, - "known_spd": 1, - "value_profile": "real only", - "binary": 44, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Pajek data sets, V. Batagelj and A. Mrvar.\n\nPajek data sets, from http://vlado.fmf.uni-lj.si/pub/networks/data/,\nVladimir Batagelj and Andrej Mrvar (2006): Pajek datasets. If the source of\nthe data set is not specified otherwise, these data sets are licensed under a\nCreative Commons Attribution-NonCommercial-ShareAlike 2.5 License.\n\nConverted to sparse adjacency matrix format by Tim Davis, October 2006.\nA(i,j) is the edge from node i to node j in the graph. If a graph is not\nlisted as weighted, and yet has non-binary entries, then the entry a(i,j)\nreflects the number of edges (i,j) in the original data (the graph is a\nmultigraph). In some cases, it was clear that the extra entries were\nduplicates, and the graph should not be a multigraph (problems GD97, EAT_RS,\nand patents).\n\nAll data was converted without loss of information, except where intentional.\nDetails are given below.\n\nThe largest graph is \"patents\", with over 3.7 million nodes and nearly\n15 million entries.\n\n\"Pajek\" is Slovenian for \"spider\" and is roughly pronounced \"Pie yeck\".\n\nThe Pajek networks are all 1-based (nodes are numbered starting at node 1).\nSome networks when included in the Pajek Dataset were converted from 0-based\nto 1-based. This was done by renaming node 0 as a new node n, and leaving\nall other nodes unchanged. This differs from the MATLAB convention, which\nis to add 1 to all row/column indices. This conversion was left as-is\n(networks: USpowerGrid, EPA, and Kleinberg) when included in the UF\nSparse Matrix Collection.\n\nRemember that in MATLAB, A(i,:) is slow to compute; A(:,i) is much faster. If\nyou want row i of a sparse matrix, access the ith column of the transpose\ninstead.\n\n\n================================================================================\nSummary:\n================================================================================\n\nCSphd:\t\tPhD's in computer science\n\t\t1882-by-1882 with 1740 nonzeros\n\t\tkind: directed graph with auxiliary node data\n\nCalifornia:\tKleinberg's web search of \"California\"\n\t\t9664-by-9664 with 16150 nonzeros\n\t\tkind: directed graph\n\nCities:\t\twww.lboro.ac.uk/gawc, data set 6\n\t\t55-by-46 with 1342 nonzeros\n\t\tkind: weighted bipartite graph with auxiliary node data\n\nEAT_RS:\t\tEdinburgh Associative Thesaurus (response-stimulus)\n\t\t23219-by-23219 with 325592 nonzeros\n\t\tkind: directed weighted graph\n\nEAT_SR:\t\tEdinburgh Associative Thesaurus (stimulus-response)\n\t\t23219-by-23219 with 325589 nonzeros\n\t\tkind: directed weighted graph\n\nEPA:\t\tKleinberg, pages linking to www.epa.gov\n\t\t4772-by-4772 with 8965 nonzeros\n\t\tkind: directed graph\n\nEVA:\t\tEVA, corporate inter-relationships\n\t\t8497-by-8497 with 6726 nonzeros\n\t\tkind: directed graph\n\nErdos02:\tErdos collaboration network\n\t\t6927-by-6927 with 16944 nonzeros\n\t\tkind: undirected graph\n\nErdos971:\tErdos collaboration network\n\t\t472-by-472 with 2628 nonzeros\n\t\tkind: undirected graph\n\nErdos972:\tErdos collaboration network\n\t\t5488-by-5488 with 14170 nonzeros\n\t\tkind: undirected graph with auxiliary node data\n\nErdos981:\tErdos collaboration network\n\t\t485-by-485 with 2762 nonzeros\n\t\tkind: undirected graph\n\nErdos982:\tErdos collaboration network\n\t\t5822-by-5822 with 14750 nonzeros\n\t\tkind: undirected graph with auxiliary node data\n\nErdos991:\tErdos collaboration network\n\t\t492-by-492 with 2834 nonzeros\n\t\tkind: undirected graph\n\nErdos992:\tErdos collaboration network\n\t\t6100-by-6100 with 15030 nonzeros\n\t\tkind: undirected graph with auxiliary node data\n\nFA:\t\tUSF Free (word) Association Norms\n\t\t10617-by-10617 with 72176 nonzeros\n\t\tkind: directed weighted graph with auxiliary node data\n\nGD00_a:\t\tGraph Drawing contest 2000\n\t\t352-by-352 with 458 nonzeros\n\t\tkind: directed graph\n\nGD00_c:\t\tGraph Drawing contest 2000\n\t\t638-by-638 with 1041 nonzeros\n\t\tkind: directed multigraph\n\nGD01_A:\t\tGraph Drawing contest 2001\n\t\t953-by-953 with 645 nonzeros\n\t\tkind: directed multigraph\n\nGD01_a:\t\tGraph Drawing contest 2000\n\t\t311-by-311 with 645 nonzeros\n\t\tkind: directed weighted graph\n\nGD01_b:\t\tGraph Drawing contest 2001\n\t\t18-by-18 with 37 nonzeros\n\t\tkind: directed graph\n\nGD01_c:\t\tGraph Drawing contest 2001\n\t\t33-by-33 with 135 nonzeros\n\t\tkind: directed multigraph with auxiliary node data\n\nGD02_a:\t\tGraph Drawing contest 2002\n\t\t23-by-23 with 87 nonzeros\n\t\tkind: directed graph\n\nGD02_b:\t\tGraph Drawing contest 2002\n\t\t80-by-80 with 232 nonzeros\n\t\tkind: directed graph\n\nGD06_Java:\tGraph Drawing contest 2006\n\t\t1538-by-1538 with 8032 nonzeros\n\t\tkind: directed graph\n\nGD06_theory:\tGraph Drawing contest 2006\n\t\t101-by-101 with 380 nonzeros\n\t\tkind: undirected graph\n\nGD95_a:\t\tGraph Drawing contest 1995\n\t\t36-by-36 with 57 nonzeros\n\t\tkind: directed graph\n\nGD95_b:\t\tGraph Drawing contest 1995\n\t\t73-by-73 with 96 nonzeros\n\t\tkind: directed graph\n\nGD95_c:\t\tGraph Drawing contest 1995\n\t\t62-by-62 with 287 nonzeros\n\t\tkind: directed graph\n\nGD96_a:\t\tGraph Drawing contest 1996\n\t\t1096-by-1096 with 1677 nonzeros\n\t\tkind: directed multigraph\n\nGD96_b:\t\tGraph Drawing contest 1996\n\t\t111-by-111 with 193 nonzeros\n\t\tkind: directed graph\n\nGD96_c:\t\tGraph Drawing contest 1996\n\t\t65-by-65 with 250 nonzeros\n\t\tkind: undirected graph\n\nGD96_d:\t\tGraph Drawing contest 1996\n\t\t180-by-180 with 229 nonzeros\n\t\tkind: directed graph\n\nGD97_a:\t\tGraph Drawing contest 1997\n\t\t84-by-84 with 332 nonzeros\n\t\tkind: directed graph\n\nGD97_b:\t\tGraph Drawing contest 1997\n\t\t47-by-47 with 264 nonzeros\n\t\tkind: undirected weighted graph\n\nGD97_c:\t\tGraph Drawing contest 1997\n\t\t452-by-452 with 460 nonzeros\n\t\tkind: directed multigraph\n\nGD98_a:\t\tGraph Drawing contest 1998\n\t\t38-by-38 with 50 nonzeros\n\t\tkind: directed graph\n\nGD98_b:\t\tGraph Drawing contest 1998\n\t\t121-by-121 with 207 nonzeros\n\t\tkind: directed graph\n\nGD98_c:\t\tGraph Drawing contest 1998\n\t\t112-by-112 with 336 nonzeros\n\t\tkind: undirected graph\n\nGD99_b:\t\tGraph Drawing contest 1999\n\t\t64-by-64 with 252 nonzeros\n\t\tkind: undirected multigraph\n\nGD99_c:\t\tGraph Drawing contest 1999\n\t\t105-by-105 with 149 nonzeros\n\t\tkind: directed graph with auxiliary node data\n\nGlossGT:\tgraph and digraph glossary\n\t\t72-by-72 with 122 nonzeros\n\t\tkind: directed graph\n\nHEP-th-new:\tHigh Energy Physics literature\n\t\t27770-by-27770 with 352807 nonzeros\n\t\tkind: directed graph with auxiliary node data\n\nHEP-th:\t\tHigh Energy Physics literature\n\t\t27240-by-27240 with 342437 nonzeros\n\t\tkind: directed graph\n\nIMDB:\t\tIMDB movie/actor network, www.imdb.com\n\t\t428440-by-896308 with 3782463 nonzeros\n\t\tkind: bipartite graph with auxiliary node data\n\nJournals:\tSlovenian journals 1999-2000\n\t\t124-by-124 with 12068 nonzeros\n\t\tkind: undirected weighted graph with auxiliary node data\n\nKohonen:\tKohonen citation network\n\t\t4470-by-4470 with 12731 nonzeros\n\t\tkind: directed graph with auxiliary node data\n\nLederberg:\tLederberg citation network\n\t\t8843-by-8843 with 41601 nonzeros\n\t\tkind: directed multigraph with auxiliary node data\n\nNotreDame_actors:\tBarabasi's actor network (of www.imdb.com)\n\t\t392400-by-127823 with 1470404 nonzeros\n\t\tkind: bipartite multigraph\n\nNotreDame_www:\tBarabasi's web page network of nd.edu\n\t\t325729-by-325729 with 929849 nonzeros\n\t\tkind: directed graph\n\nNotreDame_yeast:\tBarabasi's yeast protein interaction\n\t\t2114-by-2114 with 4480 nonzeros\n\t\tkind: undirected graph\n\nODLIS:\t\tonline dictionary of library & inf. sci\n\t\t2909-by-2909 with 18246 nonzeros\n\t\tkind: directed multigraph\n\nRagusa16:\tRagusa set\n\t\t24-by-24 with 81 nonzeros\n\t\tkind: directed weighted graph\n\nRagusa18:\tRagusa set\n\t\t23-by-23 with 64 nonzeros\n\t\tkind: directed weighted graph\n\nReuters911:\tReuters news, Sept 11 to Nov 15, 2001\n\t\t13332-by-13332 with 296076 nonzeros\n\t\tkind: temporal undirected weighted graph\n\nRoget:\t\tRoget's Thesaurus, 1879\n\t\t1022-by-1022 with 5075 nonzeros\n\t\tkind: directed graph\n\nSandi_authors:\tKlavzar bibliography\n\t\t86-by-86 with 248 nonzeros\n\t\tkind: undirected weighted graph\n\nSandi_sandi:\tKlavzar bibliography\n\t\t314-by-360 with 613 nonzeros\n\t\tkind: bipartite graph\n\nSciMet:\t\tSciMet citation network\n\t\t3084-by-3084 with 10413 nonzeros\n\t\tkind: directed multigraph with auxiliary node data\n\nSmaGri:\t\tSmaGri citation network\n\t\t1059-by-1059 with 4919 nonzeros\n\t\tkind: directed multigraph with auxiliary node data\n\nSmallW:\t\tSmallW citation network\n\t\t396-by-396 with 994 nonzeros\n\t\tkind: directed multigraph with auxiliary node data\n\nStranke94:\tSlovene Parliamentary Parties 1994\n\t\t10-by-10 with 90 nonzeros\n\t\tkind: undirected weighted graph\n\nTina_AskCal:\tstudent govt, Univ. Ljubljana, 1992 (ask opin., recall)\n\t\t11-by-11 with 29 nonzeros\n\t\tkind: directed graph\n\nTina_AskCog:\tstudent govt, Univ. Ljubljana, 1992 (ask, recognized)\n\t\t11-by-11 with 36 nonzeros\n\t\tkind: directed graph\n\nTina_DisCal:\tstudent govt, Univ. Ljubljana, 1992 (discuss, recall)\n\t\t11-by-11 with 41 nonzeros\n\t\tkind: directed graph\n\nTina_DisCog:\tstudent govt, Univ. Ljubljana, 1992 (discuss, recog.)\n\t\t11-by-11 with 48 nonzeros\n\t\tkind: directed graph\n\nUSAir97:\tUS Air flights, 1997\n\t\t332-by-332 with 4252 nonzeros\n\t\tkind: undirected weighted graph\n\nUSpowerGrid:\tUS power grid\n\t\t4941-by-4941 with 13188 nonzeros\n\t\tkind: undirected multigraph\n\nWordnet3:\tWordnet3 dictionary network\n\t\t82670-by-82670 with 132964 nonzeros\n\t\tkind: directed weighted graph with auxiliary node data\n\nWorldCities:\tworld city network\n\t\t315-by-100 with 7518 nonzeros\n\t\tkind: weighted bipartite graph with auxiliary node data\n\nZewail:\t\tZewail citation network\n\t\t6752-by-6752 with 54233 nonzeros\n\t\tkind: directed multigraph with auxiliary node data\n\ndictionary28:\tdictionary\n\t\t52652-by-52652 with 178076 nonzeros\n\t\tkind: undirected graph\n\ndivorce:\tdivorce laws in the 50 US states\n\t\t50-by-9 with 225 nonzeros\n\t\tkind: bipartite graph\n\nfoldoc:\t\tfree on-line dictionary of computing\n\t\t13356-by-13356 with 120238 nonzeros\n\t\tkind: directed weighted graph\n\nfootball:\tWorld Soccer, Paris 1998\n\t\t35-by-35 with 118 nonzeros\n\t\tkind: directed weighted graph\n\ngeom:\t\tcollaboration in computational geometry\n\t\t7343-by-7343 with 23796 nonzeros\n\t\tkind: undirected weighted graph\n\ninternet:\tconnectivity of internet routers\n\t\t124651-by-124651 with 207214 nonzeros\n\t\tkind: directed weighted graph\n\npatents:\tNBER US Patent Citations, 1963-1999, cites 1975-1999\n\t\t3774768-by-3774768 with 14970767 nonzeros\n\t\tkind: directed graph with auxiliary node data\n\npatents_main:\tmain NBER US Patent Citations, 1963-1999, cites 1975-1999\n\t\t240547-by-240547 with 560943 nonzeros\n\t\tkind: directed weighted graph with auxiliary node data\n\nyeast:\t\tyeast protein interaction network\n\t\t2361-by-2361 with 13828 nonzeros\n\t\tkind: undirected graph with auxiliary node data\n\n\n\n================================================================================\n==> Citations/Cite2001.txt <==\n================================================================================\n\nCitation networks\n\nGraph Drawing 2001 Contest - Graph A Graph Drawing 2001\n\nContest task description for Graph A. Graph A in Pajek's format. Selected\ncitation networks from Garfield's collection Citation networks in Pajek's\nformat obtained from the Garfield's collection of citation network datasets\nproduced using HistCite software. All of these networks are the result of\nsearches in the WebofScience and are used with the permission of ISI of\nPhiladelphia. Please acknowledge this when publishing results based on these\ndata.\n\n SmallW: Papers that cite S Milgram's 1967 Psychology Today paper or use\n Small World in title, Tue Jul 23 13:35:11 2002\n\n SmaGri: Citations to Small & Griffith and Descendants, Thu Nov 8 10:40:55\n 2001 \n\n SciMet: Articles from or citing Scientometrics, 1978-2000, Wed Jun\n 12 16:39:51 2002 \n\n Kohonen: Articles with topic \"self-organizing maps\"\n or references to \"Kohonen T\", Tue Jun 18 10:39:51 2002\n\n Zewail: Articles citing and by AH Zewail, 1970-2002, Wed Jul 31 15:46:38\n 2002 \n\n Lederberg: Articles by and citing J Lederberg, 1945-2002, Wed Jul 31\n 13:40:22 2002 \n\nSome references\n\n * Batagelj V.: Some Mathematics of Network Analysis. Network Seminar,\n Department of Sociology, University of Pittsburgh, January 21, 1991.\n\n * Batagelj V., Mrvar A.: Graph Drawing Contest 2001 Layouts.\n\n * Garfield E, Sher IH, and Torpie RJ.: The Use of Citation Data in Writing\n the History of Science. Philadelphia: The Institute for Scientific\n Information, December 1964.\n\n * Garfield E.: From Computational Linguistics to Algorithmic\n Historiography, paper presented at the Symposium in Honor of Casimir\n Borkowski at the University of Pittsburgh School of Information Sciences,\n September 19, 2001.\n\n * Hummon N.P., Doreian P.: Connectivity in a Citation Network: The\n Development of DNA Theory. Social Networks, 11(1989) 39-63.\n\nNetwork Data, Pajek, Vlado\n\n\n================================================================================\n==> Cities/Url.txt <==\n================================================================================\n\nhttp://www.lboro.ac.uk/gawc/data.html\n\nWorld City Relation Data\n\nThis is the raison d'\u00eatre of GaWC. For an introduction, see A Brief Guide to\nQuantitative Data Collection at GaWC, 1997-2001.\n\nINVITATION\n\nWorld city researchers with relational data on world cities are encouraged to\npost it here. Depositors may apply their own protocol for use by others.\n(Contact p.j.taylor@lboro.ac.uk) PROTOCOL for using GaWC data\n\nAs part of our mission to promote the study of non-state relations we wish to\nencourage people to use the data posted here in their research and teaching.\nHowever, we do ask that, as a matter of courtesy bordering on ethics, the\nresearch project and the information gatherers are properly acknowledged along\nwith GaWC itself. Each data set will have a statement as to whom to acknowledge\nand we ask that this be reproduced in every public use of the data.\n\n\n\nData Set 1: US Cities: Surrogate Measures of Relations, 1990 (T.R. Longcore, C.\nMcWilliams and P.J. Taylor)\n\nData Set 2: London and New York: Surrogate Measures of Relations, 1997\n\nData Set 3: Randstad Cities: Surrogate Measures of Relations, 1970-95 (A.M.\nBeerda)\n\nData Set 4: London's Relations with other Cities Using Producer Service Office\nGeographies (J.V. Beaverstock, R.G. Smith and P.J. Taylor)\n\nData Set 5: US Cities: Law Firm Office Geographies, 1998 (J.V. Beaverstock,\nR.G. Smith and P.J. Taylor)\n\nData Set 6: World Cities and Global Firms (P.J. Taylor and D.R.F. Walker)\n\nData Set 7: Inter-City Matrices (P.J. Taylor and D.R.F. Walker)\n\nData Set 8: World Cities: Regional Dimensions (P.J. Taylor, D.R.F. Walker and\nM. Hoyler)\n\nData Set 9: Cities Mentioned in Advertisements in The Economist (May 2000 -\nJanuary 2001) (P.J. Taylor)\n\nData Set 10: The Relative Centrality of Cities Based upon Air Passenger Travel,\n1977-1997 (M. Timberlake and D.A. Smith with K.-H. Shin)\n\nData Set 11: World City Network: The Basic Data (P.J. Taylor and G. Catalano)\n\nData Set 12: Global Network Service Connectivities for 315 Cities in 2000 (P.J.\nTaylor)\n\n\nDATA TOOLS\n\nData Tool 1: Macro for Calculating Connectivities (max 254 cities x 255 firms)\n (E.C. Rossi and C.C.C. Rossi)\n\nData Tool 2: Macro for Calculating Connectivities (max 1100 cities x 255\n firms) (R. Aranya)\n\n\n\n World Cities and Global Firms\n\n P.J. Taylor and D.R.F. Walker\n\nThese data consist of the distribution of offices for 46 'global' advanced\nproducer service firms over 55 world cities. Global firms are defined by having\noffices in at least 15 different cities. World cities are from the GaWC\ninventory of world cities (see GaWC Research Bulletin 6). Service values for a\nfirm in a city are given as 3, 2, 1 or 0 as defined in Data Set 4.\n\nThese data are an experimental set of data derived from Data Set 4 (43 of the\nfirms qualify as global) but with three additional law firms added which do not\nhave London offices. For publications that make use of these data, see GaWC\nResearch Bulletin 13 and GaWC Research Bulletin 17.\n\n\nKey to Data Set 6:\n\nADVANCED PRODUCER SERVICE FIRMS (SECTOR, TABLE CODE AND FIRM)\n\nAccountancy\n\nKP KPMG\nCL Coopers & Lybrand\nEY Ernst & Young International\nAA Arthur Andersen\nPW Price Waterhouse\n\nAdvertising\n\nGR Grey Worldwide\nDM DMB&B (MacManus Group)\nLH Lowe Howard -Spink\nSS Saatchi and Saatchi\nTH JWT (Thompson)\nOM Ogilvy & Mather Direct Worldwide\nDE Dentsu\nYR Young & Rubicam\nTM TMP\nPU Publicis\nAM Abbott Mead Vickers (BBDO)\n\nBanking and Finance\n\nHS HSBC\nBA Barclays\nNW NatWest Group\nSC Standard Chartered Group\nCR Creditanstalt-Bankverein\nDR Dresdner Bank Group\nMO J P Morgan\nPA Compagnie Financi\u00e8re de Paribas SA\nCS Credit Suisse\nBB BBV Group\nBT Banker's Trust\nUB UBS\nAB ABN-AMRO\nCB Citibank\n\nLaw\n\nBM Baker & McKenzie\nWC White & Case\nLL Leboeuf, Lamb, Greene & MacRae\nCO Coudert Brothers\nSK Skadden Arps\nBC Bryan Cave\nDW Dorsey & Whitney\nGJ Graham & James\nHH Hogan & Hartson\nJD Jones, Day, Reavis & Pogue\nMC Miller, Canfield, Paddock & Stone\nSQ Squire, Sanders & Dempsey\nWE Wilson, Elser, Moskowitz, Edelman & Dicker\nAO Allen & Overy\nCC Clifford Chance\nFF Freshfields\n\nAs per our data protocol, the following acknowledgement should accompany any\npublic use of the data:\n\nACKNOWLEDGEMENT: The data used is from Data Set 6 from the GaWC Study Group and\nNetwork (http://www.lboro.ac.uk/gawc/). It was created by P.J. Taylor and\nD.R.F. Walker as part of their project \"World City Network: Data Matrix\nConstruction and Analysis\" and is based on primary data collected by J.V.\nBeaverstock, R.G. Smith and P.J. Taylor (ESRC project \"The Geographical Scope\nof London as a World City\" (R000222050)).\n\n\n================================================================================\n==> Days/Days.txt <==\n================================================================================\n\nPajek datasets\n\nReuters terror news network (NOTE: renamed Reuters911 for UF sparse collection)\n\nDataset days\n\nDescription\n\ndays.net undirected temporal network with 13332 vertices and 243447 edges.\n\n\n\nDays, DaysAll. Background\n\nReuters terror news network Days.net in Pajek's format obtained from the CRA\nnetworks produced by Steve Corman and Kevin Dooley at Arizona State University.\nPlease acknowledge this when publishing results based on these data.\n\nThe Reuters terror news network is based on all stories released during 66\nconsecutive days by the news agency Reuters concerning the September 11 attack\non the U.S., beginning at 9:00 AM EST 9/11/01. The vertices of a network are\nwords (terms); there is an edge between two words iff they appear in the same\ntext unit (sentence). The weight of an edge is its frequency. The network has n\n= 13332 vertices (different words in the news) and m = 243447 edges, 50859 with\nvalue larger than 1. There are no loops in the network.\n\nThe network DaysAll.net contains the main connected component of the network\nobtained by transforming the Reuters terror news network into a combined\nnetwork for all 66 days (union of all time points). It has 13308 vertices.\n\nThe Reuters terror news network was used as a case network for the Viszards\nvisualization session on the Sunbelt XXII International Sunbelt Social Network\nConference, New Orleans, USA, 13-17. February 2002. \n\nHistory\n\n 1. 5-12. Dec 2001, networks constructed by S. Corman and his group\n \n 2. December 2001: CRA data transformed in Pajek format by V. Batagelj.\n\nReferences\n\n 1. Steven R. Corman, Timothy Kuhn, Robert D. Mcphee and Kevin J. Dooley\n (2002): Studying Complex Discursive Systems: Centering Resonance Analysis of\n Communication. (PDF)\n\n 2. Crawdad Technologies\n\n 3. Batagelj, V., & Mrvar, A. (2003): A density based approaches to network\n analysis: Analysis of Reuters terror news network, Ninth Annual ACM SIGKDD,\n Washington, D.C. (PDF); ( SVG)\n\n 4. Jeffrey C. Johnson and Lothar Krempel (2004): Network Visualization: The\n \"Bush Team\" in Reuters News Ticker 9/11-11/15/01. The Journal of Social\n Structure's, Vol. 5, No. 1. ( HTML)\n\nPajek Data; 20. April 2006 / 27. January 2004\n\n-------------------------------------------------------------------------------\nWhen converted to a sparse adjacency matrix for the UF Sparse Matrix\nCollection, Day{i} is the graph of the ith day. The diagonal entry\nDay{i}(k,k) is 1 if word k appears in any news on the ith day. Note\nthat it may not appear in conjunction with other words in the same\nsentence on that day. The sum of nnz(tril(Day{i})) for i=1:66 is 243,447.\nThe overall matrix A is the sum of the Day{i} matrices. A(i,j) is the number\nof times words i and j appear in same sentence (for i not equal to j). A(k,k)\nis the number of days the word k appears in any news report.\nNote that this network has been renamed to Reuters911 here.\n\n\n================================================================================\n==> EAT/EAT.txt <==\n================================================================================\n\n\nPajek datasets EAT The Edinburgh Associative Thesaurus\n\nDataset eat\n\nDescription\n\neatRS.net directed network with 23219 vertices and 325624 arcs (564 loops);\nstimulus X is associated with response Y N times. eatSR.net directed network\nwith 23219 vertices and 325589 arcs (564 loops); response X is associated with\nstimulus Y N times.\n\nIt seems that the SR network is incomplete and that it should be the inverse of\nRS network.\n\n\n\nEAT response-stimulus (ZIP, 1321K) EAT stimulus-response (ZIP, 1306K)\nBackground\n\nThe Edinburgh Associative Thesaurus (EAT) is a set of word association norms\nshowing the counts of word association as collected from subjects. This is not\na developed semantic network such as WordNet (3), but empirical association\ndata.\n\nThe traditional way to collect word association norms is to show or say a word\nto several people and ask them to say the word which first comes to their minds\nupon receiving the stimulus. The link established between the stimulus and the\nresponse is not semantically labelled (e.g. as synonym, antonym or by a case\nrelation) and can only be regarded as an association.\n\nThe Edinburgh association norms were collected by growing the network from a\nnucleus set of words. Responses were collected to words in this nucleus set,\nthen these responses were used to obtain further responses, and so on. In fact\nthe cycle was repeated about three times since by then the number of different\nresponses was so large that they could not be re-used as stimuli. Data\ncollection stopped when 8400 stimulus words had been used. Each stimulus word\nwas presented to 100 different subjects, each of whom received 100 words. This\ngave rise to a total of 55732 nodes in the Thesaurus network.\n\nThe subjects were mostly undergraduates from a wide variety of British\nuniversities. The age range of the subjects was from 17 to 22 with a mode of\n19. The sex distribution was 64 per cent male and 36 per cent female. The data\nwas collected between June 1968 and May 1971.\n\nThe database consists of two files. The SR (stimulus-response) file, and the RS\n(response-stimulus) file. Where words have been truncated to 19 characters to\nsave space the per cent character (%) has been placed as the 20th.\n\nThe EAT here is that included in the MRC Psycholinguistic Database (4), for use\nwith the other measures available there. EAT Data Collection Procedure (1)\nStimulus words\n\nSince the objective was to obtain a reasonably large complete mapping of the\nassociative network for a large set of words, a systematic procedure of\n'growing' the network from a small nucleus was followed. At first responses\nwere obtained from this nucleus set, then these responses were used as stimuli\nto obtain further responses, and so on. In fact, this cycle was repeated about\nthree times, since by then the number of different responses was so large that\nthey could not all be re-used as stimuli.\n\nThe nucleus set was derived from (a) the 200 stimuli used in the Palermo and\nJenkins (1964) normq (b) the 1,000 most frequent words of the Thorndike and\nLorge (1944) word frequency count and (c) the basic English vocabulary of Ogden\n(1954).\n\nData collection was stopped when 8,400 stimulus words had been used. Only a\nminimal amount of selection of stimuli was applied in each cycle of the data\ncollection. Effectively all responses which were English words or meaningful\nverbal units were included, including some phrasal forms and numerals. The data\ncover a wide range of grammatical form classes and inflexional forms.\nProcedure\n\nEach stimulus word was presented to 100 different subjects. Each subject\nrecieved a computer-printed sheet with 100 stimuli in randomised arrangement\n(to minimize priming effects). The total contribution of each subject was thus\n100 responses. The verbal environment of each word for each subject was\ndifferent. The instructions asked the subject to write down against each\nstimulus the first word it made him think of, working as quickly as possible.\nthe total time spent on this task was measured, and most subjects completed the\nsheet in five to ten minutes.\n\nMost of the data was collected in a classroom setting under supervision. Sheets\nwhich had more than 25 percent blank responses were rejected and fresh data was\ncollected.\n\nHistory\n\n 1. Original EAT: George Kiss, Christine Armstrong, Robert Milroy and J.R.I.\n Piper (collected between June 1968 and May 1971).\n\n 2. MRC Psycholinguistic Database Version modified by: Max Coltheart, S.\n James, J. Ramshaw, B.M. Philip, B. Reid, J. Benyon-Tinker and E. Doctor;\n made available by: Philip Quinlan.\n\n 3. The present version was re-structured and documented by Michael Wilson at\n the Rutherford Appleton Laboratory in 1988 (2).\n\n 4. transformed in Pajek format: V. Batagelj, 31. July 2003.\n\n\nReferences\n\n 1. Kiss, G.R., Armstrong, C., Milroy, R., and Piper, J. (1973) An\n associative thesaurus of English and its computer analysis. In Aitken, A.J.,\n Bailey, R.W. and Hamilton-Smith, N. (Eds.), The Computer and Literary\n Studies. Edinburgh: University Press.\n\n 2. The present version of The Edinburgh Associative Thesaurus (ZIP, 2.7M)\n\n 3. WordNet\n\n 4. MRC Psycholinguistic Database\n\n 5. Coltheart, M. (1981) MRC Psycholinguistic Database. Quarterly Journal of\n Experimental Psychology, 3A, 497-505.\n\n 6. MRC Psycholinguistic Database 2\n\nPajek Data; 31. July 2003\n\n-------------------------------------------------------------------------------\nNOTE regarding conversion for UF sparse matrix collection: in the original data\nthere are 325,624 weighted edges. Of those only 32 edges are duplicates, and \nall of them have identical edge weights as the edges they are duplicates of \nThese extraneous edges have been removed, since this this appears to be a \ngraph, not a multigraph. \n\n\n================================================================================\n==> EVA/EVA.txt <==\n================================================================================\n\nPajek datasets\n\nEVA Extraction, Visualization & Analysis of corporate\ninter-relationships\n\nDataset EVA\n\nDescription\n\nEVA.net directed network with 8343 vertices and 6726 arcs.\n\n\n\nEVA.net (ZIP, 204K); included also original files names.txt and ownership.txt.\nBackground\n\nEVA / Denali is a multidisciplinary research project combining information\nextraction, information visualization, and social network analysis techniques\nto bring greater transparency to the public disclosure of inter-relationships\nbetween corporations. The project is described in the paper [1].\n\nAbstract: We present EVA, a prototype system for extracting, visualizing, and\nanalyzing corporate ownership information as a social network. Using\nprobabilistic information retrieval and extraction techniques, we automatically\nextract ownership relationships from heterogeneous sources of online text,\nincluding corporate annual reports (10-Ks) filed with the U.S. Securities and\nExchange Commission (SEC). A browser-based visualization interface allows users\nto query the relationship database and explore large networks of companies.\nApplying the system and methodology to the telecommunications and media\nindustries, we construct an ownership network with 6,726 relationships among\n8,343 companies. Analysis reveals a highly clustered network, with over 50% of\nall companies connected to one another in a single component. Furthermore,\nownership activity is highly skewed: 90% of companies are involved in no more\nthan one relationship, but the top ten companies are parents for over 24% of\nall relationships. We are also able to identify the most influential companies\nin the network using social network analysis metrics such as degree,\nbetweenness, cutpoints, and cliques. We believe this methodology and tool can\naid government regulators, policy researchers, and the general public to\ninterpret complex corporate ownership structures, thereby bringing greater\ntransparency to the public disclosure of corporate inter-relationships.\n\nNote that we do not have ownership relationships for all companies, so there\nwill be companies without links.\n\nAn arc (X,Y) from company X to company Y exists in the network if in the\ncompany X is an owner of company Y.\n\nCopyright 2002 by Denali Project. If you use this dataset in your research,\nplease use the citation to paper [1] as the source of the data.\n\n\"Denali\" is the Native American name for the tallest peak in North America. It\nmeans \"the Great One.\"\n\nIf you have any questions, please contact: John Chuang, Mike Gebbie, Gabe\nLucas, Kim Norlen. \n\nHistory\n\n 1. 2002 collection of original data by the EVA group; 2. March 6, 2004:\n original data transformed into Pajek format EVA.net by V. Batagelj.\n\nReferences\n\n 1. Kim Norlen, Gabriel Lucas, Mike Gebbie, and John Chuang. EVA: Extraction,\n Visualization and Analysis of the Telecommunications and Media Ownership\n Network. Proceedings of International Telecommunications Society 14th\n Biennial Conference, Seoul Korea, August 2002. (paper berkeley / local;\n slides berkeley / local)\n\nPajek Data; 6. March 2004\n\n\n================================================================================\n==> Foldoc/Foldoc.txt <==\n================================================================================\n\nPajek datasets FOLDOC Free On-line Dictionary of Computing\n\nDataset Foldoc\n\nDescription\n\nfoldoc.net valued directed network with 13380 vertices and 120700 arcs, value\nis the multiplicity of arc.\n\n\n\nfoldoc.net (ZIP, 517K) Background\n\nFOLDOC is a searchable dictionary of acronyms, jargon, programming languages,\ntools, architecture, operating systems, networking, theory, conventions,\nstandards, mathematics, telecoms, electronics, institutions, companies,\nprojects, products, history, in fact anything to do with computing.\n\nThe dictionary has been growing since 1985 and now contains over 13000\ndefinitions totalling nearly five megabytes of text. Entries are\ncross-referenced to each other and to related resources elsewhere on the net.\n\nAn arc (X,Y) from term X to term Y exists in the network iff in the FOLDOC\ndictionary the term Y is used to describe the meaning of term X.\n\nCopyright 1993 by Denis Howe. Permission is granted to copy, distribute and/or\nmodify the FOLDOC dictionary under the terms of the GNU Free Documentation\nLicense, Version 1.1 or any later version published by the Free Software\nFoundation.\n\nPlease refer to the dictionary as \"The Free On-line Dictionary of Computing,\nhttp://www.foldoc.org/, Editor Denis Howe\" or similar. \n\nHistory\n\n 1. FOLDOC started in 1985 by Denis Howe;\n\n 2. in 1990 put on FTP, other sources included;\n\n 3. from 1994 available on the WWW, hundreds of contributors;\n\n 4. February/June 2002: Foldoc.net transformed in Pajek format and 'cleaned'\nby V. Batagelj and A. Mrvar.\n\nReferences\n\n 1. Denis Howe, Editor: FOLDOC (2002): Free on-line dictionary of computing.\n\n 2. V. Batagelj, A. Mrvar, M. Zave\u0161nik: Network analysis of texts. Language\nTechnologies 2002, Ljubljana, 14 - 15th October 2002, p. 143-148.\n\n 3. V. Batagelj, A. Mrvar, M. Zave\u0161nik: Network analysis of dictionaries.\nLanguage Technologies 2002, Ljubljana, 14 - 15th October 2002, p. 135-142.\n\n\nPajek Data; 30. January 2004\n\n\n================================================================================\n==> Football/Football.txt <==\n================================================================================\n\nPajek datasets World Soccer Data Paris 1998\n\nDataset Football\n\nDescription\n\nfootball.net - valued network with 35 vertices\n\n\n\ncomplete dataset\n\nBackground\n\nOur network example describes the 22 soccer teams which participated in the\nWorld Championship in Paris, 1998.\n\nPlayers of the national team often have contracts in other countries. This\nconstitutes a players market where national teams export players to other\ncountries. Members of the 22 teams had contracts in altogether 35 countries.\n\nCounting which team exports how many players to which country can be described\nwith a valued, asymmetric graph. The graph is highly unsymmetric: some\ncountries only export players, some countries are only importers. \n\nHistory\n\n 1. Data collected by Lothar Krempel, October 5, 1999\n\n 2. Transformed in Pajek format by V. Batagelj, February 9, 2001.\n\nReferences\n\n 1. Dagstuhl seminar: Link Analysis and Visualization, Dagstuhl 1-6. July\n2001\n\nPajek Data; 21. September 2005\n\n\n================================================================================\n==> Geom/Geom.txt <==\n================================================================================\n\n\nPajek datasets Geom Collaboration network in computational geometry\n\nDataset Geom\n\nDescription\n\nGeom.net valued undirected network with 7343 vertices and 11898 edges; author X\nwrote a joint work with author Y; value is the number of joint works.\n\n\n\nGeom.net (ZIP, 139K)\n\nBackground\n\nThe network Geom.net is based on the file geombib.bib that contains\nComputational Geometry Database, version February 2002.\n\nThe authors collaboration network in computational geometry was produced from\nthe BibTeX bibliography [Beebe, 2002] obtained from the Computational Geometry\nDatabase geombib, version February 2002 [Jones, 2002].\n\nTwo authors are linked with an edge, iff they wrote a common work (paper, book,\n...). The value of an edge is the number of common works. Using a simple\nprogram written in programming language Python, the BibTeX data were\ntransformed into the corresponding network, and output to the file in Pajek\nformat.\n\nThe obtained network has 9072 vertices (authors) and 22577 edges (common papers\nor books) / 13567 edges as a simple network - multiple edges between a pair of\nauthors are replaced with a single edge.\n\nThe problem with the obtained network is that, because of non standardized\nwriting of the author's name, it contains several vertices corresponding to the\nsame author. For example:\n\n R.S. Drysdale, Robert L. Drysdale, Robert L. Scot Drysdale, R.L. Drysdale,\nS. Drysdale, R. Drysdale, and R.L.S. Drysdale;\n\nor:\n\n Pankaj K. Agarwal, P. Agarwal, Pankaj Agarwal, and P.K. Agarwal\n\nthat are easy to guess; but an 'insider' information is needed to know that\nOtfried Schwarzkopf and Otfried Cheong are the same person. Also, no provision\nis made in the database to discern two persons with the same name. We manually\nproduced the name equivalence partition and then shrank (in Pajek) the network\naccording to it.\n\nThe reduced simple network contains 7343 vertices and 11898 edges. It is a\nsparse network - its average degree is 2m/n = 3.24.\n\nHistory\n\n 1. Computational Geometry Database started in 1986 by merging two lists of\nreferences - one compiled by Edelsbrunner and van Leeuwen and the other by\nGuibas and Stolfi;\n\n 2. Computational Geometry Database, February 2002 Edition;\n\n 3. March-April 2002: Geom.bib transformed in Pajek format and 'cleaned' by\nV. Batagelj and M. Zaver\u0161nik.\n\nReferences\n\n 1. Beebe, N.H.F. (2002): Nelson H.F. Beebe's Bibliographies Page.\n\n 2. Jones, B., Computational Geometry Database, February 2002; FTP / HTTP\n\nPajek Data; 27. January 2004\n\n\n================================================================================\n==> GlossGT/GlossGT.txt <==\n================================================================================\n\nPajek datasets Graph and Digraph Glossary\n\nDataset glossGT\n\nDescription\n\nglossGT.net mixed network with 72 vertices and 114 arcs and 4 edges; word X is\nrelated to word Y.\n\n\n\nglossTG.paj (4K)\n\nBackground\n\nThe network GlossGT.net is based on the file glossary.html containing Bill\nCherowitzo's Graph and Digraph Glossary. An arc (X,Y) from term X to term Y\nexists in the network iff in the Graph and Digraph Glossary the term Y is used\nto describe the meaning of term X.\n\nHistory\n\n 1. 1998-2001, Bill Cherowitzo prepared the glossary. 2. Graph and Digraph\n Glossary transformed in Pajek format: Barbara Zemlji\"c, 2. nov 2003.\n\nReferences\n\n 1. Bill Cherowitzo: Graph and Digraph Glossary, version 03-Feb-2001; (Course\n page)\n\nPajek Data; 25. January 2004\n\n\n================================================================================\n==> HEP-th/HEP.txt <==\n================================================================================\n\n\nPajek datasets KDD Cup 2003 High Energy Particle Physics (HEP) literature\n\nDataset hep-th\n\nDescription\n\nhep-th.net directed network with 27240 vertices and 342437 arcs (39 loops).\nhep-th-new.net directed network with 27770 vertices and 352807 arcs (39 loops).\ndate-new.vec integer vector on 27770 vertices. year-new.vec integer vector on\n27770 vertices.\n\n\n\ncomplete dataset (ZIP, 2607K)\n\nBackground Citation data from KDD Cup 2003, a knowledge discovery and data\nmining competition held in conjunction with the Ninth Annual ACM SIGKDD\nConference.\n\nThe Stanford Linear Accelerator Center SPIRES-HEP database has been\ncomprehensively cataloguing the High Energy Particle Physics (HEP) literature\nonline since 1974, and indexes more than 500,000 high-energy physics related\narticles including their full citation tree.\n\nThe network contains a citation graph of the hep-th portion of the arXiv. The\nunits names are the arXiv IDs of papers; the relation is X cites Y . Note that\nrevised papers may have updated citations. As such, citations may refer to\nfuture papers, i.e. a paper may cite another paper that was published after the\nfirst paper.\n\nThe SLAC/SPIRES dates for all hep-th papers are given. Some older papers were\nuploaded years after their intial publication and the arXiv submission date\nfrom the abstracts may not correspond to the publication date. An alternative\ndate has been provided from SLAC/SPIRES that may be a better estimate for the\ninitial publication of these old papers.\n\nThe first version of data was updated on May 12, 2003.\n\nhep-th.net X cites Y relation, first version. hep-th-new.net X cites Y\nrelation, updated version. date-new.vec SLAC date of paper was transformed to\nthe number of days since August 1, 1991, updated version. year-new.vec year\nfrom the SLAC date of paper, updated version.\n\nReferences\n\n 1. KDD Cup 2003\n \n 2. arXiv\n\nTransformed in Pajek format by V. Batagelj, 26. July 2003\n\n\n\n================================================================================\n==> IMDB/IMDB.txt <==\n================================================================================\n\nPajek data set: IMDB, the Internet Movie Database, http://www.imdb.com.\n\n-------------------------------------------------------------------------------'\nPajek network converted to sparse adjacency matrix for inclusion in UF sparse\nmatrix collection, Tim Davis. For Pajek datasets, See V. Batagelj & A. Mrvar,\nhttp://vlado.fmf.uni-lj.si/pub/networks/data/.\n-------------------------------------------------------------------------------\nA(i,j)=1 if actor j played in movie i. colname(j,:) is the name of the actor.\nColumn j = 362,181 is Kevin Bacon. Year of movie i is year(i).\ncategory(i) gives the category of movie i, use code(category(i),:).\nNote that movie names are not provided.\n\n1: Drama\n2: Short\n3: Documentary\n4: Comedy\n5: Western\n6: Family\n7: Mystery\n8: Thriller\n9: -\n10: Music\n11: Crime\n12: Sci-Fi\n13: Horror\n14: War\n15: Fantasy\n16: Romance\n17: Adventure\n18: Animation\n19: Action\n20: Musical\n21: Film-Noir\n99: Unknown.\n\nRemember that in MATLAB, A(i,:) is slow to compute; A(:,i) is faster. If you\nwant row i of a sparse matrix, access the ith column of the transpose instead.\n-------------------------------------------------------------------------------\n\nA \"Bacon number\" is a measure of separation in the graph. Kevin Bacon has\na Bacon number of zero. Any actor who played in a movie with Kevin Bacon\nhas a Bacon number of 1. In general, an actor has a Bacon number equal to\n1 + the minimum Bacon number of any actor he or she has been in a movie with.\n\nA similar definition can be extended to movies; a movie in which Kevin Bacon\nappears has a Bacon number of 0. In general, the Movie-Bacon number is\nthe smallest Bacon number of any actor in that movie.\n\nThe following code was used to compute the Bacon numbers:\n\n Bacon = Problem.aux.KevinBacon ;\n A = Problem.A ;\n [m n] = size (A) ;\n C = [speye(m) A ; A' speye(n)] ;\n x = zeros (m+n,1) ;\n B = inf * ones (m+n,1) ;\n x (m + Bacon) = 1 ;\n B (m + Bacon) = 0 ;\n tlen = 1 ;\n for k = 1:m+n\n\tx = x + C*x ;\n\tt = find (x) ;\n\tif (tlen == length (t))\n\t break\n\tend\n\ttlen = length (t) ;\n\tB (t) = min (B (t), k) ;\n end\n MovieBacon = (B (1:m) - 1) / 2 ;\n ActorBacon = B (m+1:end) / 2 ;\n\nNote that the movie names have been intentionally omitted from this version of\nthe data in the UF Sparse Matrix Collection, as has the name of movie code 9.\n\nThe above MATLAB code is not terribly efficient; it makes k passes over the\nmatrix, each taking O(nnz(A)) time. Fortunately, k is a small constant (8).\nA proper breadth-first search would take O(nnz(A)), regardless of k.\n\n================================================================================\n==> Journals/Journals.txt <==\n================================================================================\n\nPajek datasets Slovenian magazines and journals 1999 and 2000\n\nDataset Journals\n\nDescription\n\nRevije.net - valued network with 124 vertices Revije.clu - partition with 124\nvertices Revije.paj - Pajek project file with complete dataset.\n\n\n\ncomplete dataset (ZIP, 3K)\n\nBackground\n\nOver 100.000 people have been asked which magazines and journals they read\n(survey conducted in 1999 and 2000, source CATI Center Ljubljana). They listed\n124 different magazines and journals. The collected data can be represented as\n2-mode network:\n\n Delo Dnevnik Sl.novice ...\nReader1 X X ...\nReader2 X ...\nReader3 X ...\n............ ..... ....... ......... ...\n\nObtaining 1-mode network\n\nFrom 2-mode network reader/journal we generated ordinary network, where the\nvertices are journals\n\n * undirected edge with value a between journals means the number of readers\n * of both journals. loop on selected journal means the number of all\n * readers that read this journal.\n\nObtained matrix (A):\n\n Delo Dnevnik Sl.novice ...\nDelo 20714 3219 4214 ...\nDnevnik 15992 3642 ...\nSl. novice 31997 ...\n........ ...... ..... ..... ...\n\nThe second ordinary network on readers would be huge (more than 100.000\nvertices) containing large cliques (readers of particular journal).\n\nHistory\n\n 1. Transformed in journal X journal matrix, 26. December 2000.\n\n 2. Transformed in Pajek format by V. Batagelj, 21. December 2003.\n\nReferences\n\n 1. Dagstuhl seminar: Link Analysis and Visualization, Dagstuhl 1-6. July 2001\n\nPajek Data; 21. December 2003\n\n\n================================================================================\n==> KEDS/KEDS.txt <==\n================================================================================\n\nNOTE: the KEDS data has not been included.\n\nPajek datasets KEDS The Kansas Event Data System\n\nDataset KEDS\n\nDescription\n\nGulfLDays.net directed multirelational temporal network with 174 vertices and\n57131 arcs. From 'leads' Gulf event data, granularity is 1 day.\n\nGulfLMonths.net directed multirelational temporal network with 174 vertices and\n57131 arcs. From 'leads' Gulf event data, granularity is 1 month.\n\nGulfLDow.net directed multirelational temporal network with 174 vertices and\n57131 arcs. From 'leads' Gulf event data, in day of the week classes.\n\nGulfADays.net directed multirelational temporal network with 202 vertices and\n304401 arcs. From Gulf event data, granularity is 1 day.\n\nGulfAMonths.net directed multirelational temporal network with 202 vertices and\n304401 arcs. From Gulf event data, granularity is 1 month.\n\nLevantDays.net directed multirelational temporal network with 485 vertices and\n196364 arcs. From Levant event data, granularity is 1 day.\n\nLevantMonths.net directed multirelational temporal network with 485 vertices\nand 196364 arcs. From Levant event data, granularity is 1 month.\n\nBalkanDays.net directed multirelational temporal network with 325 vertices and\n78667 arcs. From Balkan event data, granularity is 1 day.\n\nBalkanMonths.net directed multirelational temporal network with 325 vertices\nand 78667 arcs. From Balkan event data, granularity is 1 month.\n\n\n\nGulfLDays.net (ZIP, 239K)\n\nGulfLMonths.net (ZIP, 197K)\n\nGulfLDow.net (ZIP, 213K)\n\nGulfADays.net (ZIP, 1078K)\n\nGulfAMonths.net (ZIP, 941K)\n\nLevantDays.net (ZIP, 855K)\n\nLevantMonths.net (ZIP, 735K)\n\nBalkanDays.net (ZIP, 335K)\n\nBalkanMonths.net (ZIP, 288K)\n\nBackground\n\nKEDS - The Kansas Event Data System uses automated coding of English-language\nnews reports to generate political event data focusing on the Middle East,\nBalkans, and West Africa. These data are used in statistical early warning\nmodels to predict political change. The ten-year project is based in the\nDepartment of Political Science at the University of Kansas; it has been funded\nprimarily by the U.S. National Science Foundation. KEDS data sets from KEDS\ndata collection.\n\nGulf data set: This data set covers the states of the Gulf region and the\nArabian peninsula for the period 15 April 1979 to 31 March 1999. The source\ntexts prior to 10 June 97 were located using a NEXIS search command\nspecifically designed to return relevant data. There are two versions of the\ndata: a set coded from the lead sentences only (57,000 events), and a set coded\nfrom full stories (304,000 events). There are some errors in the GulfAll data.\nEvents 118196 and 118197 have REUT-0 in place of the date; and in event 173526\nthe first actor is missing. In Gulf99All.dat the wrong dates are replaced with\n890319, and the incomplete event is skiped.\n\nLevant data set: Folder containing WEIS-coded events (N=196,337) for dyadic\ninteractions within the following set of countries: Egypt, Israel, Jordan,\nLebanon, Palestinians, Syria, USA, and USSR/Russia. Coverage is April 1979 to\nJune 2004. TABARI coding dictionaries are also included. There are some errors\n(333) in data set - relation codes 012], O24, O53, 213] are replaced with 012,\n024, 053, 213 in Levant.dat. Some events don't have description codes - they\nare marked with *** in relation labels in *.net files.\n\nBalkans data set, 1989-2003: Folder containing WEIS-coded events (N = 78,667)\nfor the major actors (including ethnic groups) involved in the conflicts in the\nformer Yugoslavia. Coverage is April 1989 through July 2003. TABARI coding\ndictionaries are included in the folder. There are some errors (197) in data\nset - relation codes ---], O24, O53 are replaced with 000, 024, 053 in\nBalkan.dat. Some events don't have description codes - they are marked with ***\nin relation labels in *.net files.\n\nThe original data sets are on MAC files. They should be saved as PC files\nbefore processing. \n\nHistory\n\n 1. Program Recode (in Delphi) by Vladimir Batagelj, Ljubljana, July 25, 2003\n\n 2. Program KEDSrec adapted for KEDS from Recode; Gulf (leads) data recoded\ninto Pajek's format, by Vladimir Batagelj, Ljubljana, November 3, 2003\n\n 3. Support for multiple relations networks added to program KEDSrec by\nVladimir Batagelj, Ljubljana, November 22, 2004\n\n 4. KedsR - functionality of KEDSrec implemented in R; Gulf (leads) data\nrecoded into Pajek's format (days, months, day of the week), by Vladimir\nBatagelj, Ljubljana, November 27, 2004\n\n 5. WeisR - commands from KedsR adapted for WEIS format (similar to KEDS but\nTAB delimited); Balkan and Levant data recoded into Pajek's format (days,\nmonths), by Vladimir Batagelj, Ljubljana, November 28, 2004\n\n 6. Gulf99All is a large data set - sapply commands in KedsR had to be\nreplaced by while loops; Gulf (all) data recoded into Pajek's format (days,\nmonths), by Vladimir Batagelj, Ljubljana, November 29, 2004\n\n\nReferences\n\n 1. StuffIt - uncompress program for SIT files\n\nPajek Data; 29. November 2004 / 24. November 2004\n\n\n================================================================================\n==> Mixed/Mixed.txt <==\n================================================================================\n\n\nPajek datasets from different sources\n\nUS power grid\n\nDataset: USpowerGrid\n\nUS power grid - unweighted network from Panayiotis Tsaparas' page. Adapted for\nPajek by V. Batagelj, March 19, 2006\n\nFile: USpowerGrid.net - undirected network with 4941 vertices and 6594 edges\n\n\n\nKleinberg's web graphs\n\nDataset: California - Pages matching the query \"California\".\n\nThis graph was constructed by expanding a 200-page response set to a search\nengine query 'California', as in the hub/authority algorithm. Obtained from Jon\nKleinberg's page. Adapted for Pajek by V. Batagelj, March 19, 2006\n\nFile: California.net - directed network with 9664 vertices and 16150 arcs.\n\n\nReferences: Amy N. Langville and Carl D. Meyer: A Reordering for the PageRank\nproblem (March 2004) PDF J. Kleinberg. Authoritative sources in a hyperlinked\nenvironment. Proc. 9th ACM-SIAM Symposium on Discrete Algorithms, 1998.\nExtended version in Journal of the ACM 46(1999). PDF\n\nDataset: Epa - Pages linking to www.epa.gov.\n\nThis graph was constructed by expanding a 200-page response set to a search\nengine query, as in the hub/authority algorithm. Obtained from Jon Kleinberg's\npage. Adapted for Pajek by V. Batagelj, March 19, 2006\n\nFile: Epa.net - directed network with 4772 vertices and 8965 arcs.\n\n\n Stanford web graphs\n\nDataset: StanfordWeb - Stanford Web Matrix.\n\nThis graph was obtained from a September 2002 crawl (281903 pages, 2382912\nlinks). The matrix rows represent the inlinks of a page, and the columns\nrepresent the outlinks. Downloaded from Sepandar D. Kamvar's page. Adapted for\nPajek by V. Batagelj, March 19, 2006\n\nFile: StanfordWeb.net - directed network with 281903 vertices and 2382912 arcs.\n\n\nReferences: Sepandar D. Kamvar, Taher H. Haveliwala, Christopher D. Manning,\nand Gene H. Golub, \"Exploiting the Block Structure of the Web for Computing\nPageRank.\" Preprint (March, 2003). PDF\n\nDataset: StanfordBerkeleyWeb - Stanford-Berkeley Web Matrix.\n\nThis graph was obtained from a December 2002 crawl (685230 pages, 8006115\nlinks). The matrix rows represent the inlinks of a page, and the columns\nrepresent the outlinks. Downloaded from Sepandar D. Kamvar's page. Adapted for\nPajek by V. Batagelj, March 19, 2006\n\nNOTE by Tim Davis: nodes 683,447 to 685,230 in the Stanford Berkeley web data,\ndiscussed above, are not part of the true results. Kamvar's MATLAB script for\nprocessing the data deletes those nodes. This graph is already in the UF\nSparse Matrix Collection, in the correct size. Also note that Kamvar doesn't\nconsider multiple links from page i to j to be significant. Thus, duplicate\nedges (i,j) in this graph should be ignored. The graph in the UF collection is\nthus binary.\n\nFile: StanfordBerkeleyWeb.net - directed network with 685230 vertices and\n8006115 arcs.\n\n\nReferences: Sepandar D. Kamvar, Taher H. Haveliwala, Christopher D. Manning,\nand Gene H. Golub, \"Exploiting the Block Structure of the Web for Computing\nPageRank.\" Preprint (March, 2003). PDF\n\nWorld City Network\n\nDataset: WorldCities\n\nThese data constitute the empirical basis for measuring the world city network\nas described in P.J. Taylor (2004) World City Network: A Global Urban Analysis\n(London: Routledge). The data were produced by P.J. Taylor and G. Catalano.\nThese data were collected in 2000. They are the service values (indicating the\nimportance of a city in the office network of a firm) of 100 global service\nfirms distributed across 315 cities worldwide. All firms supply advanced\nproducer services (accountancy, advertising, banking/finance, insurance, law,\nand management consultancy) through offices in at least 15 cities (including at\nleast one in each of Pacific Asia, western Europe and northern America). The\nfollowing coding information is given to help understand and evaluate the data.\nDownloaded from GaWC Data Set 11 page. Transformed in Pajek format by V.\nBatagelj, March 20, 2006\n\nFiles:\n\nWorldCities.net - two-mode network with 415=315+100 vertices and 7518 arcs.\n\nWorldCities.mat - same network in Pajek matrix format.\n\nWorldCities.net - type of service partition: 0 - city; 1 - accountancy; 2 -\nadvertising; 3 - banking/finance; 4 - insurance; 5 - law; 6 - management\nconsultancy.\n\n\n\nReferences: Taylor, P.J. (2004): World city network: a global urban analysis.\nLondon and New York: Routledge.\n\nPajek Data; 19. March 2006\n\n\n================================================================================\n==> ND/ND.txt <==\n================================================================================\n\n\nPajek datasets Notre Dame Self-Organized Networks Database\n\nDatasets NDwww, NDactors, NDyeast\n\nDescription\n\nNotre Dame Self-Organized Networks:\n\n 1. NDwww.net directed network with 325729 vertices and 1497135 arcs (27455\nloops); page X is linked to page Y.\n\n 2. NDactors.net undirected two-mode network with 520223 vertices (392400\nplayers, 127823 movies) and 1470418 edges; player X plays in movie Y.\n\n 3. NDyeast.net undirected network with 2114 vertices and 2277 edges (74\nloops); protein X interacts with protein Y.\n\n\n\nNDwww.net (ZIP, 2050K) NDactors.net (ZIP, 4150K) NDyeast.net (ZIP, 7K)\n\nBackground\n\nThe networks ND*.net are based on the files from Notre Dame Self-Organized\nNetworks Database. To transform the data into Pajek format: vertex 0 was\nreplaced by the vertex number equal to the number of vertices in a network;\nPajek keywords were inserted; and the network was saved in the short (as lists\nof neighbors) format.\n\n 1. World-Wide-Web:: Each number represents webpage within nd.edu domain.\nArcs: From page -> To page R\u00e9ka Albert, Hawoong Jeong and Albert-L\u00e1szl\u00f3\nBarab\u00e1si: Diameter of the World Wide Web, Nature 401, 130 (1999) [ PDF ] See\nalso a decompostion of this network in V. Batgelj, A. Mrvar: How to analyze\nlarge networks with Pajek?\n\n 2. Actor: Actor network data: (based on www.imdb.com) In the original ND\nnetwork file: each line corresponds to one movie, each number represents actor:\nnumber_1 number_2 ... number_k (k actors who play in the same movie).\nAlbert-L\u00e1szl\u00f3 Barab\u00e1si, R\u00e9ka Albert: Emergence of scaling in random networks,\nScience 286, 509 (1999) [ PDF ]\n\n 3. Protein Interaction Network for Yeast: Each number represents protein in\nprotein interaction network of yeast. Edges: From protein -> To protein. For\nother datasets used in supplementary material, please refer indicated\nreferences. Hawoong Jeong, Sean Mason, Albert-L\u00e1szl\u00f3 Barab\u00e1si and Zolt\u00e1n N.\nOltvai: Centrality and lethality of protein networks, Nature 411, 41 (2001) [\nPDF ] See also Yeast data\n\nHistory\n\n 1. Notre Dame Networks Database put on WWW by the Notre Dame team, 2001;\n\n 2. 23-25. July 2001: ND nets transformed in Pajek format by V. Batagelj.\n\n 3. 23. May 2004: ND nets in Pajek format transformed in short (lists of\nneighbors) Pajek format by V. Batagelj.\n\nReferences\n\n 1. Self-Organized Networks Database, University of Notre Dame.\n\nCopyright Extract from the Notre Dame Networks Database page: \"... Feel free to\nuse these data in your research.\" Mail to Hawoong Jeong (author of original ND\nnetworks).\n\nPajek Data; 23. May 2004\n\n\n================================================================================\n==> ODLIS/ODLIS.txt <==\n================================================================================\n\n\nPajek datasets\n\n ODLIS Online Dictionary of Library and Information Science\n\nDataset odlis\n\nDescription\n\nodlis.net directed network with 2909 vertices and 18419 arcs (5 loops).\n\n\n\nodlis.net (ZIP, 62K) Background\n\nThe network Odlis.net is based on the ODLIS: Online Dictionary of Library and\nInformation Science. version December 2000.\n\nODLIS is designed to be a hypertext reference resource for library and\ninformation science professionals, university students and faculty, and users\nof all types of libraries. The primary criterion for including a new term is\nwhether a librarian or other information professional might reasonably be\nexpected to encounter it at some point in his (or her) career, or be required\nto know its meaning in the course of executing his or her responsibilities as a\nlibrarian. The vocabulary of publishing, printing, book history, literature,\nand computer science has been included when, in the author's judgment, a\ndefinition might prove helpful, not only to library and information\nprofessionals, but also to laypersons.\n\nAn arc (X,Y) from term X to term Y exists in the network iff in the ODLIS\ndictionary the term Y is used to describe the meaning of term X.\n\nODLIS is the work of Joan M. Reitz, Assistant Professor/Instruction Librarian\nat the Ruth A. Haas Library, Western Connecticut State University (WCSU) in\nDanbury, CT. Ms. Reitz holds an M.L.I.S. degree (1991) from the University of\nWashington in Seattle and an M.A. degree (1998) in European History from\nWestern Connecticut State University. Her primary research interests are the\nhistory of the book and history of political and social revolutions. \n\nHistory\n\n 1. ODLIS began at the Haas Library in 1994 as a five-page photocopied\nhandout intended for undergraduates not fluent in English, and students with\nlimited exposure to library terminology.\n\n 2. In 1996, it was expanded and converted to HTML format for installation on\nthe WCSU Libraries HomePage under the title Hypertext Library Lingo: A Glossary\nof Library Terminology.\n\n 3. In 1997, many more hypertext links were added and the format improved in\nresponse to suggestions from users.\n\n 4. During the summer of 1999, several hundred terms and definitions were\nadded, and a generic version created which omitted all references to the\nspecific conditions and practices at the Haas Library.\n\n 5. In the fall of 1999, the glossary was expanded to 1,800 terms, renamed to\nreflect its extended scope, and copyrighted.\n\n 6. In February, 2000, ODLIS was indexed in Yahoo! under \"Reference -\nDictionaries - Subject.\" It is also indexed in the WorldCat database in OCLC\nFirstSearch.\n\n 7. During the year 2000, the dictionary was expanded to 2,600 terms. On\naverage, it has received over 6,200 visits per month since January 2, 2000.\n\n 8. December 2000: ODLIS transformed in Pajek format and 'cleaned' by A.\nMrvar and V. Batagelj.\n\nReferences\n\n 1. Joan M. Reitz (2002): ODLIS: Online Dictionary of Library and Information\nScience.\n\nPajek Data; 27. January 2004\n\n\n================================================================================\n==> Patents/Patents.txt <==\n================================================================================\n\n\nPajek datasets Patents The NBER U.S. Patent Citations Data File\n\nDataset patents\n\nDescription\n\npatents.net directed network with 3,774,768 vertices and 16,522,438 arcs (1\nloop).\n\n\n\nNET, NAM, Year, Date, Cat, Class, Country, Subcat, AppYear Patents NET / main\nsubnetwork Background\n\nThe network Patents is based on the The NBER U.S. Patent Citations Data File,\nversion 2001.\n\nThese data comprise detail information on almost 3 million U.S. patents granted\nbetween January 1963 and December 1999, all citations made to these patents\nbetween 1975 and 1999 (over 16 million), and a reasonably broad match of\npatents to Compustat (the data set of all firms traded in the U.S. stock\nmarket).\n\nThese data are described in detail in Hall, B. H., A. B. Jaffe, and M.\nTratjenberg (2001). \"The NBER Patent Citation Data File: Lessons, Insights and\nMethodological Tools.\" NBER Working Paper 8498. ALL USERS OF THESE DATA SHOULD\nREAD THIS PAPER, AND SHOULD CITE IT AS THE SOURCE OF THE DATA\n\nFurther documentation on uses of the patent citation data, including the\nmethodology paper and a CD containing the complete dataset itself, is available\nin the book Patents, Citations and Innovations: A Window on the Knowledge\nEconomy by Adam Jaffe and Manuel Trajtenberg, MIT Press, Cambridge (2002). The\nbook may be ordered from MIT Press. ISBN 0-262-10095-9.\n\nHistory\n\n 1. Data produced by United States Patent and Trademark Office\n\n 2. Hall, B. H., A. B. Jaffe, and M. Tratjenberg prepared the NBER dataset,\n 2001\n\n 3. July 10, 2003: NBER data transformed in Pajek format by V. Batagelj.\n\n\nReferences\n\n 1. Hall, B. H., A. B. Jaffe, and M. Tratjenberg (2001): The NBER U.S. Patent\n Citations Data File.\n\n 2. United States Patent and Trademark Office, Patent Number Search\n\n 3. Batagelj V. (2003): Efficient Algorithms for Citation Network Analysis.\n\nPajek Data; 27. January 2004\n\n-------------------------------------------------------------------------------\nNOTE regarding conversion for UF sparse matrix collection: in original the data\nthere are 14,973,817 edges (unweighted). Of this, 3050 are duplicates \nThis graph is binary; the duplicates have been removed. \nAlso, the original data has auxiliary data for all 6,009,554 US Patents in this\ntime period. This patent network has only 3,774,768 patents, and the auxiliary\ndata (appyear, class, etc.) is matched here to the nodes of the graph. \n\n================================================================================\n==> Roget/Roget.txt <==\n================================================================================\n\n\nPajek datasets Roget Roget's Thesaurus, 1879\n\nDataset Roget\n\nDescription\n\nroget.net directed network with 1022 vertices and 5075 arcs (1 loop); word X is\nrelated to word Y.\n\n\n\nRoget.net (ZIP, 17K)\n\nBackground\n\nThe network Roget.net is based on the file roget.dat from the Stanford\nGraphBase that contains cross-references in Roget's Thesaurus, 1879.\n\nDr. Peter Mark Roget (1779-1869) philologist, scientist, physician. The name\nRoget could soon become a virtual synonym for the word \"synonym\". For those who\nuse Roget's Thesaurus it is one of the three most important books ever\nprinted...along with The Bible and Webster's Dictionary. In order to\ncommunicate one's exact intention...or one's precise meaning, the Thesaurus,\nbeing a list of synonyms or verbal equivalents, is a necessary tool. The first\ndraft of the Thesaurus was written in 1805, two years before Webster started on\nhis dictionary. However for a period of 47 years Dr. Roget used his manuscript\nas his personal, secret, treasure trove. Not until he was 73 years old did he\ndecide to reveal and publish this great manuscript.\n\nSince 1852, Roget's Thesaurus has never been out of print. In fact, each\nsucceeding edition has increased the popularity of the work. The original\n15,000 words included in the 1805 manuscript has increased to over a quarter of\na million in the 1992 edition (the tenth printing). With such an increase in\nsize, it is encouraging to notice that the basic content still remains\nintact..... for example, where the 1805 Thesaurus traces the word: existence:\n\"Ens, entity, being, existence, essence...\", the 1992 Thesaurus contains\nexistence: \"existence, being, entity, ens,...essence...\"\n\nEach vertex of the graph corresponds to one of the 1022 categories in the 1879\nedition of Peter Mark Roget's Thesaurus of English Words and Phrases, edited by\nJohn Lewis Roget. An arc goes from one category to another if Roget gave a\nreference to the latter among the words and phrases of the former, or if the\ntwo categories were directly related to each other by their positions in\nRoget's book. For example, the vertex for category 312 (`ascent') has arcs to\nthe vertices for categories 224 (`obliquity'), 313 (`descent'), and 316\n(`leap'), because Roget gave explicit cross-references from 312 to 224 and 316,\nand because category 312 was implicitly paired with 313 in his scheme.\n\nHistory\n\n 1. Original Roget's Thesaurus was published in 1852.\n\n 2. Peter's son John Luis Roget published the second, improved edition in\n1879.\n\n 3. Project Gutenberg Roget's Thesaurus (1911 edition) put into electronic\nformat in 1991.\n\n 4. Graph Roget.dat of cross-references based on the second edition was\nproduced for Stanford GraphBase (SGB) in 1992/3.\n\n 5. MICRA (Pat Cassidy) prepared the electronic version of the 1911 Roget's\nThesaurus that is widely available on the internet.\n\n 6. SGB Roget.dat transformed in Pajek format: A. Mrvar, 5. December 1996.\n\nReferences\n\n 1. Peter Mark Roget: Roget's Thesaurus of English Words and Phrases\n\n 2. Project Gutenberg: Roget's Thesaurus\n\n 3. Donald E. Knuth: The Stanford GraphBase: A Platform for Combinatorial\nComputing . New York: ACM Press, 1993\n\n 4. The Stanford GraphBase: roget.dat, version 15.6.1993\n\n 5. Pat Cassidy: MICRA / Factotum\n\n 6. CIDE (Collaborative International Dictionary of English), GNU 1996-2002\n\nPajek Data; 23. January 2004\n\n\n================================================================================\n==> Sandi/Sandi.txt <==\n================================================================================\n\n REFERENCES\n\nDATASET SANDI\n\nDESCRIPTION 2-mode 674\u00d7314 network.\n\nBACKGROUND These data were obtained from the bibliography of the book Imrich W,\nKlav\u017ear S. (1999) Graph products. The result is a author-by-paper 2-mode\nnetwork: arc (i,j) - author i is the (co)author of the paper j.\n\nDERIVED DATA AUTHORS authors network\n\nREFERENCES\n\n * Imrich W, Klav\u017ear S. (1999). Graph products. References.\n\n\n\n================================================================================\n==> Stranke94/Slovene_Parties.txt <==\n================================================================================\n\nPajek datasets Slovene Parliamentary Parties 1994\n\nDataset Stranke94\n\nDescription\n\nStranke94.net - valued signed network with 10 vertices\n\n\n\nStranke94.net Background\n\nRelations between Slovene parliamentary political parties:\n\n * SKD - Slovene Christian Democrats;\n\n * ZLSD - Associated List of Social Democrats;\n\n * SDSS - Social Democratic Party of Slovenia;\n\n * LDS - Liberal Democratic Party;\n\n * ZSESS - first of two Green Parties, separated after 1992 elections;\n\n * ZS - second Green Party;\n\n * DS - Democratic Party;\n\n * SLS - Slovene People's Party;\n\n * SNS - Slovene National Party;\n\n * SPS SNS - a group of deputies, former members of SNS, separated after\n * 1992 elections\n\nwere estimated by the members of the Slovene National Parliament. So the\nrespondents were well informed and competent to give such estimations. In the\nquestionnaire designed by a group of experts on Parliament activities, some\nquestions about the political space and its dimensions were included and the\nfollowing question about relations between parliamentary political parties:\n\n If various criteria (or various dimensions of the political space) are\n taken into account, some parties are by average closer than others. How\n would you personally estimate distances between pairs of parties in the\n political space?\n\n Please, estimate the distance between each pair of parties on the scale\n from -3 to +3, where:\n\n -3 means that parties are very dissimilar;\n\n -2 means that parties are quite dissimilar;\n\n -1 means that parties are dissimilar;\n\n 0 means that parties are neither dissimilar nor similar (somewhere in\n between);\n\n +1 means that parties are similar;\n\n +2 means that parties are quite similar;\n\n +3 means that parties are very similar.\n\nTo collect estimations each respondent was given a 10-party by 10-party table\nwith empty cells in the upper triangle. The diagonal and the lower triangle\nwere coloured in black. Each respondent had to estimate relations between 45\npairs of parties.\n\nThe measures of central tendency were computed on the basis of the estimations\ngiven by 72 out of 90 members of the Parliament. 17 members of the Parliament\nwere not available at the time of interviewing and one refused respond. 64\nrespondents out of the 72 estimated all 45 requested party relations. Only two\nout of 8 respondents with missing values have a large number of missing values\n(namely 40), the rest of them have 5 to 10 missing values. As far as the\nparties (variables) are considered, there are from 0 to 8 missing values and a\nrecognisable pattern: relations involving ZS (which had no representatives in\nthe Parliament at the time of interviewing) include from 6 to 8 missing values,\nothers from 0 to 3 missing values.\n\nThe weights of arcs in the network are averages of values multiplied by 100 and\nrounded. The missing values were excluded. \n\nHistory\n\n 1. Stran format by V. Batagelj, 19. October 1994.\n\n 2. Transformed in Pajek format by V. Batagelj, 15. February 2004.\n\nReferences\n\n 1. Samo Kropivnik and Andrej Mrvar: An Analysis of the Slovene Parliamentary\n Parties Network. Developments in Statistics and Methodology. (A. Ferligoj,\n A. Kramberger, editors) Metodolo\u0161ki zvezki 12, FDV, Ljubljana, 1996, p.\n 209-216.\n\n 2. Patrick Doreian and Andrej Mrvar(1996): A Partitioning Approach to\n Structural Balance. Social Networks, 18, p. 149-168.\n\nPajek Data; 15. February 2004\n\n\n================================================================================\n==> Tina/Tina.txt <==\n================================================================================\n\nPajek datasets\nStudent Government of the University of Ljubljana / 1992\n\nDataset Tina\n\nDescription\n\nDisCal.net - network with 11 vertices and 41 arcs\nDisCog.net - network with 11 vertices and 48 arcs\nAskCal.net - network with 11 vertices and 29 arcs\nAskCog.net - network with 11 vertices and 36 arcs\nAnsCalT.net - network with 11 vertices and 41 arcs\nAnsCogT.net - network with 11 vertices and 42 arcs\nDisCalSn.net - network with 11 vertices and 41 arcs\nTina.paj - Pajek project file with complete dataset.\n\n\n\ncomplete dataset (ZIP, 3K)\nDisCalSn.net\nBackground\nIn the experiment two alternative methods were used for collection of network data:\n\n Recall: Members of the group were asked to identify the members of their\n egocentric networks by memory. The criteria for enumeration was frequency\n of the recalled communications.\n\n Recognition: The list of all members of the group was given to each\n member. They were first asked to identify who they communicate with and\n than to select the persons they communicate with most often.\n\nThe number of listed persons was not limited in any method.\n\nThe analyzed network consisted of communication interactions among twelve\nmembers and advisors of the Student Government at the University in Ljubljana.\nThe results of the measurement are not real interactions among actors but\ncognition about communication interactions. Data were collected with face to\nface interviews which lasted from 20 to 30 minutes. Interviews were conducted\nin May 1992.\n\nCommunication flow among actors was identified by three questions:\n\n Who of the members and advisors of the Student government do you (most\n often) informally discuss with?\n\n Which members and advisors of the Student Government do you (most often)\n ask for an opinion?\n\n Which of the members and advisors of the Student Government (most often)\n ask you for an opinion?\n\nThe content of the communication flow was limited to the matters of the Student\nGovernment. The time frame was also defined: questions were referred to the six\nmonths period (from the formation of the government to the day of the\ninterview).\n\nOnly one respondent listed all the others under the recognition method for two\nrelations (discussion, asking for an opinion). For that respondent the first\ngroup was defined arbitrary. The cut point was determined by the average number\nof selected persons at the recognition method (3.5 for the first relation and\n4.5 for the second).\n\nOne respondent refused to cooperate in the experiment. As he was not considered\nin the analysis, the network consists of eleven actors.\n\n\nHistory\n\n\n 1. Transformed in Stran format by V. Batagelj, 28. July 1993.\n 2. Transformed in Pajek format by V. Batagelj, 8. August 2003.\n\nFiles\n\nDisCal.net - discussion, recall\nDisCog.net - discussion, recognition\nAskCal.net - asking for an opinion, recall\nAskCog.net - asking for an opinion, recognition\nAnsCalT.net - being asked for an opinion, recall (transposed)\nAnsCogT.net - being asked for an opinion, recognition (transposed)\nDisCalSn.net - discussion, recall / short names\nTina.paj - Pajek project file with complete dataset.\nReferences\n\n 1. Valentina Hlebec: Recall Versus Recognition: Comparison of the Two\n Alternative Procedures for Collecting Social Network Data. Developments in\n Statistics and Methodology. (A. Ferligoj, A. Kramberger, editors)\n Metodolo\u0161ki zvezki 9, FDV, Ljubljana, 1993, p. 121-129.\n\nPajek Data; 8. August 2003\n\n\n================================================================================\n==> WassermanFaust/WassermanFaust.txt <==\n================================================================================\n\nNOTE: the Wasserman/Faust networks have not been included.\n\n\nPajek datasets\nfrom the book\nSocial Network Analysis: Methods and Applications\nWasserman and Faust, 1994\nWasserman and Faust datasets\n\nDataset WaFa\n\nThere are five network examples: Krackhardt's high-tech managers, Padgett's\nFlorentine families, Freeman's EIES network, Countries trade network, and\nGalaskiewicz's CEOs and clubs network.\n\nDescription\n\nHighTech.paj - multirelational network with 21 vertices and 190+102+20 arcs\nPadgett.paj - multirelational network with 16 vertices and 30+40 arcs\nEIES.paj - multirelational network with 48/32 vertices and 695+830/460 arcs\nTrade.paj - multirelational network with 24 vertices and 307+307+310+135+369\n arcs\nCEOs.net - two-mode network with 41 vertices and 98 edges.\n\n\nBackground\n\nComplete descriptions of these data, including references for the original\nsources of the data, can be found in Chapter 2 (pages 59- 66) and Appendix B\n(pages 738-755) of Wasserman and Faust.\n\nThe original data in ASCII, UCINET and KrackPlot formats are available at\nINSNA. \n\nHistory\n\n 1. INSNA page.\n 2. Transformed in Pajek format by V. Batagelj, 15. January 2005.\n\nFiles\n\nHighTech.paj - Krackhardt's High-tech managers\nPadgett.paj - Padgett's Florentine Families\nEIES.paj - Freeman's EIES network\nTrade.paj - Countries trade data\nCEOs.net - Galaskiewicz's CEOs and clubs.\nReferences\n\n 1. Stanley Wasserman, Katherine Faust: Social Network Analysis: Methods and\n Applications. CUP, 1994.\n\nPajek Data; 15. January 2005\n\n\n================================================================================\n==> Wordnet/Wordnet.txt <==\n================================================================================\n\n\nNOTE: this is a binary graph in the Pajek dataset, but where each edge has a\nlabel (not a weight) in the range 1 to 9. The following labels are used:\n\n1 hypernym pointer\n2 entailment pointer\n3 similar pointer\n4 member meronym pointer\n5 substance meronym pointer\n6 part meronym pointer\n7 cause pointer\n8 grouped pointer\n9 attribute pointer\n\nThis is not a multigraph. There are no edges (i,j) between the same nodes\nwith the same label. Thus, in the sparse matrix, the edge weight A(i,j)\nrepresents the label 1 through 9 of edge (i,j). No loss of information\noccurs in this translation. The above table is in aux.edgecode(1:9,:).\nEach node is a word in a dictionary. aux.category(i) gives the category\nof the word:\n 1: n (noun?) 63099 words\n 2: v (verb?) 4496 words\n 3: a (adjective?) 5501 words\n 4: r (?) 2846 words\n 5: s (?) 6728 words.\n\n\n================================================================================\n==> yeast/yeast.txt <==\n================================================================================\n\nPajek datasets\nProtein-protein interaction network in budding yeast\n\nDataset Yeast\n\nDescription\n\nyeastS.net network with 2361 vertices and 7182 edges (536 loops).\nyeastL.net network with 2361 vertices and 7182 edges (536 loops).\nyeast.clu partition of vertices.\nyeast.paj Pajek project file with complete dataset.\n\n\n\ncomplete dataset (ZIP, 134K)\n\nBackground Interaction detection methods have led to the discovery of thousands\nof interactions between proteins, and discerning relevance within large-scale\ndata sets is important to present-day biology. The dataset consists of\nprotein-protein interaction network described and analyzed in (1) and available\nas an example in the software package - PIN (2).\n\nPIN class encoding:\n1 - T, 2 - M, 3 - U, 4 - C, 5 - F, 6 - P, 7 - G, 8 - D, 9 - O,\n10 - E, 11 - R, 12 - B, 13 - A.\n\nyeastS.net X interacts with Y relation, short names. yeastL.net X interacts\nwith Y relation, long labels. yeast.clu PIN class partition of vertices, see\nencoding. yeast.paj Pajek project file with complete dataset.\n\nReferences\n\n 1. Shiwei Sun, Lunjiang Ling, Nan Zhang, Guojie Li and Runsheng Chen:\n Topological structure analysis of the protein-protein interaction network in\n budding yeast. Nucleic Acids Research, 2003, Vol. 31, No. 9 2443-2450 (PDF).\n\n 2. Software package Protein Interaction Network PIN\n\nTransformed in Pajek format by V. Batagelj, 25. July 2003\n\n\n================================================================================\n==> GD problems <==\n================================================================================\n\nThese graphs are from the Graph Drawing 1995-2006 Contests.\n\nGD97_b:\nNOTE regarding conversion for UF sparse matrix collection: in original the data\nevery edge appears exactly twice, with the same edge weight. It could be a \nmultigraph, but it looks more like a graph. The duplicate edges are removed in\nthis version. You can always add them back in yourself; just look at 2*A." - }, - { - "group": "Barabasi", - "count": 3, - "documented_meaning": "Notre Dame networks, A. Barabasi. ================================================================================", - "source_url": "https://sparse.tamu.edu/Barabasi", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 3, - "normalized_kinds": "bipartite graph (1); directed graph (1); undirected graph (1)", - "curator_value_type_count": 1, - "curator_value_types": "binary (3)", - "curator_structure_count": 3, - "curator_structures": "rectangular (1); unsymmetric (1); symmetric (1)", - "min_dimension": 2114, - "max_dimension": 392400, - "dimension_ratio": 185.61967833491013, - "size_span": "wide (>100\u00d7)", - "min_nnz": 4480, - "max_nnz": 1470404, - "shape_profile": "mixed", - "square": 2, - "rectangular": 1, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 1, - "known_spd": 0, - "value_profile": "real only", - "binary": 3, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Notre Dame networks, A. Barabasi.\n\n================================================================================\n\nThe NotreDame_actors, _www, and _yeast networks were derived from\nthe Pajek versions of the networks. Details below.\n\n================================================================================\n\nPajek data sets, from http://vlado.fmf.uni-lj.si/pub/networks/data/,\nVladimir Batagelj and Andrej Mrvar (2006): Pajek datasets. If the source of\nthe data set is not specified otherwise, these data sets are licensed under a\nCreative Commons Attribution-NonCommercial-ShareAlike 2.5 License.\n\nConverted to sparse adjacency matrix format by Tim Davis, October 2006.\nA(i,j) is the edge from node i to node j in the graph. If a graph is not\nlisted as weighted, and yet has non-binary entries, then the entry a(i,j)\nreflects the number of edges (i,j) in the original data (the graph is a\nmultigraph).\n\nAll data was converted without loss of information, except where intentional.\nDetails are given below.\n\n\"Pajek\" is Slovenian for \"spider\" and is roughly pronounced \"Pie yeck\".\n\nThe Pajek networks are all 1-based (nodes are numbered starting at node 1).\n\n\n================================================================================\nSummary:\n================================================================================\n\nNotreDame_actors:\tBarabasi's actor network (of www.imdb.com)\n\t\t392400-by-127823 with 1470404 nonzeros\n\t\tkind: bipartite multigraph\n\nNotreDame_www:\tBarabasi's web page network of nd.edu\n\t\t325729-by-325729 with 929849 nonzeros\n\t\tkind: directed graph\n\nNotreDame_yeast:\tBarabasi's yeast protein interaction\n\t\t2114-by-2114 with 4480 nonzeros\n\t\tkind: undirected graph\n\n================================================================================\n\nPajek datasets Notre Dame Self-Organized Networks Database\n\nDatasets NDwww, NDactors, NDyeast\n\nDescription\n\nNotre Dame Self-Organized Networks:\n\n 1. NDwww.net directed network with 325729 vertices and 1497135 arcs (27455\nloops); page X is linked to page Y.\n\n 2. NDactors.net undirected two-mode network with 520223 vertices (392400\nplayers, 127823 movies) and 1470418 edges; player X plays in movie Y.\n\n 3. NDyeast.net undirected network with 2114 vertices and 2277 edges (74\nloops); protein X interacts with protein Y.\n\n\n\nNDwww.net (ZIP, 2050K) NDactors.net (ZIP, 4150K) NDyeast.net (ZIP, 7K)\n\nBackground\n\nThe networks ND*.net are based on the files from Notre Dame Self-Organized\nNetworks Database. To transform the data into Pajek format: vertex 0 was\nreplaced by the vertex number equal to the number of vertices in a network;\nPajek keywords were inserted; and the network was saved in the short (as lists\nof neighbors) format.\n\n 1. World-Wide-Web:: Each number represents webpage within nd.edu domain.\nArcs: From page -> To page R\u00e9ka Albert, Hawoong Jeong and Albert-L\u00e1szl\u00f3\nBarab\u00e1si: Diameter of the World Wide Web, Nature 401, 130 (1999) [ PDF ] See\nalso a decompostion of this network in V. Batgelj, A. Mrvar: How to analyze\nlarge networks with Pajek?\n\n 2. Actor: Actor network data: (based on www.imdb.com) In the original ND\nnetwork file: each line corresponds to one movie, each number represents actor:\nnumber_1 number_2 ... number_k (k actors who play in the same movie).\nAlbert-L\u00e1szl\u00f3 Barab\u00e1si, R\u00e9ka Albert: Emergence of scaling in random networks,\nScience 286, 509 (1999) [ PDF ]\n\n 3. Protein Interaction Network for Yeast: Each number represents protein in\nprotein interaction network of yeast. Edges: From protein -> To protein. For\nother datasets used in supplementary material, please refer indicated\nreferences. Hawoong Jeong, Sean Mason, Albert-L\u00e1szl\u00f3 Barab\u00e1si and Zolt\u00e1n N.\nOltvai: Centrality and lethality of protein networks, Nature 411, 41 (2001) [\nPDF ] See also Yeast data\n\nHistory\n\n 1. Notre Dame Networks Database put on WWW by the Notre Dame team, 2001;\n\n 2. 23-25. July 2001: ND nets transformed in Pajek format by V. Batagelj.\n\n 3. 23. May 2004: ND nets in Pajek format transformed in short (lists of\nneighbors) Pajek format by V. Batagelj.\n\nReferences\n\n 1. Self-Organized Networks Database, University of Notre Dame.\n\nCopyright Extract from the Notre Dame Networks Database page: \"... Feel free to\nuse these data in your research.\" Mail to Hawoong Jeong (author of original ND\nnetworks).\n\nPajek Data; 23. May 2004\n\n================================================================================\n\n\nNetwork Databases\n\nThe aim of this page is to give access to the network topologies we have\nstudied. We are in the process of expanding this database. Feel free to use\nthese data in your research.\n\n World-Wide-Web: [README] [DATA]\n R\u00e9ka Albert, Hawoong Jeong and Albert-L\u00e1szl\u00f3 Barab\u00e1si:\n Diameter of the World Wide Web Nature 401, 130 (1999) [ PDF ]\n\n Actor: [README] [DATA]\n Albert-L\u00e1szl\u00f3 Barab\u00e1si, R\u00e9ka Albert:\n Emergence of scaling in random networks Science 286, 509 (1999) [ PDF ]\n\n Celluar Network: [README] [Whole Cellular Network]\n [Metabolic Network Only]\n Hawoong Jeong, B\u00e1lint Tombor, R\u00e9ka Albert,\n Zolt\u00e1n N. Oltvai and Albert-L\u00e1szl\u00f3 Barab\u00e1si:\n The large-scale organization of metabolic networks\n Nature 407, 651 (2000) [ PDF ]\n\n Protein Interaction Network: [README] [DATA for yeast]\n Hawoong Jeong, Sean Mason, Albert-L\u00e1szl\u00f3 Barab\u00e1si and Zolt\u00e1n N. Oltvai:\n Centrality and lethality of protein networks Nature 411, 41 (2001) [ PDF ]\n [ Supplementary Material 1 & 2 ]\n\n Enron Email Dataset\n [ Website ]\n\n Network Dynamic Data will be available soon!" - }, - { - "group": "QCD", - "count": 14, - "documented_meaning": "Quantum Chromodynamics, B. Medeke, Univ. Wuppertal, Germany Source: Bjoern Medeke, Department of Mathematics, Institute of Applied", - "source_url": "https://sparse.tamu.edu/QCD", - "coarse_kind_families_interpretive": "chemistry/biology", - "dominant_coarse_family_interpretive": "chemistry/biology", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "theoretical/quantum chemistry problem (14)", - "curator_value_type_count": 1, - "curator_value_types": "complex (14)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (14)", - "min_dimension": 3072, - "max_dimension": 49152, - "dimension_ratio": 16.0, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 119808, - "max_nnz": 1916928, - "shape_profile": "square only", - "square": 14, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "complex only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Quantum Chromodynamics, B. Medeke, Univ. Wuppertal, Germany\n\n\nSource:\t Bjoern Medeke, Department of Mathematics, Institute of Applied\nComputer Science, University of Wuppertal, 42097 Wuppertal, Germany. Phone: +49\n202 439-3776. Email: medeke at math.uni-wuppertal.de \nDiscipline:\tPhysics\nAccession (in Matrix Market collection): December 2000\n\nBackground. Lattice gauge theory is a discretization of quantum chromodynamics\nwhich is generally accepted to be the fundamental physical theory of strong\ninteractions among the quarks as constituents of matter. The most\ntime-consuming part of a numerical simulation in lattice gauge theory with\nWilson fermions on the lattice is the computation of quark propagators within a\nchromodynamic background gauge field. These computations use up a major part of\nthe world's high performance computing power.\n\nQuark propagators are obtained by solving the inhomogeneous lattice Dirac\nequation Ax = b, where A = I - kD with 0 <= k < kc is a large but sparse\ncomplex non-Hermitian matrix representing a periodic nearest-neighbour coupling\non a four-dimensional Euclidean space-time lattice.\n\nFrom the physical theory it is clear that the matrix A should be positive real\n(all eigenvalues lie in the right half plane) for 0 <= k < kc. Here, kc\nrepresents a critical parameter which depends on the given matrix D. Denoting\n\n\\\\gamma_5 = \\\\left( \\\\begin{array}{cccc}\n 0 & 0 & 1 & 0 \\\\\\\\\n 0 & 0 & 0 & 1 \\\\\\\\\n 1 & 0 & 0 & 0 \\\\\\\\\n 0 & 1 & 0 & 0\n \\\\end{array}\\\\right)\n\n the Wilson fermion matrix A is Gamma-5 symmetric,\n\n\\\\Gamma_5 A = A^H \\\\Gamma_5,\\\\;\\\\;\\\\;\\\\;\\\\Gamma_5 = I \\\\otimes ( \\\\gamma_5 \\\\otimes I_3 )\n\nDue to the nearest neighbour coupling, the matrix A has 'property A'. This\nmeans that with a red-black (or odd-even) ordering of the grid points the\nmatrix becomes\n\nA = I - kD\n\nwith\n\nD = \\\\left( \\\\begin{array}{cc}\n 0 & D_{\\\\rm oe} \\\\\\\\D_{\\\\rm eo} & 0\n \\\\end{array}\\\\right)\n\nSet of QCD Matrices. The QCD matrices provided in the set QCD consist of\nrealistic matrices D generated at different physical temperatures b.\n\nmatrix D b\torder\tnonzeros kc\nconf5.0-00l4x4-1000.mtx\t 5.0\t3072\t119808\t0.20611\nconf5.0-00l4x4-1400.mtx\t 5.0\t3072\t119808\t0.20328\nconf5.0-00l4x4-1800.mtx\t 5.0\t3072\t119808\t0.20265\nconf5.0-00l4x4-2200.mtx\t 5.0\t3072\t119808\t0.20235\nconf5.0-00l4x4-2600.mtx\t 5.0\t3072\t119808\t0.21070\nconf6.0-00l4x4-2000.mtx\t 6.0\t3072\t119808\t0.15968\nconf6.0-00l4x4-3000.mtx\t 6.0\t3072\t119808\t0.16453\nconf5.4-00l8x8-0500.mtx\t 5.4\t49152\t1916928\t0.17865\nconf5.4-00l8x8-1000.mtx\t 5.4\t49152\t1916928\t0.17843\nconf5.4-00l8x8-1500.mtx\t 5.4\t49152\t1916928\t0.17689\nconf5.4-00l8x8-2000.mtx\t 5.4\t49152\t1916928\t0.17835\nconf6.0-00l8x8-2000.mtx\t 6.0\t49152\t1916928\t0.15717\nconf6.0-00l8x8-3000.mtx\t 6.0\t49152\t1916928\t0.15649\nconf6.0-00l8x8-8000.mtx\t 6.0\t49152\t1916928\t0.15623\n\nReferences. A survey of lattice gauge theory is given in\n\n 1. M. Creutz: Quarks, Gluons, and Lattices,\n Cambridge University Press, (1986)\n\n 2. I. Montvay and G. Munster: Quantum Fields on the Lattice,\n Cambridge University Press (1994) \n\nMore background information can be found at various locations on the Web.\nSearch for \"High Energy Physics\" (HEP). A list of HEP Web sites is available at\nCERN (European Laboratory for Particle Physics).\n\nA PostScript version of this information is also available." - }, - { - "group": "Cylshell", - "count": 7, - "documented_meaning": "Finite element analysis of cylindrical shells, R. Kouhia, Helsinki. Set CYLSHELL", - "source_url": "https://sparse.tamu.edu/Cylshell", - "coarse_kind_families_interpretive": "structures/materials", - "dominant_coarse_family_interpretive": "structures/materials", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "structural problem (7)", - "curator_value_type_count": 1, - "curator_value_types": "real (7)", - "curator_structure_count": 1, - "curator_structures": "symmetric (7)", - "min_dimension": 5357, - "max_dimension": 5489, - "dimension_ratio": 1.0246406570841888, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 207123, - "max_nnz": 263351, - "shape_profile": "square only", - "square": 7, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 7, - "structurally_symmetric_square": 7, - "known_spd": 7, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 7, - "cohesion": "coherent family/series", - "official_notes": "Finite element analysis of cylindrical shells, R. Kouhia, Helsinki.\n\nSet CYLSHELL\n\nSource:\t Reijo Kouhia, Helsinki University of Technology, reijo.kouhia at hut.fi\n\nDiscipline:\tStructural mechanics\nAccession (in Matrix Market collection): Summer 1997\n\nThese matrices result from finite element discretization of an octant of a\ncylindrical shell. The ends of the cylinder are free. A summary of the problem\nis given below; further details, including plots of the finite element meshes\ncan be found in a separate description.\n\nNotation: T indicates the Radius to thickness ratio, R/t = 10T; MO indicates\nthe model type: DK = Discrete-Kirchhoff, RM = Reissner-Mindlin; EL indicates\nelement type: T3 = 3-node triangular, Q4 = 4-node quadrilateral, etc.; MX\nindicates the mesh type: M1 = 30x30 uniform, M2 = 100x150 uniform, M3 = graded\nmesh of 1666 triangles (grading near one boundary and one vertex). The naming\nconvention, cTMOELMX, is based on these.\n\nThe finite element is a facet type shell element (3/4 nodes) with drilling\nrotation incorporated by the Hughes-Brezzi technique and using the penalty\nparameter value of G/1000 (regularization parameter) , where G is the shear\nmodulus. In order to improve the coarse mesh accuracy the membrane\ninterpolation is amended by the Allman type quadratic modes linked to the\ndrilling rotation. The bending formulation utilizes the stabilized MITC\ntechnique with the stabilization parameter equal to 0.4.\n\nFor iterative conjugate gradient type solvers, the problem gets harder when the\nradius to thickness ratio R/t increases. For quadrilateral meshes using the\nIC(0) preconditioner, iteration counts are about 100 (for R/t=10) and 180 (for\nR/t=1000) in reaching the relative residual norm of 10-9 depending slightly on\nthe right-hand side vector. For matrices corresponding to triangular meshes the\nnumber of iterations doubles in comparison to quadrilateral ones.\n\nThe matrices are all symmetric positive definite.\n\nThe following estimates of largest and smallest eigenvalues and spectral\ncondition numbers were provided by the author. These were computed using the\nLanczos algorithm from QMRPACK (subroutine DSLAL) with 100 steps.\n\nMatrix\tMax eigenvalue\tMin eigenvalue\tCondition No.\nS1RMQ4M1 6.8743215039276076E+05 3.7969627252353000E-01 1.810479E+06\nS2RMQ4M1 6.8743398166879822E+04 3.8746327712024738E-04 1.774191E+08\nS3RMQ4M1 6.8745248885112433E+03 3.8936812501755643E-07 1.765559E+10\nS1RMT3M1 9.6684220885668334E+05 3.7976650205204426E-01 2.545886E+06\nS2RMT3M1 9.6681138121311058E+04 3.8747124280659988E-04 2.495182E+08\nS3RMT3M1 9.6688590706791765E+03 3.8956266582265278E-07 2.481978E+10\nS3DKQ4M2 4.6016534362482462E+03 2.4268618877322254E-08 1.896133E+11\nS3DKT3M2 8.7984363691286471E+03 2.4269393992599454E-08 3.625322E+11\nS3RMT3M3 9.5986080894852857E+03 3.9983547883056739E-07 2.400639E+10" - }, - { - "group": "POLYFLOW", - "count": 2, - "documented_meaning": "Parasol matrices, collected by Jacko Koster", - "source_url": "https://sparse.tamu.edu/POLYFLOW", - "coarse_kind_families_interpretive": "fluids/transport", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computational fluid dynamics problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (2)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (2)", - "min_dimension": 29957, - "max_dimension": 30412, - "dimension_ratio": 1.015188436759355, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 1793881, - "max_nnz": 1990919, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 1, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 2, - "cohesion": "coherent family/series", - "official_notes": "Parasol matrices, collected by Jacko Koster\nhttp://www.parallab.uib.no/projects/parasol/data\n\n\nProvider: Polyflow, Louvain La Neuve, Belgium (home page)\nContact: Jean-Marie Marchal\n\nname\t description\tformat\torder\t#entries\n\nINVEXTR1 Extrusion. Unsymmetric matrix. RUA\t \n\t order 30,412 #entries 1,793,881\n\nMIXTANK\t Mixing tank. Unsymmetric matrix.\tRUA\n\t order 29,957 #entries 1,995,041\n\nINVEXTR1_NEW Extrusion. Unsymmetric matrix. Modified version of INVEXTR1 to\navoid underflow errors on some machines.\n\nMIXTANK_NEW\tMixing tank. Unsymmetric matrix. Modified version of MIXTANK to\navoid underflow errors on some machines.\n\nLast update: Mon Feb 07 2005" - }, - { - "group": "INPRO", - "count": 1, - "documented_meaning": "Parasol matrices, collected by Jacko Koster", - "source_url": "https://sparse.tamu.edu/INPRO", - "coarse_kind_families_interpretive": "structures/materials", - "dominant_coarse_family_interpretive": "structures/materials", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "structural problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "symmetric (1)", - "min_dimension": 415863, - "max_dimension": 415863, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 19173163, - "max_nnz": 19173163, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 1, - "known_spd": 1, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "Parasol matrices, collected by Jacko Koster\n\nhttp://www.parallab.uib.no/projects/parasol/data\n\nProvider: INPRO, Berlin, Germany (home page)\nContact: Jochen Weiher\n\nname\tdescription\tformat\torder\t#entries\nHOOD\tHood\t\t RSA\t 220,542\t5,494,489\nOILPAN\tOilpan\t\t RSA\t 73,752\t1,835,470\nQUER\t.\t\t RSA\t 59,122\t1,462,811\nSMDOOR\tSmall door\t RSA\t 162,610\t4,036,144\nMSDOOR\tMedium size door RSA\t 415,863\t10,328,399\nLDOOR\tLarge door\t RSA\t 952,203\t23,737,339" - }, - { - "group": "Mittelmann", - "count": 30, - "documented_meaning": "Linear programming problems from H. Mittelmann 12 Jul 2006", - "source_url": "https://sparse.tamu.edu/Mittelmann", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "linear programming problem (30)", - "curator_value_type_count": 2, - "curator_value_types": "integer (20); real (10)", - "curator_structure_count": 1, - "curator_structures": "rectangular (30)", - "min_dimension": 24460, - "max_dimension": 1961394, - "dimension_ratio": 80.18781684382665, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 71264, - "max_nnz": 46168124, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 30, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Linear programming problems from H. Mittelmann\n\n12 Jul 2006\n\n ====================================\n Benchmark of commercial LP solvers\n ====================================\n\nH. Mittelmann (mittelmann@asu.edu)\n\nLogfiles of these runs at: http://plato.asu.edu/ftp/lp_logs/\n\nThis benchmark was run on a Linux-PC (3.2 GHz P4, 4 GB RDRAM, Linux-2.6).\nThe MPS-datafiles for all testcases are in one of (see column \"s\")\n\n http://miplib.zib.de/ [1]\n http://plato.asu.edu/ftp/lptestset/ [2]\n http://www.sztaki.hu/~meszaros/public_ftp/lptestset/\n(NETLIB[3], MISC[4], PROBLEMATIC[5], STOCHLP[6], KENNINGTON[7], INFEAS[8])\n\nNOTE: files in [2-8] need to be expanded with emps in same directory!\n\nB/D/P: barrier/dual/primal simplex\n\nThe following codes were tested:\n\n XP-B/D/P http://www.dashoptimization.com/ (XPRESS-MP-15.20)\n CPLEX-B/D/P http://www.cplex.com/ (ILOG-CPLEX-10.0)\n MOSEK-4.0.0.32 http://www.mosek.com (barrier)\n LOQO-6.06 http://www.princeton.edu/~rvdb/\n LIPSOL linprog in Matlab 7.0\n\nTimes are user times in secs including input and crossover to a feasible basis\nfor all codes except LOQO and LIPSOL. \"$\" without crossover.\n\n=====================================================\ns problem CPLEX-B CPLEX-D/P MOSEK LOQO LIPSOL\n=====================================================\n2 cont1 952 2174/1530 5855 - -\n2 cont11 1983 / 45727 - -\n2 cont4 3505 2929/736 686 - -\n2 cont1_l$ 3171 7102\n2 cont11_l$ f 2081\n1 dano3mip 16 36/23 14 85 14\n4 dbic1 112 116/23 129 156 104\n3 dfl001 14 20/46 12 152 13\n2 fome12 169 141/407 57 485 88\n2 fome13 87 669/1170 88 963 85\n5 gen4 31 2/71 24 33 233\n7 ken-18 11 12/68 21 274 25\n5 l30 f 28/80 3 3 1691\n4 lp22 6 40/72 7 62 9\n4 mod2 10 82/192 20 61 25\n2 neos 109 27/154 167 754 321\n2 neos1 30 808/16 35 153 37\n2 neos2 21 492/29 30 92 29\n2 neos3 201 3208/5915 562 1505 502\n4 nsct2 69 2/2 50 840 131\n4 nug15 77 2912/1278 111 1855 118\n2 nug20 1276 1490 26402 3092\n2 nug08-3rd 1279 2367/ 1925 2004\n2 pds-40 142 37/166 183 15908 252\n2 pds-100 740 160/1586 1951 1673\n3 qap12 10 203/112 15 201 17\n3 qap15 74 2871/1230 107 1978 145\n2 rail4284 239 5235/6476 386 4123 534\n4 rlfprim 4 1/6 5 64 7\n8 self 64 113/100 203 85 3194\n2 sgpf5y6 11 3/2 18 fail 23\n2 spal_004$ 4669 8020 m\n4 stat96v1 390 188/641 47 \n4 stat96v2 >125000 29364/fail 390 \n4 stat96v4 8 396/553 30 \n6 storm-125 18 15/26 62 35 62\n2 storm_1000 319 632/1482 916 1269 802\n1 stp3d 168 1067/11715 196 2347 218\n2 watson_2 49 192/346 66 313 70\n4 world 10 94/237 25 44 28\n=====================================================\n\n\n\nProblem sizes\n\n\nproblem rows columns nonzeros\n===============================================\n cont1 160793 40398 399991\n cont11 160793 80396 439989\n cont4 160793 40398 398399\n cont1_l 1918399 641598 5752001\n cont11_l 1468599 981396 4403001\n dano3mip_lp 3203 13873 79656\n dbic1 43200 183235 1038761 \n dfl001 6072 12230 41873\n fome12 24285 48920 167492\n fome13 48569 97840 334984\n gen4 1538 4297 110174\n ken-18 105128 154699 512719\n l30 2702 15380 64790\n lp22 2959 13434 78994\n mod2 35665 31728 220116\n neos 479120 36786 1084461\n neos1 131582 1892 468094\n neos2 132569 1560 552596\n neos3 512209 6624 1542816\n nsct2 23004 14981 686396\n nug15 6331 22275 110700\n nug20 15240 72600 304800\n nug08-3rd 19728 20448 139008\n pds-40 66845 212859 605678\n pds-100 156244 505360 1390539\n qap12 3193 8856 44244\n qap15 6331 22275 110700\n rail4284 4284 1092610 12372358\n rlfprim 57422 8052 264483\n self 960 7364 1148845\n sgpf5y6 246078 308634 902275\n spal_004 10203 321696 46167908\n stat96v1 5995 197472 588798\n stat96v2 29089 957432 2852184\n stat96v4 3173 62212 490472\n stormG2-125 66186 157496 529317\n stormG2_1000 528186 1259121 4228817\n stp3d 159488 204880 662128\n watson_2 352014 671861 1843716\n world 35511 32734 220748\n===============================================" - }, - { - "group": "Meszaros", - "count": 166, - "documented_meaning": "Linear programming problems from C. Me'sza'ros email: mcsaba at oplab.sztaki.hu", - "source_url": "https://sparse.tamu.edu/Meszaros", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "linear programming problem (166)", - "curator_value_type_count": 3, - "curator_value_types": "real (107); integer (41); binary (18)", - "curator_structure_count": 2, - "curator_structures": "rectangular (165); unsymmetric (1)", - "min_dimension": 16, - "max_dimension": 1113780, - "dimension_ratio": 69611.25, - "size_span": "wide (>100\u00d7)", - "min_nnz": 41, - "max_nnz": 11537419, - "shape_profile": "mixed", - "square": 1, - "rectangular": 165, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 1, - "known_spd": 0, - "value_profile": "real only", - "binary": 18, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Linear programming problems from C. Me'sza'ros\n\nemail: mcsaba at oplab.sztaki.hu\nhttp://www.sztaki.hu/~meszaros/\nhttp://www.sztaki.hu/~meszaros/public_ftp/lptestset\n\nhttp://www.sztaki.hu/~meszaros/bpmpd/\n\nLinear Programming Test Problems\n\nI'm collecting linear programming problems (in MPS format, formulated as\nminimization) for testing. You can find them in the following subdirectories of\nhttp://www.sztaki.hu/~meszaros/public_ftp/lptestset:\n\n * NETLIB, contains the \"usual\" NETLIB LP problems.\n\n * KENNINGTON, contains the \"Kennington\" collection, also available from\n NETLIB.\n\n * INFEAS, infeasible LP problems (most of them are available from NETLIB).\n\n * STOCHLP, deterministic equvivalent of stochastic linear programming\n problems (mostly 2-stage models).\n\n * MISC, in which you can find a number of different LP problems not\n available from NETLIB.\n\n * PROBLEMATIC, in which you can find \"problematical\" problems. These\n problems are, usually the results of wrong modelling. In the README file\n you can find a short explanation about the modelling mistakes.\n\n * QPDATA, where you can find sets of convex quadratic testproblems. See the\n README for more details. NETLIB.\n\nThe problems are compressed in two levels. To uncompress them, you have to\ngunzip the files and then use the empc utility to create the MPS file. Please\nlet you submit me your problems (if you have an interesting one) by sending me\na mail message.\n\nI would like to emphasize my sincerely thanks to those people who helped me\nto extend the MISC collection, namely to\n\n * Prof. G. Mitra at the Brunel University ,\n\n * Prof. T. Terlaky at the University of Technology Delft,\n\n * E.D. Andersen at the Univeristy Odensee." - }, - { - "group": "Gleich", - "count": 10, - "documented_meaning": "Web matrices and wikipedia matrices from David Gleich, Stanford. dgleich at standford dot edu.", - "source_url": "https://sparse.tamu.edu/Gleich", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 2, - "normalized_kinds": "directed graph (7); undirected graph (3)", - "curator_value_type_count": 1, - "curator_value_types": "binary (10)", - "curator_structure_count": 2, - "curator_structures": "unsymmetric (7); symmetric (3)", - "min_dimension": 2642, - "max_dimension": 9845725, - "dimension_ratio": 3726.618092354277, - "size_span": "wide (>100\u00d7)", - "min_nnz": 6606, - "max_nnz": 57156537, - "shape_profile": "square only", - "square": 10, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 3, - "structurally_symmetric_square": 3, - "known_spd": 0, - "value_profile": "real only", - "binary": 10, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Web matrices and wikipedia matrices from David Gleich, Stanford.\ndgleich at standford dot edu.\n\nA(i,j) = 1 if page i links to page j. Node names are provided in separate\nfiles at http://www.cise.ufl.edu/reseach/sparse/aux/Gleich." - }, - { - "group": "UTEP", - "count": 3, - "documented_meaning": "Matrices from Lenka Dubcova, Jakub Cerveny, and Pavel Solin Univ. of Texas at El Paso. From a PDE solver. The fill-in when working on", - "source_url": "https://sparse.tamu.edu/UTEP", - "coarse_kind_families_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "2D/3D problem (3)", - "curator_value_type_count": 1, - "curator_value_types": "real (3)", - "curator_structure_count": 1, - "curator_structures": "symmetric (3)", - "min_dimension": 16129, - "max_dimension": 146689, - "dimension_ratio": 9.094736189472378, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 253009, - "max_nnz": 3636643, - "shape_profile": "square only", - "square": 3, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 3, - "structurally_symmetric_square": 3, - "known_spd": 3, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 3, - "cohesion": "coherent family/series", - "official_notes": "Matrices from Lenka Dubcova, Jakub Cerveny, and Pavel Solin\nUniv. of Texas at El Paso. From a PDE solver. The fill-in when working on\na leading submatrix can be higher than for the whole matrix.\n\nSubsets of interest are the first k rows and columns where:\n\n matrix Dubcova1\tk = 12033\n matrix Dubcova2\tk = 48641\n matrix Dubcova3\tk = 81153\n\n\nDetails below:\n\n\n1 Dubcova1 ==========================================================\n\n---------------- complete matrix:\n name: 'UTEP/Dubcova1'\n title: 'Univ. Texas at El Paso, from a PDE solver'\n id: 1847\n A: [16129x16129 double]\n date: '2007'\n author: 'L. Dubcova, J. Cerveny, and P. Solin'\n ed: 'T. Davis'\n kind: '2D/3D problem'\n\nUMFPACK resid 1.23002e-10, tot time 0.374201 with AMD\nnnz L: 576080 (with AMD), ordering time: 0.035546\nnnz L: 608040 with METIS, ordering time: 0.264172\nUMFPACK resid 1.22938e-10, tot time 0.625973 with METIS\n\n---------------- small subset:\nUMFPACK resid 1.20162e-10, tot time 0.406472 with AMD\nnnz L: 814164 (with AMD), ordering time: 0.024683\nnnz L: 571918 with METIS, ordering time: 0.169995\nUMFPACK resid 1.19592e-10, tot time 0.458673 with METIS\n\n\n2 Dubcova2 ==========================================================\n\n---------------- complete matrix:\n name: 'UTEP/Dubcova2'\n title: 'Univ. Texas at El Paso, from a PDE solver'\n id: 1848\n A: [65025x65025 double]\n date: '2007'\n author: 'L. Dubcova, J. Cerveny, and P. Solin'\n ed: 'T. Davis'\n kind: '2D/3D problem'\n\nUMFPACK resid 1.91486e-09, tot time 1.991 with AMD\nnnz L: 2956869 (with AMD), ordering time: 0.168376\nnnz L: 3018738 with METIS, ordering time: 1.42383\nUMFPACK resid 1.9355e-09, tot time 3.30308 with METIS\n\n---------------- small subset:\nUMFPACK resid 1.89023e-09, tot time 2.54658 with AMD\nnnz L: 4357099 (with AMD), ordering time: 0.121351\nnnz L: 2835807 with METIS, ordering time: 0.92858\nUMFPACK resid 1.87189e-09, tot time 2.48607 with METIS\n\n\n3 Dubcova3 ==========================================================\n\n---------------- complete matrix:\n name: 'UTEP/Dubcova3'\n title: 'Univ. Texas at El Paso, from a PDE solver'\n id: 1849\n A: [146689x146689 double]\n date: '2007'\n author: 'L. Dubcova, J. Cerveny, and P. Solin'\n ed: 'T. Davis'\n kind: '2D/3D problem'\n Zeros: [146689x146689 double]\n\nUMFPACK resid 2.04934e-09, tot time 5.38897 with AMD\nnnz L: 7252162 (with AMD), ordering time: 0.331024\nnnz L: 7539100 with METIS, ordering time: 2.49977\nUMFPACK resid 2.05082e-09, tot time 7.39527 with METIS\n\n---------------- small subset:\nUMFPACK resid 2.04213e-09, tot time 5.87802 with AMD\nnnz L: 10085274 (with AMD), ordering time: 0.195067\nnnz L: 6536588 with METIS, ordering time: 1.11139\nUMFPACK resid 2.03453e-09, tot time 4.6961 with METIS" - }, - { - "group": "BenElechi", - "count": 1, - "documented_meaning": "Matrix from S. Ben Elechi, Arcelor, Inc. with many sparse right-hand sides.", - "source_url": "https://sparse.tamu.edu/BenElechi", - "coarse_kind_families_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "2D/3D problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "symmetric (1)", - "min_dimension": 245874, - "max_dimension": 245874, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 13150496, - "max_nnz": 13150496, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 1, - "known_spd": 1, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "Matrix from S. Ben Elechi, Arcelor, Inc. with many sparse right-hand sides." - }, - { - "group": "Botonakis", - "count": 6, - "documented_meaning": "FEM thermal/mechanical problems from Ioannis Botonakis, Germany. FEM_3D_thermal1 and FEM_3D_thermal2 are", - "source_url": "https://sparse.tamu.edu/Botonakis", - "coarse_kind_families_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "thermal problem (6)", - "curator_value_type_count": 1, - "curator_value_types": "real (6)", - "curator_structure_count": 2, - "curator_structures": "unsymmetric (3); symmetric (3)", - "min_dimension": 17880, - "max_dimension": 204316, - "dimension_ratio": 11.427069351230426, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 430740, - "max_nnz": 3489300, - "shape_profile": "square only", - "square": 6, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 3, - "structurally_symmetric_square": 6, - "known_spd": 3, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 6, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "FEM thermal/mechanical problems from Ioannis Botonakis, Germany.\n\nFEM_3D_thermal1 and FEM_3D_thermal2 are\nFEM 3D nonlinear thermal problems, wth 8-node bricks as volume elements.\n\nThe thermomech_* are the temperature and deformation of a steel cylinder." - }, - { - "group": "Wissgott", - "count": 1, - "documented_meaning": "Parabolic FEM problem from Phillip Wissgott, TU Vienna. A diffusion-convection reaction. The parabolic_fem matrix uses constant", - "source_url": "https://sparse.tamu.edu/Wissgott", - "coarse_kind_families_interpretive": "fluids/transport", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computational fluid dynamics problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "symmetric (1)", - "min_dimension": 525825, - "max_dimension": 525825, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 3674625, - "max_nnz": 3674625, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 1, - "known_spd": 1, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "Parabolic FEM problem from Phillip Wissgott, TU Vienna.\nA diffusion-convection reaction. The parabolic_fem matrix uses constant\nhomogeneous diffusion which leads to a symmetric positive definite matrix." - }, - { - "group": "Watson", - "count": 2, - "documented_meaning": "Chemical reaction simulation matrices from Layne Watson and Jingwei Zhang. Virginia Tech, ltw at cs dot vtu doe yes, and jwzhang at the same domain.", - "source_url": "https://sparse.tamu.edu/Watson", - "coarse_kind_families_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "2D/3D problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (2)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (2)", - "min_dimension": 40401, - "max_dimension": 112211, - "dimension_ratio": 2.7774312517016906, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 201201, - "max_nnz": 748331, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 2, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 2, - "cohesion": "coherent family/series", - "official_notes": "Chemical reaction simulation matrices from Layne Watson and Jingwei Zhang.\nVirginia Tech, ltw at cs dot vtu doe yes, and jwzhang at the same domain.\n\nThe ODE system \\\\frac{dp}{dt}=Qp is what we call a chemical master equation (a \nKolmogorov's backward/forward equation). Q is a sparse asymmetric matrix, \nwhose off-diagonal entries are non-negative and row sum to zero. On each row, \nq_{ij}h gives the probability the system makes a transition from current state\ni to some other state j, in small time interval h. By \"state\", we mean a \npossible combination of number of molecules in each chemical species. Now, h \nis small enough so that only one reaction happens. In this way q_{ij} is \nnonzero only if there exists a chemical reaction that connects state i and j, \ni.e. j=i+s_k, s_k's are constant state vectors that correspond to every \nreaction. Say we have M reactions, then there are at most M+1 nonzero \nentries on each row of Q. On the other hand, the number of possible \ncombination of molecules is huge, which means the dimension of Q is huge. \nThe linear system we want to solve is (I - Q/lambda)x=b, and we have \nto solve it several times. (Here lambda is a constant). Problem.A is the Q \nmatrix." - }, - { - "group": "Sinclair", - "count": 2, - "documented_meaning": "3-D spectral-element elastic wave modelling in freq. domain, C. Sinclair, Univ. Adelaide, Australia.", - "source_url": "https://sparse.tamu.edu/Sinclair", - "coarse_kind_families_interpretive": "structures/materials", - "dominant_coarse_family_interpretive": "structures/materials", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "materials problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "complex (2)", - "curator_structure_count": 1, - "curator_structures": "Hermitian (2)", - "min_dimension": 292008, - "max_dimension": 680943, - "dimension_ratio": 2.331932686775705, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 12935272, - "max_nnz": 30290827, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 2, - "structurally_symmetric_square": 2, - "known_spd": 0, - "value_profile": "complex only", - "binary": 0, - "geometric_2d3d": 2, - "cohesion": "coherent family/series", - "official_notes": "3-D spectral-element elastic wave modelling in freq. domain, C. Sinclair,\nUniv. Adelaide, Australia.\n\ncatherine dot sinclair at adelaide dot edu dot au.\n\n3Dspectralwave:\n\nThe A matrix is produced using 3-D spectral-element elastic wave modelling in\nthe frequency domain. The medium is homogeneous and isotropic with elastic\ncoefficients: c11 = 6.30, c44 = 1.00 The B matrix represents a real\ny-directed source, placed approximately in the centre. The model size in\nelements is 20x20x20. Each element is 1m x1m x 1m. Each element is a 4x4x4\nGauss-Lobbato-Legendre mesh, so the height, width and depth of the system is\n61 nodes. There are 3 unknown components at each node - the x, y and z\ndisplacements. The A matrix therefore has dimension 680943 x 680943, where\n((20 x 4) - (20 - 1))^3 * 3 = 680943. The problem domain is earth sciences.\nNote that A is complex and b is sparse and real (b has a single nonzero).\n\nThe A matrix was provided with a nonzero imaginary part, but was otherwise\ncomplex Hermitian. To save space in the Matrix Market and Rutherford/Boeing\nformats, the A matrix here has had this imaginary diagonal removed. The\nshift can be found in the aux.shift auxiliary matrix. To reproduce the\noriginal A matrix, use A = Problem.A + Problem.aux.shift ;\n\nAdded to the collection in May, 2007, by Tim Davis\n\n--------------------------------------------------------------------------------\n3Dspectralwave2 is a smaller version:\n\nThe A matrix is produced using 3-D spectral-element elastic wave modelling in \nthe frequency domain. The medium is homogeneous and isotropic with \nelastic coefficients: c11 = 6.30, c44 = 1.00 The B matrix \nrepresents a real y-directed source, placed approximately in the centre.\n\nThe model size in elements is 20x20x20. Each element is 1m x1m x 1m. Each \nelement is a 4x4x4 Gauss-Lobbato-Legendre mesh, so the height, width and \ndepth of the system is 61 nodes. There are 3 unknown components at each node \n- the x, y and z displacements. The A matrix therefore has dimension \n680943 x 680943. \n\n((20 x 4) - (20 - 1))^3 * 3 = 680943 \n\nAdded to the collection in June, 2007, by Tim Davis" - }, - { - "group": "QLi", - "count": 3, - "documented_meaning": "Mixed complementarity optimization problems from Qaun Li and Michael Ferris. QLi/crashbasis and QLi/majorbasis have the same nonzero pattern. However,", - "source_url": "https://sparse.tamu.edu/QLi", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "optimization problem (3)", - "curator_value_type_count": 1, - "curator_value_types": "real (3)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (3)", - "min_dimension": 160000, - "max_dimension": 440020, - "dimension_ratio": 2.750125, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 1750416, - "max_nnz": 5240084, - "shape_profile": "square only", - "square": 3, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 1, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Mixed complementarity optimization problems from Qaun Li and Michael Ferris.\n\nQLi/crashbasis and QLi/majorbasis have the same nonzero pattern. However,\nUMFPACK 5.1 is much slower for majorbasis than for crashbasis, because of\nthe extensive amount of denormal arithmetic that occurs during the factorization\nof majorbasis. This does not occur in the crashbasis matrix.\n\nQLi/largebasis added in 2008. The matrix is unsymmetric." - }, - { - "group": "Springer", - "count": 1, - "documented_meaning": "Tim Springer, Solenix (www.solenix.ch), Navigation Support Office, OPS-GN (www.nng.esoc.de), European Space Operations Centre, Germany.", - "source_url": "https://sparse.tamu.edu/Springer", - "coarse_kind_families_interpretive": "other", - "dominant_coarse_family_interpretive": "other", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "least squares problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "rectangular (1)", - "min_dimension": 327062, - "max_dimension": 327062, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 6019939, - "max_nnz": 6019939, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 1, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "single matrix", - "official_notes": "Tim Springer, Solenix (www.solenix.ch), Navigation Support Office,\nOPS-GN (www.nng.esoc.de), European Space Operations Centre, Germany.\n\nOrbit estimates (at the 10mm level) and reference fram (station coordinates)\nat the few mm level. Speed of factorization is important, since this matrix\nis one part of the reprocessing of 15 years of data." - }, - { - "group": "Szczerba", - "count": 1, - "documented_meaning": "Ill-conditioned CFD matrix from D. Szczerba, ETH Computer Vision Lab, Zurich.", - "source_url": "https://sparse.tamu.edu/Szczerba", - "coarse_kind_families_interpretive": "fluids/transport", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computational fluid dynamics problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 20896, - "max_dimension": 20896, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 191368, - "max_nnz": 191368, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "Ill-conditioned CFD matrix from D. Szczerba, ETH Computer Vision Lab, Zurich." - }, - { - "group": "Muite", - "count": 4, - "documented_meaning": "Ill-conditioned Chebyshev integration matrices from Benson Muite, Oxford.", - "source_url": "https://sparse.tamu.edu/Muite", - "coarse_kind_families_interpretive": "structures/materials", - "dominant_coarse_family_interpretive": "structures/materials", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "structural problem (4)", - "curator_value_type_count": 1, - "curator_value_types": "real (4)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (4)", - "min_dimension": 261, - "max_dimension": 68121, - "dimension_ratio": 261.0, - "size_span": "wide (>100\u00d7)", - "min_nnz": 2319, - "max_nnz": 5377761, - "shape_profile": "square only", - "square": 4, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 4, - "cohesion": "coherent family/series", - "official_notes": "Ill-conditioned Chebyshev integration matrices from Benson Muite, Oxford.\nhttp://www.maths.ox.ac.uk/~muite" - }, - { - "group": "Quaglino", - "count": 2, - "documented_meaning": "FEM discretization of a viscoplastic collision problem, Alessio Quaglino.", - "source_url": "https://sparse.tamu.edu/Quaglino", - "coarse_kind_families_interpretive": "structures/materials", - "dominant_coarse_family_interpretive": "structures/materials", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "materials problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (2)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (2)", - "min_dimension": 4326, - "max_dimension": 32769, - "dimension_ratio": 7.574895977808599, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 61166, - "max_nnz": 381326, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 2, - "cohesion": "coherent family/series", - "official_notes": "FEM discretization of a viscoplastic collision problem, Alessio Quaglino." - }, - { - "group": "YCheng", - "count": 3, - "documented_meaning": "Power system state simulation matrices Yunzhi Cheng, UT Arlington. In MATLAB, the solution to x=A\\\\b is desired, but this can be slow in", - "source_url": "https://sparse.tamu.edu/YCheng", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "power network problem (3)", - "curator_value_type_count": 1, - "curator_value_types": "real (3)", - "curator_structure_count": 1, - "curator_structures": "rectangular (3)", - "min_dimension": 14318, - "max_dimension": 28634, - "dimension_ratio": 1.9998603156865484, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 57376, - "max_nnz": 115262, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 3, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Power system state simulation matrices Yunzhi Cheng, UT Arlington.\nIn MATLAB, the solution to x=A\\\\b is desired, but this can be slow in \nMATLAB 7.3 because of the speed of sparse QR as compared to sparse \nCholesky. Using x=(A'*A)\\\\(A'*b) is faster, but of course yields \nslightly less accurate (but still acceptable) results. Note that an \ninitial guess to the solution is provided, for use by an iterative \nmethod. However, sparse Cholesky with an AMD ordering is very fast \nfor this matrix and thus iterative methods are unlikely to be \ncompetitive." - }, - { - "group": "Dehghani", - "count": 1, - "documented_meaning": "Light transport in soft tissue, Hamid Dehghani, Univ. of Exeter, UK", - "source_url": "https://sparse.tamu.edu/Dehghani", - "coarse_kind_families_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "electromagnetics problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "complex (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 29282, - "max_dimension": 29282, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 406084, - "max_nnz": 406084, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 1, - "known_spd": 0, - "value_profile": "complex only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "Light transport in soft tissue, Hamid Dehghani, Univ. of Exeter, UK" - }, - { - "group": "HVDC", - "count": 2, - "documented_meaning": "Power simulation matrices from Alex Wang, Manitoba HVDC, Canada.", - "source_url": "https://sparse.tamu.edu/HVDC", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "power network problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (2)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (2)", - "min_dimension": 24842, - "max_dimension": 189860, - "dimension_ratio": 7.642701875855406, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 158426, - "max_nnz": 1339638, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Power simulation matrices from Alex Wang, Manitoba HVDC, Canada." - }, - { - "group": "Zaoui", - "count": 1, - "documented_meaning": "Optimal power flow, nonlinear optimization (KKT), Fabrice Zaoui, RTE, France", - "source_url": "https://sparse.tamu.edu/Zaoui", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "optimization problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "symmetric (1)", - "min_dimension": 2063494, - "max_dimension": 2063494, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 12771361, - "max_nnz": 12771361, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 1, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "single matrix", - "official_notes": "Optimal power flow, nonlinear optimization (KKT), Fabrice Zaoui, RTE, France" - }, - { - "group": "Rost", - "count": 1, - "documented_meaning": "semiconductor device simulation, Johann Rost, Politechnia Univ., Bucharest. Numerical Methods Laboratory, Politehnica Univ., Bucharest, Romania.", - "source_url": "https://sparse.tamu.edu/Rost", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "semiconductor device problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "complex (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 74104, - "max_dimension": 74104, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 365580, - "max_nnz": 365580, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "complex only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "semiconductor device simulation, Johann Rost, Politechnia Univ., Bucharest.\n\nNumerical Methods Laboratory, Politehnica Univ., Bucharest, Romania.\nJohann Rost, et al. RF semiconductors and electronic device simulation\nin the frequency domain. UMFPACK (x=A\\\\b in MATLAB) experiences high\nfill-in for this matrix. KLU is worse." - }, - { - "group": "Lee", - "count": 2, - "documented_meaning": "FEM, Electromagnetics, Center for Computational Electromagnetics, UIUC From the Univ of Illinois at Urbana-Champaign, Center for Computational", - "source_url": "https://sparse.tamu.edu/Lee", - "coarse_kind_families_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "electromagnetics problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "complex (2)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (2)", - "min_dimension": 74062, - "max_dimension": 491100, - "dimension_ratio": 6.630930841727202, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 1731206, - "max_nnz": 20239237, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 1, - "known_spd": 0, - "value_profile": "complex only", - "binary": 0, - "geometric_2d3d": 2, - "cohesion": "coherent family/series", - "official_notes": "FEM, Electromagnetics, Center for Computational Electromagnetics, UIUC\n\nFrom the Univ of Illinois at Urbana-Champaign, Center for Computational\nElectromagnetics (development and application of the finite element\nmethod for analyzing antennas, high-frequency circuits, high-speed\ncircuits, and so on). The governing equations are Maxwell's equations.\nThe matrix results from the finite-element discretization of a bandpass\nmicrowave filter at 500 MHz. The first-order vector element is employed.\nThe absorbing boundary condition is applied on the outer boundary of the\nstructure for emulating the open space. The port boundary condition is\napplied on each port of the circuit for the truncating the computational\ndomain and exciting the circuit. Due to these boundary conditions, the\nfinite-element system matrix is complex." - }, - { - "group": "Mancktelow", - "count": 1, - "documented_meaning": "FEM viscoelastic behavior of rocks, Neil Mancktelow, ETH-Zentrum", - "source_url": "https://sparse.tamu.edu/Mancktelow", - "coarse_kind_families_interpretive": "structures/materials", - "dominant_coarse_family_interpretive": "structures/materials", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "materials problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 37762, - "max_dimension": 37762, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 1133641, - "max_nnz": 1133641, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 1, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "FEM viscoelastic behavior of rocks, Neil Mancktelow, ETH-Zentrum" - }, - { - "group": "Rudnyi", - "count": 1, - "documented_meaning": "3D fluid flow: water in a tank. Evgenii Rudnyi, IMTEK", - "source_url": "https://sparse.tamu.edu/Rudnyi", - "coarse_kind_families_interpretive": "fluids/transport", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computational fluid dynamics problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 60740, - "max_dimension": 60740, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 2035281, - "max_nnz": 2035281, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "3D fluid flow: water in a tank. Evgenii Rudnyi, IMTEK" - }, - { - "group": "Rucci", - "count": 1, - "documented_meaning": "ill-conditioned least-squares problem, A. Rucci", - "source_url": "https://sparse.tamu.edu/Rucci", - "coarse_kind_families_interpretive": "other", - "dominant_coarse_family_interpretive": "other", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "least squares problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "rectangular (1)", - "min_dimension": 1977885, - "max_dimension": 1977885, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 7791168, - "max_nnz": 7791168, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 1, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "single matrix", - "official_notes": "ill-conditioned least-squares problem, A. Rucci" - }, - { - "group": "McRae", - "count": 2, - "documented_meaning": "circuitscape: circuit theory applied to animal/gene flow. B. McRae, UCSB Landscape ecology problem, using electrical network theory to model", - "source_url": "https://sparse.tamu.edu/McRae", - "coarse_kind_families_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "2D/3D problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (2)", - "curator_structure_count": 1, - "curator_structures": "symmetric (2)", - "min_dimension": 999999, - "max_dimension": 1000000, - "dimension_ratio": 1.000001000001, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 4995991, - "max_nnz": 4996000, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 2, - "structurally_symmetric_square": 2, - "known_spd": 1, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 2, - "cohesion": "coherent family/series", - "official_notes": "circuitscape: circuit theory applied to animal/gene flow. B. McRae, UCSB\n\nLandscape ecology problem, using electrical network theory to model\nanimal movement an gene flow.\n\n Brad McRae\n National Center for Ecological Analysis and Synthesis\n 735 State Street, Suite 300\n Santa Barbara, CA 93101-5504\n Tel: (541) 223-1170\n FAX: (805) 892-2510\n email: mcrae at nceas.ucsb.edu\n http://www.nceas.ucsb.edu/~mcrae/\n\n--------------------------------------------------------------------------------\n\nNOTE (added Feb 9, 2012): The McRae/ecology1 matrix is tagged in the UF Sparse\nMatrix Collection as 'not positive definite', but it appears to be very close\nto being positive definite. See below for more details.\n\n--------------------------------------------------------------------------------\n\nOn 01/09/2012 02:20 PM, Alexander Andrianov wrote:\nDear Professor Davis,\n\nI am trying to test an eigensolver on your sparse matrix collection but am\nhaving difficulty figuring out the \"indefiniteness\" of the following instance:\necology1. It looks like the smallest eigenvalue is so close to the machine\nprecision that running the eigensolver with different settings results in a\nrange of the estimates from 9.2271971e-15 to -4.8936273e-14. Running the dense\neigensolver is not an option due to the problems size (1000000). To make things\nmore interesting, both LDL and (somewhat modified for semi-definite case)\nCholesky factorizations seem to factorize that matrix fine even though Cholesky\nresults in a normalized residual which may vary between 1.0e-11 and 1.e-16\nwhile LDL factorization's normalized residual is around the machine precision.\n\nSo I was wondering if you have some eigenvalue info on that matrix. And more\ngenerally would it be possible for you to include more information about each\nmatrix in your collection such as the eigenvalue range (smallest and largest)\nas well as some conditioning info? That would most certainly help in instance\nselection for test sets for various solvers.\n\nThank you for nay help with this.\n\nBest regards,\nAlexander Andrianov SAS Institute Inc.\n\n--------------------------------------------------------------------------------\n\nFrom: Tim Davis\nSent: Monday, January 09, 2012 2:26 PM\nTo: Alexander Andrianov\nCc: Leslie Foster\nSubject: Re: UF collection matrices info\n\nI've begun to collect the singular values, but so far I have only matrices of\ndimension 25,000 or less (or so). I don't have eigenvalue info for any matrix,\nbut it's something I've considered. I just haven't done it yet.\n\nThe difficulty I would have for this matrix is the same that you have, though.\nI might report -1e-14, or 1e-16, depending on how I compute it.\n\nLes Foster may have more info. You could try our recent spqr_rank pagckage\nto find the rank. It's pretty accurate and doesn't require an SVD.\n\nThanks,\nTim" - }, - { - "group": "NYPA", - "count": 8, - "documented_meaning": "Rank-deficient least squares problems. D. Maragal, New York Power Authority These matrices are both structurally and numerically singular. The numerical", - "source_url": "https://sparse.tamu.edu/NYPA", - "coarse_kind_families_interpretive": "other", - "dominant_coarse_family_interpretive": "other", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "least squares problem (8)", - "curator_value_type_count": 1, - "curator_value_types": "real (8)", - "curator_structure_count": 1, - "curator_structures": "rectangular (8)", - "min_dimension": 32, - "max_dimension": 75077, - "dimension_ratio": 2346.15625, - "size_span": "wide (>100\u00d7)", - "min_nnz": 234, - "max_nnz": 1308415, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 8, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Rank-deficient least squares problems. D. Maragal, New York Power Authority\n\nThese matrices are both structurally and numerically singular. The numerical\nrank is less than the structural rank (rank(A) < sprank(A) in MATLAB).\n\nDeepak Maragal\nNew York Power Authority\nWhite Plains, NY\nemail: Deepak.Maragal at nypa.gov" - }, - { - "group": "Marini", - "count": 1, - "documented_meaning": "Economic Time Series, T. Di Fonzo (Univ Padua) and M. Marini, (ISTAT, Italy) Description of the Sparse Linear System arising from the", - "source_url": "https://sparse.tamu.edu/Marini", - "coarse_kind_families_interpretive": "other", - "dominant_coarse_family_interpretive": "other", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "economic problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "symmetric (1)", - "min_dimension": 7245, - "max_dimension": 7245, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 46142, - "max_nnz": 46142, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 1, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "single matrix", - "official_notes": "Economic Time Series, T. Di Fonzo (Univ Padua) and M. Marini, (ISTAT, Italy)\n\nDescription of the Sparse Linear System arising from the \nReconciliation Problem of a System of Economic Time Series \n\nNotes for Professor Tim Davis \n\nby Tommaso Di Fonzo and Marco Marini \nJuly 3, 2008 \n\nDear Prof. Davis, \nwe have read your message Sparse QR mexFunction on the Matlab Central \nwebsite, in which you invite the community to provide sparse problems \narising from real applications for testing a new sparse QR function. In \nparticular, you seem particularly interested in rank deficient problems. We \nhave a problem with such characteristics, so we have decided to upload in the \nCISE website the elements of this linear system (coefficient matrix and right-\nhand side). This problem is generally known as the reconciliation problem \nin the statistics and economics literature, which we intend to describe you \nvery briefly in this note for your information. \n\nEconomic statistics are often published in the form of time series, as a collec\ntion of observations sampled at equally-spaced time periods (months, quar\nters). Economic concepts behind such statistics are often linked by a system \nof linear relationships, deriving from the economic theory. However, these \nrestrictions are rarely met by the original time series for various reasons. \nThen, data sets of real-world variables generally show discrepancies with \nrespect to prior restrictions on their values. Such discrepancies are rarely \naccepted because their existence generally causes confusion among users and \nembarrassment to data producers. The adjustment of a set of data in order \nto satisfy a number of accounting restrictions -and thus to remove any dis\ncrepancy -is generally known as the reconciliation problem. \n\nThe reconciliation problem can be seen as the solution of a linear system. \nThe dimension of the system increases rapidly as the number of time pe\nriods, the number of variables and the number of constraints grow. Since \nit is convenient to solve the system simultaneously (that is, considering all \nperiods, all variables, all constraints), the use of sparse matrices and algo\nrithms is strongly suggested for solving practical applications. However, as \nfar as we know, such problems are generally solved by data producers by \nusing dense matrices and/or iterative methods (i.e. the conjugate gradi\nent method). Our intention is to encourage the use of direct methods with \nsparse matrices for solving this kind of problems. \n\nA formalization of the problem is helpful to understand the features of this \nlinear system. Let p be an n-dimensional vector containing all the obser-\nvations of the system to be reconciled (where n is the number of variables \nmultiplied by the number of periods in which such variables are observed). \nLet H be the matrix defining the relationships between such observations. \nSupposing there are k constraints, the matrix H has dimension k \u00d7 n: for \neach constraint we have a known total that must be satisfied by the obser\nvations, collected together in the k-dimensional vector w. Notice that (i) \nk 5.9e7).\n\nSo in my problem, I consider\nK = span is the kernel of A, and\nL = span = orthogonal(K) is isomorph to Im(A).\n\nThe rank deficient is not quite clear since of A is a Jacobian of \ncertain non-linear operator. One can prove this non-linear operator has \na same value on a manifold of dimension 7. This manifold has some sort \nof curvature.\n\nBest regards,\n\nBruno" - }, - { - "group": "YZhou", - "count": 1, - "documented_meaning": "Circuit simulation problem, Y. Zhou, Carleton Univ, Canada", - "source_url": "https://sparse.tamu.edu/YZhou", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "circuit simulation problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 1020, - "max_dimension": 1020, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 5883, - "max_nnz": 5883, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "single matrix", - "official_notes": "Circuit simulation problem, Y. Zhou, Carleton Univ, Canada" - }, - { - "group": "CEMW", - "count": 4, - "documented_meaning": "Electromagnetic problems from D. Isaak, Computational_EM_Works - cemw_vfem is a vector finite element complex symmetric matrix with 93,476", - "source_url": "https://sparse.tamu.edu/CEMW", - "coarse_kind_families_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "electromagnetics problem (4)", - "curator_value_type_count": 3, - "curator_value_types": "real (2); integer (1); complex (1)", - "curator_structure_count": 2, - "curator_structures": "unsymmetric (3); symmetric (1)", - "min_dimension": 93476, - "max_dimension": 921632, - "dimension_ratio": 9.85955753348453, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 1434636, - "max_nnz": 5080961, - "shape_profile": "square only", - "square": 4, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 3, - "known_spd": 1, - "value_profile": "real + complex", - "binary": 0, - "geometric_2d3d": 4, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Electromagnetic problems from D. Isaak, Computational_EM_Works\n\n - cemw_vfem is a vector finite element complex symmetric matrix with 93,476\n unknowns and 1,434,636 nonzeros.\n\n - cemw_t2em.mat is a T2EM real symmetric matrix with 921,632 unknowns and\n 6,442,778 nonzeros.\n\n - cemw_tmt_sym.mat is a TmT real symmetric matrix with 726,713 unknowns and\n 5,080,961 nonzeros.\n\n - cemw_tmt_nosym.mat is a TmT real non-symmetric matrix with 917,825 unknowns\n and 4,584,801 nonzeros." - }, - { - "group": "Schenk", - "count": 5, - "documented_meaning": "Symmetric indefinite KKT matrices, O. Schenk, Univ. of Basel, Switzerland Nonlinear programming problems for a 3D PDE-constrained optimization", - "source_url": "https://sparse.tamu.edu/Schenk", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "optimization problem (5)", - "curator_value_type_count": 1, - "curator_value_types": "real (5)", - "curator_structure_count": 1, - "curator_structures": "symmetric (5)", - "min_dimension": 1062400, - "max_dimension": 27993600, - "dimension_ratio": 26.349397590361445, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 28192672, - "max_nnz": 760648352, - "shape_profile": "square only", - "square": 5, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 5, - "structurally_symmetric_square": 5, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Symmetric indefinite KKT matrices, O. Schenk, Univ. of Basel, Switzerland\nNonlinear programming problems for a 3D PDE-constrained optimization\nproblem with boundary control as a function of the discretization\nparameter N using 2nd-order finite difference approximations.\n\nO. Schenk, A. W\\\\\"achter, and M. Weiser, Inertia Revealing\nPreconditioning For Large-Scale Nonconvex Constrained Optimization,\nTechnical Report, Unversity of Basel, 2008, submitted. \n\nAbstract: Fast nonlinear programming methods following the\nall-at-once approach usually employ Newton's method for solving\nlinearized Karush-Kuhn-Tucker (KKT) systems. In nonconvex problems,\nthe Newton direction is only guaranteed to be a descent direction if\nthe Hessian of the Lagrange function is positive definite on the\nnullspace of the active constraints, otherwise some modifications to\nNewton's method are necessary. This condition can be verified using\nthe signs of the KKT's eigenvalues (inertia), which are usually\navailable from direct solvers for the arising linear saddle point\nproblems. Iterative solvers are mandatory for very large-scale\nproblems, but in general do not provide the inertia. Here we present\na preconditioner based on a multilevel incomplete LBL^T\nfactorization, from which an approximation of the inertia can be\nobtained. The suitability of the heuristics for application in\noptimization methods is verified on an interior point method applied\nto the CUTE and COPS test problems, on large-scale 3D PDE-constrained\noptimal control problems, as well as 3D PDE-constrained optimization\nin biomedical cancer hyperthermia treatment planning. The efficiency\nof the preconditioner is demonstrated on convex and nonconvex\nproblems with 1503 state variables and 1502 control variables, both\nsubject to bound constraints.\n\nNote that the 3 largest matrices (nlpkkt160, nlpkkt200, and nlpkkt240)\nrequire MATLAB 7.3 or later to read them." - }, - { - "group": "TKK", - "count": 12, - "documented_meaning": "Matrices from Reijo Kouhia, Structural Mechanics, Helsinki Univ. of Technology (Teknillinen Korkeakoulo)", - "source_url": "https://sparse.tamu.edu/TKK", - "coarse_kind_families_interpretive": "structures/materials", - "dominant_coarse_family_interpretive": "structures/materials", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "structural problem (12)", - "curator_value_type_count": 3, - "curator_value_types": "real (5); integer (4); binary (3)", - "curator_structure_count": 2, - "curator_structures": "symmetric (10); unsymmetric (2)", - "min_dimension": 1282, - "max_dimension": 143571, - "dimension_ratio": 111.98985959438377, - "size_span": "wide (>100\u00d7)", - "min_nnz": 30644, - "max_nnz": 4706073, - "shape_profile": "square only", - "square": 12, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 10, - "structurally_symmetric_square": 12, - "known_spd": 3, - "value_profile": "real only", - "binary": 3, - "geometric_2d3d": 12, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Matrices from Reijo Kouhia, Structural Mechanics, Helsinki Univ. of Technology\n(Teknillinen Korkeakoulo)\n\nhttp://users.tkk.fi/~kouhia/sparse.html\n\nSparse matrix test problems\nThese matrices are stored either in the compressed column format (Harwell-Boeing format, see I. Duff et al.: Sparse Matrix Test Problems, ACM Transactions on Mathematical Software, Vol. 15 No. 1, March 1989, 1-14) or in the MatrixMarket coordinate format (R.F. Boisvert et al.: The Matrix Market Exchange Formats: Initial Design, NISTIR 5935, 1996). Description of both formats can be found from MatrixMarket.\n\n * Cylindrical shell problems: Short description (html), and slightly more\n informative documentation (gzipped postcript-file cylshell.ps.gz, size\n 205 k).\n\n The following matrices can be found from the MatrixMarket in the set CYLSHELL\n\n o Cyl shell, R/t = 10, unif 30 x 30 quadrilateral mesh, N = 5489,\n NZ = 143300, triangular mesh NZ = 112505,\n o Cyl shell, R/t = 100, unif 30 x 30 quadrilateral mesh, N = 5489,\n NZ = 143300, triangular mesh NZ = 112505,\n o Cyl shell, R/t = 1000, unif 30 x 30 quadrilateral mesh, N = 5489,\n NZ = 143300, triangular mesh NZ = 112505\n o Cyl shell, R/t = 1000, unif 100 x 150 quadrilateral mesh, N = 90449,\n NZ = 2455670, triangular mesh NZ = 1921955\n o Cyl shell graded mesh with 1666 triangles R/t = 1000, N = 5357,\n NZ = 106526,\n\nMatrices in this group:\n\n Kouhia/s4dkt3m2\n Kouhia/g3rmt3m3\n Kouhia/t520\n Kouhia/smt\n Kouhia/engine\n Kouhia/plbuckle\n Kouhia/cbuckle\n Kouhia/cyl6\n Kouhia/tube1\n Kouhia/tube2\n Kouhia/t2d_q4\n Kouhia/t2d_q9\n\nMatrices from Reijo Kouhia in other matrix groups:\n\n Cylshell/s1rmq4m1\n Cylshell/s2rmq4m1\n Cylshell/s3rmq4m1\n Cylshell/s1rmt3m1\n Cylshell/s2rmt3m1\n Cylshell/s3rmt3m1\n Cylshell/s3rmt3m3\n GHS_psdef/s3dkq4m2\n GHS_psdef/s3dkt3m2\n\n\n--------------------------------------------------------------------------------\ns4dkt3m2\n--------------------------------------------------------------------------------\n\n Some additional cases not in MatrixMarket:\n o Cyl shell R/t = 10000, unif 100 x 150 triangular mesh,\n s4dkt3m2.dat.gz , N = 90449, NZ = 1921955\n\n--------------------------------------------------------------------------------\ng3rmt3m3\n--------------------------------------------------------------------------------\n\n o g3rmt3m3.dat.gz \n\n--------------------------------------------------------------------------------\nt520.hb.gz, t520.load.gz\n--------------------------------------------------------------------------------\n\n * T-beam: t520.hb.gz , load vector, t520.load.gz ,\n\n--------------------------------------------------------------------------------\nsmt.hb.gz, smt.mtx.gz\n--------------------------------------------------------------------------------\n\n * 3-D solid model from a thermal stress analysis of a surface mounted\n transistor, N = 25710, NZ = 1889447,\n 1704 triquadratic Serendipity 20 node brick elements, four different\n materials. HB-format file smt.hb.gz, MM-format file smt.mtx.gz,\n figure of the FE-model: smt.ps.gz\n\n--------------------------------------------------------------------------------\nengine.hb.gz, engine.rhs.gz\n--------------------------------------------------------------------------------\n\n * 3-D solid model from a thermal stress analysis of an engine head: matrix\n in HB-format: engine.hb.gz, rhs-vector: engine.rhs.gz\n\n--------------------------------------------------------------------------------\neigen/plb_k, eigen/plb_g -> renamed plbuckle\n--------------------------------------------------------------------------------\n\n * Double eigenvalue plate buckling problem: stiffness matrix and initial\n stress matrix . Symmetric matrices, upper triangle is stored in coordinate\n format.\n\n--------------------------------------------------------------------------------\nck.dat, cg.dat -> renamed cbuckle\n--------------------------------------------------------------------------------\n\n * Compressed cylindrical shell -- buckling problem: stiffness matrix and\n initial stress matrix . Symmetric matrices, lower triangle is stored in\n coordinate format N = 13681.\n\n--------------------------------------------------------------------------------\ncyl6, v_6_[0,1,2]\n--------------------------------------------------------------------------------\n\n * Cylindrical shell, from non-linear analysis, indefinite: HB-format file\n cyl6.dat.gz Symmetric matrix N = 13681, NZ = 363961.\n Some rhs vectors at step 6 v_6_0.rhs, v_6_1.rhs, v_6_2.rhs\n\n--------------------------------------------------------------------------------\ntube*\n--------------------------------------------------------------------------------\n\n * Tube, SPD matrix: HB-format file tube1.dat.gz Symmetric matrix N = 21498,\n NZ = 459277. HB-format file tube1-2.dat.gz Symmetric matrix N = 21498,\n NZ = 459277, load vector tube1-2.load.gz\n\n--------------------------------------------------------------------------------\nt2d_q4\n--------------------------------------------------------------------------------\n\n * Non-linear diffusion problem, sequence of matrices and rhs-vectors. These\n matrices are in the compressed sparse row format (CSR). 100x100 bilinear\n mesh (41 MB)\n\n--------------------------------------------------------------------------------\nt2d_q9\n--------------------------------------------------------------------------------\n\n * Non-linear diffusion problem, sequence of matrices and rhs-vectors. These\n matrices are in the compressed sparse row format (CSR)\n 50x50 biquadratic mesh (87 MB). Temperature field. \n\n\nThe following data was not imported into the UF Sparse Matrix Collection:\n\n--------------------------------------------------------------------------------\nmesh2\n--------------------------------------------------------------------------------\n\n Node-unknown data for mesh 2 (100 x 150 element mesh)" - }, - { - "group": "Um", - "count": 2, - "documented_meaning": "FEM, electromagnetics, 2 cubes in a sphere. Evan Um, Geophysics, Stanford A matrix from Evan Um, Geophysics, Stanford. Studying finite-element", - "source_url": "https://sparse.tamu.edu/Um", - "coarse_kind_families_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "electromagnetics problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (2)", - "curator_structure_count": 1, - "curator_structures": "symmetric (2)", - "min_dimension": 101492, - "max_dimension": 259789, - "dimension_ratio": 2.559699286643282, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 1647264, - "max_nnz": 4242673, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 2, - "structurally_symmetric_square": 2, - "known_spd": 2, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 2, - "cohesion": "coherent family/series", - "official_notes": "FEM, electromagnetics, 2 cubes in a sphere. Evan Um, Geophysics, Stanford\n\nA matrix from Evan Um, Geophysics, Stanford. Studying finite-element\ntime domain solvers for electromagnetic diffusion equations. The 3-D\ncomputational domain consists of 88,213 tetrahedral elements. The\ncomputational domain consists of the two parts. First, there are two\n300m x 300m x 150m boxes where a fine mesh is used. Second, the two\nboxes are enclosed by a large sphere whose radius is 10 km. An element\ngrowth factor is used to increase the mesh size gradually inside the\nsphere. This is because absorbing boundary conditions are not very\ngood choices for these problems. The finite element technique is\nedge-based rather than node-based. Therefore, the unknowns are\namplitudes of electromagnetic fields on an edge of each element." - }, - { - "group": "JGD_BIBD", - "count": 21, - "documented_meaning": "Balanced Incomplete Block Design from Mark Giesbrecht, Univ. of Waterloo From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,", - "source_url": "https://sparse.tamu.edu/JGD_BIBD", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "combinatorial problem (21)", - "curator_value_type_count": 1, - "curator_value_types": "binary (21)", - "curator_structure_count": 2, - "curator_structures": "rectangular (20); symmetric (1)", - "min_dimension": 84, - "max_dimension": 319770, - "dimension_ratio": 3806.785714285714, - "size_span": "wide (>100\u00d7)", - "min_nnz": 252, - "max_nnz": 8953560, - "shape_profile": "mixed", - "square": 1, - "rectangular": 20, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 1, - "known_spd": 1, - "value_profile": "real only", - "binary": 21, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Balanced Incomplete Block Design from Mark Giesbrecht, Univ. of Waterloo\nFrom Jean-Guillaume Dumas' Sparse Integer Matrix Collection,\nhttp://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html" - }, - { - "group": "JGD_CAG", - "count": 3, - "documented_meaning": "CAG matrix set from Michael Monagan, Simon Fraser Univ., Canada From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,", - "source_url": "https://sparse.tamu.edu/JGD_CAG", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "combinatorial problem (3)", - "curator_value_type_count": 1, - "curator_value_types": "integer (3)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (3)", - "min_dimension": 72, - "max_dimension": 1916, - "dimension_ratio": 26.61111111111111, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 1012, - "max_nnz": 195985, - "shape_profile": "square only", - "square": 3, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "CAG matrix set from Michael Monagan, Simon Fraser Univ., Canada\nFrom Jean-Guillaume Dumas' Sparse Integer Matrix Collection,\nhttp://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html\n\nStrongly Connected Graph Components and Computing\nCharacteristic Polynomials of Integer Matrices in Maple,\nSimon Lo, Michael Monagan, Allan Wittkopf\n{sclo,mmonagan,wittkopf} at cecm.sfu.ca\nCentre for Experimental and Constructive Mathematics,\nDepartment of Mathematics, Simon Fraser University,\nBurnaby, B.C., V5A 1S6, Canada.\n\nabstract:\nLet A be an n x n matrix of integers. We present details of our Maple\nimplementation of a simple modular method for computing the\ncharacteristic polynomial of A. We consider several different\nrepresentations for the computation modulo primes, in particular, the\nuse of double precision floats. The algorithm used in Maple releases\n7-10 is the Berkowitz algorithm. We present some timings comparing the\ntwo algorithms on a sequence of matrices arising from an application in\ncombinatorics of Jocelyn Quaintance. These matrices have a hidden block\nstructure. Once identified, we can further reduce the computing time\ndramatically. This work has been incorporated into Maple 11's\nLinearAlgebra package.\n\nhttp://www.cecm.sfu.ca/~monaganm/papers/CP8.pdf" - }, - { - "group": "JGD_Forest", - "count": 10, - "documented_meaning": "Forests and Trees from Nicolas Thiery From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,", - "source_url": "https://sparse.tamu.edu/JGD_Forest", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "combinatorial problem (10)", - "curator_value_type_count": 1, - "curator_value_types": "integer (10)", - "curator_structure_count": 1, - "curator_structures": "rectangular (10)", - "min_dimension": 107, - "max_dimension": 317955, - "dimension_ratio": 2971.542056074766, - "size_span": "wide (>100\u00d7)", - "min_nnz": 622, - "max_nnz": 4370721, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 10, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Forests and Trees from Nicolas Thiery\nFrom Jean-Guillaume Dumas' Sparse Integer Matrix Collection,\nhttp://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html\n\nTF10 : rank = 99 , 0.0263529 s, Gauss: 0.00382595 \nTF11 : rank = 216 , 0.136469 s, Gauss: 0.0256469\nTF12 : rank = 488 , 0.83511 s, Gauss: 0.295295\nTF13 : rank = 1121 , 6.06873 s, Gauss: 4.97585\nTF14 : rank = 2644 , 36.9781 s, Gauss: 81.5788\nTF15 : rank = 6334 , 229.029 s, Gauss: 1309.48\nTF16 : rank = 15437 , 1442.08 s, Gauss: 36000\nTF17 : rank = 38132 , 9706.03 s, Gauss:\n\nhttp://www.lapcs.univ-lyon1.fr/~nthiery" - }, - { - "group": "JGD_Franz", - "count": 11, - "documented_meaning": "Cohomology of various rings, from Matthias Franz, Univ. Konstanz, Germany From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,", - "source_url": "https://sparse.tamu.edu/JGD_Franz", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "combinatorial problem (11)", - "curator_value_type_count": 1, - "curator_value_types": "integer (11)", - "curator_structure_count": 1, - "curator_structures": "rectangular (11)", - "min_dimension": 2240, - "max_dimension": 47104, - "dimension_ratio": 21.02857142857143, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 5120, - "max_nnz": 329728, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 11, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Cohomology of various rings, from Matthias Franz, Univ. Konstanz, Germany\nFrom Jean-Guillaume Dumas' Sparse Integer Matrix Collection,\nhttp://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html" - }, - { - "group": "JGD_G5", - "count": 13, - "documented_meaning": "Decomposable subspaces at degree d of the invariant ring of G5, Nicolas Thiery. Univ. Paris Sud.", - "source_url": "https://sparse.tamu.edu/JGD_G5", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "combinatorial problem (13)", - "curator_value_type_count": 1, - "curator_value_types": "integer (13)", - "curator_structure_count": 1, - "curator_structures": "rectangular (13)", - "min_dimension": 77, - "max_dimension": 47894, - "dimension_ratio": 622.0, - "size_span": "wide (>100\u00d7)", - "min_nnz": 251, - "max_nnz": 1790490, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 13, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Decomposable subspaces at degree d of the invariant ring of G5, Nicolas Thiery.\nUniv. Paris Sud.\n\nFrom Jean-Guillaume Dumas' Sparse Integer Matrix Collection,\nhttp://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html\n\nhttp://www.lapcs.univ-lyon1.fr/~nthiery/LinearAlgebra/\n\nLinear Algebra for combinatorics\n\nAbstract: Computations in algebraic combinatorics often boils down to\nsparse linear algebra over some exact field. Such computations are\nusually done in high level computer algebra systems like MuPAD or\nMaple, which are reasonnably efficient when the ground field requires\nsymbolic computations. However, when the ground field is, say Q or\nZ/pZ, the use of external specialized libraries becomes necessary. This\ndocument, geared toward developpers of such libraries, present a brief\noverview of my needs, which seems to be fairly typical in the\ncommunity.\n\nIG5-6: 30 x 77 : rang = 30 (Iteratif: 0.01 s, Gauss: 0.01 s)\nIG5-7: 62 x 150 : rang = 62 (Iteratif: 0.02 s, Gauss: 0.01 s)\nIG5-8: 156 x 292 : rang = 154 (Iteratif: 0.08 s, Gauss: 0.01 s)\nIG5-9: 342 x 540 : rang = 308 (Iteratif: 0.46 s, Gauss: 0.02 s)\nIG5-10: 652 x 976 : rang = 527 (Iteratif: 2.1 s, Gauss: 0.07 s)\nIG5-11: 1227 x 1692 : rang = 902 (Iteratif: 7.5 s, Gauss: 0.22 s)\nIG5-12: 2296 x 2875 : rang = 1578 (Iteratif: 26 s, Gauss: 0.93 s)\nIG5-13: 3994 x 4731 : rang = 2532 (Iteratif: 80 s, Gauss: 3.35 s)\nIG5-14: 6727 x 7621 : rang = 3906 (Iteratif: 244 s, Gauss: 10.06 s)\nIG5-15: 11358 x 11987 : rang = 6146 (Iteratif: s, Gauss: 29.74 s)\nIG5-16: 18485 x 18829 : rang = 9519 (Iteratif: s, Gauss: 621.97 s)\nIG5-17: 27944 x 30131 : rang = 14060 (Iteratif: s, Gauss: 1973.8 s)" - }, - { - "group": "JGD_GL6", - "count": 5, - "documented_meaning": "Differentials of the Voronoi complex of perfect forms of rank 6 mod GL_6(Z), from Philippe Elbaz-Vincent, Institut Fourier, Grenoble, France.", - "source_url": "https://sparse.tamu.edu/JGD_GL6", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "combinatorial problem (5)", - "curator_value_type_count": 1, - "curator_value_types": "integer (5)", - "curator_structure_count": 1, - "curator_structures": "rectangular (5)", - "min_dimension": 341, - "max_dimension": 637, - "dimension_ratio": 1.8680351906158357, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 2053, - "max_nnz": 6153, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 5, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Differentials of the Voronoi complex of perfect forms of rank 6 mod GL_6(Z),\nfrom Philippe Elbaz-Vincent, Institut Fourier, Grenoble, France.\n\nFrom Jean-Guillaume Dumas' Sparse Integer Matrix Collection,\nhttp://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html\n\nhttp://www-fourier.ujf-grenoble.fr/-Informations-personnelles-.html?P=pev\n\nD_6 Smith Invariants = [ 1:156 ]\nD_7 Smith Invariants = [ 1:307 2:3 60:2 ]\nD_8 Smith Invariants = [ 1:320 2:1 6:2 12:1 ]\nD_9 Smith Invariants = [ 1:217 2:3 ]\nD_10 Smith Invariants = [ 1:120 ]" - }, - { - "group": "JGD_GL7d", - "count": 17, - "documented_meaning": "Differentials of the Voronoi complex of perfect forms of rank 7 mod GL_7(Z) equivalences, (related to the cohomology of GL_7(Z) and the K-theory of Z).", - "source_url": "https://sparse.tamu.edu/JGD_GL7d", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "combinatorial problem (17)", - "curator_value_type_count": 1, - "curator_value_types": "integer (17)", - "curator_structure_count": 1, - "curator_structures": "rectangular (17)", - "min_dimension": 60, - "max_dimension": 1955309, - "dimension_ratio": 32588.483333333334, - "size_span": "wide (>100\u00d7)", - "min_nnz": 8, - "max_nnz": 37322725, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 17, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Differentials of the Voronoi complex of perfect forms of rank 7 mod GL_7(Z)\nequivalences, (related to the cohomology of GL_7(Z) and the K-theory of Z).\nfrom Philippe Elbaz-Vincent, Institut Fourier, Grenoble, France.\n\nFrom Jean-Guillaume Dumas' Sparse Integer Matrix Collection,\nhttp://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html\n\nhttp://www-fourier.ujf-grenoble.fr/-Informations-personnelles-.html?P=pev\n\nmtx rank n m ker rank/min(n,m) homology\n10 1 60 1 59 \n11 59 1019 60 960 0,98333 0 \n12 960 8899 1019 7939 0,94210 1 \n13 7938 47271 8899 39333 0,89201 1 \n14 39332 171375 47271 132043 0,83205 0 \n15 132043 460261 171375 328218 0,77049 0 \n16 328218 955128 460261 626910 0,71311 0 \n17 626910 1548650 955128 921740 0,65636 0* \n18 921740* 1955309 1548650 1033569* 0,60* 1/0* \n19 103356(8/9)* 1911130 1955309 87756(2/1)* 0,54* 0/1* \n20 877562 1437547 1911130 559985 0,61 0 \n21 559985 822922 1437547 262937 0,68048 0 \n22 262937 349443 822922 86506 0,75245 0 \n23 86505 105054 349443 18549 0,82343 1 \n24 18549 21074 105054 2525 0,88018 0 \n25 2525 2798 21074 273 0,90243 0 \n26 273 305 2798 32 0,89508 0 \n\nfile size elements rank SF\nGL7d10 1 x 60 8 1 1 (1)\nGL7d11 60 x 1019 1513 59 1 (59)\nGL7d12 1019 x 8899 37519 960 1 (958), 2 (2)\nGL7d13 8899 x 47271 356232 7938 1 (7937), 2 (1)\nGL7d14 47271 x 171375 1831183 39332 1 (39300),2 (29),4 (3)\nGL7d15 171375 x 460261 6080381 132043 1 (131993), 2*??? (46), 6*??? (4)\nGL7d16 955128 x 460261 14488881 328218\nGL7d17 1548650 x 955128 25978098\nGL7d18 1955309 x 1548650 35590540\nGL7d19 1911130 x 1955309 37322725\nGL7d20 1437547 x 1911130 29893084 877562\nGL7d21 822922 x 1437547 18174775 559985\nGL7d22 349443 x 822922 8251000 262937\nGL7d23 105054 x 349443 2695430 86505 1 (86488), 2*??? (12), 6*??? (5)\nGL7d24 21074 x 105054 593892 18549 1 (18544),2 (4),4 (1)\nGL7d25 21074 x 2798 81671 2525 1 (2507), 2 (18)\nGL7d26 2798 x 305 7412 273 1 (258), 2 (7), 6 (7), 36 (1)" - }, - { - "group": "JGD_Groebner", - "count": 8, - "documented_meaning": "Gro\"bner basis from Jean-Charles Fauge`res, From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,", - "source_url": "https://sparse.tamu.edu/JGD_Groebner", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "combinatorial problem (8)", - "curator_value_type_count": 2, - "curator_value_types": "integer (7); binary (1)", - "curator_structure_count": 1, - "curator_structures": "rectangular (8)", - "min_dimension": 404, - "max_dimension": 5761, - "dimension_ratio": 14.259900990099009, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 15118, - "max_nnz": 2462970, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 8, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 1, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Gro\"bner basis from Jean-Charles Fauge`res, \nFrom Jean-Guillaume Dumas' Sparse Integer Matrix Collection,\nhttp://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html\n\nhttp://www-calfor.lip6.fr/~jcf/" - }, - { - "group": "JGD_Homology", - "count": 128, - "documented_meaning": "Simplicial complexes from Homology from Volkmar Welker. From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,", - "source_url": "https://sparse.tamu.edu/JGD_Homology", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "combinatorial problem (128)", - "curator_value_type_count": 1, - "curator_value_types": "integer (128)", - "curator_structure_count": 2, - "curator_structures": "rectangular (122); unsymmetric (6)", - "min_dimension": 15, - "max_dimension": 564480, - "dimension_ratio": 37632.0, - "size_span": "wide (>100\u00d7)", - "min_nnz": 18, - "max_nnz": 3386880, - "shape_profile": "mixed", - "square": 6, - "rectangular": 122, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 1, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Simplicial complexes from Homology from Volkmar Welker.\nFrom Jean-Guillaume Dumas' Sparse Integer Matrix Collection,\nhttp://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html\n\nhttp://www.mathematik.uni-marburg.de/~welker/" - }, - { - "group": "QY", - "count": 2, - "documented_meaning": "Transient stabilty constrained interior pt. optimal power flow, J. Quanyuan Two problem sets from Dr. Jiang Quanyuan from Zhejiang University,", - "source_url": "https://sparse.tamu.edu/QY", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "power network problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (2)", - "curator_structure_count": 1, - "curator_structures": "symmetric (2)", - "min_dimension": 14454, - "max_dimension": 40216, - "dimension_ratio": 2.78234398782344, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 147972, - "max_nnz": 1042160, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 2, - "structurally_symmetric_square": 2, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Transient stabilty constrained interior pt. optimal power flow, J. Quanyuan\nTwo problem sets from Dr. Jiang Quanyuan from Zhejiang University,\nHangzhou, China, March, 2008, used in an electrical power system.\nEach matrix A is solved sequentially with two right-hand-sides, b1 and\nb2, one at a time. In the UF collection, the sequence of all first\nand second right-hand-sides is in Problem.aux.b2 and Problem.aux.b1.\nThese matrices are symmetric indefinite (x=A\\\\b uses MA57).\nNote that the last matrices in the sequence are ill-conditioned.\n\nTransient Stability Constrained Interior Point Optimal Power Flow Program\n Version 1.0 -- Developed by Dr. Jiang Quanyuan, March 2008\n\ncase9.m - TSOPF converges after 12 iterations\n object = 3.945939E+03\n max_equ = 3.287326E-11\n low_inequ = None\n up_inequ = None\n\ncase39.m - TSOPF converges after 13 iterations\n object = 2.696268E+04\n max_equ = 5.684342E-14\n low_inequ = -1.110223E-16\n up_inequ = None" - }, - { - "group": "JGD_Kocay", - "count": 12, - "documented_meaning": "Brute force disjoint product matrices in tree algebra on n nodes, Nicolas Thiery From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,", - "source_url": "https://sparse.tamu.edu/JGD_Kocay", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "combinatorial problem (12)", - "curator_value_type_count": 2, - "curator_value_types": "integer (11); binary (1)", - "curator_structure_count": 1, - "curator_structures": "rectangular (12)", - "min_dimension": 2, - "max_dimension": 15905, - "dimension_ratio": 7952.5, - "size_span": "wide (>100\u00d7)", - "min_nnz": 1, - "max_nnz": 2872265, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 12, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 1, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Brute force disjoint product matrices in tree algebra on n nodes, Nicolas Thiery\nFrom Jean-Guillaume Dumas' Sparse Integer Matrix Collection,\nhttp://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html\n\nhttp://www.lapcs.univ-lyon1.fr/~nthiery/LinearAlgebra\n\nLinear algebra for combinatorics\n\nAbstract: Computations in algebraic combinatorics often boils down to\nsparse linear algebra over some exact field. Such computations are\nusually done in high level computer algebra systems like MuPAD or\nMaple, which are reasonnably efficient when the ground field requires\nsymbolic computations. However, when the ground field is, say Q or\nZ/pZ, the use of external specialized libraries becomes necessary. This\ndocument, geared toward developpers of such libraries, present a brief\noverview of my needs, which seems to be fairly typical in the\ncommunity." - }, - { - "group": "JGD_Margulies", - "count": 23, - "documented_meaning": "Combinatorial optimization as polynomial eqns, Susan Margulies, UC Davis From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,", - "source_url": "https://sparse.tamu.edu/JGD_Margulies", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "combinatorial problem (23)", - "curator_value_type_count": 1, - "curator_value_types": "binary (23)", - "curator_structure_count": 2, - "curator_structures": "rectangular (22); unsymmetric (1)", - "min_dimension": 25, - "max_dimension": 902103, - "dimension_ratio": 36084.12, - "size_span": "wide (>100\u00d7)", - "min_nnz": 74, - "max_nnz": 2170814, - "shape_profile": "mixed", - "square": 1, - "rectangular": 22, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 23, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Combinatorial optimization as polynomial eqns, Susan Margulies, UC Davis\nFrom Jean-Guillaume Dumas' Sparse Integer Matrix Collection,\nhttp://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html\n\nhttp://arxiv.org/abs/0706.0578\n\nExpressing Combinatorial Optimization Problems by Systems of Polynomial\nEquations and the Nullstellensatz\n\nAuthors: J.A. De Loera, J. Lee, Susan Margulies, S. Onn\n\n(Submitted on 5 Jun 2007)\n\nAbstract: Systems of polynomial equations over the complex or real\nnumbers can be used to model combinatorial problems. In this way, a\ncombinatorial problem is feasible (e.g. a graph is 3-colorable,\nhamiltonian, etc.) if and only if a related system of polynomial\nequations has a solution. In the first part of this paper, we construct\nnew polynomial encodings for the problems of finding in a graph its\nlongest cycle, the largest planar subgraph, the edge-chromatic number,\nor the largest k-colorable subgraph. For an infeasible polynomial\nsystem, the (complex) Hilbert Nullstellensatz gives a certificate that\nthe associated combinatorial problem is infeasible. Thus, unless P =\nNP, there must exist an infinite sequence of infeasible instances of\neach hard combinatorial problem for which the minimum degree of a\nHilbert Nullstellensatz certificate of the associated polynomial system\ngrows. We show that the minimum-degree of a Nullstellensatz\ncertificate for the non-existence of a stable set of size greater than\nthe stability number of the graph is the stability number of the graph.\nMoreover, such a certificate contains at least one term per stable set\nof G. In contrast, for non-3- colorability, we found only graphs with\nNullstellensatz certificates of degree four." - }, - { - "group": "JGD_Relat", - "count": 15, - "documented_meaning": "Relations from Florent Hiver, Univ. Paris-Est Marne-La-Valle'e From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,", - "source_url": "https://sparse.tamu.edu/JGD_Relat", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "combinatorial problem (15)", - "curator_value_type_count": 1, - "curator_value_types": "integer (15)", - "curator_structure_count": 1, - "curator_structures": "rectangular (15)", - "min_dimension": 12, - "max_dimension": 12360060, - "dimension_ratio": 1030005.0, - "size_span": "wide (>100\u00d7)", - "min_nnz": 18, - "max_nnz": 38955420, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 15, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Relations from Florent Hiver, Univ. Paris-Est Marne-La-Valle'e\nFrom Jean-Guillaume Dumas' Sparse Integer Matrix Collection,\nhttp://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html\n\nhttp://www-igm.univ-mlv.fr/~hivert/" - }, - { - "group": "JGD_SL6", - "count": 7, - "documented_meaning": "Differentials of the Voronoi complex of perfect forms from Philippe Elbaz-Vincent, Institut Fourier, Grenoble, France.", - "source_url": "https://sparse.tamu.edu/JGD_SL6", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "combinatorial problem (7)", - "curator_value_type_count": 1, - "curator_value_types": "integer (7)", - "curator_structure_count": 1, - "curator_structures": "rectangular (7)", - "min_dimension": 434, - "max_dimension": 1271, - "dimension_ratio": 2.9285714285714284, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 1832, - "max_nnz": 14966, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 7, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Differentials of the Voronoi complex of perfect forms\nfrom Philippe Elbaz-Vincent, Institut Fourier, Grenoble, France.\n\nFrom Jean-Guillaume Dumas' Sparse Integer Matrix Collection,\nhttp://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html\n\nhttp://www-fourier.ujf-grenoble.fr/-Informations-personnelles-.html?P=pev\n\nD_5 Smith Invariants = [ 1:92 3:2 18:1 ]\nD_6 Smith Invariants = [ 1:338 2:1 ]\nD_7 Smith Invariants = [ 1:621 2:5 6:1 60:2 ]\nD_8 Smith Invariants = [ 1:637 3:3 12:1 ]\nD_9 Smith Invariants = [ 1:491 ]\nD_10 Smith Invariants = [ 1:318 2:3 4:2 ]\nD_11 Smith Invariants = [ 1:129 2:6 6:1 ]" - }, - { - "group": "JGD_SPG", - "count": 7, - "documented_meaning": "Symmetric powers of graphs from Gordon Royle, Univ Western Australia From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,", - "source_url": "https://sparse.tamu.edu/JGD_SPG", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "combinatorial problem (7)", - "curator_value_type_count": 2, - "curator_value_types": "binary (6); integer (1)", - "curator_structure_count": 2, - "curator_structures": "symmetric (6); unsymmetric (1)", - "min_dimension": 300, - "max_dimension": 6545, - "dimension_ratio": 21.816666666666666, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 592, - "max_nnz": 295680, - "shape_profile": "square only", - "square": 7, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 6, - "structurally_symmetric_square": 6, - "known_spd": 0, - "value_profile": "real only", - "binary": 6, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Symmetric powers of graphs from Gordon Royle, Univ Western Australia\nFrom Jean-Guillaume Dumas' Sparse Integer Matrix Collection,\nhttp://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html\n\nhttp://www.csse.uwa.edu.au/~gordon/sympower.html" - }, - { - "group": "JGD_Taha", - "count": 2, - "documented_meaning": "Taha matrix set from Abdul Salam Jarrah, New Mexico State Univ. From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,", - "source_url": "https://sparse.tamu.edu/JGD_Taha", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "combinatorial problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "integer (2)", - "curator_structure_count": 1, - "curator_structures": "rectangular (2)", - "min_dimension": 14596, - "max_dimension": 37932, - "dimension_ratio": 2.5987941901890927, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 51307, - "max_nnz": 137228, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 2, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Taha matrix set from Abdul Salam Jarrah, New Mexico State Univ.\nFrom Jean-Guillaume Dumas' Sparse Integer Matrix Collection,\nhttp://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html\n\nhttp://www.math.nmsu.edu/~ajarrah" - }, - { - "group": "JGD_Trefethen", - "count": 11, - "documented_meaning": "Diagonal matrices with primes, Nick Trefethen, Oxford Univ. From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,", - "source_url": "https://sparse.tamu.edu/JGD_Trefethen", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "combinatorial problem (11)", - "curator_value_type_count": 1, - "curator_value_types": "integer (11)", - "curator_structure_count": 1, - "curator_structures": "symmetric (11)", - "min_dimension": 19, - "max_dimension": 20000, - "dimension_ratio": 1052.6315789473683, - "size_span": "wide (>100\u00d7)", - "min_nnz": 147, - "max_nnz": 554466, - "shape_profile": "square only", - "square": 11, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 11, - "structurally_symmetric_square": 11, - "known_spd": 11, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Diagonal matrices with primes, Nick Trefethen, Oxford Univ.\nFrom Jean-Guillaume Dumas' Sparse Integer Matrix Collection,\nhttp://ljk.imag.fr/membres/Jean-Guillaume.Dumas/simc.html\n\nProblem 7 of the Hundred-dollar, Hundred-digit Challenge Problems,\nSIAM News, vol 35, no. 1.\n\n7. Let A be the 20,000 x 20,000 matrix whose entries are zero\neverywhere except for the primes 2, 3, 5, 7, . . . , 224737 along the\nmain diagonal and the number 1 in all the positions A(i,j) with\n|i-j| = 1,2,4,8, . . . ,16384. What is the (1,1) entry of inv(A)?\n\nhttp://www.siam.org/news/news.php?id=388" - }, - { - "group": "FreeFieldTechnologies", - "count": 1, - "documented_meaning": "3D vibro-acoustic problem, aircraft engine nacelle, from M. Gontier Free Field Technologies, Mont-Saint Guibert, Belguim (", - "source_url": "https://sparse.tamu.edu/FreeFieldTechnologies", - "coarse_kind_families_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "acoustics problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "complex (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 169410, - "max_dimension": 169410, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 5033796, - "max_nnz": 5033796, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 1, - "known_spd": 0, - "value_profile": "complex only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "3D vibro-acoustic problem, aircraft engine nacelle, from M. Gontier\nFree Field Technologies, Mont-Saint Guibert, Belguim (http://www.fft.be)" - }, - { - "group": "Freescale", - "count": 8, - "documented_meaning": "Circuit simulation matrices K. Gullapalli, Freescale Semiconductor", - "source_url": "https://sparse.tamu.edu/Freescale", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 2, - "normalized_kinds": "circuit simulation problem (7); circuit simulation matrix (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (8)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (8)", - "min_dimension": 178866, - "max_dimension": 5558326, - "dimension_ratio": 31.07536368007335, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 961368, - "max_nnz": 59524291, - "shape_profile": "square only", - "square": 8, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Circuit simulation matrices K. Gullapalli, Freescale Semiconductor" - }, - { - "group": "TSOPF", - "count": 27, - "documented_meaning": "Transient stability-constrained optimal power flow, G. Geng, Zhejiang Univ. comments on the TSOPF_RS_b2303 matrix:", - "source_url": "https://sparse.tamu.edu/TSOPF", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "power network problem (27)", - "curator_value_type_count": 1, - "curator_value_types": "real (27)", - "curator_structure_count": 2, - "curator_structures": "unsymmetric (15); symmetric (12)", - "min_dimension": 2454, - "max_dimension": 120216, - "dimension_ratio": 48.987775061124694, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 25032, - "max_nnz": 16171169, - "shape_profile": "square only", - "square": 27, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 12, - "structurally_symmetric_square": 12, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Transient stability-constrained optimal power flow, G. Geng, Zhejiang Univ.\n\ncomments on the TSOPF_RS_b2303 matrix:\nWe are now trying UMFPACK in MATLAB to do some research work\npower system optimization. UMFPACK LU function (5 outputs) performs much\nfaster than normal LU function (3 outputs) of MATLAB for most test cases in\nour experiment. However, in one of our test, UMFPACK runs out of memory when\nperforming LU decomposition to an unsymmetric, non-positive definite matrix\nCk (uploaded on the website). But the normal LU function with COLAMD\nsuccessfully completes LU decomposition without any warning and error. All\ntests are done in MATLAB 2008b x64 on a PC with 4Gb RAM. We have tried both\nUMFPACK 5.0 and 5.2 but neither works. We would very appreciate it if you\ncould give some suggestions on our implementation.\n\ncomments from Guangchao Geng regarding the TSOPF_FS_300 matrix:\nKLU (which you recommend us in your last letter) does a perfect\na job in our application of power system optimization. It's faster than\nUMFPACK and SuperLU about 10 times in LU factorization phase and 5 times in\nbackward substitution phase. It greatly speeds up our research work. However,\nwhen we try UMFPACK and KLU to solve a symmetric sparse linear system in\nanother program, both of the two fails to exceed backslash in MATLAB.\nThe left-hand side matrix A and right-hand side vector b1 are uploaded in a\nsingle .MAT file at the website. We wonder if MATLAB backslash is the\nperformance limit for matrices of this kind, or we have other external\nsolvers worth to try.\n\nThank you for your consideration. Guangchao Geng\n \n-------------- Guangchao Geng E-mail: genggc at gmail dot com\nInstitute of Power System, College of Electrical Engineering,\nZhejiang University, Hangzhou, 310027, China.\n\n\nremainder: \nHere is a matrix collection of our power system optimization application,\nThis .zip file contains matrices collection of Transient Stability\nconstrained Optimal Power Flow (TSOPF). All matrices are stored in MAT format\nand divided in two groups: Full-Space (FS) group and Reduced-Space (RS)\ngroup.\n\nFS group: \n - Left-hand side: A (symmetric & indefinite)\n - Right-hand side: b1\n - Solve fastest with Matlab backslash '\\\\' (actually MA57)\n\nRS group:\n - Left-hand side: Ck (asymmetric & indefinite)\n - Right-hand side: Nk (sparse with 300+ columns for large systems)\n - Solve fastest with KLU\n\nGuangchao Geng (genggc at gmail dot com)\n2009.01.08\n\n(Update: the titles of the matrices were corrected on March 6, 2009)." - }, - { - "group": "Buss", - "count": 2, - "documented_meaning": "Website recommendation system, Steven Buss, Univ. Florida connectus:", - "source_url": "https://sparse.tamu.edu/Buss", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "undirected bipartite graph (2)", - "curator_value_type_count": 1, - "curator_value_types": "binary (2)", - "curator_structure_count": 1, - "curator_structures": "rectangular (2)", - "min_dimension": 394792, - "max_dimension": 872622, - "dimension_ratio": 2.2103335427262962, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 1127525, - "max_nnz": 22624727, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 2, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 2, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Website recommendation system, Steven Buss, Univ. Florida\n\nconnectus:\nThis is a matrix from Steven Buss, for a website recommendation system (to\nbe called www.connectious.com). Each row of the matrix is a unique user.\nEach column of the matrix is a web page. A(i,j)=1 if user i has tagged\nweb page j as favorable, and a 0 represents no opinion. The data was\nobtained from digg.com. Note that the matrix A*A' contains one\nconnected component of size 342, and 170 singletons (nodes with no edges)\nThis means that a group of 341 users share some interests, at least\nindirectly, and that there are 171 other users that share no interests\nat all with any other users (they have no pages in common with anyone).\n\n12month1: another matrix from the same problem, with 12,646 users\nand 394,792 web pages, from July 2008 to July 2009." - }, - { - "group": "Yoshiyasu", - "count": 2, - "documented_meaning": "Image editting and mesh deformation problem from Y. Yoshiyasu, Keio Univ., Japan I uploaded 5 matrices A1,b1,A2,b2 and D (for x=(A1'*A1)/(A1'*b1) and", - "source_url": "https://sparse.tamu.edu/Yoshiyasu", - "coarse_kind_families_interpretive": "imaging/graphics/robotics", - "dominant_coarse_family_interpretive": "imaging/graphics/robotics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computer graphics/vision problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (2)", - "curator_structure_count": 1, - "curator_structures": "rectangular (2)", - "min_dimension": 234023, - "max_dimension": 240000, - "dimension_ratio": 1.0255402246787708, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 711683, - "max_nnz": 853829, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 2, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 2, - "cohesion": "coherent family/series", - "official_notes": "Image editting and mesh deformation problem from Y. Yoshiyasu, Keio Univ., Japan\n\nI uploaded 5 matrices A1,b1,A2,b2 and D (for x=(A1'*A1)/(A1'*b1) and\nv=(A2'*D*A2)/(A2'*D*b2)). What I'm doing is template-mesh deformation to match\nwith silhouettes. In this process, there are two kinds of linear systems that\nI have to solve.\n\nFirst one is smooth vector field construction from images, which is harmonic\ninterpolation (minimizing laplacian: Lx=0) of intensity gradient field p. I'm\nsolving this by normal equation and cholesky factorization.\n\nx=(A1'*A1)/(A1'*b1)\n\nwhere A1=[L;C] and b1=[zeros(size(length(L),1);1);C*p]. C is a square diagonal\nmatrix containing weights. I'm doing this on a 400x300 image, so\nIx=reshape(x,400,300) must be done to get the vector field. After solving y\ndirection for Iy, the result is visualized with quiver(Ix,Iy). At each\niteration the both C submatrix and the right-hand-side change but L remains\nunchanged.\n\nSecond one is template deformation using above vector field. I'm solving this\nby:\n\nv=(A2'*D*A2)/(A2'*D*b2)\n\nwhere D is diagonal matrix containing weights. Structures of A2 and b2 are a\nlittle bit more complex than A1 and b1, but they are similar.\n\nBoth systems are not quite time-consuming for themselves, but I iterate this\nprocess more than 10 times for 10 views. So I would like them to be faster and\nalso I'd like to reuse and update the factored matrix (if it's possible).\n\n[Reply: if all of C changes at each step, this is a nearly n-rank change,\nso an update/downdate process would be much more expensive than refactorizing\nthe matrix. Update/downdate is effective for low-rank changes.\n\nIn the collection,]" - }, - { - "group": "Priebel", - "count": 6, - "documented_meaning": "Quadratic sieve matrices from David Priebel, Tenn. Tech. Univ. Each column in the matrix corresponds to a number in the factor base", - "source_url": "https://sparse.tamu.edu/Priebel", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "combinatorial problem (6)", - "curator_value_type_count": 1, - "curator_value_types": "binary (6)", - "curator_structure_count": 1, - "curator_structures": "rectangular (6)", - "min_dimension": 584, - "max_dimension": 24430, - "dimension_ratio": 41.832191780821915, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 6120, - "max_nnz": 299756, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 6, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 6, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Quadratic sieve matrices from David Priebel, Tenn. Tech. Univ.\n\nEach column in the matrix corresponds to a number in the factor base\nless than some bound B. Each row corresponds to a smooth number (able\nto be completely factored over the factor base). Each value in a row\nbinary vector corresponds to the exponent of the factor base mod 2.\nFor example:\n\n factor base: 2 7 23\n smooth numbers: 46, 28, 322\n 2^1 * 23^1 = 46\n 2^2 * 7^1 = 28\n 2^1 * 7^1 * 23^1 = 322\n Matrix:\n 101\n 010\n 111\n\nA solution to the matrix is considered to be a set of rows which when\ncombined in GF2 produce a null vector. Thus, if you multiply each of\nthe smooth numbers which correspond to that particular set of rows you\nwill get a number with only even exponents, making it a perfect\nsquare. In the above example you can see that combining the 3 vectors\nresults in a null vector and, indeed, it is a perfect square: 644^2.\n\nProblem.A: A GF(2) matrix constructed from the exponents of the\nfactorization of the smooth numbers over the factor base. A solution of\nthis matrix is a kernel (nullspace). Such a solution has a 1/2 chance of\nbeing a factorization of N.\n\nProblem.aux.factor_base: The factor base used. factor_base(j) corresponds\nto column j of the matrix. Note that a given column may or may not have\nnonzero elements in the matrix.\n\nProblem.aux.smooth_number: The smooth numbers, smooth over the factor\nbase. smooth_number(i) corresponds to row i of the matrix.\n\nProblem.aux.solution: A sample solution to the matrix. Combine, in GF(2)\nthe rows with these indicies to produce a solution to the matrix with the\nadditional property that it factors N (a matrix solution only has 1/2\nprobability of factoring N).\n\nProblem specific information:\n\nn = 787911249838484617926390474950774839909 (130-bits)\nDivisor: 23850290715477455017 (probably prime)\nDivisor: 33035708421246870877 (probably prime)\n\nn = 27393004579711727757848513391018843988362569 (145-bits)\nDivisor: 4762476283061573160587 (probably prime)\nDivisor: 5751840629031342254587 (probably prime)\n\nn = 3408489886335277144344023699527218196631767672957 (162-bits)\nDivisor: 1816046478796474796528999 (probably prime)\nDivisor: 1876873706775468629074043 (probably prime)\n\nn = 73363722971930954428433124842779099222294372095286387 (176-bits)\nDivisor: 236037985789994529800050193 (probably prime)\nDivisor: 310813205452462332837525059 (probably prime)\n\nn = 4232562527578032866150921497850842593296760823796443077101 (192-bits)\nDivisor: 83135929635332984850508004533 (probably prime)\nDivisor: 50911351399373573113182167897 (probably prime)\n\nn = 239380926372595066574100671394554319947805305453767699448870971 (208-bits)\nDivisor: 12216681953629826483019726942851 (probably prime)\nDivisor: 19594594283554225566102143686121 (probably prime)" - }, - { - "group": "MaxPlanck", - "count": 2, - "documented_meaning": "Weather shallow water equations, Max-Planck Institute of Meteoroloy The two shallow_water* matrices arise from weather shallow water equations", - "source_url": "https://sparse.tamu.edu/MaxPlanck", - "coarse_kind_families_interpretive": "fluids/transport", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computational fluid dynamics problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (2)", - "curator_structure_count": 1, - "curator_structures": "symmetric (2)", - "min_dimension": 81920, - "max_dimension": 81920, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 327680, - "max_nnz": 327680, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 2, - "structurally_symmetric_square": 2, - "known_spd": 2, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 2, - "cohesion": "coherent family/series", - "official_notes": "Weather shallow water equations, Max-Planck Institute of Meteoroloy\n\nThe two shallow_water* matrices arise from weather shallow water equations\n(see http://www.icon.enes.org), from the Max-Plank Institute of Meteorology.\nThe problem gives rise to an automatic differentiation problem. An iterative\nsolver is used for the larger problem, but a direct sovler is used for\nfinding the adjoints of a linear problem. The velocity field is integrated\nover a time loop with a semi-implicit method. The implicit part leads to\na linear problem A*x=b, whose entries vary with time. Two of these matrices\nA are in this collection, with shallow_water1 at dtime=100 and shallow_water2\nat dtime=200. The nonzero patterns of the two matrices are the same, but\nshallow_water1 is much slower. The reason is that many denormals appear\nduring factorization, which greatly slows down the BLAS. This can be solved\nby compiling with gcc -ffast-math, to flush denormals to zero." - }, - { - "group": "Clark", - "count": 1, - "documented_meaning": "Tomographic reconstruction of a particle field in fluid flow, T. Clark See", - "source_url": "https://sparse.tamu.edu/Clark", - "coarse_kind_families_interpretive": "imaging/graphics/robotics", - "dominant_coarse_family_interpretive": "imaging/graphics/robotics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computer graphics/vision problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "rectangular (1)", - "min_dimension": 73159, - "max_dimension": 73159, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 647495, - "max_nnz": 647495, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 1, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "Tomographic reconstruction of a particle field in fluid flow, T. Clark\n\nSee http://www.mathworks.com/matlabcentral/newsreader/view_thread/254747\nfor a discussion.\n\nThe problem can be solved via lsqlin in MATLAB:\nlb = zeros (n,1) ;\nx = lsqlin (A, b, [ ], [ ], [ ], [ ], lb) ;" - }, - { - "group": "Bourchtein", - "count": 4, - "documented_meaning": "Atmospheric modeling problems from Andrei Bourchtein These matrices arise in the numerical weather prediction and atmospheric", - "source_url": "https://sparse.tamu.edu/Bourchtein", - "coarse_kind_families_interpretive": "fluids/transport", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computational fluid dynamics problem (4)", - "curator_value_type_count": 2, - "curator_value_types": "real (3); integer (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (4)", - "min_dimension": 1270432, - "max_dimension": 1489752, - "dimension_ratio": 1.1726341905745448, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 8814880, - "max_nnz": 10319760, - "shape_profile": "square only", - "square": 4, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 4, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 4, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Atmospheric modeling problems from Andrei Bourchtein\n\nThese matrices arise in the numerical weather prediction and atmospheric\nmodeling. Such matrices usually appear in semi-implicit schemes applied to\nthree-dimensional Euler or Navier-Stokes equations (called nonhydrostatic\nmodels in the atmospheric sciences) or to their simplified form with\nhydrostatic balance equation instead of vertical momentum equation (called\nhydrostatic or primitive equation models in the atmospheric sciences).\n\nSuch linear systems represent discretization of three-dimensional elliptic\nproblems (frequently Dirichlet or Neumann problems for Helmholtz or\nquasi-Helmholtz equations), arising at each time step of semi-implicit\nalgorithms due to implicit time approximation of some linear terms in the\ngoverning equations. If spectral spatial approximation is applied, then the\nelliptic problem is usually transformed to the linear system with a diagonal\nmatrix solved trivially. If finite-difference or finite-element approximation\nis used, then the linear systems with the sparse matrices of the coefficients\nsimilar to the four submitted matrices arise. However, the semi-implicit\nschemes usually do not require explicit construction of the matrix of\ncoefficients, neither do iterative methods used to solve these systems in the\natmospheric models. Besides, avoiding construction of the matrix of\ncoefficients allows reducing the required computer memory. Due to these\nreasons, as far as I know, the explicit form of matrices of coefficients\nusually is not described, except for the local structure of the respective\ndifference equations.\n\nThe two right-hand sides b(:,1) and b(:,2) refer to the long wave or\nshort wave perturbation of atmospheric fields, respectively.\n\nThe description of such semi-implicit algorithms together with arising elliptic\nproblems can be found, for example, in the following recent papers (and\nreferences therein):\n\n1. Steppeler J., Hess R., Schattler U., Bonaventura L.: Review of numerical\nmethods for nonhydrostatic weather prediction models. Met. Atm Phys. 82 (2003)\n287-301.\n\n This is a review paper on nonhydrostatic models, including particularly,\n semi-implicit time differencing. Some description of arising elliptic\n problems and their solvers used in atmospheric models can be found on\n pp.294-296.\n\n2. Cote J., Gravel S., Methot A., Patoine A., Roch M., Staniforth A.: The CMC-MRB\nglobal environmental multiscale (GEM) model. Part I: Design considerations and\nformulation. Mon. Wea. Rev. 126 (1998) 1373-1395.\n\n3. Yeh K.S., Cote J., Gravel S., Methot A., Patoine A., Roch M., Staniforth A.: The\nCMC-MRB global environmental multiscale (GEM) model. Part III: Nonhydrostatic\nformulation. Mon. Wea. Rev. 130 (2002) 339-356.\n\n This pair of papers is about hydrostatic and nonhydrostatic versions of the\n modern semi-implicit Canadian model. Some description of elliptic problems\n and their solution can be found on p.1389 of the first paper and p.343 of\n the second paper.\n\n4. Davies T., Cullen M.J.P., Malcolm A.J., Mawson M.H., Staniforths A., White A.A.,\nWood N.: A new dynamical core for the Met Office\u2019s global and regional modeling.\nQ. J. Roy. Met. Soc. 131 (2005) 1759-1782.\n\n This is a brief report on United Kingdom modern semi-implicit model. Some\n description of elliptic problem can be found on p.1778 and its solution\n on pp.1771-1772." - }, - { - "group": "Puri", - "count": 4, - "documented_meaning": "ABAQUS benchmarks: pt.loaded fluid-filled shell. Srinivasan Puri ROM benchmark test matrices.", - "source_url": "https://sparse.tamu.edu/Puri", - "coarse_kind_families_interpretive": "eigenvalue/model reduction", - "dominant_coarse_family_interpretive": "eigenvalue/model reduction", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "model reduction problem (4)", - "curator_value_type_count": 2, - "curator_value_types": "complex (3); real (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (4)", - "min_dimension": 23412, - "max_dimension": 23412, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 218484, - "max_nnz": 218484, - "shape_profile": "square only", - "square": 4, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 4, - "known_spd": 0, - "value_profile": "real + complex", - "binary": 0, - "geometric_2d3d": 4, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "ABAQUS benchmarks: pt.loaded fluid-filled shell. Srinivasan Puri\nROM benchmark test matrices.\n\nhttp://sites.google.com/site/srinivaspuri/Home/rom-test-matrices\n\nThe problem can be found in any ABAQUS Benchmark manual: Section\n1.10.2, and is often known as the \"acid-test\" within the\nstructural-acoustic community.\n\nA small description of the problem follows.\n\nThe Abaqus/Standard Model:\n\nThis problem involves the steady-state vibration of a point-loaded\nspherical shell coupled to an acoustic fluid that fills its interior.\nIt is modeled using axisymmetric shell and acoustic elements. The\nclosed form solution of Stepanishen and Cox (2000) is used for\nvalidation of the analysis. The basis of the coupled\nacoustic-structural vibration capability in Abaqus is described in\n\"Coupled acoustic-structural medium analysis,\" Section 2.9.1 of the\nAbaqus Theory Manual, and \"Acoustic, shock, and coupled\nacoustic-structural analysis,\" Section 6.9.1 of the Abaqus Analysis\nUser's Manual.\n\nProblem description\n\nThe model is a semicircular shell and fluid mesh of radius 2.286 m. A\npoint load on the symmetry axis of magnitude 1.0 N is applied to the\nshell. The shells are 0.0254 m in thickness and have a Young's modulus\nof 206.8 GPa, a Poisson's ratio of 0.3, and a mass density of 7800.0\nkg/m^{3}. The acoustic fluid has a density, of 1000 kg/m^{3} and a bulk\nmodulus, of 2.25 GPa. The response of the coupled system is calculated\nfor frequencies ranging from 100 to 1000 Hz in 5 Hz increments. There\nare two different finite element meshes used: one with explicitly\ndefined acoustic-structural interaction elements and one that uses the\n*TIE option. The former model consists of 220 SAX1 elements surrounding\na mesh of 15848 ACAX4 elements. Coupling is effected using 220 ASI2A\nelements. The latter model uses 80 SAX2 elements surrounding a mesh of\n965 ACAX8 elements. For this mesh, coupling is effected using the *TIE\noption to generate the acoustic-structural interaction elements\ninternally.\n\nA dummy part is included in the models to ensure that the analytical\nsolution appears in the output database. This part consists of a single\npoint mass, uncoupled from the model described above, with a\ndisplacement boundary condition on degree of freedom 1. This imposed\ndisplacement uses an amplitude table consisting of the Stepanishen/Cox\nanalytical solution for the drive point admittance.\n\nThe ANSYS Model & the Higher Dimensional Coupled System Matrices:\n\nWe generate the required matrices in ANSYS by reading through the\nABAQUS documentation and preparing the relevant finite element mesh in\nANSYS. This particular model was done with ANSYS V10/V11. A detailed\ndescription can be found in and R. S. Puri, D Morrey, J. L. Cipolla\n(2007) and R. S. Puri (2008). In the ANSYS model, we sweep from\n100-1000Hz in 1Hz increments as opposed to 5Hz increments in the ABAQUS\nmodel. This is simply for ease of comparison with analytical solutions\n- since we have closed form solutions at every 1Hz. increments for the\nundamped case.\n\nA diagram of the coupled Finite Element mesh along with the point\nloading condition/location & the sparsity plots of the coupled\nstiffness and mass matrices are given at\nhttp://sites.google.com/site/srinivaspuri/Home/rom-test-matrices\n\nFor the undamped model, the matrices can be found in BM_Uda.zip in the\nAttachments section.\n\nFor damped models, we have three sub test cases: Low Damping, Moderate\nDamping, High Damping. The higher dimensional matrices can be found in\nBM_Ld.zip ; BM_Md.zip ; BM_Hd.zip in the Attachments sections (see\n\"contd..\" sub page also).\n\nGeneral file description are as follows:\n\nfull* represents higher dimensional system.\n\nfull.M: Coupled Mass matrix: Unsymm. (explicit zeros in Mzeros matrix)\nfull.K: Coupled Stiffness matrix: Unsymm.\nfull.E: Structural-Acoustic damping matrix\n (In this case, we vary only structural damping).\nfull.B: Structural-Acoustic input vector\n (i.e forcing function for the freq domain simulation)\nfull.C: Output measurement matrix.\nfull.C.names: Description of the output measurement:\n Not required for model reduction.\nfrequency.txt: Frequencies for frequency domain simulation.\n (100-1000Hz.)\nN10945_.txt: Results from direct inversion of the\n higher dimensional system: Coupled nodal displacements.\nTSSOAR_N10945_.txt: Results from Two Sided Arnoldi\n projection: Coupled nodal displacements.\nclosedform.txt: Velocity Results from analytical solution\n [modal expansion] for the problem.\n\nKey: : undamped;\n : low damping;\n : moderate damping;\n : high damping.\n\nCoupled System Matrix Properties:\n\nType: Second order, SISO.\nFormat: Matrix Market.\nDimension: 23412 x 23412.\nMass Matrix: Coupled, Unsymmetric.\nStiffness Matrix: Coupled, Unsymmetric.\nDamping Matrix: Undamped & Structurally Damped Symmetric.\n\nApproximation Properties:\n\nState Variable to be approximated: Coupled Displacement at driving point location.\nEigen Solution:\n 50 coupled vectors for say Coupled Lanczos projection procedure ;\n Uncoupled approach: 50 structural and uncoupled acoustic modes\n till 4000Hz for AMLS projection.\n\nArnoldi Vectors:\n 100 for undamped via One Sided or Two Sided Arnoldi Process [OSA-TSA];\n 110 for damped via Two-Sided Second Order Arnoldi Process [SOAR].\n\nMatrix Extraction Information:\n\n[1.0] Undamped Matrices:\n\nBM_Uda.zip : No issues to report. You should be able to generate the\nplots in Figure: 3 and Figure: 4 with minimal effort.\n\n[1.1] Damped Matrices:\n\nBM_.zip : No issues with Mass and Stiffness Matrices. The\ndamping matrices for the weakly non-linear test cases are assembled\nfrom the total Dynamic matrix in ANSYS. That is, ANSYS writes the\ndynamic, coupled structural-acoustic \"supermatrix\" as follows:\n\n[A] {x} = F ; -- Equation (1)\n\nwhere, [A] is the dynamic \"super matrix\" which is computed as follows:\n\n[A] = -Omega^{2} [M] + i*Omega [E] + [K] ; -- Equation (2)\n\nwhich is directly written to the ANSYS FULL file. It follows from\nabove that the damping matrix can be \"back calculated\" by using the\nfollowing relationship:\n\n[E] = [Ai] / Omega ; -- Equation (3)\n\nwhere, [Ai] represents the imaginary part of the dynamic coupled\nstructural-acoustic \"super matrix\".\n\nUnfortunately, at this moment, there is no other direct way to extract\nthe damping matrix including linearly varying damping.\n\nShortly speaking, the damping matrix [E] for the coupled\nstructural-acoustic system can be extracted by dividing the imaginary\npart of the \"super matrix\" with a high enough frequency.\n\nRemarks on Matrix Extraction for Damped Test Cases:\n\n\nFor the damped cases, the [E] matrices for the benchmark models were\nextracted at around 1000Hz, which is close to the end frequency of the\nanalysis range.\n\nThis above extraction procedure sometimes results in a \"non-symmetric\"\ndamping matrix rather than a symmetric one, which can be attributed to\nthe very small \"round-off\" differences at different frequencies. Note\nalso that this \"unsymmetricity\" is not always the case. In many cases\n(e.g. ones with lower dimension), the damping matrices are indeed\nsymmetric.\n\nSaying that, we note that this should not yield any issues/differences\nin the dimension reduction process.\n\nPlease let me know if you do encounter any difficulties or questions\non the matrices/reduction process. Results for the linearly damped\ncases can be found in R. S. Puri (2008).\n\nReferences:\n\nStepanishen, P., and D. L. Cox, \"Structural-Acoustic Analysis of an\nInternally Fluid-Loaded Spherical Shell: Comparison of Analytical and\nFinite Element Modeling Results,\" NUWC Technical Memorandum 00-118,\nNewport, Rhode Island, 2000.\n\nPuri, R. S., D. Morrey, and J. L. Cipolla (2007), A comparison between\nOne-sided and Two-sided Arnoldi based model order reduction techniques\nfor fully coupled structural acoustic analysis. In 153rd Meeting of\nthe Acoustical Society of America, The Journal of Acoustical Society\nof America, Volume 121.\n\nR.S. Puri (2008), Krylov Subspace Based Direct Projection Techniques\nFor Low Frequency, Fully Coupled, Structural Acoustic Analysis and\nOptimization, Ph.D. Thesis, School of Technology, Department of\nMechanical Engineering and Mathematical Sciences, Oxford Brookes\nUniversity, Oxford, UK.\n\nE. B. Rudnyi and J. G. Korvink (2006), Model Order Reduction for Large\nScale Engineering Models Developed in ANSYS. Lecture Notes in Computer\nScience, v. 3732, pp. 349-356.\n\nIf you run into any trouble, do ping me:\nsrinivaspuri[at]gmail[dot].com" - }, - { - "group": "Stevenson", - "count": 1, - "documented_meaning": "Circuit optimization for digital circuits. John Peter Stevenson, Stanford. LargeRegFile is a matrix representing an optimization problem.", - "source_url": "https://sparse.tamu.edu/Stevenson", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "circuit simulation problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "rectangular (1)", - "min_dimension": 2111154, - "max_dimension": 2111154, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 4944201, - "max_nnz": 4944201, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 1, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "single matrix", - "official_notes": "Circuit optimization for digital circuits. John Peter Stevenson, Stanford.\n\nLargeRegFile is a matrix representing an optimization problem.\nSpecifically, it is a geometric program, a kind of convex optimization\nproblem. The matrix entries are the exponents found in the geometric\nprogram constraints; specifically, this matrix is a portion of the\ninput required by mskexpopt:\nhttp://docs.mosek.com/6.0/capi/node008.html\n\nThis matrix represents a digital circuit. The circuit is a large\nregister file: 256 registers, 64 bits each, 2 read ports, 1 write port.\nThe optimization problem is to minimize circuit delay, subject to\nconstraints on energy cost and area cost. The optimization variables\nare the transistor size, the supply voltage, and the threshold voltage.\nThe circuit optimization method is described by Patil, et. al.: Robust\nenergy-efficient adder topologies. In ARITH07: Proceedings of the 18th\nIEEE Symposium on Computer Arithmetic, pages 16-28, Washington, DC,\nUSA, 2007. IEEE Computer Society." - }, - { - "group": "Belcastro", - "count": 3, - "documented_meaning": "Gene networks from V. Belcastro, TIGEM, Naples, Italy Currently working on gene regulatory network inference. Obtained", - "source_url": "https://sparse.tamu.edu/Belcastro", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "undirected weighted graph (3)", - "curator_value_type_count": 1, - "curator_value_types": "real (3)", - "curator_structure_count": 1, - "curator_structures": "symmetric (3)", - "min_dimension": 14340, - "max_dimension": 45101, - "dimension_ratio": 3.1451185495118548, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 18068388, - "max_nnz": 28967291, - "shape_profile": "square only", - "square": 3, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 3, - "structurally_symmetric_square": 3, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Gene networks from V. Belcastro, TIGEM, Naples, Italy\n\nCurrently working on gene regulatory network inference. Obtained \nhuman as well as mouse gene regulatory networks by analyzing gene \nexpression profiles from both the species.\n\nVincenzo Belcastro, M.Sc.\nResearch Associate\nTelethon Institute of Genetics and Medicine\n(TIGEM) - Systems Biology Group\nVia P. Castellino 111\n80131, Napoli\nItaly" - }, - { - "group": "SNAP", - "count": 68, - "documented_meaning": "Networks from SNAP (Stanford Network Analysis Platform) Network Data Sets, Jure Leskovec", - "source_url": "https://sparse.tamu.edu/SNAP", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 8, - "normalized_kinds": "directed graph (30); undirected graph (14); directed temporal multigraph (8); undirected graph with communities (6); directed weighted graph (5); directed graph with communities (2); directed weighted temporal graph (2); directed weighted temporal multigraph (1)", - "curator_value_type_count": 2, - "curator_value_types": "binary (53); integer (15)", - "curator_structure_count": 2, - "curator_structures": "unsymmetric (47); symmetric (21)", - "min_dimension": 1005, - "max_dimension": 65608366, - "dimension_ratio": 65281.95621890547, - "size_span": "wide (>100\u00d7)", - "min_nnz": 20296, - "max_nnz": 3612134270, - "shape_profile": "square only", - "square": 68, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 21, - "structurally_symmetric_square": 22, - "known_spd": 0, - "value_profile": "real only", - "binary": 53, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Networks from SNAP (Stanford Network Analysis Platform) Network Data Sets,\nJure Leskovec http://snap.stanford.edu/data/index.html\nemail jure at cs.stanford.edu\n\nCitation for the SNAP collection:\n\n@misc{snapnets,\n author = {Jure Leskovec and Andrej Krevl},\n title = {{SNAP Datasets}: {Stanford} Large Network Dataset Collection},\n howpublished = {\\\\url{http://snap.stanford.edu/data}},\n month = jun,\n year = 2014\n }\n\nThe following matrices/graphs were added to the collection in June 2010\nby Tim Davis (problem id and name):\n--------------------------------------------------------------------------------\n\n2284 SNAP/soc-Epinions1 who-trusts-whom network of Epinions.com\n2285 SNAP/soc-LiveJournal1 LiveJournal social network\n2286 SNAP/soc-Slashdot0811 Slashdot social network, Nov 2008\n2287 SNAP/soc-Slashdot0902 Slashdot social network, Feb 2009\n2288 SNAP/wiki-Vote Wikipedia who-votes-on-whom network\n2289 SNAP/email-EuAll Email network from a EU research institution\n2290 SNAP/email-Enron Email communication network from Enron\n2291 SNAP/wiki-Talk Wikipedia talk (communication) network\n2292 SNAP/cit-HepPh Arxiv High Energy Physics paper citation network\n2293 SNAP/cit-HepTh Arxiv High Energy Physics paper citation network\n2294 SNAP/cit-Patents Citation network among US Patents\n2295 SNAP/ca-AstroPh Collaboration network of Arxiv Astro Physics\n2296 SNAP/ca-CondMat Collaboration network of Arxiv Condensed Matter\n2297 SNAP/ca-GrQc Collaboration network of Arxiv General Relativity\n2298 SNAP/ca-HepPh Collaboration network of Arxiv High Energy Physics\n2299 SNAP/ca-HepTh Collaboration network of Arxiv High Energy Physics Theory\n2300 SNAP/web-BerkStan Web graph of Berkeley and Stanford\n2301 SNAP/web-Google Web graph from Google\n2302 SNAP/web-NotreDame Web graph of Notre Dame\n2303 SNAP/web-Stanford Web graph of Stanford.edu\n2304 SNAP/amazon0302 Amazon product co-purchasing network from March 2 2003\n2305 SNAP/amazon0312 Amazon product co-purchasing network from March 12 2003\n2306 SNAP/amazon0505 Amazon product co-purchasing network from May 5 2003\n2307 SNAP/amazon0601 Amazon product co-purchasing network from June 1 2003\n2308 SNAP/p2p-Gnutella04 Gnutella peer to peer network from August 4 2002\n2309 SNAP/p2p-Gnutella05 Gnutella peer to peer network from August 5 2002\n2310 SNAP/p2p-Gnutella06 Gnutella peer to peer network from August 6 2002\n2311 SNAP/p2p-Gnutella08 Gnutella peer to peer network from August 8 2002\n2312 SNAP/p2p-Gnutella09 Gnutella peer to peer network from August 9 2002\n2313 SNAP/p2p-Gnutella24 Gnutella peer to peer network from August 24 2002\n2314 SNAP/p2p-Gnutella25 Gnutella peer to peer network from August 25 2002\n2315 SNAP/p2p-Gnutella30 Gnutella peer to peer network from August 30 2002\n2316 SNAP/p2p-Gnutella31 Gnutella peer to peer network from August 31 2002\n2317 SNAP/roadNet-CA Road network of California\n2318 SNAP/roadNet-PA Road network of Pennsylvania\n2319 SNAP/roadNet-TX Road network of Texas\n2320 SNAP/as-735 733 daily instances(graphs) from November 8 1997 to January 2 2000\n2321 SNAP/as-Skitter Internet topology graph, from traceroutes run daily in 2005\n2322 SNAP/as-caida The CAIDA AS Relationships Datasets, from January 2004 to November 2007\n2323 SNAP/Oregon-1 AS peering information inferred from Oregon route-views between March 31 and May 26 2001\n2324 SNAP/Oregon-2 AS peering information inferred from Oregon route-views between March 31 and May 26 2001\n2325 SNAP/soc-sign-epinions Epinions signed social network\n2326 SNAP/soc-sign-Slashdot081106 Slashdot Zoo signed social network from November 6 2008\n2327 SNAP/soc-sign-Slashdot090216 Slashdot Zoo signed social network from February 16 2009\n2328 SNAP/soc-sign-Slashdot090221 Slashdot Zoo signed social network from February 21 2009\n\nThen the following problems were added in July 2018. All data and\nmetadata from the SNAP data set was imported into the SuiteSparse\nMatrix Collection.\n\n2777 SNAP/CollegeMsg Messages on a Facebook-like platform at UC-Irvine\n2778 SNAP/com-Amazon Amazon product network\n2779 SNAP/com-DBLP DBLP collaboration network\n2780 SNAP/com-Friendster Friendster online social network\n2781 SNAP/com-LiveJournal LiveJournal online social network\n2782 SNAP/com-Orkut Orkut online social network\n2783 SNAP/com-Youtube Youtube online social network\n2784 SNAP/email-Eu-core E-mail network\n2785 SNAP/email-Eu-core-temporal E-mails between users at a research institution\n2786 SNAP/higgs-twitter twitter messages re: Higgs boson on 4th July 2012.\n2787 SNAP/loc-Brightkite Brightkite location based online social network\n2788 SNAP/loc-Gowalla Gowalla location based online social network\n2789 SNAP/soc-Pokec Pokec online social network\n2790 SNAP/soc-sign-bitcoin-alpha Bitcoin Alpha web of trust network\n2791 SNAP/soc-sign-bitcoin-otc Bitcoin OTC web of trust network\n2792 SNAP/sx-askubuntu Comments, questions, and answers on Ask Ubuntu\n2793 SNAP/sx-mathoverflow Comments, questions, and answers on Math Overflow\n2794 SNAP/sx-stackoverflow Comments, questions, and answers on Stack Overflow\n2795 SNAP/sx-superuser Comments, questions, and answers on Super User\n2796 SNAP/twitter7 A collection of 476 million tweets collected between June-Dec 2009\n2797 SNAP/wiki-RfA Wikipedia Requests for Adminship (with text)\n2798 SNAP/wiki-talk-temporal Users editing talk pages on Wikipedia\n2799 SNAP/wiki-topcats Wikipedia hyperlinks (with communities)\n\nThe following 13 graphs/networks were in the SNAP data set in July 2018 but\nhave not yet been imported into the SuiteSparse Matrix Collection. They may be\nadded in the future:\n\n amazon-meta\n ego-Facebook\n ego-Gplus\n ego-Twitter\n gemsec-Deezer\n gemsec-Facebook\n ksc-time-series\n memetracker9\n web-flickr\n web-Reddit\n web-RedditPizzaRequests\n wiki-Elec\n wiki-meta\n wikispeedia\n\nThe 2010 description of the SNAP data set gave these categories:\n\n * Social networks: online social networks, edges represent interactions\n between people\n\n * Communication networks: email communication networks with edges\n representing communication\n\n * Citation networks: nodes represent papers, edges represent citations\n\n * Collaboration networks: nodes represent scientists, edges represent\n collaborations (co-authoring a paper)\n\n * Web graphs: nodes represent webpages and edges are hyperlinks\n\n * Blog and Memetracker graphs: nodes represent time stamped blog posts,\n edges are hyperlinks [revised below]\n\n * Amazon networks : nodes represent products and edges link commonly\n co-purchased products\n\n * Internet networks : nodes represent computers and edges communication\n\n * Road networks : nodes represent intersections and edges roads connecting\n the intersections\n\n * Autonomous systems : graphs of the internet\n\n * Signed networks : networks with positive and negative edges (friend/foe,\n trust/distrust)\n\nBy July 2018, the following categories had been added:\n\n * Networks with ground-truth communities : ground-truth network communities\n in social and information networks\n\n * Location-based online social networks : Social networks with geographic\n check-ins\n\n * Wikipedia networks, articles, and metadata : Talk, editing, voting, and\n article data from Wikipedia\n\n * Temporal networks : networks where edges have timestamps\n\n * Twitter and Memetracker : Memetracker phrases, links and 467 million\n Tweets\n\n * Online communities : Data from online communities such as Reddit and Flickr\n\n * Online reviews : Data from online review systems such as BeerAdvocate and\n Amazon\n\n================================================================================\nNote that some versions of these graphs already appear in the SuiteSparse\ncollection. Some have similar names:\n\nweb-BerkStan Kamvar/Stanford_Berkeley\n in SNAP/: n: 685,230 nz: 7,600,595\n in Kamvar/ n: 683,446 nz: 7,583,376\n\n I obtained the Kamvar/Stanford_Berkeley directly\n from Sep Kamvar. It is slightly smaller than the\n version in SNAP. It is thus likely that Sep created\n multiple versions of the graph.\n\nweb-Google appears only in SNAP.\n\nweb-NotreDame Barabasi/NotreDame_www\n in SNAP/: n: 325,729 nz: 1,497,134\n in Barabasi/: n: same nz: 929,849\n\n The Barabasi/NotreDame_www is an exact copy of\n the graph of that name in the Pajek data set.\n The SNAP collection has a different version of this\n graph, of which SNAP/web-NotreDame is an exact copy.\n It is possible that Barabasi's version of the graph is yet\n a 3rd version of this graph.\n\nweb-Stanford Kamvar/Stanford (same size and nnz)\n n: 281,903 nz: 2,312,497\n\n The SNAP/web-Stanford graph and the Kamvar/Stanford\n graphs have the same number of nodes and edges.\n However, they differ in nonzero pattern.\n\ncit-HepTh Pajek/HEP-th-new is identical to the\n SNAP/cit-HeptTh graph. Since it's small, I have\n decided to include both in the collection, to\n keep the SNAP/ collection complete.\n n: 27,770 nz: 352,807\n\ncit-HepPh appears only in SNAP\nca-HepPh appears only in SNAP\nca-HepTh appears only in SNAP\n\nPajek/HEP-th appears only in the Pajek collection\n\ncit-Patents in SNAP n: 3,774,768 nz: 16,518,948\n Pajek/patents n: same nz: 14,970,767\n\n Both of these come from the NBER data. However,\n the edges are not the same. The SNAP/cit-Patents\n data is a strict superset of the Pajek/patents graph.\n If A0 = Pajek/patents and A1 = SNAP/cit-Patents,\n then nnz(A1-A0) = nnz(A1)-nnz(A0) = 1,548,181.\n All edges in A0 appear in A1.\n\n The aux data is not the same. Pajek/patents contains\n more auxiliary data for each node. This data can be\n used to interpret the SNAP/cit-Patents graph as well,\n since the nodes match up from one graph to the other.\n\nNOTE: the 2018 GraphChallenge data sets include many of these matrices,\nat https://graphchallenge.mit.edu. In the GraphChallenge data set,\nthe graphs have all been made symmetric, and the diagonal entries\n(self edges) have been removed. The meta data has been preserved\nin the SuiteSparse Matrix Collection, but does not appear in the\n2018 GraphChallenge data sets. Finally, the node ordering differs\nbetween the two; the SuiteSparse ordering either matches the SNAP\nnode ids 1:n or 0:n-1, or when the graph is a subset of node ids,\nthe node number is provided here in a Problem.aux.nodeid, or .userid\ncomponent of the problem. This information is not provided in the\nGraphChallenge data sets." - }, - { - "group": "Dziekonski", - "count": 5, - "documented_meaning": "High order vector finite element method in electromagnetics The matrices came from analysis of a 9-th order microwave", - "source_url": "https://sparse.tamu.edu/Dziekonski", - "coarse_kind_families_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "electromagnetics problem (5)", - "curator_value_type_count": 2, - "curator_value_types": "real (3); complex (2)", - "curator_structure_count": 1, - "curator_structures": "symmetric (5)", - "min_dimension": 420408, - "max_dimension": 1157456, - "dimension_ratio": 2.7531731080283914, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 21758924, - "max_nnz": 89306020, - "shape_profile": "square only", - "square": 5, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 3, - "structurally_symmetric_square": 5, - "known_spd": 0, - "value_profile": "real + complex", - "binary": 0, - "geometric_2d3d": 5, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "High order vector finite element method in electromagnetics\n\nThe matrices came from analysis of a 9-th order microwave\ncombline filter with second order (LT\\\\QN) vector finite elements\nwith different mesh quality. The matrices were used as an\nexample in our paper [1].\n\n- gsm_106857 - real symmetric matrix (589446 x 589446) and\n 21758924 nonzero elements. First 98577 unknowns corresponds\n to lowest level (CT\\\\LN) base functions.\n\nAll matrices are sparse and come with right-hand-sides.\n\n[1] GPU Acceleration of Multilevel Solvers for Analysis of\nMicrowave Components with Finite Element Method, A. Dziekonski,\nA. Lamecki, A., and M. Mrozowski, M., IEEE Microwave and\nWireless Components Letters, vol 20, number 12,\nDec 2010. http://dx.doi.org/10.1109/LMWC.2010.2089974\n\n-------\n\nThe dielFilter* matrices came from analysis of a 4th-pole\ndielectric resonator [4] generated with Finite Element Method.\nThe tetrahedral mesh of the structure was generated with the\nNetgen mesher [2]. The matrices were used as an example in\nour paper [3].\n\ndielFilterV2clx - complex symmetric matrix (607,232 x 607,232),\n 25,309,272 nonzero (real) and 728,900 nonzero (imag) elements.\n First 109,108 unknowns correspond to lowest level base\n functions.\n\ndielFilterV2real - real symmetric matrix (1,157,456 x 1,157,456)\n and 48,538,952 nonzero elements. First 209,432 unknowns\n correspond to lowest level base functions.\n\ndielFilterV3clx - complex symmetric matrix (420,408 x 420,408),\n 32,886,208 nonzero (real) and 3,706,513 (imag) elements. First\n 24,716 unknowns correspond to lowest level base functions,\n next 116,152 unknowns correspond to the second level.\n\ndielFilterV3real - real symmetric matrix (1,102,824 x 1,102,824)\n and 89,306,020 nonzero elements. First 66,353 unknowns\n correspond to lowest level base functions, next 305,729\n unknowns correspond to the second level.\n\nAll matrices are sparse and come with right-hand-sides.\n\n[2] J. Schoberl, \"NETGEN An advancing front 2D/3D-mesh\ngenerator based on abstract rules,\" Computing and Visualization\nin Science, vol. 1, No. 1, pp. 41-52, July 1997\n\n[3] A. Dziekonski, A. Lamecki, M. Mrozowski, Tuning A\nHybrid GPU-CPU V-cycle Multilevel Preconditioner for Solving\nLarge Real and Complex Systems of FEM Equations.\n\n[4] F. Alessandri, M. Chiodetti, A. Giugliarelli; D. Maiarelli,\nG. Martirano, D. Schmitt, L. Vanni and F. Vitulli. The\nelectric-field Integral-equation method for the analysis and\ndesign of a class of rectangular cavity filters loaded by\ndielectric and metallic cylindrical pucks, Microwave Theory\nand Techniques, IEEE Transactions on, vol. 52, no 8, pp.\n1790-1797, Aug. 2004." - }, - { - "group": "Fluorem", - "count": 5, - "documented_meaning": "CFD matrices from Francois Pacull, FLUOREM, in Lyon, France We are dealing with CFD and more precisely steady flow", - "source_url": "https://sparse.tamu.edu/Fluorem", - "coarse_kind_families_interpretive": "fluids/transport", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computational fluid dynamics problem (5)", - "curator_value_type_count": 1, - "curator_value_types": "real (5)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (5)", - "min_dimension": 903, - "max_dimension": 2017169, - "dimension_ratio": 2233.8527131782944, - "size_span": "wide (>100\u00d7)", - "min_nnz": 11766, - "max_nnz": 283073458, - "shape_profile": "square only", - "square": 5, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 5, - "cohesion": "coherent family/series", - "official_notes": "CFD matrices from Francois Pacull, FLUOREM, in Lyon, France\n\nWe are dealing with CFD and more precisely steady flow\nparametrization. The equations involved are the compressible\nNavier-Stokes ones (RANS). These matrices are real, square and\nindefinite, they correspond to the Jacobian with respect the\nconservative fluid variables of the discretized governing\nequations (finite-volume discretization). Thus they have a\nblock structure (corresponding to the mesh nodes: the block\nsize is the number of variables per mesh node), they are not\nsymmetric (however, their blockwise structure has a high level\nof symmetry) and they often show some kind of hyperbolic\nbehavior. They have not been scaled or reordered.\n\nThey are generated through automatic differentiation of the\nflow solver around a steady state. A right hand-side is also\ngiven for each matrix: this represents the derivative of the\nequations with respect to a parameter (of operation or shape).\nSince they are generated automatically, they may have \"silent\"\nvariables: these are variables corresponding to an identity\nsubmatrix associated with a null right hand-side, for example\none of the three velocity components in a 2D case, or the\nturbulent variables in a \"frozen\" turbulence case.\n\nWe believe that these matrices are good test cases when\nstudying preconditioning methods for iterative methods, such as\nblock incomplete factorization, or when studying domain\ndecomposition methods or deflation. They are actually being\nstudied by a few researchers in France regarding numerical\nmethods, through the LIBRAERO research project of the ANR (national\nresearch agency): ANR-07-TLOG-011.\n\nFrancois Pacull, Lyon, France. fpacull at fluorem.com\n\n Reference: \"A Study of ILU Factorization for Schwartz \n Preconditioners with Application to Computational Fluid \n Dynamics\", F. Pacull, S. Aubert, M. Buisson, Proceedings \n of the 2nd Intl Conf on Parallel, Distributed, Grid, and \n Cloud Computing for Engineering, B.H.V Topping and P. \n Iva'nyi, Editors. Civil-Comp Press, Stirlingshire, \n Scotland, 2011. \n\n\nSpecific problem descriptions:\n\n DK01R: 1D turbulent case\n number of mesh nodes: 129\n block size: 7\n variables: [rho,rho*u,rho*v,rho*w,rho*E,rho*k,rho*omega]\n (rho v and rho w are \"silent\", the third and fourth rows\n and columns\n in each block can be removed)\n matrix order: 903\n nnz: 11766\n comments: The DK01R matrix corresponds to a small 1D turbulent\n case. The grid has 129 nodes, non-uniformly spaced\n (geometrical distribution). The number of unknowns per node is\n 7, leading to a linear system of 903 real algebraic equations.\n The 1D discretization of the partial differential equations\n uses a 5 points stencil, leading to a block penta-diagonal\n matrix, each block having size 7 by 7. Each diagonal block is\n related to two up- and two down-stream neighboring nodes,\n corresponding respectively to the 14 upper and 14 lower matrix\n rows, the node ordering being coherent with the 1D spatial\n node distribution. The stationary flow on which the matrix is\n based on is dominated by advection, characterized by a Mach\n number around 0.3.\n\n GT01R: 2D inviscid case\n number of mesh nodes: 1596\n block size: 5\n variables: [rho,rho*u,rho*v,rho*w,rho*E]\n (rho w is \"silent\", the fourth row and column in each\n block can be removed)\n matrix order: 7980\n nnz: 430909\n comments: This is a 2D linear cascade turbine case. The grid\n corresponds to one inter-blade channel. The stencil involved\n by the convective scheme uses 9 nodes. Thus, there are 9\n non-zero blocks for each node in the matrix. The specificity\n is that the computational domain is periodic, which introduces\n some non-zeros elements far away from the diagonal.\n\n PR02R: 2D turbulent case\n number of mesh nodes: 23010\n block size: 7\n variables: [rho,rho*u,rho*v,rho*w,rho*E,rho*k,rho*omega]\n (rho v is \"silent\", the third row and column in each block\n can be removed)\n matrix order: 161070\n nnz: 8185136\n comments: This is a 2D turbulent case, dominated by\n convection. The geometry is a RAE wing profile and the mesh\n is C-shaped.\n\n RM07R: 3D viscous case with \"frozen\" turbulence\n number of mesh nodes: 54527\n block size: 7\n variables: [rho,rho*u,rho*v,rho*w,rho*E,rho*k,rho*omega]\n (rho k and rho omega are \"silent\", the sixth and seventh rows\n and columns in each block can be removed)\n matrix order: 381689\n nnz: 37464962\n comments: The geometry is a jet engine compressor.\n\n HV15R: 3D engine fan. The flow has a low Mach number.\n This is a 3D Reynolds-Averaged-Navier-Stokes case.\n Number of mesh nodes: 288,167\n block size: 7\n variables: [rho, rho*u, rho*v, rho*w, rho*E, rho*k, rho*omega]\n matrix order: 2,017,169\n nnz: 283,073,458" - }, - { - "group": "Rommes", - "count": 35, - "documented_meaning": "Power system models from Joost Rommes, Nelson Martins, Francisco Freitas This collection of power system models originates from real power systems,", - "source_url": "https://sparse.tamu.edu/Rommes", - "coarse_kind_families_interpretive": "eigenvalue/model reduction", - "dominant_coarse_family_interpretive": "eigenvalue/model reduction", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "eigenvalue/model reduction problem (35)", - "curator_value_type_count": 1, - "curator_value_types": "real (35)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (35)", - "min_dimension": 66, - "max_dimension": 40366, - "dimension_ratio": 611.6060606060606, - "size_span": "wide (>100\u00d7)", - "min_nnz": 1194, - "max_nnz": 145019, - "shape_profile": "square only", - "square": 35, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 16, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 35, - "cohesion": "coherent family/series", - "official_notes": "Power system models from Joost Rommes, Nelson Martins, Francisco Freitas\n\nThis collection of power system models originates from real power systems,\nmostly based on Brazilian interconection power systems (BIPS) models (the file\nnames refer to the actual power system related to a given year electric load\nscenario). These systems [E dx/dt = Ax + Bu ; y=Cx + Du] are interesting\nbenchmarks for several numerical algorithms, including eigenvalue algorithms\n(dominant modes/poles/zeros, stability analysis, computing rightmost\neigenvalues and/or with smallest damping ratio, eigenvalue parameter\nsensitivity) and model order reduction (large-scale DAEs ). Refer to the\ncorresponding publications for more details on the systems and numerical\nresults of several eigenvalue/model order reduction algorithms. For\ncorresponding software, see http://sites.google.com/site/rommes/software\n\nIf E is not present in the problem, then E=I should be assumed.\nIf D is not present, D=0 should be assumed. (Note that as of Jan 2011,\nno problem has a nonzero D).\n\nThe iv vector in some of the files is a vector with nonzeros (ones) at indices\nthat represent state-variables (the zeros are algebraic variables). One can\nconstruct the descriptor matrix E by E=spdiags(iv,0,n,n). This iv vector is\ngenerated by the Brazilian power system simulation software, and can be more\nefficient to compute with.\n\n\nTest systems:\n\nAll power system models originate from CEPEL ( http://www.cepel.br/ )\n\npower system file n #inputs #outputs references\n------------ ---- ------ ------- -------- -- \nNew England ww_36_pmec_36 66 1 1 [1]\nBIPS/97 ww_vref_6405 13251 1 1 [1]\nBIPS/2007 xingo_afonso_itaipu 13250 1 1 [2]\nBIPS/97 mimo8x8_system 13309 8 8 [3]\nBIPS/97 mimo28x28_system 13251 28 28 [3]\nBIPS/97 mimo46x46_system 13250 46 46 [4]\nJuba5723 juba40k 40337 2 1 [5]\nBauru5727 bauru5727 40366 2 2 [5]\nzeros_nopss zeros_nopss_13k 13296 46 46 [5]\nxingo6u descriptor_xingo6u 20738 1 6 [5]\nnopss nopss_11k 11685 1 1 [5]\nxingo3012 xingo3012 20944 2 2 [5]\nbips98_606 bips98_606 7135 4 4 [6]\nbips98_1142 bips98_1142 9735 4 4 [6]\nbips98_1450 bips98_1450 11305 4 4 [6]\nbips07_1693 bips07_1693 13275 4 4 [6]\nbips07_1998 bips07_1998 15066 4 4 [6]\nbips07_2476 bips07_2476 16861 4 4 [6]\nbips07_3078 bips07_3078 21128 4 4 [6]\n\nSeveral SISO/MIMO test systems, whose main components are transmission lines\n(TL) are available. TLs are modeled by ladder networks, comprised of cascaded\nRLC PI-circuits, having fixed parameters.\n\n Transmission lines with 10--80 PI Sections are considered.\n PIsections10to80.zip [Submitted]\n\n There are two kinds of files for representing a same system: the file with\n termination '_n' refers to an index-2 system DAE model, while '_n1' means\n a model of the same system, but for an index-1 DAE representation. The\n representation of each test system has the form [E dx/dt = Ax + Bu ; y=Cx] \n\n MIMO_PI_n:\n M10PI_n\n M20PI_n\n M40PI_n\n M80PI_n\n MIMO_PI_n1:\n M10PI_n1\n M20PI_n1\n M40PI_n1\n M80PI_n1\n SISO_PI_n:\n S10PI_n\n S20PI_n\n S40PI_n\n S80PI_n\n SISO_PI_n1:\n S10PI_n1\n S20PI_n1\n S40PI_n1\n S80PI_n1\n\nReferences:\n\n[1] ROMMES, J., MARTINS, N., Efficient computation of transfer function\n dominant poles using subspace acceleration. IEEE Trans. on Power Systems,\n Vol. 21, Issue 3, Aug. 2006, pp. 1218-1226 \n\n[2] ROMMES, J., MARTINS, N., Computing large-scale system eigenvalues most\n sensitive to parameter changes, with applications to power system\n small-signal stability , IEEE Transactions on Power Systems, Vol. 23, Issue\n 2, May 2008, pp. 434-442 \n\n[3] ROMMES, J., MARTINS, N., Efficient computation of multivariable transfer\n function dominant poles using subspace acceleration. 2006, IEEE Trans. on\n Power Systems, Vol. 21, Issue 4, Nov. 2006, pp. 1471-1483.\n\n[4] MARTINS, N., PELLANDA, P.C.,ROMMES, J., Computation of transfer function\n dominant zeros with applications to oscillation damping control of large\n power systems, IEEE Trans. on Power Systems, Vol. 22, Issue 4, Nov. 2007,\n pp. 1657-1664 \n\n[5] ROMMES, J., MARTINS, N., FREITAS, F., Computing Rightmost Eigenvalues for\n Small-Signal Stability Assessment of Large-Scale Power Systems, IEEE\n Transactions on Power Systems, Vol. 25, Issue 2, May 2010, pp.929-938\n\n[6] FREITAS, F., ROMMES, J., MARTINS, N., Gramian-Based Reduction Method\n Applied to Large Sparse Power System Descriptor Models, IEEE Transactions\n on Power Systems, Vol. 23, Issue 3, August 2008, pp. 1258-1270" - }, - { - "group": "Williams", - "count": 7, - "documented_meaning": "Matrices used by S. Williams et al for sparse matrix multiplication on GPUs. 14 matrices were used in the following paper:", - "source_url": "https://sparse.tamu.edu/Williams", - "coarse_kind_families_interpretive": "generic spatial PDE/thermal; graph/network data; other", - "dominant_coarse_family_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_share": 0.5714285714285714, - "normalized_kind_count": 4, - "normalized_kinds": "2D/3D problem (4); weighted undirected graph (1); economic problem (1); weighted directed graph (1)", - "curator_value_type_count": 2, - "curator_value_types": "real (6); integer (1)", - "curator_structure_count": 2, - "curator_structures": "symmetric (4); unsymmetric (3)", - "min_dimension": 36417, - "max_dimension": 1000005, - "dimension_ratio": 27.459840184529202, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 1273389, - "max_nnz": 6010480, - "shape_profile": "square only", - "square": 7, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 4, - "structurally_symmetric_square": 4, - "known_spd": 3, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 4, - "cohesion": "multi-domain/mixed", - "official_notes": "Matrices used by S. Williams et al for sparse matrix multiplication on GPUs.\n\n14 matrices were used in the following paper:\n\n S. Williams, L. Oliker, R. Vuduc, J. Shalf, K. Yelick, J. Demmel,\n \"Optimization of Sparse Matrix-Vector Multiplication on Emerging Multicore\n Platforms\", Parallel Computing Volume 35, Issue 3, March 2009, Pages\n 178-194. Special issue on Revolutionary Technologies for Acceleration of\n Emerging Petascale Applications.\n\n https://hpcrd.lbl.gov/~swwilliams/research/papers/parco08_spmv.pdf\n http://dx.doi.org/10.1016/j.parco.2008.12.006\n\nThis same set of 14 matrices was also used in a subsequent technical report by\nNVIDIA:\n\n http://www.nvidia.com/object/nvidia_research_pub_001.html \"Efficient Sparse\n Matrix-Vector Multiplication on CUDA\" Nathan Bell and Michael Garland, in,\n \"NVIDIA Technical Report NVR-2008-004\", December 2008\n\nfile Name dim* nnz description\n\ndense2 Dense 2K 4.0M dense matrix in sparse format\npdb1HYS Protein 36K 4.3M protein data bank 1HYS\nconsph FEM/Spheres 83K 6.0M FEM concentric spheres\ncant FEM/Cantilever 62K 4.0M FEM cantilever\npwtk Wind Tunnel 218K 11.6M pressurized wind tunnel\nrma10 FEM/Harbor 47K 2.37M 3D CFD of Charleston Harbor\nqcd5_4 QCD 49K 1.90M quark propagators (QCD/LGT)\nshipsec1 FEM/Ship 141K 3.98M FEM Ship section / detail\nmac_econ_fwd500 Economics 207K 1.27M Macroeconomic model\nmc2depi Epidemiology 526K 2.1M 2D Markov model of epidemic\ncop20k_A FEM/Accelerator 121K 2.62M Accelerator cavity design\nscircuit Circuit 171K 959K Motorola circuit simulation\nwebbase-1M webbase 1M 3.1M Web connectivity matrix\nrail4284 LP 4Kx1.1M 11.3M Railways set cover,\n constraint matrix\n\n(*) the matrix is square if only one dimension listed.\n\nSix of the matrices are nearly identical to the matrices already in the\nUF Collection. They are thus not included in the UF Collection:\n\n pwtk Boeing/pwtk. The matrix here differs in only 2 entries.\n The pattern is the same. The norm of the difference between\n the two is round-off error (5e-26). Tim Davis obtained the\n matrix directly from Roger Grimes, and was careful to preserve\n each significant bit. The Boeing/pwtk matrix in the UF\n Collection is thus the correct one.\n\n qcd5_4 this is the binary pattern of QCD/conf5_4-* and QCD/conf6_0-*,\n ids: 1598:1604 in the UF Collection. These are from B.\n Medeke, and were orginally collected by R. Boisvert et al.\n for the Matrix Market.\n\n rma10 Bova/rma10. The matrix here is binary; Bova/rma10 is not.\n The rma10 matrix here is binary pattern of Problem.A +\n Problem.Zeros in the UF Collection. Tim Davis obtained\n Bova/rma10 directly from the matrix creator, Steve Bova.\n\n scircuit Hamm/scircuit. The pattern is the same. The 1-norm of the\n difference is 5e-10, which is round-off error. This matrix was\n obtained by Tim Davis from Steve Hamm (at Motorola) directly,\n and thus the Hamm/scircuit matrix already in the UF Collection\n is the correct version.\n\n shipsec1 similar to DNVS/shipsec1, but different patterns. It appears\n that the shipsec1 matrix here is missing many entries in\n the lower right quadrant. The pattern differs by 0.9M\n entries. The matrix is from C. Damhaug, collected by\n J. Koster.\n\n rail4284 an exact submatrix of Mittelmann/rail4284. The UF matrix has\n exactly 4284 more columns than the rail4284 matrix in this\n collection. Let A1 be the Mittelman/rail4284 matrix, and let\n A2 be the rail4284 matrix in this collection. Then A2 is\n identical to A1(:,m+1:end). The submatrix A1(:,1:4284) of the\n matrix in the UF collection is exactly equal to -speye(m).\n Tim Davis obtained the matrix directly from Mittelmann's\n test set. It's possible that multiple versions exist, or\n that the identity was added as the initial basis. In any\n case, the rail4284 matrix from Williams' test set is not\n added to the UF Collection.\n\nThe remaining eight matrices are unique to this set of matrices, but one is of\nno interest (dense2). Thus, there are seven matrices in the Williams/ group in\nthe UF Collection:\n\n pdb1HYS\n consph\n cant\n mac_econ_fwd500\n mc2depi\n cop20k_A\n webbase-1M\n\nI presume the pdb1HYS matrix comes from this source:\n\n http://www.rcsb.org/pdb/explore.do?structureId=1HYS\n http://dx.doi.org/10.2210/pdb1hys/pdb\n Crystal structure of HIV-1 reverse transcriptase in complex with a\n polypurine tract RNA:DNA.\n Sarafianos, S.G., Das, K., Tantillo, C., Clark Jr., A.D., Ding,\n J., Whitcomb, J.M., Boyer, P.L., Hughes, S.H., Arnold, E.\n Journal: (2001) EMBO J. 20: 1449-1461\n PubMed: 11250910 \n PubMedCentral: PMC145536 \n DOI: 10.1093/emboj/20.6.1449 \n Search Related Articles in PubMed \n PubMed Abstract:\n We have determined the 3.0 A resolution structure of wild-type HIV-1\n reverse transcriptase in complex with an RNA:DNA oligonucleotide whose\n sequence includes a purine-rich segment from the HIV-1 genome called the\n polypurine tract (PPT). The PPT is resistant to ribonuclease... [ Read\n More & Search PubMed Abstracts ] We have determined the 3.0 A resolution\n structure of wild-type HIV-1 reverse transcriptase in complex with an\n RNA:DNA oligonucleotide whose sequence includes a purine-rich segment from\n the HIV-1 genome called the polypurine tract (PPT). The PPT is resistant\n to ribonuclease H (RNase H) cleavage and is used as a primer for second\n DNA strand synthesis. The 'RNase H primer grip', consisting of amino\n acids that interact with the DNA primer strand, may contribute to RNase H\n catalysis and cleavage specificity. Cleavage specificity is also\n controlled by the width of the minor groove and the trajectory of the\n RNA:DNA, both of which are sequence dependent. An unusual 'unzipping' of 7\n bp occurs in the adenine stretch of the PPT: an unpaired base on the\n template strand takes the base pairing out of register and then, following\n two offset base pairs, an unpaired base on the primer strand\n re-establishes the normal register. The structural aberration extends to\n the RNase H active site and may play a role in the resistance of PPT to\n RNase H cleavage." - }, - { - "group": "Dattorro", - "count": 3, - "documented_meaning": "Dattorro Convex Optimization of Eternity II, Jon Dattoro", - "source_url": "https://sparse.tamu.edu/Dattorro", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "optimization problem (3)", - "curator_value_type_count": 1, - "curator_value_types": "integer (3)", - "curator_structure_count": 1, - "curator_structures": "rectangular (3)", - "min_dimension": 150638, - "max_dimension": 262144, - "dimension_ratio": 1.740224910049257, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 782087, - "max_nnz": 1503732, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 3, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Dattorro Convex Optimization of Eternity II, Jon Dattoro\n\nhttp://www.convexoptimization.com/wikimization/index.php/Dattorro_Convex_Optimization_of_Eternity_II\n\nAn Eternity II puzzle (http://www.eternityii.com/) problem\nformulation A*x=b is discussed thoroughly in section 4.6.0.0.15 of\nthe book Convex Optimization & Euclidean Distance Geometry which\nis freely available. That A matrix is obtained by presolving a\nsparse 864,593-by-1,048,576 system. The 3 problems in this set\ncontains three successive reductions, each equivalent to that\nlarger system:\n\n * EternityII_E: a 11077-by-262144 system E*x=tau, where tau is\n 11077-by-1. This is the million column Eternity II matrix\n having redundant rows and columns removed analytically.\n In the UF Collection, E is the Problem.A matrix, and tau\n is Problem.b. All entries in E are from the set {-1,0,1,2}.\n tau is binary.\n\n * EternityII_Etilde: a 10054-by-204304 system Etilde*x=tautilde\n with tautilde of size 10054-by-1. The system has columns\n removed corresponding to some known zero variables\n (removal produced dependent rows). In the UF Collection,\n Etilde is the Problem.A matrix, and tautilde is Problem.b.\n All entries in Etilde are from the set {-1,0,1}.\n tautilde is binary.\n\n * EternityII_A: a 7362-by-150638 system A*x=b, where b is\n 7362-by-1. This system has columns removed not in\n smallest face (containing tautilde) of polyhedral cone\n K = { Etilde*x | x >= 0 }.\n\nThe following linear program is a very difficult problem that\nremains unsolved:\n\n maximize_x z'*x, subject to A*x=b and x >= 0\n\nThe matrix A in the EternityII_A problem is sparse, having only\n782,087 nonzeros. All entries of A are integers from the set\n{-1,0,1}. The vector b is binary, with only 358 nonzeros.\n\nDirection vector z is determined by Convex Iteration: \n\n maximize_z z'*x^{star}\n subject to 0 <= z <= 1 and z'*1 = 256\n\n(for a vector x, x >= 0 means all(x>=0) in MATLAB notation).\n\nThese two problems are iterated to find a minimal cardinality\nsolution x. Constraint A*x=b bounds the variable from above by 1.\nAny minimal cardinality solution is binary and solves the Eternity\nII puzzle. The Eternity II puzzle is solved when\nz^{star}'*x^{star} = 256.\n\nMinimal cardinality of this Eternity II problem is equal to number\nof puzzle pieces, 256.\n\nComment: The technique, convex iteration, requires no modification\n(and works very well) when applied instead to mixed integer\nprogramming (MIP, not discussed in book). There is no modification\nto the linear program statement here except 256 variables,\ncorresponding to the largest entries of iterate x^{star}, are\ndeclared binary." - }, - { - "group": "Newman", - "count": 16, - "documented_meaning": "Network graph collection from Mark Newman, University of Michigan", - "source_url": "https://sparse.tamu.edu/Newman", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 5, - "normalized_kinds": "undirected weighted graph (7); undirected graph (6); directed weighted graph (1); undirected multigraph (1); directed multigraph (1)", - "curator_value_type_count": 3, - "curator_value_types": "binary (6); real (6); integer (4)", - "curator_structure_count": 2, - "curator_structures": "symmetric (14); unsymmetric (2)", - "min_dimension": 34, - "max_dimension": 40421, - "dimension_ratio": 1188.8529411764705, - "size_span": "wide (>100\u00d7)", - "min_nnz": 156, - "max_nnz": 351382, - "shape_profile": "square only", - "square": 16, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 14, - "structurally_symmetric_square": 14, - "known_spd": 0, - "value_profile": "real only", - "binary": 6, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Network graph collection from Mark Newman, University of Michigan\nhttp://www-personal.umich.edu/~mejn/netdata/\n\nProblems in this data set:\n\nadjnoun Common adjective and nouns in \"David Copperfield\"\nas-22july06 (symmetrized) structure of internet routers as of July 22, 2006\nastro-ph collaboration network, preprints in astrophysics archive, www.arxiv.org\ncelegansneural Neural network of the nematode C. Elegans\ncond-mat collaboration network, preprints in condensed matter archive, www.arxiv.org\ncond-mat-2003 collaboration network, preprints in condensed matter archive, www.arxiv.org\ncond-mat-2005 collaboration network, preprints in condensed matter archive, www.arxiv.org\ndolphins social network of dolphins, Doubtful Sound, New Zealand\nfootball American football games between Div IA colleges, Fall 2000\nhep-th collaboration network, preprints in high-energy physics, www.arxiv.org\nkarate Karate club, from Wayne Zachary, 1977\nlesmis co-appearences of characters in Les Miserables by Victor Hugo\nnetscience co-authoship of scientists in network theory & experiments\npolblogs Political blogosphere Feb. 2005\npolbooks Books about US politics, compiled by V. Krebs\npower Western States Power Grid, Watts & Strogatz" - }, - { - "group": "Arenas", - "count": 4, - "documented_meaning": "Networks from Alex Arenas, Univeristy Rovira i Virgili, Tarragona, Spain", - "source_url": "https://sparse.tamu.edu/Arenas", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 2, - "normalized_kinds": "undirected multigraph (3); undirected graph (1)", - "curator_value_type_count": 2, - "curator_value_types": "integer (3); binary (1)", - "curator_structure_count": 1, - "curator_structures": "symmetric (4)", - "min_dimension": 198, - "max_dimension": 10680, - "dimension_ratio": 53.93939393939394, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 4065, - "max_nnz": 48632, - "shape_profile": "square only", - "square": 4, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 4, - "structurally_symmetric_square": 4, - "known_spd": 0, - "value_profile": "real only", - "binary": 1, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Networks from Alex Arenas, Univeristy Rovira i Virgili, Tarragona, Spain\nhttp://deim.urv.cat/~aarenas/data/welcome.htm\n\nPGPgiantcompo:\n\n Graph of the largest component of the network of users of the\n Pretty-Good-Privacy algorithm for secure information interchange.\n Reference: M. Boguna, R. Pastor-Satorras, A. Diaz-Guilera and\n A. Arenas, Physical Review E, vol. 70, 056122 (2004).\n\njazz:\n\n Network of Jazz musicians.\n Reference: P.Gleiser and L. Danon , Adv. Complex Syst.6, 565 (2003).\n\ncelegans_metabolic:\n\n Graph of the metabolic network of C.elegans.\n Reference: Community identification using Extremal Optimization,\n J. Duch and A. Arenas, Physical Review E , vol. 72, 027104, (2005).\n\nemail:\n\n Network of e-mail interchanges between members of\n the Univeristy Rovira i Virgili (Tarragona).\n Reference: R. Guimera, L. Danon, A. Diaz-Guilera, F. Giralt and A. Arenas\n Physical Review E , vol. 68, 065103(R), (2003).\n\nAll data compiled by members of Alex Arenas' group." - }, - { - "group": "AG-Monien", - "count": 32, - "documented_meaning": "AG-Monien Graph Collection, Ralf Diekmann and Robert Preis", - "source_url": "https://sparse.tamu.edu/AG-Monien", - "coarse_kind_families_interpretive": "generic spatial PDE/thermal; graph/network data", - "dominant_coarse_family_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_share": 0.8125, - "normalized_kind_count": 2, - "normalized_kinds": "2D/3D problem (26); undirected graph (6)", - "curator_value_type_count": 1, - "curator_value_types": "binary (32)", - "curator_structure_count": 1, - "curator_structures": "symmetric (32)", - "min_dimension": 224, - "max_dimension": 1048576, - "dimension_ratio": 4681.142857142857, - "size_span": "wide (>100\u00d7)", - "min_nnz": 840, - "max_nnz": 4194298, - "shape_profile": "square only", - "square": 32, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 32, - "structurally_symmetric_square": 32, - "known_spd": 0, - "value_profile": "real only", - "binary": 32, - "geometric_2d3d": 26, - "cohesion": "multi-domain/mixed", - "official_notes": "AG-Monien Graph Collection, Ralf Diekmann and Robert Preis\nhttp://www2.cs.uni-paderborn.de/fachbereich/AG/monien/RESEARCH/PART/graphs.html\n\nA collection of test graphs from various sources. Many of the graphs\ninclude XY or XYZ coordinates. This set also includes some graphs from\nthe Harwell-Boeing collection, the NASA matrices, and some random matrices\nwhich are not included here in the AG-Monien/ group of the UF Collection.\nIn addition, two graphs already appear in other groups:\n\n AG-Monien/big : same as Nasa/barth5, Pothen/barth5 (not included here)\n AG-Monien/cage_3_11 : same as Pajek/GD98_c (included here)\n\nThe AG-Monien/GRID subset is not included. It contains square grids that\nare already well-represented in the UF Collection.\n\nThese graphs appear in this set, as individual graphs, all with XY or XYZ\ncoordinates:\n\n AG-Monien/3elt\n AG-Monien/3elt_dual\n AG-Monien/airfoil1\n AG-Monien/airfoil1_dual\n AG-Monien/big_dual\n AG-Monien/crack\n AG-Monien/crack_dual\n AG-Monien/grid1\n AG-Monien/grid1_dual\n AG-Monien/grid2\n AG-Monien/grid2_dual\n AG-Monien/netz4504\n AG-Monien/netz4504_dual\n AG-Monien/ukerbe1\n AG-Monien/ukerbe1_dual\n AG-Monien/whitaker3\n AG-Monien/whitaker3_dual\n AG-Monien/brack2\n AG-Monien/wave\n AG-Monien/diag\n AG-Monien/L\n AG-Monien/L-9\n AG-Monien/stufe\n AG-Monien/stufe-10\n AG-Monien/biplane-9\n AG-Monien/shock-9\n\nNote that L-9, stufe-10, biplane-9 and shock-9 were L.9, stufe.10,\netc, in the AG-Monien set. The UF Collection does not permit \".\" in\nthe matrix name.\n\nSix more problem sets are included as sequences, each sequence being\na single problem instance in the UF Collection:\n\n AG-Monien/bfly: 10 butterfly graphs 3..12\n AG-Monien/cage: 45 cage graphs 3..12\n AG-Monien/cca: 10 cube-connected cycle graphs, no wrap\n AG-Monien/ccc: 10 cube-connected cycle graphs, with wrap\n AG-Monien/debr: 18 De Bruijn graphs\n AG-Monien/se: 13 shuffle-exchange graphs\n\nThe primary graph (Problem.A) in each sequence is the last graph\nin the sequence. In the Matrix Market and Rutherford-Boeing\nformats, the filenames will differ from the names given below,\nbecause in the UF Collection, the file name gives the place of\na graph in its sequence. The correspondence with the original\ngraph names is given below.\n\nGraphs in the bfly sequence:\n\n 1 : BFLY3 : 24 nodes 48 edges 96 nonzeros\n 2 : BFLY4 : 64 nodes 128 edges 256 nonzeros\n 3 : BFLY5 : 160 nodes 320 edges 640 nonzeros\n 4 : BFLY6 : 384 nodes 768 edges 1536 nonzeros\n 5 : BFLY7 : 896 nodes 1792 edges 3584 nonzeros\n 6 : BFLY8 : 2048 nodes 4096 edges 8192 nonzeros\n 7 : BFLY9 : 4608 nodes 9216 edges 18432 nonzeros\n 8 : BFLY10 : 10240 nodes 20480 edges 40960 nonzeros\n 9 : BFLY11 : 22528 nodes 45056 edges 90112 nonzeros\n 10 : BFLY12 : 49152 nodes 98304 edges 196608 nonzeros\n\nGraphs in the cage sequence:\n\n 1 : cage_3_5 : 10 nodes 15 edges 30 nonzeros\n 2 : cage_3_6 : 14 nodes 21 edges 42 nonzeros\n 3 : cage_3_7 : 24 nodes 36 edges 72 nonzeros\n 4 : cage_3_8 : 30 nodes 45 edges 90 nonzeros\n 5 : cage_3_9.1 : 58 nodes 87 edges 174 nonzeros\n 6 : cage_3_9.2 : 58 nodes 87 edges 174 nonzeros\n 7 : cage_3_9.3 : 58 nodes 87 edges 174 nonzeros\n 8 : cage_3_9.4 : 58 nodes 87 edges 174 nonzeros\n 9 : cage_3_9.5 : 58 nodes 87 edges 174 nonzeros\n 10 : cage_3_9.6 : 58 nodes 87 edges 174 nonzeros\n 11 : cage_3_9.7 : 58 nodes 87 edges 174 nonzeros\n 12 : cage_3_9.8 : 58 nodes 87 edges 174 nonzeros\n 13 : cage_3_9.9 : 58 nodes 87 edges 174 nonzeros\n 14 : cage_3_9.10 : 58 nodes 87 edges 174 nonzeros\n 15 : cage_3_9.11 : 58 nodes 87 edges 174 nonzeros\n 16 : cage_3_9.12 : 58 nodes 87 edges 174 nonzeros\n 17 : cage_3_9.13 : 58 nodes 87 edges 174 nonzeros\n 18 : cage_3_9.14 : 58 nodes 87 edges 174 nonzeros\n 19 : cage_3_9.15 : 58 nodes 87 edges 174 nonzeros\n 20 : cage_3_9.16 : 58 nodes 87 edges 174 nonzeros\n 21 : cage_3_9.17 : 58 nodes 87 edges 174 nonzeros\n 22 : cage_3_9.18 : 58 nodes 87 edges 174 nonzeros\n 23 : cage_3_10.1 : 70 nodes 105 edges 210 nonzeros\n 24 : cage_3_10.2 : 70 nodes 105 edges 210 nonzeros\n 25 : cage_3_10.3 : 70 nodes 105 edges 210 nonzeros\n 26 : cage_3_11 : 112 nodes 168 edges 336 nonzeros\n 27 : cage_3_12 : 126 nodes 189 edges 378 nonzeros\n 28 : cage_3_13 : 272 nodes 408 edges 816 nonzeros\n 29 : cage_3_14 : 406 nodes 609 edges 1218 nonzeros\n 30 : cage_3_15 : 620 nodes 930 edges 1860 nonzeros\n 31 : cage_4_5 : 19 nodes 38 edges 76 nonzeros\n 32 : cage_4_6 : 26 nodes 52 edges 104 nonzeros\n 33 : cage_4_7 : 76 nodes 152 edges 304 nonzeros\n 34 : cage_4_8 : 80 nodes 160 edges 320 nonzeros\n 35 : cage_5_5 : 30 nodes 75 edges 150 nonzeros\n 36 : cage_5_6 : 42 nodes 105 edges 210 nonzeros\n 37 : cage_6_6 : 62 nodes 186 edges 372 nonzeros\n 38 : cage_7_5 : 50 nodes 175 edges 350 nonzeros\n 39 : cage_8_5 : 94 nodes 376 edges 752 nonzeros\n 40 : cage_8_6 : 114 nodes 456 edges 912 nonzeros\n 41 : cage_9_5 : 118 nodes 531 edges 1062 nonzeros\n 42 : cage_9_6 : 146 nodes 657 edges 1314 nonzeros\n 43 : cage_10_6 : 182 nodes 910 edges 1820 nonzeros\n 44 : cage_12_6 : 266 nodes 1596 edges 3192 nonzeros\n 45 : cage_14_6 : 366 nodes 2562 edges 5124 nonzeros\n\nGraphs in the cca sequence:\n\n 1 : CCA3 : 24 nodes 28 edges 56 nonzeros\n 2 : CCA4 : 64 nodes 80 edges 160 nonzeros\n 3 : CCA5 : 160 nodes 208 edges 416 nonzeros\n 4 : CCA6 : 384 nodes 512 edges 1024 nonzeros\n 5 : CCA7 : 896 nodes 1216 edges 2432 nonzeros\n 6 : CCA8 : 2048 nodes 2816 edges 5632 nonzeros\n 7 : CCA9 : 4608 nodes 6400 edges 12800 nonzeros\n 8 : CCA10 : 10240 nodes 14336 edges 28672 nonzeros\n 9 : CCA11 : 22528 nodes 31744 edges 63488 nonzeros\n 10 : CCA12 : 49152 nodes 69632 edges 139264 nonzeros\n\nGraphs in the ccc sequence:\n\n 1 : CCC3 : 24 nodes 36 edges 72 nonzeros\n 2 : CCC4 : 64 nodes 96 edges 192 nonzeros\n 3 : CCC5 : 160 nodes 240 edges 480 nonzeros\n 4 : CCC6 : 384 nodes 576 edges 1152 nonzeros\n 5 : CCC7 : 896 nodes 1344 edges 2688 nonzeros\n 6 : CCC8 : 2048 nodes 3072 edges 6144 nonzeros\n 7 : CCC9 : 4608 nodes 6912 edges 13824 nonzeros\n 8 : CCC10 : 10240 nodes 15360 edges 30720 nonzeros\n 9 : CCC11 : 22528 nodes 33792 edges 67584 nonzeros\n 10 : CCC12 : 49152 nodes 73728 edges 147456 nonzeros\n\nGraphs in the debr sequence:\n\n 1 : DEBR3 : 8 nodes 13 edges 26 nonzeros\n 2 : DEBR4 : 16 nodes 29 edges 58 nonzeros\n 3 : DEBR5 : 32 nodes 61 edges 122 nonzeros\n 4 : DEBR6 : 64 nodes 125 edges 250 nonzeros\n 5 : DEBR7 : 128 nodes 253 edges 506 nonzeros\n 6 : DEBR8 : 256 nodes 509 edges 1018 nonzeros\n 7 : DEBR9 : 512 nodes 1021 edges 2042 nonzeros\n 8 : DEBR10 : 1024 nodes 2045 edges 4090 nonzeros\n 9 : DEBR11 : 2048 nodes 4093 edges 8186 nonzeros\n 10 : DEBR12 : 4096 nodes 8189 edges 16378 nonzeros\n 11 : DEBR13 : 8192 nodes 16381 edges 32762 nonzeros\n 12 : DEBR14 : 16384 nodes 32765 edges 65530 nonzeros\n 13 : DEBR15 : 32768 nodes 65533 edges 131066 nonzeros\n 14 : DEBR16 : 65536 nodes 131069 edges 262138 nonzeros\n 15 : DEBR17 : 131072 nodes 262141 edges 524282 nonzeros\n 16 : DEBR18 : 262144 nodes 524285 edges 1048570 nonzeros\n 17 : DEBR19 : 524288 nodes 1048573 edges 2097146 nonzeros\n 18 : DEBR20 : 1048576 nodes 2097149 edges 4194298 nonzeros\n\nGraphs in the se sequence:\n\n 1 : SE3 : 8 nodes 10 edges 20 nonzeros\n 2 : SE4 : 16 nodes 21 edges 42 nonzeros\n 3 : SE5 : 32 nodes 46 edges 92 nonzeros\n 4 : SE6 : 64 nodes 93 edges 186 nonzeros\n 5 : SE7 : 128 nodes 190 edges 380 nonzeros\n 6 : SE8 : 256 nodes 381 edges 762 nonzeros\n 7 : SE9 : 512 nodes 766 edges 1532 nonzeros\n 8 : SE10 : 1024 nodes 1533 edges 3066 nonzeros\n 9 : SE11 : 2048 nodes 3070 edges 6140 nonzeros\n 10 : SE12 : 4096 nodes 6141 edges 12282 nonzeros\n 11 : SE13 : 8192 nodes 12286 edges 24572 nonzeros\n 12 : SE14 : 16384 nodes 24573 edges 49146 nonzeros\n 13 : SE15 : 32768 nodes 49150 edges 98300 nonzeros" - }, - { - "group": "LAW", - "count": 15, - "documented_meaning": "Laboratory for Web Algorithmics (LAW), Universit\u00e0 degli Studi di Milano", - "source_url": "https://sparse.tamu.edu/LAW", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 2, - "normalized_kinds": "directed graph (13); undirected graph (2)", - "curator_value_type_count": 1, - "curator_value_types": "binary (15)", - "curator_structure_count": 2, - "curator_structures": "unsymmetric (13); symmetric (2)", - "min_dimension": 69244, - "max_dimension": 118142155, - "dimension_ratio": 1706.171726070129, - "size_span": "wide (>100\u00d7)", - "min_nnz": 276143, - "max_nnz": 1949412601, - "shape_profile": "square only", - "square": 15, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 2, - "structurally_symmetric_square": 2, - "known_spd": 0, - "value_profile": "real only", - "binary": 15, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Laboratory for Web Algorithmics (LAW), Universit\u00e0 degli Studi di Milano\nhttp://law.di.unimi.it/index.php\n\nWhen using matrices in the LAW/ group in the collection, please follow the\ncitation instructions at http://law.di.unimi.it/datasets.php\n\nIf you publish results based on these graphs, please acknowledge the usage of\nWebGraph and LLP by quoting the following papers:\n\n@inproceedings{BoVWFI,\n author =\"Paolo Boldi and Sebastiano Vigna\",\n title = \"The {W}eb{G}raph Framework {I}: {C}ompression Techniques\",\n year = 2004,\n booktitle=\"Proc. of the Thirteenth International World Wide Web Conference (WWW 2004)\",\n address=\"Manhattan, USA\",\n pages=\"595--601\",\n publisher=\"ACM Press\"\n}\n\n@inproceedings{BRSLLP,\n author = \"Paolo Boldi and Marco Rosa and Massimo Santini and Sebastiano Vigna\",\n title = \"Layered Label Propagation: A MultiResolution Coordinate-Free Ordering\n for Compressing Social Networks\",\n booktitle=\"Proceedings of the 20th international conference on World Wide Web\",\n year = 2011,\n publisher=\"ACM Press\"\n}\n\nIf the graphs you are using were gathered by UbiCrawler, please acknowledge the\nusage of UbiCrawler by quoting the following paper:\n\n@article{BCSU3,\n author=\"Paolo Boldi and Bruno Codenotti and Massimo Santini and Sebastiano Vigna\",\n title=\"UbiCrawler: A Scalable Fully Distributed Web Crawler\",\n journal=\"Software: Practice and Experience\",\n year=2004,\n volume=34,\n number=8,\n pages=\"711--726\"\n}\n\nNotes on the graphs in the UF Collection:\n\n The node labels have not been imported into the UF Collection,\n since they are not yet compatible with the UF format.\n They are available at the http://law.di.unimi.it/index.php site\n as separate files.\n\n The MATLAB Problem.A matrix is stored in double precision, but\n these files are all binary. These graphs are also quite huge.\n If you have problems with memory usage, you can convert the\n graphs to the logical format. For example:\n\n Problem = UFget ('LAW/uk-2002') ;\n A = logical (Problem.A) ;\n clear Problem\n\n The above transformation will temporarily take additional space,\n but once the transformation is made, A will be about half as\n big as the original Problem.A. This will free up memory for\n other algorithms to operate on the graph (assuming that they\n can operate on logical sparse matrices).\n\nFor a complete list of the graphs in the LAW collection, and\nfor a wealth of statistical data on the graphs, please\nrefer to http://law.di.unimi.it/datasets.php for details." - }, - { - "group": "DIMACS10", - "count": 151, - "documented_meaning": "10th DIMACS Implementation Challenge Updated July 2012", - "source_url": "https://sparse.tamu.edu/DIMACS10", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 6, - "normalized_kinds": "undirected graph (67); undirected weighted graph (50); random unweighted graph (15); undirected random graph (10); undirected multigraph (6); random undirected graph (3)", - "curator_value_type_count": 2, - "curator_value_types": "binary (95); integer (56)", - "curator_structure_count": 1, - "curator_structures": "symmetric (151)", - "min_dimension": 39, - "max_dimension": 50912018, - "dimension_ratio": 1305436.358974359, - "size_span": "wide (>100\u00d7)", - "min_nnz": 340, - "max_nnz": 265114400, - "shape_profile": "square only", - "square": 151, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 151, - "structurally_symmetric_square": 151, - "known_spd": 0, - "value_profile": "real only", - "binary": 95, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "10th DIMACS Implementation Challenge\n\nUpdated July 2012\n\nhttp://www.cc.gatech.edu/dimacs10/index.shtml\nhttp://www.cise.ufl.edu/research/sparse/dimacs10\n\nAs stated on their main website (\nhttp://dimacs.rutgers.edu/Challenges/ ), the \"DIMACS Implementation\nChallenges address questions of determining realistic algorithm\nperformance where worst case analysis is overly pessimistic and\nprobabilistic models are too unrealistic: experimentation can provide\nguides to realistic algorithm performance where analysis fails.\"\n\nFor the 10th DIMACS Implementation Challenge, the two related\nproblems of graph partitioning and graph clustering were chosen.\nGraph partitioning and graph clustering are among the aforementioned\nquestions or problem areas where theoretical and practical results\ndeviate significantly from each other, so that experimental outcomes\nare of particular interest.\n\nProblem Motivation\n\nGraph partitioning and graph clustering are ubiquitous subtasks in\nmany application areas. Generally speaking, both techniques aim at\nthe identification of vertex subsets with many internal and few\nexternal edges. To name only a few, problems addressed by graph\npartitioning and graph clustering algorithms are:\n\n * What are the communities within an (online) social network?\n * How do I speed up a numerical simulation by mapping it\n efficiently onto a parallel computer?\n * How must components be organized on a computer chip such that\n they can communicate efficiently with each other?\n * What are the segments of a digital image?\n * Which functions are certain genes (most likely) responsible\n for?\n\nChallenge Goals\n\n * One goal of this Challenge is to create a reproducible picture\n of the state-of-the-art in the area of graph partitioning\n (GP) and graph clustering (GC) algorithms. To this end we\n are identifying a standard set of benchmark instances and\n generators.\n\n * Moreover, after initiating a discussion with the community, we\n would like to establish the most appropriate problem\n formulations and objective functions for a variety of\n applications.\n\n * Another goal is to enable current researchers to compare their\n codes with each other, in hopes of identifying the most\n effective algorithmic innovations that have been proposed.\n\n * The final goal is to publish proceedings containing results\n presented at the Challenge workshop, and a book containing\n the best of the proceedings papers.\n\nProblems Addressed\n\nThe precise problem formulations need to be established in the course\nof the Challenge. The descriptions below serve as a starting point.\n\n * Graph partitioning:\n\n The most common formulation of the graph partitioning problem\n for an undirected graph G = (V,E) asks for a division of V into\n k pairwise disjoint subsets (partitions) such that all\n partitions are of approximately equal size and the edge-cut,\n i.e., the total number of edges having their incident nodes in\n different subdomains, is minimized. The problem is known to be\n NP-hard.\n\n * Graph clustering:\n\n Clustering is an important tool for investigating the\n structural properties of data. Generally speaking, clustering\n refers to the grouping of objects such that objects in the same\n cluster are more similar to each other than to objects of\n different clusters. The similarity measure depends on the\n underlying application. Clustering graphs usually refers to the\n identification of vertex subsets (clusters) that have\n significantly more internal edges (to vertices of the same\n cluster) than external ones (to vertices of another cluster).\n\nThere are 12 data sets in the DIMACS10 collection:\n\nclustering: real-world graphs commonly used as benchmarks\ncoauthor: citation and co-author networks\nDelaunay: Delaunay triangulations of random points in the plane\ndyn-frames: frames from a 2D dynamic simulation\nKronecker: synthetic graphs from the Graph500 benchmark\nnumerical: graphs from numerical simulation\nrandom: random geometric graphs (random points in the unit square)\nstreets: real-world street networks\nWalshaw: Chris Walshaw's graph partitioning archive\nmatrix: graphs from the UF collection (not added here)\nredistrict: census networks\nstar-mixtures : artificially generated from sets of real graphs\n\nSome of the graphs already exist in the UF Collection. In some cases,\nthe original graph is unsymmetric, with values, whereas the DIMACS\ngraph is the symmetrized pattern of A+A'. Rather than add duplicate\npatterns to the UF Collection, a MATLAB script is provided at\nhttp://www.cise.ufl.edu/research/sparse/dimacs10 which downloads\neach matrix from the UF Collection via UFget, and then performs whatever\noperation is required to convert the matrix to the DIMACS graph problem.\nAlso posted at that page is a MATLAB code (metis_graph) for reading the\nDIMACS *.graph files into MATLAB.\n\n--------------------------------------------------------------------------------\nclustering: Clustering Benchmarks\n--------------------------------------------------------------------------------\n\n These real-world graphs are often used as benchmarks in the graph\n clustering and community detection communities. All but 4 of the 27\n graphs already appear in the UF collection in other groups. The\n DIMACS10 version is always symmetric, binary, and with zero-free\n diagonal. The version in the UF collection may not have those\n properties, but in those cases, if the pattern of the UF matrix\n is symmetrized and the diagonal removed, the result is the DIMACS10\n graph.\n\n DIMACS10 graph: new? UF matrix:\n --------------- ---- -------------\n clustering/adjnoun Newman/adjoun\n clustering/as-22july06 Newman/as-22july06\n clustering/astro-ph Newman/astro-ph\n clustering/caidaRouterLevel * DIMACS10/caidaRouterLevel\n clustering/celegans_metabolic Arenas/celegans_metabolic\n clustering/celegansneural Newman/celegansneural\n clustering/chesapeake * DIMACS10/chesapeake\n clustering/cnr-2000 LAW/cnr-2000\n clustering/cond-mat-2003 Newman/cond-mat-2003\n clustering/cond-mat-2005 Newman/cond-mat-2005\n clustering/cond-mat Newman/cond-mat\n clustering/dolphins Newman/dolphins\n clustering/email Arenas/email\n clustering/eu-2005 LAW/eu-2005\n clustering/football Newman/football\n clustering/hep-th Newman/hep-th\n clustering/in-2004 LAW/in-2004\n clustering/jazz Arenas/jazz\n clustering/karate Arenas/karate\n clustering/lesmis Newman/lesmis\n clustering/netscience Newman/netscience\n clustering/PGPgiantcompo Arenas/PGPgiantcompo\n clustering/polblogs Newman/polblogs\n clustering/polbooks Newman/polbooks\n clustering/power Newman/power\n clustering/road_central * DIMACS10/road_central\n clustering/road_usa * DIMACS10/road_usa\n\n the following graphs were added on July 2012:\n\n G_n_pin_pout\n preferentialAttachment\n smallworld\n\n uk-2002 was 'added' on July 2012 to the dimacs10 MATLAB interface,\n but it already appears as the LAW/uk-2002 matrix.\n\n uk-2007-05 is in the DIMACS10 collection but is not yet added here,\n because it's too large for the file format of the UF collection.\n\n--------------------------------------------------------------------------------\ncoauthor: Citation Networks\n--------------------------------------------------------------------------------\n\n These graphs are examples of social networks used in R. Geisberger, P.\n Sanders, and D. Schultes. Better approximation of betweenness\n centrality. In 10th Workshop on Algorithm Engineering and\n Experimentation, pages 90-108, San Francisco, 2008. SIAM.\n\n--------------------------------------------------------------------------------\nDelaunay: Delaunay Graphs\n--------------------------------------------------------------------------------\n\n These files have been generated as Delaunay triangulations of random\n points in the unit square.\n\n Engineering a scalable high quality graph partitioner,\n M. Holtgrewe, P. Sanders, C. Schulz, IPDPS 2010\n http://dx.doi.org/10.1109/IPDPS.2010.5470485\n\n--------------------------------------------------------------------------------\ndyn-frames: Frames from 2D Dynamic Simulations\n--------------------------------------------------------------------------------\n\n These files have been created with the generator described in Oliver\n Marquardt, Stefan Schamberger: Open Benchmarks for Load Balancing\n Heuristics in Parallel Adaptive Finite Element Computations. In Proc.\n International Conference on Parallel and Distributed Processing\n Techniques and Applications (PDPTA 2005), Volume 2, pp. 685-691. CSREA\n Press 2005, ISBN 1-932415-59-9685-691.\n\n The graphs are meshes taken from indivudual frames of a dynamic\n sequence that resembles two-dimensional adaptive numerical simulations.\n Smaller versions of these files (and their dynamic sequences as videos)\n can be found on Stefan Schamberger's website (\n http://www.upb.de/cs/schaum/benchmark.html ) dedicated to these\n benchmarks. The files presented here are the frames 0, 10, and 20 of\n the sequences, respectively.\n\n--------------------------------------------------------------------------------\nKronecker: Kronecker Generator Graphs\n--------------------------------------------------------------------------------\n\n The original Kronecker files contain self-loops and multiple edges.\n These properties are also present in real-world data sets. However,\n some tools cannot handle these \"artifacts\" at the moment. That is why\n we present \"cleansed\" versions of the data sets as well. For the\n Challenge you should expect to be confronted with the original data\n with self-loops and multiple edges. However, the final decision on\n this issue will be made based on participant feedback.\n\n All files have been generated with the R-MAT parameters A=0.57, B=0.19,\n C=0.19, and D=1-(A+B+C)=0.05 and edgefactor=48, i.e., the number of\n edges equals 48*n, where n is the number of vertices. Details about the\n generator and the parameter meanings can be found on the Graph500\n website. ( http://www.graph500.org/Specifications.html )\n\n There are 12 graphs in the DIMACS10 test set at\n http://www.cc.gatech.edu/dimacs10/index.shtml . Them come in 6 pairs.\n One graph in each pair is a multigraph, with self-edges. The other\n graph is the nonzero pattern of the first (binary), with self-edges\n removed. MATLAB cannot directly represent multigraph, so in the UF\n Collection the unweighted multigraph G is represented as a matrix A\n where A(i,j) is an integer equal to the number edges (i,j) in G.\n\n The binary graphs include the word 'simple' in their\n name In the UF Collection, only the multigraph is included,\n since the simple graph can be constructed from the multigraph.\n If A is the multigraph, the simple graph S can be computed as:\n\n S = spones (tril (A,-1)) + spones (triu (A,1)) ;\n\n DIMACS10 graph: UF matrix:\n --------------- -------------\n kronecker/kron_g500-logn16 DIMACS10/kron_g500-logn16\n kronecker/kron_g500-simple-logn16\n\n kronecker/kron_g500-logn17 DIMACS10/kron_g500-logn17\n kronecker/kron_g500-simple-logn17\n\n kronecker/kron_g500-logn18 DIMACS10/kron_g500-logn18\n kronecker/kron_g500-simple-logn18\n\n kronecker/kron_g500-logn19 DIMACS10/kron_g500-logn19\n kronecker/kron_g500-simple-logn19\n\n kronecker/kron_g500-logn20 DIMACS10/kron_g500-logn20\n kronecker/kron_g500-simple-logn20\n\n kronecker/kron_g500-logn21 DIMACS10/kron_g500-logn21\n kronecker/kron_g500-simple-logn21\n\n\n References: \"Introducing the Graph 500,\" Richard C. Murphy, Kyle B.\n Wheeler, Brian W. Barrett, James A. Ang, Cray User's Group (CUG), May\n 5, 2010.\n\n D.A. Bader, J. Feo, J. Gilbert, J. Kepner, D. Koester, E. Loh, K.\n Madduri, W. Mann, Theresa Meuse, HPCS Scalable Synthetic Compact\n Applications #2 Graph Analysis (SSCA#2 v2.2 Specification), 5\n September 2007.\n\n D. Chakrabarti, Y. Zhan, and C. Faloutsos, R-MAT: A recursive model\n for graph mining, SIAM Data Mining 2004.\n\n Section 17.6, Algorithms in C (third edition). Part 5 Graph\n Algorithms, Robert Sedgewick (Programs 17.7 and 17.8)\n\n P. Sanders, Random Permutations on Distributed, External and\n Hierarchical Memory, Information Processing Letters 67 (1988) pp\n 305-309.\n\n \"DFS: A Simple to Write Yet Difficult to Execute Benchmark,\" Richard C.\n Murphy, Jonathan Berry, William McLendon, Bruce Hendrickson, Douglas\n Gregor, Andrew Lumsdaine, IEEE International Symposium on Workload\n Characterizations 2006 (IISWC06), San Jose, CA, 25-27 October 2006.\n\n ---- sample code for generating these matrices:\n\n function ij = kronecker_generator (SCALE, edgefactor)\n %% Generate an edgelist according to the Graph500\n %% parameters. In this sample, the edge list is\n %% returned in an array with two rows, where StartVertex\n %% is first row and EndVertex is the second. The vertex\n %% labels start at zero.\n %%\n %% Example, creating a sparse matrix for viewing:\n %% ij = kronecker_generator (10, 16);\n %% G = sparse (ij(1,:)+1, ij(2,:)+1, ones (1, size (ij, 2)));\n %% spy (G);\n %% The spy plot should appear fairly dense. Any locality\n %% is removed by the final permutations.\n\n %% Set number of vertices.\n N = 2^SCALE;\n\n %% Set number of edges.\n M = edgefactor * N;\n\n %% Set initiator probabilities.\n [A, B, C] = deal (0.57, 0.19, 0.19);\n\n %% Create index arrays.\n ij = ones (2, M);\n %% Loop over each order of bit.\n ab = A + B;\n c_norm = C/(1 - (A + B));\n a_norm = A/(A + B);\n\n for ib = 1:SCALE,\n %% Compare with probabilities and set bits of indices.\n ii_bit = rand (1, M) > ab;\n jj_bit = rand (1, M) > ( c_norm * ii_bit + a_norm * not (ii_bit) );\n ij = ij + 2^(ib-1) * [ii_bit; jj_bit];\n end\n\n %% Permute vertex labels\n p = randperm (N);\n ij = p(ij);\n\n %% Permute the edge list\n p = randperm (M);\n ij = ij(:, p);\n\n %% Adjust to zero-based labels.\n ij = ij - 1;\n\n function G = kernel_1 (ij)\n %% Compute a sparse adjacency matrix representation\n %% of the graph with edges from ij.\n\n %% Remove self-edges.\n ij(:, ij(1,:) == ij(2,:)) = [];\n %% Adjust away from zero labels.\n ij = ij + 1;\n %% Find the maximum label for sizing.\n N = max (max (ij));\n %% Create the matrix, ensuring it is square.\n G = sparse (ij(1,:), ij(2,:), ones (1, size (ij, 2)), N, N);\n %% Symmetrize to model an undirected graph.\n G = spones (G + G.');\n\n--------------------------------------------------------------------------------\nnumerical: graphs from numerical simulations\n--------------------------------------------------------------------------------\n\n For the graphs adaptive and venturiLevel3, please refer to the preprint:\n\n Hartwig Anzt, Werner Augustin, Martin Baumann, Hendryk Bockelmann,\n Thomas Gengenbach, Tobias Hahn, Vincent Heuveline, Eva Ketelaer,\n Dimitar Lukarski, Andrea Otzen, Sebastian Ritterbusch, Bjo\"rn Rocker,\n Staffan Ronn\u00e5s, Michael Schick, Chandramowli Subramanian, Jan-Philipp\n Weiss, and Florian Wilhelm. Hiflow3 - a flexible and hardware-aware\n parallel Finite element package. In Parallel/High-Performance Object-\n Oriented Scientific Computing (POOSC'10).\n\n For the graphs channel-500x100x100-b050 and packing-500x100x100-b050,\n please refer to:\n\n Markus Wittmann, Thomas Zeiser. Technical Note: Data Structures of\n ILBDC Lattice Boltzmann Solver.\n http://www.cc.gatech.edu/dimacs10/archive/numerical-overview-Erlangen.pdf\n\n The instances NACA0015, M6, 333SP, AS365, and NLR are 2-dimensional FE\n triangular meshes with coordinate information. 333SP and AS365 are actually\n converted from existing 3-dimensional models to 2D places, while the rest\n are created from geometry. The corresponding coordinate files have been\n assembled in one archive. They have been created and contributed by Chan\n Siew Yin with the help of Jian Tao Zhang, Department of Mechanical\n Engineering, University of New Brunswick, Fredericton, Canada. \n\n--------------------------------------------------------------------------------\nrandom: Random Geometric Graphs\n--------------------------------------------------------------------------------\n\n rggX is a random geometric graph with 2^X vertices. Each vertex is a\n random point in the unit square and edges connect vertices whose\n Euclidean distance is below 0.55 (ln n)/n. This threshold was choosen\n in order to ensure that the graph is almost connected.\n\n Note: the UF Collection is a collection of matrices primarily from\n real applications. The only random matrices I add to the collection\n are those used in established benchmarks (such as DIMACS10).\n\n Engineering a scalable high quality graph partitioner,\n M. Holtgrewe, P. Sanders, C. Schulz, IPDPS 2010.\n http://dx.doi.org/10.1109/IPDPS.2010.5470485\n\n--------------------------------------------------------------------------------\nsteets: Street Networks\n--------------------------------------------------------------------------------\n\n The graphs Asia, Belgium, Europe, Germany, Great-Britain, Italy,\n Luxemburg and Netherlands are (roughly speaking) undirected and\n unweighted versions of the largest strongly connected component\n of the corresponding Open Street Map road networks. The Open\n Street Map road networks have been taken from\n http://download.geofabrik.de and have been converted for DIMACS10\n by Moritz Kobitzsch (kobitzsch at kit.edu) as follows:\n\n First, we took the corresponding graph and extracted all routeable\n streets. Routable streets are objects in this file that are tagged\n using one of the following tags: motorway, motorway_link, trunk\n trunk_link, primary, primary_link, secondary, secondary_link,\n tertiary, tertiary_link, residential, unclassified, road,\n living_street, and service. Next, we now compute the largest\n strongly connected component of this extracted open street map graph.\n Self-edges and parallel edges are removed afterwards.\n The DIMACS 10 graph is now the undirected and unweighted version\n of the constructed graph, i.e. if there is an edge (u,v) but the\n reverse edge (v,u) is missing, we insert the reverse edge into the\n graph. The XY coordinates of each node in the graph are preserved.\n\n--------------------------------------------------------------------------------\nWalshaw: Chris Walshaw's graph partitioning archive\n--------------------------------------------------------------------------------\n\n Chris Walshaw's graph partitioning archive contains 34 graphs that\n have been very popular as benchmarks for graph partitioning algorithms\n ( http://staffweb.cms.gre.ac.uk/~wc06/partition/ ).\n\n 17 of them are already in the UF Collection. Only the 17 new graphs\n not yet in the collection are added here in the DIMACS10 set.\n\n DIMACS10 graph: new? UF matrix:\n --------------- ---- -------------\n walshaw/144 * DIMACS10/144\n walshaw/3elt AG-Monien/3elt\n walshaw/4elt Pothen/barth5\n walshaw/598a * DIMACS10/598a\n walshaw/add20 Hamm/add20\n walshaw/add32 Hamm/add32\n walshaw/auto * DIMACS10/auto\n walshaw/bcsstk29 HB/bcsstk29\n walshaw/bcsstk30 HB/bcsstk30\n walshaw/bcsstk31 HB/bcsstk31\n walshaw/bcsstk32 HB/bcsstk32\n walshaw/bcsstk33 HB/bcsstk33\n walshaw/brack2 AG-Monien/brack2\n walshaw/crack AG-Monient/crack\n walshaw/cs4 * DIMACS10/cs4\n walshaw/cti * DIMACS10/cti\n walshaw/data * DIMACS10/data\n walshaw/fe_4elt2 * DIMACS10/fe_4elt2\n walshaw/fe_body * DIMACS10/fe_body\n walshaw/fe_ocean * DIMACS10/fe_ocean\n walshaw/fe_pwt Pothen/pwt\n walshaw/fe_rotor * DIMACS10/fe_rotor\n walshaw/fe_sphere * DIMACS10/fe_sphere\n walshaw/fe_tooth * DIMACS10/fe_tooth\n walshaw/finan512 Mulvey/finan512\n walshaw/m14b * DIMACS10/m14b\n walshaw/memplus Hamm/memplus\n walshaw/t60k * DIMACS10/t60k\n walshaw/uk * DIMACS10/uk\n walshaw/vibrobox Cote/vibrobox\n walshaw/wave AG-Monien/wave\n walshaw/whitaker3 AG-Monien/whitaker3\n walshaw/wing * DIMACS10/wing\n walshaw/wing_nodal * DIMACS10/wing_nodal\n\n--------------------------------------------------------------------------------\nredistrict: census networks\n--------------------------------------------------------------------------------\n\n These graphs represent US states. They are used for solving the\n redistricting problem. All data have been provided by Will Zhao. As stated\n on the project website, The nodes are Census2010 blocks. Two nodes have an\n edge linking them if they share a line segment on their borders, i.e.\n rook-style neighboring. The nodes weights are the POP100 variable of\n Census2010-PL94, and the area of eache district.\n\n--------------------------------------------------------------------------------\nstar-mixtures : artificially generated from sets of real graphs\n--------------------------------------------------------------------------------\n\n Each graph in this benchmark represents a star-like structure of different\n graphs S0 , . . . , St. Graphs S1 , . . . , St are weakly connected to the\n center S0 by random edges. The total number of edges between each Si and S0\n was less than 3% out of the total number of edges in Si . The graphs are\n mixtures of the following structures: social networks, finite-element\n graphs, VLSI chips, peer-to-peer networks, and matrices from optimization\n solvers.\n\n More info can be found in the paper I. Safro, P. Sanders, C. Schulz:\n Advanced Coarsening Schemes for Graph Partitioning, SEA 2012.\n\n Communicated by Christian Schulz, uploaded on March 30, 2012." - }, - { - "group": "IPSO", - "count": 7, - "documented_meaning": "Power simulation matrices, Li Peijie, Inst. of Power Systems, Guangxi Univ. Three different kinds of optimization methods (OPF, TSCOPF, HTC)", - "source_url": "https://sparse.tamu.edu/IPSO", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "power network problem (7)", - "curator_value_type_count": 1, - "curator_value_types": "real (7)", - "curator_structure_count": 1, - "curator_structures": "symmetric (7)", - "min_dimension": 8140, - "max_dimension": 226340, - "dimension_ratio": 27.805896805896804, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 141478, - "max_nnz": 2012833, - "shape_profile": "square only", - "square": 7, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 7, - "structurally_symmetric_square": 7, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Power simulation matrices, Li Peijie, Inst. of Power Systems, Guangxi Univ.\n\nThree different kinds of optimization methods (OPF, TSCOPF, HTC)\napplied to power system simulation. All matrices are symmetric\nindefinite. From Li Peijie, Institute of Power Systems (IPSO),\nGuangxi University (beyondpeijie at gxu.edu.cn). Collected by\nJonathan Hogg, Rutherford Appleton Laboratory, Oxfordshire, UK." - }, - { - "group": "Janna", - "count": 18, - "documented_meaning": "Finite element simulations: gas reservoir and structural problems. Univ Padova. C. Janna, Univ. of Padova. Authors: Carlo Janna and Massimiliano Ferronato", - "source_url": "https://sparse.tamu.edu/Janna", - "coarse_kind_families_interpretive": "fluids/transport; generic spatial PDE/thermal; structures/materials", - "dominant_coarse_family_interpretive": "structures/materials", - "dominant_coarse_family_share": 0.7777777777777778, - "normalized_kind_count": 3, - "normalized_kinds": "structural problem (14); 2D/3D problem (3); computational fluid dynamics problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (18)", - "curator_structure_count": 2, - "curator_structures": "symmetric (14); unsymmetric (4)", - "min_dimension": 377002, - "max_dimension": 4147110, - "dimension_ratio": 11.000233420512359, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 17277420, - "max_nnz": 316548962, - "shape_profile": "square only", - "square": 18, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 14, - "structurally_symmetric_square": 18, - "known_spd": 10, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 18, - "cohesion": "multi-domain/mixed", - "official_notes": "Finite element simulations: gas reservoir and structural problems. Univ Padova.\nC. Janna, Univ. of Padova. Authors: Carlo Janna and Massimiliano Ferronato\nAll matrices in the set are symmetric and positive definite.\n\nSerena:\n\n Gas reservoir simulation for CO2 sequestration.\n\n # equations: 1,391,349\n # non-zeroes: 64,531,701\n Problem description: structural problem\n\n The matrix Serena is obtained from a structural problem discretizing a gas\n reservoir with tetrahedral Finite Elements. The medium is strongly\n heterogeneous and characterized by a complex geometry consisting of\n alternating sequences of thin clay and sand layers. References: [1]\n\nEmilia_923:\n\n # equations: 923136\n # non-zeroes: 41005206\n Problem description: Geomechanical problem\n\n The matrix Emilia_923 is obtained from a structural problem discretizing a\n gas reservoir with tetrahedral Finite Elements. Due to the complex\n geometry of the geological formation it was not possible to obtain a\n computational grid characterized by regularly shaped elements. The\n problem arises from a 3D discretization with three displacement unknowns\n associated to each node of the grid. References: [1], [2]\n\nFault_639:\n\n # equations: 638802\n # non-zeroes: 28614564\n Problem description: contact mechanics\n\n The matrix Fault_639 is obtained from a structural problem discretizing a\n faulted gas reservoir with tetrahedral Finite Elements and triangular\n Interface Elements. The Interface Elements are used with a Penalty\n formulation to simulate the faults behaviour. The problem arises from a\n 3D discretization with three displacement unknowns associated to each node\n of the grid. References [3,4,5,6]\n\nFlan_1565:\n\n # equations: 1564794\n # non-zeroes: 117406044\n Problem description: Structural problem\n\n The matrix Flan_1565 is obtained from a 3D mechanical problem discretizing\n a steel flange with hexahedral Finite Elements. Due to the regular shape\n of the mechanical piece, the computational grid is a structured mesh with\n regularly shaped elements. Three displacement unknowns are associated to\n each node of the grid. References [6,7]\n\nGeo_1438:\n\n # equations: 1437960\n # non-zeroes: 63156690\n Problem description: Geomechanical problem\n\n The matrix Geo_1438 is obtained from a geomechanical problem discretizing\n a region of the earth crust subject to underground deformation. The\n computational domain is a box with an areal extent of 50 x 50 km and 10 km\n deep consisting of regularly shaped tetrahedral Finite Elements. The\n problem arises from a 3D discretization with three displacement unknowns\n associated to each node of the grid. Reference: [6]\n\nHook_1498:\n\n # equations: 1498023\n # non-zeroes: 60917445\n Problem description: Structural problem\n\n The matrix Hook_1498 is obtained from a 3D mechanical problem discretizing\n a steel hook with tetrahedral Finite Elements. The computational grid\n consists of regularly shaped elements with three displacement unknowns\n associated to each node.\n\nStocF-1465:\n\n # equations: 1465137\n # non-zeroes: 21005389\n Problem description: Flow in porous medium with a stochastic permeabilies\n\n The matrix StocF_1465 is obtained from a fluid-dynamical problem of flow\n in porous medium. The computational grid consists of tetrahedral Finite\n Elements discretizing an underground aquifer with stochastic\n permeabilties. References: [2,8]\n\nReferences:\n\n[1] M. Ferronato, G. Gambolati, C. Janna, P. Teatini. \"Geomechanical\n issues of anthropogenic CO2 sequestration in exploited gas fields\", Energy\n Conversion and Management, 51, pp. 1918-1928, 2010.\n\n[2] C. Janna, M. Ferronato. \"Adaptive pattern research for block FSAI\n preconditionig\". SIAM Journal on Scientific Computing, to appear.\n\n[3] M. Ferronato, G. Gambolati, C. Janna, P. Teatini. \"Numerical modelling\n of regional faults in land subsidence prediction above gas/oil\n reservoirs\", International Journal for Numerical and Analytical Methods in\n Geomechanics, 32, pp. 633-657, 2008.\n\n[4] M. Ferronato, C. Janna, G. Gambolati. \"Mixed constraint preconditioning\n in computational contact mechanics\", Computer Methods in Applied Mechanics\n and Engineering, 197, pp. 3922-3931, 2008. \n\n[5] C. Janna, M. Ferronato, G. Gambolati. \"Multilevel incomplete\n factorizations for the iterative solution of non-linear FE problems\".\n International Journal for Numerical Methods in Engineering, 80, pp.\n 651-670, 2009. \n\n[6] C. Janna, M. Ferronato, G. Gambolati. \"A Block FSAI-ILU parallel\n preconditioner for symmetric positive definite linear systems\". SIAM\n Journal on Scientific Computing, 32, pp. 2468-2484, 2010.\n\n[7] C. Janna, A. Comerlati, G. Gambolati. \"A comparison of projective and\n direct solvers for finite elements in elastostatics\". Advances in\n Engineering Software, 40, pp. 675-685, 2009.\n\n[8] M. Ferronato, C. Janna, G. Pini. \"Shifted FSAI preconditioners for the\n efficient parallel solution of non-linear groundwater flow models\".\n International Journal for Numerical Methods in Engineering, to appear.\n\n\n--------------------------------------------------------------------------------\nCube_Coup_* and Long_Coup_* matrices:\n--------------------------------------------------------------------------------\n\nAuthors: Carlo Janna and Massimiliano Ferronato\n\nCube_Coup_*: Symmetric Indefinite Matrix\n# equations: 2,164,760\n# non-zeroes: 127,206,144\nProblem description: Coupled consolidation problem\n\nThe matrix Cube_Coup is obtained from a 3D coupled consolidation\nproblem of a cube discretized with tetrahedral Finite Elements. The\ncomputational grid is characterized by regularly shaped elements. The\ncopuled consolidation problem gives rise to a matrix having 4 unknowns\nassociated to each node: the first three are displacement unknowns, the\nfourth is a pressure. Coupled consolidation is a transient problem with\nthe matrix ill-conditioning strongly depending on the time step size.\nWe provide a relatively simple problem, \"dt0\" with a time step size of\n10^0 seconds, and a more difficult one, \"dt6\" with a time step of 10^6\nseconds. The two Cube_Coup_* matrices are symmetric indefinite.\n\nLong_Coup_*: Symmetric Indefinite Matrix\n# equations: 1,470,152\n# non-zeroes: 87,088,992\nProblem description: Coupled consolidation problem\n\nThe matrix Long_Coup is obtained from a 3D coupled consolidation\nproblem of a geological formation discretized with tetrahedral Finite\nElements. Due its complex geometry it was not possible to obtain a\ncomputational grid characterized by regularly shaped elements. The\ncopuled consolidation problem gives rise to a matrix having 4 unknowns\nassociated to each node: the first three are displacement unknowns, the\nfourth is a pressure. Coupled consolidation is a transient problem with\nthe matrix ill-conditioning strongly depending on the time step size.\nWe provide a relatively simple problem, \"dt0\" with a time step size of\n10^0 seconds, and a more difficult one, \"dt6\" with a time step of 10^6\nseconds. The two Long_Coup_* matrices are symmetric indefinite.\n\nFurther information on the 4 matrices may be found in the following papers:\n\n1) C. Janna, M. Ferronato, G. Gambolati. \"Parallel inexact constraint\npreconditioning for ill-conditioned consolidation problems\".\nComputational Geosciences, submitted.\n\n2) M. Ferronato, L. Bergamaschi, G. Gambolati. \"Performance and\nrobustness of block constraint preconditioners in FE coupled\nconsolidation problems\". International Journal for Numerical Methods\nin Engineering, 81, pp. 381-402, 2010.\n\n3) L. Bergamaschi, M. Ferronato, G. Gambolati. \"Mixed constraint\npreconditioners for the iterative solution to FE coupled consolidation\nequations\". Journal of Computational Physics, 227, pp. 9885-9897, 2008.\n\n4) L. Bergamaschi, M. Ferronato, G. Gambolati. \"Novel preconditioners\nfor the iterative solution to FE-discretized coupled consolidation\nequations\". Computer Methods in Applied Mechanics and Engineering, 196,\npp. 2647-2656, 2007.\n\n--------------------------------------------------------------------------------\nJanna/CoupCons3D\n--------------------------------------------------------------------------------\n\n Authors: Carlo Janna, Massimiliano Ferronato Giorgio Pini\n Matrix type: Unsymmetric\n # equations: 416,800\n # non-zeroes: 22,322,336\n\n Problem description: Fully coupled poroelastic problem\n (structural problem)\n\n The matrix CoupCons3D has been obtained through a Finite Element\n transient simulation of a fully coupled consolidation problem on\n a three-dimensional domain using Finite Differences for the\n discretization in time.\n\n Further information can be found in the following papers:\n\n 1) M. Ferronato, G. Pini, and G. Gambolati. The role of\n preconditioning in the solution to FE coupled consolidation\n equations by Krylov subspace methods. International Journal for\n Numerical and Analytical Methods in Geomechanics 33 (2009), pp.\n 405-423.\n\n 2) M. Ferronato, C. Janna, and G. Pini. Parallel solution to\n ill-conditioned FE geomechanical problems. International Journal\n for Numerical and Analytical Methods in Geomechanics 36 (2012),\n pp. 422-437.\n\n 3) M. Ferronato, C. Janna and G. Pini. A generalized Block FSAI\n preconditioner for unsymmetric indefinite matrices. Journal of\n Computational and Applied Mathematics (2012), submitted.\n Authors: Carlo Janna, Massimiliano Ferronato, Giorgio Pini\n\n--------------------------------------------------------------------------------\nJanna/ML_Laplace\n--------------------------------------------------------------------------------\n\n Authors: Carlo Janna, Massimiliano Ferronato Giorgio Pini\n Matrix type: Unsymmetric\n # equations: 377,002\n # non-zeroes: 27,689,972\n\n Problem description: Poisson problem\n\n The matrix ML_Laplace has been obtained by discretizing a 2D\n Poisson equation with a Meshless Local Petrov-Galerkin method.\n\n Further information can be found in the following papers:\n\n 1) G. Pini, A. Mazzia, and F. Sartoretto. Accurate MLPG solution\n of 3D potential problems. CMES - Computer Modeling in Engineering\n & Sciences 36 (2008), pp. 43-64.\n\n 2) M. Ferronato, C. Janna and G. Pini. A generalized Block FSAI\n preconditioner for unsymmetric indefinite matrices. Journal of\n Computational and Applied Mathematics (2012), submitted.\n Authors: Carlo Janna, Massimiliano Ferronato, Giorgio Pini\n\n--------------------------------------------------------------------------------\nJanna/Transport\n--------------------------------------------------------------------------------\n\n Authors: Carlo Janna, Massimiliano Ferronato Giorgio Pini\n Matrix type: Unsymmetric\n # equations: 1,602,111\n # non-zeroes: 23,500,731\n\n Problem description: 3D Finite Element flow and transport\n\n The matrix Transport has been obtained by a FE tetrahedral\n discretization of a density driven coupled flow and transport.\n\n Further information can be found in the following papers:\n\n 1) A. Mazzia, and M. Putti. High order Godunov mixed methods on\n tetrahedral meshes for density driven flow simulations in porous\n media. Journal of Computational Physics 208 (2005), pp. 154-174.\n\n 2) M. Ferronato, C. Janna and G. Pini. A generalized Block FSAI\n preconditioner for unsymmetric indefinite matrices. Journal of\n Computational and Applied Mathematics (2012), submitted.\n\n--------------------------------------------------------------------------------\nJanna/ML_Geer\n--------------------------------------------------------------------------------\n\n Authors: Carlo Janna, Massimiliano Ferronato, Giorgio Pini\n Matrix type: Unsymmetric\n # equations: 1,504,002\n # non-zeroes: 110,879,972\n\n Problem description: Poroelastic problem (structural problem)\n\n The matrix ML_Geer has been obtained to find through a Meshless\n Petrov-Galerkin discretization the deformed configuration of an\n axial-symmetric porous medium subject to a pore-pressure drawdown.\n\n Further information can be found in the following papers:\n\n 1) M. Ferronato, A. Mazzia, G. Pini, and G. Gambolati. A meshless\n method for axi-symmetric poroelastic simulations: numerical\n study. International Journal for Numerical Methods in Engineering\n 70 (2007), pp. 1346-1365.\n\n 2) M. Ferronato, C. Janna and G. Pini. A generalized Block FSAI\n preconditioner for unsymmetric indefinite matrices. Journal of\n Computational and Applied Mathematics (2012), submitted.\n\n--------------------------------------------------------------------------------\nBump_2991\n--------------------------------------------------------------------------------\n\n Matrix Name: Bump_2911\n\n Authors: Carlo Janna and Massimiliano Ferronato\n Symmetric Positive Definite Matrix\n # equations: 2,911,419\n # non-zeroes: 130,378,257\n Problem description: 3D geomechanical reservoir simulation\n\n The matrix Bump_2911 is obtained from the 3D geomechanical\n simulation of a gas-reservoir discretized by linear tetrahedral\n Finite Elements. The mechanical properties of the medium vary\n with the depth and the geological formation. Zero displacement\n are applied on bottom and lateral boundary, while a traction-free\n top boundary is assumed.\n\n Further information may be found in the following papers:\n\n 1) C. Janna, M. Ferronato, G. Gambolati. \"Enhanced Block FSAI\n preconditioning using Domain Decomposition techniques\". SIAM\n Journal on Scientific Computing, 35, pp. S229-S249, 2013.\n\n 2) C. Janna, M. Ferronato, G. Gambolati. \"The use of supernodes\n in factored sparse approximate inverse preconditioning\". SIAM\n Journal on Scientific Computing, submitted.\n\n--------------------------------------------------------------------------------\nQueen_4147\n--------------------------------------------------------------------------------\n\n Matrix Name: Queen_4147\n\n Authors: Carlo Janna and Massimiliano Ferronato\n Symmetric Positive Definite Matrix\n # equations: 4,147,110\n # non-zeroes: 329,499,288\n Problem description: 3D structural problem\n\n The matrix Queen_4147 is obtained from the 3D discretizaion\n of a structural problem by isoparametric hexahedral Finite\n Elements. The solid material is strongly heterogeneous and\n several elements exhibit shape distortion thus producing an\n ill-conditioned stiffness matrix.\n\n Further information may be found in the following paper:\n\n 1) C. Janna, M. Ferronato, G. Gambolati. \"The use of supernodes\n in factored sparse approximate inverse preconditioning\". SIAM\n Journal on Scientific Computing, submitted.\n\n--------------------------------------------------------------------------------\nPFlow_742\n--------------------------------------------------------------------------------\n\n Matrix Name: PFlow_742\n\n Authors: Carlo Janna and Massimiliano Ferronato\n Symmetric Positive Definite Matrix\n # equations: 742,793\n # non-zeroes: 37,138,461\n Problem description: 3D pressure-temperature evolution\n in porous media\n\n The matrix PFlow_742 is obtained from a 3D simulation of the\n pressure-temperature field in a multilayered porous media\n discretized by hexahedral Finite Elements. The ill-conditioning\n of the matrix is due to the strong contrasts in the material\n properties fo different layers.\n\n Further information may be found in the following paper:\n\n 1) C. Janna, M. Ferronato, G. Gambolati. \"The use of supernodes\n in factored sparse approximate inverse preconditioning\". SIAM\n Journal on Scientific Computing, submitted." - }, - { - "group": "Schulthess", - "count": 3, - "documented_meaning": "Biochemical networks from Pascal Schulthess, Inst. Pathology, Berlin Matrices from Pascal Schulthess, Institute for Pathology,", - "source_url": "https://sparse.tamu.edu/Schulthess", - "coarse_kind_families_interpretive": "chemistry/biology", - "dominant_coarse_family_interpretive": "chemistry/biology", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "biochemical network (3)", - "curator_value_type_count": 1, - "curator_value_types": "integer (3)", - "curator_structure_count": 1, - "curator_structures": "rectangular (3)", - "min_dimension": 1996, - "max_dimension": 16559, - "dimension_ratio": 8.296092184368737, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 4335, - "max_nnz": 43816, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 3, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Biochemical networks from Pascal Schulthess, Inst. Pathology, Berlin\n\nMatrices from Pascal Schulthess, Institute for Pathology, \nChariteplatz 1, 10117 Berlin, Germany. \n \nThree large biochemical networks (N_biocarta, N_pid, and N_reactome).\nThese are stoichiometric matrices extracted from three biochemical \ndatabases (BioCarta, PID, and REACTOME) describing cell signaling \npathways and protein-protein interaction networks. The goal is to \nfind the left nullspace of the matrix; in MATLAB notation: \n \nN = null (Problem.A') ; \n \nThe matrix (Problem.A')*N will thus be essentially zero. \nThis can be done much more efficiently with the spqr_rank toolbox by \nLeslie Foster and Tim Davis, as: \n \nN = spqr_null (Problem.A') ; \n \nResults: \nThe matrix A is transposed, then N = null (A) or N = spqr_null (A) \nis computed. The size statistic is the memory taken by N. \nspqr_null can compute either an explicit matrix N, or an implicit \nHouseholder-based representation. The latter takes less memory. \n \nMatrix: N_biocarta size: 1996 by 1922 (transposed) \n \nspqr_null stats: \n flag: 0 \n rank: 1023 \n tol: 3.5456e-12 \n est_sval_upper_bounds: [0.1689 3.4534e-15] \n est_sval_lower_bounds: [0.1203 0] \n sval_numbers_for_bounds: [1023 1024] \n est_norm_A_times_N: 2.4349e-15 \n \nspqr_null, implicit: 0.03 sec, norm(A*N) 9e-15 size: 0.08 MB \nspqr_null, explicit: 0.10 sec, norm(A*N) 9e-15 size: 0.11 MB \nMATLAB null: 3.31 sec, norm(A*N) 2e-13 size: 13.82 MB \nall report dim(N) of 899. \n \nMatrix: N_pid size: 3923 by 3625 (transposed) \n \nspqr_null stats: \n flag: 0 \n rank: 2048 \n tol: 1.3937e-11 \n est_sval_upper_bounds: [0.0922 5.1310e-15] \n est_sval_lower_bounds: [0.0585 0] \n sval_numbers_for_bounds: [2048 2049] \n est_norm_A_times_N: 1.6751e-15 \n \nspqr_null, implicit: 0.05 sec, norm(A*N) 4e-14 size: 0.21 MB \nspqr_null, explicit: 0.34 sec, norm(A*N) 4e-14 size: 1.32 MB \nMATLAB null: 24.86 sec, norm(A*N) 9e-13 size: 45.73 MB \nall report dim(N) of 1577 \n \nMatrix: N_reactome size: 16559 by 10204 (transposed) \n \nspqr_null stats: \n flag: 0 \n rank: 9025 \n tol: 1.1766e-10 \n est_sval_upper_bounds: [0.6722 1.3042e-14] \n est_sval_lower_bounds: [0.0106 0] \n sval_numbers_for_bounds: [9025 9026] \n est_norm_A_times_N: 9.4695e-15 \n \nspqr_null, implicit: 0.95 sec, norm(A*N) 2e-13 size: 7.5 MB \nspqr_null, explicit: 3.53 sec, norm(A*N) 2e-13 size: 25.2 MB \nMATLAB null: 904.54 sec, norm(A*N) 2e-10 size: 96.2 MB \nall report dim(N) of 1179." - }, - { - "group": "Chevron", - "count": 4, - "documented_meaning": "2D Helmholtz; Temporal freq. domain seismic modeling. J. Washbourne, Chevron Linearized for Gauss-Newton method.", - "source_url": "https://sparse.tamu.edu/Chevron", - "coarse_kind_families_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "2D/3D problem (4)", - "curator_value_type_count": 1, - "curator_value_types": "complex (4)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (4)", - "min_dimension": 37365, - "max_dimension": 711450, - "dimension_ratio": 19.04054596547571, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 330633, - "max_nnz": 6376412, - "shape_profile": "square only", - "square": 4, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "complex only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "2D Helmholtz; Temporal freq. domain seismic modeling. J. Washbourne, Chevron\nLinearized for Gauss-Newton method." - }, - { - "group": "CPM", - "count": 9, - "documented_meaning": "Closest Point Method, Yujia Chen, Andy Wathen, Shengxin Zhu A method for computing on surfaces. For more information see:", - "source_url": "https://sparse.tamu.edu/CPM", - "coarse_kind_families_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_interpretive": "generic spatial PDE/thermal", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "2D/3D problem (9)", - "curator_value_type_count": 1, - "curator_value_types": "real (9)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (9)", - "min_dimension": 148, - "max_dimension": 40948, - "dimension_ratio": 276.6756756756757, - "size_span": "wide (>100\u00d7)", - "min_nnz": 1527, - "max_nnz": 412148, - "shape_profile": "square only", - "square": 9, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 9, - "cohesion": "coherent family/series", - "official_notes": "Closest Point Method, Yujia Chen, Andy Wathen, Shengxin Zhu \n\nA method for computing on surfaces. For more information see:\nhttp://people.maths.ox.ac.uk/~macdonald/closestpoint/\n\nOne of the matrices in this collection (cz10228) is solved poorly\nby x=A\\\\b in MATLAB R2012a. The solution is to use this setting:\n\n spparms ('piv_tol', 0.5)\n x = A\\\\b\n\nwhich changes the pivot tolerance. MATLAB has a test which\nchecks the accuracy of x=A\\\\b when using UMFPACK, and if the\naccuracy is poor, it refactorizes the matrix with a piv_tol\nof 1.0 (standard partial pivoting). For the cz10228 matrix,\nthe test is nearly, but not quite, triggered. The other matrices\ndo not cause this problem.\n\nFurther details from Shengxin Zhu:\n\nGenerally speaking, the matrix comes from the numerical solution of\nPoisson equation on a unit circle by solving an embedding PDE posed\nin a narrow band around the circle. Of course the easiest way to\nsolve PDE on a unit circle is to parametrize it and then solve a 1D\nproblem; but here we just want to test the effectiveness of the\nembedding method for solving PDEs on general curves or surfaces.\nThe method extends the original surface PDE to a band around the\nsurface, and then solve the extended PDE on a Cartesian grid by a\nfinite difference scheme. In order to define such a PDE, one has to\nboth define proper embedding differential operator and extend the\nsolution from the surface to the embedding space. One natural way\nis to enforce the solution being constant along the normal\ndirection of the surface so that the surface differential operator\ncould be replaced by standard Cartesian differential operator. Here\nin the case of Poisson equation on a unit circle, we solve a\nstandard Poisson equation on a 2D cartesian grid around the circle\nwith suitable right hand side and Neumann boundary condition on the\nboundary of the band. We enforce the Neumann boundary condition by\ntaking the value of each boundary point to be the value of its\nclosest point on the circle; and since the closest point is usually\nnot a grid point, its value is obtained by interpolation of values\nof the surrounding points. Putting the process into a sparse\nmatrix, and change one row of the matrix to fix the value of one\npoint of the solution and ensure the matrix is nonsingular.\n\nI tried different bandwidth, different interpolation order for the\nvalue of the closest points, 2nd order and 4-th order finite\ndifference scheme to the Laplace operator. In most cases, MATLAB\nbackslash works pretty well in solving the resulting linear system.\nThe size of the matrix which makes MATLAB backslash not work is not\nthe largest among all, and its condition number is not largest\namong all. By the way, the AMG solver by Notay and a geometric\nmultigrid solver written by myself works pretty well for this case." - }, - { - "group": "Davis", - "count": 1, - "documented_meaning": "Analysis of the MATLAB FileExchange, March 2010 For the 8th Edition of the MATLAB Primer (published by CRC Press)", - "source_url": "https://sparse.tamu.edu/Davis", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "term/document graph (1)", - "curator_value_type_count": 1, - "curator_value_types": "integer (1)", - "curator_structure_count": 1, - "curator_structures": "rectangular (1)", - "min_dimension": 9498, - "max_dimension": 9498, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 301899, - "max_nnz": 301899, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 1, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "single matrix", - "official_notes": "Analysis of the MATLAB FileExchange, March 2010 \n \nFor the 8th Edition of the MATLAB Primer (published by CRC Press) \nI wrote a script that downloaded and parsed all 9489 submissions \nto the MATLAB File Exchange (as of March 12, 2010, 8am), at: \nhttp://www.mathworks.com/matlabcentral/fileexchange/ . \nEach submission was parsed to determine how many times each of \n1319 keywords appears. The list of keywords to search for was \nobtained by parsing the MATLAB Help documentation. In the primary \nmatrix (Problem.A), the entry A(i,j) is the nubmer of times that \nthe ith keyword appears in the jth \"file\" (each of the submissions \nto the MATLAB File Exchange is called a \"file\", although each \nsubmission often consists of more than one actual file). \nProblem.aux.key{i} is the ith keyword, and Problem.aux.id(j) is \nthe MATLAB File ID corresponding to the jth column of the matrix A.\nThe first 454 rows of the A matrix correspond to the 454 functions \nor keywords that appear in the \"MATLAB Top 500\" list of most- \nfrequently used functions, keywords, and operators in the 8th \nEdition of the MATLAB Primer. The remaining are operators \n(+ - / * . etc) to round out the \"Top 500\" (these do not appear \nin the Problem.A matrix since parsing them proved difficult). \n \nNote that some editorial adjustments were made to the Top 500. \nIn particular, deprecated functions were removed. Some of the \ntrigonometric functions are rarely used, but all of them were \nincluded in the \"Top 500\" to make a complete set. You can \nvisualize this with semilogy(sum(A,2)). The rows of A are \nmostly sorted in decreasing row-sum, except for rows 430 to \n470 or so. \n \nThis matrix was constructed in March 2010 by Tim Davis and added \nto the collection in July 2012; Tim Davis is solely responsible \nfor this matrix. It was not submitted by or authorized by \nThe MathWorks, Inc, but since the File Exchange is now under \na BSD license, this is included in the collection as a legitimate \nderivative work." - }, - { - "group": "Bodendiek", - "count": 5, - "documented_meaning": "Curl-Curl operator of 2nd order Maxwell's equations, A. Bodendiek From Andre' Bodendiek, Institut Computational Mathematics,", - "source_url": "https://sparse.tamu.edu/Bodendiek", - "coarse_kind_families_interpretive": "eigenvalue/model reduction", - "dominant_coarse_family_interpretive": "eigenvalue/model reduction", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "model reduction problem (5)", - "curator_value_type_count": 1, - "curator_value_types": "real (5)", - "curator_structure_count": 1, - "curator_structures": "symmetric (5)", - "min_dimension": 11083, - "max_dimension": 2380515, - "dimension_ratio": 214.78976811332672, - "size_span": "wide (>100\u00d7)", - "min_nnz": 113343, - "max_nnz": 26515867, - "shape_profile": "square only", - "square": 5, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 5, - "structurally_symmetric_square": 5, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 5, - "cohesion": "coherent family/series", - "official_notes": "Curl-Curl operator of 2nd order Maxwell's equations, A. Bodendiek\n\nFrom Andre' Bodendiek, Institut Computational Mathematics,\nTU Braunschweig\n\nThe following matrix collection consists of the curl-curl-operator\nof a second-order Maxwell's equations with PEC boundary conditions,\ni.e. E x n = 0, where E and n denote the electric field strength\nand the unit outer normal of the computational domain. The\ncurl-curl-operator has been discretized using the Finite Element\nMethod with first-order Nedelec elements resulting in the weak\nformulation\n\n 1/mu0 ( curl E, curl v ),\n\nwhere v resembles a test function of H(curl) and\nmu0 = 1.25 1e-9 H / mm denotes the magnetic permeability of vacuum,\nsee [Hipt02]. \n\nIn general, the underlying model problem of Maxwell's equations\nresults from a Coplanar Waveguide, which will be considered for\nthe analysis of parasitic effects in the development of new\nsemiconductors. Since the corresponding dynamical systems are often\nhigh-dimensional, model order reduction techniques have become an\nappealing approach for the efficient simulation and accurate analysis\nof the parasitic effects. However, different kinds of model order\ntechniques require the repeated solution of high-dimensional linear\nsystems of the original model problem, see [Bai02,An09]. Therefore,\nthe development of efficient solvers resembles an important task\nin model order reduction.\n\nEach matrix CurlCurl_ consists of a different number of degrees\nof freedom, given in the following table:\n\n = 0: 11083\n = 1: 226451\n = 2: 806529\n = 3: 1219574\n = 4: 2380515\n\nReferences.\n\n@ARTICLE{Bai02,\n author = {Z. Bai},\n title = {Krylov subspace techniques for reduced-order modeling\n of large-scale dynamical systems},\n journal = {Applied Numerical Mathematics},\n year = {2002},\n volume = {43},\n pages = {9--44},\n number = {1--2}\n}\n\n@ARTICLE{Hipt02,\n author = {R. Hiptmair},\n title = {Finite elements in computational electromagnetism},\n journal = {Acta Numerica, Cambridge University Press},\n year = {2002},\n pages = {237-339}\n}\n\n@BOOK{An09,\n title = {Approximation of {L}arge-{S}cale {D}ynamical {S}ystems},\n publisher = {Society for Industrial Mathematics},\n year = {2009},\n author = {Athanasios C. Antoulas}\n}" - }, - { - "group": "ANSYS", - "count": 3, - "documented_meaning": "Underdetermined systems needing well-conditioned bases to be found. The goal is to find a permutation or factorization that places A in upper", - "source_url": "https://sparse.tamu.edu/ANSYS", - "coarse_kind_families_interpretive": "other", - "dominant_coarse_family_interpretive": "other", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "least squares problem (3)", - "curator_value_type_count": 1, - "curator_value_types": "real (3)", - "curator_structure_count": 1, - "curator_structures": "rectangular (3)", - "min_dimension": 887058, - "max_dimension": 1823928, - "dimension_ratio": 2.056154163538348, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 652140, - "max_nnz": 4211599, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 3, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Underdetermined systems needing well-conditioned bases to be found.\n\nThe goal is to find a permutation or factorization that places A in upper\ntrapezoidal form, [R1 R2] where R1 is well-conditioned, square, and upper\ntriangular, and where R1\\\\R2 is as sparse as possible. Submitted to the\nUF collection by Emmannuel Delor, ANSYS.\n\n opts.tol = 0.01 ;\n [m n] = size(A) ;\n x = ones (n,1) ;\n y = A*x ;\n [c R P info] = spqr (A, y, opts) ;\n info\n Rs = R (:, 1:m) ; % must be well conditioned\n fprintf ('condest(Rs) %g\\\\n', condest (Rs)) ;\n xs = x (1:m) ;\n xm = x (m+1:n) ;\n A2 = -Rs \\\\ R (:, m+1:n) ;\n y2 = Rs \\\\ c ;\n norm(A2*xm + y2 - xs) % should be very small\n nnz (A2) % should also be as small as possible" - }, - { - "group": "Sorensen", - "count": 1, - "documented_meaning": "Call graph of the Linux kernel. The sparse matrix represents a call-graph made from the source code of", - "source_url": "https://sparse.tamu.edu/Sorensen", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "directed weighted graph (1)", - "curator_value_type_count": 1, - "curator_value_types": "integer (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 324085, - "max_dimension": 324085, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 1208908, - "max_nnz": 1208908, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "single matrix", - "official_notes": "Call graph of the Linux kernel.\n\nThe sparse matrix represents a call-graph made from the source code of\nthe Linux kernel (version 3.7.10)\n\nSvend Haugaard S\u00f8rensen \nMay 6 2013" - }, - { - "group": "LeGresley", - "count": 3, - "documented_meaning": "Power flow analyses of electrical grids. Submitted by Patrick LeGresley, Stanford, July 2013", - "source_url": "https://sparse.tamu.edu/LeGresley", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "power network problem (3)", - "curator_value_type_count": 1, - "curator_value_types": "real (3)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (3)", - "min_dimension": 2508, - "max_dimension": 87936, - "dimension_ratio": 35.0622009569378, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 16727, - "max_nnz": 593276, - "shape_profile": "square only", - "square": 3, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Power flow analyses of electrical grids.\nSubmitted by Patrick LeGresley, Stanford, July 2013" - }, - { - "group": "Brogan", - "count": 1, - "documented_meaning": "specular surface reconstruction problem, Submitted by Joel Brogan, Notre Dame, July 2014", - "source_url": "https://sparse.tamu.edu/Brogan", - "coarse_kind_families_interpretive": "imaging/graphics/robotics", - "dominant_coarse_family_interpretive": "imaging/graphics/robotics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computer vision problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "rectangular (1)", - "min_dimension": 477976, - "max_dimension": 477976, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 7647040, - "max_nnz": 7647040, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 1, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "specular surface reconstruction problem,\nSubmitted by Joel Brogan, Notre Dame, July 2014\nRequires a sparse QR factorization, x=A\\\\b" - }, - { - "group": "Moqri", - "count": 1, - "documented_meaning": "Document similarity graph for MIS articles, including metadata KnowledgeMap -- Visualizing Invisible Networks of Knowledge", - "source_url": "https://sparse.tamu.edu/Moqri", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "undirected weighted graph (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "symmetric (1)", - "min_dimension": 2427, - "max_dimension": 2427, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 57022, - "max_nnz": 57022, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 1, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "single matrix", - "official_notes": "Document similarity graph for MIS articles, including metadata\n\nKnowledgeMap -- Visualizing Invisible Networks of Knowledge\nin a Scientific Discipline, by M. Moqri, Univ of Florida,\nWarrington College of Business Administration.\nhttp://misknowledgemap.warrington.ufl.edu/\nhttp://www.moqri.com/\n\nThis undirected wieghted graph describes the similarities\nbetween a set of 2427 articles. A(i,j) is 0 if articles\ni and j are not similar at all, 1 if they are extremely\nsimilar. The similarity metric is based on LSA, co-citation,\nand bibliographic coupling of the articles. Information\nabout each node is as follows:\n\nlabel: the title of the document (text)\nyear: year published (ranges from 2005 to 2014) (number)\nsource: journal (text)\nauthors: list of authors (text)\nkeywords: list of keywords separated by semicolons (text)\ntopics: list of keywords separated by semicolons (text)\nabstract: (text)\ntimes cited: (number)" - }, - { - "group": "Mazaheri", - "count": 1, - "documented_meaning": "Sparse bundle adjustment problem from Mehdi Mazaheri, University of Calgary A is symmetric positive definite, coming from bundle adjustment of 1700 images", - "source_url": "https://sparse.tamu.edu/Mazaheri", - "coarse_kind_families_interpretive": "imaging/graphics/robotics", - "dominant_coarse_family_interpretive": "imaging/graphics/robotics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computer vision problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "symmetric (1)", - "min_dimension": 513351, - "max_dimension": 513351, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 20207907, - "max_nnz": 20207907, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 1, - "known_spd": 1, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "Sparse bundle adjustment problem from Mehdi Mazaheri, University of Calgary\n\nA is symmetric positive definite, coming from bundle adjustment of 1700 images\nto optimize the trajectory of a multi-camera data acqusition system. The\ntrajectory is a loop inside a building at the University of Calgary.\n\nMehdi Mazaheri\nDept of Geomatics, University of Calgary" - }, - { - "group": "VDOL", - "count": 91, - "documented_meaning": "Optimal control problems, Vehicle Dynamics & Optimization Lab, UF Anil Rao and Begum Senses, University of Florida", - "source_url": "https://sparse.tamu.edu/VDOL", - "coarse_kind_families_interpretive": "optimization/control", - "dominant_coarse_family_interpretive": "optimization/control", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "optimal control problem (91)", - "curator_value_type_count": 1, - "curator_value_types": "real (91)", - "curator_structure_count": 1, - "curator_structures": "symmetric (91)", - "min_dimension": 99, - "max_dimension": 18476, - "dimension_ratio": 186.62626262626262, - "size_span": "wide (>100\u00d7)", - "min_nnz": 927, - "max_nnz": 224897, - "shape_profile": "square only", - "square": 91, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 91, - "structurally_symmetric_square": 91, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Optimal control problems, Vehicle Dynamics & Optimization Lab, UF\nAnil Rao and Begum Senses, University of Florida\nhttp://vdol.mae.ufl.edu\n\nEach optimal control problem is described below. Each of these\nproblems gives rise to a sequence of matrices of different sizes\nwhen they are being solved inside GPOPS, an optimal control\nsolver created by Anil Rao, Begum Senses, and others at in VDOL\nlab at the University of Florida. The matrices are all\nsymmetric indefinite.\n\nRao, Senses, and Davis have created a graph coarsening strategy\nthat matches pairs of nodes. The mapping is given for each matrix,\nwhere map(i)=k means that node i in the original graph is mapped to\nnode k in the smaller graph. map(i)=map(j)=k means that both nodes\ni and j are mapped to the same node k, and thus nodes i and j have\nbeen merged.\n\nEach matrix consists of a set of nodes (rows/columns) and the\nnames of these rows/cols are given for each matrix.\n\nAnil Rao, Begum Sense, and Tim Davis, 2015.\n\n----------------------------------------------------------------------\nVDOL/dynamicSoaring\n----------------------------------------------------------------------\n\nDynamic soaring optimal control problem is taken from\nRef.~\\\\cite{zhao2004optimal} where the dynamics of a glider is\nderived using a point mass model under the assumption of a flat\nEarth and stationary winds. The goal of the dynamic soaring\nproblem is to determine the state and the control that minimize\nthe average wind gradient slope that can sustain a powerless\ndynamic soaring flight. The state of the system is defined by the\nair speed, heading angle, air-realtive flight path angle,\naltitude, and the position of the glider and the control of the\nsystem is the lift coefficient. The specified accuracy tolerance\nof $10^{-7}$ were satisfied after eight mesh iterations. As the\nmesh refinement proceeds, the size of the KKT matrices increases\nfrom 647 to 3543.\n\n@article{zhao2004optimal,\n title={Optimal Patterns of Glider Dynamic Soaring},\n author={Zhao, Yiyuan J},\n journal={Optimal Control applications and methods},\n volume={25},\n number={2},\n pages={67--89},\n year={2004},\n publisher={Wiley Online Library}\n}\n\n\n----------------------------------------------------------------------\nVDOL/freeFlyingRobot\n----------------------------------------------------------------------\n\nFree flying robot optimal control problem is taken from\nRef.~\\\\cite{sakawa1999trajectory}. Free flying robot technology is\nexpected to play an important role in unmanned space missions.\nAlthough NASA currently has free flying robots, called spheres,\ninside the International Space Station (ISS), these free flying\nrobots have neither the technology nor the hardware to complete\ninside and outside inspection and maintanance. NASA's new plan is to\nsend new free flying robots to ISS that are capable of completing\nhousekeeping of ISS during off hours and working in extreme\nenvironments for the external maintanance of ISS. As a result, the\ncrew in ISS can have more time for science experiments. The current\nfree flying robots in ISS works are equipped with a propulsion\nsystem. The goal of the free flying robot optimal control problem is\nto determine the state and the control that minimize the magnitude of\nthrust during a mission. The state of the system is defined by the\ninertial coordinates of the center of gravity, the corresponding\nvelocity, thrust direction, and the anglular velocity and the control\nis the thrust from two engines. The specified accuracy tolerance of\n$10^{-6}$ were satisfied after eight mesh iterations. As the mesh\nrefinement proceeds, the size of the KKT matrices increases from 798\nto 6078.\n\n\n----------------------------------------------------------------------\nVDOL/goddardRocketProblem\n----------------------------------------------------------------------\n\nGoddard rocket maximum ascent optimal control problem is taken from\nRef.~\\\\cite{goddard1920method}. The goal of the Goddard rocket maximum\nascent problem is to determine the state and the control that\nmaximize the final altitude of an ascending rocket. The state of the\nsystem is defined by the altitude, velocity, and the mass of the\nrocket and the control of the system is the thrust. The Goddard\nrocket problem contains a singular arc where the continuous-time\noptimality conditions are indeterminate, thereby the nonlinear\nprogramming problem solver will have difficulty determining the\noptimal control during the singular arc. In order to prevent this\ndifficulty and obtain more accurate solutions the Goddard rocket\nproblem is posed as a three-phase optimal control problem. Phase one\nand phase three contains the same dynamics and the path constraints\nas the original problem, while phase two contains an additional path\nconstraint and an event constraint. The specified accuracy tolerance\nof $10^{-8}$ were satisfied after two mesh iterations. As the mesh\nrefinement proceeds, the size of the KKT matrices increases from 831\nto 867.\n\n@article{goddard1920method,\n title={A Method of Reaching Extreme Altitudes.},\n author={Goddard, Robert H},\n journal={Nature},\n volume={105},\n pages={809--811},\n year={1920}\n}\n\n----------------------------------------------------------------------\nVDOL/hangGlider\n----------------------------------------------------------------------\n\nRange maximization of a hang glider optimal control problem is taken\nfrom Ref.~\\\\cite{bulirsch1993combining}. The goal of the optimal\ncontrol problem is to determine the state and the control that\nmaximize the range of the hang glider in the presence of a thermal\nupdraft. The state of the system is defined by horizontal distance,\naltitude, horizontal velocity, and the vertical velocity and the\ncontrol is the lift coefficient. The specified accuracy tolerance of\n$10^{-8}$ were satisfied after five mesh iterations. As the mesh\nrefinement proceeds, the size of the KKT matrices increases from 360\nto 16011. This problem is sensitive to accuracy of the mesh and it\nrequires excessive number of collocation points to be able to satisfy\nthe accuracy tolerance. Thus, the size of the KKT matrices changes\ndrastically.\n\n@book{bulirsch1993combining,\n title={Combining Direct and Indirect Methods in Optimal Control:\n Range Maximization of a Hang Glider},\n author={Bulirsch, Roland and Nerz, Edda and Pesch, Hans Josef and\n von Stryk, Oskar},\n year={1993},\n publisher={Springer}\n}\n\n----------------------------------------------------------------------\nVDOL/kineticBatchReactor\n----------------------------------------------------------------------\n\n\n----------------------------------------------------------------------\nVDOL/lowThrust\n----------------------------------------------------------------------\n\nLow-thrust orbit transfer optimal control problem is taken from\nRef.~\\\\cite{betts2010practical}. The goal of the low-thrust orbit\ntransfer problem is to determine the state and the control that\nminimize the fuel consumption during the orbit transfer of a\nspacecraft that starts from a low-earth orbit and terminates at the\ngeostationary orbit via low-thrust propulsion systems. The highly\nnonlinear dynamics of the low-thrust orbit transfer problem is given\nin modified equinoctial elements (state of the system) and the thrust\ndirection (control of the system). Furthermore, the low-thrust\noptimal control problem is a badly scaled problem because of the\nsmall thrust-to-initial-mass ratio, that is typically on the order of\n$O(10^{-4})$, and the long orbit transfer duration. Badly scaling of\nthe problem leads to a lot of delayed pivots. The specified accuracy\ntolerance of $10^{-8}$ were satisfied after thirteen mesh iterations.\nAs the mesh refinement proceeds, the size of the KKT matrices\nincreases from 584 to 18476. \n\n@book{betts2010practical,\n title={Practical Methods for Optimal Control and Estimation Using\n Nonlinear Programming},\n author={Betts, John T},\n volume={19},\n year={2010},\n publisher={SIAM Press},\n address = {Philadelphia, Pennsylvania},\n}\n\n\n\n----------------------------------------------------------------------\nVDOL/orbitRaising\n----------------------------------------------------------------------\n\nOrbit raising problem that is taken from\nRef.~\\\\cite{bryson1975applied}. The goal of the optimal control\nproblem is to determine the state and the control that maximize the\nradius of an orbit transfer in a given time. The state of the system\nis defined by radial distance of the spacecraft from the attracting\ncenter (e.g Earth, Mars, etc.) and velocity of the spacecraft and the\ncontrol is the thrust direction. The specified accuracy tolerance of\n$10^{-8}$ were satisfied after four mesh iterations. As the mesh\nrefinement proceeds, the size of the KKT matrices increases from 442\nto 915.\n\n@book{bryson1975applied,\n title={Applied Optimal Control: Optimization, Estimation, and\n Control},\n author={Bryson, Arthur Earl},\n year={1975},\n publisher={CRC Press}\n}\n\n\n\n----------------------------------------------------------------------\nVDOL/reorientation\n----------------------------------------------------------------------\n\nMinimum-time reorientation of an asymmetric rigid body optimal\ncontrol problem is taken from Ref.~\\\\cite{betts2010practical}. The\ngoal of the problem is to determine the state and the control that\nminimize the time that is required to reorient a rigid body. The\nstate of the system is defined by quaternians that gives the\norientation of the spacecraft and the angular velocity of the\nspacecraft and the control of the system is torque. The vehicle data\nthat is used to model the dynamics are taken from NASA X-ray Timing\nExplorer spacecraft. The specified accuracy tolerance of $10^{-8}$\nwere satisfied after eight mesh iterations. As the mesh refinement\nproceeds, the size of the KKT matrices increases from 677 to 3108.\n\n@book{betts2010practical,\n title={Practical Methods for Optimal Control and Estimation\n Using Nonlinear Programming},\n author={Betts, John T},\n volume={19},\n year={2010},\n publisher={SIAM Press},\n address = {Philadelphia, Pennsylvania},\n}\n\n----------------------------------------------------------------------\nVDOL/spaceShuttleEntry\n----------------------------------------------------------------------\n\nSpace shuttle launch vehicle reentry optimal control problem is taken\nfrom Ref.~\\\\cite{betts2010practical}. The goal of the optimal control\nproblem is to determine the state and the control that maximize the\ncross range (maximize the final latitude) during the atmospheric\nentry of a reusable launch vehicle. State of the system is defined by\nthe position, velocity, and the orientation of the space shuttle and\nthe control of the system is the angle of attack and the bank angle\nof the space shuttle. The specified accuracy tolerance of $10^{-8}$\nwere satisfied after two mesh iterations. As the mesh refinement\nproceeds, the size of the KKT matrices increases from 560 to 2450.\n\n\n----------------------------------------------------------------------\nVDOL/spaceStation\n----------------------------------------------------------------------\n\nSpace station attitude optimal control problem is taken from\nRef.~\\\\cite{betts2010practical}. The goal of the space station\nattitude control problem is to determine the state and the control\nthat minimize the magnitude of the final momentum while the space\nstatition reaches an orientation at the final time that can be\nmaintained without utilizing additional control torque. The state of\nthe system is defined by the angular velocity of the spacecraft with\nrespect to an inertial reference frame, Euler-Rodriguez parameters\nused to defined the vehicle attitude, and the angular momentum of the\ncontrol moment gyroscope and the control of the system is the torque.\nThe specified accuracy tolerance of $10^{-7}$ were satisfied after\nthirteen mesh iterations. As the mesh refinement proceeds, the size\nof the KKT matrices increases from 99 to 1640.\n\n\n----------------------------------------------------------------------\nVDOL/tumorAntiogenesis\n----------------------------------------------------------------------\n\nTumor anti-angiogenesis optimal control problem is taken from\nRef.~\\\\cite{ledzewicz2008analysis}. A tumor first uses the blood\nvessels of its host but as the tumor grows oxygen that is carried by\nthe blood vessels of its host cannot defuse very far into the tumor.\nTherefore, the tumor grows its own blood vessels by producing\nvascular endothelial growth factor (VEGF). This process is called\nangiogenesis. But blood vessels have a defense mechanism, called\nendostatin, that tries to impede the development of new blood cells\nby targeting VEGF. In addition, new pharmacological therapies that is\ndeveloped for tumor-type cancers also targets VEGF. The goal of the\ntumor anti-angiogenesis problem is to determine the state and control\nthat minimizing the size of the tumor at the final time. The state of\nthe system is defined by the tumor volume, carrying capacity of a\nvessel, and the total anti-angiogenic treatment administered and the\ncontrol of the system is the angiogenic dose rate. The specified\naccuracy tolerance of $10^{-7}$ were satisfied after eight mesh\niterations. As the mesh refinement proceeds, the size of the KKT\nmatrices increases from 205 to 490.\n\n@article{ledzewicz2008analysis,\n title={Analysis of Optimal Controls for a Mathematical Model of\n Tumour Anti-Angiogenesis},\n author={Ledzewicz, Urszula and Sch{\\\\\"a}ttler, Heinz},\n journal={Optimal Control Applications and Methods},\n volume=29,\n number=1,\n pages={41--57},\n year=2008,\n publisher={Wiley Online Library}\n}" - }, - { - "group": "Meng", - "count": 1, - "documented_meaning": "Jacobian matrix from a computational chemistry problem, iChem, China' Lingyi Meng, Collaborative Innovation Center of Chemistry for", - "source_url": "https://sparse.tamu.edu/Meng", - "coarse_kind_families_interpretive": "chemistry/biology", - "dominant_coarse_family_interpretive": "chemistry/biology", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computational chemistry problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "complex (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 274087, - "max_dimension": 274087, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 4137369, - "max_nnz": 4137369, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "complex only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "single matrix", - "official_notes": "Jacobian matrix from a computational chemistry problem, iChem, China'\nLingyi Meng, Collaborative Innovation Center of Chemistry for\nEnergy Materials (iChem), Xiamen University, Fujian, China,\nwww.2011-ichem.org. The matrix is complex, and has a sparse\nand real right-hand-side.\n\nReferences:\nLingyi Meng, ChiYung Yam, SiuKong Koo, Quan Chen, Ngai Wong, and\nGuanHua Chen, Dynamic Multiscale Quantum Mechanics/Electromagnetics\nSimulation Method, J. of Chemical Theory and Computation, 2012,\nvol 8, pp 1190-1199, dx.doi.org/10.1021/ct200859h\n\nLingyi Meng, Zhenyu Yin, ChiYung Yam, SiuKong Koo, Quan Chen,\nNgai Wong, and GuanHua Chen, Frequency-domain multiscale quantum\nmechanics/electromagnetics simulation method, J. of Chemical\nPhysics 139, 244111 (2013); doi: 10.1063/1.4853635" - }, - { - "group": "Harvard_Seismology", - "count": 1, - "documented_meaning": "Earth's crust underneath Japan, Harvard Seismology Group. This matrix represents the forward operator of a linearized seismic", - "source_url": "https://sparse.tamu.edu/Harvard_Seismology", - "coarse_kind_families_interpretive": "imaging/graphics/robotics", - "dominant_coarse_family_interpretive": "imaging/graphics/robotics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "tomography problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "rectangular (1)", - "min_dimension": 87616, - "max_dimension": 87616, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 13734559, - "max_nnz": 13734559, - "shape_profile": "rectangular only", - "square": 0, - "rectangular": 1, - "symmetry_profile": "N/A (rectangular only)", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "single matrix", - "official_notes": "Earth's crust underneath Japan, Harvard Seismology Group.\n\nThis matrix represents the forward operator of a linearized seismic\ntomography problem for imaging Japan's crust and upper mantle. The\nrows of the matrix correspond to 87,616 P-wave ray paths, from 125\nmoderate size earthquakes (stars), deeper that ~150 km that occurred\nthe last decade, and recorded from seismic stations of the Hi-net\narray (triangles). The 3-Dimensional space is parameterized using a\ngrid of 67,320 parameters that correspond to the columns of the matrix.\n\nThe matrix is structurally and numerically rank deficient. This is\nbecause some parts of the Earth are not imaged well from the data,\ndue to the lack of signals (earthquakes) and receivers (seismic stations\nin those areas. This leads to an under-determined part of the system.\nOther parts of the problem are overdetermined, because deep earthquakes\nrepeat in the same region of the Earth, over time, and the seismic\nstations do not move. Repeated earthquakes from the same source\nare recorded by the same seismic stations, leading to an over-\ndetermined problem for those regions of the Earth.\n\nIn MATLAB, cs_dmspy(Problem.A) displays these two parts of the matrix,\nusing a function from CSparse, available at http://suitesparse.com.\n\nPetros Bogiatzis, Miaki Ishii, Harvard Seismology Group, 2015\nhttp://www.seismology.harvard.edu\n\nReferences:\n\nBogiatzis, P., & Ishii, M., 2014. Resolution Analysis and Jointly Inverted P-\nand S-Wave Models of Japan's Crust and Upper Mantle. American Geophysical\nUnion, Fall Meeting 2014, S33A-4501." - }, - { - "group": "Mycielski", - "count": 19, - "documented_meaning": "Mycielski graph adjacency matrices matrices/Mycielski/README. Scott Kolodziej, June 27, 2018.", - "source_url": "https://sparse.tamu.edu/Mycielski", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "undirected graph (19)", - "curator_value_type_count": 1, - "curator_value_types": "binary (19)", - "curator_structure_count": 1, - "curator_structures": "symmetric (19)", - "min_dimension": 2, - "max_dimension": 786431, - "dimension_ratio": 393215.5, - "size_span": "wide (>100\u00d7)", - "min_nnz": 2, - "max_nnz": 2710370560, - "shape_profile": "square only", - "square": 19, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 19, - "structurally_symmetric_square": 19, - "known_spd": 0, - "value_profile": "real only", - "binary": 19, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Mycielski graph adjacency matrices \n \nmatrices/Mycielski/README. Scott Kolodziej, June 27, 2018. \n \nThe Mycielskian graph sequence generates graphs that are triangle-free \nand with a known chromatic number (i.e. the minimum number of colors \nrequired to color the vertices of the graph). The sequence starts with a \n2 vertex, 1 edge graph (denoted M2) and repeatedly generates subsequent \ngraphs by applying the Mycielskian operation. \n \nIn MATLAB, the (sparse) Mycielskian operation can be written as follows: \n \nfunction M_new = mycielski(M) \n [n,~] = size(M); \n m = nnz(M); \n M_new = sparse([], [], [], 2*n+1, 2*n+1, 3*m+n); \n M_new(1:n, 1:n) = M; \n M_new((n+1):2*n, :) = sparse([M sparse(n,n) ones(n,1)]); \n M_new(:, (n+1):2*n) = sparse([M; sparse(n,n); ones(1,n)]); \nend \n \nMore abstractly, the structure of the Mycielskian operation can be represented \nby the block matrix below: \n n n 1 \n _____ _____ _ \n | | | | \n n | M | M |0| \n |_____|_____|_| \n M_new = | | | | \n n | M | 0 |1| \n |_____|_____|_| \n 1 | 0 | 1 |0| \n ------------- \n \nKnown properties of the Mycielskian operation include the following: \n \n * If a graph has a minimum chromatic number k, the Mycielskian of that graph \n has a minimum chromatic number of k + 1. \n * If a graph is triangle-free (i.e. no cycles of length 3 exist), so is the \n Mycielskian of that graph. \n * If a graph has a Hamiltonian cycle, so does the Mycielskian of that graph. \n * If a graph is factor-critical, so is the Mycielskian of that graph, meaning \n that every subgraph of |V|-1 vertices has a perfect matching. \n \nMycielski graphs were first described by Jan Mycielski in the following \npublication: \n \n Mycielski, J., 1955. Sur le coloriage des graphes. Colloq. Math., \n 3: 161-162. \n \n--------------------------------------------------------------------------------" - }, - { - "group": "MAWI", - "count": 5, - "documented_meaning": "MAWI Working Group Traffic Archive ( The MAWI (Measurement and Analysis on the WIDE Internet) Working", - "source_url": "https://sparse.tamu.edu/MAWI", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "undirected weighted graph (5)", - "curator_value_type_count": 1, - "curator_value_types": "integer (5)", - "curator_structure_count": 1, - "curator_structures": "symmetric (5)", - "min_dimension": 18571154, - "max_dimension": 226196185, - "dimension_ratio": 12.179974653163718, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 38040320, - "max_nnz": 480047894, - "shape_profile": "square only", - "square": 5, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 5, - "structurally_symmetric_square": 5, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "MAWI Working Group Traffic Archive (http://mawi.wide.ad.jp/mawi/):\nThe MAWI (Measurement and Analysis on the WIDE Internet) Working\nGroup is a working group that has carried out network traffic\nmeasurement, analysis, evaluation, and verification from the\nbeginning of the WIDE Project. The graphs provided here were\ngenerated from packet trace data from the WIDE backbone maintained by\nthe MAWI Working Group.\n\nMAWI Datasets: Name, link, and Description\n\nGraph 1 Num. vertices : 18571154, Edge count : 38040320\n http://mawi.wide.ad.jp/mawi/ditl/ditl2015/201512012345.html\n in 2018 GraphChallenge: 201512012345.v18571154_e38040320.tsv.gz\n in the SuiteSparse Matrix Collection: MAWI/mawi_201512012345\n\nGraph 2 Num. vertices : 35991342, Edge count : 74485420\n http://mawi.wide.ad.jp/mawi/ditl/ditl2015/201512020000.html\n in 2018 GraphChallenge: 201512020000.v35991342_e74485420.tsv.gz\n in the SuiteSparse Matrix Collection: MAWI/mawi_201512020000\n\nGraph 3 Num. vertices : 68863315, Edge count : 143414960\n http://mawi.wide.ad.jp/mawi/ditl/ditl2015/201512020030.html\n in 2018 GraphChallenge: 201512020030.v68863315_e143414960.tsv.gz\n in the SuiteSparse Matrix Collection: MAWI/mawi_201512020030\n\nGraph 4 Num. vertices : 128568730, Edge count : 270234840\n http://mawi.wide.ad.jp/mawi/ditl/ditl2015/201512020130.html\n in 2018 GraphChallenge: 201512020130.v128568730_e270234840.tsv.gz\n in the SuiteSparse Matrix Collection: MAWI/mawi_201512020130\n\nGraph 5 Num. vertices : 226196185, Edge count : 480047894\n http://mawi.wide.ad.jp/mawi/ditl/ditl2015/201512020330.html\n in 2018 GraphChallenge: 201512020330.v226196185_e480047894.tsv.gz\n in the SuiteSparse Matrix Collection: MAWI/mawi_201512020330\n\nThese matrices appear in the 2018 GraphChallenge:\nsee https://graphchallenge.mit.edu/data-sets\n\nMAWILab is a database that assists researchers to evaluate their\ntraffic anomaly detection methods. It consists of a set of labels\nlocating traffic anomalies in the MAWI archive (samplepoints B and\nF). The labels are obtained using an advanced graph-based methodology\nthat compares and combines different and independent anomaly\ndetectors. The data set is daily updated to include new traffic from\nupcoming applications and anomalies.\n\nYou may use WIDE traffic data for only research purposes. Actions\nthat trespass upon users'' privacy are prohibited.\n\nGuidelines for protecting user privacy in WIDE traffic archive:\nhttp://mawi.wide.ad.jp/mawi/guideline.txt\n\nIn the 2018 GraphChallenge data set (and thus also in the SuiteSparse\nMatrix Collection), only the connectivity of the graph is provided,\nwith no user information at all on the nodes or edges. Privacy\nguidelines are thus met.\n\nRelated Papers:\n\nKenjiro Cho. Recursive Lattice Search: Hierarchical Heavy Hitters\nRevisited. ACM IMC 2017, London, UK, November 2017.\n\nRomain Fontugne, Patrice Abry, Kensuke Fukuda, Darryl Veitch, Kenjiro\nCho, Pierre Borgnat, Herwig Wendt. Scaling in Internet Traffic: a 14\nyear and 3 day longitudinal study, with multiscale analyses and\nrandom projections. IEEE/ACM Transactions on Networking, vol.25(4).\npp2152--2165. August 2017.\n\nMidori Kato, Kenjiro Cho, Michio Honda, Hideyuki Tokuda. Monitoring\nthe Dynamics of Network Traffic by Recursive Multi-dimensional\nAggregation. OSDI2012 MAD Workshop. Hollywood, CA. October 2012.\n\nR. Fontugne, P. Borgnat, P. Abry, K. Fukuda. \"MAWILab: Combining\ndiverse anomaly detectors for automated anomaly labeling and\nperformance benchmarking\". ACM CoNEXT 2010. Philadelphia, PA.\nDecember 2010.\n\nPierre Borgnat, Guillaume Dewaele, Kensuke Fukuda, Patrice Abry,\nKenjiro Cho. \"Seven Years and One Day: Sketching the Evolution of\nInternet Traffic.\" INFOCOM2009. Rio de Janeiro, Brazil. April 2009.\n\nGuilaume Dewaele, Kensuke Fukuda, Pierre Borgnat, Patrice Abry,\nKenjiro Cho. \"Extracting Hidden Anomalies using Sketch and Non\nGaussian Multiresolution Statistical Detection Procedures\"\nSIGCOMM2007 LSAD Workshop, Kyoto Japan. August 2007..\n\nKenjiro Cho, Ryo Kaizaki and Akira Kato. \"Aguri: An\nAggregation-based Traffic Profiler\" In Proceedings of QofIS2001\n(published by Springer-Verlag in the LCNS series). September 2001.\n\nKenjiro Cho, Koushirou Mitsuya and Akira Kato. \"Traffic Data\nRepository at the WIDE Project\" USENIX 2000 FREENIX Track, San Diego,\nCA, June 2000.\n\nAkira KATO, Jun MURAI, Satoshi KATSUNO and Tohru ASAMI. \"An Internet\nTraffic Data Repository: The Architecture and the Design Policy\"\nINET99, San Jose, CA, June 1999.\n\nMAWI data set imported into the SuiteSparse Matrix Collection on July\n2018, from the 2018 GraphChallenge data set" - }, - { - "group": "GenBank", - "count": 5, - "documented_meaning": "Protein k-mer graphs generated using data from GenBank are available below.", - "source_url": "https://sparse.tamu.edu/GenBank", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "undirected graph (5)", - "curator_value_type_count": 1, - "curator_value_types": "binary (5)", - "curator_structure_count": 1, - "curator_structures": "symmetric (5)", - "min_dimension": 55042369, - "max_dimension": 214005017, - "dimension_ratio": 3.888005201956333, - "size_span": "narrow (\u22644\u00d7)", - "min_nnz": 117217600, - "max_nnz": 465410904, - "shape_profile": "square only", - "square": 5, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 5, - "structurally_symmetric_square": 5, - "known_spd": 0, - "value_profile": "real only", - "binary": 5, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "Protein k-mer graphs generated using data from GenBank\nhttps://www.ncbi.nlm.nih.gov/genbank/ are available below.\nNodes of the graph represent segments of amino acids.\n\nProtein k-mer graphs\nName Description\nA2a.tsv Graph 1 Num. vertices : 170728175 Edge count : 360585172\nP1a.tsv Graph 2 Num. vertices : 139353211 Edge count : 297829984\nU1a.tsv Graph 3 Nun. vertices : 67716231 Edge count : 138778562\nV1r.tsv Graph 4 Num. vertices : 214005017 Edge count : 465410904\nV2a.tsv Graph 5 Num. vertices : 55042369 Edge count : 117217600\n\nFrom the 2018 GraphChallenge, https://graphchallenge.mit.edu.\nImported into the SuiteSparse Matrix Collection as \"GenBank/kmer_*\"\nwhere \"*\" is the 3-letter name above, on July 2018." - }, - { - "group": "Sybrandt", - "count": 2, - "documented_meaning": "Sybrandt: machine learning graphs from document analysis ----------------------------------------------------------------------", - "source_url": "https://sparse.tamu.edu/Sybrandt", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 2, - "normalized_kinds": "undirected weighted graph (1); undirected graph (1)", - "curator_value_type_count": 2, - "curator_value_types": "real (1); binary (1)", - "curator_structure_count": 1, - "curator_structures": "symmetric (2)", - "min_dimension": 30239687, - "max_dimension": 183964077, - "dimension_ratio": 6.083531122527822, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 6669254694, - "max_nnz": 11588725964, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 2, - "structurally_symmetric_square": 2, - "known_spd": 0, - "value_profile": "real only", - "binary": 1, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Sybrandt: machine learning graphs from document analysis\n\n----------------------------------------------------------------------\nMOLIERE Hypothesis Generation Network, J. Sybrandt, Clemson Univ.\n----------------------------------------------------------------------\n\nJustin Sybrandt, jsybran at clemson.edu\nMatrix Name: MOLIERE_2016 Hypothesis Generation Network\nKind/Problem Domain: Term/Document Graph\n\nThis network was presented in \"MOLIERE: Automatic Biomedical Hypothesis\nGeneration System (KDD''17).\" Nodes in this network are either MEDLINE\ndocuments, UMLS terms, or n-grams we extracted through ToPMine.\n\ncitation:\nJustin Sybrandt (Clemson University, Clemson, SC, USA),\nMichael Shtutman (University of South Carolina, Columbia, SC, USA)\nIlya Safro (Clemson University, Clemson, SC, USA),\n\"MOLIERE: Automatic Biomedical Hypothesis Generation System\",\n23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining\nAugust 13-17, 2017, Halifax, Nova Scotia - Canada\nhttps://doi.org/10.1145/3097983.3098057\n\nThe original data was 0-based with nodes numbered 0 to n-1. Converted\nto 1-based for the SuiteSparse Matrix Collection, July 2018.\n\nThe graph has n=30,239,687 nodes. Node labels are held in the char array\nProblem.aux.labels. Nodes 1 to 22,281,874 all have labels starting with\nthe capital letter \"P\", and are PubMed indentification numbers of the\nMEDLINE documents. Nodes 27,683,534 to n all start with the letter \"C\",\nand refer to UMLS terms. Nodes between these two sets of nodes refer to\nn-grams; none of them start with P or C (or any capital letter). The\nlabel of the kth node is also the kth line in the labels text file, and\nto the kth row and column of the matrix.\n\nThe graph has 4,023,336 explicit zero edges, which are very important\nto the problem. They link automatically mined n-grams to UMLS terms\nrepresenting the same concept. The goal is to find shortest-paths,\nso an edge of zero-length is important, and not the same as no edge\nat all. The pattern of the explicit zeros is held in Problem.Zeros\nin the MATLAB representation, and are part of the files for the Matrix\nMarket and Rutherford-Boeing formats. To operate on the graph G in\nMATLAB, use G = Problem.A + 1e-100 * Problem.Zeros, or some other\nsuitable tiny value. The nonzero edge weights in the graph range in\nvalue between 5e-15 and 3.0. There are no negative edge weights.\n\nThe graph also has d=3,106,164 duplicate edges; it could be considered\na multigraph. However, since the problem is to find shortest paths, the\nduplicate edges are not needed. The matrix in the SuiteSparse Matrix\nCollection holds the smallest edge weight for any duplicate. The\nduplicates not in the matrix are held in Problem.aux.duplicate_edges,\nas a d-by-3 dense matrix, where each row holds [i j eij] for the edge\n(i,j) with weight eij.\n\n----------------------------------------------------------------------\nSybrandt/AGATHA_2015: deep-learning graph\n----------------------------------------------------------------------\n\nAGATHA: Automatic Graph-mining And Transformer based Hypothesis\nGeneration Approach\n\nJustin Sybrandt, Ilya Tyagin, Michael Shtutman, Ilya Safro\nClemson University\npaper: https://arxiv.org/abs/2002.05635.\n\nabstract: Medical research is risky and expensive. Drug discovery, as\nan example, requires that researchers efficiently winnow thousands of\npotential targets to a small candidate set for more thorough\nevaluation. However, research groups spend significant time and money\nto perform the experiments necessary to determine this candidate set\nlong before seeing intermediate results. Hypothesis generation systems\naddress this challenge by mining the wealth of publicly available\nscientific information to predict plausible research directions. We\npresent AGATHA, a deep-learning hypothesis generation system that can\nintroduce data-driven insights earlier in the discovery process.\nThrough a learned ranking criteria, this system quickly prioritizes\nplausible term-pairs among entity sets, allowing us to recommend new\nresearch directions. We massively validate our system with a temporal\nholdout wherein we predict connections first introduced after 2015\nusing data published beforehand. We additionally explore biomedical\nsub-domains, and demonstrate AGATHA's predictive capacity across the\ntwenty most popular relationship types. This system achieves\nbest-in-class performance on an established benchmark, and demonstrates\nhigh recommendation scores across subdomains. Reproducibility: All\ncode, experimental data, and pre-trained models are available online:\nhttp://sybrandt.com/2020/agatha .\n\nAppears in the 29TH ACM Intl. Conf. on Information and Knowledge\nManagement, Oct 2020. https://www.cikm2020.org/\n\nDetails of this matrix:\n\nThis matrix represents the semantic graph associated with the Agatha\nhypothesis generation system: https://arxiv.org/abs/2002.05635.\n\nThe matrix was created by selecting all biomedical papers published\nprior to January 1st 2015. We extracted terms, phrases, entities, and\nauthor-supplied metadata keywords for each. In addition we identify all\nSemRep predicate arguments. Each sentence, term, phrase, entity, and\npredicate represents a node. Edges follow a particular schema,\ndescribed in detail in the paper. Most edges indicate that two nodes\nco-occur. For instance, if a sentence contains a term, then an edge\nexists between the two nodes. We introduce a set of edges between\nsentences based on the nearest-neighbors network of sentence\nembeddings.\n\nThe graph is really big, consisting of 183,964,077 nodes and\n11,588,725,964 entries (half that many edges since the matrix is\nsymmetric).\n\nEach node has a label, consisting of a\ncharacter string, with a length ranging from 0 to 782 characters.\nThe mean string lenth is 17.4. A single node (171,649,101 in 1-based\nnotation) has a label of length zero, and is an artifact from how the\noriginal data was processed. All other node labels range in length\nfrom 3 to 782. The longest string comes from the following paper:\nhttps://pubmed.ncbi.nlm.nih.gov/183954/ which states: \"The primary\nstructure of the enzyme was determined: Ac-Met-Glu-...--Ala-Leu-Lys.\"\n\nTo save space in the MATLAB representation, the node labels are held\nin a single character array, Problem.aux.names, where each label is\nterminated with a newline character. To extract the label of any\ngiven node, do the following:\n\n names = Problem.aux.names ;\n p = [1 find(names==10)+1] ;\n label = names (p(i):p(i+1)-2) ;\n\nFor example, to list all nodes with labels of length 0 to 4:\n\n p = [1 find(names==10)+1] ;\n d = diff (p) ;\n for len = 0:4\n fprintf ('\\\\nnodes with labels of length %d:\\\\n', len) ;\n nodes = find (d == len+1) ;\n for i = nodes\n fprintf ('%12d: [%s]\\\\n', i, names (p(i):p(i+1)-2)) ;\n end\n end" - }, - { - "group": "Guettel", - "count": 3, - "documented_meaning": "3D Transient Electromagnetic Modelleing, Stefan Guettel, Univ Manchester The TEM problem relates to the time-dependent modelling of a transient", - "source_url": "https://sparse.tamu.edu/Guettel", - "coarse_kind_families_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_interpretive": "electromagnetics/acoustics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "electromagnetics problem (3)", - "curator_value_type_count": 1, - "curator_value_types": "real (3)", - "curator_structure_count": 1, - "curator_structures": "symmetric (3)", - "min_dimension": 27623, - "max_dimension": 181302, - "dimension_ratio": 6.563443507222242, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 439469, - "max_nnz": 7839010, - "shape_profile": "square only", - "square": 3, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 3, - "structurally_symmetric_square": 3, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 3, - "cohesion": "coherent family/series", - "official_notes": "3D Transient Electromagnetic Modelleing, Stefan Guettel, Univ Manchester\n\nThe TEM problem relates to the time-dependent modelling of a transient\nelectromagnetic field in geophysical exploration. The set contains a\nmatrix pencil (C,M) and an initial value vector q, corresponding to a\nNedelec finite element discretization of Maxwell's equations for the\nelectric field density e(t). The curl-curl matrix C is symmetric\npositive semi-definite and the mass matrix M is symmetric positive\ndefinite. The problem to be solved is a linear initial value problem\n\n M*e'(t) = C*e(t), M*e(0) = q,\n\nfor logarithmically distributed time points t in the interval\n[1e-6,1e-3].\n\nThere are three test sets which are described in the following\npublication:\n\n@article{BEG2015,\n title={Three-dimensional transient electromagnetic modelling using\n rational {K}rylov methods},\n author={B{\\\\\"o}rner, Ralph-Uwe and Ernst, Oliver G and G{\\\\\"u}ttel,\n Stefan},\n journal={Geophysical Journal International},\n volume={202},\n number={3},\n pages={2025--2043},\n year={2015},\n publisher={Oxford University Press}\n}\n\nThe problem details are\n\nTEM27623: section 5.1 in the above paper; layered half-space problem\ndiscretized by 24582 tetrahedral elements of order 1 giving rise to\n27623 degrees of freedom.\n\nTEM152078: section 5.1 in the above paper; layered half-space problem\ndiscretized by 24582 tetrahedral elements of order 2 giving rise to\n152078 degrees of freedom.\n\nTEM181302: section 5.2 in the above paper; homogeneous subsurface with\ntopography discretized by 28849 tetrahedral elements of order 2 giving\nrise to 181302 degrees of freedom." - }, - { - "group": "Grueninger", - "count": 2, - "documented_meaning": "Wind tunnel evaporation, Christoph Grueninger, Univ. Stuttgart Source: Christoph Grueninger, pr at grueninger.de", - "source_url": "https://sparse.tamu.edu/Grueninger", - "coarse_kind_families_interpretive": "fluids/transport", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computational fluid dynamics problem (2)", - "curator_value_type_count": 1, - "curator_value_types": "real (2)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (2)", - "min_dimension": 8256, - "max_dimension": 40816, - "dimension_ratio": 4.9437984496124034, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 109368, - "max_nnz": 803978, - "shape_profile": "square only", - "square": 2, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 2, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 2, - "cohesion": "coherent family/series", - "official_notes": "Wind tunnel evaporation, Christoph Grueninger, Univ. Stuttgart\n\nSource: Christoph Grueninger, pr at grueninger.de\nStuttgart Research Centre for Simulation Technology,\nUniv. of Stuttgart, Germany\n\nMatrix files: windtunnel-evaporation-(2|3)d-time4-newton1.mat\nRight-hand sides: windtunnel-evaporation-(2|3)d-time4-newton1-rhs.mat\n\n== Background ==\nThe matrix origins from a simulation of soil-water evaporation from a\nwater-filled sand box to air in a pipe. It is based on a coupled\nNavier-Stokes/Darcy model. The Navier-Stokes model features one fluid\nphase, the Darcy model two fluid phases. Each fluid phase may be composed\nof two components, in addition, non-isothermal processes are considered.\nA cell-centered finite volume method (FVM) is combined with a marker and\ncell (MAC) scheme. The coupled problem is compiled in one monolithic\nsystem and solved using Newton's method. The given matrix occurs at the\n4th time-step in the 1st Newton step.\n\n== Publication and software==\nThe related model and the software is described in:\n Christoph Gr\\\\\"uninger, Thomas Fetzer, Bernd Flemisch, Rainer Helmig:\n \"Coupling DuMuX and DUNE-PDELab to investigate evaporation\n at the interface between Darcy and Navier-Stokes flow\"\n SimTech Technical Report 2017 - 1, 2017.\n doi: 10.18419/opus-9360\n https://elib.uni-stuttgart.de/handle/11682/9377?mode=simple\n\nabstract:\nAn implementation of a coupled Navier-Stokes/Darcy model based on\ndifferent Dune discretization modules is presented. The Darcy model is\ntaken from DuMuX, the Navier-Stokes model is implemented on top of\nDune-PDELab, and the coupling is done with help of Dune-MultiDomain\ntogether with some project-specific auxiliary code. The Navier-Stokes\nmodel features one fluid phase, the Darcy model two fluid phases. Each\nfluid phase may be composed of two components, in addition,\nnon-isothermal processes are considered. The coupling between free and\nporous-medium flow uses a sharp interface between both subdomains and\nconserves mass, momentum, and energy by accounting for the corresponding\nfluxes across the interface. A cell-centered finite volume method (FVM)\nis combined with a marker and cell (MAC) scheme. It solves the coupled\nproblem in one monolithic system using a Newton method and a direct\nlinear solver. Numerical results demonstrate the basic functioning and a\nlab-scale reference application.\n\nThe software to reproduce the matrices is available as open source:\nhttps://git.iws.uni-stuttgart.de/dumux-pub/Grueninger2017b" - }, - { - "group": "PowerSystem", - "count": 1, - "documented_meaning": "Power system matrix", - "source_url": "https://sparse.tamu.edu/PowerSystem", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "power network problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 197156, - "max_dimension": 197156, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 805839, - "max_nnz": 805839, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "single matrix", - "official_notes": "Power system matrix" - }, - { - "group": "TAMU_SmartGridCenter", - "count": 3, - "documented_meaning": "Texas A&M Smart Grid Center: synthetic (yet realistic) power system grids Group name: TAMU_SmartGridCenter", - "source_url": "https://sparse.tamu.edu/TAMU_SmartGridCenter", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "power network problem (3)", - "curator_value_type_count": 1, - "curator_value_types": "real (3)", - "curator_structure_count": 2, - "curator_structures": "unsymmetric (2); symmetric (1)", - "min_dimension": 4000, - "max_dimension": 69999, - "dimension_ratio": 17.49975, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 28505, - "max_nnz": 238627, - "shape_profile": "square only", - "square": 3, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 3, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Texas A&M Smart Grid Center: synthetic (yet realistic) power system grids\n\nGroup name: TAMU_SmartGridCenter\n\nSynthetic electric grid models are fictitious representations that are\ndesigned to be statistically and functionally similar to actual electric\ngrids while containing no confidential critical energy infrastructure\ninformation (CEII). Some of these cases were developed with the support\nof the U.S. DOE ARPA-E Grid Data program; their support is gratefully\nacknowledged. \n\nFor more information and full datasets, see:\nhttps://electricgrids.engr.tamu.edu/electric-grid-test-cases/ \n\nA. B. Birchfield; T. Xu; K. M. Gegner; K. S. Shetye; T. J. Overbye, \"Grid\nStructural Characteristics as Validation Criteria for Synthetic Networks,\"\nin IEEE Transactions on Power Systems, vol. 32, no. 4, pp. 3258-3265, July\n2017.\n\nA. B. Birchfield, E. Schweitzer, H. Athari, T. Xu, T. J. Overbye, A.\nScaglione, and Z.Wang, \"Validation metrics to assess the realism of\nsynthetic power grids,\" Energies, vol. 10, no. 8, p. 1233, Aug. 2017.\n\nA. B. Birchfield, T. Xu, and T. J. Overbye, \"Power flow convergence and\nreactive power planning in the creation of large synthetic grids,\" in IEEE\nTransactions on Power Systems, 2018.\n\n----------------------------------------------------------------------\nMatrix: ACTIVSg2000\n\nTitle: Power flow Jacobian for a synthetic 2000-bus power grid\n\nDescription:\nACTIVSg2000 is a 2000-bus power system dataset that is entirely synthetic,\nbuilt from public information and a statistical analysis of real power\nsystems. Its geographic footprint is Texas, but it bears no relation to\nany actual grid, except that generation and load profiles are similar,\nbased on public data. This matrix is the power flow Jacobian for the peak\nplanning operating point.\n\nThe full dataset can be downloaded at:\nhttps://electricgrids.engr.tamu.edu/electric-grid-test-cases/activsg2000/\n\n----------------------------------------------------------------------\nMatrix: ACTIVSg10K\nTitle: Power flow Jacobian for a synthetic 10,000-bus power grid\nDescription:\n\nACTIVSg10K is a 10,000-bus power system dataset that is entirely\nsynthetic, built from public information and a statistical analysis of\nreal power systems. Its geographic footprint is the Western United\nStates, but it bears no relation to any actual grid, except that\ngeneration and load profiles are similar, based on public data. This\nmatrix is the power flow Jacobian for the peak planning operating point.\n\nThe full dataset can be downloaded at:\nhttps://electricgrids.engr.tamu.edu/electric-grid-test-cases/activsg10K/\n\n----------------------------------------------------------------------\nMatrix: ACTIVSg70K\nTitle: Linearized or \"dc\" power flow susceptance matrix for a synthetic\n70,000-bus power grid\n\nDescription:\nACTIVSg70K is a 70,000-bus power system dataset that is entirely\nsynthetic, built from public information and a statistical analysis of\nreal power systems. Its geographic footprint is the Eastern United\nStates, but it bears no relation to any actual grid, except that\ngeneration and load profiles are similar, based on public data. This\nmatrix is the linearized or \"dc\" power flow susceptance matrix. \n\nThe full dataset can be downloaded at:\nhttps://electricgrids.engr.tamu.edu/electric-grid-test-cases/activsg70K/" - }, - { - "group": "Embree", - "count": 1, - "documented_meaning": "Fluid stability problem from IFISS package, Mark Embree, Virginia Tech The matrix comes from a shift-invert eigenvalue computation that arises", - "source_url": "https://sparse.tamu.edu/Embree", - "coarse_kind_families_interpretive": "fluids/transport", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computational fluid dynamics (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 96307, - "max_dimension": 96307, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 3599932, - "max_nnz": 3599932, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 1, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 1, - "cohesion": "single matrix", - "official_notes": "Fluid stability problem from IFISS package, Mark Embree, Virginia Tech\n\nThe matrix comes from a shift-invert eigenvalue computation that arises\nduring linear stability analysis of a 2D backward facing step. The\ndiscretizations were created with IFISS, which we also use to compute the\nsteady-state flow whose stability we are assessing.\n\nCreated by the IFISS package, by David Silvester (School of Mathematics,\nUniv. of Manchester), Howard Elman (Computer Science, Univ. of Maryland),\nand Alison Ramage (Dept. of Mathematics and Statistics, Univ. of\nStrathclyde). http://www.maths.manchester.ac.uk/~djs/ifiss/\n\nThis matrix requires one step of iterative refinement after LU\nfactorization. x=A\\\\b in MATLAB (using UMFPACK) does iterative refinement\nby default but with just lu, no iterative refinement is done:\n\n In MATLAB R2014a:\n x = randn(96307,1);\n b = A*x;\n x1 = A\\\\b;\n norm(x-x1) is 1.1045984e-12\n norm(b-A*x1) is 9.3538018e-15\n\n [L,U,P,Q,R] = lu(A);\n x2 = Q*(U\\\\(L\\\\(P*(R\\\\b))));\n norm(x-x2) is 4.9150874e-05\n norm(b-A*x2) is 4.0055911e-07\n\nThe matrix is well conditioned, with singular values ranging from\n1.003 to 2e-5. The singluar values themselves are in\nProblem.aux.singular_values. The need for iterative refinement\ncomes from the threshold partial pivoting in UMFPACK, which tries\nto balance reduction in fill with finding good numerical pivots.\nThus UMFPACK uses iterative refinement with sparse backward error using\nthe method described in Arioli, Demmel, and Duff, \"Sovling sparse linear\nsystems with sparse backward error\", SIAM J. Matrix Analysis & Applic,\nvol 10, no 2, pp 165-190, Apr 1989).\n\nThe matrix itself is described in this paper:\n\nhttps://arxiv.org/abs/1601.00044\n\nPseudospectra of Matrix Pencils for Transient Analysis of\nDifferential-Algebraic Equations\n\nMark Embree, Blake Keeler\n(Submitted on 1 Jan 2016 (v1),\nlast revised 27 Jun 2017 (this version, v3))\n\nTo understand the solution of a linear, time-invariant\ndifferential-algebraic equation, one must analyze a matrix pencil (A,E)\nwith singular E. Even when this pencil is stable (all its finite\neigenvalues fall in the left-half plane), the solution can exhibit\ntransient growth before its inevitable decay. When the equation results\nfrom the linearization of a nonlinear system, this transient growth gives\na mechanism that can promote nonlinear instability. One might hope to\nenrich the conventional large-scale eigenvalue calculation used for\nlinear stability analysis to signal the potential for such transient\ngrowth. Toward this end, we introduce a new definition of the\npseudospectrum of a matrix pencil, use it to bound transient growth,\nexplain how to incorporate a physically-relevant norm, and derive\napproximate pseudospectra using the invariant subspace computed in\nconventional linear stability analysis. We apply these tools to several\ncanonical test problems in fluid mechanics, an important source of\ndifferential-algebraic equations." - }, - { - "group": "Hardesty", - "count": 3, - "documented_meaning": "Surface fitting problem for visualization, Sean Hardesty Visualization of 3D structures in the earth", - "source_url": "https://sparse.tamu.edu/Hardesty", - "coarse_kind_families_interpretive": "imaging/graphics/robotics", - "dominant_coarse_family_interpretive": "imaging/graphics/robotics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computer graphics/vision problem (3)", - "curator_value_type_count": 2, - "curator_value_types": "real (2); integer (1)", - "curator_structure_count": 2, - "curator_structures": "rectangular (2); symmetric (1)", - "min_dimension": 929901, - "max_dimension": 8217820, - "dimension_ratio": 8.837306336911134, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 4020731, - "max_nnz": 40451632, - "shape_profile": "mixed", - "square": 1, - "rectangular": 2, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 1, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 3, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "Surface fitting problem for visualization, Sean Hardesty\n\nVisualization of 3D structures in the earth\n\nThe Hardesty3 matrix is an interpolation matrix stacked above a\nweighted Laplacian, to to fit a surface z(x,y) to a set of points\nin R^3 subject to a smoothness constraint enforced via regularization.\nHardesty2 is a smaller version of this problem.\n\nFor the big matrix (Hardesty/Hardesty3), sparse QR (via SuiteSparseQR,\nor SPQR) finds an R factor and a set of Householder vectors (Q.H) with\nabout 150 million nonzeros. Sparse LU factorization (with UMFPACK\nv5.7.1) sees very high fillin (about 2.5 billion nonzeros in L+U).\n\nThe Hardesty1 matrix is a simple discretization of a 2D biharmonic\noperator with some Lagrange multiplier constraints used for smoothing." - }, - { - "group": "VLSI", - "count": 14, - "documented_meaning": "VLSI: semiconductor device and process simulation Process and Device Simulation matrices: nv1 and nv2 are very ill-conditioned.", - "source_url": "https://sparse.tamu.edu/VLSI", - "coarse_kind_families_interpretive": "electrical/circuit/device", - "dominant_coarse_family_interpretive": "electrical/circuit/device", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 2, - "normalized_kinds": "semiconductor device problem (8); semiconductor process problem (6)", - "curator_value_type_count": 1, - "curator_value_types": "real (14)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (14)", - "min_dimension": 46994, - "max_dimension": 11449533, - "dimension_ratio": 243.63818785376856, - "size_span": "wide (>100\u00d7)", - "min_nnz": 845089, - "max_nnz": 349321980, - "shape_profile": "square only", - "square": 14, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 8, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 14, - "cohesion": "coherent family/series", - "official_notes": "VLSI: semiconductor device and process simulation\n\nProcess and Device Simulation matrices: nv1 and nv2 are very ill-conditioned.\nDevice simulation matrices in this set:\n nv1, nv2, imagesensor, mosfet2, power9, radiation, test1, dgreen\nProcess simulation matrices in this set:\n vas_stokes_1M, vas_stokes_2M, vas_stokes_4M, stokes, ss1, ss\n\nSource: these are real matrices from industry. The submittor\nrequested that their name and company be withheld.\n\nAdded to the collection in January 2019." - }, - { - "group": "Martin", - "count": 1, - "documented_meaning": "Martin/marine1: chemical oceanography; a marine nitrogen cycle inverse model A matrix submitted by Taylor Martin, Stanford, discussed the following paper:", - "source_url": "https://sparse.tamu.edu/Martin", - "coarse_kind_families_interpretive": "fluids/transport", - "dominant_coarse_family_interpretive": "fluids/transport", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "chemical oceanography problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "unsymmetric (1)", - "min_dimension": 400320, - "max_dimension": 400320, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 6226538, - "max_nnz": 6226538, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "no square matrix exactly symmetric/Hermitian", - "exact_symmetric_square": 0, - "structurally_symmetric_square": 0, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "single matrix", - "official_notes": "Martin/marine1: chemical oceanography; a marine nitrogen cycle inverse model\n\nA matrix submitted by Taylor Martin, Stanford, discussed the following paper:\n\nTitle: Modeling oceanic nitrite concentrations and isotopes using a 3D\ninverse N cycle model\nAuthors: Taylor S. Martin(1), Francois Primeau(2), and Karen L. Casciotti(1)\n(1) Stanford University, Department of Earth System Science\n(2) University of California, Irvine, Department of Earth System Science\nReceived: 05 Sep 2018\nAbstract. Nitrite (NO2-) is a key intermediate in the marine nitrogen (N) cycle\nand a substrate in nitrification, which produces nitrate (NO3-), as well as\nwater column N loss processes, denitrification and anammox. In models of the\nmarine N cycle, NO2- is often not considered as a separate state variable,\nsince NO3- occurs in much higher concentrations in the ocean. In oxygen\ndeficient zones (ODZs), however, NO2- represents a substantial fraction of the\nbioavailable N, and modeling its production and consumption is important to\nunderstanding the N cycle processes occurring there, especially those where\nbioavailable N is lost from or retained within the water column. Here we\npresent the expansion of a global 3D inverse N cycle model to include NO2- as a\nreactive intermediate as well as the processes that produce and consume NO2- in\nmarine ODZs. NO2- accumulation in ODZs is accurately represented by the model\ninvolving NO3- reduction, NO2- reduction, NO2- oxidation, and anammox. We model\nboth 14N and 15N and use a compilation of oceanographic measurements of NO3-\nand NO2- concentrations and isotopes to place a better constraint on the N\ncycle processes occurring. The model is optimized using a range of isotope\neffects for denitrification and NO2- oxidation, and we find that the larger\n(more negative) inverse isotope effects for NO2- oxidation along with\nrelatively high rates of NO2- oxidation give a better simulation of NO3- and\nNO2- concentrations and isotopes in marine ODZs.\n\nHow to cite: Martin, T. S., Primeau, F., and Casciotti, K. L.: Modeling\noceanic nitrite concentrations and isotopes using a 3D inverse N cycle model,\nBiogeosciences Discuss., https://doi.org/10.5194/bg-2018-397, in review, 2018." - }, - { - "group": "Negre", - "count": 1, - "documented_meaning": "Negre: phenyl dendrimer density matrix, for graph partitioning The phenyl dendrimer structure is composed of 22 covalently bonded", - "source_url": "https://sparse.tamu.edu/Negre", - "coarse_kind_families_interpretive": "chemistry/biology", - "dominant_coarse_family_interpretive": "chemistry/biology", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "computational chemistry problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "symmetric (1)", - "min_dimension": 730, - "max_dimension": 730, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 63024, - "max_nnz": 63024, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 1, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "single matrix", - "official_notes": "Negre: phenyl dendrimer density matrix, for graph partitioning\n\nThe phenyl dendrimer structure is composed of 22 covalently bonded\nphenyl groups. The density matrix hereby uploaded accounts for the\nconnectivity of 730 orbitals. This matrix has been used as a substrate\nfor implementing a graph-partitioning method using the D-wave quantum\nannealer. See: https://dl.acm.org/citation.cfm?id=3149531\n\nChristian F. A. Negre, Los Alamos National Lab, cnegre@lanl.gov\n\nGraph Partitioning Using Quantum Annealing on the D-Wave System,\nH. Ushijima-Mwesigwa, C. F. A. Negre, and S. M. Mniszewski,\nProc. of 2nd Intl. Workshop on Post Moore's Era Supercomputing,\n(PMES17) 2017, Denver, CO, USA, 22--29, ACM.\n\nAbstract:\nGraph partitioning (GP) applications are ubiquitous throughout\nmathematics, computer science, chemistry, physics, bio-science, machine\nlearning, and complex systems. Post Moore's era supercomputing has\nprovided us an opportunity to explore new approaches for traditional\ngraph algorithms on quantum computing architectures. In this work, we\nexplore graph partitioning using quantum annealing on the D-Wave 2X\nmachine. Motivated by a recently proposed graph-based electronic\nstructure theory applied to quantum molecular dynamics (QMD) simulations,\ngraph partitioning is used for reducing the calculation of the density\nmatrix into smaller subsystems rendering the calculation more\ncomputationally efficient. Unconstrained graph partitioning as community\nclustering based on the modularity metric can be naturally mapped into\nthe Hamiltonian of the quantum annealer. On the other hand, when\nconstraints are imposed for partitioning into equal parts and minimizing\nthe number of cut edges between parts, a quadratic unconstrained binary\noptimization (QUBO) reformulation is required. This reformulation may\nemploy the graph complement to fit the problem in the Chimera graph of\nthe quantum annealer. Partitioning into 2 parts and k parts concurrently\nfor arbitrary k are demonstrated with benchmark graphs, random graphs,\nand small material system density matrix based graphs. Results for graph\npartitioning using quantum and hybrid classical-quantum approaches are\nshown to be comparable to current \"state of the art\" methods and\nsometimes better." - }, - { - "group": "Precima", - "count": 1, - "documented_meaning": "Precima: data analytics problem Submitted by Carl Ugaz, Precima, Inc. CUgaz@precima.com", - "source_url": "https://sparse.tamu.edu/Precima", - "coarse_kind_families_interpretive": "data analytics", - "dominant_coarse_family_interpretive": "data analytics", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "data analytics problem (1)", - "curator_value_type_count": 1, - "curator_value_types": "real (1)", - "curator_structure_count": 1, - "curator_structures": "symmetric (1)", - "min_dimension": 303813, - "max_dimension": 303813, - "dimension_ratio": 1.0, - "size_span": "one size", - "min_nnz": 2006126, - "max_nnz": 2006126, - "shape_profile": "square only", - "square": 1, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 1, - "structurally_symmetric_square": 1, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "single matrix", - "official_notes": "Precima: data analytics problem\n\nSubmitted by Carl Ugaz, Precima, Inc. CUgaz@precima.com\n\nThe matrix is nonsingular, ill-conditioned,, and symmetric indefinite.\nIf factorized with KLU, best results are obtained if the permutation to\nblock triangular form (BTF) is not used, because it breaks the symmetry\nof the pattern.\n\nThe matrix is nonsingular, and symmetric indefinite. If factorized with\nKLU, best results are obtained if the permutation to block triangular\nform (BTF) is not used, because it breaks the symmetry of the pattern.\n\nIgnoring the diagonal (which has some zero entries and some nonzero)\nthe undirected graph of A divides into 41,422 connected components.\nOf those, 40,966 are single nodes, and 451 components have between\n4 and 90 nodes. The remaining 5 components have 93738, 89686, 40319,\n15884, and 4289 nodes, respectively." - }, - { - "group": "GAP", - "count": 5, - "documented_meaning": "GAP Benchmark matrices: Scott Beamer, Krste Asanovic', and David Patterson. as described in \"The GAP Benchmark Suite\", .", - "source_url": "https://sparse.tamu.edu/GAP", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 2, - "normalized_kinds": "directed weighted graph (3); random undirected weighted graph (2)", - "curator_value_type_count": 1, - "curator_value_types": "integer (5)", - "curator_structure_count": 2, - "curator_structures": "symmetric (3); unsymmetric (2)", - "min_dimension": 23947347, - "max_dimension": 134217728, - "dimension_ratio": 5.604701347502084, - "size_span": "moderate (4\u2013100\u00d7)", - "min_nnz": 57708624, - "max_nnz": 4294966740, - "shape_profile": "square only", - "square": 5, - "rectangular": 0, - "symmetry_profile": "mixed among square matrices", - "exact_symmetric_square": 3, - "structurally_symmetric_square": 3, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "one broad domain; mixed matrix forms", - "official_notes": "GAP Benchmark matrices: Scott Beamer, Krste Asanovic', and David Patterson.\nas described in \"The GAP Benchmark Suite\", https://arxiv.org/abs/1508.03619 .\n\n(1) GAP-twitter (|V|=61.6M, |E|=1,468.4M, directed) is an example of a\n social network topology [18]. This particular crawl of Twitter\n has been commonly used by researchers and thus eases comparisons\n with prior work. By virtue of it coming from real-world data, it\n has interesting irregularities and the skew in its degree\n distribution can be a challenge for some implementations.\n\n [18] Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon.\n What is Twitter, a social network or a news media? International\n World Wide Web Conference (WWW), 2010.\n\n A permuted version of this matrix appears as SNAP/twitter7 in\n the SuiteSparse Matrix Collection.\n\n(2) GAP-web (|V|=50.6M, |E|=1,949.4M, directed) is a web-crawl of the\n .sk domain (sk-2005) [9]. Despite its large size, it exhibits\n substantial locality due to its topology and high average\n degree.\n\n The matrix comes from the Laboratory for Web Algorithmics (LAW), Universita\n degli Studi di Milano, http://law.di.unimi.it/index.php.\n\n The pattern of this GAP-web matrix also appears as LAW/sk-2005, in the\n SuiteSparse Matrix Collection.\n\n(3) GAP-road (|V|=23.9M, |E|=58.3M, directed) is the distances of all of\n the roads in the USA [10]. Although it is substantially smaller\n than the rest of the graphs, it has a high diameter which can\n cause some synchronous implementations to have long runtimes.\n\n [10] 9th DIMACS implementation challenge -- shortest paths.\n http://www.dis.uniroma1.it/challenge9/, 2006.\n\n The pattern of the GAP-road matrix also appears as DIMACS10/road_usa\n in the SuiteSparse Matrix Collection.\n\n(4) GAP-kron (|V|=134.2M, |E|=2,111.6M, undirected) uses the Kronecker\n synthetic graph generator [19] with the same parameters as\n Graph 500 (A=0.57, B=C=0.19, D=0.05) [14]. It has been used\n frequently in research due to Graph 500, so it also provides\n continuity with prior work.\n\n [19] Jurij Leskovec, Deepayan Chakrabarti, Jon Kleinberg, and\n Christos Faloutsos. Realistic, mathematically tractable graph\n generation and evolution, using Kronecker multiplication.\n European Conference on Principles and Practice of Knowledge\n Discovery in Databases, 2005.\n\n [14] Graph500 benchmark. www.graph500.org.\n\n(5) GAP-urand (|V|=134.2M, |E|=2,147.4M, undirected) is synthetically\n generated by the Erdos\u2013 Reyni model (Uniform Random) [11]. With\n respect to locality, it represents the worst case as every\n vertex has equal probability of being a neighbor of every other\n vertex. When contrasted with the similarly sized kron graph, it\n demonstrates the impact of kron\u2019s scale-free property.\n\n [11] Paul Erdos and Alfred Reyni. On random graphs. I.\n Publicationes Mathematicae, 6:290\u2013297, 1959." - }, - { - "group": "ML_Graph", - "count": 36, - "documented_meaning": "ML_Graph: adjacency matrices from machine learning datasets, Olaf Schenk. The following adjacency matrices were used in the numerical experiments in:", - "source_url": "https://sparse.tamu.edu/ML_Graph", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "undirected weighted graph (36)", - "curator_value_type_count": 1, - "curator_value_types": "real (36)", - "curator_structure_count": 1, - "curator_structures": "symmetric (36)", - "min_dimension": 106, - "max_dimension": 38547, - "dimension_ratio": 363.6509433962264, - "size_span": "wide (>100\u00d7)", - "min_nnz": 1412, - "max_nnz": 618158, - "shape_profile": "square only", - "square": 36, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 36, - "structurally_symmetric_square": 36, - "known_spd": 0, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "ML_Graph: adjacency matrices from machine learning datasets, Olaf Schenk.\n\nThe following adjacency matrices were used in the numerical experiments in:\n\nD. Pasadakis, C.L. Alappat, O. Schenk, and G. Wellein, \"Multiway p-spectral\ngraph cuts on Grassmann manifolds,\" Machine Learning 111, 791\u2013829, 2022,\nhttps://doi.org/10.1007/s10994-021-06108-1\n\n-------------------------------------------------------------------------------\nGraph Creation\n-------------------------------------------------------------------------------\n\nFor all the graphs in the folder, for $n$ data points, the connectivity matrix\n$G \\\\in \\\\mathbb{R}^{n\\\\times n}$ is created from a k nearest neighbors routine,\nwith k set such that the resulting graph is connected. The similarity matrix\n$S \\\\in \\\\mathbb{R}^{n\\\\times n}$ between the data points is defined as\n\\\\begin{equation}\n s_{ij} = \\\\max\\\\{s_i(j), s_j(i)\\\\} \\\\;\\\\; \\\\text{with}\\\\;\n s_i(j) = \\\\exp (-4 \\\\frac{\\\\|x_i - x_j \\\\|^2}{\\\\sigma_i^2} )\n\\\\end{equation}\nwith $\\\\sigma_i$ standing for the Euclidean distance between the $i$th data point\nand its nearest k-nearest neighbor. The adjacency matrix $W$ is then created\nas\n\n\\\\begin{equation}\n W = G \\\\odot S.\n\\\\end{equation}\n\nBesides the adjacency matrices $W$, the node labels for each graph are part of\nthe submission. If the graph has c classes, the node labels are integers in\nthe range 0 to c-1.\n\n-------------------------------------------------------------------------------\nDescription of matrices\n-------------------------------------------------------------------------------\n\nFor a more detailed description of the datasets, and references of the sources\nthe datasets were obtained from, see the paper cited in the beginning of this\ndescription.\n\n Graphs from various machine learning datasets\n\n Name Neighbours Nodes Edges Classes\nhar_10NN 10 10299 75868 6 \nindianpines_10NN 10 9144 62328 8 \nJapaneseVowels_10NN 10 9961 65572 9 \nworms20_10NN 10 20055 120413 20 \noptdigits_10NN 10 5620 39825 10 \nVehicle_10NN 10 846 5447 4 \nmfeatkarhunen_10NN 10 2000 13834 10 \nmfeatfactors_10NN 10 2000 13721 10 \nmfeatmorphological_10NN 10 2000 11416 10 \nmfeatpixel_10NN 10 2000 13966 10 \nmfeatzernike_10NN 10 2000 13707 10 \nsemeion_10NN 10 1593 11113 10 \nmice_10NN 10 1077 6742 8 \nyeast_30NN 30 1484 31175 12 \ncnae9_10NN 10 1080 9139 9 \ndermatology_5NN 5 366 1220 6 \niris_30NN 30 150 2759 3 \nEcoli_10NN 10 336 2280 8 \nBinaryalphadigs_10NN 10 1404 9696 36 \nGlass_10NN 10 214 1493 6 \ncollins_15NN 15 1000 8246 30 \nmicromass_10NN 10 571 4834 20 \nbreasttissue_10NN 10 106 706 6 \nPlants_10NN 10 1600 10965 100 \nplantsmargin_12NN 12 1600 12741 100 \nplantstexture_10NN 10 1599 10602 100 \nSpectro_10NN 10 531 3711 48 \n\n Graphs from image classification problems. The RGB values are normalised \n in the interval [0,1].\n\n Name Neighbours Nodes Edges Classes\nusps_norm_5NN 5 11000 40556 10 \nFashion_MNIST_norm_10NN 10 10000 79152 10 \nkmnist_norm_10NN 10 10000 78466 10 \nk49_norm_10NN 10 38547 309079 49 \nmnist_test_norm_10NN 10 10000 72800 10 \nYaleB_10NN 10 2414 8568 10 \numistfacesnorm_10NN 10 575 3495 20 \nYaleA_10NN 10 165 1134 10 \nOlivetti_norm_10NN 10 400 2828 40" - }, - { - "group": "FlowIPM22", - "count": 11, - "documented_meaning": "FlowIPM22: Laplacians from Newton-Steps of Maxflow Short-step IPM on Chimera Graphs A collection of matrices arising from solving maximum flow problems on a", - "source_url": "https://sparse.tamu.edu/FlowIPM22", - "coarse_kind_families_interpretive": "graph/network data", - "dominant_coarse_family_interpretive": "graph/network data", - "dominant_coarse_family_share": 1.0, - "normalized_kind_count": 1, - "normalized_kinds": "undirected weighted graph (11)", - "curator_value_type_count": 1, - "curator_value_types": "real (11)", - "curator_structure_count": 1, - "curator_structures": "symmetric (11)", - "min_dimension": 100000, - "max_dimension": 72180402, - "dimension_ratio": 721.80402, - "size_span": "wide (>100\u00d7)", - "min_nnz": 499696, - "max_nnz": 216902404, - "shape_profile": "square only", - "square": 11, - "rectangular": 0, - "symmetry_profile": "all square matrices symmetric/Hermitian", - "exact_symmetric_square": 11, - "structurally_symmetric_square": 11, - "known_spd": 4, - "value_profile": "real only", - "binary": 0, - "geometric_2d3d": 0, - "cohesion": "coherent family/series", - "official_notes": "FlowIPM22: Laplacians from Newton-Steps of Maxflow Short-step IPM on Chimera Graphs\n\nA collection of matrices arising from solving maximum flow problems on a\ndiverse collection of graphs (known as Chimeras) using a short-step\ninterior point method. The Laplacian matrices arise when computing Newton\nsteps of the interior point method.\n\nThe matrices are symmetric graph Laplacians, meaning they are symmetric\ndiagonally dominant matrices with non-positive off-diagonals and row sums\nequal to zero.\n\nGenerated by Yuan Gao, Rasmus Kyng, and Daniel Spielman. 2022.\n\nReferences:\n\ndata set: https://www.spe.org/web/csp/datasets/set02.htm\n\nAn Algebraic Sparsified Nested Dissection Algorithm Using Low-Rank\nApproximations L\u00e9opold Cambier, Chao Chen, Erik G. Boman, Sivasankaran\nRajamanickam, Raymond S. Tuminaro, and Eric Darve\nSIAM Journal on Matrix Analysis and Applications, vol 41, no 2,\npp 715-746, 2020. https://epubs.siam.org/doi/10.1137/19M123806X" - } - ] -} \ No newline at end of file diff --git a/report_notes/group_taxonomy/ss_index.mat b/report_notes/group_taxonomy/ss_index.mat deleted file mode 100644 index 4b422f4..0000000 Binary files a/report_notes/group_taxonomy/ss_index.mat and /dev/null differ diff --git a/report_notes/group_taxonomy/ssstats.csv b/report_notes/group_taxonomy/ssstats.csv deleted file mode 100644 index 89a0c4d..0000000 --- a/report_notes/group_taxonomy/ssstats.csv +++ /dev/null @@ -1,2906 +0,0 @@ -2904 -31-Oct-2023 18:12:37 -HB,1138_bus,1138,1138,4054,1,0,0,1,1,1,power network problem,4054 -HB,494_bus,494,494,1666,1,0,0,1,1,1,power network problem,1666 -HB,662_bus,662,662,2474,1,0,0,1,1,1,power network problem,2474 -HB,685_bus,685,685,3249,1,0,0,1,1,1,power network problem,3249 -HB,abb313,313,176,1557,1,1,0,0,0,0,least squares problem,1557 -HB,arc130,130,130,1037,1,0,1,0,0.7586805555555556,0,materials problem,1282 -HB,ash219,219,85,438,1,1,0,0,0,0,least squares problem,438 -HB,ash292,292,292,2208,1,1,0,0,1,1,least squares problem,2208 -HB,ash331,331,104,662,1,1,0,0,0,0,least squares problem,662 -HB,ash608,608,188,1216,1,1,0,0,0,0,least squares problem,1216 -HB,ash85,85,85,523,1,1,0,0,1,1,least squares problem,523 -HB,ash958,958,292,1916,1,1,0,0,0,0,least squares problem,1916 -HB,bcspwr01,39,39,131,1,1,0,0,1,1,power network problem,131 -HB,bcspwr02,49,49,167,1,1,0,0,1,1,power network problem,167 -HB,bcspwr03,118,118,476,1,1,0,0,1,1,power network problem,476 -HB,bcspwr04,274,274,1612,1,1,0,0,1,1,power network problem,1612 -HB,bcspwr05,443,443,1623,1,1,0,0,1,1,power network problem,1623 -HB,bcspwr06,1454,1454,5300,1,1,0,0,1,1,power network problem,5300 -HB,bcspwr07,1612,1612,5824,1,1,0,0,1,1,power network problem,5824 -HB,bcspwr08,1624,1624,6050,1,1,0,0,1,1,power network problem,6050 -HB,bcspwr09,1723,1723,6511,1,1,0,0,1,1,power network problem,6511 -HB,bcspwr10,5300,5300,21842,1,1,0,0,1,1,power network problem,21842 -HB,bcsstk01,48,48,400,1,0,1,1,1,1,structural problem,400 -HB,bcsstk02,66,66,4356,1,0,1,1,1,1,structural problem,4356 -HB,bcsstk03,112,112,640,1,0,1,1,1,1,structural problem,640 -HB,bcsstk04,132,132,3648,1,0,1,1,1,1,structural problem,3648 -HB,bcsstk05,153,153,2423,1,0,1,1,1,1,structural problem,2423 -HB,bcsstk06,420,420,7860,1,0,1,1,1,1,structural problem,7860 -HB,bcsstk07,420,420,7860,1,0,1,1,1,1,duplicate structural problem,7860 -HB,bcsstk08,1074,1074,12960,1,0,1,1,1,1,structural problem,12960 -HB,bcsstk09,1083,1083,18437,1,0,1,1,1,1,structural problem,18437 -HB,bcsstk10,1086,1086,22070,1,0,1,1,1,1,structural problem,22070 -HB,bcsstk11,1473,1473,34241,1,0,1,1,1,1,structural problem,34241 -HB,bcsstk12,1473,1473,34241,1,0,1,1,1,1,duplicate structural problem,34241 -HB,bcsstk13,2003,2003,83883,1,0,1,1,1,1,computational fluid dynamics problem,83883 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-HB,bcsstk29,13992,13992,619488,1,1,1,0,1,1,structural problem,619488 -HB,bcsstk30,28924,28924,2043492,1,1,1,0,1,1,structural problem,2043492 -HB,bcsstk31,35588,35588,1181416,1,1,1,0,1,1,structural problem,1181416 -HB,bcsstk32,44609,44609,2014701,1,1,1,0,1,1,structural problem,2014701 -HB,bcsstk33,8738,8738,591904,1,1,1,0,1,1,structural problem,591904 -HB,bcsstm01,48,48,24,1,0,1,0,1,1,structural problem,48 -HB,bcsstm02,66,66,66,1,0,1,1,1,1,structural problem,66 -HB,bcsstm03,112,112,72,1,0,1,0,1,1,structural problem,112 -HB,bcsstm04,132,132,66,1,0,1,0,1,1,structural problem,132 -HB,bcsstm05,153,153,153,1,0,1,1,1,1,structural problem,153 -HB,bcsstm06,420,420,420,1,0,1,1,1,1,structural problem,420 -HB,bcsstm07,420,420,7252,1,0,1,1,1,1,structural problem,7252 -HB,bcsstm08,1074,1074,1074,1,0,1,1,1,1,structural problem,1074 -HB,bcsstm09,1083,1083,1083,1,0,1,1,1,1,structural problem,1083 -HB,bcsstm10,1086,1086,22092,1,0,1,0,1,1,structural problem,22092 -HB,bcsstm11,1473,1473,1473,1,0,1,1,1,1,structural problem,1473 -HB,bcsstm12,1473,1473,19659,1,0,1,1,1,1,structural problem,19659 -HB,bcsstm13,2003,2003,21181,1,0,1,0,1,1,computational fluid dynamics problem,21943 -HB,bcsstm19,817,817,817,1,0,1,1,1,1,structural problem,817 -HB,bcsstm20,485,485,485,1,0,1,1,1,1,structural problem,485 -HB,bcsstm21,3600,3600,3600,1,0,1,1,1,1,structural problem,3600 -HB,bcsstm22,138,138,138,1,0,1,1,1,1,structural problem,138 -HB,bcsstm23,3134,3134,3134,1,0,1,1,1,1,structural problem,3134 -HB,bcsstm24,3562,3562,3562,1,0,1,1,1,1,structural problem,3562 -HB,bcsstm25,15439,15439,15439,1,0,1,1,1,1,structural problem,15439 -HB,bcsstm26,1922,1922,1922,1,0,1,1,1,1,structural problem,1922 -HB,bcsstm27,1224,1224,56126,1,0,1,0,1,1,structural problem,56126 -HB,beacxc,497,506,50409,1,0,0,0,0,0,economic problem,50409 -HB,beaflw,497,507,53403,1,0,0,0,0,0,economic problem,53403 -HB,beause,497,507,44551,1,0,0,0,0,0,economic problem,44551 -HB,blckhole,2132,2132,14872,1,1,1,0,1,1,structural problem,14872 -HB,bp_0,822,822,3276,1,0,0,0,0.009160305343511451,0.001221374045801527,optimization problem sequence,3276 -HB,bp_1000,822,822,4661,1,0,0,0,0.009448142581060769,0.0004294610264118531,subsequent optimization problem,4661 -HB,bp_1200,822,822,4726,1,0,0,0,0.009322033898305085,0.000423728813559322,subsequent optimization problem,4726 -HB,bp_1400,822,822,4790,1,0,0,0,0.01044932079414838,0.0004179728317659352,subsequent optimization problem,4790 -HB,bp_1600,822,822,4841,1,0,0,0,0.01075491209927611,0.0004136504653567735,subsequent optimization problem,4841 -HB,bp_200,822,822,3802,1,0,0,0,0.00631578947368421,0,subsequent optimization problem,3802 -HB,bp_400,822,822,4028,1,0,0,0,0.00546448087431694,0.0004967709885742673,subsequent optimization problem,4028 -HB,bp_600,822,822,4172,1,0,0,0,0.006235011990407674,0.0004796163069544365,subsequent optimization problem,4172 -HB,bp_800,822,822,4534,1,0,0,0,0.005298013245033113,0.0004415011037527594,subsequent optimization problem,4534 -HB,can_1054,1054,1054,12196,1,1,1,0,1,1,structural problem,12196 -HB,can_1072,1072,1072,12444,1,1,1,0,1,1,structural problem,12444 -HB,can_144,144,144,1296,1,1,1,0,1,1,structural problem,1296 -HB,can_161,161,161,1377,1,1,1,0,1,1,structural problem,1377 -HB,can_187,187,187,1491,1,1,1,0,1,1,structural problem,1491 -HB,can_229,229,229,1777,1,1,1,0,1,1,structural problem,1777 -HB,can_24,24,24,160,1,1,1,0,1,1,structural problem,160 -HB,can_256,256,256,2916,1,1,1,0,1,1,structural problem,2916 -HB,can_268,268,268,3082,1,1,1,0,1,1,structural problem,3082 -HB,can_292,292,292,2540,1,1,1,0,1,1,structural problem,2540 -HB,can_445,445,445,3809,1,1,1,0,1,1,structural problem,3809 -HB,can_61,61,61,557,1,1,1,0,1,1,structural problem,557 -HB,can_62,62,62,218,1,1,1,0,1,1,structural problem,218 -HB,can_634,634,634,7228,1,1,1,0,1,1,structural problem,7228 -HB,can_715,715,715,6665,1,1,1,0,1,1,structural problem,6665 -HB,can_73,73,73,377,1,1,1,0,1,1,structural problem,377 -HB,can_838,838,838,10010,1,1,1,0,1,1,structural problem,10010 -HB,can_96,96,96,768,1,1,1,0,1,1,structural problem,768 -HB,curtis54,54,54,291,1,1,1,0,0.9535864978902954,0.9535864978902954,2D/3D problem,291 -HB,dwt_1005,1005,1005,8621,1,1,1,0,1,1,structural problem,8621 -HB,dwt_1007,1007,1007,8575,1,1,1,0,1,1,structural problem,8575 -HB,dwt_1242,1242,1242,10426,1,1,1,0,1,1,structural problem,10426 -HB,dwt_162,162,162,1182,1,1,1,0,1,1,structural problem,1182 -HB,dwt_193,193,193,3493,1,1,1,0,1,1,structural problem,3493 -HB,dwt_198,198,198,1392,1,1,1,0,1,1,structural problem,1392 -HB,dwt_209,209,209,1743,1,1,1,0,1,1,structural problem,1743 -HB,dwt_221,221,221,1629,1,1,1,0,1,1,structural problem,1629 -HB,dwt_234,234,234,834,1,1,1,0,1,1,structural problem,834 -HB,dwt_245,245,245,1461,1,1,1,0,1,1,structural problem,1461 -HB,dwt_2680,2680,2680,25026,1,1,1,0,1,1,structural problem,25026 -HB,dwt_307,307,307,2523,1,1,1,0,1,1,structural problem,2523 -HB,dwt_310,310,310,2448,1,1,1,0,1,1,structural problem,2448 -HB,dwt_346,346,346,3226,1,1,1,0,1,1,structural problem,3226 -HB,dwt_361,361,361,2953,1,1,1,0,1,1,structural problem,2953 -HB,dwt_419,419,419,3563,1,1,1,0,1,1,structural problem,3563 -HB,dwt_492,492,492,3156,1,1,1,0,1,1,structural problem,3156 -HB,dwt_503,503,503,6027,1,1,1,0,1,1,structural problem,6027 -HB,dwt_512,512,512,3502,1,1,1,0,1,1,structural problem,3502 -HB,dwt_59,59,59,267,1,1,1,0,1,1,structural problem,267 -HB,dwt_592,592,592,5104,1,1,1,0,1,1,structural problem,5104 -HB,dwt_607,607,607,5131,1,1,1,0,1,1,structural problem,5131 -HB,dwt_66,66,66,320,1,1,1,0,1,1,structural problem,320 -HB,dwt_72,72,72,222,1,1,1,0,1,1,structural problem,222 -HB,dwt_758,758,758,5994,1,1,1,0,1,1,structural problem,5994 -HB,dwt_869,869,869,7285,1,1,1,0,1,1,structural problem,7285 -HB,dwt_87,87,87,541,1,1,1,0,1,1,structural problem,541 -HB,dwt_878,878,878,7448,1,1,1,0,1,1,structural problem,7448 -HB,dwt_918,918,918,7384,1,1,1,0,1,1,structural problem,7384 -HB,dwt_992,992,992,16744,1,1,1,0,1,1,structural problem,16744 -HB,eris1176,1176,1176,18552,1,1,0,0,1,1,power network problem,18552 -HB,fs_183_1,183,183,998,1,0,1,0,0.417607223476298,0,2D/3D problem sequence,1069 -HB,fs_183_3,183,183,1069,1,0,1,0,0.417607223476298,0,subsequent 2D/3D problem,1069 -HB,fs_183_4,183,183,1069,1,0,1,0,0.417607223476298,0,subsequent 2D/3D problem,1069 -HB,fs_183_6,183,183,1000,1,0,1,0,0.417607223476298,0,subsequent 2D/3D problem,1069 -HB,fs_541_1,541,541,4282,1,0,1,0,0.6826923076923077,0,2D/3D problem sequence,4285 -HB,fs_541_2,541,541,4282,1,0,1,0,0.6826923076923077,0,subsequent 2D/3D problem,4285 -HB,fs_541_3,541,541,4282,1,0,1,0,0.6826923076923077,0,subsequent 2D/3D problem,4285 -HB,fs_541_4,541,541,4273,1,0,1,0,0.6826923076923077,0,subsequent 2D/3D problem,4285 -HB,fs_680_1,680,680,2184,1,0,1,0,0.5106815869786369,0,2D/3D problem sequence,2646 -HB,fs_680_2,680,680,2424,1,0,1,0,0.5106815869786369,0,subsequent 2D/3D problem,2646 -HB,fs_680_3,680,680,2471,1,0,1,0,0.5106815869786369,0,subsequent 2D/3D problem,2646 -HB,fs_760_1,760,760,5739,1,0,1,0,0.6510736196319018,0,2D/3D problem sequence,5976 -HB,fs_760_2,760,760,5739,1,0,1,0,0.6510736196319018,0,subsequent 2D/3D problem,5976 -HB,fs_760_3,760,760,5816,1,0,1,0,0.6510736196319018,0,subsequent 2D/3D problem,5976 -HB,gemat1,4929,10595,46591,1,0,0,0,0,0,power network problem,47369 -HB,gemat11,4929,4929,33108,1,0,0,0,0.001326419872181358,0,power network problem sequence,33185 -HB,gemat12,4929,4929,33044,1,0,0,0,0.001450063440275512,0,subsequent power network problem,33111 -HB,gent113,113,113,655,1,1,0,0,0.05663716814159292,0.05663716814159292,statistical/mathematical problem,655 -HB,gr_30_30,900,900,7744,1,0,1,1,1,1,2D/3D problem,7744 -HB,gre_1107,1107,1107,5664,1,0,0,0,0,0,directed weighted graph,5664 -HB,gre_115,115,115,421,1,0,0,0,0.2549019607843137,0,directed weighted graph,421 -HB,gre_185,185,185,975,1,0,0,0,0.4146341463414634,0,directed weighted graph,1005 -HB,gre_216a,216,216,812,1,0,0,0,0,0,directed weighted graph,876 -HB,gre_216b,216,216,812,1,0,0,0,0,0,directed weighted graph,876 -HB,gre_343,343,343,1310,1,0,0,0,0,0,directed weighted graph,1435 -HB,gre_512,512,512,1976,1,0,0,0,0,0,directed weighted graph,2192 -HB,hor_131,434,434,4182,1,0,0,0,1,0.3740661686232657,directed weighted graph,4710 -HB,ibm32,32,32,126,1,1,0,0,0.0851063829787234,0.0851063829787234,directed graph,126 -HB,illc1033,1033,320,4719,1,0,0,0,0,0,least squares problem,4732 -HB,illc1850,1850,712,8636,1,0,0,0,0,0,least squares problem,8758 -HB,impcol_a,207,207,572,1,0,0,0,0.02482269503546099,0.01063829787234043,chemical process simulation problem,572 -HB,impcol_b,59,59,271,1,0,0,0,0.09491525423728814,0,chemical process simulation problem,312 -HB,impcol_c,137,137,400,1,0,0,0,0.04444444444444445,0.01146131805157593,chemical process simulation problem,411 -HB,impcol_d,425,425,1255,1,0,0,0,0.05674846625766871,0.0228198859005705,chemical process simulation problem,1339 -HB,impcol_e,225,225,1303,1,0,0,0,0.1038338658146965,0.001603849238171612,chemical process simulation problem,1308 -HB,jagmesh1,936,936,6264,1,1,1,0,1,1,2D/3D problem,6264 -HB,jagmesh2,1009,1009,6865,1,1,1,0,1,1,2D/3D problem,6865 -HB,jagmesh3,1089,1089,7361,1,1,1,0,1,1,2D/3D problem,7361 -HB,jagmesh4,1440,1440,9504,1,1,1,0,1,1,2D/3D problem,9504 -HB,jagmesh5,1180,1180,7750,1,1,1,0,1,1,2D/3D problem,7750 -HB,jagmesh6,1377,1377,8993,1,1,1,0,1,1,2D/3D problem,8993 -HB,jagmesh7,1138,1138,7450,1,1,1,0,1,1,2D/3D problem,7450 -HB,jagmesh8,1141,1141,7465,1,1,1,0,1,1,2D/3D problem,7465 -HB,jagmesh9,1349,1349,9101,1,1,1,0,1,1,2D/3D problem,9101 -HB,jgl009,9,9,50,1,1,0,0,0.4761904761904762,0.4761904761904762,counter-example problem,50 -HB,jgl011,11,11,76,1,1,0,0,0.5151515151515151,0.5151515151515151,counter-example problem,76 -HB,jpwh_991,991,991,6027,1,0,1,0,0.9364575059571089,0.9364575059571089,semiconductor device problem,6027 -HB,lns_131,131,131,536,1,0,1,0,0.7028301886792453,0.01886792452830189,computational fluid dynamics problem,536 -HB,lns_3937,3937,3937,25407,1,0,1,0,0.849666726715907,0.001351107908484958,computational fluid dynamics problem,25407 -HB,lns_511,511,511,2796,1,0,1,0,0.7994945240101096,0.02021903959561921,computational fluid dynamics problem,2796 -HB,lnsp3937,3937,3937,25407,1,0,1,0,0.849666726715907,0.001351107908484958,computational fluid dynamics problem,25407 -HB,lnsp_131,131,131,536,1,0,1,0,0.7028301886792453,0.01886792452830189,computational fluid dynamics problem,536 -HB,lnsp_511,511,511,2796,1,0,1,0,0.7994945240101096,0.02021903959561921,computational fluid dynamics problem,2796 -HB,lshp1009,1009,1009,6865,1,1,1,0,1,1,duplicate thermal problem,6865 -HB,lshp1270,1270,1270,8668,1,1,1,0,1,1,thermal problem,8668 -HB,lshp1561,1561,1561,10681,1,1,1,0,1,1,thermal problem,10681 -HB,lshp1882,1882,1882,12904,1,1,1,0,1,1,thermal problem,12904 -HB,lshp2233,2233,2233,15337,1,1,1,0,1,1,thermal problem,15337 -HB,lshp2614,2614,2614,17980,1,1,1,0,1,1,thermal problem,17980 -HB,lshp3025,3025,3025,20833,1,1,1,0,1,1,thermal problem,20833 -HB,lshp3466,3466,3466,23896,1,1,1,0,1,1,thermal problem,23896 -HB,lshp_265,265,265,1753,1,1,1,0,1,1,thermal problem,1753 -HB,lshp_406,406,406,2716,1,1,1,0,1,1,thermal problem,2716 -HB,lshp_577,577,577,3889,1,1,1,0,1,1,thermal problem,3889 -HB,lshp_778,778,778,5272,1,1,1,0,1,1,thermal problem,5272 -HB,lund_a,147,147,2449,1,0,1,1,1,1,structural problem,2449 -HB,lund_b,147,147,2441,1,0,1,1,1,1,structural problem,2441 -HB,mahindas,1258,1258,7682,1,0,0,0,0.01663146779303062,0,economic problem,7682 -HB,mbeacxc,496,496,49920,1,0,0,0,0.3162614104531868,0,economic problem,49920 -HB,mbeaflw,496,496,49920,1,0,0,0,0.3162614104531868,0,economic problem,49920 -HB,mbeause,496,496,41063,1,0,0,0,0.2190689324447286,0.007771192484568281,economic problem,41063 -HB,mcca,180,180,2659,1,0,1,0,0.6446147640177491,0,2D/3D problem,2659 -HB,mcfe,765,765,24382,1,0,1,0,0.699411440911208,0,2D/3D problem,24382 -HB,nnc1374,1374,1374,8588,1,0,1,0,0.8169596690796277,0.5918631769888573,2D/3D problem,8606 -HB,nnc261,261,261,1500,1,0,1,0,0.8237037037037037,0.6133333333333333,2D/3D problem,1500 -HB,nnc666,666,666,4032,1,0,1,0,0.817831590533847,0.5919381557150746,2D/3D problem,4044 -HB,nos1,237,237,1017,1,0,1,1,1,1,structural problem,1017 -HB,nos2,957,957,4137,1,0,1,1,1,1,structural problem,4137 -HB,nos3,960,960,15844,1,0,1,1,1,1,structural problem,15844 -HB,nos4,100,100,594,1,0,1,1,1,1,structural problem,594 -HB,nos5,468,468,5172,1,0,1,1,1,1,structural problem,5172 -HB,nos6,675,675,3255,1,0,1,1,1,1,2D/3D problem,3255 -HB,nos7,729,729,4617,1,0,1,1,1,1,2D/3D problem,4617 -HB,orani678,2529,2529,90158,1,0,0,0,0.07068607068607069,2.223531635296341e-05,economic problem,90158 -HB,orsirr_1,1030,1030,6858,1,0,1,0,1,0.4094028826355525,computational fluid dynamics problem,6858 -HB,orsirr_2,886,886,5970,1,0,1,0,1,0.4118804091266719,computational fluid dynamics problem,5970 -HB,orsreg_1,2205,2205,14133,1,0,1,0,1,0.4119718309859155,computational fluid dynamics problem,14133 -HB,plat1919,1919,1919,32399,1,0,1,1,1,1,2D/3D problem,32399 -HB,plat362,362,362,5786,1,0,1,1,1,1,2D/3D problem,5786 -HB,plsk1919,1919,1919,9662,1,0,1,0,1,0,2D/3D problem,9662 -HB,plskz362,362,362,1760,1,0,1,0,1,0,2D/3D problem,1760 -HB,pores_1,30,30,180,1,0,1,0,0.6266666666666667,0.2933333333333333,computational fluid dynamics problem,180 -HB,pores_2,1224,1224,9613,1,0,1,0,0.6119918941470974,0.3914650137084277,computational fluid dynamics problem,9613 -HB,pores_3,532,532,3474,1,0,1,0,0.7423521414004078,0.3052345343303875,computational fluid dynamics problem,3474 -HB,psmigr_1,3140,3140,543160,1,0,0,0,0.4786508697793794,0.01044772250019444,economic problem,543162 -HB,psmigr_2,3140,3140,540022,1,0,0,0,0.4786508697793794,0,economic problem,540022 -HB,psmigr_3,3140,3140,543160,1,0,0,0,0.4786508697793794,0,economic problem,543162 -HB,rgg010,10,10,76,1,1,0,0,0.6363636363636364,0.6363636363636364,counter-example problem,76 -HB,saylr1,238,238,1128,1,0,1,0,1,0.647191011235955,computational fluid dynamics problem,1128 -HB,saylr3,1000,1000,3750,1,0,1,0,1,1,computational fluid dynamics problem,3750 -HB,saylr4,3564,3564,22316,1,0,1,0,1,1,computational fluid dynamics problem,22316 -HB,sherman1,1000,1000,3750,1,0,1,0,1,1,computational fluid dynamics problem,3750 -HB,sherman2,1080,1080,23094,1,0,1,0,0.6708458253838466,0.004905968928863451,computational fluid dynamics problem,23094 -HB,sherman3,5005,5005,20033,1,0,1,0,1,0.4532871972318339,computational fluid dynamics problem,20033 -HB,sherman4,1104,1104,3786,1,0,1,0,1,0,computational fluid dynamics problem,3786 -HB,sherman5,3312,3312,20793,1,0,1,0,0.7386305131285396,0.1534237171786511,computational fluid dynamics problem,20793 -HB,shl_0,663,663,1687,1,0,0,0,0,0,optimization problem sequence,1687 -HB,shl_200,663,663,1726,1,0,0,0,0,0,subsequent optimization problem,1726 -HB,shl_400,663,663,1712,1,0,0,0,0,0,subsequent optimization problem,1712 -HB,sstmodel,3345,3345,22749,1,1,1,0,1,1,structural problem,22749 -HB,steam1,240,240,2248,1,0,1,0,1,0.2729083665338645,computational fluid dynamics problem,3762 -HB,steam2,600,600,5660,1,0,1,0,1,0.1877470355731225,computational fluid dynamics problem,13760 -HB,steam3,80,80,314,1,0,1,0,1,0.3247863247863248,computational fluid dynamics problem,928 -HB,str_0,363,363,2454,1,0,0,0,0.004893964110929853,0,optimization problem sequence,2454 -HB,str_200,363,363,3068,1,0,0,0,0.01108936725375081,0,subsequent optimization problem,3068 -HB,str_400,363,363,3157,1,0,0,0,0.0196513470681458,0,subsequent optimization problem,3157 -HB,str_600,363,363,3279,1,0,0,0,0.01893129770992366,0,subsequent optimization problem,3279 -HB,watt_1,1856,1856,11360,1,0,1,0,0.9865319865319865,0.9865319865319865,computational fluid dynamics problem,11360 -HB,watt_2,1856,1856,11550,1,0,1,0,0.9804002475758201,0.9671962038374252,computational fluid dynamics problem,11550 -HB,well1033,1033,320,4732,1,0,0,0,0,0,least squares problem,4732 -HB,well1850,1850,712,8755,1,0,0,0,0,0,least squares problem,8758 -HB,west0067,67,67,294,1,0,0,0,0.03424657534246575,0,chemical process simulation problem,294 -HB,west0132,132,132,413,1,0,0,0,0.0196078431372549,0.004914004914004914,chemical process simulation problem,414 -HB,west0156,156,156,362,1,0,0,0,0,0,chemical process simulation problem,371 -HB,west0167,167,167,506,1,0,0,0,0.01622718052738337,0,chemical process simulation problem,507 -HB,west0381,381,381,2134,1,0,0,0,0.005565862708719851,0.0009376465072667605,chemical process simulation problem,2157 -HB,west0479,479,479,1888,1,0,0,0,0.01366982124079916,0,chemical process simulation problem,1910 -HB,west0497,497,497,1721,1,0,0,0,0.006972690296339338,0,chemical process simulation problem,1727 -HB,west0655,655,655,2808,1,0,0,0,0.005616005616005616,0,chemical process simulation problem,2854 -HB,west0989,989,989,3518,1,0,0,0,0.01812004530011325,0.003985197836606889,chemical process simulation problem,3537 -HB,west1505,1505,1505,5414,1,0,0,0,0.001102941176470588,0,chemical process simulation problem,5445 -HB,west2021,2021,2021,7310,1,0,0,0,0.00326619488296135,0,chemical process simulation problem,7353 -HB,will199,199,199,701,1,1,1,0,0.05596465390279824,0.05596465390279824,structural problem,701 -HB,will57,57,57,281,1,1,1,0,0.8660714285714286,0.8660714285714286,semiconductor device problem,281 -HB,wm1,207,277,2909,1,0,0,0,0,0,economic problem,2909 -HB,wm2,207,260,2942,1,0,0,0,0,0,economic problem,2942 -HB,wm3,207,260,2948,1,0,0,0,0,0,economic problem,2948 -HB,young1c,841,841,4089,0,0,1,0,1,0.8479064039408867,acoustics problem,4089 -HB,young2c,841,841,4089,0,0,1,0,1,0.8479064039408867,duplicate acoustics problem,4089 -HB,young3c,841,841,3988,1,0,1,0,0.9380362249761678,0.8071178900540197,acoustics problem,3988 -HB,young4c,841,841,4089,0,0,1,0,1,0.8004926108374384,acoustics problem,4089 -HB,zenios,2873,2873,1314,1,0,0,0,1,1,optimization problem,27191 -ATandT,onetone1,36057,36057,335552,1,0,0,0,0.0742956282869174,0.04331285111284008,frequency-domain circuit simulation problem,341088 -ATandT,onetone2,36057,36057,222596,1,0,0,0,0.1128682425939654,0.06622764699821082,frequency-domain circuit simulation problem,227628 -ATandT,pre2,659033,659033,5834044,1,0,0,0,0.3317760910564062,0.06536187196755584,frequency-domain circuit simulation problem,5959282 -ATandT,twotone,120750,120750,1206265,1,0,0,0,0.2445668624008717,0.1058542372000797,frequency-domain circuit simulation problem,1224224 -Averous,epb0,1794,1794,7764,1,0,1,0,0.5008375209380235,0,thermal problem,7764 -Averous,epb1,14734,14734,95053,1,0,1,0,0.7293915511896314,0,thermal problem,95053 -Averous,epb2,25228,25228,175027,1,0,1,0,0.6705385216189694,0,thermal problem,175027 -Averous,epb3,84617,84617,463625,1,0,1,0,0.6674107142857143,0.03155606214116852,thermal problem,463625 -Bai,af23560,23560,23560,460598,1,0,1,0,0.9973084203031934,0,computational fluid dynamics problem,484256 -Bai,bfwa398,398,398,3678,1,0,1,0,0.9890243902439024,0.9432926829268292,electromagnetics problem,3678 -Bai,bfwa62,62,62,450,1,0,1,0,0.9690721649484536,0.8144329896907216,electromagnetics problem,450 -Bai,bfwa782,782,782,7514,1,0,1,0,0.9916815210932858,0.9604872251931076,electromagnetics problem,7514 -Bai,bfwb398,398,398,2910,1,0,1,0,1,1,electromagnetics problem,2910 -Bai,bfwb62,62,62,342,1,0,1,0,1,1,electromagnetics problem,342 -Bai,bfwb782,782,782,5982,1,0,1,0,1,1,electromagnetics problem,5982 -Bai,bwm200,200,200,796,1,0,0,0,1,0.6644295302013423,chemical process simulation problem,796 -Bai,bwm2000,2000,2000,7996,1,0,0,0,1,0.6664442961974649,chemical process simulation problem,7996 -Bai,cdde1,961,961,4681,1,0,1,0,1,0,computational fluid dynamics problem sequence,4681 -Bai,cdde2,961,961,4681,1,0,1,0,1,0,subsequent computational fluid dynamics problem,4681 -Bai,cdde3,961,961,4681,1,0,1,0,1,0,subsequent computational fluid dynamics problem,4681 -Bai,cdde4,961,961,4681,1,0,1,0,1,0,subsequent computational fluid dynamics problem,4681 -Bai,cdde5,961,961,4681,1,0,1,0,1,0,subsequent computational fluid dynamics problem,4681 -Bai,cdde6,961,961,4681,1,0,1,0,1,0,subsequent computational fluid dynamics problem,4681 -Bai,ck104,104,104,992,1,0,1,0,1,0,2D/3D problem,992 -Bai,ck400,400,400,2860,1,0,1,0,0.9886178861788618,0.001626016260162602,2D/3D problem,2860 -Bai,ck656,656,656,3884,1,0,1,0,0.9913258983890955,0,2D/3D problem,3884 -Bai,dw1024,2048,2048,10114,1,0,1,0,0.9848747830399207,0.9481775353334987,electromagnetics problem,10114 -Bai,dw256A,512,512,2480,1,0,1,0,0.9796747967479674,0.9146341463414634,electromagnetics problem,2480 -Bai,dw256B,512,512,2500,1,0,1,0,0.9698189134808853,0.8832997987927566,electromagnetics problem,2500 -Bai,dw4096,8192,8192,41746,1,0,1,0,0.9626274065685164,0.9154199201287477,electromagnetics problem,41746 -Bai,lop163,163,163,935,1,0,0,0,0.4507772020725389,0,statistical/mathematical problem,935 -Bai,mhda416,416,416,8562,1,0,1,0,0.7675276752767528,0.2675871920009523,electromagnetics problem,8562 -Bai,mhdb416,416,416,2312,1,0,1,1,1,1,electromagnetics problem,2312 -Bai,odepa400,400,400,1201,1,0,1,0,0.9962546816479401,0.9937578027465668,2D/3D problem,1201 -Bai,odepb400,400,400,399,1,0,1,0,1,1,2D/3D problem,399 -Bai,olm100,100,100,396,1,0,1,0,0.668918918918919,0.3310810810810811,computational fluid dynamics problem,396 -Bai,olm1000,1000,1000,3996,1,0,1,0,0.6668891855807744,0.3331108144192256,computational fluid dynamics problem,3996 -Bai,olm2000,2000,2000,7996,1,0,1,0,0.6667778519012675,0.3332221480987325,computational fluid dynamics problem,7996 -Bai,olm500,500,500,1996,1,0,1,0,0.6671122994652406,0.3328877005347594,computational fluid dynamics problem,1996 -Bai,olm5000,5000,5000,19996,1,0,1,0,0.6667111229661243,0.3332888770338757,computational fluid dynamics problem,19996 -Bai,pde225,225,225,1065,1,0,1,0,1,0.5,2D/3D problem,1065 -Bai,pde2961,2961,2961,14585,1,0,1,0,1,0.5013764624913971,2D/3D problem,14585 -Bai,pde900,900,900,4380,1,0,1,0,1,0.5,2D/3D problem,4380 -Bai,qc2534,2534,2534,463360,0,0,1,0,1,0,electromagnetics problem,463360 -Bai,qc324,324,324,26730,0,0,1,0,1,0,electromagnetics problem,26730 -Bai,qh882,882,882,3354,1,0,0,0,0.9264705882352942,0,power network problem,3354 -Bai,rbsa480,480,480,17088,1,0,1,0,0.07485627126598615,0.0007039774727208729,robotics problem,17088 -Bai,rbsb480,480,480,17088,1,0,1,0,0.07568201818715166,0.0008213552361396304,robotics problem,17088 -Bai,rdb2048,2048,2048,12032,1,0,1,0,1,0.7948717948717948,computational fluid dynamics problem,12032 -Bai,rdb5000,5000,5000,29600,1,0,1,0,1,0.7967479674796748,computational fluid dynamics problem,29600 -Bai,rdb968,968,968,5632,1,0,1,0,1,0.7924528301886793,computational fluid dynamics problem,5632 -Bai,rw136,136,136,479,1,0,0,0,0.44258872651357,0.02922755741127349,statistical/mathematical problem,479 -Bai,rw496,496,496,1859,1,0,0,0,0.4690693921463152,0,statistical/mathematical problem,1859 -Bai,rw5151,5151,5151,20199,1,0,0,0,0.4902222882320907,0,statistical/mathematical problem,20199 -Bai,tub100,100,100,396,1,0,1,0,1,0,computational fluid dynamics problem,396 -Bai,tub1000,1000,1000,3996,1,0,1,0,1,0,computational fluid dynamics problem,3996 -Boeing,bcsstk34,588,588,21418,1,0,1,1,1,1,structural problem,21418 -Boeing,bcsstk35,30237,30237,1450163,1,0,1,0,1,1,structural problem,1450163 -Boeing,bcsstk36,23052,23052,1143140,1,0,1,1,1,1,structural problem,1143140 -Boeing,bcsstk37,25503,25503,1140977,1,0,1,0,1,1,structural problem,1140977 -Boeing,bcsstk38,8032,8032,355460,1,0,1,1,1,1,structural problem,355460 -Boeing,bcsstm34,588,588,24270,1,0,1,0,1,1,structural problem,24270 -Boeing,bcsstm35,30237,30237,20619,1,0,1,0,1,1,structural problem,20619 -Boeing,bcsstm36,23052,23052,320606,1,0,1,0,1,1,structural problem,320606 -Boeing,bcsstm37,25503,25503,15525,1,0,1,0,1,1,structural problem,15525 -Boeing,bcsstm38,8032,8032,10485,1,0,1,0,1,1,structural problem,10485 -Boeing,bcsstm39,46772,46772,46772,1,0,1,1,1,1,structural problem,46772 -Boeing,crystk01,4875,4875,315891,1,0,1,0,1,1,materials problem,315891 -Boeing,crystk02,13965,13965,968583,1,0,1,0,1,1,materials problem,968583 -Boeing,crystk03,24696,24696,1751178,1,0,1,0,1,1,materials problem,1751178 -Boeing,crystm01,4875,4875,105339,1,0,1,1,1,1,materials problem,105339 -Boeing,crystm02,13965,13965,322905,1,0,1,1,1,1,materials problem,322905 -Boeing,crystm03,24696,24696,583770,1,0,1,1,1,1,materials problem,583770 -Boeing,ct20stif,52329,52329,2600295,1,0,1,1,1,1,structural problem,2698463 -Boeing,msc00726,726,726,34518,1,0,1,1,1,1,structural problem,34518 -Boeing,msc01050,1050,1050,26198,1,0,1,1,1,1,structural problem,29156 -Boeing,msc01440,1440,1440,44998,1,0,1,1,1,1,structural problem,46270 -Boeing,msc04515,4515,4515,97707,1,0,1,1,1,1,structural problem,97707 -Boeing,msc10848,10848,10848,1229776,1,0,1,1,1,1,structural problem,1229778 -Boeing,msc23052,23052,23052,1142686,1,0,1,1,1,1,structural problem,1154814 -Boeing,nasa1824,1824,1824,39208,1,0,1,0,1,1,duplicate structural problem,39208 -Boeing,nasa2910,2910,2910,174296,1,1,1,0,1,1,duplicate structural problem,174296 -Boeing,nasa4704,4704,4704,104756,1,1,1,0,1,1,duplicate structural problem,104756 -Boeing,pcrystk02,13965,13965,968583,1,1,1,0,1,1,duplicate materials problem,968583 -Boeing,pcrystk03,24696,24696,1751178,1,1,1,0,1,1,duplicate materials problem,1751178 -Boeing,pct20stif,52329,52329,2698463,1,1,1,0,1,1,structural problem,2698463 -Boeing,pwtk,217918,217918,11524432,1,0,1,1,1,1,structural problem,11634424 -Bomhof,circuit_1,2624,2624,35823,1,0,0,0,0.999548641410646,0.2060602413263924,circuit simulation problem,35823 -Bomhof,circuit_2,4510,4510,21199,1,0,0,0,0.8065636603186011,0.4152593124925141,circuit simulation problem,21199 -Bomhof,circuit_3,12127,12127,48137,1,0,0,0,0.7700717835450027,0.3001104362230811,circuit simulation problem,48137 -Bomhof,circuit_4,80209,80209,307604,1,0,0,0,0.8292028705104302,0.3635452210926232,circuit simulation problem,307604 -Bova,rma10,46835,46835,2329092,1,0,1,0,1,0.2355549300354827,computational fluid dynamics problem,2374001 -Brethour,coater1,1348,1348,19457,1,0,1,0,0.5922916666666667,0.395,computational fluid dynamics problem,19457 -Brethour,coater2,9540,9540,207308,1,0,1,0,0.5941884735804569,0.1280874338061923,computational fluid dynamics problem,207308 -Brunetiere,thermal,3456,3456,66528,1,0,1,0,1,0.0730593607305936,thermal problem,66528 -Cote,mplate,5962,5962,142190,0,0,1,0,1,0.05311683354376486,acoustics problem,142190 -Cote,vibrobox,12328,12328,301700,1,0,1,0,1,1,acoustics problem,342828 -DRIVCAV,cavity01,317,317,7280,1,0,1,0,0.8037831733483908,0.5198237885462555,computational fluid dynamics problem sequence,7327 -DRIVCAV,cavity02,317,317,5923,1,0,1,0,0.8037831733483908,0,subsequent computational fluid dynamics problem,7327 -DRIVCAV,cavity03,317,317,7311,1,0,1,0,0.8037831733483908,0,subsequent computational fluid dynamics problem,7327 -DRIVCAV,cavity04,317,317,5923,1,0,1,0,0.8037831733483908,0,subsequent computational fluid dynamics problem,7327 -DRIVCAV,cavity05,1182,1182,32632,1,0,1,0,0.9049083605322621,0.6103499322813317,computational fluid dynamics problem sequence,32747 -DRIVCAV,cavity06,1182,1182,29675,1,0,1,0,0.9049083605322621,0,subsequent computational fluid dynamics problem,32747 -DRIVCAV,cavity07,1182,1182,32702,1,0,1,0,0.9049083605322621,0,subsequent computational fluid dynamics problem,32747 -DRIVCAV,cavity08,1182,1182,29675,1,0,1,0,0.9049083605322621,0,subsequent computational fluid dynamics problem,32747 -DRIVCAV,cavity09,1182,1182,32702,1,0,1,0,0.9049083605322621,0,subsequent computational fluid dynamics problem,32747 -DRIVCAV,cavity10,2597,2597,76171,1,0,1,0,0.9372682821986997,0.6400172395216033,computational fluid dynamics problem sequence,76367 -DRIVCAV,cavity11,2597,2597,71601,1,0,1,0,0.9372682821986997,0,subsequent computational fluid dynamics problem,76367 -DRIVCAV,cavity12,2597,2597,76258,1,0,1,0,0.9372682821986997,0,subsequent computational fluid dynamics problem,76367 -DRIVCAV,cavity13,2597,2597,71601,1,0,1,0,0.9372682821986997,0,subsequent computational fluid dynamics problem,76367 -DRIVCAV,cavity14,2597,2597,76258,1,0,1,0,0.9372682821986997,0,subsequent computational fluid dynamics problem,76367 -DRIVCAV,cavity15,2597,2597,71601,1,0,1,0,0.9372682821986997,0,subsequent computational fluid dynamics problem,76367 -DRIVCAV,cavity16,4562,4562,137887,1,0,1,0,0.9531982436361479,0.6542773036781541,computational fluid dynamics problem sequence,138187 -DRIVCAV,cavity17,4562,4562,131735,1,0,1,0,0.9531982436361479,0,subsequent computational fluid dynamics problem,138187 -DRIVCAV,cavity18,4562,4562,138040,1,0,1,0,0.9531982436361479,0,subsequent computational fluid dynamics problem,138187 -DRIVCAV,cavity19,4562,4562,131735,1,0,1,0,0.9531982436361479,0,subsequent computational fluid dynamics problem,138187 -DRIVCAV,cavity20,4562,4562,138040,1,0,1,0,0.9531982436361479,0,subsequent computational fluid dynamics problem,138187 -DRIVCAV,cavity21,4562,4562,131735,1,0,1,0,0.9531982436361479,0,subsequent computational fluid dynamics problem,138187 -DRIVCAV,cavity22,4562,4562,138040,1,0,1,0,0.9531982436361479,0,subsequent computational fluid dynamics problem,138187 -DRIVCAV,cavity23,4562,4562,131735,1,0,1,0,0.9531982436361479,0,subsequent computational fluid dynamics problem,138187 -DRIVCAV,cavity24,4562,4562,138040,1,0,1,0,0.9531982436361479,0,subsequent computational fluid dynamics problem,138187 -DRIVCAV,cavity25,4562,4562,131735,1,0,1,0,0.9531982436361479,0,subsequent computational fluid dynamics problem,138187 -DRIVCAV,cavity26,4562,4562,138040,1,0,1,0,0.9531982436361479,0,subsequent computational fluid dynamics problem,138187 -FIDAP,ex1,216,216,4317,1,0,1,0,1,0.9980657640232108,computational fluid dynamics problem,4352 -FIDAP,ex10,2410,2410,54840,1,0,1,1,1,1,computational fluid dynamics problem,54840 -FIDAP,ex10hs,2548,2548,57308,1,0,1,1,1,1,computational fluid dynamics problem,57308 -FIDAP,ex11,16614,16614,1096948,1,0,1,0,1,0.9999981487206734,computational fluid dynamics problem,1096948 -FIDAP,ex12,3973,3973,79077,1,0,1,0,1,1,computational fluid dynamics problem,80211 -FIDAP,ex13,2568,2568,75628,1,0,1,1,1,1,computational fluid dynamics problem,75628 -FIDAP,ex14,3251,3251,65875,1,0,1,0,1,1,computational fluid dynamics problem,66775 -FIDAP,ex15,6867,6867,98671,1,0,1,1,1,1,computational fluid dynamics problem,98671 -FIDAP,ex18,5773,5773,71701,1,0,1,0,1,0.4973910933139182,computational fluid dynamics problem,71805 -FIDAP,ex19,12005,12005,259577,1,0,1,0,1,0.4284006268883396,computational fluid dynamics problem,259879 -FIDAP,ex2,441,441,26839,1,0,1,0,1,1,computational fluid dynamics problem,26839 -FIDAP,ex20,2203,2203,67830,1,0,1,0,1,0.9705407160221662,computational fluid dynamics problem,69981 -FIDAP,ex21,656,656,18964,1,0,1,0,1,0.9422327996538296,computational fluid dynamics problem,19144 -FIDAP,ex22,839,839,22460,1,0,1,0,1,0.88617551561714,computational fluid dynamics problem,22715 -FIDAP,ex23,1409,1409,42760,1,0,1,0,1,0.9386855719486492,computational fluid dynamics problem,43703 -FIDAP,ex24,2283,2283,47901,1,0,1,0,1,0.862361129122898,computational fluid dynamics problem,48737 -FIDAP,ex25,848,848,24369,1,0,1,0,1,0.9755481671646816,computational fluid dynamics problem,24612 -FIDAP,ex26,2163,2163,74464,1,0,1,0,1,0.7325380026938618,computational fluid dynamics problem,94033 -FIDAP,ex27,974,974,37652,1,0,1,0,1,0.5304041225928939,computational fluid dynamics problem,40782 -FIDAP,ex28,2603,2603,77031,1,0,1,0,1,0.9877623772912288,computational fluid dynamics problem,77781 -FIDAP,ex29,2870,2870,23754,1,0,1,0,1,0.9963608504117986,computational fluid dynamics problem,23754 -FIDAP,ex3,1821,1821,52685,1,0,1,1,1,1,computational fluid dynamics problem,52685 -FIDAP,ex31,3909,3909,91223,1,0,1,0,1,0.7266897116346767,computational fluid dynamics problem,115357 -FIDAP,ex32,1159,1159,11047,1,0,1,0,1,1,computational fluid dynamics problem,11343 -FIDAP,ex33,1733,1733,22189,1,0,1,1,1,1,computational fluid dynamics problem,22189 -FIDAP,ex35,19716,19716,227872,1,0,1,0,1,0.4608754972232364,computational fluid dynamics problem,228208 -FIDAP,ex36,3079,3079,53099,1,0,1,0,1,0.9952497822816879,computational fluid dynamics problem,53843 -FIDAP,ex37,3565,3565,67591,1,0,1,0,1,0.9536750695030144,computational fluid dynamics problem,67591 -FIDAP,ex4,1601,1601,31849,1,0,1,0,1,1,computational fluid dynamics problem,32299 -FIDAP,ex40,7740,7740,456188,1,0,1,0,1,0.7709917560940942,computational fluid dynamics problem,458012 -FIDAP,ex5,27,27,279,1,0,1,1,1,1,computational fluid dynamics problem,279 -FIDAP,ex6,1651,1651,49062,1,0,1,0,1,0.9792406332233408,computational fluid dynamics problem,49533 -FIDAP,ex7,1633,1633,46626,1,0,1,0,1,0.8240392950014447,computational fluid dynamics problem,54543 -FIDAP,ex8,3096,3096,90841,1,0,1,0,1,0.4310900906034532,computational fluid dynamics problem,106344 -FIDAP,ex9,3363,3363,99471,1,0,1,1,1,1,computational fluid dynamics problem,99471 -Gaertner,big,13209,13209,91465,1,0,0,0,1,0.999974442854222,directed weighted graph,91465 -Gaertner,nopoly,10774,10774,70842,1,0,0,0,1,1,undirected weighted graph,70842 -Gaertner,pesa,11738,11738,79566,1,0,0,0,1,0.999970513652179,directed weighted graph,79566 -Garon,garon1,3175,3175,84723,1,0,1,0,1,0.6720847366622736,computational fluid dynamics problem,88927 -Garon,garon2,13535,13535,373235,1,0,1,0,1,0.6726819817326322,computational fluid dynamics problem,390607 -Goodwin,goodwin,7320,7320,324772,1,0,1,0,0.6353114022282706,0.001199255331506196,computational fluid dynamics problem,324784 -Goodwin,rim,22560,22560,1014951,1,0,1,0,0.6390640220938991,0,computational fluid dynamics problem,1014951 -Graham,graham1,9035,9035,335472,1,0,1,0,0.718197839525901,0.0004322586733311619,computational fluid dynamics problem,335504 -Grund,b1_ss,7,7,15,1,0,0,0,0,0,chemical process simulation problem,15 -Grund,b2_ss,1089,1089,3895,1,0,0,0,0.0146710837671557,0.0005136106831022085,chemical process simulation problem,4228 -Grund,b_dyn,1089,1089,4144,1,0,0,0,0.01454716095729704,0.0004828585224529213,chemical process simulation problem,4264 -Grund,bayer01,57735,57735,275094,1,0,0,0,0.0002304047924196823,2.908118011428904e-05,chemical process simulation problem,277774 -Grund,bayer02,13935,13935,63307,1,0,0,0,0.001884570082449941,0.000284346713425904,chemical process simulation problem,63679 -Grund,bayer03,6747,6747,29195,1,0,0,0,0.003132397173723459,0.0002740852405097985,chemical process simulation problem,56196 -Grund,bayer04,20545,20545,85537,1,0,0,0,0.001584088708967702,0.0002104352502425851,chemical process simulation problem,159082 -Grund,bayer05,3268,3268,20712,1,0,0,0,0.006682474671265359,0.00135200386286818,chemical process simulation problem,27836 -Grund,bayer06,3008,3008,20715,1,0,0,0,0.006745484877058098,0.001351808043257857,chemical process simulation problem,27576 -Grund,bayer07,3268,3268,20963,1,0,0,0,0.006682474671265359,0.0013358141310052,chemical process simulation problem,27836 -Grund,bayer08,3008,3008,20698,1,0,0,0,0.006745484877058098,0.001352918438345574,chemical process simulation problem,27576 -Grund,bayer09,3083,3083,11767,1,0,0,0,0.02112012068640392,0.0001700246535747683,chemical process simulation problem,21216 -Grund,bayer10,13436,13436,71594,1,0,0,0,0.002528365095919851,0.0002234917796929782,chemical process simulation problem,94926 -Grund,d_dyn,87,87,230,1,0,0,0,0.07692307692307693,0.01769911504424779,chemical process simulation problem,238 -Grund,d_dyn1,87,87,232,1,0,0,0,0.07692307692307693,0.01754385964912281,chemical process simulation problem,238 -Grund,d_ss,53,53,144,1,0,0,0,0.04137931034482759,0,chemical process simulation problem,149 -Grund,meg1,2904,2904,58142,1,0,0,0,0.002374602082078637,0.001996042329863202,circuit simulation problem,58142 -Grund,meg4,5860,5860,25258,1,0,0,0,1,1,circuit simulation problem,46842 -Grund,poli,4008,4008,8188,1,0,0,0,0.0291866028708134,0.002392344497607655,economic problem,8188 -Grund,poli_large,15575,15575,33033,1,0,0,0,0.003543059603405909,0.0004582426394776034,economic problem,33074 -Gset,G1,800,800,38352,1,1,0,0,1,1,undirected random graph,38352 -Gset,G10,800,800,38352,1,0,0,0,1,1,undirected weighted random graph,38352 -Gset,G11,800,800,3200,1,0,0,0,1,1,undirected weighted random graph,3200 -Gset,G12,800,800,3200,1,0,0,0,1,1,undirected weighted random graph,3200 -Gset,G13,800,800,3200,1,0,0,0,1,1,undirected weighted random graph,3200 -Gset,G14,800,800,9388,1,1,0,0,1,1,duplicate undirected random graph,9388 -Gset,G15,800,800,9322,1,1,0,0,1,1,duplicate undirected random graph,9322 -Gset,G16,800,800,9344,1,1,0,0,1,1,duplicate undirected random graph,9344 -Gset,G17,800,800,9334,1,1,0,0,1,1,duplicate undirected random graph,9334 -Gset,G18,800,800,9388,1,0,0,0,1,1,undirected weighted random graph,9388 -Gset,G19,800,800,9322,1,0,0,0,1,1,undirected weighted random graph,9322 -Gset,G2,800,800,38352,1,1,0,0,1,1,undirected random graph,38352 -Gset,G20,800,800,9344,1,0,0,0,1,1,undirected weighted random graph,9344 -Gset,G21,800,800,9334,1,0,0,0,1,1,undirected weighted random graph,9334 -Gset,G22,2000,2000,39980,1,1,0,0,1,1,undirected random graph,39980 -Gset,G23,2000,2000,39980,1,1,0,0,1,1,undirected random graph,39980 -Gset,G24,2000,2000,39980,1,1,0,0,1,1,undirected random graph,39980 -Gset,G25,2000,2000,39980,1,1,0,0,1,1,undirected random graph,39980 -Gset,G26,2000,2000,39980,1,1,0,0,1,1,undirected random graph,39980 -Gset,G27,2000,2000,39980,1,0,0,0,1,1,undirected weighted random graph,39980 -Gset,G28,2000,2000,39980,1,0,0,0,1,1,undirected weighted random graph,39980 -Gset,G29,2000,2000,39980,1,0,0,0,1,1,undirected weighted random graph,39980 -Gset,G3,800,800,38352,1,1,0,0,1,1,undirected random graph,38352 -Gset,G30,2000,2000,39980,1,0,0,0,1,1,undirected weighted random graph,39980 -Gset,G31,2000,2000,39980,1,0,0,0,1,1,undirected weighted random graph,39980 -Gset,G32,2000,2000,8000,1,0,0,0,1,1,undirected weighted random graph,8000 -Gset,G33,2000,2000,8000,1,0,0,0,1,1,undirected weighted random graph,8000 -Gset,G34,2000,2000,8000,1,0,0,0,1,1,undirected weighted random graph,8000 -Gset,G35,2000,2000,23556,1,1,0,0,1,1,duplicate undirected random graph,23556 -Gset,G36,2000,2000,23532,1,1,0,0,1,1,duplicate undirected random graph,23532 -Gset,G37,2000,2000,23570,1,1,0,0,1,1,duplicate undirected random graph,23570 -Gset,G38,2000,2000,23558,1,1,0,0,1,1,duplicate undirected random graph,23558 -Gset,G39,2000,2000,23556,1,0,0,0,1,1,undirected weighted random graph,23556 -Gset,G4,800,800,38352,1,1,0,0,1,1,undirected random graph,38352 -Gset,G40,2000,2000,23532,1,0,0,0,1,1,undirected weighted random graph,23532 -Gset,G41,2000,2000,23570,1,0,0,0,1,1,undirected weighted random graph,23570 -Gset,G42,2000,2000,23558,1,0,0,0,1,1,undirected weighted random graph,23558 -Gset,G43,1000,1000,19980,1,1,0,0,1,1,undirected random graph,19980 -Gset,G44,1000,1000,19980,1,1,0,0,1,1,undirected random graph,19980 -Gset,G45,1000,1000,19980,1,1,0,0,1,1,undirected random graph,19980 -Gset,G46,1000,1000,19980,1,1,0,0,1,1,undirected random graph,19980 -Gset,G47,1000,1000,19980,1,1,0,0,1,1,undirected random graph,19980 -Gset,G48,3000,3000,12000,1,1,0,0,1,1,undirected random graph,12000 -Gset,G49,3000,3000,12000,1,1,0,0,1,1,undirected random graph,12000 -Gset,G5,800,800,38352,1,1,0,0,1,1,undirected random graph,38352 -Gset,G50,3000,3000,12000,1,1,0,0,1,1,undirected random graph,12000 -Gset,G51,1000,1000,11818,1,1,0,0,1,1,undirected random graph,11818 -Gset,G52,1000,1000,11832,1,1,0,0,1,1,undirected random graph,11832 -Gset,G53,1000,1000,11828,1,1,0,0,1,1,undirected random graph,11828 -Gset,G54,1000,1000,11832,1,1,0,0,1,1,undirected random graph,11832 -Gset,G55,5000,5000,24996,1,1,0,0,1,1,duplicate undirected random graph,24996 -Gset,G56,5000,5000,24996,1,0,0,0,1,1,undirected weighted random graph,24996 -Gset,G57,5000,5000,20000,1,0,0,0,1,1,undirected weighted random graph,20000 -Gset,G58,5000,5000,59140,1,1,0,0,1,1,duplicate undirected random graph,59140 -Gset,G59,5000,5000,59140,1,0,0,0,1,1,undirected weighted random graph,59140 -Gset,G6,800,800,38352,1,0,0,0,1,1,undirected weighted random graph,38352 -Gset,G60,7000,7000,34296,1,1,0,0,1,1,duplicate undirected random graph,34296 -Gset,G61,7000,7000,34296,1,0,0,0,1,1,undirected weighted random graph,34296 -Gset,G62,7000,7000,28000,1,0,0,0,1,1,undirected weighted random graph,28000 -Gset,G63,7000,7000,82918,1,1,0,0,1,1,duplicate undirected random graph,82918 -Gset,G64,7000,7000,82918,1,0,0,0,1,1,undirected weighted random graph,82918 -Gset,G65,8000,8000,32000,1,0,0,0,1,1,undirected weighted random graph,32000 -Gset,G66,9000,9000,36000,1,0,0,0,1,1,undirected weighted random graph,36000 -Gset,G67,10000,10000,40000,1,0,0,0,1,1,undirected weighted random graph,40000 -Gset,G7,800,800,38352,1,0,0,0,1,1,undirected weighted random graph,38352 -Gset,G8,800,800,38352,1,0,0,0,1,1,undirected weighted random graph,38352 -Gset,G9,800,800,38352,1,0,0,0,1,1,undirected weighted random graph,38352 -Gupta,gupta1,31802,31802,2164210,1,1,0,0,1,1,optimization problem,2164210 -Gupta,gupta2,62064,62064,4248286,1,1,0,0,1,1,optimization problem,4248286 -Gupta,gupta3,16783,16783,9323427,1,1,0,0,1,1,optimization problem,9323427 -Hamm,add20,2395,2395,13151,1,0,0,0,1,0.5265898103384158,circuit simulation problem,17319 -Hamm,add32,4960,4960,19848,1,0,0,0,1,0.3052122514777001,circuit simulation problem,23884 -Hamm,bcircuit,68902,68902,375558,1,0,0,0,1,0.9084315976207868,circuit simulation problem,375558 -Hamm,hcircuit,105676,105676,513072,1,0,0,0,0.9999410961997232,0.1951139297670355,circuit simulation problem,513072 -Hamm,memplus,17758,17758,99147,1,0,0,0,1,0.4962095614886533,circuit simulation problem,126150 -Hamm,scircuit,170998,170998,958936,1,0,0,0,0.9999467019956295,0.8008355096685118,circuit simulation problem,958936 -Hollinger,g7jac010,2880,2880,18229,1,0,0,0,0.07102357505222322,0,economic problem,19635 -Hollinger,g7jac010sc,2880,2880,18229,1,0,0,0,0.07102357505222322,0,economic problem,19635 -Hollinger,g7jac020,5850,5850,42568,1,0,0,0,0.06184526063359838,0,economic problem,45465 -Hollinger,g7jac020sc,5850,5850,42568,1,0,0,0,0.06184526063359838,0,economic problem,45465 -Hollinger,g7jac040,11790,11790,107383,1,0,0,0,0.04830823961664447,0,economic problem,114671 -Hollinger,g7jac040sc,11790,11790,107383,1,0,0,0,0.04830823961664447,0,economic problem,114671 -Hollinger,g7jac050sc,14760,14760,145157,1,0,0,0,0.04349647420233192,0,economic problem,157990 -Hollinger,g7jac060,17730,17730,183325,1,0,0,0,0.04035864774282543,0,economic problem,203316 -Hollinger,g7jac060sc,17730,17730,183325,1,0,0,0,0.04035864774282543,0,economic problem,203316 -Hollinger,g7jac080,23670,23670,259648,1,0,0,0,0.03703210435580416,0,economic problem,293976 -Hollinger,g7jac080sc,23670,23670,259648,1,0,0,0,0.03703210435580416,0,economic problem,293976 -Hollinger,g7jac100,29610,29610,335972,1,0,0,0,0.03529318979455026,0,economic problem,384636 -Hollinger,g7jac100sc,29610,29610,335972,1,0,0,0,0.03529318979455026,0,economic problem,384636 -Hollinger,g7jac120,35550,35550,412306,1,0,0,0,0.0342243022108217,0,economic problem,475296 -Hollinger,g7jac120sc,35550,35550,412306,1,0,0,0,0.0342243022108217,0,economic problem,475296 -Hollinger,g7jac140,41490,41490,488633,1,0,0,0,0.03350074170680273,0,economic problem,565956 -Hollinger,g7jac140sc,41490,41490,488633,1,0,0,0,0.03350074170680273,0,economic problem,565956 -Hollinger,g7jac160,47430,47430,564952,1,0,0,0,0.03297843351620031,0,economic problem,656616 -Hollinger,g7jac160sc,47430,47430,564952,1,0,0,0,0.03297843351620031,0,economic problem,656616 -Hollinger,g7jac180,53370,53370,641290,1,0,0,0,0.03258366406977314,0,economic problem,747276 -Hollinger,g7jac180sc,53370,53370,641290,1,0,0,0,0.03258366406977314,0,economic problem,747276 -Hollinger,g7jac200,59310,59310,717620,1,0,0,0,0.03227480202305086,0,economic problem,837936 -Hollinger,g7jac200sc,59310,59310,717620,1,0,0,0,0.03227480202305086,0,economic problem,837936 -Hollinger,jan99jac020,6774,6774,33744,1,0,0,0,0.003759634062284604,0,economic problem,38692 -Hollinger,jan99jac020sc,6774,6774,33744,1,0,0,0,0.003759634062284604,0,economic problem,38692 -Hollinger,jan99jac040,13694,13694,72734,1,0,0,0,0.003470816220281136,0,economic problem,82842 -Hollinger,jan99jac040sc,13694,13694,72734,1,0,0,0,0.003470816220281136,0,economic problem,82842 -Hollinger,jan99jac060,20614,20614,111903,1,0,0,0,0.003378124765408002,0,economic problem,127182 -Hollinger,jan99jac060sc,20614,20614,111903,1,0,0,0,0.003378124765408002,0,economic problem,127182 -Hollinger,jan99jac080,27534,27534,151063,1,0,0,0,0.003333611134261188,0,economic problem,171522 -Hollinger,jan99jac080sc,27534,27534,151063,1,0,0,0,0.003333611134261188,0,economic problem,171522 -Hollinger,jan99jac100,34454,34454,190224,1,0,0,0,0.003307461633445052,0,economic problem,215862 -Hollinger,jan99jac100sc,34454,34454,190224,1,0,0,0,0.003307461633445052,0,economic problem,215862 -Hollinger,jan99jac120,41374,41374,229385,1,0,0,0,0.003290255360374358,0,economic problem,260202 -Hollinger,jan99jac120sc,41374,41374,229385,1,0,0,0,0.003290255360374358,0,economic problem,260202 -Hollinger,mark3jac020,9129,9129,52883,1,0,0,0,0.07363006419249245,0.01351166293878669,economic problem,56175 -Hollinger,mark3jac020sc,9129,9129,52883,1,0,0,0,0.07363006419249245,0.007349249098461679,economic problem,56175 -Hollinger,mark3jac040,18289,18289,106803,1,0,0,0,0.07382338721543732,0.01389985784236298,economic problem,113435 -Hollinger,mark3jac040sc,18289,18289,106803,1,0,0,0,0.07382338721543732,0.01173364623056615,economic problem,113435 -Hollinger,mark3jac060,27449,27449,160723,1,0,0,0,0.07388687991287715,0.01402730528871371,economic problem,170695 -Hollinger,mark3jac060sc,27449,27449,160723,1,0,0,0,0.07388687991287715,0.0121839735039789,economic problem,170695 -Hollinger,mark3jac080,36609,36609,214643,1,0,0,0,0.07391845139171971,0.01409066941898426,economic problem,227955 -Hollinger,mark3jac080sc,36609,36609,214643,1,0,0,0,0.07391845139171971,0.0125985842018466,economic problem,227955 -Hollinger,mark3jac100,45769,45769,268563,1,0,0,0,0.07393733869014309,0.01412857360574466,economic problem,285215 -Hollinger,mark3jac100sc,45769,45769,268563,1,0,0,0,0.07393733869014309,0.01261351717213372,economic problem,285215 -Hollinger,mark3jac120,54929,54929,322483,1,0,0,0,0.07394990714529154,0.01415379562098285,economic problem,342475 -Hollinger,mark3jac120sc,54929,54929,322483,1,0,0,0,0.07394990714529154,0.01276529035436745,economic problem,342475 -Hollinger,mark3jac140,64089,64089,376395,1,0,0,0,0.07395887333678935,0.01417215070061332,economic problem,399735 -Hollinger,mark3jac140sc,64089,64089,376395,1,0,0,0,0.07395887333678935,0.0128291219789848,economic problem,399735 -LPnetlib,lp_25fv47,821,1876,10705,1,0,0,0,0,0,linear programming problem,10705 -LPnetlib,lp_80bau3b,2262,12061,23264,1,0,0,0,0,0,linear programming problem,23264 -LPnetlib,lp_adlittle,56,138,424,1,0,0,0,0,0,linear programming problem,424 -LPnetlib,lp_afiro,27,51,102,1,0,0,0,0,0,linear programming problem,102 -LPnetlib,lp_agg,488,615,2862,1,0,0,0,0,0,linear programming problem,2862 -LPnetlib,lp_agg2,516,758,4740,1,0,0,0,0,0,linear programming problem,4740 -LPnetlib,lp_agg3,516,758,4756,1,0,0,0,0,0,linear programming problem,4756 -LPnetlib,lp_bandm,305,472,2494,1,0,0,0,0,0,linear programming problem,2494 -LPnetlib,lp_beaconfd,173,295,3408,1,0,0,0,0,0,linear programming problem,3408 -LPnetlib,lp_blend,74,114,522,1,0,0,0,0,0,linear programming problem,522 -LPnetlib,lp_bnl1,643,1586,5532,1,0,0,0,0,0,linear programming problem,5532 -LPnetlib,lp_bnl2,2324,4486,14996,1,0,0,0,0,0,linear programming problem,14996 -LPnetlib,lp_bore3d,233,334,1448,1,0,0,0,0,0,linear programming problem,1448 -LPnetlib,lp_brandy,220,303,2202,1,0,0,0,0,0,linear programming problem,2202 -LPnetlib,lp_capri,271,482,1896,1,0,0,0,0,0,linear programming problem,1896 -LPnetlib,lp_cre_a,3516,7248,18168,1,0,0,0,0,0,linear programming problem,18168 -LPnetlib,lp_cre_b,9648,77137,260785,1,0,0,0,0,0,linear programming problem,260785 -LPnetlib,lp_cre_c,3068,6411,15977,1,0,0,0,0,0,linear programming problem,15977 -LPnetlib,lp_cre_d,8926,73948,246614,1,0,0,0,0,0,linear programming problem,246614 -LPnetlib,lp_cycle,1903,3371,21234,1,0,0,0,0,0,linear programming problem,21234 -LPnetlib,lp_czprob,929,3562,10708,1,0,0,0,0,0,linear programming problem,10708 -LPnetlib,lp_d2q06c,2171,5831,33081,1,0,0,0,0,0,linear programming problem,33081 -LPnetlib,lp_d6cube,415,6184,37704,1,0,0,0,0,0,linear programming problem,37704 -LPnetlib,lp_degen2,444,757,4201,1,0,0,0,0,0,linear programming problem,4201 -LPnetlib,lp_degen3,1503,2604,25432,1,0,0,0,0,0,linear programming problem,25432 -LPnetlib,lp_dfl001,6071,12230,35632,1,0,0,0,0,0,linear programming problem,35632 -LPnetlib,lp_e226,223,472,2768,1,0,0,0,0,0,linear programming problem,2768 -LPnetlib,lp_etamacro,400,816,2537,1,0,0,0,0,0,linear programming problem,2537 -LPnetlib,lp_fffff800,524,1028,6401,1,0,0,0,0,0,linear programming problem,6401 -LPnetlib,lp_finnis,497,1064,2760,1,0,0,0,0,0,linear programming problem,2760 -LPnetlib,lp_fit1d,24,1049,13427,1,0,0,0,0,0,linear programming problem,13427 -LPnetlib,lp_fit1p,627,1677,9868,1,0,0,0,0,0,linear programming problem,9868 -LPnetlib,lp_fit2d,25,10524,129042,1,0,0,0,0,0,linear programming problem,129042 -LPnetlib,lp_fit2p,3000,13525,50284,1,0,0,0,0,0,linear programming problem,50284 -LPnetlib,lp_ganges,1309,1706,6937,1,0,0,0,0,0,linear programming problem,6937 -LPnetlib,lp_gfrd_pnc,616,1160,2445,1,0,0,0,0,0,linear programming problem,2445 -LPnetlib,lp_greenbea,2392,5598,31070,1,0,0,0,0,0,linear programming problem,31070 -LPnetlib,lp_greenbeb,2392,5598,31070,1,0,0,0,0,0,linear programming problem,31070 -LPnetlib,lp_grow15,300,645,5620,1,0,0,0,0,0,linear programming problem,5620 -LPnetlib,lp_grow22,440,946,8252,1,0,0,0,0,0,linear programming problem,8252 -LPnetlib,lp_grow7,140,301,2612,1,0,0,0,0,0,linear programming problem,2612 -LPnetlib,lp_israel,174,316,2443,1,0,0,0,0,0,linear programming problem,2443 -LPnetlib,lp_kb2,43,68,313,1,0,0,0,0,0,linear programming problem,313 -LPnetlib,lp_ken_07,2426,3602,8404,1,0,0,0,0,0,linear programming problem,8404 -LPnetlib,lp_ken_11,14694,21349,49058,1,0,0,0,0,0,linear programming problem,49058 -LPnetlib,lp_ken_13,28632,42659,97246,1,0,0,0,0,0,linear programming problem,97246 -LPnetlib,lp_ken_18,105127,154699,358171,1,0,0,0,0,0,linear programming problem,358171 -LPnetlib,lp_lotfi,153,366,1136,1,0,0,0,0,0,linear programming problem,1136 -LPnetlib,lp_maros,846,1966,10137,1,0,0,0,0,0,linear programming problem,10137 -LPnetlib,lp_maros_r7,3136,9408,144848,1,0,0,0,0,0,linear programming problem,144848 -LPnetlib,lp_modszk1,687,1620,3168,1,0,0,0,0,0,linear programming problem,3168 -LPnetlib,lp_osa_07,1118,25067,144812,1,0,0,0,0,0,linear programming problem,144812 -LPnetlib,lp_osa_14,2337,54797,317097,1,0,0,0,0,0,linear programming problem,317097 -LPnetlib,lp_osa_30,4350,104374,604488,1,0,0,0,0,0,linear programming problem,604488 -LPnetlib,lp_osa_60,10280,243246,1408073,1,0,0,0,0,0,linear programming problem,1408073 -LPnetlib,lp_pds_02,2953,7716,16571,1,0,0,0,0,0,linear programming problem,16571 -LPnetlib,lp_pds_06,9881,29351,63220,1,0,0,0,0,0,linear programming problem,63220 -LPnetlib,lp_pds_10,16558,49932,107605,1,0,0,0,0,0,linear programming problem,107605 -LPnetlib,lp_pds_20,33874,108175,232647,1,0,0,0,0,0,linear programming problem,232647 -LPnetlib,lp_perold,625,1506,6148,1,0,0,0,0,0,linear programming problem,6148 -LPnetlib,lp_pilot,1441,4860,44375,1,0,0,0,0,0,linear programming problem,44375 -LPnetlib,lp_pilot4,410,1123,5264,1,0,0,0,0,0,linear programming problem,5264 -LPnetlib,lp_pilot87,2030,6680,74949,1,0,0,0,0,0,linear programming problem,74949 -LPnetlib,lp_pilot_ja,940,2267,14977,1,0,0,0,0,0,linear programming problem,14977 -LPnetlib,lp_pilot_we,722,2928,9265,1,0,0,0,0,0,linear programming problem,9265 -LPnetlib,lp_pilotnov,975,2446,13331,1,0,0,0,0,0,linear programming problem,13331 -LPnetlib,lp_qap12,3192,8856,38304,1,0,0,0,0,0,linear programming problem,38304 -LPnetlib,lp_qap15,6330,22275,94950,1,0,0,0,0,0,linear programming problem,94950 -LPnetlib,lp_qap8,912,1632,7296,1,0,0,0,0,0,linear programming problem,7296 -LPnetlib,lp_recipe,91,204,687,1,0,0,0,0,0,linear programming problem,687 -LPnetlib,lp_sc105,105,163,340,1,0,0,0,0,0,linear programming problem,340 -LPnetlib,lp_sc205,205,317,665,1,0,0,0,0,0,linear programming problem,665 -LPnetlib,lp_sc50a,50,78,160,1,0,0,0,0,0,linear programming problem,160 -LPnetlib,lp_sc50b,50,78,148,1,0,0,0,0,0,linear programming problem,148 -LPnetlib,lp_scagr25,471,671,1725,1,0,0,0,0,0,linear programming problem,1725 -LPnetlib,lp_scagr7,129,185,465,1,0,0,0,0,0,linear programming problem,465 -LPnetlib,lp_scfxm1,330,600,2732,1,0,0,0,0,0,linear programming problem,2732 -LPnetlib,lp_scfxm2,660,1200,5469,1,0,0,0,0,0,linear programming problem,5469 -LPnetlib,lp_scfxm3,990,1800,8206,1,0,0,0,0,0,linear programming problem,8206 -LPnetlib,lp_scorpion,388,466,1534,1,0,0,0,0,0,linear programming problem,1534 -LPnetlib,lp_scrs8,490,1275,3288,1,0,0,0,0,0,linear programming problem,3288 -LPnetlib,lp_scsd1,77,760,2388,1,0,0,0,0,0,linear programming problem,2388 -LPnetlib,lp_scsd6,147,1350,4316,1,0,0,0,0,0,linear programming problem,4316 -LPnetlib,lp_scsd8,397,2750,8584,1,0,0,0,0,0,linear programming problem,8584 -LPnetlib,lp_sctap1,300,660,1872,1,0,0,0,0,0,linear programming problem,1872 -LPnetlib,lp_sctap2,1090,2500,7334,1,0,0,0,0,0,linear programming problem,7334 -LPnetlib,lp_sctap3,1480,3340,9734,1,0,0,0,0,0,linear programming problem,9734 -LPnetlib,lp_share1b,117,253,1179,1,0,0,0,0,0,linear programming problem,1179 -LPnetlib,lp_share2b,96,162,777,1,0,0,0,0,0,linear programming problem,777 -LPnetlib,lp_shell,536,1777,3558,1,0,0,0,0,0,linear programming problem,3558 -LPnetlib,lp_ship04l,402,2166,6380,1,0,0,0,0,0,linear programming problem,6380 -LPnetlib,lp_ship04s,402,1506,4400,1,0,0,0,0,0,linear programming problem,4400 -LPnetlib,lp_ship08l,778,4363,12882,1,0,0,0,0,0,linear programming problem,12882 -LPnetlib,lp_ship08s,778,2467,7194,1,0,0,0,0,0,linear programming problem,7194 -LPnetlib,lp_ship12l,1151,5533,16276,1,0,0,0,0,0,linear programming problem,16276 -LPnetlib,lp_ship12s,1151,2869,8284,1,0,0,0,0,0,linear programming problem,8284 -LPnetlib,lp_sierra,1227,2735,8001,1,0,0,0,0,0,linear programming problem,8001 -LPnetlib,lp_stair,356,614,4003,1,0,0,0,0,0,linear programming problem,4003 -LPnetlib,lp_standata,359,1274,3230,1,0,0,0,0,0,linear programming problem,3230 -LPnetlib,lp_standgub,361,1383,3338,1,0,0,0,0,0,linear programming problem,3339 -LPnetlib,lp_standmps,467,1274,3878,1,0,0,0,0,0,linear programming problem,3878 -LPnetlib,lp_stocfor1,117,165,501,1,0,0,0,0,0,linear programming problem,501 -LPnetlib,lp_stocfor2,2157,3045,9357,1,0,0,0,0,0,linear programming problem,9357 -LPnetlib,lp_stocfor3,16675,23541,72721,1,0,0,0,0,0,linear programming problem,76473 -LPnetlib,lp_truss,1000,8806,27836,1,0,0,0,0,0,linear programming problem,27836 -LPnetlib,lp_tuff,333,628,4561,1,0,0,0,0,0,linear programming problem,4561 -LPnetlib,lp_vtp_base,198,346,1051,1,0,0,0,0,0,linear programming problem,1051 -LPnetlib,lp_wood1p,244,2595,70216,1,0,0,0,0,0,linear programming problem,70216 -LPnetlib,lp_woodw,1098,8418,37487,1,0,0,0,0,0,linear programming problem,37487 -LPnetlib,lpi_bgdbg1,348,629,1662,1,0,0,0,0,0,linear programming problem,1662 -LPnetlib,lpi_bgetam,400,816,2537,1,0,0,0,0,0,linear programming problem,2537 -LPnetlib,lpi_bgindy,2671,10880,66266,1,0,0,0,0,0,linear programming problem,66266 -LPnetlib,lpi_bgprtr,20,40,70,1,0,0,0,0,0,linear programming problem,70 -LPnetlib,lpi_box1,231,261,651,1,0,0,0,0,0,linear programming problem,651 -LPnetlib,lpi_ceria3d,3576,4400,21178,1,0,0,0,0,0,linear programming problem,21178 -LPnetlib,lpi_chemcom,288,744,1590,1,0,0,0,0,0,linear programming problem,1590 -LPnetlib,lpi_cplex1,3005,5224,10947,1,0,0,0,0,0,linear programming problem,10947 -LPnetlib,lpi_cplex2,224,378,1215,1,0,0,0,0,0,linear programming problem,1215 -LPnetlib,lpi_ex72a,197,215,467,1,0,0,0,0,0,linear programming problem,467 -LPnetlib,lpi_ex73a,193,211,457,1,0,0,0,0,0,linear programming problem,457 -LPnetlib,lpi_forest6,66,131,246,1,0,0,0,0,0,linear programming problem,246 -LPnetlib,lpi_galenet,8,14,22,1,0,0,0,0,0,linear programming problem,22 -LPnetlib,lpi_gosh,3792,13455,99953,1,0,0,0,0,0,linear programming problem,99953 -LPnetlib,lpi_gran,2658,2525,20111,1,0,0,0,0,0,linear programming problem,20111 -LPnetlib,lpi_greenbea,2393,5596,31074,1,0,0,0,0,0,linear programming problem,31074 -LPnetlib,lpi_itest2,9,13,26,1,0,0,0,0,0,linear programming problem,26 -LPnetlib,lpi_itest6,11,17,29,1,0,0,0,0,0,linear programming problem,29 -LPnetlib,lpi_klein1,54,108,750,1,0,0,0,0,0,linear programming problem,750 -LPnetlib,lpi_klein2,477,531,5062,1,0,0,0,0,0,linear programming problem,5062 -LPnetlib,lpi_klein3,994,1082,13101,1,0,0,0,0,0,linear programming problem,13101 -LPnetlib,lpi_mondou2,312,604,1208,1,0,0,0,0,0,linear programming problem,1208 -LPnetlib,lpi_pang,361,741,2933,1,0,0,0,0,0,linear programming problem,2933 -LPnetlib,lpi_pilot4i,410,1123,5264,1,0,0,0,0,0,linear programming problem,5264 -LPnetlib,lpi_qual,323,464,1646,1,0,0,0,0,0,linear programming problem,1646 -LPnetlib,lpi_reactor,318,808,2591,1,0,0,0,0,0,linear programming problem,2591 -LPnetlib,lpi_refinery,323,464,1626,1,0,0,0,0,0,linear programming problem,1626 -LPnetlib,lpi_vol1,323,464,1646,1,0,0,0,0,0,linear programming problem,1646 -LPnetlib,lpi_woodinfe,35,89,140,1,0,0,0,0,0,linear programming problem,140 -Li,li,22695,22695,1215181,1,0,1,0,1,0.8685670104303111,electromagnetics problem,1350309 -Li,pli,22695,22695,1350309,1,1,1,0,1,1,duplicate electromagnetics problem,1350309 -Mallya,lhr01,1477,1477,18427,1,0,0,0,0.007324823611784348,0,chemical process simulation problem,18592 -Mallya,lhr02,2954,2954,36875,1,0,0,0,0.007320486597050275,0,chemical process simulation problem,37206 -Mallya,lhr04,4101,4101,81057,1,0,0,0,0.01515885315769082,0,chemical process simulation problem,82682 -Mallya,lhr04c,4101,4101,82682,1,0,0,0,0.01515885315769082,0,chemical process simulation problem,82682 -Mallya,lhr07,7337,7337,154660,1,0,0,0,0.0173680609288971,0,chemical process simulation problem,156508 -Mallya,lhr07c,7337,7337,156508,1,0,0,0,0.0173680609288971,0,chemical process simulation problem,156508 -Mallya,lhr10,10672,10672,228395,1,0,0,0,0.008791586915847653,1.752395305332977e-05,chemical process simulation problem,232633 -Mallya,lhr10c,10672,10672,232633,1,0,0,0,0.008791586915847653,1.720467106819501e-05,chemical process simulation problem,232633 -Mallya,lhr11,10964,10964,231806,1,0,0,0,0.008200580375103791,3.452591601570929e-05,chemical process simulation problem,233741 -Mallya,lhr11c,10964,10964,233741,1,0,0,0,0.008200580375103791,4.280052387841227e-05,chemical process simulation problem,233741 -Mallya,lhr14,14270,14270,305750,1,0,0,0,0.006622946983926842,0,chemical process simulation problem,307858 -Mallya,lhr14c,14270,14270,307858,1,0,0,0,0.006622946983926842,0,chemical process simulation problem,307858 -Mallya,lhr17,17576,17576,379761,1,0,0,0,0.001513390098553639,0.0001527473453564807,chemical process simulation problem,381975 -Mallya,lhr17c,17576,17576,381975,1,0,0,0,0.001513390098553639,5.236643939631969e-06,chemical process simulation problem,381975 -Mallya,lhr34,35152,35152,746972,1,0,0,0,0.00151326330781556,0.0001553140154832006,chemical process simulation problem,764014 -Mallya,lhr34c,35152,35152,764014,1,0,0,0,0.00151326330781556,5.236205217354879e-06,chemical process simulation problem,764014 -Mallya,lhr71,70304,70304,1494006,1,0,0,0,0.001513199920413014,0.0001553075692357127,chemical process simulation problem,1528092 -Mallya,lhr71c,70304,70304,1528092,1,0,0,0,0.001513199920413014,5.235985883782057e-06,chemical process simulation problem,1528092 -Mulvey,finan512,74752,74752,596992,1,0,0,1,1,1,economic problem,596992 -Mulvey,pfinan512,74752,74752,596992,1,1,0,0,1,1,duplicate economic problem,596992 -Nasa,barth,6691,6691,26439,1,1,1,0,0,0,duplicate structural problem,26439 -Nasa,barth4,6019,6019,23492,1,1,1,0,0,0,duplicate structural problem,23492 -Nasa,barth5,15606,15606,61484,1,1,1,0,0,0,duplicate structural problem,61484 -Nasa,nasa1824,1824,1824,39208,1,0,1,1,1,1,structural problem,39208 -Nasa,nasa2146,2146,2146,72250,1,0,1,1,1,1,structural problem,72250 -Nasa,nasa2910,2910,2910,174296,1,0,1,1,1,1,structural problem,174296 -Nasa,nasa4704,4704,4704,104756,1,0,1,1,1,1,structural problem,104756 -Nasa,nasasrb,54870,54870,2677324,1,0,1,1,1,1,structural problem,2677324 -Nasa,pwt,36519,36519,326107,1,1,1,0,1,1,duplicate structural problem,326107 -Nasa,shuttle_eddy,10429,10429,103599,1,1,1,0,1,1,duplicate structural problem,103599 -Nasa,skirt,12598,12598,196520,1,1,1,0,1,1,duplicate structural problem,196520 -Nemeth,nemeth01,9506,9506,725054,1,0,0,0,1,1,theoretical/quantum chemistry problem sequence,725054 -Nemeth,nemeth02,9506,9506,394808,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,394808 -Nemeth,nemeth03,9506,9506,394808,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,394808 -Nemeth,nemeth04,9506,9506,394808,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,394808 -Nemeth,nemeth05,9506,9506,394808,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,394808 -Nemeth,nemeth06,9506,9506,394808,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,394808 -Nemeth,nemeth07,9506,9506,394812,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,394812 -Nemeth,nemeth08,9506,9506,394816,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,394816 -Nemeth,nemeth09,9506,9506,395506,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,395506 -Nemeth,nemeth10,9506,9506,401448,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,401448 -Nemeth,nemeth11,9506,9506,408264,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,408264 -Nemeth,nemeth12,9506,9506,446818,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,446818 -Nemeth,nemeth13,9506,9506,474472,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,474472 -Nemeth,nemeth14,9506,9506,496144,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,496144 -Nemeth,nemeth15,9506,9506,539802,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,539802 -Nemeth,nemeth16,9506,9506,587012,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,587012 -Nemeth,nemeth17,9506,9506,629620,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,629620 -Nemeth,nemeth18,9506,9506,695234,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,695234 -Nemeth,nemeth19,9506,9506,818302,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,818302 -Nemeth,nemeth20,9506,9506,971870,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,971870 -Nemeth,nemeth21,9506,9506,1173746,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,1173746 -Nemeth,nemeth22,9506,9506,1358832,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,1358832 -Nemeth,nemeth23,9506,9506,1506810,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,1506810 -Nemeth,nemeth24,9506,9506,1506550,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,1506550 -Nemeth,nemeth25,9506,9506,1511758,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,1511758 -Nemeth,nemeth26,9506,9506,1511760,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,1511760 -Okunbor,aft01,8205,8205,125567,1,0,1,1,1,1,acoustics problem,125567 -Okunbor,aft02,8184,8184,127762,0,0,1,0,1,0.9963538443526401,acoustics problem,127762 -Qaplib,lp_nug05,210,225,1050,1,0,0,0,0,0,linear programming problem,1050 -Qaplib,lp_nug06,372,486,2232,1,0,0,0,0,0,linear programming problem,2232 -Qaplib,lp_nug07,602,931,4214,1,0,0,0,0,0,linear programming problem,4214 -Qaplib,lp_nug08,912,1632,7296,1,0,0,0,0,0,linear programming problem,7296 -Qaplib,lp_nug12,3192,8856,38304,1,0,0,0,0,0,linear programming problem,38304 -Qaplib,lp_nug15,6330,22275,94950,1,0,0,0,0,0,linear programming problem,94950 -Qaplib,lp_nug20,15240,72600,304800,1,0,0,0,0,0,linear programming problem,304800 -Qaplib,lp_nug30,52260,379350,1567800,1,0,0,0,0,0,linear programming problem,1567800 -Ronis,xenon1,48600,48600,1181120,1,0,1,0,1,0.775813230671423,materials problem,1181120 -Ronis,xenon2,157464,157464,3866688,1,0,1,0,1,0.7756792256277864,materials problem,3866688 -Rothberg,3dtube,45330,45330,3213618,1,1,1,0,1,1,computational fluid dynamics problem,3213618 -Rothberg,cfd1,70656,70656,1825580,1,0,1,1,1,1,computational fluid dynamics problem,1828364 -Rothberg,cfd2,123440,123440,3085406,1,0,1,1,1,1,computational fluid dynamics problem,3087898 -Rothberg,gearbox,153746,153746,9080404,1,1,1,0,1,1,structural problem,9080404 -Rothberg,struct3,53570,53570,1173694,1,1,1,0,1,1,structural problem,1173694 -Rothberg,struct4,4350,4350,237798,1,1,1,0,1,1,structural problem,237798 -Shyy,shyy161,76480,76480,329762,1,0,1,0,0.7263358696509635,0.1824427535660244,computational fluid dynamics problem,329762 -Shyy,shyy41,4720,4720,20042,1,0,1,0,0.7234369459874719,0.1843753693416854,computational fluid dynamics problem,20042 -Simon,appu,14000,14000,1853104,1,0,0,0,1,0.9429754924136972,directed weighted random graph,1853104 -Simon,bbmat,38744,38744,1771722,1,0,1,0,0.5295358625441292,0.0005851199495896658,computational fluid dynamics problem,1771722 -Simon,olafu,16146,16146,1015156,1,0,1,1,1,1,structural problem,1015156 -Simon,raefsky1,3242,3242,293409,1,0,1,0,1,0.09040311269027836,computational fluid dynamics problem sequence,294276 -Simon,raefsky2,3242,3242,293551,1,0,1,0,1,0.09753745147411895,subsequent computational fluid dynamics problem,294276 -Simon,raefsky3,21200,21200,1488768,1,0,1,0,1,0.4763745189320018,computational fluid dynamics problem,1488768 -Simon,raefsky4,19779,19779,1316789,1,0,1,1,1,1,structural problem,1328611 -Simon,raefsky5,6316,6316,167178,1,0,1,0,0,0,structural problem,168658 -Simon,raefsky6,3402,3402,130371,1,0,1,0,0,0,structural problem,137845 -Simon,venkat01,62424,62424,1717792,1,0,1,0,1,0.05542936676316082,computational fluid dynamics problem sequence,1717792 -Simon,venkat25,62424,62424,1717763,1,0,1,0,1,0.05539650790563141,subsequent computational fluid dynamics problem,1717792 -Simon,venkat50,62424,62424,1717777,1,0,1,0,1,0.05539603939461855,subsequent computational fluid dynamics problem,1717792 -TOKAMAK,utm1700b,1700,1700,21509,1,0,1,0,0.5232974910394266,0,electromagnetics problem,21509 -TOKAMAK,utm300,300,300,3155,1,0,1,0,0.4651488616462347,0,electromagnetics problem,3155 -TOKAMAK,utm3060,3060,3060,42211,1,0,1,0,0.524686470332814,0,electromagnetics problem,42211 -TOKAMAK,utm5940,5940,5940,83842,1,0,1,0,0.5290493183743677,0,electromagnetics problem,83842 -Vavasis,av41092,41092,41092,1683902,1,0,1,0,0.0009359570219938022,0,2D/3D problem,1683902 -Wang,swang1,3169,3169,20841,1,0,1,0,1,0,semiconductor device problem sequence,20841 -Wang,swang2,3169,3169,20841,1,0,1,0,1,0,subsequent semiconductor device problem,20841 -Wang,wang1,2903,2903,19093,1,0,1,0,1,0.808153180975911,semiconductor device problem sequence,19093 -Wang,wang2,2903,2903,19093,1,0,1,0,1,0.808153180975911,subsequent semiconductor device problem,19093 -Wang,wang3,26064,26064,177168,1,0,1,0,1,0.9780019059720457,semiconductor device problem,177168 -Wang,wang4,26068,26068,177196,1,0,1,0,1,0.04647715843523371,semiconductor device problem,177196 -Zhao,Zhao1,33861,33861,166453,1,0,1,0,0.9224990949680222,0,electromagnetics problem,166453 -Zhao,Zhao2,33861,33861,166453,1,0,1,0,0.9224990949680222,0,electromagnetics problem,166453 -Zitney,extr1,2837,2837,10967,1,0,0,0,0.004209049456331112,0.000364797081623347,chemical process simulation problem,11407 -Zitney,hydr1,5308,5308,22680,1,0,0,0,0.004126315789473685,0.0006173383896287151,chemical process simulation problem,23752 -Zitney,radfr1,1048,1048,13299,1,0,0,0,0.05374309287714783,0,chemical process simulation problem,13299 -Zitney,rdist1,4134,4134,94408,1,0,0,0,0.05878038669628716,0,chemical process simulation problem,94408 -Zitney,rdist2,3198,3198,56834,1,0,0,0,0.04562603575332323,0,chemical process simulation problem,56934 -Zitney,rdist3a,2398,2398,61896,1,0,0,0,0.1404154186743065,0,chemical process simulation problem,61896 -Cunningham,k3plates,11107,11107,378927,1,0,1,0,1,0.999260507857104,acoustics problem,378927 -Cunningham,m3plates,11107,11107,6639,1,0,1,0,1,1,acoustics problem,6639 -Cunningham,qa8fk,66127,66127,1660579,1,0,1,0,1,1,acoustics problem,1660579 -Cunningham,qa8fm,66127,66127,1660579,1,0,1,1,1,1,acoustics problem,1660579 -Boeing,bcsstk39,46772,46772,2060662,1,0,1,0,1,1,structural problem,2089294 -Chen,pkustk01,22044,22044,979380,1,1,1,0,1,1,structural problem,979380 -Chen,pkustk02,10800,10800,810000,1,1,1,0,1,1,structural problem,810000 -Chen,pkustk03,63336,63336,3130416,1,1,1,0,1,1,structural problem,3130416 -Chen,pkustk04,55590,55590,4218660,1,1,1,0,1,1,structural problem,4218660 -Chen,pkustk05,37164,37164,2205144,1,1,1,0,1,1,structural problem,2205144 -Chen,pkustk06,43164,43164,2571768,1,1,1,0,1,1,structural problem,2571768 -Chen,pkustk07,16860,16860,2418804,1,1,1,0,1,1,structural problem,2418804 -Chen,pkustk08,22209,22209,3226671,1,1,1,0,1,1,structural problem,3226671 -Chen,pkustk09,33960,33960,1583640,1,1,1,0,1,1,structural problem,1583640 -Chen,pkustk10,80676,80676,4308984,1,1,1,0,1,1,structural problem,4308984 -Chen,pkustk11,87804,87804,5217912,1,1,1,0,1,1,structural problem,5217912 -Chen,pkustk12,94653,94653,7512317,1,1,1,0,1,1,structural problem,7512317 -Chen,pkustk13,94893,94893,6616827,1,1,1,0,1,1,structural problem,6616827 -Chen,pkustk14,151926,151926,14836504,1,1,1,0,1,1,structural problem,14836504 -MathWorks,pivtol,102,102,306,1,0,0,0,0.9901960784313726,0,statistical/mathematical problem,306 -Kim,kim1,38415,38415,933195,0,0,1,0,0.9897360244976419,0,2D/3D problem,933195 -Kim,kim2,456976,456976,11330020,0,0,1,0,0.9970363405132914,0,2D/3D problem,11330020 -Langemyr,comsol,1500,1500,97645,1,0,1,0,0.9990327110094128,0.0008944822923708982,structural problem,97645 -Pothen,barth,6691,6691,46187,1,1,1,0,1,1,structural problem,46187 -Pothen,barth4,6019,6019,40965,1,1,1,0,1,1,structural problem,40965 -Pothen,barth5,15606,15606,107362,1,1,1,0,1,1,structural problem,107362 -Pothen,bodyy4,17546,17546,121550,1,0,1,1,1,1,structural problem,121938 -Pothen,bodyy5,18589,18589,128853,1,0,1,1,1,1,structural problem,129281 -Pothen,bodyy6,19366,19366,134208,1,0,1,1,1,1,structural problem,134748 -Pothen,commanche_dual,7920,7920,31680,1,1,1,0,1,1,structural problem,31680 -Pothen,mesh1e1,48,48,306,1,0,1,1,1,1,structural problem,306 -Pothen,mesh1em1,48,48,306,1,0,1,1,1,1,structural problem,306 -Pothen,mesh1em6,48,48,306,1,0,1,1,1,1,structural problem,306 -Pothen,mesh2e1,306,306,2018,1,0,1,1,1,1,structural problem,2018 -Pothen,mesh2em5,306,306,2018,1,0,1,1,1,1,structural problem,2018 -Pothen,mesh3e1,289,289,1377,1,0,1,1,1,1,structural problem,1889 -Pothen,mesh3em5,289,289,1377,1,0,1,1,1,1,structural problem,1889 -Pothen,onera_dual,85567,85567,419201,1,1,1,0,1,1,structural problem,419201 -Pothen,pwt,36519,36519,326107,1,1,1,0,1,1,structural problem,326107 -Pothen,shuttle_eddy,10429,10429,103599,1,1,1,0,1,1,structural problem,103599 -Pothen,skirt,12598,12598,196520,1,1,1,0,1,1,structural problem,196520 -Pothen,sphere2,66,66,450,1,1,1,0,1,1,structural problem,450 -Pothen,sphere3,258,258,1794,1,1,1,0,1,1,structural problem,1794 -Pothen,tandem_dual,94069,94069,460493,1,1,1,0,1,1,structural problem,460493 -Pothen,tandem_vtx,18454,18454,253350,1,1,1,0,1,1,structural problem,253350 -Norris,fv1,9604,9604,85264,1,0,1,1,1,1,2D/3D problem,85264 -Norris,fv2,9801,9801,87025,1,0,1,1,1,1,2D/3D problem,87025 -Norris,fv3,9801,9801,87025,1,0,1,1,1,1,2D/3D problem,87025 -Norris,heart1,3557,3557,1385317,1,0,1,0,1,0,2D/3D problem,1387773 -Norris,heart2,2339,2339,680341,1,0,1,0,1,0,2D/3D problem,682797 -Norris,heart3,2339,2339,680341,1,0,1,0,1,0,2D/3D problem,682797 -Norris,lung1,1650,1650,7419,1,0,1,0,0.5713295198474606,0,computational fluid dynamics problem,7419 -Norris,lung2,109460,109460,492564,1,0,1,0,0.5714270798529903,0,computational fluid dynamics problem,492564 -Norris,stomach,213360,213360,3021648,1,0,1,0,0.8474914253808726,0,2D/3D problem,3021648 -Norris,torso1,116158,116158,8516500,1,0,1,0,0.4243874832715144,0,2D/3D problem,8516500 -Norris,torso2,115967,115967,1033473,1,0,1,0,0.9916360219987662,0,2D/3D problem,1033473 -Norris,torso3,259156,259156,4429042,1,0,1,0,0.9513722917125312,0,2D/3D problem,4429042 -Shen,e40r0100,17281,17281,553562,1,0,1,0,0.3079877899832364,0.001965387548691824,2D/3D problem,553562 -Shen,shermanACa,3432,3432,25220,1,0,1,0,0.163759867817147,0.02157150725169818,2D/3D problem,25220 -Shen,shermanACb,18510,18510,145149,1,0,1,0,0.1493062958488302,0.02665845434660729,2D/3D problem,145149 -Shen,shermanACd,6136,6136,53329,1,0,1,0,0.1520140698832454,0.01665501239590617,2D/3D problem,53329 -Pereyra,landmark,71952,2704,1146848,1,0,0,0,0,0,least squares problem,1151232 -vanHeukelum,cage3,5,5,19,1,0,0,0,1,0,directed weighted graph,19 -vanHeukelum,cage4,9,9,49,1,0,0,0,1,0,directed weighted graph,49 -vanHeukelum,cage5,37,37,233,1,0,0,0,1,0.04081632653061224,directed weighted graph,233 -vanHeukelum,cage6,93,93,785,1,0,0,0,1,0.09248554913294797,directed weighted graph,785 -vanHeukelum,cage7,340,340,3084,1,0,0,0,1,0.1020408163265306,directed weighted graph,3084 -vanHeukelum,cage8,1015,1015,11003,1,0,0,0,1,0.144973968762515,directed weighted graph,11003 -vanHeukelum,cage9,3534,3534,41594,1,0,0,0,1,0.1535470310036784,directed weighted graph,41594 -vanHeukelum,cage10,11397,11397,150645,1,0,0,0,1,0.1743077099850626,directed weighted graph,150645 -vanHeukelum,cage11,39082,39082,559722,1,0,0,0,1,0.1821988322065151,directed weighted graph,559722 -vanHeukelum,cage12,130228,130228,2032536,1,0,0,0,1,0.1935123018985359,directed weighted graph,2032536 -vanHeukelum,cage13,445315,445315,7479343,1,0,0,0,1,0.2004785878020389,directed weighted graph,7479343 -vanHeukelum,cage14,1505785,1505785,27130349,1,0,0,0,1,0.2073316837703073,directed weighted graph,27130349 -vanHeukelum,cage15,5154859,5154859,99199551,1,0,0,0,1,0.2131193964673732,directed weighted graph,99199551 -Hohn,fd12,7500,7500,28462,1,0,1,0,0.0002810765230834094,0,materials problem,28462 -Hohn,fd15,11532,11532,44206,1,0,1,0,0,0,materials problem,44206 -Hohn,fd18,16428,16428,63406,1,0,1,0,3.154275620603729e-05,0,materials problem,63406 -Hohn,sinc12,7500,7500,283992,1,0,1,0,0.01755337541442645,0,materials problem,294986 -Hohn,sinc15,11532,11532,551184,1,0,1,0,0.01378652867239141,0,materials problem,568526 -Hohn,sinc18,16428,16428,948696,1,0,1,0,0.01157085557378834,0,materials problem,973826 -Alemdar,Alemdar,6245,6245,42581,1,0,1,0,1,1,2D/3D problem,42581 -Andrews,Andrews,60000,60000,760154,1,0,1,1,1,1,computer graphics/vision problem,760154 -FEMLAB,ns3Da,20414,20414,1679599,1,0,1,0,0.9999825339398674,0,computational fluid dynamics problem,1679599 -FEMLAB,poisson2D,367,367,2417,1,0,1,0,1,0,computational fluid dynamics problem,2417 -FEMLAB,poisson3Da,13514,13514,352762,1,0,1,0,1,0,computational fluid dynamics problem,352762 -FEMLAB,poisson3Db,85623,85623,2374949,1,0,1,0,1,0,computational fluid dynamics problem,2374949 -FEMLAB,problem1,415,415,2779,1,0,1,0,1,0.6548223350253807,2D/3D problem,2779 -FEMLAB,sme3Da,12504,12504,874887,1,0,1,0,0.9999710105602732,0.4370888572710733,structural problem,874887 -FEMLAB,sme3Db,29067,29067,2081063,1,0,1,0,0.999978557463075,0.4406373111838425,structural problem,2081063 -FEMLAB,sme3Dc,42930,42930,3148656,1,0,1,0,0.9999793929020139,0.4391179389295772,structural problem,3148656 -FEMLAB,waveguide3D,21036,21036,303468,0,0,1,0,1,0,electromagnetics problem,303468 -Lucifora,cell1,7055,7055,30082,1,0,0,0,0.9970503597122302,0,directed weighted graph,34855 -Lucifora,cell2,7055,7055,30082,1,0,0,0,0.9970503597122302,0.004342728101793546,directed weighted graph,34855 -ND,nd3k,9000,9000,3279690,1,0,1,1,1,1,2D/3D problem,3279690 -ND,nd6k,18000,18000,6897316,1,0,1,1,1,1,2D/3D problem,6897316 -ND,nd12k,36000,36000,14220946,1,0,1,1,1,1,2D/3D problem,14220946 -ND,nd24k,72000,72000,28715634,1,0,1,1,1,1,2D/3D problem,28715634 -Schenk_AFE,af_shell1,504855,504855,17562051,1,0,1,0,1,1,structural problem sequence,17588875 -Schenk_AFE,af_shell2,504855,504855,17562051,1,0,1,0,1,1,subsequent structural problem,17588875 -Schenk_AFE,af_shell3,504855,504855,17562051,1,0,1,1,1,1,subsequent structural problem,17588875 -Schenk_AFE,af_shell4,504855,504855,17562051,1,0,1,1,1,1,subsequent structural problem,17588875 -Schenk_AFE,af_shell5,504855,504855,17579155,1,0,1,0,1,1,subsequent structural problem,17588875 -Schenk_AFE,af_shell6,504855,504855,17579155,1,0,1,0,1,1,subsequent structural problem,17588875 -Schenk_AFE,af_shell7,504855,504855,17579155,1,0,1,1,1,1,subsequent structural problem,17588875 -Schenk_AFE,af_shell8,504855,504855,17579155,1,0,1,1,1,1,subsequent structural problem,17588875 -Schenk_AFE,af_shell9,504855,504855,17588845,1,0,1,0,1,1,subsequent structural problem,17588875 -Schenk_IBMNA,c-62,41731,41731,559341,1,0,0,0,1,1,optimization problem sequence,559343 -Schenk_IBMNA,c-64,51035,51035,707985,1,0,0,0,1,1,optimization problem sequence,717841 -Schenk_IBMNA,c-66,49989,49989,444853,1,0,0,0,1,1,optimization problem sequence,499007 -Schenk_IBMNA,c-67,57975,57975,530229,1,0,0,0,1,1,optimization problem sequence,531935 -Schenk_IBMNA,c-73,169422,169422,1279274,1,0,0,0,1,1,optimization problem sequence,1279274 -Schenk_IBMSDS,2D_27628_bjtcai,27628,27628,206670,1,0,1,0,0.9995761793531919,0.2185967538342959,semiconductor device problem,442898 -Schenk_IBMSDS,2D_54019_highK,54019,54019,486129,1,0,1,0,0.9980889117620531,0.189734095484946,semiconductor device problem,996414 -Schenk_IBMSDS,3D_28984_Tetra,28984,28984,285092,1,0,1,0,0.9874041102377119,0.3613007012666531,semiconductor device problem,599170 -Schenk_IBMSDS,3D_51448_3D,51448,51448,537038,1,0,1,0,0.9920908271502504,0.1870343293725159,semiconductor device problem,1056610 -Schenk_IBMSDS,ibm_matrix_2,51448,51448,537038,1,0,1,0,0.9920908271502504,0.185666920653226,semiconductor device problem,1056610 -Schenk_IBMSDS,matrix_9,103430,103430,1205518,1,0,1,0,0.9966503478484926,0.1744833443427385,semiconductor device problem,2121550 -Schenk_IBMSDS,matrix-new_3,125329,125329,893984,1,0,1,0,0.9871118002084263,0.2826079320371298,semiconductor device problem,2678750 -Schenk_ISEI,barrier2-10,115625,115625,2158759,1,0,1,0,1,0.1989334147759642,subsequent semiconductor device problem,3897557 -Schenk_ISEI,barrier2-11,115625,115625,2158759,1,0,1,0,1,0.1988805553760946,subsequent semiconductor device problem,3897557 -Schenk_ISEI,barrier2-12,115625,115625,2158759,1,0,1,0,1,0.1983617501551521,subsequent semiconductor device problem,3897557 -Schenk_ISEI,barrier2-1,113076,113076,2129496,1,0,1,0,1,0.1972208170916773,semiconductor device problem sequence,3805068 -Schenk_ISEI,barrier2-2,113076,113076,2129496,1,0,1,0,1,0.1971642812509299,subsequent semiconductor device problem,3805068 -Schenk_ISEI,barrier2-3,113076,113076,2129496,1,0,1,0,1,0.1980361234266671,subsequent semiconductor device problem,3805068 -Schenk_ISEI,barrier2-4,113076,113076,2129496,1,0,1,0,1,0.189249263546285,subsequent semiconductor device problem,3805068 -Schenk_ISEI,barrier2-9,115625,115625,2158759,1,0,1,0,1,0.2036036406422221,semiconductor device problem sequence,3897557 -Schenk_ISEI,igbt3,10938,10938,130500,1,0,1,0,1,0.1720279018417223,semiconductor device problem,234006 -Schenk_ISEI,nmos3,18588,18588,237130,1,0,1,0,1,0.1670891636390259,semiconductor device problem,386594 -Schenk_ISEI,ohne2,181343,181343,6869939,1,0,1,0,1,0.09493801090692276,semiconductor device problem,11063545 -Schenk_ISEI,para-10,155924,155924,2094873,1,0,1,0,1,0.1980866954210761,subsequent semiconductor device problem,5416358 -Schenk_ISEI,para-4,153226,153226,2930882,1,0,1,0,1,0.2900222345747638,semiconductor device problem,5326228 -Schenk_ISEI,para-5,155924,155924,2094873,1,0,1,0,1,0.3692588097985042,semiconductor device problem sequence,5416358 -Schenk_ISEI,para-6,155924,155924,2094873,1,0,1,0,1,0.3692588097985042,subsequent semiconductor device problem,5416358 -Schenk_ISEI,para-7,155924,155924,2094873,1,0,1,0,1,0.3692588097985042,subsequent semiconductor device problem,5416358 -Schenk_ISEI,para-8,155924,155924,2094873,1,0,1,0,1,0.1779654854253516,subsequent semiconductor device problem,5416358 -Schenk_ISEI,para-9,155924,155924,2094873,1,0,1,0,1,0.1810599453621524,subsequent semiconductor device problem,5416358 -Kamvar,Stanford,281903,281903,2312497,1,1,0,0,0.2766368994208425,0.2766368994208425,directed graph,2312497 -Kamvar,Stanford_Berkeley,683446,683446,7583376,1,1,0,0,0.2502051856587356,0.2502051856587356,directed graph,7583376 -Sumner,graphics,29493,11822,117954,1,0,1,0,0,0,computer graphics/vision problem,117954 -Tromble,language,399130,399130,1216334,1,0,0,0,0.05790231080611451,0,directed weighted graph,1216334 -Sanghavi,ecl32,51993,51993,380415,1,0,1,0,0.9219175329301935,0.6030290297239527,semiconductor device problem,380415 -Sandia,adder_dcop_01,1813,1813,11156,1,0,0,0,0.6544094067343666,0.01582041688936398,subsequent circuit simulation problem,11156 -Sandia,adder_dcop_02,1813,1813,11246,1,0,0,0,0.6481736368448915,0.02604552673372154,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_03,1813,1813,11148,1,0,0,0,0.6429870546699475,0.03509147319995721,subsequent circuit simulation problem,11148 -Sandia,adder_dcop_04,1813,1813,11107,1,0,0,0,0.6505480335267569,0.003868471953578337,subsequent circuit simulation problem,11107 -Sandia,adder_dcop_05,1813,1813,11097,1,0,0,0,0.6473752151462995,0.01333907056798623,subsequent circuit simulation problem,11097 -Sandia,adder_dcop_06,1813,1813,11224,1,0,0,0,0.6486257030669638,0.01464501751034702,subsequent circuit simulation problem,11224 -Sandia,adder_dcop_07,1813,1813,11226,1,0,0,0,0.6484880636604774,0.005941644562334218,subsequent circuit simulation problem,11226 -Sandia,adder_dcop_08,1813,1813,11242,1,0,0,0,0.6478127317021501,0.005296049147336087,subsequent circuit simulation problem,11242 -Sandia,adder_dcop_09,1813,1813,11239,1,0,0,0,0.6484424666242848,0.006145369781733418,subsequent circuit simulation problem,11239 -Sandia,adder_dcop_10,1813,1813,11232,1,0,0,0,0.6478634291167427,0.00572579790054077,subsequent circuit simulation problem,11232 -Sandia,adder_dcop_11,1813,1813,11243,1,0,0,0,0.6481677610675705,0.00487184918449481,subsequent circuit simulation problem,11243 -Sandia,adder_dcop_12,1813,1813,11246,1,0,0,0,0.6481736368448915,0.004446797247220751,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_13,1813,1813,11245,1,0,0,0,0.6482422702244811,0.002753070732740364,subsequent circuit simulation problem,11245 -Sandia,adder_dcop_14,1813,1813,11246,1,0,0,0,0.6481736368448915,0.008258337744838539,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_15,1813,1813,11246,1,0,0,0,0.6481736368448915,0.005929062996294336,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_16,1813,1813,11246,1,0,0,0,0.6481736368448915,0.008258337744838539,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_17,1813,1813,11246,1,0,0,0,0.6481736368448915,0.009105346744309158,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_18,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_19,1813,1813,11245,1,0,0,0,0.6482422702244811,0.007623888182973317,subsequent circuit simulation problem,11245 -Sandia,adder_dcop_20,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_21,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_22,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_23,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_24,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_25,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_26,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_27,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_28,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_29,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_30,1813,1813,11246,1,0,0,0,0.6481736368448915,0.008046585494970884,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_31,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_32,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_33,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_34,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_35,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_36,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_37,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_38,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_39,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_40,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_41,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_42,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_43,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_44,1813,1813,11245,1,0,0,0,0.6482422702244811,0.007623888182973317,subsequent circuit simulation problem,11245 -Sandia,adder_dcop_45,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_46,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_47,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_48,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_49,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_50,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_51,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_52,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_53,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_54,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_55,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_56,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_57,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_58,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_59,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_60,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_61,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_62,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_63,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_64,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_65,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_66,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_67,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_68,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_69,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_trans_01,1814,1814,14579,1,0,0,0,1,0.03115703773289494,circuit simulation problem sequence,14579 -Sandia,adder_trans_02,1814,1814,14579,1,0,0,0,1,0.03115703773289494,subsequent circuit simulation problem,14579 -Sandia,fpga_dcop_01,1220,1220,5892,1,0,0,0,0.8183347350714887,0.2733389402859546,circuit simulation problem sequence,5892 -Sandia,fpga_dcop_02,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3271656854499579,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_03,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3448275862068966,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_04,1220,1220,5884,1,0,0,0,0.818871103622578,0.3546756529064869,subsequent circuit simulation problem,5884 -Sandia,fpga_dcop_05,1220,1220,5852,1,0,0,0,0.8210347752332485,0.359626802374894,subsequent circuit simulation problem,5852 -Sandia,fpga_dcop_06,1220,1220,5860,1,0,0,0,0.8204911092294666,0.3539373412362405,subsequent circuit simulation problem,5860 -Sandia,fpga_dcop_07,1220,1220,5855,1,0,0,0,0.8205128205128205,0.3432930705657978,subsequent circuit simulation problem,5855 -Sandia,fpga_dcop_08,1220,1220,5888,1,0,0,0,0.8186026936026936,0.3425925925925926,subsequent circuit simulation problem,5888 -Sandia,fpga_dcop_09,1220,1220,5888,1,0,0,0,0.8186026936026936,0.3337542087542087,subsequent circuit simulation problem,5888 -Sandia,fpga_dcop_10,1220,1220,5884,1,0,0,0,0.818871103622578,0.3323504633529907,subsequent circuit simulation problem,5884 -Sandia,fpga_dcop_11,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3301093355761144,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_12,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3216989066442388,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_13,1220,1220,5892,1,0,0,0,0.8183347350714887,0.325063078216989,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_14,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3187552565180824,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_15,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3221194280908327,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_16,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3221194280908327,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_17,1220,1220,5892,1,0,0,0,0.8183347350714887,0.31959629941127,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_18,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3191757779646762,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_19,1220,1220,5892,1,0,0,0,0.8183347350714887,0.31959629941127,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_20,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3212783851976451,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_21,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3216989066442388,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_22,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3263246425567704,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_23,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3216989066442388,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_24,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3254835996635829,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_25,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3259041211101766,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_26,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3254835996635829,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_27,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3271656854499579,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_28,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3284272497897393,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_29,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3280067283431455,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_30,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3292682926829268,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_31,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3284272497897393,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_32,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3280067283431455,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_33,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3280067283431455,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_34,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3275862068965517,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_35,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3288477712363331,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_36,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3288477712363331,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_37,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3296888141295206,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_38,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3296888141295206,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_39,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3296888141295206,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_40,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3313708999158957,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_41,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3301093355761144,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_42,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3313708999158957,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_43,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3317914213624895,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_44,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3322119428090833,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_45,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3322119428090833,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_46,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3317914213624895,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_47,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3317914213624895,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_48,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3317914213624895,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_49,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3317914213624895,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_50,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3317914213624895,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_51,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3322119428090833,subsequent circuit simulation problem,5892 -Sandia,fpga_trans_01,1220,1220,7382,1,0,0,0,1,0.214193962748876,circuit simulation problem sequence,7382 -Sandia,fpga_trans_02,1220,1220,7382,1,0,0,0,1,0.2138728323699422,subsequent circuit simulation problem,7382 -Sandia,init_adder1,1813,1813,11156,1,0,0,0,0.6544094067343666,0.01582041688936398,circuit simulation problem sequence,11156 -Sandia,mult_dcop_01,25187,25187,193276,1,0,0,0,0.6144759258929472,0.003383612629779353,circuit simulation problem sequence,193276 -Sandia,mult_dcop_02,25187,25187,193276,1,0,0,0,0.6144759258929472,0.003383612629779353,subsequent circuit simulation problem,193276 -Sandia,mult_dcop_03,25187,25187,193216,1,0,0,0,0.6142197994049846,0.01294544504421048,subsequent circuit simulation problem,193216 -Sandia,oscil_dcop_01,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,circuit simulation problem sequence,1544 -Sandia,oscil_dcop_02,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_03,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_04,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_05,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_06,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_07,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_08,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_09,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_10,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_11,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_12,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_13,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_14,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_15,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_16,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_17,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_18,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_19,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_20,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_21,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_22,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_23,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_24,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_25,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_26,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_27,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_28,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_29,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_30,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_31,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_32,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_33,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_34,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_35,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_36,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_37,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_38,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_39,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_40,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_41,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_42,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_43,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_44,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_45,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_46,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_47,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_48,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_49,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_50,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_51,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_52,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_53,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_54,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_55,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_56,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_57,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_trans_01,430,430,1614,1,0,0,0,0.9737274220032841,0.7175697865353038,circuit simulation problem,1614 -MathWorks,Pd,8081,8081,13036,1,0,0,0,0,0,counter-example problem,13036 -MathWorks,Pd_rhs,8081,12406,6323,1,0,0,0,0,0,counter-example problem,6323 -MathWorks,Harvard500,500,500,2636,1,1,0,0,0.4057744830277019,0.4057744830277019,directed graph,2636 -HB,cegb2802,2802,2802,277362,1,1,1,0,1,1,structural problem,277362 -HB,cegb2919,2919,2919,321543,1,1,1,0,1,1,structural problem,321543 -HB,cegb3024,3024,3024,79848,1,1,1,0,1,1,structural problem,79848 -HB,cegb3306,3306,3306,74916,1,1,1,0,1,1,structural problem,74916 -HB,lap_25,25,25,169,1,1,1,0,1,1,structural problem,169 -HB,lock1074,1074,1074,51588,1,1,1,0,1,1,structural problem,51588 -HB,lock2232,2232,2232,80352,1,1,1,0,1,1,structural problem,80352 -HB,lock3491,3491,3491,160444,1,1,1,0,1,1,structural problem,160444 -HB,lock_700,700,700,22175,1,1,1,0,1,1,structural problem,22175 -HB,man_5976,5976,5976,225046,1,1,1,0,1,1,structural problem,225046 -IBM_Austin,coupled,11341,11341,97193,1,0,0,0,1,0.7823373235828404,circuit simulation problem,98523 -ACUSIM,Pres_Poisson,14822,14822,715804,1,0,1,1,1,1,computational fluid dynamics problem,715804 -Rajat,rajat01,6833,6833,43250,1,1,0,0,0.9957479284779764,0.9957479284779764,circuit simulation problem,43250 -Rajat,rajat02,1960,1960,11187,1,1,0,0,1,1,circuit simulation problem,11187 -Rajat,rajat03,7602,7602,32653,1,0,0,0,1,0.4024380527448012,circuit simulation problem,32653 -Rajat,rajat04,1041,1041,8725,1,0,0,0,1,0.03991188285603214,circuit simulation problem,9642 -Rajat,rajat05,301,301,1250,1,0,0,0,0.7697974217311234,0.7058823529411765,circuit simulation problem,1384 -Rajat,rajat06,10922,10922,46983,1,1,0,0,1,1,circuit simulation problem,46983 -Rajat,rajat07,14842,14842,63913,1,1,0,0,1,1,circuit simulation problem,63913 -Rajat,rajat08,19362,19362,83443,1,1,0,0,1,1,circuit simulation problem,83443 -Rajat,rajat09,24482,24482,105573,1,1,0,0,1,1,circuit simulation problem,105573 -Rajat,rajat10,30202,30202,130303,1,1,0,0,1,1,circuit simulation problem,130303 -Rajat,rajat11,135,135,665,1,0,0,0,0.8911764705882353,0.6303939962476548,circuit simulation problem,812 -Rajat,rajat12,1879,1879,12818,1,0,0,0,1,0.4548776032151991,circuit simulation problem,12926 -Rajat,rajat13,7598,7598,48762,1,0,0,0,1,0.2965265970366772,circuit simulation problem,48922 -Rajat,rajat14,180,180,1475,1,0,0,0,1,0.0245398773006135,circuit simulation problem,1503 -Hamrle,Hamrle1,32,32,98,1,0,0,0,0.06451612903225806,0,circuit simulation problem,98 -Hamrle,Hamrle2,5952,5952,22162,1,0,0,0,0.0009930486593843098,0.0001805543017062382,circuit simulation problem,22162 -Hamrle,Hamrle3,1447360,1447360,5514242,1,0,0,0,2.538882993038383e-06,0,circuit simulation problem,5514242 -Oberwolfach,gyro_k,17361,17361,1021159,1,0,1,1,1,1,duplicate model reduction problem,1021159 -Oberwolfach,gyro_m,17361,17361,340431,1,0,1,1,1,1,duplicate model reduction problem,340431 -Oberwolfach,t2dah_a,11445,11445,176117,1,0,1,0,1,1,duplicate model reduction problem,176117 -Oberwolfach,t2dah_e,11445,11445,176117,1,0,1,1,1,1,duplicate model reduction problem,176117 -Oberwolfach,t2dal_a,4257,4257,37465,1,0,1,0,1,1,duplicate model reduction problem,37465 -Oberwolfach,t2dal_e,4257,4257,4257,1,0,1,1,1,1,duplicate model reduction problem,4257 -Oberwolfach,t3dh_a,79171,79171,4352105,1,0,1,0,1,1,duplicate model reduction problem,4352105 -Oberwolfach,t3dh_e,79171,79171,4352105,1,0,1,0,1,1,duplicate model reduction problem,4352105 -Oberwolfach,t3dl_a,20360,20360,509866,1,0,1,0,1,1,duplicate model reduction problem,509866 -Oberwolfach,t3dl_e,20360,20360,20360,1,0,1,1,1,1,duplicate model reduction problem,20360 -LiuWenzhuo,powersim,15838,15838,64424,1,0,0,0,0.5913695769855386,0.5337339974478245,power network problem,67562 -Lin,Lin,256000,256000,1766400,1,0,1,0,1,1,structural problem,1766400 -Cannizzo,sts4098,4098,4098,72356,1,0,1,1,1,1,structural problem,72356 -GHS_indef,aug2d,29008,29008,76832,1,0,1,0,1,1,2D/3D problem,76832 -GHS_indef,aug2dc,30200,30200,80000,1,0,1,0,1,1,2D/3D problem,80000 -GHS_indef,aug3d,24300,24300,69984,1,0,1,0,1,1,2D/3D problem,69984 -GHS_indef,aug3dcqp,35543,35543,128115,1,0,1,0,1,1,2D/3D problem,128115 -GHS_indef,bmw3_2,227362,227362,11288630,1,0,1,0,1,1,structural problem,11288630 -GHS_indef,c-55,32780,32780,403450,1,0,0,0,1,1,optimization problem,403450 -GHS_indef,c-58,37595,37595,552551,1,0,0,0,1,1,optimization problem,552557 -GHS_indef,c-59,41282,41282,480536,1,0,0,0,1,1,optimization problem,480536 -GHS_indef,c-62ghs,41731,41731,559339,1,0,0,0,1,1,subsequent optimization problem,559343 -GHS_indef,c-63,44234,44234,434704,1,0,0,0,1,1,optimization problem,434704 -GHS_indef,c-68,64810,64810,565996,1,0,0,0,1,1,optimization problem,566006 -GHS_indef,c-69,67458,67458,623914,1,0,0,0,1,1,optimization problem,623970 -GHS_indef,c-70,68924,68924,658986,1,0,0,0,1,1,optimization problem,658986 -GHS_indef,c-71,76638,76638,859520,1,0,0,0,1,1,optimization problem,859554 -GHS_indef,c-72,84064,84064,707546,1,0,0,0,1,1,optimization problem,707558 -GHS_indef,copter2,55476,55476,759952,1,0,1,0,1,1,computational fluid dynamics problem,759952 -GHS_indef,d_pretok,182730,182730,1641672,1,0,1,0,1,1,2D/3D problem,1641672 -GHS_indef,darcy003,389874,389874,2097566,1,0,1,0,1,1,2D/3D problem,2101242 -GHS_indef,dawson5,51537,51537,1010777,1,0,1,0,1,1,structural problem,1010777 -GHS_indef,dtoc,24993,24993,69972,1,0,0,0,1,1,optimization problem,69972 -GHS_indef,exdata_1,6001,6001,2269500,1,0,0,0,1,1,optimization problem,2269501 -GHS_indef,helm2d03,392257,392257,2741935,1,0,1,0,1,1,2D/3D problem,2741935 -GHS_indef,helm3d01,32226,32226,428444,1,0,1,0,1,1,2D/3D problem,428444 -GHS_indef,k1_san,67759,67759,559774,1,0,1,0,1,1,2D/3D problem,559774 -GHS_indef,laser,3002,3002,9000,1,0,1,0,1,1,materials problem,9000 -GHS_indef,mario001,38434,38434,204912,1,0,1,0,1,1,2D/3D problem,206156 -GHS_indef,mario002,389874,389874,2097566,1,0,1,0,1,1,duplicate 2D/3D problem,2101242 -GHS_indef,ncvxqp1,12111,12111,73963,1,0,0,0,1,1,optimization problem,73963 -GHS_indef,ncvxqp9,16554,16554,54040,1,0,0,0,1,1,optimization problem,54040 -GHS_indef,olesnik0,88263,88263,744216,1,0,1,0,1,1,2D/3D problem,744216 -GHS_indef,sit100,10262,10262,61046,1,0,1,0,1,1,2D/3D problem,61046 -GHS_indef,stokes128,49666,49666,558594,1,0,1,0,1,1,computational fluid dynamics problem,558594 -GHS_indef,stokes64,12546,12546,140034,1,0,1,0,1,1,computational fluid dynamics problem,140034 -GHS_indef,stokes64s,12546,12546,140034,1,0,1,0,1,1,computational fluid dynamics problem,140034 -GHS_indef,tuma1,22967,22967,87760,1,0,1,0,1,1,2D/3D problem,87760 -GHS_indef,tuma2,12992,12992,49365,1,0,1,0,1,1,2D/3D problem,49365 -GHS_indef,turon_m,189924,189924,1690876,1,0,1,0,1,1,2D/3D problem,1690876 -GHS_psdef,audikw_1,943695,943695,77651847,1,0,1,1,1,1,structural problem,77651847 -GHS_psdef,bmw7st_1,141347,141347,7318399,1,0,1,1,1,1,structural problem,7339667 -GHS_psdef,bmwcra_1,148770,148770,10641602,1,0,1,1,1,1,structural problem,10644002 -GHS_psdef,copter1,17222,17222,211064,1,1,1,0,1,1,computational fluid dynamics problem,211064 -GHS_psdef,copter2,55476,55476,759952,1,1,1,0,1,1,duplicate computational fluid dynamics problem,759952 -GHS_psdef,crankseg_1,52804,52804,10614210,1,0,1,1,1,1,structural problem,10614210 -GHS_psdef,crankseg_2,63838,63838,14148858,1,0,1,1,1,1,structural problem,14148858 -DNVS,crplat2,18010,18010,960946,1,1,1,0,1,1,structural problem,960946 -DNVS,fcondp2,201822,201822,11294316,1,1,1,0,1,1,structural problem,11294316 -GHS_psdef,finance256,37376,37376,298496,1,1,0,0,1,1,optimization problem,298496 -GHS_psdef,ford1,18728,18728,101576,1,1,1,0,1,1,structural problem,101576 -GHS_psdef,ford2,100196,100196,544688,1,1,1,0,1,1,structural problem,544688 -DNVS,fullb,199187,199187,11708077,1,1,1,0,1,1,structural problem,11708077 -DNVS,halfb,224617,224617,12387821,1,1,1,0,1,1,structural problem,12387821 -GHS_psdef,hood,220542,220542,9895422,1,0,1,1,1,1,structural problem,10768436 -GHS_psdef,inline_1,503712,503712,36816170,1,0,1,1,1,1,structural problem,36816342 -GHS_psdef,ldoor,952203,952203,42493817,1,0,1,1,1,1,structural problem,46522475 -DNVS,m_t1,97578,97578,9753570,1,0,1,1,1,1,structural problem,9753570 -GHS_psdef,oilpan,73752,73752,2148558,1,0,1,1,1,1,structural problem,3597188 -GHS_psdef,opt1,15449,15449,1930655,1,1,1,0,1,1,structural problem,1930655 -GHS_psdef,pds10,16558,16558,149658,1,1,0,0,1,1,optimization problem,149658 -GHS_psdef,pwt,36519,36519,326107,1,1,1,0,1,1,duplicate structural problem,326107 -GHS_psdef,ramage02,16830,16830,2866352,1,1,1,0,1,1,computational fluid dynamics problem,2866352 -GHS_psdef,s3dkq4m2,90449,90449,4427725,1,0,1,1,1,1,structural problem,4820891 -GHS_psdef,s3dkt3m2,90449,90449,3686223,1,0,1,1,1,1,structural problem,3753461 -DNVS,ship_001,34920,34920,3896496,1,0,1,1,1,1,structural problem,4644230 -DNVS,ship_003,121728,121728,3777036,1,0,1,1,1,1,structural problem,8086034 -DNVS,shipsec1,140874,140874,3568176,1,0,1,1,1,1,structural problem,7813404 -DNVS,shipsec5,179860,179860,4598604,1,0,1,1,1,1,structural problem,10113096 -DNVS,shipsec8,114919,114919,3303553,1,0,1,1,1,1,structural problem,6653399 -GHS_psdef,srb1,54924,54924,2962152,1,1,1,0,1,1,structural problem,2962152 -DNVS,thread,29736,29736,4444880,1,0,1,1,1,1,structural problem,4470048 -DNVS,trdheim,22098,22098,1935324,1,1,1,0,1,1,structural problem,1935324 -DNVS,troll,213453,213453,11985111,1,1,1,0,1,1,structural problem,11985111 -DNVS,tsyl201,20685,20685,2454957,1,1,1,0,1,1,structural problem,2454957 -GHS_psdef,vanbody,47072,47072,2329056,1,0,1,1,1,1,structural problem,2336898 -GHS_psdef,wathen100,30401,30401,471601,1,0,1,1,1,1,random 2D/3D problem,471601 -GHS_psdef,wathen120,36441,36441,565761,1,0,1,1,1,1,random 2D/3D problem,565761 -DNVS,x104,108384,108384,8713602,1,0,1,1,1,1,structural problem,10167624 -GHS_indef,a0nsdsil,80016,80016,355034,1,0,0,0,1,1,optimization problem,355034 -GHS_indef,a2nnsnsl,80016,80016,347222,1,0,0,0,1,1,optimization problem,347222 -GHS_indef,a5esindl,60008,60008,255004,1,0,0,0,1,1,optimization problem,255004 -GHS_indef,blockqp1,60012,60012,640033,1,0,0,0,1,1,optimization problem,640033 -GHS_indef,bloweya,30004,30004,150009,1,0,1,0,1,1,materials problem,150009 -GHS_indef,boyd1,93279,93279,1211231,1,0,0,0,1,1,optimization problem,1211231 -GHS_indef,boyd2,466316,466316,1500397,1,0,0,0,1,1,optimization problem,1500397 -GHS_indef,brainpc2,27607,27607,179395,1,0,0,0,1,1,optimization problem,179395 -GHS_indef,bratu3d,27792,27792,173796,1,0,1,0,1,1,2D/3D problem,173796 -GHS_indef,cont-201,80595,80595,438795,1,0,0,0,1,1,optimization problem,438795 -GHS_indef,cont-300,180895,180895,988195,1,0,0,0,1,1,optimization problem,988195 -GHS_indef,dixmaanl,60000,60000,299998,1,0,0,0,1,1,optimization problem,299998 -GHS_indef,linverse,11999,11999,95977,1,0,0,0,1,1,statistical/mathematical problem,95977 -GHS_indef,ncvxbqp1,50000,50000,349968,1,0,0,0,1,1,optimization problem,349968 -GHS_indef,ncvxqp3,75000,75000,499964,1,0,0,0,1,1,optimization problem,499964 -GHS_indef,ncvxqp5,62500,62500,424966,1,0,0,0,1,1,optimization problem,424966 -GHS_indef,ncvxqp7,87500,87500,574962,1,0,0,0,1,1,optimization problem,574962 -GHS_indef,sparsine,50000,50000,1548988,1,0,1,0,1,1,structural problem,1548988 -GHS_indef,spmsrtls,29995,29995,229947,1,0,0,0,1,1,statistical/mathematical problem,229947 -GHS_psdef,cvxbqp1,50000,50000,349968,1,0,0,1,1,1,optimization problem,349968 -GHS_psdef,gridgena,48962,48962,512084,1,0,0,1,1,1,optimization problem,512084 -GHS_psdef,jnlbrng1,40000,40000,199200,1,0,0,1,1,1,optimization problem,199200 -GHS_psdef,minsurfo,40806,40806,203622,1,0,0,1,1,1,optimization problem,203622 -GHS_psdef,obstclae,40000,40000,197608,1,0,0,1,1,1,optimization problem,197608 -GHS_psdef,torsion1,40000,40000,197608,1,0,0,1,1,1,duplicate optimization problem,197608 -Rajat,rajat15,37261,37261,443573,1,0,0,0,1,0.943060217954145,circuit simulation problem,443573 -Bydder,mri1,65536,147456,589824,1,0,1,0,0,0,computer graphics/vision problem,589824 -Bydder,mri2,63240,147456,569160,1,0,1,0,0,0,computer graphics/vision problem,569160 -Engwirda,airfoil_2d,14214,14214,259688,1,0,1,0,0.981668119637925,0,computational fluid dynamics problem,259688 -IBM_EDA,dc1,116835,116835,766396,1,0,0,0,0.8542477149952045,0.2426284829292399,circuit simulation problem sequence,766396 -IBM_EDA,dc2,116835,116835,766396,1,0,0,0,0.8542477149952045,0.2426284829292399,subsequent circuit simulation problem,766396 -IBM_EDA,dc3,116835,116835,766396,1,0,0,0,0.8542477149952045,0.2426407989395915,subsequent circuit simulation problem,766396 -IBM_EDA,trans4,116835,116835,749800,1,0,0,0,0.8542477149952045,0.3020988522272163,circuit simulation problem sequence,766396 -IBM_EDA,trans5,116835,116835,749800,1,0,0,0,0.8542477149952045,0.3018997890878642,subsequent circuit simulation problem,766396 -Morandini,robot,120,120,870,1,0,1,0,0.2177215189873418,0.05316455696202532,robotics problem,870 -Morandini,rotor1,100,100,708,1,0,1,0,0.514018691588785,0.07165109034267912,structural problem,708 -Morandini,rotor2,791,791,10685,1,0,1,0,0.2341063574570172,0.1155537784886046,structural problem,10685 -MathWorks,tomography,500,500,28726,1,0,1,0,1,0.9331113158081201,computer graphics/vision problem,28726 -Kemelmacher,Kemelmacher,28452,9693,100875,1,0,1,0,0,0,computer graphics/vision problem,100875 -MathWorks,Kuu,7102,7102,340200,1,0,1,1,1,1,structural problem,340200 -MathWorks,Muu,7102,7102,170134,1,0,1,1,1,1,structural problem,170134 -Toledo,deltaX,68600,21961,247424,1,0,0,0,0,0,counter-example problem,247424 -VanVelzen,std1_Jac2_db,21982,21982,498771,1,0,0,0,0.3261820218167785,8.389455293641422e-06,chemical process simulation problem,498771 -VanVelzen,std1_Jac2,21982,21982,1248213,1,0,0,0,1.441471454060305e-05,0,chemical process simulation problem,1248731 -VanVelzen,std1_Jac3_db,21982,21982,531826,1,0,0,0,0.3593020610225873,7.84553706623987e-06,chemical process simulation problem,531826 -VanVelzen,std1_Jac3,21982,21982,1455374,1,0,0,0,1.37377639453759e-05,0,chemical process simulation problem,1455848 -VanVelzen,Zd_Jac2_db,22835,22835,676439,1,0,0,0,0.3038965489807284,3.059956793410077e-06,chemical process simulation problem,676439 -VanVelzen,Zd_Jac2,22835,22835,1642407,1,0,0,0,0.000249569948369456,0,chemical process simulation problem,1642833 -VanVelzen,Zd_Jac3_db,22835,22835,713907,1,0,0,0,0.3299540424152621,2.894054454528616e-06,chemical process simulation problem,713907 -VanVelzen,Zd_Jac3,22835,22835,1915726,1,0,0,0,0.0002609405864378739,0,chemical process simulation problem,1916152 -VanVelzen,Zd_Jac6_db,22835,22835,663643,1,0,0,0,0.3245527521504101,3.121059662176502e-06,chemical process simulation problem,663643 -VanVelzen,Zd_Jac6,22835,22835,1711557,1,0,0,0,0.0002161245251101651,0,chemical process simulation problem,1711983 -Rajat,rajat16,94294,94294,476766,1,0,0,0,0.9900237892855388,0.6363336955309653,circuit simulation problem,641159 -Rajat,rajat17,94294,94294,479246,1,0,0,0,0.9900237892855388,0.2705152684049277,circuit simulation problem,641159 -Rajat,rajat18,94294,94294,479151,1,0,0,0,0.626033640830624,0.2849580148146621,circuit simulation problem,485143 -Rajat,rajat19,1157,1157,3699,1,0,0,0,0.9041281299345816,0.9193154034229829,circuit simulation problem,5399 -Lourakis,bundle1,10581,10581,770811,1,0,1,1,1,1,computer graphics/vision problem,770901 -PARSEC,benzene,8219,8219,242669,1,0,0,0,1,1,theoretical/quantum chemistry problem,242669 -PARSEC,CO,221119,221119,7666057,1,0,0,0,1,1,theoretical/quantum chemistry problem,7666057 -PARSEC,Ga10As10H30,113081,113081,6115633,1,0,0,0,1,1,theoretical/quantum chemistry problem,6115633 -PARSEC,Ga19As19H42,133123,133123,8884839,1,0,0,0,1,1,theoretical/quantum chemistry problem,8884839 -PARSEC,Ga3As3H12,61349,61349,5970947,1,0,0,0,1,1,theoretical/quantum chemistry problem,5970947 -PARSEC,Ga41As41H72,268096,268096,18488476,1,0,0,0,1,1,theoretical/quantum chemistry problem,18488476 -PARSEC,GaAsH6,61349,61349,3381809,1,0,0,0,1,1,theoretical/quantum chemistry problem,3381809 -PARSEC,Ge87H76,112985,112985,7892195,1,0,0,0,1,1,theoretical/quantum chemistry problem,7892195 -PARSEC,Ge99H100,112985,112985,8451395,1,0,0,0,1,1,theoretical/quantum chemistry problem,8451395 -PARSEC,H2O,67024,67024,2216736,1,0,0,0,1,1,theoretical/quantum chemistry problem,2216736 -PARSEC,Na5,5832,5832,305630,1,0,0,0,1,1,theoretical/quantum chemistry problem,305630 -PARSEC,Si10H16,17077,17077,875923,1,0,0,0,1,1,theoretical/quantum chemistry problem,875923 -PARSEC,Si2,769,769,17801,1,0,0,0,1,1,theoretical/quantum chemistry problem,17801 -PARSEC,Si34H36,97569,97569,5156379,1,0,0,0,1,1,theoretical/quantum chemistry problem,5156379 -PARSEC,Si41Ge41H72,185639,185639,15011265,1,0,0,0,1,1,theoretical/quantum chemistry problem,15011265 -PARSEC,Si5H12,19896,19896,738598,1,0,0,0,1,1,theoretical/quantum chemistry problem,738598 -PARSEC,Si87H76,240369,240369,10661631,1,0,0,0,1,1,theoretical/quantum chemistry problem,10661631 -PARSEC,SiH4,5041,5041,171903,1,0,0,0,1,1,theoretical/quantum chemistry problem,171903 -PARSEC,SiNa,5743,5743,198787,1,0,0,0,1,1,theoretical/quantum chemistry problem,198787 -PARSEC,SiO2,155331,155331,11283503,1,0,0,0,1,1,theoretical/quantum chemistry problem,11283503 -PARSEC,SiO,33401,33401,1317655,1,0,0,0,1,1,theoretical/quantum chemistry problem,1317655 -Rajat,rajat20,86916,86916,604299,1,0,0,0,0.9906228863063395,0.1122042623922186,circuit simulation problem,605045 -Rajat,rajat21,411676,411676,1876011,1,0,0,0,0.7647530446640561,0.6145057622905241,circuit simulation problem,1893370 -Rajat,rajat22,39899,39899,195429,1,0,0,0,0.9800702079331211,0.3342735182643515,circuit simulation problem,197264 -Rajat,rajat23,110355,110355,555441,1,0,0,0,0.9891581094231137,0.3324988529606978,circuit simulation problem,556938 -Rajat,rajat24,358172,358172,1946979,1,0,0,0,0.996935347128458,0.2716106951495262,circuit simulation problem,1948235 -Rajat,rajat25,87190,87190,606489,1,0,0,0,0.9906572831072957,0.1118151951820715,circuit simulation problem,607235 -Rajat,rajat26,51032,51032,247528,1,0,0,0,0.9853537517311467,0.3271800826884766,circuit simulation problem,249302 -Rajat,rajat27,20640,20640,97353,1,0,0,0,0.9645908149382882,0.3035747337767303,circuit simulation problem,99777 -Rajat,rajat28,87190,87190,606489,1,0,0,0,0.9906572831072957,0.1072533437604223,circuit simulation problem,607235 -MathWorks,Sieber,2290,2290,14873,1,0,0,0,0.7272727272727273,0,counter-example problem,14873 -Andrianov,ex3sta1,16782,16782,678998,1,1,0,0,1,1,optimization problem,678998 -Andrianov,fxm3_6,5026,5026,94026,1,1,0,0,1,1,optimization problem,94026 -Andrianov,fxm4_6,18892,18892,497844,1,1,0,0,1,1,optimization problem,497844 -Andrianov,ins2,309412,309412,2751484,1,1,0,0,1,1,optimization problem,2751484 -Andrianov,lp1,534388,534388,1643420,1,1,0,0,1,1,optimization problem,1643420 -Andrianov,lpl1,32460,32460,328036,1,1,0,0,1,1,optimization problem,328036 -Andrianov,mip1,66463,66463,10352819,1,1,0,0,1,1,optimization problem,10352819 -Andrianov,net100,29920,29920,2033200,1,1,0,0,1,1,optimization problem,2033200 -Andrianov,net125,36720,36720,2577200,1,1,0,0,1,1,optimization problem,2577200 -Andrianov,net150,43520,43520,3121200,1,1,0,0,1,1,optimization problem,3121200 -Andrianov,net25,9520,9520,401200,1,1,0,0,1,1,optimization problem,401200 -Andrianov,net4-1,88343,88343,2441727,1,1,0,0,1,1,optimization problem,2441727 -Andrianov,net50,16320,16320,945200,1,1,0,0,1,1,optimization problem,945200 -Andrianov,net75,23120,23120,1489200,1,1,0,0,1,1,optimization problem,1489200 -Andrianov,pattern1,19242,19242,9323432,1,1,0,0,1,1,optimization problem,9323432 -Andrianov,pf2177,9728,9728,725144,1,1,0,0,1,1,optimization problem,725144 -MKS,fp,7548,7548,834222,1,0,1,0,0.7575174939506899,0.002912877385765126,electromagnetics problem,848553 -Rajat,rajat29,643994,643994,3760246,1,0,0,0,0.6856406275660725,0.6910598008173955,circuit simulation problem,4866270 -Rajat,rajat30,643994,643994,6175244,1,0,0,0,0.9945912407336421,0.009194565053000587,circuit simulation problem,6175377 -Rajat,rajat31,4690002,4690002,20316253,1,0,0,0,1,0.4000960614178773,circuit simulation problem,20316253 -Raju,laminar_duct3D,67173,67173,3788857,1,0,1,0,0.9022908019592145,0.5518386226500883,computational fluid dynamics problem,3833077 -Bates,Chem97Zt,2541,31022,62044,1,1,0,0,0,0,statistical/mathematical problem,62044 -Bates,Chem97ZtZ,2541,2541,7361,1,0,0,1,1,1,statistical/mathematical problem,7361 -Schmid,thermal1,82654,82654,574458,1,0,1,1,1,1,thermal problem,574458 -Schmid,thermal2,1228045,1228045,8580313,1,0,1,1,1,1,thermal problem,8580313 -MathWorks,Kaufhold,8765,8765,42471,1,0,0,0,1,0.778437073518068,counter-example problem,42471 -Bindel,ted_A,10605,10605,424587,1,0,1,0,0.5657922767749357,0.1057079149564699,thermal problem,424587 -Bindel,ted_B,10605,10605,144579,1,0,1,1,1,1,thermal problem,144579 -Bindel,ted_AB,10605,10605,522387,0,0,1,0,0.6415700434950818,0,thermal problem,522387 -Bindel,ted_A_unscaled,10605,10605,424587,1,0,1,0,0.5657922767749357,0.1057079149564699,thermal problem,424587 -Bindel,ted_B_unscaled,10605,10605,144579,1,0,1,1,1,1,thermal problem,144579 -Bindel,ted_AB_unscaled,10605,10605,522387,0,0,1,0,0.6415700434950818,0,thermal problem,522387 -Koutsovasilis,F1,343791,343791,26837113,1,0,1,0,1,1,structural problem,26837113 -AMD,G2_circuit,150102,150102,726674,1,0,0,1,1,1,circuit simulation problem,726674 -IBM_EDA,ckt11752_dc_1,49702,49702,333029,1,0,0,0,0.9838102263462359,0.7355387943965807,circuit simulation problem,333029 -IBM_EDA,ckt11752_tr_0,49702,49702,332807,1,0,0,0,0.9838102263462359,0.7360237367761078,circuit simulation problem,333029 -Sandia,ASIC_100k,99340,99340,940621,1,0,0,0,1,0.002631466347182405,circuit simulation problem,954163 -Sandia,ASIC_100ks,99190,99190,578890,1,0,0,0,1,0.0007546383156139254,circuit simulation problem,578890 -Sandia,ASIC_320k,321821,321821,1931828,1,0,0,0,1,0.3578041366837217,circuit simulation problem,2635364 -Sandia,ASIC_320ks,321671,321671,1316085,1,0,0,0,1,0.2891089626654492,circuit simulation problem,1827807 -Sandia,ASIC_680k,682862,682862,2638997,1,0,0,0,1,0.004444308126428144,circuit simulation problem,3871773 -Sandia,ASIC_680ks,682712,682712,1693767,1,0,0,0,1,0.005801860432914134,circuit simulation problem,2329176 -AMD,G3_circuit,1585478,1585478,7660826,1,0,0,1,1,1,circuit simulation problem,7660826 -GHS_psdef,apache1,80800,80800,542184,1,0,1,1,1,1,structural problem,542184 -GHS_psdef,apache2,715176,715176,4817870,1,0,1,1,1,1,structural problem,4817870 -GHS_indef,bloweybl,30003,30003,109999,1,0,1,0,1,1,materials problem,120000 -GHS_indef,bloweybq,10001,10001,49999,1,0,1,1,1,1,materials problem,69991 -GHS_indef,cvxqp3,17500,17500,114962,1,0,0,0,1,1,optimization problem,122462 -GHS_indef,qpband,20000,20000,45000,1,0,0,0,1,1,optimization problem,45000 -Oberwolfach,chipcool0,20082,20082,281150,1,0,1,0,1,0.8688617524936032,model reduction problem,281150 -Oberwolfach,chipcool1,20082,20082,281150,1,0,1,0,1,0.08556391438245974,model reduction problem,281150 -Oberwolfach,filter2D,1668,1668,10750,1,0,1,0,1,1,model reduction problem,10750 -Oberwolfach,filter3D,106437,106437,2707179,1,0,1,0,1,1,model reduction problem,2707179 -Oberwolfach,flowmeter0,9669,9669,67391,1,0,1,0,1,1,model reduction problem,67391 -Oberwolfach,flowmeter5,9669,9669,67391,1,0,1,0,1,0.06222930598385364,model reduction problem,67391 -Oberwolfach,gas_sensor,66917,66917,1703365,1,0,1,0,1,1,model reduction problem,1703365 -Oberwolfach,gyro,17361,17361,1021159,1,0,1,1,1,1,model reduction problem,1021159 -Oberwolfach,inlet,11730,11730,328323,1,0,1,0,0.6083330964361183,9.475888601453601e-05,model reduction problem,328323 -Oberwolfach,LF10000,19998,19998,99982,1,0,1,1,1,1,model reduction problem,99982 -Oberwolfach,LF10,18,18,82,1,0,1,1,1,1,model reduction problem,82 -Oberwolfach,LFAT5000,19994,19994,79966,1,0,1,1,1,1,model reduction problem,79966 -Oberwolfach,LFAT5,14,14,46,1,0,1,1,1,1,model reduction problem,46 -Oberwolfach,piston,2025,2025,100015,1,0,1,0,1,0.03275844473925911,model reduction problem,100015 -Oberwolfach,rail_1357,1357,1357,8985,1,0,1,0,1,1,model reduction problem,8985 -Oberwolfach,rail_20209,20209,20209,139233,1,0,1,0,1,1,model reduction problem,139233 -Oberwolfach,rail_5177,5177,5177,35185,1,0,1,0,1,1,model reduction problem,35185 -Oberwolfach,rail_79841,79841,79841,553921,1,0,1,0,1,1,model reduction problem,553921 -Oberwolfach,spiral,1434,1434,18228,1,0,1,0,1,1,model reduction problem,18228 -Oberwolfach,t2dah,11445,11445,176117,1,0,1,0,1,1,model reduction problem,176117 -Oberwolfach,t2dal_bci,4257,4257,37465,1,0,1,0,1,1,model reduction problem,37465 -Oberwolfach,t2dal,4257,4257,37465,1,0,1,0,1,1,model reduction problem,37465 -Oberwolfach,t3dh,79171,79171,4352105,1,0,1,0,1,1,model reduction problem,4352105 -Oberwolfach,t3dl,20360,20360,509866,1,0,1,0,1,1,model reduction problem,509866 -Oberwolfach,windscreen,22692,22692,1482390,0,0,1,0,1,0,model reduction problem,1482390 -Oberwolfach,bone010,986703,986703,47851783,1,0,1,1,1,1,model reduction problem,71666325 -Oberwolfach,boneS01,127224,127224,5516602,1,0,1,1,1,1,model reduction problem,6715152 -Oberwolfach,boneS10,914898,914898,40878708,1,0,1,1,1,1,model reduction problem,55468422 -Pajek,California,9664,9664,16150,1,1,0,0,0.02241486068111455,0.02241486068111455,directed graph,16150 -Pajek,Cities,55,46,1342,1,0,0,0,0,0,weighted bipartite graph,1342 -Pajek,CSphd,1882,1882,1740,1,1,0,0,0,0,directed graph,1740 -Pajek,dictionary28,52652,52652,178076,1,1,0,0,1,1,undirected graph,178076 -Pajek,divorce,50,9,225,1,1,0,0,0,0,bipartite graph,225 -Pajek,EAT_RS,23219,23219,325592,1,0,0,0,0.1236262721980876,0.03102501938294547,directed weighted graph,325592 -Pajek,EAT_SR,23219,23219,325589,1,0,0,0,0.1236274132759018,0.03102530574571187,directed weighted graph,325589 -Pajek,EPA,4772,4772,8965,1,1,0,0,0.01249302844394869,0.01249302844394869,directed graph,8965 -Pajek,Erdos02,6927,6927,16944,1,1,0,0,1,1,undirected graph,16944 -Pajek,Erdos971,472,472,2628,1,1,0,0,1,1,undirected graph,2628 -Pajek,Erdos972,5488,5488,14170,1,1,0,0,1,1,undirected graph,14170 -Pajek,Erdos981,485,485,2762,1,1,0,0,1,1,undirected graph,2762 -Pajek,Erdos982,5822,5822,14750,1,1,0,0,1,1,undirected graph,14750 -Pajek,Erdos991,492,492,2834,1,1,0,0,1,1,undirected graph,2834 -Pajek,Erdos992,6100,6100,15030,1,1,0,0,1,1,undirected graph,15030 -Pajek,EVA,8497,8497,6726,1,1,0,0,0.003866745984533016,0.003866745984533016,directed graph,6726 -Pajek,FA,10617,10617,72176,1,0,0,0,0.232333869090506,0.005847142936318794,directed weighted graph,72176 -Pajek,foldoc,13356,13356,120238,1,0,0,0,0.4785009730700777,0.459472047106572,directed weighted graph,120238 -Pajek,football,35,35,118,1,0,0,0,0,0,directed weighted graph,118 -Pajek,GD00_a,352,352,458,1,1,0,0,0,0,directed graph,458 -Pajek,GD00_c,638,638,1041,1,0,0,0,0.02133850630455868,0.01551891367604268,directed multigraph,1041 -Pajek,GD01_a,311,311,645,1,0,0,0,0.01550387596899225,0.01550387596899225,directed weighted graph,645 -Pajek,GD01_Acap,953,953,645,1,0,0,0,0.01550387596899225,0.01550387596899225,directed multigraph,645 -Pajek,GD01_b,18,18,37,1,1,0,0,0.5142857142857142,0.5142857142857142,directed graph,37 -Pajek,GD01_c,33,33,135,1,0,0,0,0,0,directed multigraph,135 -Pajek,GD02_a,23,23,87,1,1,0,0,0.6436781609195402,0.6436781609195402,directed graph,87 -Pajek,GD02_b,80,80,232,1,1,0,0,0,0,directed graph,232 -Pajek,GD06_Java,1538,1538,8032,1,1,0,0,0.05353585657370518,0.05353585657370518,directed graph,8032 -Pajek,GD06_theory,101,101,380,1,1,0,0,1,1,undirected graph,380 -Pajek,GD95_a,36,36,57,1,1,0,0,0.03508771929824561,0.03508771929824561,directed graph,57 -Pajek,GD95_b,73,73,96,1,1,0,0,0,0,directed graph,96 -Pajek,GD95_c,62,62,287,1,1,0,0,0.9965156794425087,0.9965156794425087,directed graph,287 -Pajek,GD96_a,1096,1096,1677,1,0,0,0,0,0,directed multigraph,1677 -Pajek,GD96_b,111,111,193,1,1,0,0,0,0,directed graph,193 -Pajek,GD96_c,65,65,250,1,1,0,0,1,1,undirected graph,250 -Pajek,GD96_d,180,180,229,1,1,0,0,0.008733624454148471,0.008733624454148471,directed graph,229 -Pajek,GD97_a,84,84,332,1,1,0,0,1,1,directed graph,332 -Pajek,GD97_b,47,47,264,1,0,0,0,1,1,undirected weighted graph,264 -Pajek,GD97_c,452,452,460,1,0,0,0,0,0,directed multigraph,460 -Pajek,GD98_a,38,38,50,1,1,0,0,0.16,0.16,directed graph,50 -Pajek,GD98_b,121,121,207,1,1,0,0,0.7246376811594203,0.7246376811594203,directed graph,207 -Pajek,GD98_c,112,112,336,1,1,0,0,1,1,undirected graph,336 -Pajek,GD99_b,64,64,252,1,0,0,0,1,1,undirected multigraph,252 -Pajek,GD99_c,105,105,149,1,1,0,0,0.3892617449664429,0.3892617449664429,directed graph,149 -Pajek,geom,7343,7343,23796,1,0,0,0,1,1,undirected weighted graph,23796 -Pajek,GlossGT,72,72,122,1,1,0,0,0.06557377049180328,0.06557377049180328,directed graph,122 -Pajek,HEP-th,27240,27240,342437,1,1,0,0,0.002774548916757691,0.002774548916757691,directed graph,342437 -Pajek,HEP-th-new,27770,27770,352807,1,1,0,0,0.002738343613933237,0.002738343613933237,directed graph,352807 -Pajek,IMDB,428440,896308,3782463,1,1,0,0,0,0,bipartite graph,3782463 -Pajek,internet,124651,124651,207214,1,0,0,0,0.01444913320583093,0.002245819852432203,directed weighted graph,207214 -Pajek,Journals,124,124,12068,1,0,0,1,1,1,undirected weighted graph,12068 -Pajek,Kohonen,4470,4470,12731,1,1,0,0,0.001728336868567837,0.001728336868567837,directed graph,12731 -Pajek,Lederberg,8843,8843,41601,1,0,0,0,0.003029210241615579,0.003029210241615579,directed multigraph,41601 -Barabasi,NotreDame_actors,392400,127823,1470404,1,1,0,0,0,0,bipartite graph,1470404 -Barabasi,NotreDame_www,325729,325729,929849,1,1,0,0,0.3315875213545298,0.3315875213545298,directed graph,929849 -Barabasi,NotreDame_yeast,2114,2114,4480,1,1,0,0,1,1,undirected graph,4480 -Pajek,ODLIS,2909,2909,18246,1,0,0,0,0.2043747601556932,0.1997697494654898,directed multigraph,18246 -Pajek,patents_main,240547,240547,560943,1,0,0,0,0,0,directed weighted graph,560943 -Pajek,patents,3774768,3774768,14970767,1,1,0,0,0,0,directed graph,14970767 -Pajek,Ragusa16,24,24,81,1,0,0,0,0.3661971830985916,0.1971830985915493,directed weighted graph,81 -Pajek,Ragusa18,23,23,64,1,0,0,0,0.3278688524590164,0.1967213114754098,directed weighted graph,64 -Pajek,Reuters911,13332,13332,296076,1,0,0,0,1,1,undirected weighted graph sequence,296076 -Pajek,Roget,1022,1022,5075,1,1,0,0,0.5620811982656682,0.5620811982656682,directed graph,5075 -Pajek,Sandi_authors,86,86,248,1,0,0,0,1,1,undirected weighted graph,248 -Pajek,Sandi_sandi,314,360,613,1,1,0,0,0,0,bipartite graph,613 -Pajek,SciMet,3084,3084,10413,1,0,0,0,0.002497118709181713,0.002497118709181713,directed multigraph,10413 -Pajek,SmaGri,1059,1059,4919,1,0,0,0,0.0008133387555917039,0.0008133387555917039,directed multigraph,4919 -Pajek,SmallW,396,396,994,1,0,0,0,0,0,directed multigraph,994 -Pajek,Stranke94,10,10,90,1,0,0,0,1,1,undirected weighted graph,90 -Pajek,Tina_AskCal,11,11,29,1,1,0,0,0.2758620689655172,0.2758620689655172,directed graph,29 -Pajek,Tina_AskCog,11,11,36,1,1,0,0,0.5,0.5,directed graph,36 -Pajek,Tina_DisCal,11,11,41,1,1,0,0,0.4390243902439024,0.4390243902439024,directed graph,41 -Pajek,Tina_DisCog,11,11,48,1,1,0,0,0.5,0.5,directed graph,48 -Pajek,USAir97,332,332,4252,1,0,0,0,1,1,undirected weighted graph,4252 -Pajek,USpowerGrid,4941,4941,13188,1,1,0,0,1,1,undirected graph,13188 -Pajek,Wordnet3,82670,82670,132964,1,0,0,0,0.1765463136851033,0.1741230992912098,directed weighted graph,132964 -Pajek,WorldCities,315,100,7518,1,0,0,0,0,0,weighted bipartite graph,7518 -Pajek,yeast,2361,2361,13828,1,1,0,0,1,1,undirected graph,13828 -Pajek,Zewail,6752,6752,54233,1,0,0,0,0.001843929783153858,0.001843929783153858,directed multigraph,54233 -Zitney,extr1b,2836,2836,10965,1,0,0,0,0.003859310586790632,0,chemical process simulation problem sequence,11404 -Zitney,hydr1c,5308,5308,22592,1,0,0,0,0.004126315789473685,0.0006197432492253209,chemical process simulation problem sequence,23752 -Schenk_IBMNA,c-18,2169,2169,15145,1,0,0,0,1,1,optimization problem,15145 -Schenk_IBMNA,c-19,2327,2327,21817,1,0,0,0,1,1,optimization problem,21817 -Schenk_IBMNA,c-20,2921,2921,20445,1,0,0,0,1,1,optimization problem,22685 -Schenk_IBMNA,c-21,3509,3509,32145,1,0,0,0,1,1,optimization problem,32157 -Schenk_IBMNA,c-22,3792,3792,28870,1,0,0,0,1,1,optimization problem,28872 -Schenk_IBMNA,c-23,3969,3969,31079,1,0,0,0,1,1,optimization problem,31079 -Schenk_IBMNA,c-24,4119,4119,35699,1,0,0,0,1,1,optimization problem,35699 -Schenk_IBMNA,c-25,3797,3797,49635,1,0,0,0,1,1,optimization problem,49635 -Schenk_IBMNA,c-26,4307,4307,34537,1,0,0,0,1,1,optimization problem,34537 -Schenk_IBMNA,c-27,4563,4563,30927,1,0,0,0,1,1,optimization problem,31375 -Schenk_IBMNA,c-28,4598,4598,30590,1,0,0,0,1,1,optimization problem,30590 -Schenk_IBMNA,c-29,5033,5033,43731,1,0,0,0,1,1,optimization problem,43731 -Schenk_IBMNA,c-30,5321,5321,65693,1,0,0,0,1,1,optimization problem,65693 -Schenk_IBMNA,c-31,5339,5339,78571,1,0,0,0,1,1,optimization problem,78571 -Schenk_IBMNA,c-32,5975,5975,54471,1,0,0,0,1,1,optimization problem,54471 -Schenk_IBMNA,c-33,6317,6317,56123,1,0,0,0,1,1,optimization problem,56123 -Schenk_IBMNA,c-34,6611,6611,64333,1,0,0,0,1,1,optimization problem,64333 -Schenk_IBMNA,c-35,6537,6537,62891,1,0,0,0,1,1,optimization problem,62891 -Schenk_IBMNA,c-36,7479,7479,65941,1,0,0,0,1,1,optimization problem,65941 -Schenk_IBMNA,c-37,8204,8204,74676,1,0,0,0,1,1,optimization problem,74676 -Schenk_IBMNA,c-38,8127,8127,77689,1,0,0,0,1,1,optimization problem,77689 -Schenk_IBMNA,c-39,9271,9271,116587,1,0,0,0,1,1,optimization problem,136811 -Schenk_IBMNA,c-40,9941,9941,81501,1,0,0,0,1,1,optimization problem,81501 -Schenk_IBMNA,c-41,9769,9769,101635,1,0,0,0,1,1,optimization problem,101745 -Schenk_IBMNA,c-42,10471,10471,110285,1,0,0,0,1,1,optimization problem,110285 -Schenk_IBMNA,c-43,11125,11125,123659,1,0,0,0,1,1,optimization problem,123675 -Schenk_IBMNA,c-44,10728,10728,85000,1,0,0,0,1,1,optimization problem,85000 -Schenk_IBMNA,c-45,13206,13206,174452,1,0,0,0,1,1,optimization problem,174452 -Schenk_IBMNA,c-46,14913,14913,130397,1,0,0,0,1,1,optimization problem,130397 -Schenk_IBMNA,c-47,15343,15343,211401,1,0,0,0,1,1,optimization problem,211401 -Schenk_IBMNA,c-48,18354,18354,166080,1,0,0,0,1,1,optimization problem,166080 -Schenk_IBMNA,c-49,21132,21132,157040,1,0,0,0,1,1,optimization problem,157042 -Schenk_IBMNA,c-50,22401,22401,180245,1,0,0,0,1,1,optimization problem,193625 -Schenk_IBMNA,c-51,23196,23196,203048,1,0,0,0,1,1,optimization problem,203050 -Schenk_IBMNA,c-52,23948,23948,202708,1,0,0,0,1,1,optimization problem,202716 -Schenk_IBMNA,c-53,30235,30235,355139,1,0,0,0,1,1,optimization problem,372213 -Schenk_IBMNA,c-54,31793,31793,385987,1,0,0,0,1,1,optimization problem,391693 -Schenk_IBMNA,c-56,35910,35910,380240,1,0,0,0,1,1,optimization problem,380900 -Schenk_IBMNA,c-57,37833,37833,403373,1,0,0,0,1,1,optimization problem,405197 -Schenk_IBMNA,c-60,43640,43640,298570,1,0,0,0,1,1,optimization problem,298578 -Schenk_IBMNA,c-61,43618,43618,310016,1,0,0,0,1,1,optimization problem,310016 -Schenk_IBMNA,c-65,48066,48066,360428,1,0,0,0,1,1,optimization problem,360528 -Schenk_IBMNA,c-big,345241,345241,2340859,1,0,0,0,1,1,optimization problem,2341011 -Schenk_AFE,af_0_k101,503625,503625,17550675,1,0,1,1,1,1,structural problem,17550675 -Schenk_AFE,af_1_k101,503625,503625,17550675,1,0,1,1,1,1,structural problem,17550675 -Schenk_AFE,af_2_k101,503625,503625,17550675,1,0,1,1,1,1,structural problem,17550675 -Schenk_AFE,af_3_k101,503625,503625,17550675,1,0,1,1,1,1,structural problem,17550675 -Schenk_AFE,af_4_k101,503625,503625,17550675,1,0,1,1,1,1,structural problem,17550675 -Schenk_AFE,af_5_k101,503625,503625,17550675,1,0,1,1,1,1,structural problem,17550675 -Schenk_AFE,af_shell10,1508065,1508065,52259885,1,0,1,0,1,1,structural problem,52672325 -Schenk_IBMNA,c-64b,51035,51035,707601,1,0,0,0,1,1,subsequent optimization problem,717841 -Schenk_IBMNA,c-66b,49989,49989,444851,1,0,0,0,1,1,subsequent optimization problem,499007 -Schenk_IBMNA,c-67b,57975,57975,530583,1,0,0,0,1,1,subsequent optimization problem,531935 -Schenk_IBMNA,c-73b,169422,169422,1279274,1,0,0,0,1,1,subsequent optimization problem,1279274 -QCD,conf5_0-4x4-10,3072,3072,119808,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,119808 -QCD,conf5_0-4x4-14,3072,3072,119808,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,119808 -QCD,conf5_0-4x4-18,3072,3072,119808,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,119808 -QCD,conf5_0-4x4-22,3072,3072,119808,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,119808 -QCD,conf5_0-4x4-26,3072,3072,119808,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,119808 -QCD,conf6_0-4x4-20,3072,3072,119808,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,119808 -QCD,conf6_0-4x4-30,3072,3072,119808,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,119808 -QCD,conf5_4-8x8-05,49152,49152,1916928,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,1916928 -QCD,conf5_4-8x8-10,49152,49152,1916928,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,1916928 -QCD,conf5_4-8x8-15,49152,49152,1916928,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,1916928 -QCD,conf5_4-8x8-20,49152,49152,1916928,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,1916928 -QCD,conf6_0-8x8-20,49152,49152,1916928,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,1916928 -QCD,conf6_0-8x8-30,49152,49152,1916928,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,1916928 -QCD,conf6_0-8x8-80,49152,49152,1916928,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,1916928 -Cylshell,s1rmq4m1,5489,5489,262411,1,0,1,1,1,1,structural problem,281111 -Cylshell,s2rmq4m1,5489,5489,263351,1,0,1,1,1,1,structural problem,281111 -Cylshell,s3rmq4m1,5489,5489,262943,1,0,1,1,1,1,structural problem,281111 -Cylshell,s1rmt3m1,5489,5489,217651,1,0,1,1,1,1,structural problem,219521 -Cylshell,s2rmt3m1,5489,5489,217681,1,0,1,1,1,1,structural problem,219521 -Cylshell,s3rmt3m1,5489,5489,217669,1,0,1,1,1,1,structural problem,219521 -Cylshell,s3rmt3m3,5357,5357,207123,1,0,1,1,1,1,structural problem,207695 -Bai,cryg10000,10000,10000,49699,1,0,1,0,0.9974558553112169,0,materials problem,49699 -Bai,cryg2500,2500,2500,12349,1,0,1,0,0.9948218093207433,0,materials problem,12349 -Bai,dw2048,2048,2048,10114,1,0,1,0,0.9848747830399207,0.9481775353334987,electromagnetics problem,10114 -Bai,dw8192,8192,8192,41746,1,0,1,0,0.9626274065685164,0.9154199201287477,electromagnetics problem,41746 -Bai,dwa512,512,512,2480,1,0,1,0,0.9796747967479674,0.9146341463414634,electromagnetics problem,2480 -Bai,dwb512,512,512,2500,1,0,1,0,0.9698189134808853,0.8832997987927566,electromagnetics problem,2500 -Bai,dwg961a,961,961,3405,0,0,1,0,1,0.36,electromagnetics problem,3405 -Bai,dwg961b,961,961,10591,0,0,1,0,1,0.3476635514018692,electromagnetics problem,10591 -Bai,mhd1280a,1280,1280,47906,0,0,1,0,0.8978403914290535,0.3060992070187278,electromagnetics problem,47906 -Bai,mhd1280b,1280,1280,22778,0,0,1,1,1,1,electromagnetics problem,22778 -Bai,mhd3200a,3200,3200,68026,1,0,1,0,0.7676029990571827,0.2674009667619461,electromagnetics problem,68026 -Bai,mhd3200b,3200,3200,18316,1,0,1,1,1,1,electromagnetics problem,18316 -Bai,mhd4800a,4800,4800,102252,1,0,1,0,0.7677879876950033,0.2677033659541848,electromagnetics problem,102252 -Bai,mhd4800b,4800,4800,27520,1,0,1,1,1,1,electromagnetics problem,27520 -Bai,qh1484,1484,1484,6110,1,0,0,0,1,0.05377207062600321,power network problem,6110 -Bai,qh768,768,768,2934,1,0,0,0,0.9347300564061241,0,power network problem,2934 -Bai,rdb1250,1250,1250,7300,1,0,1,0,1,0.7933884297520661,computational fluid dynamics problem,7300 -Bai,rdb1250l,1250,1250,7300,1,0,1,0,1,0.7933884297520661,computational fluid dynamics problem,7300 -Bai,rdb200,200,200,1120,1,0,1,0,1,0.7826086956521739,computational fluid dynamics problem,1120 -Bai,rdb200l,200,200,1120,1,0,1,0,1,0.7826086956521739,computational fluid dynamics problem,1120 -Bai,rdb2048_noL,2048,2048,12032,1,0,1,0,1,0.7948717948717948,computational fluid dynamics problem,12032 -Bai,rdb3200l,3200,3200,18880,1,0,1,0,1,0.7959183673469388,computational fluid dynamics problem,18880 -Bai,rdb450,450,450,2580,1,0,1,0,1,0.7887323943661971,computational fluid dynamics problem,2580 -Bai,rdb450l,450,450,2580,1,0,1,0,1,0.7887323943661971,computational fluid dynamics problem,2580 -Bai,rdb800l,800,800,4640,1,0,1,0,1,0.7916666666666666,computational fluid dynamics problem,4640 -Bai,tols1090,1090,1090,3546,1,0,1,0,0.3178758414360509,0,computational fluid dynamics problem,3546 -Bai,tols2000,2000,2000,5184,1,0,1,0,0.3387276785714285,0,computational fluid dynamics problem,5184 -Bai,tols340,340,340,2196,1,0,1,0,0.2858627858627859,0,computational fluid dynamics problem,2196 -Bai,tols4000,4000,4000,8784,1,0,1,0,0.3606733524355301,0,computational fluid dynamics problem,8784 -Bai,tols90,90,90,1746,1,0,1,0,0.2688172043010753,0,computational fluid dynamics problem,1746 -POLYFLOW,invextr1_new,30412,30412,1793881,1,0,1,0,0.971442083756505,0.7213486599424203,computational fluid dynamics problem,1793881 -POLYFLOW,mixtank_new,29957,29957,1990919,1,0,1,0,1,0.9890905426943581,computational fluid dynamics problem,1995041 -INPRO,msdoor,415863,415863,19173163,1,0,1,1,1,1,structural problem,20240935 -Mittelmann,nug08-3rd,19728,29856,148416,1,0,0,0,0,0,linear programming problem,148416 -Mittelmann,pds-30,49944,158489,340635,1,0,0,0,0,0,linear programming problem,340635 -Mittelmann,pds-40,66844,217531,466800,1,0,0,0,0,0,linear programming problem,466800 -Mittelmann,pds-50,83060,275814,590833,1,0,0,0,0,0,linear programming problem,590833 -Mittelmann,pds-60,99431,336421,719557,1,0,0,0,0,0,linear programming problem,719557 -Mittelmann,pds-70,114944,390005,833465,1,0,0,0,0,0,linear programming problem,833465 -Mittelmann,pds-80,129181,434580,927826,1,0,0,0,0,0,linear programming problem,927826 -Mittelmann,pds-90,142823,475448,1014136,1,0,0,0,0,0,linear programming problem,1014136 -Mittelmann,pds-100,156243,514577,1096002,1,0,0,0,0,0,linear programming problem,1096002 -Mittelmann,rail507,507,63516,409856,1,0,0,0,0,0,linear programming problem,409856 -Mittelmann,rail516,516,47827,315412,1,0,0,0,0,0,linear programming problem,315412 -Mittelmann,rail582,582,56097,402290,1,0,0,0,0,0,linear programming problem,402290 -Mittelmann,rail2586,2586,923269,8011362,1,0,0,0,0,0,linear programming problem,8011362 -Mittelmann,rail4284,4284,1096894,11284032,1,0,0,0,0,0,linear programming problem,11284032 -Mittelmann,sgpf5y6,246077,312540,831976,1,0,0,0,0,0,linear programming problem,831976 -Mittelmann,stormG2_1000,528185,1377306,3459881,1,0,0,0,0,0,linear programming problem,3459881 -Mittelmann,watson_1,201155,386992,1055093,1,0,0,0,0,0,linear programming problem,1055093 -Mittelmann,watson_2,352013,677224,1846391,1,0,0,0,0,0,linear programming problem,1846391 -Mittelmann,cont11_l,1468599,1961394,5382999,1,0,0,0,0,0,linear programming problem,5382999 -Mittelmann,cont1_l,1918399,1921596,7031999,1,0,0,0,0,0,linear programming problem,7031999 -Mittelmann,fome11,12142,24460,71264,1,0,0,0,0,0,linear programming problem,71264 -Mittelmann,fome12,24284,48920,142528,1,0,0,0,0,0,linear programming problem,142528 -Mittelmann,fome13,48568,97840,285056,1,0,0,0,0,0,linear programming problem,285056 -Mittelmann,fome20,33874,108175,232647,1,0,0,0,0,0,linear programming problem,232647 -Mittelmann,fome21,67748,216350,465294,1,0,0,0,0,0,linear programming problem,465294 -Mittelmann,neos,479119,515905,1526794,1,0,0,0,0,0,linear programming problem,1526794 -Mittelmann,neos1,131581,133473,599590,1,0,0,0,0,0,linear programming problem,599590 -Mittelmann,neos2,132568,134128,685087,1,0,0,0,0,0,linear programming problem,685087 -Mittelmann,neos3,512209,518832,2055024,1,0,0,0,0,0,linear programming problem,2055024 -Mittelmann,spal_004,10203,321696,46168124,1,0,0,0,0,0,linear programming problem,46168124 -Meszaros,gams10am,114,171,407,1,0,0,0,0,0,linear programming problem,407 -Meszaros,gams30am,354,531,1287,1,0,0,0,0,0,linear programming problem,1287 -Meszaros,gams60am,714,1071,2607,1,0,0,0,0,0,linear programming problem,2607 -Meszaros,gas11,459,862,2166,1,0,0,0,0,0,linear programming problem,2166 -Meszaros,aa01,823,8904,72965,1,1,0,0,0,0,linear programming problem,72965 -Meszaros,aa03,825,8627,70806,1,1,0,0,0,0,linear programming problem,70806 -Meszaros,aa3,825,8627,70806,1,1,0,0,0,0,linear programming problem,70806 -Meszaros,aa4,426,7195,52121,1,1,0,0,0,0,linear programming problem,52121 -Meszaros,aa5,801,8308,65953,1,1,0,0,0,0,linear programming problem,65953 -Meszaros,aa6,646,7292,51728,1,1,0,0,0,0,linear programming problem,51728 -Meszaros,air02,50,6774,61555,1,1,0,0,0,0,linear programming problem,61555 -Meszaros,air03,124,10757,91028,1,1,0,0,0,0,linear programming problem,91028 -Meszaros,air04,823,8904,72965,1,1,0,0,0,0,linear programming problem,72965 -Meszaros,air05,426,7195,52121,1,1,0,0,0,0,linear programming problem,52121 -Meszaros,air06,825,8627,70806,1,1,0,0,0,0,linear programming problem,70806 -Meszaros,aircraft,3754,7517,20267,1,0,0,0,0,0,linear programming problem,20267 -Meszaros,bas1lp,5411,9825,587775,1,0,0,0,0,0,linear programming problem,587775 -Meszaros,baxter,27441,30733,111576,1,0,0,0,0,0,linear programming problem,111576 -Meszaros,car4,16384,33052,63724,1,0,0,0,0,0,linear programming problem,63724 -Meszaros,cari,400,1200,152800,1,0,0,0,0,0,linear programming problem,152800 -Meszaros,ch,3700,8291,24102,1,0,0,0,0,0,linear programming problem,24102 -Meszaros,co5,5774,12325,57993,1,0,0,0,0,0,linear programming problem,57993 -Meszaros,co9,10789,22924,109651,1,0,0,0,0,0,linear programming problem,109651 -Meszaros,complex,1023,1408,46463,1,0,0,0,0,0,linear programming problem,46463 -Meszaros,cq5,5048,11748,51571,1,0,0,0,0,0,linear programming problem,51571 -Meszaros,cq9,9278,21534,96653,1,0,0,0,0,0,linear programming problem,96653 -Meszaros,cr42,905,1513,6614,1,0,0,0,0,0,linear programming problem,6614 -Meszaros,crew1,135,6469,46950,1,1,0,0,0,0,linear programming problem,46950 -Meszaros,dano3mip,3202,15851,81633,1,0,0,0,0,0,linear programming problem,81633 -Meszaros,dbic1,43200,226317,1081843,1,0,0,0,0,0,linear programming problem,1081843 -Meszaros,dbir1,18804,45775,1077025,1,0,0,0,0,0,linear programming problem,1077025 -Meszaros,dbir2,18906,45877,1158159,1,0,0,0,0,0,linear programming problem,1158159 -Meszaros,df2177,630,10358,22336,1,1,0,0,0,0,linear programming problem,22336 -Meszaros,e18,24617,38602,156466,1,0,0,0,0,0,linear programming problem,156466 -Meszaros,ex3sta1,17443,17516,68779,1,0,0,0,0,0,linear programming problem,68779 -Meszaros,farm,7,17,41,1,0,0,0,0,0,linear programming problem,41 -Meszaros,gams10a,114,171,407,1,0,0,0,0,0,linear programming problem,407 -Meszaros,gams30a,354,531,1287,1,0,0,0,0,0,linear programming problem,1287 -Meszaros,ge,10099,16369,44825,1,0,0,0,0,0,linear programming problem,44825 -Meszaros,iiasa,669,3639,7317,1,0,0,0,0,0,linear programming problem,7317 -Meszaros,jendrec1,2109,4228,89608,1,0,0,0,0,0,linear programming problem,89608 -Meszaros,kl02,71,36699,212536,1,1,0,0,0,0,linear programming problem,212536 -Meszaros,l9,244,1483,4659,1,0,0,0,0,0,linear programming problem,4659 -Meszaros,lp22,2958,16392,68518,1,0,0,0,0,0,linear programming problem,68518 -Meszaros,lpl2,3294,10881,32232,1,0,0,0,0,0,linear programming problem,32232 -Meszaros,lpl3,10828,33686,100525,1,0,0,0,0,0,linear programming problem,100525 -Meszaros,mod2,34774,66409,199810,1,0,0,0,0,0,linear programming problem,199810 -Meszaros,model1,362,798,3028,1,0,0,0,0,0,linear programming problem,3028 -Meszaros,model2,379,1321,7607,1,0,0,0,0,0,linear programming problem,7607 -Meszaros,model3,1609,4578,23974,1,0,0,0,0,0,linear programming problem,23974 -Meszaros,model4,1337,4962,45753,1,0,0,0,0,0,linear programming problem,45753 -Meszaros,model5,1888,11802,89925,1,0,0,0,0,0,linear programming problem,89925 -Meszaros,model6,2096,5289,27628,1,0,0,0,0,0,linear programming problem,27628 -Meszaros,model7,3358,9582,51027,1,0,0,0,0,0,linear programming problem,51027 -Meszaros,model8,2896,6464,25277,1,0,0,0,0,0,linear programming problem,25277 -Meszaros,model9,2879,10939,55956,1,0,0,0,0,0,linear programming problem,55956 -Meszaros,model10,4400,16819,150372,1,0,0,0,0,0,linear programming problem,150372 -Meszaros,nemsafm,334,2348,2826,1,0,0,0,0,0,linear programming problem,2826 -Meszaros,nemscem,651,1712,3840,1,0,0,0,0,0,linear programming problem,3840 -Meszaros,nemsemm1,3945,75352,1053986,1,0,0,0,0,0,linear programming problem,1053986 -Meszaros,nemsemm2,6943,48878,182012,1,0,0,0,0,0,linear programming problem,182012 -Meszaros,nemspmm1,2372,8903,55867,1,0,0,0,0,0,linear programming problem,55867 -Meszaros,nemspmm2,2301,8734,68225,1,0,0,0,0,0,linear programming problem,68225 -Meszaros,nemswrld,7138,28550,192283,1,0,0,0,0,0,linear programming problem,192283 -Meszaros,nl,7039,15325,47035,1,0,0,0,0,0,linear programming problem,47035 -Meszaros,nw14,73,123409,904910,1,1,0,0,0,0,linear programming problem,904910 -Meszaros,p0033,15,48,113,1,0,0,0,0,0,linear programming problem,113 -Meszaros,p0040,23,63,133,1,0,0,0,0,0,linear programming problem,133 -Meszaros,p010,10090,19090,118000,1,0,0,0,0,0,linear programming problem,118000 -Meszaros,p0201,133,334,2056,1,0,0,0,0,0,linear programming problem,2056 -Meszaros,p0282,241,523,2207,1,0,0,0,0,0,linear programming problem,2207 -Meszaros,p0291,252,543,2283,1,0,0,0,0,0,linear programming problem,2283 -Meszaros,p0548,176,724,1887,1,0,0,0,0,0,linear programming problem,1887 -Meszaros,p05,5090,9590,59045,1,0,0,0,0,0,linear programming problem,59045 -Meszaros,p2756,755,3511,9692,1,0,0,0,0,0,linear programming problem,9692 -Meszaros,p6000,2095,7967,19826,1,0,0,0,0,0,linear programming problem,19826 -Meszaros,pcb1000,1565,2820,20463,1,0,0,0,0,0,linear programming problem,20463 -Meszaros,pcb3000,3960,7732,57479,1,0,0,0,0,0,linear programming problem,57479 -Meszaros,pf2177,9728,10178,30984,1,1,0,0,0,0,linear programming problem,30984 -Meszaros,primagaz,1554,10836,21665,1,0,0,0,0,0,linear programming problem,21665 -Meszaros,problem,12,46,86,1,0,0,0,0,0,linear programming problem,86 -Meszaros,progas,1650,1900,8897,1,0,0,0,0,0,linear programming problem,8897 -Meszaros,qiulp,1192,1900,4492,1,0,0,0,0,0,linear programming problem,4492 -Meszaros,r05,5190,9690,104145,1,0,0,0,0,0,linear programming problem,104145 -Meszaros,refine,29,62,153,1,0,0,0,0,0,linear programming problem,153 -Meszaros,rlfddd,4050,61521,264627,1,0,0,0,0,0,linear programming problem,264627 -Meszaros,rlfdual,8052,74970,282031,1,0,0,0,0,0,linear programming problem,282031 -Meszaros,rlfprim,58866,62716,320591,1,0,0,0,0,0,linear programming problem,320591 -Meszaros,rosen1,520,1544,23794,1,0,0,0,0,0,linear programming problem,23794 -Meszaros,rosen2,1032,3080,47536,1,0,0,0,0,0,linear programming problem,47536 -Meszaros,rosen7,264,776,8034,1,0,0,0,0,0,linear programming problem,8034 -Meszaros,rosen8,520,1544,16058,1,0,0,0,0,0,linear programming problem,16058 -Meszaros,rosen10,2056,6152,64192,1,0,0,0,0,0,linear programming problem,64192 -Meszaros,route,20894,43019,206782,1,0,0,0,0,0,linear programming problem,206782 -Meszaros,seymourl,4944,6316,38493,1,0,0,0,0,0,linear programming problem,38493 -Meszaros,slptsk,2861,3347,72465,1,0,0,0,0,0,linear programming problem,72465 -Meszaros,south31,18425,36321,112398,1,0,0,0,0,0,linear programming problem,112398 -Meszaros,stat96v1,5995,197472,588798,1,0,0,0,0,0,linear programming problem,588798 -Meszaros,stat96v2,29089,957432,2852184,1,0,0,0,0,0,linear programming problem,2852184 -Meszaros,stat96v3,33841,1113780,3317736,1,0,0,0,0,0,linear programming problem,3317736 -Meszaros,stat96v4,3173,63076,491336,1,0,0,0,0,0,linear programming problem,491336 -Meszaros,stat96v5,2307,75779,233921,1,0,0,0,0,0,linear programming problem,233921 -Meszaros,t0331-4l,664,46915,430982,1,1,0,0,0,0,linear programming problem,430982 -Meszaros,testbig,17613,31223,61639,1,0,0,0,0,0,linear programming problem,61639 -Meszaros,ulevimin,6590,46937,164538,1,0,0,0,0,0,linear programming problem,164538 -Meszaros,us04,163,28016,297538,1,1,0,0,0,0,linear programming problem,297538 -Meszaros,world,34506,67147,198883,1,0,0,0,0,0,linear programming problem,198883 -Meszaros,zed,116,142,666,1,0,0,0,0,0,linear programming problem,666 -Meszaros,de063155,852,1848,4913,1,0,0,0,0,0,linear programming problem,4913 -Meszaros,de063157,936,1908,5119,1,0,0,0,0,0,linear programming problem,5119 -Meszaros,de080285,936,1908,5082,1,0,0,0,0,0,linear programming problem,5082 -Meszaros,gen1,769,2561,63086,1,0,0,0,0,0,linear programming problem,63086 -Meszaros,gen2,1121,3264,81855,1,0,0,0,0,0,linear programming problem,81855 -Meszaros,gen4,1537,4298,107103,1,0,0,0,0,0,linear programming problem,107103 -Meszaros,gen,769,2561,63086,1,0,0,0,0,0,linear programming problem,63086 -Meszaros,iprob,3001,3001,9000,1,0,0,0,1,0.5,linear programming problem,9000 -Meszaros,l30,2701,16281,52070,1,0,0,0,0,0,linear programming problem,52070 -Meszaros,stoch_aircraft,3754,7517,20267,1,0,0,0,0,0,linear programming problem,20267 -Meszaros,cep1,1521,4769,8233,1,0,0,0,0,0,linear programming problem,8233 -Meszaros,deter0,1923,5468,11173,1,0,0,0,0,0,linear programming problem,11173 -Meszaros,deter1,5527,15737,32187,1,0,0,0,0,0,linear programming problem,32187 -Meszaros,deter2,6095,17313,35731,1,0,0,0,0,0,linear programming problem,35731 -Meszaros,deter3,7647,21777,44547,1,0,0,0,0,0,linear programming problem,44547 -Meszaros,deter4,3235,9133,19231,1,0,0,0,0,0,linear programming problem,19231 -Meszaros,deter5,5103,14529,29715,1,0,0,0,0,0,linear programming problem,29715 -Meszaros,deter6,4255,12113,24771,1,0,0,0,0,0,linear programming problem,24771 -Meszaros,deter7,6375,18153,37131,1,0,0,0,0,0,linear programming problem,37131 -Meszaros,deter8,3831,10905,22299,1,0,0,0,0,0,linear programming problem,22299 -Meszaros,fxm2-6,1520,2845,12812,1,0,0,0,0,0,linear programming problem,12812 -Meszaros,fxm2-16,3900,7335,32972,1,0,0,0,0,0,linear programming problem,32972 -Meszaros,fxm3_6,6200,12625,57722,1,0,0,0,0,0,linear programming problem,57722 -Meszaros,fxm3_16,41340,85575,392252,1,0,0,0,0,0,linear programming problem,392252 -Meszaros,fxm4_6,22400,47185,265442,1,0,0,0,0,0,linear programming problem,265442 -Meszaros,pgp2,4034,13254,22474,1,0,0,0,0,0,linear programming problem,22474 -Meszaros,stormg2-125,66185,172431,433256,1,0,0,0,0,0,linear programming problem,433256 -Meszaros,stormg2-27,14441,37485,94274,1,0,0,0,0,0,linear programming problem,94274 -Meszaros,stormg2-8,4409,11322,28553,1,0,0,0,0,0,linear programming problem,28553 -Meszaros,degme,185501,659415,8127528,1,0,0,0,0,0,linear programming problem,8127528 -Meszaros,karted,46502,133115,1770349,1,0,0,0,0,0,linear programming problem,1770349 -Meszaros,tp-6,142752,1014301,11537419,1,0,0,0,0,0,linear programming problem,11537419 -Meszaros,ts-palko,22002,47235,1076903,1,0,0,0,0,0,linear programming problem,1076903 -Meszaros,delf,3170,6654,15397,1,0,0,0,0,0,linear programming problem sequence,15397 -Meszaros,kleemin,8,16,44,1,0,0,0,0,0,linear programming problem sequence,44 -Meszaros,large,4282,8617,20635,1,0,0,0,0,0,linear programming problem sequence,20635 -Meszaros,nsct,23003,37563,697738,1,0,0,0,0,0,linear programming problem sequence,697738 -Meszaros,nsic,465,897,3449,1,0,0,0,0,0,linear programming problem sequence,3449 -Meszaros,nsir,4453,10057,154939,1,0,0,0,0,0,linear programming problem sequence,154939 -Meszaros,plddb,3069,5049,10839,1,0,0,0,0,0,linear programming problem sequence,10839 -Meszaros,rat,3136,9408,268908,1,0,0,0,0,0,linear programming problem sequence,268908 -Meszaros,small,677,1400,3207,1,0,0,0,0,0,linear programming problem sequence,3207 -Meszaros,pltexpa,26894,70364,143059,1,0,0,0,0,0,linear programming problem sequence,143059 -Meszaros,sc205-2r,35213,62423,123239,1,0,0,0,0,0,linear programming problem sequence,123239 -Meszaros,scagr7-2b,9743,13847,35885,1,0,0,0,0,0,linear programming problem sequence,35885 -Meszaros,scagr7-2c,2447,3479,9005,1,0,0,0,0,0,linear programming problem sequence,9005 -Meszaros,scagr7-2r,32847,46679,120141,1,0,0,0,0,0,linear programming problem sequence,120141 -Meszaros,scfxm1-2b,19036,33047,111052,1,0,0,0,0,0,linear programming problem sequence,111052 -Meszaros,scfxm1-2r,37980,65943,221388,1,0,0,0,0,0,linear programming problem sequence,221388 -Meszaros,scrs8-2b,1820,3499,7367,1,0,0,0,0,0,linear programming problem sequence,7367 -Meszaros,scrs8-2c,1820,3499,7367,1,0,0,0,0,0,linear programming problem sequence,7367 -Meszaros,scrs8-2r,14364,27691,58439,1,0,0,0,0,0,linear programming problem sequence,58439 -Meszaros,scsd8-2b,5130,35910,112770,1,0,0,0,0,0,linear programming problem sequence,112770 -Meszaros,scsd8-2c,5130,35910,112770,1,0,0,0,0,0,linear programming problem sequence,112770 -Meszaros,scsd8-2r,8650,60550,190210,1,0,0,0,0,0,linear programming problem sequence,190210 -Meszaros,sctap1-2b,15390,33858,99454,1,0,0,0,0,0,linear programming problem sequence,99454 -Meszaros,sctap1-2c,3390,7458,21854,1,0,0,0,0,0,linear programming problem sequence,21854 -Meszaros,sctap1-2r,28830,63426,186366,1,0,0,0,0,0,linear programming problem sequence,186366 -Gleich,wb-cs-stanford,9914,9914,36854,1,1,0,0,0.4572071438616228,0.4572071438616228,directed graph,36854 -Gleich,wb-edu,9845725,9845725,57156537,1,1,0,0,0.3281596169311674,0.3281596169311674,directed graph,57156537 -Gleich,wikipedia-20051105,1634989,1634989,19753078,1,1,0,0,0.1211282592272251,0.1211282592272251,directed graph,19753078 -Gleich,wikipedia-20060925,2983494,2983494,37269096,1,1,0,0,0.1182841447575249,0.1182841447575249,directed graph,37269096 -Gleich,wikipedia-20061104,3148440,3148440,39383235,1,1,0,0,0.1179357318284389,0.1179357318284389,directed graph,39383235 -Gleich,wikipedia-20070206,3566907,3566907,45030389,1,1,0,0,0.1171832378930545,0.1171832378930545,directed graph,45030389 -UTEP,Dubcova1,16129,16129,253009,1,0,1,1,1,1,2D/3D problem,253009 -UTEP,Dubcova2,65025,65025,1030225,1,0,1,1,1,1,2D/3D problem,1030225 -UTEP,Dubcova3,146689,146689,3636643,1,0,1,1,1,1,2D/3D problem,3636649 -BenElechi,BenElechi1,245874,245874,13150496,1,0,1,1,1,1,2D/3D problem,13150496 -Botonakis,FEM_3D_thermal1,17880,17880,430740,1,0,1,0,1,0.950733904955675,thermal problem,430740 -Botonakis,FEM_3D_thermal2,147900,147900,3489300,1,0,1,0,1,0.9502082959238642,thermal problem,3489300 -Wissgott,parabolic_fem,525825,525825,3674625,1,0,1,1,1,1,computational fluid dynamics problem,3674625 -Watson,chem_master1,40401,40401,201201,1,0,1,0,1,0,2D/3D problem,201201 -Watson,Baumann,112211,112211,748331,1,0,1,0,1,0,2D/3D problem,760631 -Sinclair,3Dspectralwave,680943,680943,30290827,0,0,1,0,1,1,materials problem,33650589 -Sinclair,3Dspectralwave2,292008,292008,12935272,0,0,1,0,1,1,materials problem,14322744 -QLi,crashbasis,160000,160000,1750416,1,0,0,0,0.5503013048158469,0.0002439613283568576,optimization problem,1750416 -QLi,majorbasis,160000,160000,1750416,1,0,0,0,0.5503013048158469,0.0002376736652548767,optimization problem,1750416 -Springer,ESOC,327062,37830,6019939,1,0,0,0,0,0,least squares problem,6019939 -Koutsovasilis,F2,71505,71505,5294285,1,0,1,0,1,1,structural problem,5294285 -Szczerba,Ill_Stokes,20896,20896,191368,1,0,1,0,0.9897436481194791,0.3299904380293234,computational fluid dynamics problem,191368 -Rajat,Raj1,263743,263743,1300261,1,0,0,0,0.998585875116119,0.5762090211971218,circuit simulation problem,1302464 -Muite,Chebyshev1,261,261,2319,1,0,1,0,0.5014577259475219,0,structural problem,2319 -Muite,Chebyshev2,2053,2053,18447,1,0,1,0,0.5001829937782115,0,structural problem,18447 -Muite,Chebyshev3,4101,4101,36879,1,0,1,0,0.5000915248032217,0,structural problem,36879 -Muite,Chebyshev4,68121,68121,5377761,1,0,1,0,0.3020408163265306,0.0002369275506437348,structural problem,5377761 -Quaglino,viscoplastic1,4326,4326,61166,1,0,1,0,0.7408163265306122,0,materials problem,61166 -Quaglino,viscoplastic2,32769,32769,381326,1,0,1,0,0.5699899872904575,0,materials problem,381326 -YCheng,psse0,26722,11028,102432,1,0,0,0,0,0,power network problem,102432 -YCheng,psse1,14318,11028,57376,1,0,0,0,0,0,power network problem,57376 -YCheng,psse2,28634,11028,115262,1,0,0,0,0,0,power network problem,115262 -Dehghani,light_in_tissue,29282,29282,406084,0,0,1,0,1,0,electromagnetics problem,406084 -HVDC,hvdc1,24842,24842,158426,1,0,0,0,0.9817003233707516,0.09899271099935641,power network problem,159981 -HVDC,hvdc2,189860,189860,1339638,1,0,0,0,0.9885955341075688,0.06323365458033522,power network problem,1347273 -Zaoui,kkt_power,2063494,2063494,12771361,1,0,0,0,1,1,optimization problem,14612663 -Rost,RFdevice,74104,74104,365580,0,0,1,0,0.0001203589980660497,0.0001148881345175929,semiconductor device problem,365580 -Lee,fem_filter,74062,74062,1731206,0,0,1,0,1,0.6131971633123011,electromagnetics problem,1731206 -Mancktelow,viscorocks,37762,37762,1133641,1,0,1,0,1,0.279507135368047,materials problem,1162244 -Rudnyi,water_tank,60740,60740,2035281,1,0,1,0,0.9691204183655847,0.9180067671423384,computational fluid dynamics problem,2035281 -Rucci,Rucci1,1977885,109900,7791168,1,0,0,0,0,0,least squares problem,7791168 -McRae,ecology1,1000000,1000000,4996000,1,0,1,0,1,1,2D/3D problem,4996000 -McRae,ecology2,999999,999999,4995991,1,0,1,1,1,1,2D/3D problem,4995991 -NYPA,Maragal_1,32,14,234,1,0,0,0,0,0,least squares problem,234 -NYPA,Maragal_2,555,350,4357,1,0,0,0,0,0,least squares problem,4357 -NYPA,Maragal_3,1690,860,18391,1,0,0,0,0,0,least squares problem,18391 -NYPA,Maragal_4,1964,1034,26719,1,0,0,0,0,0,least squares problem,26719 -NYPA,Maragal_5,4654,3320,93091,1,0,0,0,0,0,least squares problem,93091 -NYPA,Maragal_6,21255,10152,537694,1,0,0,0,0,0,least squares problem,537694 -NYPA,Maragal_7,46845,26564,1200537,1,0,0,0,0,0,least squares problem,1200537 -Marini,eurqsa,7245,7245,46142,1,0,0,0,1,1,economic problem,46142 -Castrillon,denormal,89400,89400,1156224,1,0,0,1,1,1,counter-example problem,1156224 -QLi,largebasis,440020,440020,5240084,1,0,0,0,1,0,optimization problem,5560100 -MathWorks,QRpivot,660,749,3808,1,0,0,0,0,0,counter-example problem,3808 -Luong,photogrammetry,1388,390,11816,1,0,1,0,0,0,computer graphics/vision problem,11816 -YZhou,circuit204,1020,1020,5883,1,0,0,0,0.4379791625712601,0.3727147631216827,circuit simulation problem,5883 -CEMW,t2em,921632,921632,4590832,1,0,1,0,0.9988182710127548,0.9988182710127548,electromagnetics problem,4590832 -CEMW,tmt_unsym,917825,917825,4584801,1,0,1,0,1,0,electromagnetics problem,4584801 -CEMW,tmt_sym,726713,726713,5080961,1,0,1,1,1,1,electromagnetics problem,5080961 -CEMW,vfem,93476,93476,1434636,0,0,1,0,1,0.8209624504160578,electromagnetics problem,1434636 -Schenk,nlpkkt80,1062400,1062400,28192672,1,0,0,0,1,1,optimization problem,28704672 -Schenk,nlpkkt120,3542400,3542400,95117792,1,0,0,0,1,1,optimization problem,96845792 -Schenk,nlpkkt160,8345600,8345600,225422112,1,0,0,0,1,1,optimization problem,229518112 -Schenk,nlpkkt200,16240000,16240000,440225632,1,0,0,0,1,1,optimization problem,448225632 -Schenk,nlpkkt240,27993600,27993600,760648352,1,0,0,0,1,1,optimization problem,774472352 -TKK,s4dkt3m2,90449,90449,3753461,1,1,1,0,1,1,structural problem,3753461 -TKK,g3rmt3m3,5357,5357,207695,1,1,1,0,1,1,structural problem,207695 -TKK,t520,5563,5563,286341,1,0,1,0,1,1,structural problem,286341 -TKK,smt,25710,25710,3749582,1,0,1,1,1,1,structural problem,3753184 -TKK,engine,143571,143571,4706073,1,0,1,0,1,1,structural problem,4706073 -TKK,plbuckle,1282,1282,30644,1,0,1,1,1,1,structural problem,30644 -TKK,cbuckle,13681,13681,676515,1,0,1,1,1,1,structural problem,676515 -TKK,cyl6,13681,13681,714241,1,0,1,0,1,1,structural problem,714241 -TKK,tube1,21498,21498,897056,1,1,1,0,1,1,structural problem,897056 -TKK,tube2,21498,21498,897056,1,0,1,0,1,1,structural problem,897056 -TKK,t2d_q4,9801,9801,87025,1,0,1,0,1,0.6937221589143272,structural problem sequence,87025 -TKK,t2d_q9,9801,9801,87025,1,0,1,0,1,0.6937221589143272,structural problem sequence,87025 -Luong,photogrammetry2,4472,936,37056,1,0,1,0,0,0,computer graphics/vision problem,37056 -Um,2cubes_sphere,101492,101492,1647264,1,0,1,1,1,1,electromagnetics problem,1647264 -JGD_BIBD,bibd_9_3,36,84,252,1,1,0,0,0,0,combinatorial problem,252 -JGD_BIBD,bibd_9_5,36,126,1260,1,1,0,0,0,0,combinatorial problem,1260 -JGD_BIBD,bibd_11_5,55,462,4620,1,1,0,0,0,0,combinatorial problem,4620 -JGD_BIBD,bibd_12_4,66,495,2970,1,1,0,0,0,0,combinatorial problem,2970 -JGD_BIBD,bibd_12_5,66,792,7920,1,1,0,0,0,0,combinatorial problem,7920 -JGD_BIBD,bibd_13_6,78,1716,25740,1,1,0,0,0,0,combinatorial problem,25740 -JGD_BIBD,bibd_14_7,91,3432,72072,1,1,0,0,0,0,combinatorial problem,72072 -JGD_BIBD,bibd_15_3,105,455,1365,1,1,0,0,0,0,combinatorial problem,1365 -JGD_BIBD,bibd_15_7,105,6435,135135,1,1,0,0,0,0,combinatorial problem,135135 -JGD_BIBD,bibd_16_8,120,12870,360360,1,1,0,0,0,0,combinatorial problem,360360 -JGD_BIBD,bibd_17_3,136,680,2040,1,1,0,0,0,0,combinatorial problem,2040 -JGD_BIBD,bibd_17_4b,136,2380,14280,1,1,0,0,0,0,combinatorial problem,14280 -JGD_BIBD,bibd_17_4,136,2380,14280,1,1,0,0,0,0,combinatorial problem,14280 -JGD_BIBD,bibd_17_8,136,24310,680680,1,1,0,0,0,0,combinatorial problem,680680 -JGD_BIBD,bibd_18_9,153,48620,1750320,1,1,0,0,0,0,combinatorial problem,1750320 -JGD_BIBD,bibd_19_9,171,92378,3325608,1,1,0,0,0,0,combinatorial problem,3325608 -JGD_BIBD,bibd_20_10,190,184756,8314020,1,1,0,0,0,0,combinatorial problem,8314020 -JGD_BIBD,bibd_22_8,231,319770,8953560,1,1,0,0,0,0,combinatorial problem,8953560 -JGD_BIBD,bibd_49_3,1176,18424,55272,1,1,0,0,0,0,combinatorial problem,55272 -JGD_BIBD,bibd_81_2,3240,3240,3240,1,1,0,1,1,1,combinatorial problem,3240 -JGD_BIBD,bibd_81_3,3240,85320,255960,1,1,0,0,0,0,combinatorial problem,255960 -JGD_CAG,CAG_mat1916,1916,1916,195985,1,0,0,0,0.3002540333592691,0.2119864584245809,combinatorial problem,195985 -JGD_CAG,CAG_mat364,364,364,13585,1,0,0,0,0.4155510173209288,0.2804628999319265,combinatorial problem,13585 -JGD_CAG,CAG_mat72,72,72,1012,1,0,0,0,0.5574468085106383,0.3340425531914893,combinatorial problem,1012 -JGD_Forest,TF10,99,107,622,1,0,0,0,0,0,combinatorial problem,622 -JGD_Forest,TF11,216,236,1607,1,0,0,0,0,0,combinatorial problem,1607 -JGD_Forest,TF12,488,552,4231,1,0,0,0,0,0,combinatorial problem,4231 -JGD_Forest,TF13,1121,1302,11185,1,0,0,0,0,0,combinatorial problem,11185 -JGD_Forest,TF14,2644,3160,29862,1,0,0,0,0,0,combinatorial problem,29862 -JGD_Forest,TF15,6334,7742,80057,1,0,0,0,0,0,combinatorial problem,80057 -JGD_Forest,TF16,15437,19321,216173,1,0,0,0,0,0,combinatorial problem,216173 -JGD_Forest,TF17,38132,48630,586218,1,0,0,0,0,0,combinatorial problem,586218 -JGD_Forest,TF18,95368,123867,1597545,1,0,0,0,0,0,combinatorial problem,1597545 -JGD_Forest,TF19,241029,317955,4370721,1,0,0,0,0,0,combinatorial problem,4370721 -JGD_Franz,Franz1,2240,768,5120,1,0,0,0,0,0,combinatorial problem,5120 -JGD_Franz,Franz2,4032,4480,21504,1,0,0,0,0,0,combinatorial problem,21504 -JGD_Franz,Franz3,1280,2800,11520,1,0,0,0,0,0,combinatorial problem,11520 -JGD_Franz,Franz4,6784,5252,46528,1,0,0,0,0,0,combinatorial problem,46528 -JGD_Franz,Franz5,7382,2882,44056,1,0,0,0,0,0,combinatorial problem,44056 -JGD_Franz,Franz6,7576,3016,45456,1,0,0,0,0,0,combinatorial problem,45456 -JGD_Franz,Franz7,10164,1740,40424,1,0,0,0,0,0,combinatorial problem,40424 -JGD_Franz,Franz8,16728,7176,100368,1,0,0,0,0,0,combinatorial problem,100368 -JGD_Franz,Franz9,19588,4164,97508,1,0,0,0,0,0,combinatorial problem,97508 -JGD_Franz,Franz10,19588,4164,97508,1,0,0,0,0,0,combinatorial problem,97508 -JGD_Franz,Franz11,47104,30144,329728,1,0,0,0,0,0,combinatorial problem,329728 -JGD_G5,IG5-6,30,77,251,1,0,0,0,0,0,combinatorial problem,251 -JGD_G5,IG5-7,62,150,549,1,0,0,0,0,0,combinatorial problem,549 -JGD_G5,IG5-8,156,292,1711,1,0,0,0,0,0,combinatorial problem,1711 -JGD_G5,IG5-9,342,540,4570,1,0,0,0,0,0,combinatorial problem,4570 -JGD_G5,IG5-10,652,976,10273,1,0,0,0,0,0,combinatorial problem,10273 -JGD_G5,IG5-11,1227,1692,22110,1,0,0,0,0,0,combinatorial problem,22110 -JGD_G5,IG5-12,2296,2875,46260,1,0,0,0,0,0,combinatorial problem,46260 -JGD_G5,IG5-13,3994,4731,91209,1,0,0,0,0,0,combinatorial problem,91209 -JGD_G5,IG5-14,6735,7621,173337,1,0,0,0,0,0,combinatorial problem,173337 -JGD_G5,IG5-15,11369,11987,323509,1,0,0,0,0,0,combinatorial problem,323509 -JGD_G5,IG5-16,18846,18485,588326,1,0,0,0,0,0,combinatorial problem,588326 -JGD_G5,IG5-17,30162,27944,1035008,1,0,0,0,0,0,combinatorial problem,1035008 -JGD_G5,IG5-18,47894,41550,1790490,1,0,0,0,0,0,combinatorial problem,1790490 -JGD_GL6,GL6_D_6,469,201,2526,1,0,0,0,0,0,combinatorial problem,2526 -JGD_GL6,GL6_D_7,636,470,5378,1,0,0,0,0,0,combinatorial problem,5378 -JGD_GL6,GL6_D_8,544,637,6153,1,0,0,0,0,0,combinatorial problem,6153 -JGD_GL6,GL6_D_9,340,545,4349,1,0,0,0,0,0,combinatorial problem,4349 -JGD_GL6,GL6_D_10,163,341,2053,1,0,0,0,0,0,combinatorial problem,2053 -JGD_GL7d,GL7d10,1,60,8,1,0,0,0,0,0,combinatorial problem,8 -JGD_GL7d,GL7d11,1019,60,1513,1,0,0,0,0,0,combinatorial problem,1513 -JGD_GL7d,GL7d12,8899,1019,37519,1,0,0,0,0,0,combinatorial problem,37519 -JGD_GL7d,GL7d13,47271,8899,356232,1,0,0,0,0,0,combinatorial problem,356232 -JGD_GL7d,GL7d14,171375,47271,1831183,1,0,0,0,0,0,combinatorial problem,1831183 -JGD_GL7d,GL7d15,460261,171375,6080381,1,0,0,0,0,0,combinatorial problem,6080381 -JGD_GL7d,GL7d16,955128,460261,14488881,1,0,0,0,0,0,combinatorial problem,14488881 -JGD_GL7d,GL7d17,1548650,955128,25978098,1,0,0,0,0,0,combinatorial problem,25978098 -JGD_GL7d,GL7d18,1955309,1548650,35590540,1,0,0,0,0,0,combinatorial problem,35590540 -JGD_GL7d,GL7d19,1911130,1955309,37322725,1,0,0,0,0,0,combinatorial problem,37322725 -JGD_GL7d,GL7d20,1437547,1911130,29893084,1,0,0,0,0,0,combinatorial problem,29893084 -JGD_GL7d,GL7d21,822922,1437547,18174775,1,0,0,0,0,0,combinatorial problem,18174775 -JGD_GL7d,GL7d22,349443,822922,8251000,1,0,0,0,0,0,combinatorial problem,8251000 -JGD_GL7d,GL7d23,105054,349443,2695430,1,0,0,0,0,0,combinatorial problem,2695430 -JGD_GL7d,GL7d24,21074,105054,593892,1,0,0,0,0,0,combinatorial problem,593892 -JGD_GL7d,GL7d25,2798,21074,81671,1,0,0,0,0,0,combinatorial problem,81671 -JGD_GL7d,GL7d26,305,2798,7412,1,0,0,0,0,0,combinatorial problem,7412 -JGD_Groebner,c8_mat11,4562,5761,2462970,1,0,0,0,0,0,combinatorial problem,2462970 -JGD_Groebner,c8_mat11_I,4562,5761,2462970,1,0,0,0,0,0,combinatorial problem,2462970 -JGD_Groebner,f855_mat9,2456,2511,171214,1,0,0,0,0,0,combinatorial problem,171214 -JGD_Groebner,f855_mat9_I,2456,2511,171214,1,0,0,0,0,0,combinatorial problem,171214 -JGD_Groebner,HFE18_96_in,2372,4096,933343,1,1,0,0,0,0,combinatorial problem,933343 -JGD_Groebner,rkat7_mat5,694,738,38114,1,0,0,0,0,0,combinatorial problem,38114 -JGD_Groebner,robot24c1_mat5,404,302,15118,1,0,0,0,0,0,combinatorial problem,15118 -JGD_Groebner,robot24c1_mat5_J,302,404,15118,1,0,0,0,0,0,combinatorial problem,15118 -JGD_Homology,ch3-3-b1,18,9,36,1,0,0,0,0,0,combinatorial problem,36 -JGD_Homology,ch3-3-b2,6,18,18,1,0,0,0,0,0,combinatorial problem,18 -JGD_Homology,ch4-4-b1,72,16,144,1,0,0,0,0,0,combinatorial problem,144 -JGD_Homology,ch4-4-b2,96,72,288,1,0,0,0,0,0,combinatorial problem,288 -JGD_Homology,ch4-4-b3,24,96,96,1,0,0,0,0,0,combinatorial problem,96 -JGD_Homology,ch5-5-b1,200,25,400,1,0,0,0,0,0,combinatorial problem,400 -JGD_Homology,ch5-5-b2,600,200,1800,1,0,0,0,0,0,combinatorial problem,1800 -JGD_Homology,ch5-5-b3,600,600,2400,1,0,0,0,0.02168473728106756,0.01417848206839033,combinatorial problem,2400 -JGD_Homology,ch5-5-b4,120,600,600,1,0,0,0,0,0,combinatorial problem,600 -JGD_Homology,ch6-6-b1,450,36,900,1,0,0,0,0,0,combinatorial problem,900 -JGD_Homology,ch6-6-b2,2400,450,7200,1,0,0,0,0,0,combinatorial problem,7200 -JGD_Homology,ch6-6-b3,5400,2400,21600,1,0,0,0,0,0,combinatorial problem,21600 -JGD_Homology,ch6-6-b4,4320,5400,21600,1,0,0,0,0,0,combinatorial problem,21600 -JGD_Homology,ch6-6-b5,720,4320,4320,1,0,0,0,0,0,combinatorial problem,4320 -JGD_Homology,ch7-6-b1,630,42,1260,1,0,0,0,0,0,combinatorial problem,1260 -JGD_Homology,ch7-6-b2,4200,630,12600,1,0,0,0,0,0,combinatorial problem,12600 -JGD_Homology,ch7-6-b3,12600,4200,50400,1,0,0,0,0,0,combinatorial problem,50400 -JGD_Homology,ch7-6-b4,15120,12600,75600,1,0,0,0,0,0,combinatorial problem,75600 -JGD_Homology,ch7-6-b5,5040,15120,30240,1,0,0,0,0,0,combinatorial problem,30240 -JGD_Homology,ch7-7-b1,882,49,1764,1,0,0,0,0,0,combinatorial problem,1764 -JGD_Homology,ch7-7-b2,7350,882,22050,1,0,0,0,0,0,combinatorial problem,22050 -JGD_Homology,ch7-7-b5,35280,52920,211680,1,0,0,0,0,0,combinatorial problem,211680 -JGD_Homology,ch7-8-b1,1176,56,2352,1,0,0,0,0,0,combinatorial problem,2352 -JGD_Homology,ch7-8-b2,11760,1176,35280,1,0,0,0,0,0,combinatorial problem,35280 -JGD_Homology,ch7-8-b3,58800,11760,235200,1,0,0,0,0,0,combinatorial problem,235200 -JGD_Homology,ch7-8-b4,141120,58800,705600,1,0,0,0,0,0,combinatorial problem,705600 -JGD_Homology,ch7-8-b5,141120,141120,846720,1,0,0,0,0.0003732057190233348,0.0002054993516141147,combinatorial problem,846720 -JGD_Homology,ch7-9-b1,1512,63,3024,1,0,0,0,0,0,combinatorial problem,3024 -JGD_Homology,ch7-9-b2,17640,1512,52920,1,0,0,0,0,0,combinatorial problem,52920 -JGD_Homology,ch7-9-b3,105840,17640,423360,1,0,0,0,0,0,combinatorial problem,423360 -JGD_Homology,ch7-9-b4,317520,105840,1587600,1,0,0,0,0,0,combinatorial problem,1587600 -JGD_Homology,ch7-9-b5,423360,317520,2540160,1,0,0,0,0,0,combinatorial problem,2540160 -JGD_Homology,ch8-8-b1,1568,64,3136,1,0,0,0,0,0,combinatorial problem,3136 -JGD_Homology,ch8-8-b2,18816,1568,56448,1,0,0,0,0,0,combinatorial problem,56448 -JGD_Homology,ch8-8-b3,117600,18816,470400,1,0,0,0,0,0,combinatorial problem,470400 -JGD_Homology,ch8-8-b4,376320,117600,1881600,1,0,0,0,0,0,combinatorial problem,1881600 -JGD_Homology,ch8-8-b5,564480,376320,3386880,1,0,0,0,0,0,combinatorial problem,3386880 -JGD_Homology,cis-n4c6-b1,210,21,420,1,0,0,0,0,0,combinatorial problem,420 -JGD_Homology,cis-n4c6-b13,6300,25605,88200,1,0,0,0,0,0,combinatorial problem,88200 -JGD_Homology,cis-n4c6-b14,920,6300,13800,1,0,0,0,0,0,combinatorial problem,13800 -JGD_Homology,cis-n4c6-b15,60,920,960,1,0,0,0,0,0,combinatorial problem,960 -JGD_Homology,cis-n4c6-b2,1330,210,3990,1,0,0,0,0,0,combinatorial problem,3990 -JGD_Homology,cis-n4c6-b3,5970,1330,23880,1,0,0,0,0,0,combinatorial problem,23880 -JGD_Homology,cis-n4c6-b4,20058,5970,100290,1,0,0,0,0,0,combinatorial problem,100290 -JGD_Homology,D6-6,120576,23740,146520,1,0,0,0,0,0,combinatorial problem,146880 -JGD_Homology,klein-b1,30,10,60,1,0,0,0,0,0,combinatorial problem,60 -JGD_Homology,klein-b2,20,30,60,1,0,0,0,0,0,combinatorial problem,60 -JGD_Homology,lutz30-23-b6,1716,3003,12012,1,0,0,0,0,0,combinatorial problem,12012 -JGD_Homology,m133-b3,200200,200200,800800,1,0,0,0,4.495504495504496e-05,2.747252747252747e-05,combinatorial problem,800800 -JGD_Homology,mk10-b1,630,45,1260,1,0,0,0,0,0,combinatorial problem,1260 -JGD_Homology,mk10-b2,3150,630,9450,1,0,0,0,0,0,combinatorial problem,9450 -JGD_Homology,mk10-b3,4725,3150,18900,1,0,0,0,0,0,combinatorial problem,18900 -JGD_Homology,mk10-b4,945,4725,4725,1,0,0,0,0,0,combinatorial problem,4725 -JGD_Homology,mk11-b1,990,55,1980,1,0,0,0,0,0,combinatorial problem,1980 -JGD_Homology,mk11-b2,6930,990,20790,1,0,0,0,0,0,combinatorial problem,20790 -JGD_Homology,mk11-b3,17325,6930,69300,1,0,0,0,0,0,combinatorial problem,69300 -JGD_Homology,mk11-b4,10395,17325,51975,1,0,0,0,0,0,combinatorial problem,51975 -JGD_Homology,mk11-b4b,9450,17325,47250,1,0,0,0,0,0,combinatorial problem,47250 -JGD_Homology,mk12-b1,1485,66,2970,1,0,0,0,0,0,combinatorial problem,2970 -JGD_Homology,mk12-b2,13860,1485,41580,1,0,0,0,0,0,combinatorial problem,41580 -JGD_Homology,mk12-b3,51975,13860,207900,1,0,0,0,0,0,combinatorial problem,207900 -JGD_Homology,mk12-b4,62370,51975,311850,1,0,0,0,0,0,combinatorial problem,311850 -JGD_Homology,mk12-b5,10395,62370,62370,1,0,0,0,0,0,combinatorial problem,62370 -JGD_Homology,mk13-b5,135135,270270,810810,1,0,0,0,0,0,combinatorial problem,810810 -JGD_Homology,mk9-b1,378,36,756,1,0,0,0,0,0,combinatorial problem,756 -JGD_Homology,mk9-b2,1260,378,3780,1,0,0,0,0,0,combinatorial problem,3780 -JGD_Homology,mk9-b3,945,1260,3780,1,0,0,0,0,0,combinatorial problem,3780 -JGD_Homology,n2c6-b10,30,306,330,1,0,0,0,0,0,combinatorial problem,330 -JGD_Homology,n2c6-b1,105,15,210,1,0,0,0,0,0,combinatorial problem,210 -JGD_Homology,n2c6-b2,455,105,1365,1,0,0,0,0,0,combinatorial problem,1365 -JGD_Homology,n2c6-b3,1365,455,5460,1,0,0,0,0,0,combinatorial problem,5460 -JGD_Homology,n2c6-b4,3003,1365,15015,1,0,0,0,0,0,combinatorial problem,15015 -JGD_Homology,n2c6-b5,4945,3003,29670,1,0,0,0,0,0,combinatorial problem,29670 -JGD_Homology,n2c6-b6,5715,4945,40005,1,0,0,0,0,0,combinatorial problem,40005 -JGD_Homology,n2c6-b7,3990,5715,31920,1,0,0,0,0,0,combinatorial problem,31920 -JGD_Homology,n2c6-b8,1470,3990,13230,1,0,0,0,0,0,combinatorial problem,13230 -JGD_Homology,n2c6-b9,306,1470,3060,1,0,0,0,0,0,combinatorial problem,3060 -JGD_Homology,n3c4-b1,15,6,30,1,0,0,0,0,0,combinatorial problem,30 -JGD_Homology,n3c4-b2,20,15,60,1,0,0,0,0,0,combinatorial problem,60 -JGD_Homology,n3c4-b3,15,20,60,1,0,0,0,0,0,combinatorial problem,60 -JGD_Homology,n3c4-b4,6,15,30,1,0,0,0,0,0,combinatorial problem,30 -JGD_Homology,n3c5-b1,45,10,90,1,0,0,0,0,0,combinatorial problem,90 -JGD_Homology,n3c5-b2,120,45,360,1,0,0,0,0,0,combinatorial problem,360 -JGD_Homology,n3c5-b3,210,120,840,1,0,0,0,0,0,combinatorial problem,840 -JGD_Homology,n3c5-b4,252,210,1260,1,0,0,0,0,0,combinatorial problem,1260 -JGD_Homology,n3c5-b5,210,252,1260,1,0,0,0,0,0,combinatorial problem,1260 -JGD_Homology,n3c5-b6,120,210,840,1,0,0,0,0,0,combinatorial problem,840 -JGD_Homology,n3c5-b7,30,120,240,1,0,0,0,0,0,combinatorial problem,240 -JGD_Homology,n3c6-b10,675,2511,7425,1,0,0,0,0,0,combinatorial problem,7425 -JGD_Homology,n3c6-b1,105,105,210,1,0,0,0,0.08571428571428572,0.0761904761904762,combinatorial problem,210 -JGD_Homology,n3c6-b11,60,675,720,1,0,0,0,0,0,combinatorial problem,720 -JGD_Homology,n3c6-b2,455,105,1365,1,0,0,0,0,0,combinatorial problem,1365 -JGD_Homology,n3c6-b3,1365,455,5460,1,0,0,0,0,0,combinatorial problem,5460 -JGD_Homology,n3c6-b4,3003,1365,15015,1,0,0,0,0,0,combinatorial problem,15015 -JGD_Homology,n3c6-b5,5005,3003,30030,1,0,0,0,0,0,combinatorial problem,30030 -JGD_Homology,n3c6-b6,6435,5005,45045,1,0,0,0,0,0,combinatorial problem,45045 -JGD_Homology,n3c6-b7,6435,6435,51480,1,0,0,0,1,0.5333333333333333,combinatorial problem,51480 -JGD_Homology,n3c6-b8,4935,6435,44415,1,0,0,0,0,0,combinatorial problem,44415 -JGD_Homology,n3c6-b9,2511,4935,25110,1,0,0,0,0,0,combinatorial problem,25110 -JGD_Homology,n4c5-b10,120,630,1320,1,0,0,0,0,0,combinatorial problem,1320 -JGD_Homology,n4c5-b1,105,15,210,1,0,0,0,0,0,combinatorial problem,210 -JGD_Homology,n4c5-b11,10,120,120,1,0,0,0,0,0,combinatorial problem,120 -JGD_Homology,n4c5-b2,455,105,1365,1,0,0,0,0,0,combinatorial problem,1365 -JGD_Homology,n4c5-b3,1350,455,5400,1,0,0,0,0,0,combinatorial problem,5400 -JGD_Homology,n4c5-b4,2852,1350,14260,1,0,0,0,0,0,combinatorial problem,14260 -JGD_Homology,n4c5-b5,4340,2852,26040,1,0,0,0,0,0,combinatorial problem,26040 -JGD_Homology,n4c5-b6,4735,4340,33145,1,0,0,0,0,0,combinatorial problem,33145 -JGD_Homology,n4c5-b7,3635,4735,29080,1,0,0,0,0,0,combinatorial problem,29080 -JGD_Homology,n4c5-b8,1895,3635,17055,1,0,0,0,0,0,combinatorial problem,17055 -JGD_Homology,n4c5-b9,630,1895,6300,1,0,0,0,0,0,combinatorial problem,6300 -JGD_Homology,n4c6-b10,132402,186558,1456422,1,0,0,0,0,0,combinatorial problem,1456422 -JGD_Homology,n4c6-b11,69235,132402,830820,1,0,0,0,0,0,combinatorial problem,830820 -JGD_Homology,n4c6-b1,210,21,420,1,0,0,0,0,0,combinatorial problem,420 -JGD_Homology,n4c6-b12,25605,69235,332865,1,0,0,0,0,0,combinatorial problem,332865 -JGD_Homology,n4c6-b13,6300,25605,88200,1,0,0,0,0,0,combinatorial problem,88200 -JGD_Homology,n4c6-b14,920,6300,13800,1,0,0,0,0,0,combinatorial problem,13800 -JGD_Homology,n4c6-b15,60,920,960,1,0,0,0,0,0,combinatorial problem,960 -JGD_Homology,n4c6-b2,1330,210,3990,1,0,0,0,0,0,combinatorial problem,3990 -JGD_Homology,n4c6-b3,5970,1330,23880,1,0,0,0,0,0,combinatorial problem,23880 -JGD_Homology,n4c6-b4,20058,5970,100290,1,0,0,0,0,0,combinatorial problem,100290 -JGD_Homology,n4c6-b5,51813,20058,310878,1,0,0,0,0,0,combinatorial problem,310878 -JGD_Homology,n4c6-b6,104115,51813,728805,1,0,0,0,0,0,combinatorial problem,728805 -JGD_Homology,n4c6-b7,163215,104115,1305720,1,0,0,0,0,0,combinatorial problem,1305720 -JGD_Homology,n4c6-b8,198895,163215,1790055,1,0,0,0,0,0,combinatorial problem,1790055 -JGD_Homology,n4c6-b9,186558,198895,1865580,1,0,0,0,0,0,combinatorial problem,1865580 -JGD_Homology,shar_te2-b1,17160,286,34320,1,0,0,0,0,0,combinatorial problem,34320 -JGD_Homology,shar_te2-b2,200200,17160,600600,1,0,0,0,0,0,combinatorial problem,600600 -JGD_Homology,shar_te2-b3,200200,200200,800800,1,0,0,0,7.492516848797264e-05,2.997006739518906e-05,combinatorial problem,800800 -QY,case39,40216,40216,1042160,1,0,0,0,1,1,power network problem sequence,1042160 -JGD_Kocay,Trec3,1,2,1,1,1,0,0,0,0,combinatorial problem,1 -JGD_Kocay,Trec4,2,3,3,1,0,0,0,0,0,combinatorial problem,3 -JGD_Kocay,Trec5,3,7,12,1,0,0,0,0,0,combinatorial problem,12 -JGD_Kocay,Trec6,6,15,40,1,0,0,0,0,0,combinatorial problem,40 -JGD_Kocay,Trec7,11,36,147,1,0,0,0,0,0,combinatorial problem,147 -JGD_Kocay,Trec8,23,84,549,1,0,0,0,0,0,combinatorial problem,549 -JGD_Kocay,Trec9,47,201,2147,1,0,0,0,0,0,combinatorial problem,2147 -JGD_Kocay,Trec10,106,478,8612,1,0,0,0,0,0,combinatorial problem,8612 -JGD_Kocay,Trec11,235,1138,35705,1,0,0,0,0,0,combinatorial problem,35705 -JGD_Kocay,Trec12,551,2726,151219,1,0,0,0,0,0,combinatorial problem,151219 -JGD_Kocay,Trec13,1301,6561,654517,1,0,0,0,0,0,combinatorial problem,654517 -JGD_Kocay,Trec14,3159,15905,2872265,1,0,0,0,0,0,combinatorial problem,2872265 -JGD_Margulies,cat_ears_2_1,85,85,254,1,1,0,0,0.03252032520325204,0.03252032520325204,combinatorial problem,254 -JGD_Margulies,cat_ears_2_4,1009,2689,7982,1,1,0,0,0,0,combinatorial problem,7982 -JGD_Margulies,cat_ears_3_1,204,181,542,1,1,0,0,0,0,combinatorial problem,542 -JGD_Margulies,cat_ears_3_4,5226,13271,39592,1,1,0,0,0,0,combinatorial problem,39592 -JGD_Margulies,cat_ears_4_1,377,313,938,1,1,0,0,0,0,combinatorial problem,938 -JGD_Margulies,cat_ears_4_4,19020,44448,132888,1,1,0,0,0,0,combinatorial problem,132888 -JGD_Margulies,flower_4_1,121,129,386,1,1,0,0,0,0,combinatorial problem,386 -JGD_Margulies,flower_4_4,1837,5529,16466,1,1,0,0,0,0,combinatorial problem,16466 -JGD_Margulies,flower_5_1,211,201,602,1,1,0,0,0,0,combinatorial problem,602 -JGD_Margulies,flower_5_4,5226,14721,43942,1,1,0,0,0,0,combinatorial problem,43942 -JGD_Margulies,flower_7_1,463,393,1178,1,1,0,0,0,0,combinatorial problem,1178 -JGD_Margulies,flower_7_4,27693,67593,202218,1,1,0,0,0,0,combinatorial problem,202218 -JGD_Margulies,flower_8_1,625,513,1538,1,1,0,0,0,0,combinatorial problem,1538 -JGD_Margulies,flower_8_4,55081,125361,375266,1,1,0,0,0,0,combinatorial problem,375266 -JGD_Margulies,kneser_10_4_1,349651,330751,992252,1,1,0,0,0,0,combinatorial problem,992252 -JGD_Margulies,kneser_6_2_1,601,676,2027,1,1,0,0,0,0,combinatorial problem,2027 -JGD_Margulies,kneser_8_3_1,15737,15681,47042,1,1,0,0,0,0,combinatorial problem,47042 -JGD_Margulies,wheel_3_1,21,25,74,1,1,0,0,0,0,combinatorial problem,74 -JGD_Margulies,wheel_4_1,36,41,122,1,1,0,0,0,0,combinatorial problem,122 -JGD_Margulies,wheel_5_1,57,61,182,1,1,0,0,0,0,combinatorial problem,182 -JGD_Margulies,wheel_601,902103,723605,2170814,1,1,0,0,0,0,combinatorial problem,2170814 -JGD_Margulies,wheel_6_1,83,85,254,1,1,0,0,0,0,combinatorial problem,254 -JGD_Margulies,wheel_7_1,114,113,338,1,1,0,0,0,0,combinatorial problem,338 -JGD_Relat,rel3,12,5,18,1,0,0,0,0,0,combinatorial problem,18 -JGD_Relat,rel4,66,12,104,1,0,0,0,0,0,combinatorial problem,104 -JGD_Relat,rel5,340,35,656,1,0,0,0,0,0,combinatorial problem,656 -JGD_Relat,rel6,2340,157,5101,1,0,0,0,0,0,combinatorial problem,5101 -JGD_Relat,rel7,21924,1045,50636,1,0,0,0,0,0,combinatorial problem,50636 -JGD_Relat,rel8,345688,12347,821839,1,0,0,0,0,0,combinatorial problem,821839 -JGD_Relat,rel9,9888048,274669,23667183,1,0,0,0,0,0,combinatorial problem,23667183 -JGD_Relat,relat3,12,5,24,1,0,0,0,0,0,combinatorial problem,24 -JGD_Relat,relat4,66,12,172,1,0,0,0,0,0,combinatorial problem,172 -JGD_Relat,relat5,340,35,1058,1,0,0,0,0,0,combinatorial problem,1058 -JGD_Relat,relat6,2340,157,8108,1,0,0,0,0,0,combinatorial problem,8108 -JGD_Relat,relat7b,21924,1045,81355,1,0,0,0,0,0,combinatorial problem,81355 -JGD_Relat,relat7,21924,1045,81355,1,0,0,0,0,0,combinatorial problem,81355 -JGD_Relat,relat8,345688,12347,1334038,1,0,0,0,0,0,combinatorial problem,1334038 -JGD_Relat,relat9,12360060,549336,38955420,1,0,0,0,0,0,combinatorial problem,38955420 -JGD_SL6,D_5,434,115,1832,1,0,0,0,0,0,combinatorial problem,1832 -JGD_SL6,D_6,970,435,6491,1,0,0,0,0,0,combinatorial problem,6491 -JGD_SL6,D_7,1270,971,12714,1,0,0,0,0,0,combinatorial problem,12714 -JGD_SL6,D_8,1132,1271,14966,1,0,0,0,0,0,combinatorial problem,14966 -JGD_SL6,D_9,815,1133,12395,1,0,0,0,0,0,combinatorial problem,12395 -JGD_SL6,D_10,460,816,7614,1,0,0,0,0,0,combinatorial problem,7614 -JGD_SL6,D_11,169,461,2952,1,0,0,0,0,0,combinatorial problem,2952 -JGD_SPG,08blocks,300,300,592,1,0,0,0,0.0273972602739726,0,combinatorial problem,592 -JGD_SPG,EX1,560,560,8736,1,1,0,0,1,1,combinatorial problem,8736 -JGD_SPG,EX2,560,560,8736,1,1,0,0,1,1,combinatorial problem,8736 -JGD_SPG,EX3,2600,2600,71760,1,1,0,0,1,1,combinatorial problem,71760 -JGD_SPG,EX4,2600,2600,71760,1,1,0,0,1,1,combinatorial problem,71760 -JGD_SPG,EX5,6545,6545,295680,1,1,0,0,1,1,combinatorial problem,295680 -JGD_SPG,EX6,6545,6545,295680,1,1,0,0,1,1,combinatorial problem,295680 -JGD_Taha,abtaha2,37932,331,137228,1,0,0,0,0,0,combinatorial problem,137228 -JGD_Taha,abtaha1,14596,209,51307,1,0,0,0,0,0,combinatorial problem,51307 -JGD_Trefethen,Trefethen_20b,19,19,147,1,0,0,1,1,1,combinatorial problem,147 -JGD_Trefethen,Trefethen_20,20,20,158,1,0,0,1,1,1,combinatorial problem,158 -JGD_Trefethen,Trefethen_150,150,150,2040,1,0,0,1,1,1,combinatorial problem,2040 -JGD_Trefethen,Trefethen_200b,199,199,2873,1,0,0,1,1,1,combinatorial problem,2873 -JGD_Trefethen,Trefethen_200,200,200,2890,1,0,0,1,1,1,combinatorial problem,2890 -JGD_Trefethen,Trefethen_300,300,300,4678,1,0,0,1,1,1,combinatorial problem,4678 -JGD_Trefethen,Trefethen_500,500,500,8478,1,0,0,1,1,1,combinatorial problem,8478 -JGD_Trefethen,Trefethen_700,700,700,12654,1,0,0,1,1,1,combinatorial problem,12654 -JGD_Trefethen,Trefethen_2000,2000,2000,41906,1,0,0,1,1,1,combinatorial problem,41906 -JGD_Trefethen,Trefethen_20000b,19999,19999,554435,1,0,0,1,1,1,combinatorial problem,554435 -JGD_Trefethen,Trefethen_20000,20000,20000,554466,1,0,0,1,1,1,combinatorial problem,554466 -QY,case9,14454,14454,147972,1,0,0,0,1,1,power network problem sequence,147972 -FreeFieldTechnologies,mono_500Hz,169410,169410,5033796,0,0,1,0,1,0.820796293715178,acoustics problem,5036288 -Freescale,Freescale1,3428755,3428755,17052626,1,0,0,0,0.9061379876258038,0.3945743467477048,circuit simulation problem,18920347 -NYPA,Maragal_8,33212,75077,1308415,1,0,0,0,0,0,least squares problem,1308415 -Bates,sls,1748122,62729,6804304,1,0,0,0,0,0,least squares problem,6804304 -TSOPF,TSOPF_RS_b2383,38120,38120,16171169,1,0,0,0,0.002485718029041526,0.0003350401831305099,power network problem,16171169 -TSOPF,TSOPF_FS_b300,29214,29214,4400122,1,0,0,0,1,1,power network problem,4400122 -TSOPF,TSOPF_FS_b162_c1,10798,10798,608540,1,0,0,0,1,1,power network problem,608540 -TSOPF,TSOPF_FS_b162_c3,30798,30798,1801300,1,0,0,0,1,1,power network problem,1801300 -TSOPF,TSOPF_FS_b162_c4,40798,40798,2398220,1,0,0,0,1,1,power network problem,2398220 -TSOPF,TSOPF_FS_b300_c1,29214,29214,4400122,1,0,0,0,1,1,power network problem,4400122 -TSOPF,TSOPF_FS_b300_c2,56814,56814,8767466,1,0,0,0,1,1,power network problem,8767466 -TSOPF,TSOPF_FS_b300_c3,84414,84414,13135930,1,0,0,0,1,1,power network problem,13135930 -TSOPF,TSOPF_FS_b39_c19,76216,76216,1977600,1,0,0,0,1,1,power network problem,1977600 -TSOPF,TSOPF_FS_b39_c30,120216,120216,3121160,1,0,0,0,1,1,power network problem,3121160 -TSOPF,TSOPF_FS_b39_c7,28216,28216,730080,1,0,0,0,1,1,power network problem,730080 -TSOPF,TSOPF_FS_b9_c1,2454,2454,25032,1,0,0,0,1,1,power network problem,25032 -TSOPF,TSOPF_FS_b9_c6,14454,14454,147972,1,0,0,0,1,1,power network problem,147972 -TSOPF,TSOPF_RS_b162_c1,5374,5374,205399,1,0,0,0,0.02981942459702805,0.002418331351367556,power network problem,205399 -TSOPF,TSOPF_RS_b162_c3,15374,15374,610299,1,0,0,0,0.02683526094131803,0.0008133934303355415,power network problem,610299 -TSOPF,TSOPF_RS_b162_c4,20374,20374,812749,1,0,0,0,0.02645844479664045,0.0006107347997698388,power network problem,812749 -TSOPF,TSOPF_RS_b2052_c1,25626,25626,6761100,1,0,0,0,0.003886561090908282,0.0002050869579089441,power network problem,6761100 -TSOPF,TSOPF_RS_b2383_c1,38120,38120,16171169,1,0,0,0,0.002485718029041526,0.0003350401831305099,power network problem,16171169 -TSOPF,TSOPF_RS_b300_c1,14538,14538,1474325,1,0,0,0,0.01034334678703541,0.0003506683588241378,power network problem,1474325 -TSOPF,TSOPF_RS_b300_c2,28338,28338,2943887,1,0,0,0,0.00991209710027776,0.0001755931670937033,power network problem,2943887 -TSOPF,TSOPF_RS_b300_c3,42138,42138,4413449,1,0,0,0,0.009768063952888569,0.0001171197776188222,power network problem,4413449 -TSOPF,TSOPF_RS_b39_c19,38098,38098,684206,1,0,0,0,0.05914025923776359,0.0001300058038305281,power network problem,684206 -TSOPF,TSOPF_RS_b39_c30,60098,60098,1079986,1,0,0,0,0.05903687108113011,8.236061201778597e-05,power network problem,1079986 -TSOPF,TSOPF_RS_b39_c7,14098,14098,252446,1,0,0,0,0.05962284731399325,0.0003524007299729407,power network problem,252446 -TSOPF,TSOPF_RS_b678_c1,18696,18696,4396289,1,0,0,0,0.004448301852317109,0.0003256971884064591,power network problem,4396289 -TSOPF,TSOPF_RS_b678_c2,35696,35696,8781949,1,0,0,0,0.004170134538170976,0.0001630280691806068,power network problem,8781949 -TSOPF,TSOPF_RS_b9_c6,7224,7224,54082,1,0,0,0,0.1544436146377894,0.0002560546249866638,power network problem,54082 -Lee,fem_hifreq_circuit,491100,491100,20239237,0,0,1,0,0.999999037883928,0.4990933575151925,electromagnetics problem,20239237 -Buss,connectus,512,394792,1127525,1,1,0,0,0,0,undirected bipartite graph,1127525 -Yoshiyasu,image_interp,240000,120000,711683,1,0,1,0,0,0,computer graphics/vision problem,711683 -Yoshiyasu,mesh_deform,234023,9393,853829,1,0,1,0,0,0,computer graphics/vision problem,853829 -Priebel,130bit,584,575,6120,1,1,0,0,0,0,combinatorial problem,6120 -Priebel,145bit,1002,993,11315,1,1,0,0,0,0,combinatorial problem,11315 -Priebel,162bit,3606,3597,37118,1,1,0,0,0,0,combinatorial problem,37118 -Priebel,176bit,7441,7431,82270,1,1,0,0,0,0,combinatorial problem,82270 -Priebel,192bit,13691,13682,154303,1,1,0,0,0,0,combinatorial problem,154303 -Priebel,208bit,24430,24421,299756,1,1,0,0,0,0,combinatorial problem,299756 -MathWorks,TS,2142,2142,45262,1,0,0,0,0.9525732031943213,0.04130434782608695,counter-example problem,45262 -Botonakis,thermomech_TC,102158,102158,711558,1,0,1,1,1,1,thermal problem,711558 -Botonakis,thermomech_TK,102158,102158,711558,1,0,1,1,1,1,thermal problem,711558 -Botonakis,thermomech_dM,204316,204316,1423116,1,0,1,1,1,1,thermal problem,1423116 -Botonakis,thermomech_dK,204316,204316,2846228,1,0,1,0,1,0.6652727267221619,thermal problem,2846228 -MaxPlanck,shallow_water1,81920,81920,327680,1,0,1,1,1,1,computational fluid dynamics problem,327680 -MaxPlanck,shallow_water2,81920,81920,327680,1,0,1,1,1,1,computational fluid dynamics problem,327680 -Buss,12month1,12471,872622,22624727,1,1,0,0,0,0,undirected bipartite graph,22624727 -Clark,tomographic1,73159,59498,647495,1,0,1,0,0,0,computer graphics/vision problem,647495 -Bourchtein,atmosmodd,1270432,1270432,8814880,1,0,1,0,1,0.6690207156308852,computational fluid dynamics problem,8814880 -Bourchtein,atmosmodj,1270432,1270432,8814880,1,0,1,0,1,0.6690207156308852,computational fluid dynamics problem,8814880 -Bourchtein,atmosmodl,1489752,1489752,10319760,1,0,1,0,1,0.6714503542918647,computational fluid dynamics problem,10319760 -Bourchtein,atmosmodm,1489752,1489752,10319760,1,0,1,0,1,0.6714503542918647,computational fluid dynamics problem,10319760 -Puri,ABACUS_shell_ud,23412,23412,218484,1,0,1,0,1,0.9753731955380578,model reduction problem,218484 -Puri,ABACUS_shell_ld,23412,23412,218484,0,0,1,0,1,0.8850885826771654,model reduction problem,218484 -Puri,ABACUS_shell_md,23412,23412,218484,0,0,1,0,1,0.8850885826771654,model reduction problem,218484 -Puri,ABACUS_shell_hd,23412,23412,218484,0,0,1,0,1,0.8850885826771654,model reduction problem,218484 -Stevenson,LargeRegFile,2111154,801374,4944201,1,0,0,0,0,0,circuit simulation problem,4944201 -Freescale,memchip,2707524,2707524,13343948,1,0,0,0,0.9058013004223939,0.4015860962358702,circuit simulation problem,14810202 -Freescale,transient,178866,178866,961368,1,0,0,0,0.9996742983044353,0.243597788123496,circuit simulation problem,961790 -Freescale,circuit5M,5558326,5558326,59524291,1,0,0,0,0.9999998517635799,0.4198865531852634,circuit simulation problem,59524291 -Freescale,circuit5M_dc,3523317,3523317,14865409,1,0,0,0,0.9072118240231114,0.7150571517141635,circuit simulation problem,19194193 -Grund,poli3,16955,16955,37849,1,0,0,0,0.00507322676366421,0.0005743275581506652,economic problem,37849 -Grund,poli4,33833,33833,73249,1,0,0,0,0.003551857113862391,0.0002029632636492795,economic problem,73249 -Belcastro,human_gene1,22283,22283,24669643,1,0,0,0,1,1,undirected weighted graph,24669643 -Belcastro,human_gene2,14340,14340,18068388,1,0,0,0,1,1,undirected weighted graph,18068388 -Belcastro,mouse_gene,45101,45101,28967291,1,0,0,0,1,1,undirected weighted graph,28967291 -Um,offshore,259789,259789,4242673,1,0,1,1,1,1,electromagnetics problem,4242673 -SNAP,soc-Epinions1,75888,75888,508837,1,1,0,0,0.405226035056413,0.405226035056413,directed graph,508837 -SNAP,soc-LiveJournal1,4847571,4847571,68993773,1,1,0,0,0.7484193555024753,0.7484193555024753,directed graph,68993773 -SNAP,soc-Slashdot0811,77360,77360,905468,1,1,0,0,0.8669352939826918,0.8669352939826918,directed graph,905468 -SNAP,soc-Slashdot0902,82168,82168,948464,1,1,0,0,0.8410650442849082,0.8410650442849082,directed graph,948464 -SNAP,wiki-Vote,8297,8297,103689,1,1,0,0,0.0564572905515532,0.0564572905515532,directed graph,103689 -SNAP,email-EuAll,265214,265214,420045,1,1,0,0,0.2600511748250413,0.2600511748250413,directed graph,420045 -SNAP,email-Enron,36692,36692,367662,1,1,0,0,1,1,directed graph,367662 -SNAP,wiki-Talk,2394385,2394385,5021410,1,1,0,0,0.1441208744157517,0.1441208744157517,directed graph,5021410 -SNAP,cit-HepPh,34546,34546,421578,1,1,0,0,0.003117186276789061,0.003117186276789061,directed graph,421578 -SNAP,cit-HepTh,27770,27770,352807,1,1,0,0,0.002738343613933237,0.002738343613933237,directed graph,352807 -SNAP,cit-Patents,3774768,3774768,16518948,1,1,0,0,0,0,directed graph,16518948 -SNAP,ca-AstroPh,18772,18772,396160,1,1,0,0,1,1,undirected graph,396160 -SNAP,ca-CondMat,23133,23133,186936,1,1,0,0,1,1,undirected graph,186936 -SNAP,ca-GrQc,5242,5242,28980,1,1,0,0,1,1,undirected graph,28980 -SNAP,ca-HepPh,12008,12008,237010,1,1,0,0,1,1,undirected graph,237010 -SNAP,ca-HepTh,9877,9877,51971,1,1,0,0,1,1,undirected graph,51971 -SNAP,web-BerkStan,685230,685230,7600595,1,1,0,0,0.2502764586193581,0.2502764586193581,directed graph,7600595 -SNAP,web-Google,916428,916428,5105039,1,1,0,0,0.3067510355944391,0.3067510355944391,directed graph,5105039 -SNAP,web-NotreDame,325729,325729,1497134,1,1,0,0,0.5165359238309862,0.5165359238309862,directed graph,1497134 -SNAP,web-Stanford,281903,281903,2312497,1,1,0,0,0.2766368994208425,0.2766368994208425,directed graph,2312497 -SNAP,amazon0302,262111,262111,1234877,1,1,0,0,0.5427018237443891,0.5427018237443891,directed graph,1234877 -SNAP,amazon0312,400727,400727,3200440,1,1,0,0,0.5315337891039982,0.5315337891039982,directed graph,3200440 -SNAP,amazon0505,410236,410236,3356824,1,1,0,0,0.5465803390347542,0.5465803390347542,directed graph,3356824 -SNAP,amazon0601,403394,403394,3387388,1,1,0,0,0.5573497928197183,0.5573497928197183,directed graph,3387388 -SNAP,p2p-Gnutella04,10879,10879,39994,1,1,0,0,0,0,directed graph,39994 -SNAP,p2p-Gnutella05,8846,8846,31839,1,1,0,0,0,0,directed graph,31839 -SNAP,p2p-Gnutella06,8717,8717,31525,1,1,0,0,0,0,directed graph,31525 -SNAP,p2p-Gnutella08,6301,6301,20777,1,1,0,0,0,0,directed graph,20777 -SNAP,p2p-Gnutella09,8114,8114,26013,1,1,0,0,0,0,directed graph,26013 -SNAP,p2p-Gnutella24,26518,26518,65369,1,1,0,0,0,0,directed graph,65369 -SNAP,p2p-Gnutella25,22687,22687,54705,1,1,0,0,0,0,directed graph,54705 -SNAP,p2p-Gnutella30,36682,36682,88328,1,1,0,0,0,0,directed graph,88328 -SNAP,p2p-Gnutella31,62586,62586,147892,1,1,0,0,0,0,directed graph,147892 -SNAP,roadNet-CA,1971281,1971281,5533214,1,1,0,0,1,1,undirected graph,5533214 -SNAP,roadNet-PA,1090920,1090920,3083796,1,1,0,0,1,1,undirected graph,3083796 -SNAP,roadNet-TX,1393383,1393383,3843320,1,1,0,0,1,1,undirected graph,3843320 -SNAP,as-735,7716,7716,26467,1,1,0,0,1,1,undirected graph sequence,26467 -SNAP,as-Skitter,1696415,1696415,22190596,1,1,0,0,1,1,undirected graph,22190596 -SNAP,as-caida,31379,31379,106762,1,0,0,0,1,0.07869841329311927,directed weighted graph sequence,106762 -SNAP,Oregon-1,11492,11492,46818,1,1,0,0,1,1,undirected graph sequence,46818 -SNAP,Oregon-2,11806,11806,65460,1,1,0,0,1,1,undirected graph sequence,65460 -SNAP,soc-sign-epinions,131828,131828,841372,1,0,0,0,0.3082520316984202,0.3018224331855771,directed weighted graph,841372 -SNAP,soc-sign-Slashdot081106,77357,77357,516575,1,0,0,0,0.1859207278710739,0.1786110438948846,directed weighted graph,516575 -SNAP,soc-sign-Slashdot090216,81871,81871,545671,1,0,0,0,0.175926519826049,0.168574104176326,directed weighted graph,545671 -SNAP,soc-sign-Slashdot090221,82144,82144,549202,1,0,0,0,0.1774246998372184,0.1703271291801559,directed weighted graph,549202 -Dziekonski,gsm_106857,589446,589446,21758924,1,0,1,0,1,1,electromagnetics problem,21758924 -Gleich,flickr,820878,820878,9837214,1,1,0,0,0.653017002578169,0.653017002578169,directed graph,9837214 -Gleich,minnesota,2642,2642,6606,1,1,0,0,1,1,undirected graph,6606 -Gleich,usroads-48,126146,126146,323900,1,1,0,0,1,1,undirected graph,323900 -Gleich,usroads,129164,129164,330870,1,1,0,0,1,1,undirected graph,330870 -Fluorem,DK01R,903,903,11766,1,0,1,0,0.9614287029365737,0,computational fluid dynamics problem,11766 -Fluorem,GT01R,7980,7980,430909,1,0,1,0,0.8811455350661695,9.457852263618717e-06,computational fluid dynamics problem,430909 -Fluorem,PR02R,161070,161070,8185136,1,0,1,0,0.9476492341912441,0.0001697393815055858,computational fluid dynamics problem,8185136 -Fluorem,RM07R,381689,381689,37464962,1,0,1,0,0.9261667922354103,4.260681089287885e-06,computational fluid dynamics problem,37464962 -Rommes,ww_36_pmec_36,66,66,1194,1,0,1,0,0.3339191564147627,0,eigenvalue/model reduction problem,1194 -Rommes,ww_vref_6405,13251,13251,48737,1,0,1,0,0.707540090603374,0.5856980072816208,eigenvalue/model reduction problem,48737 -Rommes,xingo_afonso_itaipu,13250,13250,48735,1,0,1,0,0.7075597554196776,0.5857142857142857,eigenvalue/model reduction problem,48735 -Rommes,mimo8x8_system,13309,13309,48872,1,0,1,0,0.7060291752177048,0.5844472793832159,eigenvalue/model reduction problem,48872 -Rommes,mimo28x28_system,13251,13251,48737,1,0,1,0,0.707540090603374,0.5856980072816208,eigenvalue/model reduction problem,48737 -Rommes,mimo46x46_system,13250,13250,48735,1,0,1,0,0.7075597554196776,0.5857142857142857,eigenvalue/model reduction problem,48735 -Rommes,juba40k,40337,40337,144945,1,0,1,0,0.6934462083788981,0.5830536596402579,eigenvalue/model reduction problem,144945 -Rommes,bauru5727,40366,40366,145019,1,0,1,0,0.6931521565005984,0.5828064190876452,eigenvalue/model reduction problem,145019 -Rommes,zeros_nopss_13k,13296,13296,48827,1,0,1,0,0.7083056110002218,0.5842204479929031,eigenvalue/model reduction problem,48827 -Rommes,descriptor_xingo6u,20738,20738,73916,1,0,1,0,0.6848202865775669,0.5745268504272713,eigenvalue/model reduction problem,73916 -Rommes,nopss_11k,11685,11685,44941,1,0,1,0,0.7502077151335311,0.6233827893175075,eigenvalue/model reduction problem,44941 -Rommes,xingo3012,20944,20944,74386,1,0,1,0,0.681002526603102,0.5712336093539642,eigenvalue/model reduction problem,74386 -Rommes,bips98_606,7135,7135,34738,1,0,1,0,0.9086859688195991,0.7583878152166104,eigenvalue/model reduction problem,34738 -Rommes,bips98_1142,9735,9735,40983,1,0,1,0,0.8020552507215579,0.6694154587839767,eigenvalue/model reduction problem,40983 -Rommes,bips98_1450,11305,11305,44678,1,0,1,0,0.7593684272891549,0.6277809047012471,eigenvalue/model reduction problem,44678 -Rommes,bips07_1693,13275,13275,49044,1,0,1,0,0.706761938919083,0.5845449278560985,eigenvalue/model reduction problem,49044 -Rommes,bips07_1998,15066,15066,62198,1,0,1,0,0.7978660841804587,0.6687809795849786,eigenvalue/model reduction problem,62198 -Rommes,bips07_2476,16861,16861,66498,1,0,1,0,0.7574573297176583,0.6349098739830914,eigenvalue/model reduction problem,66498 -Rommes,bips07_3078,21128,21128,75729,1,0,1,0,0.6861363964744979,0.5750975292587777,eigenvalue/model reduction problem,75729 -Rommes,M10PI_n1,528,528,1317,1,0,1,0,1,0,eigenvalue/model reduction problem,1317 -Rommes,M20PI_n1,1028,1028,2547,1,0,1,0,1,0,eigenvalue/model reduction problem,2547 -Rommes,M40PI_n1,2028,2028,5007,1,0,1,0,1,0,eigenvalue/model reduction problem,5007 -Rommes,M80PI_n1,4028,4028,9927,1,0,1,0,1,0,eigenvalue/model reduction problem,9927 -Rommes,M10PI_n,682,682,1633,1,0,1,0,1,0.1132075471698113,eigenvalue/model reduction problem,1633 -Rommes,M20PI_n,1182,1182,2881,1,0,1,0,1,0.04173622704507512,eigenvalue/model reduction problem,2881 -Rommes,M40PI_n,2182,2182,5341,1,0,1,0,1,0.02274795268425842,eigenvalue/model reduction problem,5341 -Rommes,M80PI_n,4182,4182,10261,1,0,1,0,1,0.01191043353978085,eigenvalue/model reduction problem,10261 -Rommes,S10PI_n1,528,528,1317,1,0,1,0,1,0,eigenvalue/model reduction problem,1317 -Rommes,S20PI_n1,1028,1028,2547,1,0,1,0,1,0,eigenvalue/model reduction problem,2547 -Rommes,S40PI_n1,2028,2028,5007,1,0,1,0,1,0,eigenvalue/model reduction problem,5007 -Rommes,S80PI_n1,4028,4028,9927,1,0,1,0,1,0,eigenvalue/model reduction problem,9927 -Rommes,S10PI_n,682,682,1633,1,0,1,0,1,0.1132075471698113,eigenvalue/model reduction problem,1633 -Rommes,S20PI_n,1182,1182,2881,1,0,1,0,1,0.04173622704507512,eigenvalue/model reduction problem,2881 -Rommes,S40PI_n,2182,2182,5341,1,0,1,0,1,0.02274795268425842,eigenvalue/model reduction problem,5341 -Rommes,S80PI_n,4182,4182,10261,1,0,1,0,1,0.01191043353978085,eigenvalue/model reduction problem,10261 -Williams,pdb1HYS,36417,36417,4344765,1,0,0,1,1,1,weighted undirected graph,4344765 -Williams,consph,83334,83334,6010480,1,0,1,1,1,1,2D/3D problem,6010480 -Williams,cant,62451,62451,4007383,1,0,1,1,1,1,2D/3D problem,4007383 -Williams,mac_econ_fwd500,206500,206500,1273389,1,0,0,0,0.05995735832675801,0.005599217836274841,economic problem,1273389 -Williams,mc2depi,525825,525825,2100225,1,0,1,0,0,0,2D/3D problem,2100225 -Williams,cop20k_A,121192,121192,2624331,1,0,1,0,1,1,2D/3D problem,2624331 -Williams,webbase-1M,1000005,1000005,3105536,1,0,0,0,0.09552554676231316,0.01588957844838191,weighted directed graph,3105536 -Freescale,FullChip,2987012,2987012,26621983,1,0,0,0,0.9999996192108144,0.005614865134161801,circuit simulation problem,26621990 -Dattorro,EternityII_E,11077,262144,1503732,1,0,0,0,0,0,optimization problem,1503732 -Dattorro,EternityII_Etilde,10054,204304,1170516,1,0,0,0,0,0,optimization problem,1170516 -Dattorro,EternityII_A,7362,150638,782087,1,0,0,0,0,0,optimization problem,782087 -Fluorem,HV15R,2017169,2017169,283073458,1,0,1,0,0.8446615474952066,0.0007082780488857875,computational fluid dynamics problem,283073458 -Dziekonski,dielFilterV2clx,607232,607232,25309272,0,0,1,0,1,0.9712899015627859,electromagnetics problem,25309272 -Dziekonski,dielFilterV2real,1157456,1157456,48538952,1,0,1,0,1,1,electromagnetics problem,48538952 -Dziekonski,dielFilterV3clx,420408,420408,32886208,0,0,1,0,1,0.8873627016737613,electromagnetics problem,32886208 -Dziekonski,dielFilterV3real,1102824,1102824,89306020,1,0,1,0,1,1,electromagnetics problem,89306020 -Newman,adjnoun,112,112,850,1,1,0,0,1,1,undirected graph,850 -Newman,as-22july06,22963,22963,96872,1,1,0,0,1,1,undirected graph,96872 -Newman,astro-ph,16706,16706,242502,1,0,0,0,1,1,undirected weighted graph,242502 -Newman,celegansneural,297,297,2345,1,0,0,0,0.1680170575692964,0.04946695095948828,directed weighted graph,2345 -Newman,cond-mat,16726,16726,95188,1,0,0,0,1,1,undirected weighted graph,95188 -Newman,cond-mat-2003,31163,31163,240058,1,0,0,0,1,1,undirected weighted graph,240058 -Newman,cond-mat-2005,40421,40421,351382,1,0,0,0,1,1,undirected weighted graph,351386 -Newman,dolphins,62,62,318,1,1,0,0,1,1,undirected graph,318 -Newman,football,115,115,1226,1,0,0,0,1,1,undirected multigraph,1226 -Newman,hep-th,8361,8361,31502,1,0,0,0,1,1,undirected weighted graph,31502 -Newman,karate,34,34,156,1,1,0,0,1,1,undirected graph,156 -Newman,lesmis,77,77,508,1,0,0,0,1,1,undirected weighted graph,508 -Newman,netscience,1589,1589,5484,1,0,0,0,1,1,undirected weighted graph,5484 -Newman,polblogs,1490,1490,19025,1,0,0,0,0.242561244874356,0.2414046893071181,directed multigraph,19025 -Newman,polbooks,105,105,882,1,1,0,0,1,1,undirected graph,882 -Newman,power,4941,4941,13188,1,1,0,0,1,1,undirected graph,13188 -Arenas,PGPgiantcompo,10680,10680,48632,1,0,0,0,1,1,undirected multigraph,48632 -Arenas,jazz,198,198,5484,1,0,0,0,1,1,undirected multigraph,5484 -Arenas,celegans_metabolic,453,453,4065,1,0,0,0,1,1,undirected multigraph,4065 -Arenas,email,1133,1133,10902,1,1,0,0,1,1,undirected graph,10902 -AG-Monien,3elt,4720,4720,27444,1,1,1,0,1,1,2D/3D problem,27444 -AG-Monien,3elt_dual,9000,9000,26556,1,1,1,0,1,1,2D/3D problem,26556 -AG-Monien,airfoil1,4253,4253,24578,1,1,1,0,1,1,2D/3D problem,24578 -AG-Monien,airfoil1_dual,8034,8034,23626,1,1,1,0,1,1,2D/3D problem,23626 -AG-Monien,big_dual,30269,30269,89858,1,1,1,0,1,1,2D/3D problem,89858 -AG-Monien,crack,10240,10240,60760,1,1,1,0,1,1,2D/3D problem,60760 -AG-Monien,crack_dual,20141,20141,60086,1,1,1,0,1,1,2D/3D problem,60086 -AG-Monien,grid1,252,252,952,1,1,1,0,1,1,2D/3D problem,952 -AG-Monien,grid1_dual,224,224,840,1,1,1,0,1,1,2D/3D problem,840 -AG-Monien,grid2,3296,3296,12864,1,1,1,0,1,1,2D/3D problem,12864 -AG-Monien,grid2_dual,3136,3136,12224,1,1,1,0,1,1,2D/3D problem,12224 -AG-Monien,netz4504,1961,1961,5156,1,1,1,0,1,1,2D/3D problem,5156 -AG-Monien,netz4504_dual,615,615,2342,1,1,1,0,1,1,2D/3D problem,2342 -AG-Monien,ukerbe1,5981,5981,15704,1,1,1,0,1,1,2D/3D problem,15704 -AG-Monien,ukerbe1_dual,1866,1866,7076,1,1,1,0,1,1,2D/3D problem,7076 -AG-Monien,whitaker3,9800,9800,57978,1,1,1,0,1,1,2D/3D problem,57978 -AG-Monien,whitaker3_dual,19190,19190,57162,1,1,1,0,1,1,2D/3D problem,57162 -AG-Monien,brack2,62631,62631,733118,1,1,1,0,1,1,2D/3D problem,733118 -AG-Monien,wave,156317,156317,2118662,1,1,1,0,1,1,2D/3D problem,2118662 -AG-Monien,diag,2559,2559,8184,1,1,1,0,1,1,2D/3D problem,8184 -AG-Monien,L,956,956,3640,1,1,1,0,1,1,2D/3D problem,3640 -AG-Monien,L-9,17983,17983,71192,1,1,1,0,1,1,2D/3D problem,71192 -AG-Monien,stufe,1036,1036,3736,1,1,1,0,1,1,2D/3D problem,3736 -AG-Monien,stufe-10,24010,24010,92828,1,1,1,0,1,1,2D/3D problem,92828 -AG-Monien,biplane-9,21701,21701,84076,1,1,1,0,1,1,2D/3D problem,84076 -AG-Monien,shock-9,36476,36476,142580,1,1,1,0,1,1,2D/3D problem,142580 -AG-Monien,bfly,49152,49152,196608,1,1,0,0,1,1,undirected graph sequence,196608 -AG-Monien,cage,366,366,5124,1,1,0,0,1,1,undirected graph sequence,5124 -AG-Monien,cca,49152,49152,139264,1,1,0,0,1,1,undirected graph sequence,139264 -AG-Monien,ccc,49152,49152,147456,1,1,0,0,1,1,undirected graph sequence,147456 -AG-Monien,debr,1048576,1048576,4194298,1,1,0,0,1,1,undirected graph sequence,4194298 -AG-Monien,se,32768,32768,98300,1,1,0,0,1,1,undirected graph sequence,98300 -LAW,cnr-2000,325557,325557,3216152,1,1,0,0,0.2491384628169437,0.2491384628169437,directed graph,3216152 -LAW,in-2004,1382908,1382908,16917053,1,1,0,0,0.3564974165403691,0.3564974165403691,directed graph,16917053 -LAW,eu-2005,862664,862664,19235140,1,1,0,0,0.277066804642518,0.277066804642518,directed graph,19235140 -LAW,enron,69244,69244,276143,1,1,0,0,0.1468201946046729,0.1468201946046729,directed graph,276143 -LAW,amazon-2008,735323,735323,5158388,1,1,0,0,0.6338864001699756,0.6338864001699756,directed graph,5158388 -LAW,ljournal-2008,5363260,5363260,79023142,1,1,0,0,0.7302651670539438,0.7302651670539438,directed graph,79023142 -LAW,hollywood-2009,1139905,1139905,113891327,1,1,0,0,1,1,undirected graph,113891327 -LAW,dblp-2010,326186,326186,1615400,1,1,0,0,1,1,undirected graph,1615400 -LAW,webbase-2001,118142155,118142155,1019903190,1,1,0,0,0.2780598082364509,0.2780598082364509,directed graph,1019903190 -LAW,uk-2002,18520486,18520486,298113762,1,1,0,0,0.2084315853924949,0.2084315853924949,directed graph,298113762 -LAW,indochina-2004,7414866,7414866,194109311,1,1,0,0,0.4240142027924715,0.4240142027924715,directed graph,194109311 -LAW,it-2004,41291594,41291594,1150725436,1,1,0,0,0.1906175218924703,0.1906175218924703,directed graph,1150725436 -LAW,arabic-2005,22744080,22744080,639999458,1,1,0,0,0.2447917196533005,0.2447917196533005,directed graph,639999458 -LAW,sk-2005,50636154,50636154,1949412601,1,1,0,0,0.1245713694644861,0.1245713694644861,directed graph,1949412601 -LAW,uk-2005,39459925,39459925,936364282,1,1,0,0,0.3002522210250522,0.3002522210250522,directed graph,936364282 -DIMACS10,caidaRouterLevel,192244,192244,1218132,1,1,0,0,1,1,undirected graph,1218132 -DIMACS10,chesapeake,39,39,340,1,1,0,0,1,1,undirected graph,340 -DIMACS10,road_central,14081816,14081816,33866826,1,1,0,0,1,1,undirected graph,33866826 -DIMACS10,road_usa,23947347,23947347,57708624,1,1,0,0,1,1,undirected graph,57708624 -DIMACS10,citationCiteseer,268495,268495,2313294,1,1,0,0,1,1,undirected graph,2313294 -DIMACS10,coAuthorsCiteseer,227320,227320,1628268,1,1,0,0,1,1,undirected graph,1628268 -DIMACS10,coAuthorsDBLP,299067,299067,1955352,1,1,0,0,1,1,undirected graph,1955352 -DIMACS10,coPapersCiteseer,434102,434102,32073440,1,1,0,0,1,1,undirected graph,32073440 -DIMACS10,coPapersDBLP,540486,540486,30491458,1,1,0,0,1,1,undirected graph,30491458 -DIMACS10,delaunay_n10,1024,1024,6112,1,1,0,0,1,1,undirected graph,6112 -DIMACS10,delaunay_n11,2048,2048,12254,1,1,0,0,1,1,undirected graph,12254 -DIMACS10,delaunay_n12,4096,4096,24528,1,1,0,0,1,1,undirected graph,24528 -DIMACS10,delaunay_n13,8192,8192,49094,1,1,0,0,1,1,undirected graph,49094 -DIMACS10,delaunay_n14,16384,16384,98244,1,1,0,0,1,1,undirected graph,98244 -DIMACS10,delaunay_n15,32768,32768,196548,1,1,0,0,1,1,undirected graph,196548 -DIMACS10,delaunay_n16,65536,65536,393150,1,1,0,0,1,1,undirected graph,393150 -DIMACS10,delaunay_n17,131072,131072,786352,1,1,0,0,1,1,undirected graph,786352 -DIMACS10,delaunay_n18,262144,262144,1572792,1,1,0,0,1,1,undirected graph,1572792 -DIMACS10,delaunay_n19,524288,524288,3145646,1,1,0,0,1,1,undirected graph,3145646 -DIMACS10,delaunay_n20,1048576,1048576,6291372,1,1,0,0,1,1,undirected graph,6291372 -DIMACS10,delaunay_n21,2097152,2097152,12582816,1,1,0,0,1,1,undirected graph,12582816 -DIMACS10,delaunay_n22,4194304,4194304,25165738,1,1,0,0,1,1,undirected graph,25165738 -DIMACS10,delaunay_n23,8388608,8388608,50331568,1,1,0,0,1,1,undirected graph,50331568 -DIMACS10,delaunay_n24,16777216,16777216,100663202,1,1,0,0,1,1,undirected graph,100663202 -DIMACS10,hugebubbles-00000,18318143,18318143,54940162,1,1,0,0,1,1,undirected graph,54940162 -DIMACS10,hugebubbles-00010,19458087,19458087,58359528,1,1,0,0,1,1,undirected graph,58359528 -DIMACS10,hugebubbles-00020,21198119,21198119,63580358,1,1,0,0,1,1,undirected graph,63580358 -DIMACS10,hugetrace-00000,4588484,4588484,13758266,1,1,0,0,1,1,undirected graph,13758266 -DIMACS10,hugetrace-00010,12057441,12057441,36164358,1,1,0,0,1,1,undirected graph,36164358 -DIMACS10,hugetrace-00020,16002413,16002413,47997626,1,1,0,0,1,1,undirected graph,47997626 -DIMACS10,hugetric-00000,5824554,5824554,17467046,1,1,0,0,1,1,undirected graph,17467046 -DIMACS10,hugetric-00010,6592765,6592765,19771708,1,1,0,0,1,1,undirected graph,19771708 -DIMACS10,hugetric-00020,7122792,7122792,21361554,1,1,0,0,1,1,undirected graph,21361554 -DIMACS10,kron_g500-logn16,65536,65536,4912469,1,0,0,0,1,1,undirected multigraph,4912469 -DIMACS10,kron_g500-logn17,131072,131072,10228360,1,0,0,0,1,1,undirected multigraph,10228360 -DIMACS10,kron_g500-logn18,262144,262144,21165908,1,0,0,0,1,1,undirected multigraph,21165908 -DIMACS10,kron_g500-logn19,524288,524288,43562265,1,0,0,0,1,1,undirected multigraph,43562265 -DIMACS10,kron_g500-logn20,1048576,1048576,89239674,1,0,0,0,1,1,undirected multigraph,89239674 -DIMACS10,kron_g500-logn21,2097152,2097152,182082942,1,0,0,0,1,1,undirected multigraph,182082942 -DIMACS10,adaptive,6815744,6815744,27248640,1,1,0,0,1,1,undirected graph,27248640 -DIMACS10,channel-500x100x100-b050,4802000,4802000,85362744,1,1,0,0,1,1,undirected graph,85362744 -DIMACS10,packing-500x100x100-b050,2145852,2145852,34976486,1,1,0,0,1,1,undirected graph,34976486 -DIMACS10,venturiLevel3,4026819,4026819,16108474,1,1,0,0,1,1,undirected graph,16108474 -DIMACS10,rgg_n_2_15_s0,32768,32768,320480,1,1,0,0,1,1,undirected random graph,320480 -DIMACS10,rgg_n_2_16_s0,65536,65536,684254,1,1,0,0,1,1,undirected random graph,684254 -DIMACS10,rgg_n_2_17_s0,131072,131072,1457506,1,1,0,0,1,1,undirected random graph,1457506 -DIMACS10,rgg_n_2_18_s0,262144,262144,3094566,1,1,0,0,1,1,undirected random graph,3094566 -DIMACS10,rgg_n_2_19_s0,524288,524288,6539532,1,1,0,0,1,1,undirected random graph,6539532 -DIMACS10,rgg_n_2_20_s0,1048576,1048576,13783240,1,1,0,0,1,1,undirected random graph,13783240 -DIMACS10,rgg_n_2_21_s0,2097152,2097152,28975990,1,1,0,0,1,1,undirected random graph,28975990 -DIMACS10,rgg_n_2_22_s0,4194304,4194304,60718396,1,1,0,0,1,1,undirected random graph,60718396 -DIMACS10,rgg_n_2_23_s0,8388608,8388608,127002786,1,1,0,0,1,1,undirected random graph,127002786 -DIMACS10,rgg_n_2_24_s0,16777216,16777216,265114400,1,1,0,0,1,1,undirected random graph,265114400 -DIMACS10,asia_osm,11950757,11950757,25423206,1,1,0,0,1,1,undirected graph,25423206 -DIMACS10,belgium_osm,1441295,1441295,3099940,1,1,0,0,1,1,undirected graph,3099940 -DIMACS10,europe_osm,50912018,50912018,108109320,1,1,0,0,1,1,undirected graph,108109320 -DIMACS10,germany_osm,11548845,11548845,24738362,1,1,0,0,1,1,undirected graph,24738362 -DIMACS10,great-britain_osm,7733822,7733822,16313034,1,1,0,0,1,1,undirected graph,16313034 -DIMACS10,italy_osm,6686493,6686493,14027956,1,1,0,0,1,1,undirected graph,14027956 -DIMACS10,luxembourg_osm,114599,114599,239332,1,1,0,0,1,1,undirected graph,239332 -DIMACS10,netherlands_osm,2216688,2216688,4882476,1,1,0,0,1,1,undirected graph,4882476 -DIMACS10,144,144649,144649,2148786,1,1,0,0,1,1,undirected graph,2148786 -DIMACS10,598a,110971,110971,1483868,1,1,0,0,1,1,undirected graph,1483868 -DIMACS10,auto,448695,448695,6629222,1,1,0,0,1,1,undirected graph,6629222 -DIMACS10,cs4,22499,22499,87716,1,1,0,0,1,1,undirected graph,87716 -DIMACS10,cti,16840,16840,96464,1,1,0,0,1,1,undirected graph,96464 -DIMACS10,data,2851,2851,30186,1,1,0,0,1,1,undirected graph,30186 -DIMACS10,fe_4elt2,11143,11143,65636,1,1,0,0,1,1,undirected graph,65636 -DIMACS10,fe_body,45087,45087,327468,1,1,0,0,1,1,undirected graph,327468 -DIMACS10,fe_ocean,143437,143437,819186,1,1,0,0,1,1,undirected graph,819186 -DIMACS10,fe_rotor,99617,99617,1324862,1,1,0,0,1,1,undirected graph,1324862 -DIMACS10,fe_sphere,16386,16386,98304,1,1,0,0,1,1,undirected graph,98304 -DIMACS10,fe_tooth,78136,78136,905182,1,1,0,0,1,1,undirected graph,905182 -DIMACS10,m14b,214765,214765,3358036,1,1,0,0,1,1,undirected graph,3358036 -DIMACS10,t60k,60005,60005,178880,1,1,0,0,1,1,undirected graph,178880 -DIMACS10,uk,4824,4824,13674,1,1,0,0,1,1,undirected graph,13674 -DIMACS10,wing,62032,62032,243088,1,1,0,0,1,1,undirected graph,243088 -DIMACS10,wing_nodal,10937,10937,150976,1,1,0,0,1,1,undirected graph,150976 -IPSO,HTC_336_4438,226340,226340,783496,1,0,0,0,1,1,power network problem,904522 -IPSO,HTC_336_9129,226340,226340,762969,1,0,0,0,1,1,power network problem,883422 -IPSO,OPF_10000,43887,43887,426898,1,0,0,0,1,1,power network problem,467711 -IPSO,OPF_3754,15435,15435,141478,1,0,0,0,1,1,power network problem,158273 -IPSO,OPF_6000,29902,29902,274697,1,0,0,0,1,1,power network problem,302418 -IPSO,TSC_OPF_1047,8140,8140,2012833,1,0,0,0,1,1,power network problem,2016902 -IPSO,TSC_OPF_300,9774,9774,820783,1,0,0,0,1,1,power network problem,820804 -Janna,Serena,1391349,1391349,64131971,1,0,1,1,1,1,structural problem,64531701 -Janna,Emilia_923,923136,923136,40373538,1,0,1,1,1,1,structural problem,41005206 -Janna,Fault_639,638802,638802,27245944,1,0,1,1,1,1,structural problem,28614564 -Janna,Flan_1565,1564794,1564794,114165372,1,0,1,1,1,1,structural problem,117406044 -Janna,Geo_1438,1437960,1437960,60236322,1,0,1,1,1,1,structural problem,63156690 -Janna,Hook_1498,1498023,1498023,59374451,1,0,1,1,1,1,structural problem,60917445 -Janna,StocF-1465,1465137,1465137,21005389,1,0,1,1,1,1,computational fluid dynamics problem,21005389 -Janna,Cube_Coup_dt0,2164760,2164760,124406070,1,0,1,0,1,1,structural problem,127206144 -Janna,Cube_Coup_dt6,2164760,2164760,124406070,1,0,1,0,1,1,structural problem,127206144 -Janna,Long_Coup_dt0,1470152,1470152,84422970,1,0,1,0,1,1,structural problem,87088992 -Janna,Long_Coup_dt6,1470152,1470152,84422970,1,0,1,0,1,1,structural problem,87088992 -Schulthess,N_biocarta,1922,1996,4335,1,0,0,0,0,0,biochemical network,4335 -Schulthess,N_pid,3625,3923,8054,1,0,0,0,0,0,biochemical network,8054 -Schulthess,N_reactome,10204,16559,43816,1,0,0,0,0,0,biochemical network,43816 -Chevron,Chevron1,37365,37365,330633,0,0,0,0,0.994503321194266,0.7103673090824775,other problem,330633 -Chevron,Chevron2,90249,90249,803173,0,0,0,0,0.9963698795383519,0.6400065084076283,other problem,803173 -Chevron,Chevron3,381381,381381,3413113,0,0,0,0,0.998180577966654,0.5728441696033818,other problem,3413113 -Chevron,Chevron4,711450,711450,6376412,0,0,0,0,0.9986570077610406,0.5561276492234193,other problem,6376412 -CPM,cz148,148,148,1527,1,0,1,0,0.4263959390862944,0.2306018854242204,2D/3D problem,1527 -CPM,cz308,308,308,3182,1,0,1,0,0.4342379958246347,0.2219902574808629,2D/3D problem,3182 -CPM,cz628,628,628,6346,1,0,1,0,0.441413081497027,0.2308499475341028,2D/3D problem,6346 -CPM,cz1268,1268,1268,12786,1,0,1,0,0.4386178155929849,0.2340684146553221,2D/3D problem,12786 -CPM,cz2548,2548,2548,25674,1,0,1,0,0.4322407679667906,0.2349736227622589,2D/3D problem,25674 -CPM,cz5108,5108,5108,51412,1,0,1,0,0.433914996544575,0.2362214927436075,2D/3D problem,51412 -CPM,cz10228,10228,10228,102876,1,0,1,0,0.4358431914342458,0.2371988602020551,2D/3D problem,102876 -CPM,cz20468,20468,20468,206076,1,0,1,0,0.4354769190983147,0.2377052713245119,2D/3D problem,206076 -CPM,cz40948,40948,40948,412148,1,0,1,0,0.4349030172413793,0.2372683189655172,2D/3D problem,412148 -Davis,FX_March2010,1319,9498,301899,1,0,0,0,0,0,term/document graph,301899 -Bodendiek,CurlCurl_0,11083,11083,113343,1,0,1,0,1,1,model reduction problem,113343 -Bodendiek,CurlCurl_1,226451,226451,2472071,1,0,1,0,1,1,model reduction problem,2472071 -Bodendiek,CurlCurl_2,806529,806529,8921789,1,0,1,0,1,1,model reduction problem,8921789 -Bodendiek,CurlCurl_3,1219574,1219574,13544618,1,0,1,0,1,1,model reduction problem,13544618 -Bodendiek,CurlCurl_4,2380515,2380515,26515867,1,0,1,0,1,1,model reduction problem,26515867 -DIMACS10,G_n_pin_pout,100000,100000,1002396,1,1,0,0,1,1,random undirected graph,1002396 -DIMACS10,preferentialAttachment,100000,100000,999970,1,1,0,0,1,1,random undirected graph,999970 -DIMACS10,smallworld,100000,100000,999996,1,1,0,0,1,1,random undirected graph,999996 -DIMACS10,333SP,3712815,3712815,22217266,1,1,0,0,1,1,undirected graph,22217266 -DIMACS10,AS365,3799275,3799275,22736152,1,1,0,0,1,1,undirected graph,22736152 -DIMACS10,M6,3501776,3501776,21003872,1,1,0,0,1,1,undirected graph,21003872 -DIMACS10,NACA0015,1039183,1039183,6229636,1,1,0,0,1,1,undirected graph,6229636 -DIMACS10,NLR,4163763,4163763,24975952,1,1,0,0,1,1,undirected graph,24975952 -DIMACS10,ak2010,45292,45292,217098,1,0,0,0,1,1,undirected weighted graph,217098 -DIMACS10,al2010,252266,252266,1230482,1,0,0,0,1,1,undirected weighted graph,1230482 -DIMACS10,ar2010,186211,186211,904310,1,0,0,0,1,1,undirected weighted graph,904310 -DIMACS10,az2010,241666,241666,1196094,1,0,0,0,1,1,undirected weighted graph,1196094 -DIMACS10,ca2010,710145,710145,3489366,1,0,0,0,1,1,undirected weighted graph,3489366 -DIMACS10,co2010,201062,201062,974574,1,0,0,0,1,1,undirected weighted graph,974574 -DIMACS10,ct2010,67578,67578,336352,1,0,0,0,1,1,undirected weighted graph,336352 -DIMACS10,de2010,24115,24115,116056,1,0,0,0,1,1,undirected weighted graph,116056 -DIMACS10,fl2010,484481,484481,2346294,1,0,0,0,1,1,undirected weighted graph,2346294 -DIMACS10,ga2010,291086,291086,1418056,1,0,0,0,1,1,undirected weighted graph,1418056 -DIMACS10,hi2010,25016,25016,124126,1,0,0,0,1,1,undirected weighted graph,124126 -DIMACS10,ia2010,216007,216007,1021170,1,0,0,0,1,1,undirected weighted graph,1021170 -DIMACS10,id2010,149842,149842,728264,1,0,0,0,1,1,undirected weighted graph,728264 -DIMACS10,il2010,451554,451554,2164464,1,0,0,0,1,1,undirected weighted graph,2164464 -DIMACS10,in2010,267071,267071,1281716,1,0,0,0,1,1,undirected weighted graph,1281716 -DIMACS10,ks2010,238600,238600,1121798,1,0,0,0,1,1,undirected weighted graph,1121798 -DIMACS10,ky2010,161672,161672,787778,1,0,0,0,1,1,undirected weighted graph,787778 -DIMACS10,la2010,204447,204447,980634,1,0,0,0,1,1,undirected weighted graph,980634 -DIMACS10,ma2010,157508,157508,776610,1,0,0,0,1,1,undirected weighted graph,776610 -DIMACS10,md2010,145247,145247,700378,1,0,0,0,1,1,undirected weighted graph,700378 -DIMACS10,me2010,69518,69518,335476,1,0,0,0,1,1,undirected weighted graph,335476 -DIMACS10,mi2010,329885,329885,1578090,1,0,0,0,1,1,undirected weighted graph,1578090 -DIMACS10,mn2010,259777,259777,1227102,1,0,0,0,1,1,undirected weighted graph,1227102 -DIMACS10,mo2010,343565,343565,1656568,1,0,0,0,1,1,undirected weighted graph,1656568 -DIMACS10,ms2010,171778,171778,839980,1,0,0,0,1,1,undirected weighted graph,839980 -DIMACS10,mt2010,132288,132288,638668,1,0,0,0,1,1,undirected weighted graph,638668 -DIMACS10,nc2010,288987,288987,1416620,1,0,0,0,1,1,undirected weighted graph,1416620 -DIMACS10,nd2010,133769,133769,625946,1,0,0,0,1,1,undirected weighted graph,625946 -DIMACS10,ne2010,193352,193352,913854,1,0,0,0,1,1,undirected weighted graph,913854 -DIMACS10,nh2010,48837,48837,234550,1,0,0,0,1,1,undirected weighted graph,234550 -DIMACS10,nj2010,169588,169588,829912,1,0,0,0,1,1,undirected weighted graph,829912 -DIMACS10,nm2010,168609,168609,830970,1,0,0,0,1,1,undirected weighted graph,830970 -DIMACS10,nv2010,84538,84538,416998,1,0,0,0,1,1,undirected weighted graph,416998 -DIMACS10,ny2010,350169,350169,1709544,1,0,0,0,1,1,undirected weighted graph,1709544 -DIMACS10,oh2010,365344,365344,1768240,1,0,0,0,1,1,undirected weighted graph,1768240 -DIMACS10,ok2010,269118,269118,1274148,1,0,0,0,1,1,undirected weighted graph,1274148 -DIMACS10,or2010,196621,196621,979512,1,0,0,0,1,1,undirected weighted graph,979512 -DIMACS10,pa2010,421545,421545,2058462,1,0,0,0,1,1,undirected weighted graph,2058462 -DIMACS10,ri2010,25181,25181,125750,1,0,0,0,1,1,undirected weighted graph,125750 -DIMACS10,sc2010,181908,181908,893160,1,0,0,0,1,1,undirected weighted graph,893160 -DIMACS10,sd2010,88360,88360,410722,1,0,0,0,1,1,undirected weighted graph,410722 -DIMACS10,tn2010,240116,240116,1193966,1,0,0,0,1,1,undirected weighted graph,1193966 -DIMACS10,tx2010,914231,914231,4456272,1,0,0,0,1,1,undirected weighted graph,4456272 -DIMACS10,ut2010,115406,115406,572066,1,0,0,0,1,1,undirected weighted graph,572066 -DIMACS10,va2010,285762,285762,1402128,1,0,0,0,1,1,undirected weighted graph,1402128 -DIMACS10,vt2010,32580,32580,155598,1,0,0,0,1,1,undirected weighted graph,155598 -DIMACS10,wa2010,195574,195574,947432,1,0,0,0,1,1,undirected weighted graph,947432 -DIMACS10,wi2010,253096,253096,1209404,1,0,0,0,1,1,undirected weighted graph,1209404 -DIMACS10,wv2010,135218,135218,662922,1,0,0,0,1,1,undirected weighted graph,662922 -DIMACS10,wy2010,86204,86204,427586,1,0,0,0,1,1,undirected weighted graph,427586 -DIMACS10,vsp_barth5_1Ksep_50in_5Kout,32212,32212,203610,1,1,0,0,1,1,random unweighted graph,203610 -DIMACS10,vsp_bcsstk30_500sep_10in_1Kout,58348,58348,4033156,1,1,0,0,1,1,random unweighted graph,4033156 -DIMACS10,vsp_befref_fxm_2_4_air02,14109,14109,196448,1,1,0,0,1,1,random unweighted graph,196448 -DIMACS10,vsp_bump2_e18_aa01_model1_crew1,56438,56438,601602,1,1,0,0,1,1,random unweighted graph,601602 -DIMACS10,vsp_c-30_data_data,11023,11023,124368,1,1,0,0,1,1,random unweighted graph,124368 -DIMACS10,vsp_c-60_data_cti_cs4,85830,85830,482160,1,1,0,0,1,1,random unweighted graph,482160 -DIMACS10,vsp_data_and_seymourl,9167,9167,111732,1,1,0,0,1,1,random unweighted graph,111732 -DIMACS10,vsp_finan512_scagr7-2c_rlfddd,139752,139752,1104040,1,1,0,0,1,1,random unweighted graph,1104040 -DIMACS10,vsp_mod2_pgp2_slptsk,101364,101364,778736,1,1,0,0,1,1,random unweighted graph,778736 -DIMACS10,vsp_model1_crew1_cr42_south31,45101,45101,379952,1,1,0,0,1,1,random unweighted graph,379952 -DIMACS10,vsp_msc10848_300sep_100in_1Kout,21996,21996,2442056,1,1,0,0,1,1,random unweighted graph,2442056 -DIMACS10,vsp_p0291_seymourl_iiasa,10498,10498,107736,1,1,0,0,1,1,random unweighted graph,107736 -DIMACS10,vsp_sctap1-2b_and_seymourl,40174,40174,281662,1,1,0,0,1,1,random unweighted graph,281662 -DIMACS10,vsp_south31_slptsk,39668,39668,379828,1,1,0,0,1,1,random unweighted graph,379828 -DIMACS10,vsp_vibrobox_scagr7-2c_rlfddd,77328,77328,871172,1,1,0,0,1,1,random unweighted graph,871172 -Janna,CoupCons3D,416800,416800,17277420,1,0,1,0,1,0.008881642549324996,structural problem,22322336 -Janna,ML_Laplace,377002,377002,27582698,1,0,1,0,1,7.351401706466176e-08,structural problem,27689972 -Janna,Transport,1602111,1602111,23487281,1,0,1,0,1,0,structural problem,23500731 -Janna,ML_Geer,1504002,1504002,110686677,1,0,1,0,1,2.381330188145693e-07,structural problem,110879972 -ANSYS,Delor64K,64719,1785345,652140,1,0,0,0,0,0,least squares problem,652140 -ANSYS,Delor295K,295734,1823928,2401323,1,0,0,0,0,0,least squares problem,2401323 -ANSYS,Delor338K,343236,887058,4211599,1,0,0,0,0,0,least squares problem,4211599 -Sorensen,Linux_call_graph,324085,324085,1208908,1,0,0,0,0.0006324084497715383,0.0006125422157473015,directed weighted graph,1208908 -LeGresley,LeGresley_2508,2508,2508,16727,1,0,0,0,0.8376116463886349,0.2557141852450946,power network problem,16727 -LeGresley,LeGresley_4908,4908,4908,30482,1,0,0,0,0.9773607038123168,0.02995112414467253,power network problem,30482 -LeGresley,LeGresley_87936,87936,87936,593276,1,0,0,0,0.9792020608292309,0.03427471928313486,power network problem,593276 -Brogan,specular,477976,1600,7647040,1,0,1,0,0,0,computer vision problem,7647616 -Janna,Bump_2911,2911419,2911419,127729899,1,0,1,1,1,1,2D/3D problem,127729899 -Janna,Queen_4147,4147110,4147110,316548962,1,0,1,1,1,1,2D/3D problem,329499284 -Janna,PFlow_742,742793,742793,37138461,1,0,1,1,1,1,2D/3D problem,37138461 -Freescale,Freescale2,2999349,2999349,14313235,1,0,0,0,0.9853083953765489,0.5600838032920651,circuit simulation matrix,23042677 -Moqri,MISKnowledgeMap,2427,2427,57022,1,0,0,0,1,1,undirected weighted graph,57022 -Mazaheri,bundle_adj,513351,513351,20207907,1,0,1,1,1,1,computer vision problem,20208051 -VDOL,dynamicSoaringProblem_1,647,647,5367,1,0,0,0,1,1,optimal control problem,5367 -VDOL,dynamicSoaringProblem_2,1591,1591,15588,1,0,0,0,1,1,optimal control problem,15588 -VDOL,dynamicSoaringProblem_3,2871,2871,32022,1,0,0,0,1,1,optimal control problem,32022 -VDOL,dynamicSoaringProblem_4,3191,3191,36516,1,0,0,0,1,1,optimal control problem,36516 -VDOL,dynamicSoaringProblem_5,3271,3271,36789,1,0,0,0,1,1,optimal control problem,36789 -VDOL,dynamicSoaringProblem_6,3431,3431,36741,1,0,0,0,1,1,optimal control problem,36741 -VDOL,dynamicSoaringProblem_7,3511,3511,37680,1,0,0,0,1,1,optimal control problem,37680 -VDOL,dynamicSoaringProblem_8,3543,3543,38136,1,0,0,0,1,1,optimal control problem,38136 -VDOL,freeFlyingRobot_1,798,798,5246,1,0,0,0,1,1,optimal control problem,5246 -VDOL,freeFlyingRobot_2,1338,1338,11600,1,0,0,0,1,1,optimal control problem,11600 -VDOL,freeFlyingRobot_3,1718,1718,12922,1,0,0,0,1,1,optimal control problem,12922 -VDOL,freeFlyingRobot_4,2358,2358,18218,1,0,0,0,1,1,optimal control problem,18218 -VDOL,freeFlyingRobot_5,2878,2878,24582,1,0,0,0,1,1,optimal control problem,24582 -VDOL,freeFlyingRobot_6,3358,3358,27030,1,0,0,0,1,1,optimal control problem,27030 -VDOL,freeFlyingRobot_7,3918,3918,31046,1,0,0,0,1,1,optimal control problem,31046 -VDOL,freeFlyingRobot_8,4398,4398,34958,1,0,0,0,1,1,optimal control problem,34958 -VDOL,freeFlyingRobot_9,4778,4778,39964,1,0,0,0,1,1,optimal control problem,39964 -VDOL,freeFlyingRobot_10,5218,5218,40080,1,0,0,0,1,1,optimal control problem,40080 -VDOL,freeFlyingRobot_11,5438,5438,40054,1,0,0,0,1,1,optimal control problem,40054 -VDOL,freeFlyingRobot_12,5578,5578,41940,1,0,0,0,1,1,optimal control problem,41940 -VDOL,freeFlyingRobot_13,5718,5718,43994,1,0,0,0,1,1,optimal control problem,43994 -VDOL,freeFlyingRobot_14,5958,5958,43298,1,0,0,0,1,1,optimal control problem,43298 -VDOL,freeFlyingRobot_15,6038,6038,43378,1,0,0,0,1,1,optimal control problem,43378 -VDOL,freeFlyingRobot_16,6078,6078,44006,1,0,0,0,1,1,optimal control problem,44006 -VDOL,goddardRocketProblem_1,831,831,8498,1,0,0,0,1,1,optimal control problem,8498 -VDOL,goddardRocketProblem_2,867,867,9058,1,0,0,0,1,1,optimal control problem,9058 -VDOL,hangGlider_1,360,360,3477,1,0,0,0,1,1,optimal control problem,3477 -VDOL,hangGlider_2,1647,1647,14754,1,0,0,0,1,1,optimal control problem,14754 -VDOL,hangGlider_3,10260,10260,92703,1,0,0,0,1,1,optimal control problem,92703 -VDOL,hangGlider_4,15561,15561,149532,1,0,0,0,1,1,optimal control problem,149532 -VDOL,hangGlider_5,16011,16011,155246,1,0,0,0,1,1,optimal control problem,155246 -VDOL,kineticBatchReactor_1,2052,2052,20600,1,0,0,0,1,1,optimal control problem,20600 -VDOL,kineticBatchReactor_2,4361,4361,44840,1,0,0,0,1,1,optimal control problem,44840 -VDOL,kineticBatchReactor_3,5082,5082,53219,1,0,0,0,1,1,optimal control problem,53219 -VDOL,kineticBatchReactor_4,7105,7105,74869,1,0,0,0,1,1,optimal control problem,74869 -VDOL,kineticBatchReactor_5,7641,7641,80767,1,0,0,0,1,1,optimal control problem,80767 -VDOL,kineticBatchReactor_6,7773,7773,82500,1,0,0,0,1,1,optimal control problem,82500 -VDOL,kineticBatchReactor_7,7968,7968,84508,1,0,0,0,1,1,optimal control problem,84508 -VDOL,kineticBatchReactor_8,8052,8052,85194,1,0,0,0,1,1,optimal control problem,85194 -VDOL,kineticBatchReactor_9,8115,8115,86183,1,0,0,0,1,1,optimal control problem,86183 -VDOL,lowThrust_1,584,584,6133,1,0,0,0,1,1,optimal control problem,6133 -VDOL,lowThrust_2,2312,2312,26261,1,0,0,0,1,1,optimal control problem,26261 -VDOL,lowThrust_3,7064,7064,80645,1,0,0,0,1,1,optimal control problem,80645 -VDOL,lowThrust_4,13562,13562,160947,1,0,0,0,1,1,optimal control problem,160947 -VDOL,lowThrust_5,16262,16262,198369,1,0,0,0,1,1,optimal control problem,198369 -VDOL,lowThrust_6,16928,16928,207349,1,0,0,0,1,1,optimal control problem,207349 -VDOL,lowThrust_7,17378,17378,211561,1,0,0,0,1,1,optimal control problem,211561 -VDOL,lowThrust_8,17702,17702,216445,1,0,0,0,1,1,optimal control problem,216445 -VDOL,lowThrust_9,18044,18044,219589,1,0,0,0,1,1,optimal control problem,219589 -VDOL,lowThrust_10,18260,18260,222005,1,0,0,0,1,1,optimal control problem,222005 -VDOL,lowThrust_11,18368,18368,223801,1,0,0,0,1,1,optimal control problem,223801 -VDOL,lowThrust_12,18458,18458,224593,1,0,0,0,1,1,optimal control problem,224593 -VDOL,lowThrust_13,18476,18476,224897,1,0,0,0,1,1,optimal control problem,224897 -VDOL,orbitRaising_1,442,442,2906,1,0,0,0,1,1,optimal control problem,2906 -VDOL,orbitRaising_2,739,739,6526,1,0,0,0,1,1,optimal control problem,6526 -VDOL,orbitRaising_3,761,761,6098,1,0,0,0,1,1,optimal control problem,6098 -VDOL,orbitRaising_4,915,915,7790,1,0,0,0,1,1,optimal control problem,7790 -VDOL,reorientation_1,677,677,7326,1,0,0,0,1,1,optimal control problem,7326 -VDOL,reorientation_2,1544,1544,17910,1,0,0,0,1,1,optimal control problem,17910 -VDOL,reorientation_3,2513,2513,32166,1,0,0,0,1,1,optimal control problem,32166 -VDOL,reorientation_4,2717,2717,33630,1,0,0,0,1,1,optimal control problem,33630 -VDOL,reorientation_5,2904,2904,35590,1,0,0,0,1,1,optimal control problem,35590 -VDOL,reorientation_6,3006,3006,37342,1,0,0,0,1,1,optimal control problem,37342 -VDOL,reorientation_7,3057,3057,37150,1,0,0,0,1,1,optimal control problem,37150 -VDOL,reorientation_8,3108,3108,37894,1,0,0,0,1,1,optimal control problem,37894 -VDOL,spaceShuttleEntry_1,560,560,6891,1,0,0,0,1,1,optimal control problem,6891 -VDOL,spaceShuttleEntry_2,1428,1428,24073,1,0,0,0,1,1,optimal control problem,24073 -VDOL,spaceShuttleEntry_3,1834,1834,28757,1,0,0,0,1,1,optimal control problem,28757 -VDOL,spaceShuttleEntry_4,2450,2450,39669,1,0,0,0,1,1,optimal control problem,39669 -VDOL,spaceStation_1,99,99,927,1,0,0,0,1,1,optimal control problem,927 -VDOL,spaceStation_2,329,329,5817,1,0,0,0,1,1,optimal control problem,5817 -VDOL,spaceStation_3,467,467,5103,1,0,0,0,1,1,optimal control problem,5103 -VDOL,spaceStation_4,950,950,13626,1,0,0,0,1,1,optimal control problem,13626 -VDOL,spaceStation_5,1019,1019,15219,1,0,0,0,1,1,optimal control problem,15219 -VDOL,spaceStation_6,1111,1111,17595,1,0,0,0,1,1,optimal control problem,17595 -VDOL,spaceStation_7,1134,1134,18252,1,0,0,0,1,1,optimal control problem,18252 -VDOL,spaceStation_8,1157,1157,18945,1,0,0,0,1,1,optimal control problem,18945 -VDOL,spaceStation_9,1180,1180,19674,1,0,0,0,1,1,optimal control problem,19674 -VDOL,spaceStation_10,1272,1272,17478,1,0,0,0,1,1,optimal control problem,17478 -VDOL,spaceStation_11,1364,1364,18954,1,0,0,0,1,1,optimal control problem,18954 -VDOL,spaceStation_12,1410,1410,19728,1,0,0,0,1,1,optimal control problem,19728 -VDOL,spaceStation_13,1617,1617,23649,1,0,0,0,1,1,optimal control problem,23649 -VDOL,spaceStation_14,1640,1640,24324,1,0,0,0,1,1,optimal control problem,24324 -VDOL,tumorAntiAngiogenesis_1,205,205,1783,1,0,0,0,1,1,optimal control problem,1783 -VDOL,tumorAntiAngiogenesis_2,305,305,2699,1,0,0,0,1,1,optimal control problem,2699 -VDOL,tumorAntiAngiogenesis_3,410,410,3952,1,0,0,0,1,1,optimal control problem,3952 -VDOL,tumorAntiAngiogenesis_4,455,455,4593,1,0,0,0,1,1,optimal control problem,4593 -VDOL,tumorAntiAngiogenesis_5,460,460,4690,1,0,0,0,1,1,optimal control problem,4690 -VDOL,tumorAntiAngiogenesis_6,465,465,4795,1,0,0,0,1,1,optimal control problem,4795 -VDOL,tumorAntiAngiogenesis_7,475,475,4597,1,0,0,0,1,1,optimal control problem,4597 -VDOL,tumorAntiAngiogenesis_8,490,490,4776,1,0,0,0,1,1,optimal control problem,4776 -Meng,iChem_Jacobian,274087,274087,4137369,0,0,0,0,0.9801722990969854,0.6457048695901567,computational chemistry problem,4137369 -Harvard_Seismology,JP,87616,67320,13734559,1,0,0,0,0,0,tomography problem,13734559 -Mycielski,mycielskian2,2,2,2,1,1,0,0,1,1,undirected graph,2 -Mycielski,mycielskian3,5,5,10,1,1,0,0,1,1,undirected graph,10 -Mycielski,mycielskian4,11,11,40,1,1,0,0,1,1,undirected graph,40 -Mycielski,mycielskian5,23,23,142,1,1,0,0,1,1,undirected graph,142 -Mycielski,mycielskian6,47,47,472,1,1,0,0,1,1,undirected graph,472 -Mycielski,mycielskian7,95,95,1510,1,1,0,0,1,1,undirected graph,1510 -Mycielski,mycielskian8,191,191,4720,1,1,0,0,1,1,undirected graph,4720 -Mycielski,mycielskian9,383,383,14542,1,1,0,0,1,1,undirected graph,14542 -Mycielski,mycielskian10,767,767,44392,1,1,0,0,1,1,undirected graph,44392 -Mycielski,mycielskian11,1535,1535,134710,1,1,0,0,1,1,undirected graph,134710 -Mycielski,mycielskian12,3071,3071,407200,1,1,0,0,1,1,undirected graph,407200 -Mycielski,mycielskian13,6143,6143,1227742,1,1,0,0,1,1,undirected graph,1227742 -Mycielski,mycielskian14,12287,12287,3695512,1,1,0,0,1,1,undirected graph,3695512 -Mycielski,mycielskian15,24575,24575,11111110,1,1,0,0,1,1,undirected graph,11111110 -Mycielski,mycielskian16,49151,49151,33382480,1,1,0,0,1,1,undirected graph,33382480 -Mycielski,mycielskian17,98303,98303,100245742,1,1,0,0,1,1,undirected graph,100245742 -Mycielski,mycielskian18,196607,196607,300933832,1,1,0,0,1,1,undirected graph,300933832 -Mycielski,mycielskian19,393215,393215,903194710,1,1,0,0,1,1,undirected graph,903194710 -Mycielski,mycielskian20,786431,786431,2710370560,1,1,0,0,1,1,undirected graph,2710370560 -SNAP,CollegeMsg,1899,1899,20296,1,0,0,0,0.6363815530153725,0.263204572329523,directed temporal multigraph,20296 -SNAP,com-Amazon,334863,334863,1851744,1,1,0,0,1,1,undirected graph with communities,1851744 -SNAP,com-DBLP,317080,317080,2099732,1,1,0,0,1,1,undirected graph with communities,2099732 -SNAP,com-Friendster,65608366,65608366,3612134270,1,1,0,0,1,1,undirected graph with communities,3612134270 -SNAP,com-LiveJournal,3997962,3997962,69362378,1,1,0,0,1,1,undirected graph with communities,69362378 -SNAP,com-Orkut,3072441,3072441,234370166,1,1,0,0,1,1,undirected graph with communities,234370166 -SNAP,com-Youtube,1134890,1134890,5975248,1,1,0,0,1,1,undirected graph with communities,5975248 -SNAP,email-Eu-core,1005,1005,25571,1,1,0,0,0.7112198644149385,0.7112198644149385,directed graph with communities,25571 -SNAP,email-Eu-core-temporal,1005,1005,24929,1,0,0,0,0.7112198644149385,0.1435276184363592,directed temporal multigraph,24929 -SNAP,higgs-twitter,456626,456626,14855842,1,1,0,0,0.3160251211999823,0.3160251211999823,directed temporal multigraph,14855842 -SNAP,loc-Brightkite,58228,58228,428156,1,1,0,0,1,1,undirected graph,428156 -SNAP,loc-Gowalla,196591,196591,1900654,1,1,0,0,1,1,undirected graph,1900654 -SNAP,soc-Pokec,1632803,1632803,30622564,1,1,0,0,0.5434293483720044,0.5434293483720044,directed graph,30622564 -SNAP,soc-sign-bitcoin-alpha,3783,3783,24186,1,0,0,0,0.832051600099231,0.5957165302240965,directed weighted temporal graph,24186 -SNAP,soc-sign-bitcoin-otc,5881,5881,35592,1,0,0,0,0.7923128792987189,0.5689480782198246,directed weighted temporal graph,35592 -SNAP,sx-askubuntu,159316,159316,596933,1,0,0,0,0.3265757288095759,0.1849102403322678,directed temporal multigraph,596933 -SNAP,sx-mathoverflow,24818,24818,239978,1,0,0,0,0.3509349053252102,0.1609975832379348,directed temporal multigraph,239978 -SNAP,sx-stackoverflow,2601977,2601977,36233450,1,0,0,0,0.3837726021373516,0.2153393177894375,directed temporal multigraph,36233450 -SNAP,sx-superuser,194085,194085,924886,1,0,0,0,0.3272723867800749,0.197851768013418,directed temporal multigraph,924886 -SNAP,twitter7,41652230,41652230,1468365182,1,1,0,0,0.3621059600332965,0.3621059600332965,directed graph,1468365182 -SNAP,wiki-RfA,11380,11380,188077,1,0,0,0,0.07451698016852498,0.06452676823559451,directed weighted temporal multigraph,188077 -SNAP,wiki-talk-temporal,1140149,1140149,3309592,1,0,0,0,0.2189736490582744,0.07817954976807415,directed temporal multigraph,3309592 -SNAP,wiki-topcats,1791489,1791489,28511807,1,1,0,0,0.2149515115699757,0.2149515115699757,directed graph with communities,28511807 -MAWI,mawi_201512012345,18571154,18571154,38040320,1,0,0,0,1,1,undirected weighted graph,38040320 -MAWI,mawi_201512020000,35991342,35991342,74485420,1,0,0,0,1,1,undirected weighted graph,74485420 -MAWI,mawi_201512020030,68863315,68863315,143414960,1,0,0,0,1,1,undirected weighted graph,143414960 -MAWI,mawi_201512020130,128568730,128568730,270234840,1,0,0,0,1,1,undirected weighted graph,270234840 -MAWI,mawi_201512020330,226196185,226196185,480047894,1,0,0,0,1,1,undirected weighted graph,480047894 -GenBank,kmer_A2a,170728175,170728175,360585172,1,1,0,0,1,1,undirected graph,360585172 -GenBank,kmer_P1a,139353211,139353211,297829984,1,1,0,0,1,1,undirected graph,297829984 -GenBank,kmer_U1a,67716231,67716231,138778562,1,1,0,0,1,1,undirected graph,138778562 -GenBank,kmer_V1r,214005017,214005017,465410904,1,1,0,0,1,1,undirected graph,465410904 -GenBank,kmer_V2a,55042369,55042369,117217600,1,1,0,0,1,1,undirected graph,117217600 -Sybrandt,MOLIERE_2016,30239687,30239687,6669254694,1,0,0,0,1,1,undirected weighted graph,6677301366 -Guettel,TEM27623,27623,27623,439469,1,0,1,0,1,1,electromagnetics problem,439469 -Guettel,TEM152078,152078,152078,6459326,1,0,1,0,1,1,electromagnetics problem,6459326 -Guettel,TEM181302,181302,181302,7839010,1,0,1,0,1,1,electromagnetics problem,7839010 -Grueninger,windtunnel_evap2d,8256,8256,109368,1,0,1,0,1,0,computational fluid dynamics problem,233468 -Grueninger,windtunnel_evap3d,40816,40816,803978,1,0,1,0,1,0,computational fluid dynamics problem,2730600 -PowerSystem,power197k,197156,197156,805839,1,0,0,0,0.2139400937122758,0.02415229830828888,power network problem,3698357 -Goodwin,Goodwin_010,1182,1182,32282,1,0,1,0,0.9018121596229179,0.05496990350011147,computational fluid dynamics,32282 -Goodwin,Goodwin_013,1965,1965,56069,1,0,1,0,0.9250320454129281,0.05779161325764512,computational fluid dynamics,56069 -Goodwin,Goodwin_017,3317,3317,97773,1,0,1,0,0.9419021841757411,0.06036381947504249,computational fluid dynamics,97773 -Goodwin,Goodwin_023,6005,6005,182168,1,0,1,0,0.9572036973485083,0.06214380952916754,computational fluid dynamics,182168 -Goodwin,Goodwin_030,10142,10142,312814,1,0,1,0,0.966280360610536,0.06332624905442888,computational fluid dynamics,312814 -Goodwin,Goodwin_040,17922,17922,561677,1,0,1,0,0.9746314856878265,0.06442027585251406,computational fluid dynamics,561677 -Goodwin,Goodwin_054,32510,32510,1030878,1,0,1,0,0.9804679703906704,0.06537882962720246,computational fluid dynamics,1030878 -Goodwin,Goodwin_071,56021,56021,1797934,1,0,1,0,0.9849308636424431,0.06556841497181332,computational fluid dynamics,1797934 -Goodwin,Goodwin_095,100037,100037,3226066,1,0,1,0,0.9880933163299772,0.06443620776105316,computational fluid dynamics,3226066 -Goodwin,Goodwin_127,178437,178437,5778545,1,0,1,0,0.9902002197399873,0.06589012929818107,computational fluid dynamics,5778545 -TAMU_SmartGridCenter,ACTIVSg2000,4000,4000,28505,1,0,0,0,1,0.007345439706182412,power network problem,29336 -TAMU_SmartGridCenter,ACTIVSg10K,20000,20000,135888,1,0,0,0,1,0.02328109899213033,power network problem,137736 -TAMU_SmartGridCenter,ACTIVSg70K,69999,69999,238627,1,0,0,0,1,1,power network problem,238627 -Embree,ifiss_mat,96307,96307,3599932,1,0,1,0,1,0.2845118352876235,computational fluid dynamics,3599932 -Hardesty,Hardesty1,938905,938905,12143314,1,0,1,0,1,1,computer graphics/vision problem,12143314 -Hardesty,Hardesty2,929901,303645,4020731,1,0,1,0,0,0,computer graphics/vision problem,4020731 -Hardesty,Hardesty3,8217820,7591564,40451632,1,0,1,0,0,0,computer graphics/vision problem,40451632 -VLSI,nv1,75468,75468,1635003,1,0,1,0,1,0.2136393219773843,semiconductor device problem,2449194 -VLSI,nv2,1453908,1453908,37475646,1,0,1,0,1,0.1982255825635065,semiconductor device problem,52728362 -VLSI,imagesensor,118758,118758,1446396,1,0,1,0,1,0.2826177015120085,semiconductor device problem,1893170 -VLSI,mosfet2,46994,46994,1013062,1,0,1,0,1,0.1797368301196189,semiconductor device problem,1499460 -VLSI,power9,155376,155376,1887730,1,0,1,0,1,0.3018551635520223,semiconductor device problem,2512300 -VLSI,radiation,223104,223104,5526637,1,0,1,0,1,0.1890109856957617,semiconductor device problem,7637688 -VLSI,test1,392908,392908,9447535,1,0,1,0,1,0.2651539373184561,semiconductor device problem,12968200 -VLSI,dgreen,1200611,1200611,26606169,1,0,1,0,1,0.1732208361650628,semiconductor device problem,38259877 -VLSI,vas_stokes_1M,1090664,1090664,34767207,1,0,1,0,0.420954312323566,0.1713153870930279,semiconductor process problem,34767207 -VLSI,vas_stokes_2M,2146677,2146677,65129037,1,0,1,0,0.464592974921867,0.2072952172640085,semiconductor process problem,65129037 -VLSI,vas_stokes_4M,4382246,4382246,131577616,1,0,1,0,0.4636465777016884,0.1138555436412505,semiconductor process problem,131577616 -VLSI,stokes,11449533,11449533,349321980,1,0,1,0,0.4729070672045655,0.1293167418294988,semiconductor process problem,349321980 -VLSI,ss1,205282,205282,845089,1,0,1,0,0.9936840328411537,0.04200954350296261,semiconductor process problem,845089 -VLSI,ss,1652680,1652680,34753577,1,0,1,0,0.6780372145201986,0.2889301761218133,semiconductor process problem,34753577 -Freescale,nxp1,414604,414604,2655880,1,0,0,0,0.9998398784682034,0.8220579996645768,circuit simulation problem,2656177 -Martin,marine1,400320,400320,6226538,1,0,0,0,0.922267240944297,0,chemical oceanography problem,6226538 -Negre,dendrimer,730,730,63024,1,0,0,0,1,1,computational chemistry problem,63024 -Precima,analytics,303813,303813,2006126,1,0,0,0,1,1,data analytics problem,2006126 -GAP,GAP-twitter,61578415,61578415,1468364884,1,0,0,0,0.3621059600332965,0.001422518355458029,directed weighted graph,1468364884 -GAP,GAP-web,50636151,50636151,1930292948,1,0,0,0,0.1245713694644861,0.0004892386935249789,directed weighted graph,1930292948 -GAP,GAP-road,23947347,23947347,57708624,1,0,0,0,1,1,directed weighted graph,57708624 -GAP,GAP-kron,134217726,134217726,4223264644,1,0,0,0,1,1,random undirected weighted graph,4223264644 -GAP,GAP-urand,134217728,134217728,4294966740,1,0,0,0,1,1,random undirected weighted graph,4294966740 -ML_Graph,Binaryalphadigs_10NN,1404,1404,19392,1,0,0,0,1,1,undirected weighted graph,19392 -ML_Graph,breasttissue_10NN,106,106,1412,1,0,0,0,1,1,undirected weighted graph,1412 -ML_Graph,cnae9_10NN,1080,1080,18278,1,0,0,0,1,1,undirected weighted graph,18278 -ML_Graph,collins_15NN,1000,1000,16492,1,0,0,0,1,1,undirected weighted graph,16492 -ML_Graph,dataset12mfeatfactors_10NN,2000,2000,27442,1,0,0,0,1,1,undirected weighted graph,27442 -ML_Graph,dataset16mfeatkarhunen_10NN,2000,2000,27668,1,0,0,0,1,1,undirected weighted graph,27668 -ML_Graph,dataset18mfeatmorphological_10NN,2000,2000,22832,1,0,0,0,1,1,undirected weighted graph,22832 -ML_Graph,dataset20mfeatpixel_10NN,2000,2000,27932,1,0,0,0,1,1,undirected weighted graph,27932 -ML_Graph,dataset22mfeatzernike_10NN,2000,2000,27414,1,0,0,0,1,1,undirected weighted graph,27414 -ML_Graph,dermatology_5NN,366,366,2440,1,0,0,0,1,1,undirected weighted graph,2440 -ML_Graph,Ecoli_10NN,336,336,4560,1,0,0,0,1,1,undirected weighted graph,4560 -ML_Graph,Fashion_MNIST_norm_10NN,10000,10000,158304,1,0,0,0,1,1,undirected weighted graph,158304 -ML_Graph,Glass_10NN,214,214,2986,1,0,0,0,1,1,undirected weighted graph,2986 -ML_Graph,har_10NN,10299,10299,151736,1,0,0,0,1,1,undirected weighted graph,151736 -ML_Graph,indianpines_10NN,9144,9144,124656,1,0,0,0,1,1,undirected weighted graph,124656 -ML_Graph,iris_dataset_30NN,150,150,5518,1,0,0,0,1,1,undirected weighted graph,5518 -ML_Graph,JapaneseVowelsSmall_10NN,9961,9961,131144,1,0,0,0,1,1,undirected weighted graph,131144 -ML_Graph,k49_norm_10NN,38547,38547,618158,1,0,0,0,1,1,undirected weighted graph,618158 -ML_Graph,kmnist_norm_10NN,10000,10000,156932,1,0,0,0,1,1,undirected weighted graph,156932 -ML_Graph,mice_10NN,1077,1077,13484,1,0,0,0,1,1,undirected weighted graph,13484 -ML_Graph,micromass_10NN,571,571,9668,1,0,0,0,1,1,undirected weighted graph,9668 -ML_Graph,mnist_test_norm_10NN,10000,10000,145600,1,0,0,0,1,1,undirected weighted graph,145600 -ML_Graph,Olivetti_norm_10NN,400,400,5656,1,0,0,0,1,1,undirected weighted graph,5656 -ML_Graph,optdigits_10NN,5620,5620,79650,1,0,0,0,1,1,undirected weighted graph,79650 -ML_Graph,Plants_10NN,1600,1600,21930,1,0,0,0,1,1,undirected weighted graph,21930 -ML_Graph,plantsmargin_12NN,1600,1600,25482,1,0,0,0,1,1,undirected weighted graph,25482 -ML_Graph,plantstexture_10NN,1599,1599,21204,1,0,0,0,1,1,undirected weighted graph,21204 -ML_Graph,semeion_10NN,1593,1593,22226,1,0,0,0,1,1,undirected weighted graph,22226 -ML_Graph,Spectro_10NN,531,531,7422,1,0,0,0,1,1,undirected weighted graph,7422 -ML_Graph,umistfacesnorm_10NN,575,575,6990,1,0,0,0,1,1,undirected weighted graph,6990 -ML_Graph,usps_norm_5NN,11000,11000,81112,1,0,0,0,1,1,undirected weighted graph,81112 -ML_Graph,Vehicle_10NN,846,846,10894,1,0,0,0,1,1,undirected weighted graph,10894 -ML_Graph,worms20_10NN,20055,20055,240826,1,0,0,0,1,1,undirected weighted graph,240826 -ML_Graph,YaleA_10NN,165,165,2268,1,0,0,0,1,1,undirected weighted graph,2268 -ML_Graph,YaleB_10NN,2414,2414,34808,1,0,0,0,1,1,undirected weighted graph,34808 -ML_Graph,yeast_30NN,1484,1484,62350,1,0,0,0,1,1,undirected weighted graph,62350 -Sybrandt,AGATHA_2015,183964077,183964077,11588725964,1,1,0,0,1,1,undirected graph,11588725964 -FlowIPM22,Spielman_k100,338402,338402,1025404,1,0,0,1,1,1,undirected weighted graph,1025404 -FlowIPM22,Spielman_k200,2686802,2686802,8100804,1,0,0,0,1,1,undirected weighted graph,8100804 -FlowIPM22,Spielman_k300,9045202,9045202,27226204,1,0,0,0,1,1,undirected weighted graph,27226204 -FlowIPM22,Spielman_k400,21413602,21413602,64401604,1,0,0,0,1,1,undirected weighted graph,64401604 -FlowIPM22,Spielman_k500,41792002,41792002,125627004,1,0,0,0,1,1,undirected weighted graph,125627004 -FlowIPM22,Spielman_k600,72180402,72180402,216902404,1,0,0,0,1,1,undirected weighted graph,216902404 -FlowIPM22,uni_chimera_i1,100000,100000,1100592,1,0,0,1,1,1,undirected weighted graph,1100592 -FlowIPM22,uni_chimera_i2,100000,100000,797974,1,0,0,1,1,1,undirected weighted graph,797974 -FlowIPM22,uni_chimera_i3,100000,100000,499696,1,0,0,0,1,1,undirected weighted graph,499696 -FlowIPM22,uni_chimera_i4,100000,100000,814436,1,0,0,0,1,1,undirected weighted graph,814436 -FlowIPM22,uni_chimera_i5,100000,100000,499982,1,0,0,1,1,1,undirected weighted graph,499982 diff --git a/report_notes/group_taxonomy/taxonomy_findings.md b/report_notes/group_taxonomy/taxonomy_findings.md deleted file mode 100644 index ba95112..0000000 --- a/report_notes/group_taxonomy/taxonomy_findings.md +++ /dev/null @@ -1,189 +0,0 @@ -# How to interpret SuiteSparse Matrix Collection groups - -## Executive conclusion - -A SuiteSparse **group is primarily a provenance namespace**, not a mathematical -matrix class. The older official `UFgui` help is unusually explicit: `Group` -indicates the source of a problem—“a person, organization, or other -collection”—and `Group/Name` is its full name. The current statistics page -likewise says that `Group/Name`, not `Name` alone, is the unique identifier. - -That explains the naming. `Averous`, `Bai`, `Goodwin`, and many others are -contributors; `Boeing`, `Sandia`, `IBM_EDA`, and `TAMU_SmartGridCenter` are -organizations or labs; `HB`, `LPnetlib`, `SNAP`, `LAW`, `Pajek`, and `DIMACS10` -are imported collections/benchmarks; `DRIVCAV`, `TOKAMAK`, `VLSI`, and -`FlowIPM22` identify an application or construction. A group can be a clean -parameter sequence, a domain-specific submission, or a historical bundle. -Consequently, membership is useful context, but it never guarantees equal -shape, symmetry, scalar type, definiteness, or even application kind. - -The current public CSV index has 2,904 matrices in 203 groups and reports a -`LastRevisionDate` of 31-Oct-2023 18:12:37. Its only observed curator-label -difference from the current official website source is `Chevron1`–`Chevron4`: -the CSV says `other problem`, while the current site says `2D/3D problem`; the -generated outputs use the current label. The accompanying exhaustive table is -in `group_catalogue.md`; machine-readable values and the full official notes -are in `group_metrics.csv` and `group_metrics.json`. - -## What the main kinds of group names mean - -These categories describe the naming/provenance convention, not disjoint -matrix taxonomic classes. - -| Naming pattern | What the group represents | Examples | -|---|---|---| -| Contributor or editor | Matrices submitted or assembled by a person/team; the applications can be narrow or broad | `Averous`, `Bai`, `Goodwin`, `Grund`, `Rothberg`, `Simon`, `Williams` | -| Organization, company, or lab | Problems generated by one institution/toolchain | `Boeing`, `Sandia`, `IBM_EDA`, `Freescale`, `NASA`, `TAMU_SmartGridCenter`, `VDOL` | -| Imported archive/benchmark | A pre-existing test collection; often intentionally broad | `HB` (Harwell–Boeing), `GHS_indef`, `GHS_psdef`, `Oberwolfach`, `LPnetlib`, `Qaplib` | -| Graph/network corpus | Adjacency or weighted-adjacency matrices from a data repository or challenge | `Pajek`, `SNAP`, `LAW`, `DIMACS10`, `Gset`, `GAP`, `AG-Monien`, `ML_Graph` | -| Application/code/construction | Matrices sharing a simulation, code, equation, or generative construction | `FIDAP`, `DRIVCAV`, `TOKAMAK`, `VLSI`, `FlowIPM22`, `Mycielski`, `GenBank` | -| Mathematical subcollection | Algebraic/combinatorial boundary, incidence, relation, or design matrices | the 16 `JGD_*` groups, imported from Jean-Guillaume Dumas's Sparse Integer Matrix Collection | - -Documented examples make the convention concrete: - -- `HB` is the original Harwell–Boeing collection, so it is a historical archive, - not one application. -- `Bai` is explicitly “matrices from Z. Bai, D. Day, J. Demmel, and J. - Dongarra”; its name is provenance. -- `Sandia` is a circuit-simulation collection from Sandia National - Laboratories. -- `LPnetlib` is a translation of Netlib linear-programming instances, whose - principal matrix is the rectangular constraint matrix in `Ax=b`. -- `DIMACS10` is the 10th DIMACS Implementation Challenge corpus for graph - partitioning and clustering; it deliberately combines real and synthetic - graph sources. -- `VDOL` consists of KKT matrices generated during mesh-refinement iterations - in optimal-control solves at the Vehicle Dynamics & Optimization Lab. -- `FlowIPM22` consists of graph Laplacians arising in Newton steps of a max-flow - interior-point method on Chimera graphs. -- `JGD_Homology` contains matrices for simplicial-complex homology and is one - of the `JGD_*` mathematical subcollections. - -## What the matrices themselves represent - -Across groups, the primary sparse array can encode several different objects: - -1. **Discretized operators or linear systems.** Finite-element, - finite-volume, and finite-difference simulations produce stiffness, mass, - Jacobian, saddle-point, or coupled-physics matrices. Structural mechanics, - CFD, thermal, electromagnetics, acoustics, semiconductor, and material - problems dominate this category. -2. **Optimization systems.** Linear-programming groups commonly store a - rectangular constraint matrix; nonlinear/optimal-control groups often store - a square symmetric-indefinite KKT/Newton matrix. -3. **Graphs and networks.** Here `A(i,j)` records an edge or its weight. - Undirected graphs normally yield symmetric patterns/values; directed graphs - do not. Bipartite graphs can appear as rectangular incidence-like arrays. -4. **Model reduction/eigenvalue problems.** The primary `A` may be only one - operator in a larger problem with mass/input/output matrices included as - auxiliary metadata; the primary matrix's visible properties do not describe - the entire dynamical model. -5. **Algebraic/combinatorial constructions.** Boundary/differential, - incidence, relation, design, and polynomial-system matrices are often - rectangular and integer-valued. -6. **Least-squares, inverse, imaging, and data matrices.** Interpolation, - bundle adjustment, tomography, recommendation, document, and statistical - problems can be strongly rectangular and may include multiple right-hand - sides or labels in the problem metadata. - -The official `kind` field is the best high-level application label. It is a -qualitative curation field, as are `isND` (2D/3D discretization) and `isGraph`; -it is not inferred solely from algebraic matrix properties. - -## How homogeneous are groups? Quantitative answer - -The following counts are calculations from the 2,904-row public CSV, not claims -copied from the descriptions. “Kind” below collapses only the curator's -`subsequent`, `duplicate`, and `sequence` qualifiers. “Dimension” means -`max(nrows,ncols)`. - -| Axis | Group-level result | Interpretation | -|---|---:|---| -| Application kind | 172/203 groups have one normalized `kind`; 31 have more than one | Most groups are application-coherent, but the broad legacy collections are important exceptions. The 31 multi-kind groups contain 1,083 matrices (37.3% of the collection). | -| Shape | 159 square-only; 34 rectangular-only; 10 mixed | Shape is usually homogeneous within a group. The ten mixed groups are `HB`, `MathWorks`, `Bates`, `Pajek`, `Barabasi`, `Meszaros`, `JGD_BIBD`, `JGD_Homology`, `JGD_Margulies`, and `Hardesty`. | -| Exact numerical symmetry | Among groups containing square matrices: 61 all symmetric/Hermitian, 76 none, 32 mixed; 34 are rectangular-only | Symmetry is usually consistent, but 32 groups mix symmetric and nonsymmetric square members. | -| Real/complex domain | 184 real-only; 9 complex-only; 10 mix real and complex | Complex participation is usually homogeneous. This coarse split treats binary and integer matrices as real. | -| Exact curator value type | 39 groups mix two or more of `real`, `integer`, `binary`, and `complex` | Binary/integer/real distinctions reveal more within-group variation than the coarse real/complex flag. | -| Exact curator structure | 36 groups mix two or more of symmetric, Hermitian, skew-symmetric, unsymmetric, and rectangular | This is the most direct answer to “same matrix structure?”; numerical self-adjointness is also reported separately because complex-symmetric is not Hermitian. | -| Size | 60 have one dimension, 31 span ≤4×, 60 span 4–100×, and 52 span >100× | Scale is the least safe property to infer from group membership. The >100× groups contain 2,137 matrices (73.6%). Fifty-one of the 60 “one dimension” cases are single-matrix groups. | - -Thus the typical pattern is **same provenance and often the same domain/form, -but potentially very different scale**. Groups built from iterative solves or -mesh refinement are intentionally size sequences. Historical and data-corpus -groups can be diverse on every axis. - -### Representative profiles - -| Group | What it shows | Measured profile | -|---|---|---| -| `HB` | Maximal historical diversity | 292 matrices; 16 normalized kinds; dimensions 9–44,609; 276 square + 16 rectangular; 169/276 square matrices exactly symmetric/Hermitian; real + complex; 112 binary. | -| `Bai` | Contributor collection broader than a domain | 78 matrices; 8 kinds spanning CFD, electromagnetics, generic 2D/3D, statistics, power, chemistry, robotics, and materials; all square, but only 8/78 exactly symmetric/Hermitian; real + complex. | -| `Sandia` | Same domain/form, widely varying scale | 192 real square circuit matrices; none exactly symmetric/Hermitian; dimensions 430–682,862. | -| `LPnetlib` | Very coherent algebraic form | 138 real rectangular LP constraint matrices; dimensions 13–243,246. | -| `VDOL` | A coherent numerical sequence | 91 real, square, symmetric-indefinite optimal-control KKT matrices; dimensions 99–18,476. | -| `DIMACS10` | Homogeneous graph algebra, diverse datasets/scales | 151 square symmetric undirected graphs; 95 binary and 56 weighted; dimensions 39–50,912,018. | -| `SNAP` | One broad graph domain, multiple graph types | 68 square graphs; 8 normalized graph kinds (directed, undirected, temporal, weighted, communities); only 21/68 exactly symmetric; 53 binary; dimensions 1,005–65,608,366. | -| `LAW` | Direction explains nonsymmetry | 15 binary web/social graphs: 13 directed and 2 undirected; 2/15 exactly symmetric; dimensions 69,244–118,142,155. | -| `FIDAP` | Pattern symmetry is not numerical symmetry | 35 real square CFD matrices; all 35 have symmetric sparsity patterns, but only 13 have equal mirrored numerical values. | -| `Hardesty` | Same application need not mean same shape | Three surface-fitting/smoothing matrices; one square biharmonic/Lagrange-multiplier system and two rectangular interpolation-plus-regularization systems; dimensions 929,901–8,217,820. | -| `JGD_Homology` | Mathematical family with shape/scale variation | 128 combinatorial matrices; 122 rectangular + 6 square; dimensions 15–564,480. | -| `Oberwolfach` | Benchmark archive with form/value variation | 38 square model-reduction matrices; 32 exactly symmetric/Hermitian and 6 not; real + complex; dimensions 14–986,703. | - -## Important interpretation traps - -- **Group is not `kind`.** `HB` is a source archive; “structural problem” is a - kind. A group can contain many kinds, and the same kind occurs in many - groups. -- **Do not treat a surname as a domain.** A contributor may have supplied - unrelated applications. `Bai` is the clearest example. -- **Do not equate “same group” with “same size.”** Many of the best-curated - homogeneous groups are refinement/time-step/parameter sequences and thus - have enormous size ranges. -- **Do not equate symmetric pattern with symmetric numbers.** The official - `pattern_symmetry` asks whether both `(i,j)` and `(j,i)` are present; - `numerical_symmetry` also requires conjugate-equal values. `FIDAP` shows the - difference. -- **For complex matrices, numerical symmetry means Hermitian.** The statistic - compares `A(i,j)` with the complex conjugate of `A(j,i)`. A complex-symmetric - (but non-Hermitian) matrix can therefore have a value below one. -- **Rectangular matrices have symmetry statistics set to zero by definition.** - They should be reported as “not applicable,” not lumped into nonsymmetric - square systems. -- **`posdef=0` is not uniformly a proof of indefiniteness.** The documentation - says `1` means known positive definite, `0` known not positive definite, and - `-1` may be used for symmetric cases where definiteness is unknown, but the - compact CSV/index treatment and graph conventions warrant checking the - individual problem metadata before drawing solver conclusions. -- **A graph matrix is not automatically a linear system.** Its diagonal may be - irrelevant and its structural rank may not even be computed; graph benchmark - groups should be selected separately when evaluating linear solvers. - -## Sources to cite - -- Current collection overview and domain scope: https://sparse.tamu.edu/about -- Official statistics and exact field definitions: https://sparse.tamu.edu/statistics -- Official current group pages/descriptions: `https://sparse.tamu.edu/{Group}` -- Legacy official all-group list (compact descriptions): - https://www.cise.ufl.edu/research/sparse/matrices/groups.html -- Official UFgui help explicitly defining `Group` as source/provenance: - https://www.cise.ufl.edu/research/sparse/mat/UFget/UFhelp.html -- Canonical website paper: Kolodziej et al., *The SuiteSparse Matrix Collection - Website Interface*, JOSS 4(35), 1244 (2019), - https://doi.org/10.21105/joss.01244 -- Original collection paper: Davis and Hu, *The University of Florida Sparse - Matrix Collection*, ACM TOMS 38(1), Article 1 (2011), - https://doi.org/10.1145/2049662.2049663 - -## Appendix/data files - -- `group_catalogue.md`: all 203 groups, official meaning/source synopsis, - normalized current curator kinds and counts, interpretive coarse kind family, size range, shape, - symmetry/Hermitian count, value domain, binary count, and a transparent - heuristic cohesion assessment. -- `group_metrics.csv`: same results plus exact numerical columns and the full - official group notes. -- `group_metrics.json`: structured copy including the full official notes. -- `aggregate_summary.json`: reproducible headline counts. -- `analyze_groups.py`: generation logic; domain-family and cohesion columns are - explicitly heuristic, while all dimensions/counts/profiles derive directly - from official index fields. diff --git a/reports/suitesparse-groups/GROUP_CATALOGUE.md b/reports/suitesparse-groups/GROUP_CATALOGUE.md deleted file mode 100644 index eec5fca..0000000 --- a/reports/suitesparse-groups/GROUP_CATALOGUE.md +++ /dev/null @@ -1,216 +0,0 @@ -# SuiteSparse group catalogue - -This appendix covers all 203 groups in the 2,904-problem public snapshot. -Group descriptions are condensed from each official group page; click a group -name for its full provenance notes and matrix list. `Kind` is the current website -label after collapsing only `subsequent`, `duplicate`, and `sequence` modifiers. -`Dimension` is `max(rows, columns)`. Entry types are the mutually exclusive -Rutherford–Boeing categories `real`, `binary`, `integer`, and `complex`. -The assessment is an unofficial screening aid computed from six categorical fields, -including the uncollapsed kind string; the component counts are the evidence. - -| Group | N | Source / meaning | Normalized kind(s) | Dimension | Shape | Structure | Entry type | Known PD | Assessment | -|---|---:|---|---|---:|---|---|---|---:|---| -| [ACUSIM](https://sparse.tamu.edu/ACUSIM) | 1 | Computational fluid dynamics matrices from ACUSIM, Inc. | computational fluid dynamics problem (1) | 14,822–14,822 | square (1) | symmetric (1) | real (1) | 1 | singleton; one dimension | -| [AG-Monien](https://sparse.tamu.edu/AG-Monien) | 32 | AG-Monien Graph Collection, Ralf Diekmann and Robert Preis | 2D/3D problem (26); undirected graph (6) | 224–1,048,576 | square (32) | symmetric (32) | binary (32) | 0 | some categorical mix; wide (>100×) | -| [AMD](https://sparse.tamu.edu/AMD) | 2 | Circuit simulation matrices from Ufuk Okuyucu, AMD, Inc. | circuit simulation problem (2) | 150,102–1,585,478 | square (2) | symmetric (2) | real (2) | 2 | small; uniform observed types; moderate (4–100×) | -| [ANSYS](https://sparse.tamu.edu/ANSYS) | 3 | Underdetermined systems needing well-conditioned bases to be found. | least squares problem (3) | 887,058–1,823,928 | rectangular (3) | rectangular (3) | real (3) | 0 | small; uniform observed types; narrow (≤4×) | -| [ATandT](https://sparse.tamu.edu/ATandT) | 4 | Frequency domain analysis of large nonlinear analog circuits, AT&T. | frequency-domain circuit simulation problem (4) | 36,057–659,033 | square (4) | unsymmetric (4) | real (4) | 0 | small; uniform observed types; moderate (4–100×) | -| [Alemdar](https://sparse.tamu.edu/Alemdar) | 1 | A finite-element matrix from Bulent Alemdar, RAM International. | 2D/3D problem (1) | 6,245–6,245 | square (1) | symmetric (1) | integer (1) | 0 | singleton; one dimension | -| [Andrews](https://sparse.tamu.edu/Andrews) | 1 | Large eigenvalue problem from Stuart Andrews, Brown University, Rhode Island. | computer graphics/vision problem (1) | 60,000–60,000 | square (1) | symmetric (1) | integer (1) | 1 | singleton; one dimension | -| [Andrianov](https://sparse.tamu.edu/Andrianov) | 16 | Matrices from Alexander Andrianov, SAS Institute Inc. | optimization problem (16) | 5,026–534,388 | square (16) | symmetric (16) | binary (16) | 0 | categorically uniform; wide (>100×) | -| [Arenas](https://sparse.tamu.edu/Arenas) | 4 | Networks from Alex Arenas, Univeristy Rovira i Virgili, Tarragona, Spain | undirected multigraph (3); undirected graph (1) | 198–10,680 | square (4) | symmetric (4) | integer (3); binary (1) | 0 | small; observed mix; moderate (4–100×) | -| [Averous](https://sparse.tamu.edu/Averous) | 4 | Matrices from David Averous, Institut National Polytechnique de Toulouse (INPT). | thermal problem (4) | 1,794–84,617 | square (4) | unsymmetric (4) | real (4) | 0 | small; uniform observed types; moderate (4–100×) | -| [Bai](https://sparse.tamu.edu/Bai) | 78 | Matrices from Z. Bai, D. Day, J. Demmel, and J. Dongarra. | computational fluid dynamics problem (31); electromagnetics problem (26); 2D/3D problem (8); statistical/mathematical problem (4); +4 more | 62–23,560 | square (78) | unsymmetric (66); symmetric (11); Hermitian (1) | real (70); complex (6); integer (2) | 4 | high categorical mix; wide (>100×) | -| [Barabasi](https://sparse.tamu.edu/Barabasi) | 3 | Notre Dame networks, A. Barabasi. | bipartite graph (1); directed graph (1); undirected graph (1) | 2,114–392,400 | square (2); rectangular (1) | rectangular (1); symmetric (1); unsymmetric (1) | binary (3) | 0 | small; observed mix; wide (>100×) | -| [Bates](https://sparse.tamu.edu/Bates) | 3 | Statistical matrices, D. Bates, Univ. Wisconsin | statistical/mathematical problem (2); least squares problem (1) | 2,541–1,748,122 | rectangular (2); square (1) | rectangular (2); symmetric (1) | real (2); binary (1) | 1 | small; observed mix; wide (>100×) | -| [Belcastro](https://sparse.tamu.edu/Belcastro) | 3 | Gene networks from V. Belcastro, TIGEM, Naples, Italy | undirected weighted graph (3) | 14,340–45,101 | square (3) | symmetric (3) | real (3) | 0 | small; uniform observed types; narrow (≤4×) | -| [BenElechi](https://sparse.tamu.edu/BenElechi) | 1 | Matrix from S. Ben Elechi, Arcelor, Inc. with many sparse right-hand sides. | 2D/3D problem (1) | 245,874–245,874 | square (1) | symmetric (1) | real (1) | 1 | singleton; one dimension | -| [Bindel](https://sparse.tamu.edu/Bindel) | 6 | Coupled linear thermoeslasticity equations. D Bindel, UC Berkeley | thermal problem (6) | 10,605–10,605 | square (6) | unsymmetric (4); symmetric (2) | real (4); complex (2) | 2 | high categorical mix; one dimension | -| [Bodendiek](https://sparse.tamu.edu/Bodendiek) | 5 | Curl-Curl operator of 2nd order Maxwell's equations, A. Bodendiek | model reduction problem (5) | 11,083–2,380,515 | square (5) | symmetric (5) | real (5) | 0 | categorically uniform; wide (>100×) | -| [Boeing](https://sparse.tamu.edu/Boeing) | 32 | Structural engineering matrices from Roger Grimes, Boeing. | structural problem (24); materials problem (8) | 588–217,918 | square (32) | symmetric (32) | real (27); binary (5) | 15 | high categorical mix; wide (>100×) | -| [Bomhof](https://sparse.tamu.edu/Bomhof) | 4 | Matrices from circuit simulation problems. Wim Bomhof, Univ. Utrecht. | circuit simulation problem (4) | 2,624–80,209 | square (4) | unsymmetric (4) | real (4) | 0 | small; uniform observed types; moderate (4–100×) | -| [Botonakis](https://sparse.tamu.edu/Botonakis) | 6 | FEM thermal/mechanical problems from Ioannis Botonakis, Germany. | thermal problem (6) | 17,880–204,316 | square (6) | symmetric (3); unsymmetric (3) | real (6) | 3 | high categorical mix; moderate (4–100×) | -| [Bourchtein](https://sparse.tamu.edu/Bourchtein) | 4 | Atmospheric modeling problems from Andrei Bourchtein | computational fluid dynamics problem (4) | 1,270,432–1,489,752 | square (4) | unsymmetric (4) | real (3); integer (1) | 0 | small; observed mix; narrow (≤4×) | -| [Bova](https://sparse.tamu.edu/Bova) | 1 | Model of Charleston Harbor. Steve Bova, Nichols Research Corporation. | computational fluid dynamics problem (1) | 46,835–46,835 | square (1) | unsymmetric (1) | real (1) | 0 | singleton; one dimension | -| [Brethour](https://sparse.tamu.edu/Brethour) | 2 | Numerical simulation of coating flows, from Jim Brethour. | computational fluid dynamics problem (2) | 1,348–9,540 | square (2) | unsymmetric (2) | real (2) | 0 | small; uniform observed types; moderate (4–100×) | -| [Brogan](https://sparse.tamu.edu/Brogan) | 1 | specular surface reconstruction problem, Submitted by Joel Brogan, Notre Dame, July 2014 | computer vision problem (1) | 477,976–477,976 | rectangular (1) | rectangular (1) | real (1) | 0 | singleton; one dimension | -| [Brunetiere](https://sparse.tamu.edu/Brunetiere) | 1 | A matrix from Noel Brunetiere. Finite-element discretization of thermal problem. | thermal problem (1) | 3,456–3,456 | square (1) | unsymmetric (1) | real (1) | 0 | singleton; one dimension | -| [Buss](https://sparse.tamu.edu/Buss) | 2 | Website recommendation system, Steven Buss, Univ. Florida | undirected bipartite graph (2) | 394,792–872,622 | rectangular (2) | rectangular (2) | binary (2) | 0 | small; uniform observed types; narrow (≤4×) | -| [Bydder](https://sparse.tamu.edu/Bydder) | 2 | MRI reconstruction matrices from Mark Bydder, UCSD. | computer graphics/vision problem (2) | 147,456–147,456 | rectangular (2) | rectangular (2) | real (2) | 0 | small; uniform observed types; one dimension | -| [CEMW](https://sparse.tamu.edu/CEMW) | 4 | Electromagnetic problems from D. Isaak, Computational_EM_Works | electromagnetics problem (4) | 93,476–921,632 | square (4) | unsymmetric (3); symmetric (1) | real (2); complex (1); integer (1) | 1 | small; observed mix; moderate (4–100×) | -| [CPM](https://sparse.tamu.edu/CPM) | 9 | Closest Point Method, Yujia Chen, Andy Wathen, Shengxin Zhu | 2D/3D problem (9) | 148–40,948 | square (9) | unsymmetric (9) | real (9) | 0 | categorically uniform; wide (>100×) | -| [Cannizzo](https://sparse.tamu.edu/Cannizzo) | 1 | Structural engineering matrix (finite-element) from Fabio Cannizzo. | structural problem (1) | 4,098–4,098 | square (1) | symmetric (1) | real (1) | 1 | singleton; one dimension | -| [Castrillon](https://sparse.tamu.edu/Castrillon) | 1 | Counter-example matrix (denormal in A+alpha*I), J. Castrillon, Teledyne | counter-example problem (1) | 89,400–89,400 | square (1) | symmetric (1) | real (1) | 1 | singleton; one dimension | -| [Chen](https://sparse.tamu.edu/Chen) | 14 | Finite element matrices from Pu Chen, Peking University. | structural problem (14) | 10,800–151,926 | square (14) | symmetric (14) | binary (14) | 0 | categorically uniform; moderate (4–100×) | -| [Chevron](https://sparse.tamu.edu/Chevron) | 4 | 2D Helmholtz; Temporal freq. domain seismic modeling. J. Washbourne, Chevron | 2D/3D problem (4) | 37,365–711,450 | square (4) | unsymmetric (4) | complex (4) | 0 | small; uniform observed types; moderate (4–100×) | -| [Clark](https://sparse.tamu.edu/Clark) | 1 | Tomographic reconstruction of a particle field in fluid flow, T. Clark | computer graphics/vision problem (1) | 73,159–73,159 | rectangular (1) | rectangular (1) | real (1) | 0 | singleton; one dimension | -| [Cote](https://sparse.tamu.edu/Cote) | 2 | Vibroacoustic matrices from Andre Cote, Univ. Sherbrooke, Quebec. | acoustics problem (2) | 5,962–12,328 | square (2) | symmetric (2) | complex (1); real (1) | 0 | small; observed mix; narrow (≤4×) | -| [Cunningham](https://sparse.tamu.edu/Cunningham) | 4 | Finite element matrices from Andrew Cunningham, Vibro-Acoustic Sciences, Inc. | acoustics problem (4) | 11,107–66,127 | square (4) | symmetric (3); unsymmetric (1) | real (4) | 1 | small; observed mix; moderate (4–100×) | -| [Cylshell](https://sparse.tamu.edu/Cylshell) | 7 | Finite element analysis of cylindrical shells, R. Kouhia, Helsinki. | structural problem (7) | 5,357–5,489 | square (7) | symmetric (7) | real (7) | 7 | categorically uniform; narrow (≤4×) | -| [DIMACS10](https://sparse.tamu.edu/DIMACS10) | 151 | 10th DIMACS Implementation Challenge | undirected graph (67); undirected weighted graph (50); random unweighted graph (15); undirected random graph (10); +2 more | 39–50,912,018 | square (151) | symmetric (151) | binary (95); integer (56) | 0 | some categorical mix; wide (>100×) | -| [DNVS](https://sparse.tamu.edu/DNVS) | 15 | Positive definite matrices from Christian Damhaug, DNV Software. | structural problem (15) | 18,010–224,617 | square (15) | symmetric (15) | real (8); binary (7) | 8 | some categorical mix; moderate (4–100×) | -| [DRIVCAV](https://sparse.tamu.edu/DRIVCAV) | 26 | Driven cavity, finite element matrices from Yousef Saad (SPARSKIT2 collection). | computational fluid dynamics problem (26) | 317–4,562 | square (26) | unsymmetric (26) | real (26) | 0 | some categorical mix; moderate (4–100×) | -| [Dattorro](https://sparse.tamu.edu/Dattorro) | 3 | Dattorro Convex Optimization of Eternity II, Jon Dattoro | optimization problem (3) | 150,638–262,144 | rectangular (3) | rectangular (3) | integer (3) | 0 | small; uniform observed types; narrow (≤4×) | -| [Davis](https://sparse.tamu.edu/Davis) | 1 | Analysis of the MATLAB FileExchange, March 2010 | term/document graph (1) | 9,498–9,498 | rectangular (1) | rectangular (1) | integer (1) | 0 | singleton; one dimension | -| [Dehghani](https://sparse.tamu.edu/Dehghani) | 1 | Light transport in soft tissue, Hamid Dehghani, Univ. of Exeter, UK | electromagnetics problem (1) | 29,282–29,282 | square (1) | unsymmetric (1) | complex (1) | 0 | singleton; one dimension | -| [Dziekonski](https://sparse.tamu.edu/Dziekonski) | 5 | High order vector finite element method in electromagnetics | electromagnetics problem (5) | 420,408–1,157,456 | square (5) | symmetric (5) | real (3); complex (2) | 0 | some categorical mix; narrow (≤4×) | -| [Embree](https://sparse.tamu.edu/Embree) | 1 | Fluid stability problem from IFISS package, Mark Embree, Virginia Tech | computational fluid dynamics (1) | 96,307–96,307 | square (1) | unsymmetric (1) | real (1) | 0 | singleton; one dimension | -| [Engwirda](https://sparse.tamu.edu/Engwirda) | 1 | Unstructured 2D mesh (an airfoil) from Darren Engwirda. | computational fluid dynamics problem (1) | 14,214–14,214 | square (1) | unsymmetric (1) | real (1) | 0 | singleton; one dimension | -| [FEMLAB](https://sparse.tamu.edu/FEMLAB) | 9 | Test matrices from FEMLAB, a finite-element method toolbox for MATLAB. | computational fluid dynamics problem (4); structural problem (3); 2D/3D problem (1); electromagnetics problem (1) | 367–85,623 | square (9) | unsymmetric (9) | real (8); complex (1) | 0 | high categorical mix; wide (>100×) | -| [FIDAP](https://sparse.tamu.edu/FIDAP) | 35 | Fluid dynamics, finite element matrices from Yousef Saad (SPARSKIT2 collection). | computational fluid dynamics problem (35) | 27–19,716 | square (35) | unsymmetric (22); symmetric (13) | real (35) | 8 | high categorical mix; wide (>100×) | -| [FlowIPM22](https://sparse.tamu.edu/FlowIPM22) | 11 | FlowIPM22: Laplacians from Newton-Steps of Maxflow Short-step IPM on Chimera Graphs | undirected weighted graph (11) | 100,000–72,180,402 | square (11) | symmetric (11) | real (11) | 4 | some categorical mix; wide (>100×) | -| [Fluorem](https://sparse.tamu.edu/Fluorem) | 5 | CFD matrices from Francois Pacull, FLUOREM, in Lyon, France | computational fluid dynamics problem (5) | 903–2,017,169 | square (5) | unsymmetric (5) | real (5) | 0 | categorically uniform; wide (>100×) | -| [FreeFieldTechnologies](https://sparse.tamu.edu/FreeFieldTechnologies) | 1 | 3D vibro-acoustic problem, aircraft engine nacelle, from M. Gontier | acoustics problem (1) | 169,410–169,410 | square (1) | unsymmetric (1) | complex (1) | 0 | singleton; one dimension | -| [Freescale](https://sparse.tamu.edu/Freescale) | 8 | Circuit simulation matrices K. Gullapalli, Freescale Semiconductor | circuit simulation problem (7); circuit simulation matrix (1) | 178,866–5,558,326 | square (8) | unsymmetric (8) | real (8) | 0 | some categorical mix; moderate (4–100×) | -| [GAP](https://sparse.tamu.edu/GAP) | 5 | GAP Benchmark matrices: Scott Beamer, Krste Asanovic', and David Patterson. | directed weighted graph (3); random undirected weighted graph (2) | 23,947,347–134,217,728 | square (5) | symmetric (3); unsymmetric (2) | integer (5) | 0 | high categorical mix; moderate (4–100×) | -| [GHS_indef](https://sparse.tamu.edu/GHS_indef) | 60 | Indefinite matrices collected by Nick Gould, Yifan Hu, and Jennifer Scott. | optimization problem (30); 2D/3D problem (17); computational fluid dynamics problem (4); materials problem (4); +2 more | 3,002–466,316 | square (60) | symmetric (60) | real (57); integer (3) | 1 | some categorical mix; wide (>100×) | -| [GHS_psdef](https://sparse.tamu.edu/GHS_psdef) | 32 | Positive definite matrices collected by Nick Gould, Yifan Hu, & Jennifer Scott. | structural problem (19); optimization problem (8); computational fluid dynamics problem (3); random 2D/3D problem (2) | 15,449–952,203 | square (32) | symmetric (32) | real (22); binary (10) | 22 | high categorical mix; moderate (4–100×) | -| [Gaertner](https://sparse.tamu.edu/Gaertner) | 3 | Matrices from Dr. Klaus Gaertner, Weierstrass Institute Berlin. | directed weighted graph (2); undirected weighted graph (1) | 10,774–13,209 | square (3) | unsymmetric (2); symmetric (1) | integer (3) | 0 | small; observed mix; narrow (≤4×) | -| [Garon](https://sparse.tamu.edu/Garon) | 2 | Matrices from Andre Garon, Univ. of Montreal. 2D Navier-Stokes. | computational fluid dynamics problem (2) | 3,175–13,535 | square (2) | unsymmetric (2) | real (2) | 0 | small; uniform observed types; moderate (4–100×) | -| [GenBank](https://sparse.tamu.edu/GenBank) | 5 | Protein k-mer graphs generated using data from GenBank | undirected graph (5) | 55,042,369–214,005,017 | square (5) | symmetric (5) | binary (5) | 0 | categorically uniform; narrow (≤4×) | -| [Gleich](https://sparse.tamu.edu/Gleich) | 10 | Web matrices and wikipedia matrices from David Gleich, Stanford. | directed graph (7); undirected graph (3) | 2,642–9,845,725 | square (10) | unsymmetric (7); symmetric (3) | binary (10) | 0 | high categorical mix; wide (>100×) | -| [Goodwin](https://sparse.tamu.edu/Goodwin) | 12 | Fluid mechanics matrices from Ralph Goodwin | computational fluid dynamics (10); computational fluid dynamics problem (2) | 1,182–178,437 | square (12) | unsymmetric (12) | real (12) | 0 | some categorical mix; wide (>100×) | -| [Graham](https://sparse.tamu.edu/Graham) | 1 | Finite element matrix from Dave Graham, Univ. of Illinois. | computational fluid dynamics problem (1) | 9,035–9,035 | square (1) | unsymmetric (1) | real (1) | 0 | singleton; one dimension | -| [Grueninger](https://sparse.tamu.edu/Grueninger) | 2 | Wind tunnel evaporation, Christoph Grueninger, Univ. Stuttgart | computational fluid dynamics problem (2) | 8,256–40,816 | square (2) | unsymmetric (2) | real (2) | 0 | small; uniform observed types; moderate (4–100×) | -| [Grund](https://sparse.tamu.edu/Grund) | 22 | Matrices from Friedrich Grund, WIAS Berlin. Chemical process simulation. | chemical process simulation problem (16); economic problem (4); circuit simulation problem (2) | 7–57,735 | square (22) | unsymmetric (21); symmetric (1) | real (22) | 0 | some categorical mix; wide (>100×) | -| [Gset](https://sparse.tamu.edu/Gset) | 67 | Random graphs from Yinyu Ye, Univ. of Iowa. | undirected random graph (34); undirected weighted random graph (33) | 800–10,000 | square (67) | symmetric (67) | binary (34); integer (33) | 0 | some categorical mix; moderate (4–100×) | -| [Guettel](https://sparse.tamu.edu/Guettel) | 3 | 3D Transient Electromagnetic Modelleing, Stefan Guettel, Univ Manchester | electromagnetics problem (3) | 27,623–181,302 | square (3) | symmetric (3) | real (3) | 0 | small; uniform observed types; moderate (4–100×) | -| [Gupta](https://sparse.tamu.edu/Gupta) | 3 | Linear programming matrices from Anshul Gupta, IBM T. J. Watson Research Center. | optimization problem (3) | 16,783–62,064 | square (3) | symmetric (3) | binary (3) | 0 | small; uniform observed types; narrow (≤4×) | -| [HB](https://sparse.tamu.edu/HB) | 292 | The original Harwell-Boeing collection. Iain Duff, Roger Grimes, and John Lewis. | structural problem (121); 2D/3D problem (36); computational fluid dynamics problem (27); power network problem (18); +12 more | 9–44,609 | square (276); rectangular (16) | symmetric (169); unsymmetric (105); rectangular (16); skew-symmetric (2) | real (170); binary (112); integer (7); complex (3) | 60 | high categorical mix; wide (>100×) | -| [HVDC](https://sparse.tamu.edu/HVDC) | 2 | Power simulation matrices from Alex Wang, Manitoba HVDC, Canada. | power network problem (2) | 24,842–189,860 | square (2) | unsymmetric (2) | real (2) | 0 | small; uniform observed types; moderate (4–100×) | -| [Hamm](https://sparse.tamu.edu/Hamm) | 6 | Semiconductor simulation matrices from Steve Hamm, Motorola, Inc. | circuit simulation problem (6) | 2,395–170,998 | square (6) | unsymmetric (6) | real (6) | 0 | some categorical mix; moderate (4–100×) | -| [Hamrle](https://sparse.tamu.edu/Hamrle) | 3 | Circuit simulation matrices from Jaroslav Hamrle, RIKEN, Japan. | circuit simulation problem (3) | 32–1,447,360 | square (3) | unsymmetric (3) | real (3) | 0 | small; uniform observed types; wide (>100×) | -| [Hardesty](https://sparse.tamu.edu/Hardesty) | 3 | Surface fitting problem for visualization, Sean Hardesty | computer graphics/vision problem (3) | 929,901–8,217,820 | rectangular (2); square (1) | rectangular (2); symmetric (1) | real (2); integer (1) | 0 | small; observed mix; moderate (4–100×) | -| [Harvard_Seismology](https://sparse.tamu.edu/Harvard_Seismology) | 1 | Earth's crust underneath Japan, Harvard Seismology Group. | tomography problem (1) | 87,616–87,616 | rectangular (1) | rectangular (1) | real (1) | 0 | singleton; one dimension | -| [Hohn](https://sparse.tamu.edu/Hohn) | 6 | Single-material crack problem, Michael Hohn, Univ. of Utah. | materials problem (6) | 7,500–16,428 | square (6) | unsymmetric (6) | real (6) | 0 | some categorical mix; narrow (≤4×) | -| [Hollinger](https://sparse.tamu.edu/Hollinger) | 49 | Economic models from Peter Holinger, Intex Solutions, Inc. | economic problem (49) | 2,880–64,089 | square (49) | unsymmetric (49) | real (49) | 0 | categorically uniform; moderate (4–100×) | -| [IBM_Austin](https://sparse.tamu.edu/IBM_Austin) | 1 | Circuit simulation matrix from Emrah Acar, IBM Austin. High degree of coupling. | circuit simulation problem (1) | 11,341–11,341 | square (1) | unsymmetric (1) | real (1) | 0 | singleton; one dimension | -| [IBM_EDA](https://sparse.tamu.edu/IBM_EDA) | 7 | Circuit simulation matrices from Tim Lehner, IBM EDA. | circuit simulation problem (7) | 49,702–116,835 | square (7) | unsymmetric (7) | real (7) | 0 | some categorical mix; narrow (≤4×) | -| [INPRO](https://sparse.tamu.edu/INPRO) | 1 | Parasol matrices, collected by Jacko Koster | structural problem (1) | 415,863–415,863 | square (1) | symmetric (1) | real (1) | 1 | singleton; one dimension | -| [IPSO](https://sparse.tamu.edu/IPSO) | 7 | Power simulation matrices, Li Peijie, Inst. of Power Systems, Guangxi Univ. | power network problem (7) | 8,140–226,340 | square (7) | symmetric (7) | real (7) | 0 | categorically uniform; moderate (4–100×) | -| [JGD_BIBD](https://sparse.tamu.edu/JGD_BIBD) | 21 | Balanced Incomplete Block Design from Mark Giesbrecht, Univ. of Waterloo | combinatorial problem (21) | 84–319,770 | rectangular (20); square (1) | rectangular (20); symmetric (1) | binary (21) | 1 | categorically uniform; wide (>100×) | -| [JGD_CAG](https://sparse.tamu.edu/JGD_CAG) | 3 | CAG matrix set from Michael Monagan, Simon Fraser Univ., Canada | combinatorial problem (3) | 72–1,916 | square (3) | unsymmetric (3) | integer (3) | 0 | small; uniform observed types; moderate (4–100×) | -| [JGD_Forest](https://sparse.tamu.edu/JGD_Forest) | 10 | Forests and Trees from Nicolas Thiery | combinatorial problem (10) | 107–317,955 | rectangular (10) | rectangular (10) | integer (10) | 0 | categorically uniform; wide (>100×) | -| [JGD_Franz](https://sparse.tamu.edu/JGD_Franz) | 11 | Cohomology of various rings, from Matthias Franz, Univ. Konstanz, Germany | combinatorial problem (11) | 2,240–47,104 | rectangular (11) | rectangular (11) | integer (11) | 0 | categorically uniform; moderate (4–100×) | -| [JGD_G5](https://sparse.tamu.edu/JGD_G5) | 13 | Decomposable subspaces at degree d of the invariant ring of G5, Nicolas Thiery. | combinatorial problem (13) | 77–47,894 | rectangular (13) | rectangular (13) | integer (13) | 0 | categorically uniform; wide (>100×) | -| [JGD_GL6](https://sparse.tamu.edu/JGD_GL6) | 5 | Differentials of the Voronoi complex of perfect forms of rank 6 mod GL_6(Z), from Philippe Elbaz-Vincent, Institut Fourier,… | combinatorial problem (5) | 341–637 | rectangular (5) | rectangular (5) | integer (5) | 0 | categorically uniform; narrow (≤4×) | -| [JGD_GL7d](https://sparse.tamu.edu/JGD_GL7d) | 17 | Differentials of the Voronoi complex of perfect forms of rank 7 mod GL_7(Z) | combinatorial problem (17) | 60–1,955,309 | rectangular (17) | rectangular (17) | integer (17) | 0 | categorically uniform; wide (>100×) | -| [JGD_Groebner](https://sparse.tamu.edu/JGD_Groebner) | 8 | Gro"bner basis from Jean-Charles Fauge`res, From Jean-Guillaume Dumas' Sparse Integer Matrix Collection, | combinatorial problem (8) | 404–5,761 | rectangular (8) | rectangular (8) | integer (7); binary (1) | 0 | some categorical mix; moderate (4–100×) | -| [JGD_Homology](https://sparse.tamu.edu/JGD_Homology) | 128 | Simplicial complexes from Homology from Volkmar Welker. | combinatorial problem (128) | 15–564,480 | rectangular (122); square (6) | rectangular (122); unsymmetric (6) | integer (128) | 0 | categorically uniform; wide (>100×) | -| [JGD_Kocay](https://sparse.tamu.edu/JGD_Kocay) | 12 | Brute force disjoint product matrices in tree algebra on n nodes, Nicolas Thiery | combinatorial problem (12) | 2–15,905 | rectangular (12) | rectangular (12) | integer (11); binary (1) | 0 | categorically uniform; wide (>100×) | -| [JGD_Margulies](https://sparse.tamu.edu/JGD_Margulies) | 23 | Combinatorial optimization as polynomial eqns, Susan Margulies, UC Davis | combinatorial problem (23) | 25–902,103 | rectangular (22); square (1) | rectangular (22); unsymmetric (1) | binary (23) | 0 | categorically uniform; wide (>100×) | -| [JGD_Relat](https://sparse.tamu.edu/JGD_Relat) | 15 | Relations from Florent Hiver, Univ. Paris-Est Marne-La-Valle'e | combinatorial problem (15) | 12–12,360,060 | rectangular (15) | rectangular (15) | integer (15) | 0 | categorically uniform; wide (>100×) | -| [JGD_SL6](https://sparse.tamu.edu/JGD_SL6) | 7 | Differentials of the Voronoi complex of perfect forms | combinatorial problem (7) | 434–1,271 | rectangular (7) | rectangular (7) | integer (7) | 0 | categorically uniform; narrow (≤4×) | -| [JGD_SPG](https://sparse.tamu.edu/JGD_SPG) | 7 | Symmetric powers of graphs from Gordon Royle, Univ Western Australia | combinatorial problem (7) | 300–6,545 | square (7) | symmetric (6); unsymmetric (1) | binary (6); integer (1) | 0 | high categorical mix; moderate (4–100×) | -| [JGD_Taha](https://sparse.tamu.edu/JGD_Taha) | 2 | Taha matrix set from Abdul Salam Jarrah, New Mexico State Univ. | combinatorial problem (2) | 14,596–37,932 | rectangular (2) | rectangular (2) | integer (2) | 0 | small; uniform observed types; narrow (≤4×) | -| [JGD_Trefethen](https://sparse.tamu.edu/JGD_Trefethen) | 11 | Diagonal matrices with primes, Nick Trefethen, Oxford Univ. | combinatorial problem (11) | 19–20,000 | square (11) | symmetric (11) | integer (11) | 11 | categorically uniform; wide (>100×) | -| [Janna](https://sparse.tamu.edu/Janna) | 18 | Finite element simulations: gas reservoir and structural problems. Univ Padova. | structural problem (14); 2D/3D problem (3); computational fluid dynamics problem (1) | 377,002–4,147,110 | square (18) | symmetric (14); unsymmetric (4) | real (18) | 10 | high categorical mix; moderate (4–100×) | -| [Kamvar](https://sparse.tamu.edu/Kamvar) | 2 | Web matrices from Sep Kamvar, Stanford University. | directed graph (2) | 281,903–683,446 | square (2) | unsymmetric (2) | binary (2) | 0 | small; uniform observed types; narrow (≤4×) | -| [Kemelmacher](https://sparse.tamu.edu/Kemelmacher) | 1 | Computer vision problem from Ira Kemelmacher, Weizmann Inst., Israel. | computer graphics/vision problem (1) | 28,452–28,452 | rectangular (1) | rectangular (1) | real (1) | 0 | singleton; one dimension | -| [Kim](https://sparse.tamu.edu/Kim) | 2 | 2D complex meshes, from Wonsik Kim, Korea. | 2D/3D problem (2) | 38,415–456,976 | square (2) | unsymmetric (2) | complex (2) | 0 | small; uniform observed types; moderate (4–100×) | -| [Koutsovasilis](https://sparse.tamu.edu/Koutsovasilis) | 2 | Symmetric indefinite matrix from P. Koutsovasilis, Tech. Univ. Dresden | structural problem (2) | 71,505–343,791 | square (2) | symmetric (2) | real (2) | 0 | small; uniform observed types; moderate (4–100×) | -| [LAW](https://sparse.tamu.edu/LAW) | 15 | Laboratory for Web Algorithmics (LAW), Università degli Studi di Milano | directed graph (13); undirected graph (2) | 69,244–118,142,155 | square (15) | unsymmetric (13); symmetric (2) | binary (15) | 0 | high categorical mix; wide (>100×) | -| [LPnetlib](https://sparse.tamu.edu/LPnetlib) | 138 | NETLIB Linear Programming problems, www.netlib.org. | linear programming problem (138) | 13–243,246 | rectangular (138) | rectangular (138) | real (108); integer (30) | 0 | some categorical mix; wide (>100×) | -| [Langemyr](https://sparse.tamu.edu/Langemyr) | 1 | FEM structural mechanics matrix from Lars Langemyr, COMSOL. | structural problem (1) | 1,500–1,500 | square (1) | unsymmetric (1) | real (1) | 0 | singleton; one dimension | -| [LeGresley](https://sparse.tamu.edu/LeGresley) | 3 | Power flow analyses of electrical grids. | power network problem (3) | 2,508–87,936 | square (3) | unsymmetric (3) | real (3) | 0 | small; uniform observed types; moderate (4–100×) | -| [Lee](https://sparse.tamu.edu/Lee) | 2 | FEM, Electromagnetics, Center for Computational Electromagnetics, UIUC | electromagnetics problem (2) | 74,062–491,100 | square (2) | unsymmetric (2) | complex (2) | 0 | small; observed mix; moderate (4–100×) | -| [Li](https://sparse.tamu.edu/Li) | 2 | 3D magnetohydrodynamics finite element matrix from Ben Li, Washington State Univ. | electromagnetics problem (2) | 22,695–22,695 | square (2) | symmetric (1); unsymmetric (1) | binary (1); real (1) | 0 | small; observed mix; one dimension | -| [Lin](https://sparse.tamu.edu/Lin) | 1 | Large sparse eigenvalue problem from Chun-hung (Martin) Lin. NCHC, Taiwan. | structural problem (1) | 256,000–256,000 | square (1) | symmetric (1) | real (1) | 0 | singleton; one dimension | -| [LiuWenzhuo](https://sparse.tamu.edu/LiuWenzhuo) | 1 | Power system simulation matrix from Wenzhou Liu, EPRI, China. | power network problem (1) | 15,838–15,838 | square (1) | unsymmetric (1) | real (1) | 0 | singleton; one dimension | -| [Lourakis](https://sparse.tamu.edu/Lourakis) | 1 | 3D vision matrices, M. Lourakis, Foundation for Research & Technology - Hellas. | computer graphics/vision problem (1) | 10,581–10,581 | square (1) | symmetric (1) | real (1) | 1 | singleton; one dimension | -| [Lucifora](https://sparse.tamu.edu/Lucifora) | 2 | GSM cell traffic matrices from Salvatore Lucifora, Telecom Italia Mobile. | directed weighted graph (2) | 7,055–7,055 | square (2) | unsymmetric (2) | real (2) | 0 | small; uniform observed types; one dimension | -| [Luong](https://sparse.tamu.edu/Luong) | 2 | Photogrammetry problem from Bruno Luong, FOGALE nanotech, France. | computer graphics/vision problem (2) | 1,388–4,472 | rectangular (2) | rectangular (2) | real (2) | 0 | small; uniform observed types; narrow (≤4×) | -| [MAWI](https://sparse.tamu.edu/MAWI) | 5 | MAWI Working Group Traffic Archive (http://mawi.wide.ad.jp/mawi/): The MAWI (Measurement and Analysis on the WIDE Internet)… | undirected weighted graph (5) | 18,571,154–226,196,185 | square (5) | symmetric (5) | integer (5) | 0 | categorically uniform; moderate (4–100×) | -| [MKS](https://sparse.tamu.edu/MKS) | 1 | 2-D Fokker Planck equations, electron dynamics. Dan Smith, MKS Inst. | electromagnetics problem (1) | 7,548–7,548 | square (1) | unsymmetric (1) | real (1) | 0 | singleton; one dimension | -| [ML_Graph](https://sparse.tamu.edu/ML_Graph) | 36 | ML_Graph: adjacency matrices from machine learning datasets, Olaf Schenk. | undirected weighted graph (36) | 106–38,547 | square (36) | symmetric (36) | real (36) | 0 | categorically uniform; wide (>100×) | -| [Mallya](https://sparse.tamu.edu/Mallya) | 18 | Chemical process simulation matrices, J. Mallya, S. Zitney, M. Stadtherr. | chemical process simulation problem (18) | 1,477–70,304 | square (18) | unsymmetric (18) | real (18) | 0 | categorically uniform; moderate (4–100×) | -| [Mancktelow](https://sparse.tamu.edu/Mancktelow) | 1 | FEM viscoelastic behavior of rocks, Neil Mancktelow, ETH-Zentrum | materials problem (1) | 37,762–37,762 | square (1) | unsymmetric (1) | real (1) | 0 | singleton; one dimension | -| [Marini](https://sparse.tamu.edu/Marini) | 1 | Economic Time Series, T. Di Fonzo (Univ Padua) and M. Marini, (ISTAT, Italy) | economic problem (1) | 7,245–7,245 | square (1) | symmetric (1) | real (1) | 0 | singleton; one dimension | -| [Martin](https://sparse.tamu.edu/Martin) | 1 | Martin/marine1: chemical oceanography; a marine nitrogen cycle inverse model | chemical oceanography problem (1) | 400,320–400,320 | square (1) | unsymmetric (1) | real (1) | 0 | singleton; one dimension | -| [MathWorks](https://sparse.tamu.edu/MathWorks) | 11 | Matrices contributed by The Mathworks, Inc. | counter-example problem (6); structural problem (2); computer graphics/vision problem (1); directed graph (1); +1 more | 102–12,406 | square (9); rectangular (2) | unsymmetric (7); rectangular (2); symmetric (2) | real (10); binary (1) | 2 | high categorical mix; wide (>100×) | -| [MaxPlanck](https://sparse.tamu.edu/MaxPlanck) | 2 | Weather shallow water equations, Max-Planck Institute of Meteoroloy | computational fluid dynamics problem (2) | 81,920–81,920 | square (2) | symmetric (2) | real (2) | 2 | small; uniform observed types; one dimension | -| [Mazaheri](https://sparse.tamu.edu/Mazaheri) | 1 | Sparse bundle adjustment problem from Mehdi Mazaheri, University of Calgary | computer vision problem (1) | 513,351–513,351 | square (1) | symmetric (1) | real (1) | 1 | singleton; one dimension | -| [McRae](https://sparse.tamu.edu/McRae) | 2 | circuitscape: circuit theory applied to animal/gene flow. B. McRae, UCSB | 2D/3D problem (2) | 999,999–1,000,000 | square (2) | symmetric (2) | real (2) | 1 | small; observed mix; narrow (≤4×) | -| [Meng](https://sparse.tamu.edu/Meng) | 1 | Jacobian matrix from a computational chemistry problem, iChem, China' | computational chemistry problem (1) | 274,087–274,087 | square (1) | unsymmetric (1) | complex (1) | 0 | singleton; one dimension | -| [Meszaros](https://sparse.tamu.edu/Meszaros) | 166 | Linear programming problems from C. Me'sza'ros | linear programming problem (166) | 16–1,113,780 | rectangular (165); square (1) | rectangular (165); unsymmetric (1) | real (107); integer (41); binary (18) | 0 | some categorical mix; wide (>100×) | -| [Mittelmann](https://sparse.tamu.edu/Mittelmann) | 30 | Linear programming problems from H. Mittelmann | linear programming problem (30) | 24,460–1,961,394 | rectangular (30) | rectangular (30) | integer (20); real (10) | 0 | some categorical mix; moderate (4–100×) | -| [Moqri](https://sparse.tamu.edu/Moqri) | 1 | Document similarity graph for MIS articles, including metadata | undirected weighted graph (1) | 2,427–2,427 | square (1) | symmetric (1) | real (1) | 0 | singleton; one dimension | -| [Morandini](https://sparse.tamu.edu/Morandini) | 3 | Multibody simulation problems from Marco Morandini. | structural problem (2); robotics problem (1) | 100–791 | square (3) | unsymmetric (3) | real (3) | 0 | small; observed mix; moderate (4–100×) | -| [Muite](https://sparse.tamu.edu/Muite) | 4 | Ill-conditioned Chebyshev integration matrices from Benson Muite, Oxford. | structural problem (4) | 261–68,121 | square (4) | unsymmetric (4) | real (4) | 0 | small; uniform observed types; wide (>100×) | -| [Mulvey](https://sparse.tamu.edu/Mulvey) | 2 | Multistage stochastic financial modelling. J. Mulvey, Princeton. | economic problem (2) | 74,752–74,752 | square (2) | symmetric (2) | binary (1); real (1) | 1 | small; observed mix; one dimension | -| [Mycielski](https://sparse.tamu.edu/Mycielski) | 19 | Mycielski graph adjacency matrices | undirected graph (19) | 2–786,431 | square (19) | symmetric (19) | binary (19) | 0 | categorically uniform; wide (>100×) | -| [ND](https://sparse.tamu.edu/ND) | 4 | ND problem set. 3D mesh problems. | 2D/3D problem (4) | 9,000–72,000 | square (4) | symmetric (4) | real (4) | 4 | small; uniform observed types; moderate (4–100×) | -| [NYPA](https://sparse.tamu.edu/NYPA) | 8 | Rank-deficient least squares problems. D. Maragal, New York Power Authority | least squares problem (8) | 32–75,077 | rectangular (8) | rectangular (8) | real (8) | 0 | categorically uniform; wide (>100×) | -| [Nasa](https://sparse.tamu.edu/Nasa) | 11 | NASA matrices obtained from Alex Pothen, ICASE and Old Dominion Univ. | structural problem (11) | 1,824–54,870 | square (11) | symmetric (8); unsymmetric (3) | binary (6); real (5) | 5 | high categorical mix; moderate (4–100×) | -| [Negre](https://sparse.tamu.edu/Negre) | 1 | Negre: phenyl dendrimer density matrix, for graph partitioning | computational chemistry problem (1) | 730–730 | square (1) | symmetric (1) | real (1) | 0 | singleton; one dimension | -| [Nemeth](https://sparse.tamu.edu/Nemeth) | 26 | A set of matrices from Karoly Nemeth, theoretical chemistry. | theoretical/quantum chemistry problem (26) | 9,506–9,506 | square (26) | symmetric (26) | real (26) | 0 | categorically uniform; one dimension | -| [Newman](https://sparse.tamu.edu/Newman) | 16 | Network graph collection from Mark Newman, University of Michigan | undirected weighted graph (7); undirected graph (6); directed multigraph (1); directed weighted graph (1); +1 more | 34–40,421 | square (16) | symmetric (14); unsymmetric (2) | binary (6); real (6); integer (4) | 0 | high categorical mix; wide (>100×) | -| [Norris](https://sparse.tamu.edu/Norris) | 12 | Finite element modelling of human body parts. Stuart Norris, Univ. Aukland. | 2D/3D problem (10); computational fluid dynamics problem (2) | 1,650–259,156 | square (12) | unsymmetric (9); symmetric (3) | real (12) | 3 | high categorical mix; wide (>100×) | -| [Oberwolfach](https://sparse.tamu.edu/Oberwolfach) | 38 | Oberwolfach model reduction benchmark collection | model reduction problem (38) | 14–986,703 | square (38) | symmetric (33); unsymmetric (5) | real (37); complex (1) | 13 | high categorical mix; wide (>100×) | -| [Okunbor](https://sparse.tamu.edu/Okunbor) | 2 | Acoustic radiation around aft duct fan. Walter Eversman and Daniel Okunbor. | acoustics problem (2) | 8,184–8,205 | square (2) | symmetric (1); unsymmetric (1) | complex (1); real (1) | 1 | small; observed mix; narrow (≤4×) | -| [PARSEC](https://sparse.tamu.edu/PARSEC) | 21 | PARSEC matrices, density functional theory. Zhou, Saad, Tiago, Chelikowsky, UMN. | theoretical/quantum chemistry problem (21) | 769–268,096 | square (21) | symmetric (21) | real (21) | 0 | categorically uniform; wide (>100×) | -| [POLYFLOW](https://sparse.tamu.edu/POLYFLOW) | 2 | Parasol matrices, collected by Jacko Koster | computational fluid dynamics problem (2) | 29,957–30,412 | square (2) | unsymmetric (2) | real (2) | 0 | small; observed mix; narrow (≤4×) | -| [Pajek](https://sparse.tamu.edu/Pajek) | 76 | Pajek data sets, V. Batagelj and A. Mrvar. | directed graph (28); undirected graph (13); directed multigraph (11); directed weighted graph (11); +4 more | 10–3,774,768 | square (71); rectangular (5) | unsymmetric (49); symmetric (22); rectangular (5) | binary (44); integer (28); real (4) | 1 | high categorical mix; wide (>100×) | -| [Pereyra](https://sparse.tamu.edu/Pereyra) | 1 | From Victor Pereyra, Stanford University. | least squares problem (1) | 71,952–71,952 | rectangular (1) | rectangular (1) | real (1) | 0 | singleton; one dimension | -| [Pothen](https://sparse.tamu.edu/Pothen) | 22 | Structrual engineering matrices from NASA, collected by Alex Pothen. | structural problem (22) | 48–94,069 | square (22) | symmetric (22) | binary (12); real (10) | 10 | some categorical mix; wide (>100×) | -| [PowerSystem](https://sparse.tamu.edu/PowerSystem) | 1 | Power system matrix | power network problem (1) | 197,156–197,156 | square (1) | unsymmetric (1) | real (1) | 0 | singleton; one dimension | -| [Precima](https://sparse.tamu.edu/Precima) | 1 | Precima: data analytics problem | data analytics problem (1) | 303,813–303,813 | square (1) | symmetric (1) | real (1) | 0 | singleton; one dimension | -| [Priebel](https://sparse.tamu.edu/Priebel) | 6 | Quadratic sieve matrices from David Priebel, Tenn. Tech. Univ. | combinatorial problem (6) | 584–24,430 | rectangular (6) | rectangular (6) | binary (6) | 0 | categorically uniform; moderate (4–100×) | -| [Puri](https://sparse.tamu.edu/Puri) | 4 | ABAQUS benchmarks: pt.loaded fluid-filled shell. Srinivasan Puri | model reduction problem (4) | 23,412–23,412 | square (4) | unsymmetric (4) | complex (3); real (1) | 0 | small; observed mix; one dimension | -| [QCD](https://sparse.tamu.edu/QCD) | 14 | Quantum Chromodynamics, B. Medeke, Univ. Wuppertal, Germany | theoretical/quantum chemistry problem (14) | 3,072–49,152 | square (14) | unsymmetric (14) | complex (14) | 0 | categorically uniform; moderate (4–100×) | -| [QLi](https://sparse.tamu.edu/QLi) | 3 | Mixed complementarity optimization problems from Qaun Li and Michael Ferris. | optimization problem (3) | 160,000–440,020 | square (3) | unsymmetric (3) | real (3) | 0 | small; observed mix; narrow (≤4×) | -| [QY](https://sparse.tamu.edu/QY) | 2 | Transient stabilty constrained interior pt. optimal power flow, J. Quanyuan | power network problem (2) | 14,454–40,216 | square (2) | symmetric (2) | real (2) | 0 | small; uniform observed types; narrow (≤4×) | -| [Qaplib](https://sparse.tamu.edu/Qaplib) | 8 | Linear programming, lower bounds for quadratic assignment problem, M. Resende. | linear programming problem (8) | 225–379,350 | rectangular (8) | rectangular (8) | integer (8) | 0 | categorically uniform; wide (>100×) | -| [Quaglino](https://sparse.tamu.edu/Quaglino) | 2 | FEM discretization of a viscoplastic collision problem, Alessio Quaglino. | materials problem (2) | 4,326–32,769 | square (2) | unsymmetric (2) | real (2) | 0 | small; uniform observed types; moderate (4–100×) | -| [Rajat](https://sparse.tamu.edu/Rajat) | 32 | Circuit simulation matrices from Rajat and Raj. | circuit simulation problem (32) | 135–4,690,002 | square (32) | unsymmetric (26); symmetric (6) | real (25); binary (7) | 0 | high categorical mix; wide (>100×) | -| [Raju](https://sparse.tamu.edu/Raju) | 1 | 3D laminar flow, M. Raju, Case Western Univ. | computational fluid dynamics problem (1) | 67,173–67,173 | square (1) | unsymmetric (1) | real (1) | 0 | singleton; one dimension | -| [Rommes](https://sparse.tamu.edu/Rommes) | 35 | Power system models from Joost Rommes, Nelson Martins, Francisco Freitas | eigenvalue/model reduction problem (35) | 66–40,366 | square (35) | unsymmetric (35) | real (35) | 0 | some categorical mix; wide (>100×) | -| [Ronis](https://sparse.tamu.edu/Ronis) | 2 | Crystalline compounds (zeolites,sodalites), D. Ronis, Univ. of McGill. | materials problem (2) | 48,600–157,464 | square (2) | unsymmetric (2) | real (2) | 0 | small; uniform observed types; narrow (≤4×) | -| [Rost](https://sparse.tamu.edu/Rost) | 1 | semiconductor device simulation, Johann Rost, Politechnia Univ., Bucharest. | semiconductor device problem (1) | 74,104–74,104 | square (1) | unsymmetric (1) | complex (1) | 0 | singleton; one dimension | -| [Rothberg](https://sparse.tamu.edu/Rothberg) | 6 | Structural engineering matrices from Ed Rothberg, Silicon Graphics, Inc. | computational fluid dynamics problem (3); structural problem (3) | 4,350–153,746 | square (6) | symmetric (6) | binary (4); real (2) | 2 | high categorical mix; moderate (4–100×) | -| [Rucci](https://sparse.tamu.edu/Rucci) | 1 | ill-conditioned least-squares problem, A. Rucci | least squares problem (1) | 1,977,885–1,977,885 | rectangular (1) | rectangular (1) | real (1) | 0 | singleton; one dimension | -| [Rudnyi](https://sparse.tamu.edu/Rudnyi) | 1 | 3D fluid flow: water in a tank. Evgenii Rudnyi, IMTEK | computational fluid dynamics problem (1) | 60,740–60,740 | square (1) | unsymmetric (1) | real (1) | 0 | singleton; one dimension | -| [SNAP](https://sparse.tamu.edu/SNAP) | 68 | Networks from SNAP (Stanford Network Analysis Platform) Network Data Sets, Jure Leskovec… | directed graph (30); undirected graph (14); directed temporal multigraph (8); undirected graph with communities (6); +4 more | 1,005–65,608,366 | square (68) | unsymmetric (47); symmetric (21) | binary (53); integer (15) | 0 | high categorical mix; wide (>100×) | -| [Sandia](https://sparse.tamu.edu/Sandia) | 192 | Circuit simulation matrices from Sandia National Laboratory. | circuit simulation problem (192) | 430–682,862 | square (192) | unsymmetric (192) | real (192) | 0 | categorically uniform; wide (>100×) | -| [Sanghavi](https://sparse.tamu.edu/Sanghavi) | 1 | ECL32, a semiconductor device simulation matrix. Jagesh Sanghavi, UC Berkeley. | semiconductor device problem (1) | 51,993–51,993 | square (1) | unsymmetric (1) | real (1) | 0 | singleton; one dimension | -| [Schenk](https://sparse.tamu.edu/Schenk) | 5 | Symmetric indefinite KKT matrices, O. Schenk, Univ. of Basel, Switzerland | optimization problem (5) | 1,062,400–27,993,600 | square (5) | symmetric (5) | real (5) | 0 | categorically uniform; moderate (4–100×) | -| [Schenk_AFE](https://sparse.tamu.edu/Schenk_AFE) | 16 | Sheet metal forming matrices, Olaf Schenk, Univ. of Basel. | structural problem (16) | 503,625–1,508,065 | square (16) | symmetric (16) | real (16) | 10 | some categorical mix; narrow (≤4×) | -| [Schenk_IBMNA](https://sparse.tamu.edu/Schenk_IBMNA) | 52 | Non-linear optimization matrices, Olaf Schenk, Univ. of Basel. | optimization problem (52) | 2,169–345,241 | square (52) | symmetric (52) | real (52) | 0 | some categorical mix; wide (>100×) | -| [Schenk_IBMSDS](https://sparse.tamu.edu/Schenk_IBMSDS) | 7 | Semiconductor device simulation matrices, Olaf Schenk, Univ. of Basel. | semiconductor device problem (7) | 27,628–125,329 | square (7) | unsymmetric (7) | real (7) | 0 | categorically uniform; moderate (4–100×) | -| [Schenk_ISEI](https://sparse.tamu.edu/Schenk_ISEI) | 18 | Semiconductor device simulation matrices, Olaf Schenk, Univ. of Basel. | semiconductor device problem (18) | 10,938–181,343 | square (18) | unsymmetric (18) | real (18) | 0 | some categorical mix; moderate (4–100×) | -| [Schmid](https://sparse.tamu.edu/Schmid) | 2 | Unstructured FEM, steady state thermal problem. D. Schmid, Univ. Oslo | thermal problem (2) | 82,654–1,228,045 | square (2) | symmetric (2) | real (2) | 2 | small; uniform observed types; moderate (4–100×) | -| [Schulthess](https://sparse.tamu.edu/Schulthess) | 3 | Biochemical networks from Pascal Schulthess, Inst. Pathology, Berlin | biochemical network (3) | 1,996–16,559 | rectangular (3) | rectangular (3) | integer (3) | 0 | small; uniform observed types; moderate (4–100×) | -| [Shen](https://sparse.tamu.edu/Shen) | 4 | Matrices from Kai Shen, UCSB (now at Univ. of Rochester, NY). | 2D/3D problem (4) | 3,432–18,510 | square (4) | unsymmetric (4) | real (4) | 0 | small; uniform observed types; moderate (4–100×) | -| [Shyy](https://sparse.tamu.edu/Shyy) | 2 | Computational fluid dynamics, viscous flow, Wei Shyy, Univ. Florida. | computational fluid dynamics problem (2) | 4,720–76,480 | square (2) | unsymmetric (2) | real (2) | 0 | small; uniform observed types; moderate (4–100×) | -| [Simon](https://sparse.tamu.edu/Simon) | 12 | Structural engineering, computational fluid dynamics. Horst Simon. | computational fluid dynamics problem (7); structural problem (4); directed weighted random graph (1) | 3,242–62,424 | square (12) | unsymmetric (10); symmetric (2) | real (12) | 2 | high categorical mix; moderate (4–100×) | -| [Sinclair](https://sparse.tamu.edu/Sinclair) | 2 | 3-D spectral-element elastic wave modelling in freq. domain, C. Sinclair, Univ. Adelaide, Australia. | materials problem (2) | 292,008–680,943 | square (2) | Hermitian (2) | complex (2) | 0 | small; uniform observed types; narrow (≤4×) | -| [Sorensen](https://sparse.tamu.edu/Sorensen) | 1 | Call graph of the Linux kernel. | directed weighted graph (1) | 324,085–324,085 | square (1) | unsymmetric (1) | integer (1) | 0 | singleton; one dimension | -| [Springer](https://sparse.tamu.edu/Springer) | 1 | Tim Springer, Solenix (www.solenix.ch), Navigation Support Office, OPS-GN (www.nng.esoc.de), European Space Operations… | least squares problem (1) | 327,062–327,062 | rectangular (1) | rectangular (1) | real (1) | 0 | singleton; one dimension | -| [Stevenson](https://sparse.tamu.edu/Stevenson) | 1 | Circuit optimization for digital circuits. John Peter Stevenson, Stanford. | circuit simulation problem (1) | 2,111,154–2,111,154 | rectangular (1) | rectangular (1) | real (1) | 0 | singleton; one dimension | -| [Sumner](https://sparse.tamu.edu/Sumner) | 1 | A computer graphics problem from Bob Sumner, PhD student at MIT (as of 2003). | computer graphics/vision problem (1) | 29,493–29,493 | rectangular (1) | rectangular (1) | real (1) | 0 | singleton; one dimension | -| [Sybrandt](https://sparse.tamu.edu/Sybrandt) | 2 | Sybrandt: machine learning graphs from document analysis | undirected graph (1); undirected weighted graph (1) | 30,239,687–183,964,077 | square (2) | symmetric (2) | binary (1); real (1) | 0 | small; observed mix; moderate (4–100×) | -| [Szczerba](https://sparse.tamu.edu/Szczerba) | 1 | Ill-conditioned CFD matrix from D. Szczerba, ETH Computer Vision Lab, Zurich. | computational fluid dynamics problem (1) | 20,896–20,896 | square (1) | unsymmetric (1) | real (1) | 0 | singleton; one dimension | -| [TAMU_SmartGridCenter](https://sparse.tamu.edu/TAMU_SmartGridCenter) | 3 | Texas A&M Smart Grid Center: synthetic (yet realistic) power system grids | power network problem (3) | 4,000–69,999 | square (3) | unsymmetric (2); symmetric (1) | real (3) | 0 | small; observed mix; moderate (4–100×) | -| [TKK](https://sparse.tamu.edu/TKK) | 12 | Matrices from Reijo Kouhia, Structural Mechanics, Helsinki Univ. of Technology | structural problem (12) | 1,282–143,571 | square (12) | symmetric (10); unsymmetric (2) | real (5); integer (4); binary (3) | 3 | high categorical mix; wide (>100×) | -| [TOKAMAK](https://sparse.tamu.edu/TOKAMAK) | 4 | TOKAMAK test matrices from the SPARSKIT2 collection, Yousef Saad, Univ. MN. | electromagnetics problem (4) | 300–5,940 | square (4) | unsymmetric (4) | real (4) | 0 | small; uniform observed types; moderate (4–100×) | -| [TSOPF](https://sparse.tamu.edu/TSOPF) | 27 | Transient stability-constrained optimal power flow, G. Geng, Zhejiang Univ. | power network problem (27) | 2,454–120,216 | square (27) | unsymmetric (15); symmetric (12) | real (27) | 0 | some categorical mix; moderate (4–100×) | -| [Toledo](https://sparse.tamu.edu/Toledo) | 1 | Null space problem, computer graphics domain. Matrices from Sivan Toledo. | counter-example problem (1) | 68,600–68,600 | rectangular (1) | rectangular (1) | real (1) | 0 | singleton; one dimension | -| [Tromble](https://sparse.tamu.edu/Tromble) | 1 | Finite-state machine model for natural language processing. Roy Tromble. | directed weighted graph (1) | 399,130–399,130 | square (1) | unsymmetric (1) | real (1) | 0 | singleton; one dimension | -| [UTEP](https://sparse.tamu.edu/UTEP) | 3 | Matrices from Lenka Dubcova, Jakub Cerveny, and Pavel Solin | 2D/3D problem (3) | 16,129–146,689 | square (3) | symmetric (3) | real (3) | 3 | small; uniform observed types; moderate (4–100×) | -| [Um](https://sparse.tamu.edu/Um) | 2 | FEM, electromagnetics, 2 cubes in a sphere. Evan Um, Geophysics, Stanford | electromagnetics problem (2) | 101,492–259,789 | square (2) | symmetric (2) | real (2) | 2 | small; uniform observed types; narrow (≤4×) | -| [VDOL](https://sparse.tamu.edu/VDOL) | 91 | Optimal control problems, Vehicle Dynamics & Optimization Lab, UF | optimal control problem (91) | 99–18,476 | square (91) | symmetric (91) | real (91) | 0 | categorically uniform; wide (>100×) | -| [VLSI](https://sparse.tamu.edu/VLSI) | 14 | VLSI: semiconductor device and process simulation | semiconductor device problem (8); semiconductor process problem (6) | 46,994–11,449,533 | square (14) | unsymmetric (14) | real (14) | 0 | some categorical mix; wide (>100×) | -| [VanVelzen](https://sparse.tamu.edu/VanVelzen) | 10 | Chemical process simulation matrices from Nils van Velzen, VORtech Computing. | chemical process simulation problem (10) | 21,982–22,835 | square (10) | unsymmetric (10) | real (10) | 0 | categorically uniform; narrow (≤4×) | -| [Vavasis](https://sparse.tamu.edu/Vavasis) | 1 | Irregular finite-element matrix from Steve Vavasis, Cornell. | 2D/3D problem (1) | 41,092–41,092 | square (1) | unsymmetric (1) | real (1) | 0 | singleton; one dimension | -| [Wang](https://sparse.tamu.edu/Wang) | 6 | 3D semiconductor device, 2D incompressible flow. S. Wang, Univ. New South Wales. | semiconductor device problem (6) | 2,903–26,068 | square (6) | unsymmetric (6) | real (6) | 0 | some categorical mix; moderate (4–100×) | -| [Watson](https://sparse.tamu.edu/Watson) | 2 | Chemical reaction simulation matrices from Layne Watson and Jingwei Zhang. | 2D/3D problem (2) | 40,401–112,211 | square (2) | unsymmetric (2) | real (2) | 0 | small; uniform observed types; narrow (≤4×) | -| [Williams](https://sparse.tamu.edu/Williams) | 7 | Matrices used by S. Williams et al for sparse matrix multiplication on GPUs. | 2D/3D problem (4); economic problem (1); weighted directed graph (1); weighted undirected graph (1) | 36,417–1,000,005 | square (7) | symmetric (4); unsymmetric (3) | real (6); integer (1) | 3 | high categorical mix; moderate (4–100×) | -| [Wissgott](https://sparse.tamu.edu/Wissgott) | 1 | Parabolic FEM problem from Phillip Wissgott, TU Vienna. | computational fluid dynamics problem (1) | 525,825–525,825 | square (1) | symmetric (1) | real (1) | 1 | singleton; one dimension | -| [YCheng](https://sparse.tamu.edu/YCheng) | 3 | Power system state simulation matrices Yunzhi Cheng, UT Arlington. | power network problem (3) | 14,318–28,634 | rectangular (3) | rectangular (3) | real (3) | 0 | small; uniform observed types; narrow (≤4×) | -| [YZhou](https://sparse.tamu.edu/YZhou) | 1 | Circuit simulation problem, Y. Zhou, Carleton Univ, Canada | circuit simulation problem (1) | 1,020–1,020 | square (1) | unsymmetric (1) | real (1) | 0 | singleton; one dimension | -| [Yoshiyasu](https://sparse.tamu.edu/Yoshiyasu) | 2 | Image editting and mesh deformation problem from Y. Yoshiyasu, Keio Univ., Japan | computer graphics/vision problem (2) | 234,023–240,000 | rectangular (2) | rectangular (2) | real (2) | 0 | small; uniform observed types; narrow (≤4×) | -| [Zaoui](https://sparse.tamu.edu/Zaoui) | 1 | Optimal power flow, nonlinear optimization (KKT), Fabrice Zaoui, RTE, France | optimization problem (1) | 2,063,494–2,063,494 | square (1) | symmetric (1) | real (1) | 0 | singleton; one dimension | -| [Zhao](https://sparse.tamu.edu/Zhao) | 2 | Reduced-order modeling, electromagnetics. A. Cangellaris and L. Zhao. | electromagnetics problem (2) | 33,861–33,861 | square (2) | unsymmetric (2) | real (2) | 0 | small; uniform observed types; one dimension | -| [Zitney](https://sparse.tamu.edu/Zitney) | 8 | Chemical engineering applications. S. Zitney, Cray Research. | chemical process simulation problem (8) | 1,048–5,308 | square (8) | unsymmetric (8) | real (8) | 0 | some categorical mix; moderate (4–100×) | -| [vanHeukelum](https://sparse.tamu.edu/vanHeukelum) | 13 | DNA electrophoresis model. Alexander van Heukelum, Utrect Univ, The Netherlands. | directed weighted graph (13) | 5–5,154,859 | square (13) | unsymmetric (13) | real (13) | 0 | categorically uniform; wide (>100×) | diff --git a/reports/suitesparse-groups/REPORT.md b/reports/suitesparse-groups/REPORT.md deleted file mode 100644 index 49aec7b..0000000 --- a/reports/suitesparse-groups/REPORT.md +++ /dev/null @@ -1,431 +0,0 @@ -# SuiteSparse Matrix Collection groups: meaning, contents, and within-group diversity - -Analysis date: 2026-09-02 -Public data revision served by the collection: 31-Oct-2023 18:12:37 -Scope: 2,904 primary matrix problems in 203 groups - -## Executive answer - -A SuiteSparse **group is a provenance namespace, not a mathematical matrix -class**. The official client documentation defines the group as the source of a -problem—a person, organization, or another collection—and defines the full -identifier as `Group/Name`. The collection paper makes the same distinction: -group classifies source, while `kind` describes the application domain. -[Official `ssget` help](https://github.com/DrTimothyAldenDavis/SuiteSparse/blob/dev/ssget/sshelp.html), -[Davis and Hu (2011)](https://doi.org/10.1145/2049662.2049663) - -That distinction explains the data: - -- Most groups are fairly coherent in application and shape. In the full - snapshot, 172 of 203 groups have one normalized `kind`; 159 are square-only, - 34 rectangular-only, and only 10 mix square and rectangular matrices. -- Size is much less coherent. Fifty-two groups span more than 100× in maximum - dimension, and those groups contain 2,137 matrices (73.6% of the collection). - A family can therefore be mathematically very consistent but cover many mesh - refinements or problem scales. -- Large imported archives and benchmark selections can be diverse on nearly - every axis. `HB`, `Pajek`, `Bai`, `SNAP`, `MathWorks`, and `Williams` mix - several kinds, structures, entry types, or definiteness classes. -- Group membership predicts **where a problem came from** more reliably than - whether it is symmetric, positive definite, real, square, or of a particular - size. Use `kind`, `RBtype`/structure, dimensions, symmetry statistics, and - definiteness fields for property-based selection. - -The exhaustive answer for every group is in -[GROUP_CATALOGUE.md](GROUP_CATALOGUE.md). It gives all 203 official group links, -source descriptions, kind counts, dimension ranges, square/rectangular counts, -special-structure counts, entry-type counts, known-positive-definite counts, -and a transparent diversity assessment. - -## 1. What a “group” means - -The collection uses three related but different concepts: - -| Concept | Meaning | Example | -|---|---|---| -| `Group` | Source/provenance namespace: person, team, organization, imported collection, or benchmark | `Bai`, `Sandia`, `HB`, `DIMACS10` | -| `Name` | Local problem name inside that group; not guaranteed unique collection-wide | `copter2` | -| `kind` | Curated application/domain label for the primary matrix | `structural problem`, `directed graph`, `linear programming problem` | - -Thus `GHS_indef/copter2` is unambiguous; `copter2` alone is not the canonical -identifier. Matrix IDs are also stable once assigned. The official help notes -that kind is often related to group because one submitter may work in one -domain, but explicitly warns that some groups cover many domains. -[Official `ssget` help](https://github.com/DrTimothyAldenDavis/SuiteSparse/blob/dev/ssget/sshelp.html) - -Group is not identical to `author`. Individual records separately store the -creator/author and editor/curator. A group may name a contributor, but it may -instead name an organization, the earlier archive from which the problem was -imported, or a later benchmark selection. - -### Common naming patterns - -| Pattern | What it usually represents | Examples | -|---|---|---| -| Contributor or research team | Problems submitted or assembled by a person/team | `Averous`, `Bai`, `Goodwin`, `Grund`, `Rommes` | -| Company, university, or lab | A source institution or toolchain | `Boeing`, `Sandia`, `IBM_EDA`, `Freescale`, `TAMU_SmartGridCenter` | -| Imported archive or benchmark | A pre-existing collection; often intentionally broad | `HB`, `GHS_indef`, `GHS_psdef`, `Oberwolfach`, `LPnetlib`, `Qaplib` | -| Graph/network corpus | Adjacency or weighted-adjacency data from a repository/challenge | `Pajek`, `SNAP`, `LAW`, `DIMACS10`, `Gset`, `GAP` | -| Application, solver, or construction | A simulation, equation family, or generator | `FIDAP`, `DRIVCAV`, `TOKAMAK`, `VLSI`, `FlowIPM22`, `Mycielski` | -| Mathematical subcollection | Related algebraic/combinatorial constructions | the `JGD_*` groups | - -These patterns are descriptive, not mutually exclusive. For example, -`Williams` is named for a benchmark study and contains matrices originally -drawn from multiple scientific and graph domains. -[Williams group](https://sparse.tamu.edu/Williams) - -## 2. What a collection “matrix” represents - -The collection is formally a collection of sparse **problems**. Each problem -has a primary sparse matrix `A` plus metadata and may also include explicit -stored zeros, right-hand sides, solutions, coordinates, node labels, other -matrices, or a matrix sequence in auxiliary data. The published index statistics -and this report describe the primary `A`, not every auxiliary object. -[Davis and Hu paper](https://yifanhu.net/PUB/matrices.pdf) - -The primary matrices commonly encode: - -1. **Discretized operators or linear systems.** Finite-element, - finite-volume, and finite-difference models produce stiffness, mass, - Jacobian, saddle-point, or coupled-physics matrices. Structural mechanics, - CFD, thermal, electromagnetics, acoustics, semiconductor, and materials - problems are common. -2. **Optimization systems.** Linear-programming entries often store a - rectangular constraint matrix. Nonlinear and optimal-control problems often - store square KKT or Newton systems, frequently symmetric indefinite. -3. **Graphs and networks.** `A(i,j)` represents an edge or weight. Undirected - graphs normally give symmetric adjacency matrices; directed graphs are - generally unsymmetric; bipartite data may be rectangular. -4. **Model-reduction and eigenvalue problems.** The visible `A` can be one - operator in a larger dynamical model whose mass, input, or output matrices - appear in auxiliary fields. -5. **Algebraic and combinatorial constructions.** Boundary, differential, - incidence, relation, design, and polynomial-system matrices are often - integer-valued and rectangular. -6. **Least-squares, inverse, imaging, and data problems.** Examples include - tomography, bundle adjustment, interpolation, recommendation, documents, - and statistical designs. - -The official overview lists both geometric PDE domains and non-geometric areas -such as optimization, economics, circuits, chemistry, power networks, and -graph data. [Collection overview](https://sparse.tamu.edu/about) - -## 3. How to interpret the attributes - -The collection’s [statistics documentation](https://sparse.tamu.edu/statistics) -defines the fields used below. - -| Attribute | Meaning | Important caution | -|---|---|---| -| `nrows`, `ncols` | Shape of primary `A` | Use both; “size” is ambiguous for rectangular problems. This report uses `max(nrows,ncols)` for group dimension spans. | -| `nnz` | Numerically nonzero entries in `A` | Excludes explicitly stored zeros supplied by the author. `nentries = nnz + nzero`. | -| Entry type | Mutually exclusive RB category: real non-integer/non-binary, complex, integer, or binary | The separate `isReal` flag means non-complex, so integer and binary matrices also count as real under that flag. | -| Structure | Rectangular; square unsymmetric, symmetric, Hermitian, or skew-symmetric | This is the categorical algebraic structure, encoded by the second RB letter. | -| `pattern_symmetry` | Fraction of off-diagonal structural entries having a transposed partner | It is a ratio in `[0,1]`, not a Boolean. Rectangular matrices are assigned 0. | -| `numerical_symmetry` | Fraction of off-diagonal values having a conjugate-equal transposed partner | For complex matrices it measures Hermitian, not plain complex-symmetric, agreement. Rectangular matrices are assigned 0. | -| `posdef` | 1 known positive definite; 0 known not positive definite; historically −1 symmetric/Hermitian but unknown | No −1 cases occur in this snapshot. `cholcand` is only a Cholesky-candidate heuristic, not proof of positive definiteness. | -| `kind` | Curated problem-domain phrase | Semi-controlled vocabulary, not an exhaustive or immutable ontology. Modifiers such as `sequence`, `subsequent`, `duplicate`, and `random` carry useful provenance. | -| `isND`, `isGraph` | Curator judgments for 2D/3D discretization or graph interpretation | These are qualitative problem assessments, not intrinsic numeric properties. | -| `sprank`, `nblocks`, `ncc` | Structural rank, Dulmage–Mendelsohn blocks, and graph components | Graph problems may omit structural-rank data because their diagonal is often irrelevant. | - -### Symmetric structure versus symmetry score - -Two notions must not be conflated: - -- The website/Rutherford–Boeing structure says whether the stored matrix is - exactly symmetric, Hermitian, skew-symmetric, unsymmetric, or rectangular. -- The two symmetry scores quantify how much of a square matrix’s off-diagonal - pattern or values match their transpose/conjugate transpose. - -For example, a matrix can have a fully symmetric sparsity pattern but unequal -mirrored values. Conversely, complex symmetric and Hermitian are different -classes. This is why the snapshot has 1,201 matrices labelled symmetric but -1,196 with `numerical_symmetry == 1`; those columns answer related, not identical, -questions. - -## 4. Snapshot and collection-wide profile - -The official site displayed 2,904 problems when checked on 2026-09-02. -The current downloadable `ssstats.csv` and `ss_index.mat` still embed a last -revision of 31-Oct-2023; their hashes and HTTP metadata are recorded in -[data/provenance.json](data/provenance.json). The live total and the download -agree. [Collection index](https://sparse.tamu.edu/) - -### Scale - -| Measure | Minimum | Median | Maximum | -|---|---:|---:|---:| -| Rows | 1 | 4,942.5 | 226,196,185 | -| Columns | 2 | 6,139.5 | 226,196,185 | -| Numerically nonzero entries | 1 | 57,092 | 11,588,725,964 | -| Stored pattern entries | 1 | 57,235 | 11,588,725,964 | - -There are 432 matrices with at least one explicit stored zero. Across those -records, the index reports 259,828,688 explicit-zero entries, which is why -`nentries` should not automatically be substituted for `nnz`. - -The largest dimension belongs to `MAWI/mawi_201512020330` -(226,196,185 square; 480,047,894 nonzeros). The largest `nnz` belongs to -`Sybrandt/AGATHA_2015` (183,964,077 square; 11,588,725,964 nonzeros). - -### Shape, structure, entry type, and flags - -| Axis | Category | Count | Share | -|---|---|---:|---:| -| Shape | Square | 2,226 | 76.65% | -| | Rectangular | 678 | 23.35% | -| Structure | Symmetric | 1,201 | 41.36% | -| | Unsymmetric square | 1,020 | 35.12% | -| | Rectangular | 678 | 23.35% | -| | Hermitian | 3 | 0.10% | -| | Skew-symmetric | 2 | 0.07% | -| Entry type | Real, excluding integer/binary | 1,732 | 59.64% | -| | Binary | 601 | 20.70% | -| | Integer | 522 | 17.98% | -| | Complex | 49 | 1.69% | -| Curated flag | Graph | 543 | 18.70% | -| | 2D/3D discretization | 899 | 30.96% | -| Definiteness | Known positive definite | 240 | 8.26% | - -The continuous symmetry scores give another partition: 1,196 exact -numerically symmetric/Hermitian matrices; 222 with symmetric pattern but not -matching values; 768 partly pattern-symmetric; 40 square pattern-unsymmetric; -and 678 rectangular. - -### Most common normalized kinds - -This table uses the current website kind and collapses only `subsequent`, -`duplicate`, and `sequence` modifiers. It does not merge scientifically -different kinds into a subjective super-category. - -| Normalized kind | Matrices | Share | -|---|---:|---:| -| Linear programming problem | 342 | 11.8% | -| Structural problem | 302 | 10.4% | -| Combinatorial problem | 299 | 10.3% | -| Circuit simulation problem | 258 | 8.9% | -| Computational fluid dynamics problem | 174 | 6.0% | -| Optimization problem | 138 | 4.8% | -| 2D/3D problem | 138 | 4.8% | -| Undirected graph | 138 | 4.8% | -| Undirected weighted graph | 122 | 4.2% | -| Optimal control problem | 91 | 3.1% | -| Directed graph | 83 | 2.9% | -| Economic problem | 71 | 2.4% | -| Power network problem | 70 | 2.4% | -| Chemical process simulation problem | 70 | 2.4% | -| Theoretical/quantum chemistry problem | 61 | 2.1% | - -The normalized current website vocabulary has 58 values; the unnormalized CSV -has 91 exact strings. - -## 5. Group sizes are strongly imbalanced - -| Statistic | Result | -|---|---:| -| Groups | 203 | -| Matrices per group, minimum / median / mean / maximum | 1 / 4 / 14.31 / 292 | -| Singleton groups | 51 (25.1%) | -| Groups with fewer than five matrices | 114 (56.2%) | -| Matrices in the ten largest groups | 1,380 (47.5%) | - -The largest groups are: - -| Group | Matrices | Character | -|---|---:|---| -| [HB](https://sparse.tamu.edu/HB) | 292 | Historical Harwell–Boeing archive; many domains | -| [Sandia](https://sparse.tamu.edu/Sandia) | 192 | Circuit-simulation sequences | -| [Meszaros](https://sparse.tamu.edu/Meszaros) | 166 | Linear-programming problems | -| [DIMACS10](https://sparse.tamu.edu/DIMACS10) | 151 | Graph partitioning/clustering benchmark | -| [LPnetlib](https://sparse.tamu.edu/LPnetlib) | 138 | Netlib LP constraint matrices | -| [JGD_Homology](https://sparse.tamu.edu/JGD_Homology) | 128 | Homology/combinatorial matrices | -| [VDOL](https://sparse.tamu.edu/VDOL) | 91 | Optimal-control KKT systems | -| [Bai](https://sparse.tamu.edu/Bai) | 78 | Multi-domain contributor collection | -| [Pajek](https://sparse.tamu.edu/Pajek) | 76 | Heterogeneous graph/network datasets | -| [SNAP](https://sparse.tamu.edu/SNAP) | 68 | Stanford graph datasets | -| [Gset](https://sparse.tamu.edu/Gset) | 67 | Generated random graph families | -| [GHS_indef](https://sparse.tamu.edu/GHS_indef) | 60 | Indefinite test-matrix collection | -| [Schenk_IBMNA](https://sparse.tamu.edu/Schenk_IBMNA) | 52 | Nonlinear-optimization/KKT matrices | - -Consequently, “sample groups uniformly” and “sample matrices uniformly” are -very different experimental designs. - -## 6. Are matrices within a group similar or diverse? - -### Direct group-level answer - -Across all 203 groups, after collapsing only the official sequence/duplicate -modifiers: - -| Axis | Group profile | Conclusion | -|---|---:|---| -| Application kind | 172 one-kind; 31 multi-kind | Usually coherent, but the multi-kind groups hold 1,083 matrices (37.3%). | -| Shape | 159 square-only; 34 rectangular-only; 10 mixed | Shape is the most reliable within-group categorical property. | -| Exact numerical/Hermitian symmetry among square members | 61 all exact; 76 none exact; 32 mixed; 34 rectangular-only | Usually consistent, but not guaranteed. Pattern symmetry must still be checked separately. | -| Complex versus non-complex | 184 non-complex-only; 9 complex-only; 10 mixed | Broad scalar domain is usually consistent. Integer/binary/non-integer real may still mix. | -| Maximum-dimension span | 60 one dimension; 31 ≤4×; 60 between 4× and 100×; 52 >100× | Size is the least safe attribute to infer from group. | - -The ten shape-mixed groups are `HB`, `MathWorks`, `Bates`, `Pajek`, -`Barabasi`, `Meszaros`, `JGD_BIBD`, `JGD_Homology`, `JGD_Margulies`, and -`Hardesty`. - -### More conservative analysis of groups with at least five matrices - -Small groups can look homogeneous merely because they contain too few samples. -Among the 89 groups with at least five matrices, the number exactly uniform on -each attribute is: - -| Attribute | Exactly uniform groups | Share of 89 | -|---|---:|---:| -| Shape | 82 | 92.1% | -| Website structure | 62 | 69.7% | -| Mutually exclusive entry type | 61 | 68.5% | -| Exact-symmetry class | 55 | 61.8% | -| Positive-definite status | 66 | 74.2% | -| Exact, unnormalized `kind` string | 51 | 57.3% | -| Normalized `kind` used in the catalogue | 64 | 71.9% | - -Only 31 of these 89 groups are uniform across all six categorical attributes -used by the reproducible analysis (shape, structure, entry type, exact-symmetry -class, positive-definite status, and unnormalized kind). Even then, scale may -vary substantially. - -The report’s optional diversity score is the mean fraction outside the modal -category across those six axes. It is a screening aid, not an official -SuiteSparse statistic. Groups with fewer than five members are not assigned a -strong homogeneous/heterogeneous label. The complete components are in -[output/group_summary.csv](output/group_summary.csv), so readers need not rely -on the score. - -### Representative profiles - -| Group | What it demonstrates | Measured profile | -|---|---|---| -| [HB](https://sparse.tamu.edu/HB) | Historical archive: very broad diversity | 292 matrices; 16 normalized kinds; dimensions 9–44,609; 276 square + 16 rectangular; 169 symmetric, 105 unsymmetric, 16 rectangular, 2 skew; all four entry types; 60 known PD. | -| [Bai](https://sparse.tamu.edu/Bai) | A contributor name need not mean one domain | 78 square matrices across eight normalized kinds; 66 unsymmetric, 11 symmetric, 1 Hermitian; real, complex, and integer entries; dimensions 62–23,560. | -| [Pajek](https://sparse.tamu.edu/Pajek) | One graph corpus, many graph forms | 76 matrices; 71 square + 5 rectangular; directed, undirected, weighted, multigraph, and bipartite kinds; binary, integer, and real entries; dimensions 10–3,774,768. | -| [Williams](https://sparse.tamu.edu/Williams) | Benchmark selection cuts across sources | Seven square matrices spanning spatial, economic, and graph problems; four symmetric + three unsymmetric; dimensions 36,417–1,000,005. | -| [Sandia](https://sparse.tamu.edu/Sandia) | Same domain/form, very different scale | 192 real square unsymmetric circuit matrices; rows 430–682,862; `nnz` 1,544–2,638,997. | -| [Gset](https://sparse.tamu.edu/Gset) | Related generated graph corpus, mixed value type | 67 square symmetric random graphs across even, 2-D torus, and skew pattern families; 34 binary + 33 integer/weighted; dimensions 800–10,000. | -| [LPnetlib](https://sparse.tamu.edu/LPnetlib) | Coherent algebraic form | 138 rectangular LP constraint matrices; 108 real + 30 integer; dimensions 13–243,246. | -| [VDOL](https://sparse.tamu.edu/VDOL) | Coherent numerical family | 91 real square symmetric, non-PD optimal-control systems; dimensions 99–18,476. | -| [QCD](https://sparse.tamu.edu/QCD) | Coherent complex family | 14 complex square unsymmetric quantum-chromodynamics matrices; dimensions 3,072–49,152. | -| [FlowIPM22](https://sparse.tamu.edu/FlowIPM22) | Structure/domain uniform; scale varies; metadata caveat | 11 real square symmetric graph Laplacians; dimensions 100,000–72,180,402. The index marks four PD and seven not, but the group notes say every matrix has zero row sums, implying singular positive-semidefinite Laplacians rather than PD matrices. | -| [FIDAP](https://sparse.tamu.edu/FIDAP) | Pattern symmetry differs from numerical symmetry | 35 real square CFD matrices; all have symmetric patterns, but only 13 have exact mirrored numerical values. | -| [JGD_Homology](https://sparse.tamu.edu/JGD_Homology) | Same combinatorial subject, shape/scale variation | 128 integer combinatorial matrices; 122 rectangular + 6 square; dimensions 15–564,480. | -| [Mycielski](https://sparse.tamu.edu/Mycielski) | Categorical uniformity does not imply size uniformity | 19 square symmetric binary graph matrices; dimensions 2–786,431 and an `nnz` span exceeding nine orders of magnitude. | - -These examples support a continuum rather than a binary rule: - -- **Tight families:** same generator, equation, structure, and entry type; - often different resolution or parameter. -- **Domain-coherent but form-diverse collections:** all graph, circuit, CFD, - or structural problems, yet different symmetry, weighting, or shape. -- **Provenance-only bundles:** common source/archive or benchmark study, but - several scientific domains and matrix forms. - -## 7. Practical consequences for using the collection - -1. **Select by properties, not group name alone.** For a symmetric solver, - filter categorical structure or exact symmetry. For SPD work, require - `posdef == 1`. For least squares, require rectangular shape and inspect kind. -2. **Use group for provenance-aware sampling.** Stratifying or holding out - entire groups can reduce leakage between refinement steps and related - problem sequences, but group is not a perfect independence boundary. - Related or duplicate problems can occur across groups, especially when one - group is an imported collection. -3. **Balance large groups deliberately.** The median group has four matrices, - while `HB` has 292 and the ten largest groups hold nearly half the collection. - Report whether results are matrix-weighted or group-weighted. -4. **Separate graphs from linear systems when appropriate.** A graph adjacency - matrix is not automatically a meaningful `Ax=b` test. Diagonal, rank, and - positive-definiteness conventions differ. -5. **Do not bucket rectangular matrices as “nonsymmetric.”** Symmetry is not - applicable; the collection assigns their symmetry ratios zero by definition. -6. **Preserve exact identifiers and metadata.** Use `Group/Name`, retain the - matrix-specific citation/notes, and state transformations. The collection’s - matrices are CC BY 4.0. [License and citation guidance](https://sparse.tamu.edu/about) - -For the LinearSolverBench repository specifically, a robust sampling scheme -would treat group as a provenance stratum, then balance independently across -kind, shape/structure, entry type, size bands, rank, and definiteness. It should -also audit `duplicate`, `subsequent`, and `sequence` metadata before splitting -training/development/test sets. - -## 8. Method and reproducibility - -The analysis used: - -- The official [`ssstats.csv`](https://sparse.tamu.edu/files/ssstats.csv) and - [`ss_index.mat`](https://sparse.tamu.edu/files/ss_index.mat) endpoints, - retrieved 2026-09-02. -- The official website’s public metadata repository at commit - [`0316f79`](https://github.com/ScottKolo/suitesparse-matrix-collection-website/tree/0316f796684820b319a8601110e1ce51617c9ee0/db/collection_data), - used for categorical structure, entry type, current kind, and group notes. -- Metadata only; no full matrix payloads were downloaded. - -Numeric group summaries deliberately use the downloadable CSV kind so the -snapshot remains reproducible. The current website disagrees only for -`Chevron/Chevron1`–`Chevron/Chevron4`: the CSV says `other problem`, while the -current site says `2D/3D problem`. The readable catalogue uses the current site -label, and [output/kind_source_mismatches.csv](output/kind_source_mismatches.csv) -records the four differences. - -There is also a documentation-version nuance around explicit zeros: the -statistics page defines pattern symmetry from the numerically nonzero `A`, -while the newer client help describes a pattern including `A + Zeros`. This -report consumes the published symmetry scores rather than recomputing them, so -the ambiguity does not change the reported results; it matters if independently -reproducing those scores from matrix files. - -Run the analysis after checking out the website metadata repository: - -```bash -git clone https://github.com/ScottKolo/suitesparse-matrix-collection-website.git \ - /path/to/suitesparse-matrix-collection-website -git -C /path/to/suitesparse-matrix-collection-website \ - checkout 0316f796684820b319a8601110e1ce51617c9ee0 - -python3 reports/suitesparse-groups/analyze_groups.py \ - --stats reports/suitesparse-groups/data/ssstats.csv \ - --website-repo /path/to/suitesparse-matrix-collection-website \ - --output-dir reports/suitesparse-groups/output - -python3 reports/suitesparse-groups/build_catalogue.py \ - --group-summary reports/suitesparse-groups/output/group_summary.csv \ - --matrix-metadata reports/suitesparse-groups/output/matrix_metadata.csv \ - --website-repo /path/to/suitesparse-matrix-collection-website \ - --output-md reports/suitesparse-groups/GROUP_CATALOGUE.md \ - --output-csv reports/suitesparse-groups/output/group_catalogue.csv -``` - -### Delivered files - -| File | Purpose | -|---|---| -| [GROUP_CATALOGUE.md](GROUP_CATALOGUE.md) | Human-readable all-203-group appendix | -| [output/group_catalogue.csv](output/group_catalogue.csv) | Compact, filterable all-group catalogue | -| [output/group_summary.csv](output/group_summary.csv) | Full group-level counts, ranges, medians, symmetry scores, and outliers | -| [output/matrix_metadata.csv](output/matrix_metadata.csv) | Joined 2,904-row matrix metadata table | -| [output/group_diversity_ranking.csv](output/group_diversity_ranking.csv) | Groups sorted by the unofficial categorical-diversity score | -| [output/collection_attribute_distributions.csv](output/collection_attribute_distributions.csv) | Overall kind/shape/structure/type distributions | -| [output/group_size_distribution.csv](output/group_size_distribution.csv) | Exact group-size frequency table | -| [output/largest_matrices.csv](output/largest_matrices.csv) | Largest rows/columns/`nnz`/stored-entry cases | -| [output/collection_summary.json](output/collection_summary.json) | Machine-readable headline statistics and definitions | -| [data/provenance.json](data/provenance.json) | Retrieval date, URLs, hashes, ETags, revision, and metadata commit | -| [analyze_groups.py](analyze_groups.py) | Standard-library analysis generator | -| [build_catalogue.py](build_catalogue.py) | All-group catalogue generator | - -## 9. Sources - -- Timothy A. Davis and Yifan Hu, *The University of Florida Sparse Matrix - Collection*, ACM TOMS 38(1), 2011. - [DOI](https://doi.org/10.1145/2049662.2049663) · - [author PDF](https://yifanhu.net/PUB/matrices.pdf) -- Scott P. Kolodziej et al., *The SuiteSparse Matrix Collection Website - Interface*, JOSS 4(35), 2019. - [Paper](https://joss.theoj.org/papers/10.21105/joss.01244) -- [Official collection overview](https://sparse.tamu.edu/about) -- [Official statistics definitions](https://sparse.tamu.edu/statistics) -- [Official `ssget`/`ssgui` help](https://github.com/DrTimothyAldenDavis/SuiteSparse/blob/dev/ssget/sshelp.html) -- [Official current group pages](https://sparse.tamu.edu/groups?per_page=All) diff --git a/reports/suitesparse-groups/analyze_groups.py b/reports/suitesparse-groups/analyze_groups.py deleted file mode 100644 index e454c27..0000000 --- a/reports/suitesparse-groups/analyze_groups.py +++ /dev/null @@ -1,576 +0,0 @@ -#!/usr/bin/env python3 -"""Reproducible group-level analysis of the SuiteSparse Matrix Collection. - -Inputs ------- -* ``ssstats.csv`` downloaded from https://sparse.tamu.edu/files/ssstats.csv -* An optional checkout of the collection website repository: - https://github.com/ScottKolo/suitesparse-matrix-collection-website - -The first source supplies the canonical index and continuous symmetry scores. The -second supplies the website's categorical ``rb_type`` and ``structure`` labels, -including the distinction between real, complex, integer, and binary matrices. - -This script deliberately uses only the Python standard library. -""" - -from __future__ import annotations - -import argparse -import csv -import json -import math -import re -import statistics -import subprocess -from collections import Counter, defaultdict -from pathlib import Path -from typing import Callable, Iterable - - -CSV_FIELDS = ( - "group", - "name", - "nrows", - "ncols", - "nnz", - "is_real", - "is_binary", - "is_2d3d", - "posdef_index", - "pattern_symmetry", - "numerical_symmetry", - "kind", - "nentries", -) - -WEBSITE_FIELDS = ( - "rb_type", - "structure", - "positive_definite", - "cholesky_candidate", - "structural_full_rank", - "website_kind", -) - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--stats", type=Path, required=True) - parser.add_argument( - "--website-repo", - type=Path, - help="Checkout containing db/collection_data/matrices (recommended)", - ) - parser.add_argument("--output-dir", type=Path, required=True) - return parser.parse_args() - - -def load_stats(path: Path) -> tuple[list[dict], str, int]: - with path.open(newline="", encoding="utf-8") as handle: - reader = csv.reader(handle) - declared_count = int(next(reader)[0]) - revision = next(reader)[0] - matrices: list[dict] = [] - for matrix_id, values in enumerate(reader, 1): - if len(values) != len(CSV_FIELDS): - raise ValueError( - f"{path}: matrix {matrix_id}: expected {len(CSV_FIELDS)} " - f"columns, found {len(values)}" - ) - item = dict(zip(CSV_FIELDS, values)) - item["id"] = matrix_id - for key in ( - "nrows", - "ncols", - "nnz", - "nentries", - "is_real", - "is_binary", - "is_2d3d", - "posdef_index", - ): - item[key] = int(item[key]) - for key in ("pattern_symmetry", "numerical_symmetry"): - item[key] = float(item[key]) - matrices.append(item) - if len(matrices) != declared_count: - raise ValueError( - f"{path}: declared {declared_count} matrices but contained {len(matrices)}" - ) - return matrices, revision, declared_count - - -def ruby_scalar(text: str, key: str) -> str: - """Extract one simple scalar from the website's Ruby hash fixture.""" - match = re.search( - rf"^\s*{re.escape(key)}:\s*(?:'([^']*)'|\"([^\"]*)\"|([^,\n]*)),?\s*$", - text, - re.MULTILINE, - ) - if not match: - return "missing" - value = next((part for part in match.groups() if part is not None), "") - return value.strip() - - -def enrich_from_website(matrices: list[dict], repo: Path | None) -> dict: - provenance = { - "available": False, - "repository": "https://github.com/ScottKolo/suitesparse-matrix-collection-website", - } - if repo is None: - for item in matrices: - for key in WEBSITE_FIELDS: - item[key] = "missing" - return provenance - - metadata_root = repo / "db" / "collection_data" / "matrices" - if not metadata_root.is_dir(): - raise ValueError(f"website metadata directory not found: {metadata_root}") - - for item in matrices: - path = metadata_root / item["group"] / f"{item['name']}.rb" - if not path.is_file(): - raise ValueError(f"website metadata record not found: {path}") - text = path.read_text(encoding="utf-8", errors="replace") - if ruby_scalar(text, "matrix_id") != str(item["id"]): - raise ValueError(f"matrix-id mismatch between index and {path}") - for key in WEBSITE_FIELDS: - ruby_key = "kind" if key == "website_kind" else key - item[key] = ruby_scalar(text, ruby_key) - - try: - commit = subprocess.run( - ["git", "-C", str(repo), "rev-parse", "HEAD"], - check=True, - text=True, - capture_output=True, - ).stdout.strip() - except (OSError, subprocess.CalledProcessError): - commit = "unknown" - provenance.update( - { - "available": True, - "checkout": str(repo.resolve()), - "commit": commit, - "metadata_records": len(matrices), - } - ) - return provenance - - -def shape_class(item: dict) -> str: - return "square" if item["nrows"] == item["ncols"] else "rectangular" - - -def exact_symmetry_class(item: dict) -> str: - """Mutually exclusive class based on continuous ssstats symmetry metrics.""" - if item["nrows"] != item["ncols"]: - return "rectangular" - psym = item["pattern_symmetry"] - nsym = item["numerical_symmetry"] - if psym < 0 or nsym < 0: - return "unknown" - if math.isclose(nsym, 1.0, abs_tol=1e-12): - return "exact_numerical_or_hermitian" - if math.isclose(psym, 1.0, abs_tol=1e-12): - return "pattern_symmetric_only" - if math.isclose(psym, 0.0, abs_tol=1e-12): - return "pattern_unsymmetric" - return "partially_pattern_symmetric" - - -def posdef_class(item: dict) -> str: - return {1: "yes", 0: "no", -1: "unknown"}.get( - item["posdef_index"], f"other_{item['posdef_index']}" - ) - - -def size_class(item: dict) -> str: - dimension = max(item["nrows"], item["ncols"]) - if dimension < 1_000: - return "lt_1k" - if dimension < 10_000: - return "1k_to_10k" - if dimension < 100_000: - return "10k_to_100k" - if dimension < 1_000_000: - return "100k_to_1m" - return "ge_1m" - - -def counts(items: Iterable[dict], classifier: str | Callable[[dict], str]) -> Counter: - if isinstance(classifier, str): - return Counter(str(item[classifier]) for item in items) - return Counter(classifier(item) for item in items) - - -def counts_text(values: Counter) -> str: - return "; ".join(f"{key}:{values[key]}" for key in sorted(values)) - - -def dominant(values: Counter) -> tuple[str, float]: - # Deterministic alphabetical tie breaking. - key, count = sorted(values.items(), key=lambda pair: (-pair[1], pair[0]))[0] - return key, count / sum(values.values()) - - -def impurity(values: Counter) -> float: - """Fraction outside the modal category; zero means categorical homogeneity.""" - return 1.0 - max(values.values()) / sum(values.values()) - - -def log10_span(values: Iterable[int]) -> float: - positive = [value for value in values if value > 0] - if not positive: - return 0.0 - return math.log10(max(positive) / min(positive)) - - -def median_number(values: Iterable[int | float]) -> int | float: - value = statistics.median(values) - return int(value) if float(value).is_integer() else value - - -def numeric_summary(items: list[dict], key: str) -> dict: - values = [item[key] for item in items] - return { - "min": min(values), - "median": median_number(values), - "max": max(values), - } - - -def outlier_names(items: list[dict], key: str, largest: bool) -> str: - target = (max if largest else min)(item[key] for item in items) - return "; ".join( - f"{item['group']}/{item['name']}={target}" - for item in items - if item[key] == target - ) - - -def group_row(group: str, items: list[dict]) -> dict: - category_specs: list[tuple[str, str | Callable[[dict], str]]] = [ - ("shape", shape_class), - ("structure", "structure"), - ("entry_type", "rb_type"), - ("exact_symmetry", exact_symmetry_class), - ("positive_definite", posdef_class), - ("kind", "kind"), - ("structural_full_rank", "structural_full_rank"), - ("is_2d3d", lambda item: "yes" if item["is_2d3d"] == 1 else "no"), - ("cholesky_candidate", "cholesky_candidate"), - ("size_band", size_class), - ] - categorical: dict[str, Counter] = { - label: counts(items, classifier) for label, classifier in category_specs - } - dominants = {label: dominant(value) for label, value in categorical.items()} - - # The score is descriptive, not an official SuiteSparse metric. It is the - # mean fraction outside the modal category for six matrix attributes. Kind - # is included because a provenance group may span application domains. - diversity_attributes = ( - "shape", - "structure", - "entry_type", - "exact_symmetry", - "positive_definite", - "kind", - ) - categorical_diversity = statistics.mean( - impurity(categorical[label]) for label in diversity_attributes - ) - - max_dims = [max(item["nrows"], item["ncols"]) for item in items] - dim_span = log10_span(max_dims) - nnz_span = log10_span(item["nnz"] for item in items) - nentries_span = log10_span(item["nentries"] for item in items) - - # Labels help readers screen the full table; the component metrics remain - # the evidence. Small groups are marked as insufficient rather than being - # mistaken for strongly homogeneous populations. - mixed_count = sum( - dominants[label][1] < 0.90 for label in diversity_attributes - ) - if len(items) < 5: - diversity_label = "small_group_lt5" - elif mixed_count >= 3: - diversity_label = "categorically_heterogeneous" - elif mixed_count == 0: - diversity_label = ( - "type_homogeneous_size_diverse" - if max(dim_span, nnz_span) >= 3.0 - else "homogeneous" - ) - else: - diversity_label = "mixed_some_attributes" - - row: dict[str, object] = { - "group": group, - "matrix_count": len(items), - "diversity_label": diversity_label, - "categorical_diversity_score": round(categorical_diversity, 6), - "mixed_attribute_count_dominant_share_lt_0_90": mixed_count, - "distinct_kinds": len(categorical["kind"]), - "dimension_log10_span": round(dim_span, 6), - "nnz_log10_span": round(nnz_span, 6), - "nentries_log10_span": round(nentries_span, 6), - "matrices_with_explicit_zeros": sum( - item["nentries"] > item["nnz"] for item in items - ), - "explicit_zero_count_total": sum( - item["nentries"] - item["nnz"] for item in items - ), - } - for key in ( - "nrows", - "ncols", - "nnz", - "nentries", - "pattern_symmetry", - "numerical_symmetry", - ): - summary = numeric_summary(items, key) - row.update({f"{key}_{stat}": value for stat, value in summary.items()}) - for label, value in categorical.items(): - row[f"{label}_counts"] = counts_text(value) - row[f"dominant_{label}"] = dominants[label][0] - row[f"dominant_{label}_share"] = round(dominants[label][1], 6) - for key in ("nrows", "ncols", "nnz", "nentries"): - row[f"smallest_{key}_matrices"] = outlier_names(items, key, False) - row[f"largest_{key}_matrices"] = outlier_names(items, key, True) - return row - - -def write_csv(path: Path, rows: list[dict], fieldnames: list[str] | None = None) -> None: - if not rows: - raise ValueError(f"refusing to write an empty table: {path}") - if fieldnames is None: - fieldnames = list(rows[0]) - with path.open("w", newline="", encoding="utf-8") as handle: - writer = csv.DictWriter(handle, fieldnames=fieldnames, extrasaction="ignore") - writer.writeheader() - writer.writerows(rows) - - -def make_distribution_rows( - matrices: list[dict], specifications: list[tuple[str, str | Callable[[dict], str]]] -) -> list[dict]: - rows = [] - for attribute, classifier in specifications: - value_counts = counts(matrices, classifier) - for value, count in sorted(value_counts.items()): - rows.append( - { - "attribute": attribute, - "value": value, - "count": count, - "share": round(count / len(matrices), 8), - } - ) - return rows - - -def main() -> None: - args = parse_args() - matrices, revision, declared_count = load_stats(args.stats) - website_provenance = enrich_from_website(matrices, args.website_repo) - output_dir = args.output_dir - output_dir.mkdir(parents=True, exist_ok=True) - - by_group: dict[str, list[dict]] = defaultdict(list) - for item in matrices: - by_group[item["group"]].append(item) - group_rows = [group_row(group, by_group[group]) for group in sorted(by_group)] - - matrix_fields = ["id", *CSV_FIELDS, *WEBSITE_FIELDS] - write_csv(output_dir / "matrix_metadata.csv", matrices, matrix_fields) - write_csv(output_dir / "group_summary.csv", group_rows) - write_csv( - output_dir / "group_diversity_ranking.csv", - sorted( - group_rows, - key=lambda row: ( - -float(row["categorical_diversity_score"]), - -int(row["matrix_count"]), - str(row["group"]), - ), - ), - ) - - group_size_counts = Counter(len(items) for items in by_group.values()) - group_size_rows = [] - for size, number_of_groups in sorted(group_size_counts.items()): - matrices_at_size = size * number_of_groups - group_size_rows.append( - { - "group_size": size, - "number_of_groups": number_of_groups, - "number_of_matrices": matrices_at_size, - "share_of_groups": round(number_of_groups / len(by_group), 8), - "share_of_matrices": round(matrices_at_size / len(matrices), 8), - } - ) - write_csv(output_dir / "group_size_distribution.csv", group_size_rows) - - distribution_specs: list[tuple[str, str | Callable[[dict], str]]] = [ - ("shape", shape_class), - ("website_structure", "structure"), - ("entry_type", "rb_type"), - ( - "numeric_domain_isReal_flag", - lambda item: "real_or_noncomplex" if item["is_real"] == 1 else "complex", - ), - ( - "binary_isBinary_flag", - lambda item: "binary" if item["is_binary"] == 1 else "not_binary", - ), - ("exact_symmetry_class", exact_symmetry_class), - ("positive_definite", posdef_class), - ("is_2d3d", lambda item: str(item["is_2d3d"])), - ("structural_full_rank", "structural_full_rank"), - ("kind", "kind"), - ("size_band_by_max_dimension", size_class), - ] - distribution_rows = make_distribution_rows(matrices, distribution_specs) - write_csv(output_dir / "collection_attribute_distributions.csv", distribution_rows) - - largest_rows = [] - for key in ("nrows", "ncols", "nnz", "nentries"): - for rank, item in enumerate( - sorted(matrices, key=lambda value: (-value[key], value["id"]))[:20], 1 - ): - largest_rows.append( - { - "metric": key, - "rank": rank, - "id": item["id"], - "matrix": f"{item['group']}/{item['name']}", - "value": item[key], - "nrows": item["nrows"], - "ncols": item["ncols"], - "nnz": item["nnz"], - "nentries": item["nentries"], - "structure": item["structure"], - "entry_type": item["rb_type"], - "kind": item["kind"], - } - ) - write_csv(output_dir / "largest_matrices.csv", largest_rows) - - kind_mismatches = [ - { - "id": item["id"], - "matrix": f"{item['group']}/{item['name']}", - "ssstats_kind": item["kind"], - "website_repo_kind": item["website_kind"], - } - for item in matrices - if item["website_kind"] != "missing" and item["website_kind"] != item["kind"] - ] - if kind_mismatches: - write_csv(output_dir / "kind_source_mismatches.csv", kind_mismatches) - - group_sizes = [len(items) for items in by_group.values()] - summary = { - "source": { - "ssstats_url": "https://sparse.tamu.edu/files/ssstats.csv", - "ss_index_url": "https://sparse.tamu.edu/files/ss_index.mat", - "ssstats_revision_embedded": revision, - "website_metadata": website_provenance, - }, - "collection": { - "declared_matrix_count": declared_count, - "parsed_matrix_count": len(matrices), - "group_count": len(by_group), - "group_size": { - "min": min(group_sizes), - "median": median_number(group_sizes), - "mean": statistics.mean(group_sizes), - "max": max(group_sizes), - "singleton_groups": sum(value == 1 for value in group_sizes), - "groups_lt_5": sum(value < 5 for value in group_sizes), - }, - "nrows": numeric_summary(matrices, "nrows"), - "ncols": numeric_summary(matrices, "ncols"), - "nnz": numeric_summary(matrices, "nnz"), - "nentries": numeric_summary(matrices, "nentries"), - "matrices_with_explicit_zeros": sum( - item["nentries"] > item["nnz"] for item in matrices - ), - "explicit_zero_count_total": sum( - item["nentries"] - item["nnz"] for item in matrices - ), - "pattern_symmetry": numeric_summary(matrices, "pattern_symmetry"), - "numerical_symmetry": numeric_summary(matrices, "numerical_symmetry"), - "pattern_symmetry_missing_count": sum( - item["pattern_symmetry"] < 0 for item in matrices - ), - "numerical_symmetry_missing_count": sum( - item["numerical_symmetry"] < 0 for item in matrices - ), - "ssstats_distinct_kind_count": len({item["kind"] for item in matrices}), - "website_distinct_kind_count": len( - { - item["website_kind"] - for item in matrices - if item["website_kind"] != "missing" - } - ), - "ssstats_vs_website_kind_mismatch_count": len(kind_mismatches), - }, - "definitions_and_cautions": { - "group": ( - "A provenance/source label, typically a person, organization, or " - "earlier collection; it is not a homogeneity guarantee." - ), - "nnz": "Numerically nonzero entries in A; explicit stored zeros are excluded.", - "nentries": ( - "All stored pattern entries, equal to nnz plus explicit zeros supplied " - "by the matrix author." - ), - "entry_type": ( - "The website's mutually exclusive Rutherford-Boeing entry-type label: " - "real, complex, integer, or binary. In the ssstats flags, integer and " - "binary matrices are also real/non-complex; isBinary is a separate flag." - ), - "pattern_symmetry": ( - "Matched off-diagonal structural entries divided by all off-diagonal " - "entries; rectangular matrices are assigned 0." - ), - "numerical_symmetry": ( - "Matched conjugate off-diagonal values divided by all off-diagonal " - "entries; this measures Hermitian symmetry and rectangular matrices " - "are assigned 0." - ), - "posdef_index": ( - "1 known positive definite; 0 known not positive definite; -1 symmetric/" - "Hermitian with unknown definiteness. No -1 values occur in this snapshot." - ), - "categorical_diversity_score": ( - "Unofficial descriptive metric: the mean fraction outside the modal " - "category across shape, website structure, entry type, exact symmetry " - "class, positive-definite status, and problem kind." - ), - "diversity_label": ( - "Unofficial screening rule. Groups with fewer than five matrices are " - "not classified; heterogeneous means at least three attributes have a " - "modal share below 90%; homogeneous means none do." - ), - "kind_source_discrepancy": ( - "Group statistics use the ssstats.csv kind. Four Chevron matrices are " - "'other problem' in ssstats.csv but '2D/3D problem' in the website " - "repository; see kind_source_mismatches.csv." - ), - }, - } - (output_dir / "collection_summary.json").write_text( - json.dumps(summary, indent=2, sort_keys=True) + "\n", encoding="utf-8" - ) - - -if __name__ == "__main__": - main() diff --git a/reports/suitesparse-groups/build_catalogue.py b/reports/suitesparse-groups/build_catalogue.py deleted file mode 100644 index c78b99b..0000000 --- a/reports/suitesparse-groups/build_catalogue.py +++ /dev/null @@ -1,198 +0,0 @@ -#!/usr/bin/env python3 -"""Build the readable all-group catalogue from the verified analysis outputs. - -The numeric inputs are produced by ``analyze_groups.py``. Group descriptions -come from the official SuiteSparse Matrix Collection website repository at the -commit recorded in ``data/provenance.json``. -""" - -from __future__ import annotations - -import argparse -import csv -import re -import textwrap -from collections import Counter, defaultdict -from pathlib import Path - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--group-summary", type=Path, required=True) - parser.add_argument("--matrix-metadata", type=Path, required=True) - parser.add_argument("--website-repo", type=Path, required=True) - parser.add_argument("--output-md", type=Path, required=True) - parser.add_argument("--output-csv", type=Path, required=True) - return parser.parse_args() - - -def read_csv(path: Path) -> list[dict[str, str]]: - with path.open(newline="", encoding="utf-8") as handle: - return list(csv.DictReader(handle)) - - -def normalize_kind(kind: str) -> str: - for prefix in ("subsequent ", "duplicate "): - if kind.startswith(prefix): - kind = kind[len(prefix) :] - if kind.endswith(" sequence"): - kind = kind[: -len(" sequence")] - return kind - - -def group_description(repo: Path, group: str) -> str: - path = repo / "db" / "collection_data" / "groups" / f"{group}.rb" - text = path.read_text(encoding="utf-8", errors="replace") - match = re.search(r"\bnotes:\s*'(.*)'\s*,\s*\n\s*}\s*$", text, re.DOTALL) - if not match: - raise ValueError(f"could not parse group notes: {path}") - notes = match.group(1).replace("\\'", "'") - lines = [ - line.strip() - for line in notes.splitlines() - if line.strip() and not set(line.strip()) <= {"=", "-"} - ] - description = lines[0] if lines else "" - # Some official first lines deliberately continue after a comma/colon. - if description.endswith((",", ":")) and len(lines) > 1: - description += " " + lines[1] - return textwrap.shorten(description, width=125, placeholder="…") - - -def count_text(counts: Counter[str], limit: int | None = None) -> str: - ordered = sorted(counts.items(), key=lambda item: (-item[1], item[0])) - shown = ordered if limit is None else ordered[:limit] - result = "; ".join(f"{name} ({count})" for name, count in shown) - if limit is not None and len(ordered) > limit: - result += f"; +{len(ordered) - limit} more" - return result - - -def parse_count_field(value: str) -> Counter[str]: - result: Counter[str] = Counter() - for part in value.split("; "): - name, count = part.rsplit(":", 1) - result[name] = int(count) - return result - - -def size_profile(dimensions: list[int]) -> str: - ratio = max(dimensions) / min(dimensions) - if ratio == 1: - return "one dimension" - if ratio <= 4: - return "narrow (≤4×)" - if ratio <= 100: - return "moderate (4–100×)" - return "wide (>100×)" - - -def assessment(summary: dict[str, str], n: int, dimensions: list[int]) -> str: - label = summary["diversity_label"] - if n == 1: - categorical = "singleton" - elif label == "small_group_lt5": - score = float(summary["categorical_diversity_score"]) - categorical = "small; observed mix" if score > 0 else "small; uniform observed types" - elif label == "categorically_heterogeneous": - categorical = "high categorical mix" - elif label == "mixed_some_attributes": - categorical = "some categorical mix" - else: - categorical = "categorically uniform" - return f"{categorical}; {size_profile(dimensions)}" - - -def main() -> None: - args = parse_args() - summaries = {row["group"]: row for row in read_csv(args.group_summary)} - matrices = read_csv(args.matrix_metadata) - - by_group: dict[str, list[dict[str, str]]] = defaultdict(list) - for matrix in matrices: - by_group[matrix["group"]].append(matrix) - - rows: list[dict[str, str | int]] = [] - for group in sorted(by_group): - items = by_group[group] - summary = summaries[group] - dimensions = [max(int(item["nrows"]), int(item["ncols"])) for item in items] - kinds = Counter( - normalize_kind(item["website_kind"] if item["website_kind"] != "missing" else item["kind"]) - for item in items - ) - shapes = Counter("square" if item["nrows"] == item["ncols"] else "rectangular" for item in items) - structures = Counter(item["structure"] for item in items) - entry_types = Counter(item["rb_type"] for item in items) - pd_counts = Counter(item["positive_definite"] for item in items) - rows.append( - { - "group": group, - "matrix_count": len(items), - "source_or_meaning": group_description(args.website_repo, group), - "normalized_kind_counts": count_text(kinds), - "dimension_min": min(dimensions), - "dimension_max": max(dimensions), - "size_profile": size_profile(dimensions), - "shape_counts": count_text(shapes), - "structure_counts": count_text(structures), - "entry_type_counts": count_text(entry_types), - "known_positive_definite": pd_counts.get("yes", 0), - "assessment": assessment(summary, len(items), dimensions), - "categorical_diversity_score": summary["categorical_diversity_score"], - "source_url": f"https://sparse.tamu.edu/{group}", - } - ) - - args.output_csv.parent.mkdir(parents=True, exist_ok=True) - with args.output_csv.open("w", newline="", encoding="utf-8") as handle: - writer = csv.DictWriter(handle, fieldnames=list(rows[0])) - writer.writeheader() - writer.writerows(rows) - - lines = [ - "# SuiteSparse group catalogue", - "", - "This appendix covers all 203 groups in the 2,904-problem public snapshot.", - "Group descriptions are condensed from each official group page; click a group", - "name for its full provenance notes and matrix list. `Kind` is the current website", - "label after collapsing only `subsequent`, `duplicate`, and `sequence` modifiers.", - "`Dimension` is `max(rows, columns)`. Entry types are the mutually exclusive", - "Rutherford–Boeing categories `real`, `binary`, `integer`, and `complex`.", - "The assessment is an unofficial screening aid computed from six categorical fields,", - "including the uncollapsed kind string; the component counts are the evidence.", - "", - "| Group | N | Source / meaning | Normalized kind(s) | Dimension | Shape | Structure | Entry type | Known PD | Assessment |", - "|---|---:|---|---|---:|---|---|---|---:|---|", - ] - for row in rows: - group = str(row["group"]) - kind_counts = Counter() - for part in str(row["normalized_kind_counts"]).split("; "): - match = re.match(r"(.*) \((\d+)\)$", part) - if match: - kind_counts[match.group(1)] = int(match.group(2)) - compact_kinds = count_text(kind_counts, limit=4) - lines.append( - "| " - + " | ".join( - [ - f"[{group}](https://sparse.tamu.edu/{group})", - str(row["matrix_count"]), - str(row["source_or_meaning"]).replace("|", "\\|"), - compact_kinds.replace("|", "\\|"), - f"{int(row['dimension_min']):,}–{int(row['dimension_max']):,}", - str(row["shape_counts"]), - str(row["structure_counts"]), - str(row["entry_type_counts"]), - str(row["known_positive_definite"]), - str(row["assessment"]), - ] - ) - + " |" - ) - args.output_md.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -if __name__ == "__main__": - main() diff --git a/reports/suitesparse-groups/data/provenance.json b/reports/suitesparse-groups/data/provenance.json deleted file mode 100644 index 404e69c..0000000 --- a/reports/suitesparse-groups/data/provenance.json +++ /dev/null @@ -1,34 +0,0 @@ -{ - "retrieved_on": "2026-09-02", - "retrieval_timezone": "America/Los_Angeles", - "files": { - "ssstats.csv": { - "url": "https://sparse.tamu.edu/files/ssstats.csv", - "sha256": "9bc797ab989331afbc9e0e51236d9145c2ac7f76c0913e9677ae07c4913a2aad", - "bytes": 254328, - "embedded_revision": "31-Oct-2023 18:12:37", - "http_last_modified": "Thu, 02 Nov 2023 22:07:25 GMT", - "http_etag": "c0a17979013144c0b0cf643a0be2566e" - }, - "ss_index.mat": { - "url": "https://sparse.tamu.edu/files/ss_index.mat", - "sha256": "f040a883a523c4621c5986aa66c39eeaa21bcae79c45596e953e7ea5e7ccbdb4", - "bytes": 164169, - "http_last_modified": "Thu, 02 Nov 2023 22:07:25 GMT", - "http_etag": "73085697a647ec01c968810fd913c655" - } - }, - "website_metadata": { - "repository": "https://github.com/ScottKolo/suitesparse-matrix-collection-website", - "commit": "0316f796684820b319a8601110e1ce51617c9ee0", - "selected_fields_captured_in": "../output/matrix_metadata.csv", - "selected_fields": [ - "rb_type", - "structure", - "positive_definite", - "cholesky_candidate", - "structural_full_rank", - "website_kind" - ] - } -} diff --git a/reports/suitesparse-groups/data/ss_index.mat b/reports/suitesparse-groups/data/ss_index.mat deleted file mode 100644 index 4b422f4..0000000 Binary files a/reports/suitesparse-groups/data/ss_index.mat and /dev/null differ diff --git a/reports/suitesparse-groups/data/ssstats.csv b/reports/suitesparse-groups/data/ssstats.csv deleted file mode 100644 index 89a0c4d..0000000 --- a/reports/suitesparse-groups/data/ssstats.csv +++ /dev/null @@ -1,2906 +0,0 @@ -2904 -31-Oct-2023 18:12:37 -HB,1138_bus,1138,1138,4054,1,0,0,1,1,1,power network problem,4054 -HB,494_bus,494,494,1666,1,0,0,1,1,1,power network problem,1666 -HB,662_bus,662,662,2474,1,0,0,1,1,1,power network problem,2474 -HB,685_bus,685,685,3249,1,0,0,1,1,1,power network problem,3249 -HB,abb313,313,176,1557,1,1,0,0,0,0,least squares problem,1557 -HB,arc130,130,130,1037,1,0,1,0,0.7586805555555556,0,materials problem,1282 -HB,ash219,219,85,438,1,1,0,0,0,0,least squares problem,438 -HB,ash292,292,292,2208,1,1,0,0,1,1,least squares problem,2208 -HB,ash331,331,104,662,1,1,0,0,0,0,least squares problem,662 -HB,ash608,608,188,1216,1,1,0,0,0,0,least squares problem,1216 -HB,ash85,85,85,523,1,1,0,0,1,1,least squares problem,523 -HB,ash958,958,292,1916,1,1,0,0,0,0,least 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-HB,can_229,229,229,1777,1,1,1,0,1,1,structural problem,1777 -HB,can_24,24,24,160,1,1,1,0,1,1,structural problem,160 -HB,can_256,256,256,2916,1,1,1,0,1,1,structural problem,2916 -HB,can_268,268,268,3082,1,1,1,0,1,1,structural problem,3082 -HB,can_292,292,292,2540,1,1,1,0,1,1,structural problem,2540 -HB,can_445,445,445,3809,1,1,1,0,1,1,structural problem,3809 -HB,can_61,61,61,557,1,1,1,0,1,1,structural problem,557 -HB,can_62,62,62,218,1,1,1,0,1,1,structural problem,218 -HB,can_634,634,634,7228,1,1,1,0,1,1,structural problem,7228 -HB,can_715,715,715,6665,1,1,1,0,1,1,structural problem,6665 -HB,can_73,73,73,377,1,1,1,0,1,1,structural problem,377 -HB,can_838,838,838,10010,1,1,1,0,1,1,structural problem,10010 -HB,can_96,96,96,768,1,1,1,0,1,1,structural problem,768 -HB,curtis54,54,54,291,1,1,1,0,0.9535864978902954,0.9535864978902954,2D/3D problem,291 -HB,dwt_1005,1005,1005,8621,1,1,1,0,1,1,structural problem,8621 -HB,dwt_1007,1007,1007,8575,1,1,1,0,1,1,structural problem,8575 -HB,dwt_1242,1242,1242,10426,1,1,1,0,1,1,structural problem,10426 -HB,dwt_162,162,162,1182,1,1,1,0,1,1,structural problem,1182 -HB,dwt_193,193,193,3493,1,1,1,0,1,1,structural problem,3493 -HB,dwt_198,198,198,1392,1,1,1,0,1,1,structural problem,1392 -HB,dwt_209,209,209,1743,1,1,1,0,1,1,structural problem,1743 -HB,dwt_221,221,221,1629,1,1,1,0,1,1,structural problem,1629 -HB,dwt_234,234,234,834,1,1,1,0,1,1,structural problem,834 -HB,dwt_245,245,245,1461,1,1,1,0,1,1,structural problem,1461 -HB,dwt_2680,2680,2680,25026,1,1,1,0,1,1,structural problem,25026 -HB,dwt_307,307,307,2523,1,1,1,0,1,1,structural problem,2523 -HB,dwt_310,310,310,2448,1,1,1,0,1,1,structural problem,2448 -HB,dwt_346,346,346,3226,1,1,1,0,1,1,structural problem,3226 -HB,dwt_361,361,361,2953,1,1,1,0,1,1,structural problem,2953 -HB,dwt_419,419,419,3563,1,1,1,0,1,1,structural problem,3563 -HB,dwt_492,492,492,3156,1,1,1,0,1,1,structural problem,3156 -HB,dwt_503,503,503,6027,1,1,1,0,1,1,structural problem,6027 -HB,dwt_512,512,512,3502,1,1,1,0,1,1,structural problem,3502 -HB,dwt_59,59,59,267,1,1,1,0,1,1,structural problem,267 -HB,dwt_592,592,592,5104,1,1,1,0,1,1,structural problem,5104 -HB,dwt_607,607,607,5131,1,1,1,0,1,1,structural problem,5131 -HB,dwt_66,66,66,320,1,1,1,0,1,1,structural problem,320 -HB,dwt_72,72,72,222,1,1,1,0,1,1,structural problem,222 -HB,dwt_758,758,758,5994,1,1,1,0,1,1,structural problem,5994 -HB,dwt_869,869,869,7285,1,1,1,0,1,1,structural problem,7285 -HB,dwt_87,87,87,541,1,1,1,0,1,1,structural problem,541 -HB,dwt_878,878,878,7448,1,1,1,0,1,1,structural problem,7448 -HB,dwt_918,918,918,7384,1,1,1,0,1,1,structural problem,7384 -HB,dwt_992,992,992,16744,1,1,1,0,1,1,structural problem,16744 -HB,eris1176,1176,1176,18552,1,1,0,0,1,1,power network problem,18552 -HB,fs_183_1,183,183,998,1,0,1,0,0.417607223476298,0,2D/3D problem sequence,1069 -HB,fs_183_3,183,183,1069,1,0,1,0,0.417607223476298,0,subsequent 2D/3D problem,1069 -HB,fs_183_4,183,183,1069,1,0,1,0,0.417607223476298,0,subsequent 2D/3D problem,1069 -HB,fs_183_6,183,183,1000,1,0,1,0,0.417607223476298,0,subsequent 2D/3D problem,1069 -HB,fs_541_1,541,541,4282,1,0,1,0,0.6826923076923077,0,2D/3D problem sequence,4285 -HB,fs_541_2,541,541,4282,1,0,1,0,0.6826923076923077,0,subsequent 2D/3D problem,4285 -HB,fs_541_3,541,541,4282,1,0,1,0,0.6826923076923077,0,subsequent 2D/3D problem,4285 -HB,fs_541_4,541,541,4273,1,0,1,0,0.6826923076923077,0,subsequent 2D/3D problem,4285 -HB,fs_680_1,680,680,2184,1,0,1,0,0.5106815869786369,0,2D/3D problem sequence,2646 -HB,fs_680_2,680,680,2424,1,0,1,0,0.5106815869786369,0,subsequent 2D/3D problem,2646 -HB,fs_680_3,680,680,2471,1,0,1,0,0.5106815869786369,0,subsequent 2D/3D problem,2646 -HB,fs_760_1,760,760,5739,1,0,1,0,0.6510736196319018,0,2D/3D problem sequence,5976 -HB,fs_760_2,760,760,5739,1,0,1,0,0.6510736196319018,0,subsequent 2D/3D problem,5976 -HB,fs_760_3,760,760,5816,1,0,1,0,0.6510736196319018,0,subsequent 2D/3D problem,5976 -HB,gemat1,4929,10595,46591,1,0,0,0,0,0,power network problem,47369 -HB,gemat11,4929,4929,33108,1,0,0,0,0.001326419872181358,0,power network problem sequence,33185 -HB,gemat12,4929,4929,33044,1,0,0,0,0.001450063440275512,0,subsequent power network problem,33111 -HB,gent113,113,113,655,1,1,0,0,0.05663716814159292,0.05663716814159292,statistical/mathematical problem,655 -HB,gr_30_30,900,900,7744,1,0,1,1,1,1,2D/3D problem,7744 -HB,gre_1107,1107,1107,5664,1,0,0,0,0,0,directed weighted graph,5664 -HB,gre_115,115,115,421,1,0,0,0,0.2549019607843137,0,directed weighted graph,421 -HB,gre_185,185,185,975,1,0,0,0,0.4146341463414634,0,directed weighted graph,1005 -HB,gre_216a,216,216,812,1,0,0,0,0,0,directed weighted graph,876 -HB,gre_216b,216,216,812,1,0,0,0,0,0,directed weighted graph,876 -HB,gre_343,343,343,1310,1,0,0,0,0,0,directed weighted graph,1435 -HB,gre_512,512,512,1976,1,0,0,0,0,0,directed weighted graph,2192 -HB,hor_131,434,434,4182,1,0,0,0,1,0.3740661686232657,directed weighted graph,4710 -HB,ibm32,32,32,126,1,1,0,0,0.0851063829787234,0.0851063829787234,directed graph,126 -HB,illc1033,1033,320,4719,1,0,0,0,0,0,least squares problem,4732 -HB,illc1850,1850,712,8636,1,0,0,0,0,0,least squares problem,8758 -HB,impcol_a,207,207,572,1,0,0,0,0.02482269503546099,0.01063829787234043,chemical process simulation problem,572 -HB,impcol_b,59,59,271,1,0,0,0,0.09491525423728814,0,chemical process simulation problem,312 -HB,impcol_c,137,137,400,1,0,0,0,0.04444444444444445,0.01146131805157593,chemical process simulation problem,411 -HB,impcol_d,425,425,1255,1,0,0,0,0.05674846625766871,0.0228198859005705,chemical process simulation problem,1339 -HB,impcol_e,225,225,1303,1,0,0,0,0.1038338658146965,0.001603849238171612,chemical process simulation problem,1308 -HB,jagmesh1,936,936,6264,1,1,1,0,1,1,2D/3D problem,6264 -HB,jagmesh2,1009,1009,6865,1,1,1,0,1,1,2D/3D problem,6865 -HB,jagmesh3,1089,1089,7361,1,1,1,0,1,1,2D/3D problem,7361 -HB,jagmesh4,1440,1440,9504,1,1,1,0,1,1,2D/3D problem,9504 -HB,jagmesh5,1180,1180,7750,1,1,1,0,1,1,2D/3D problem,7750 -HB,jagmesh6,1377,1377,8993,1,1,1,0,1,1,2D/3D problem,8993 -HB,jagmesh7,1138,1138,7450,1,1,1,0,1,1,2D/3D problem,7450 -HB,jagmesh8,1141,1141,7465,1,1,1,0,1,1,2D/3D problem,7465 -HB,jagmesh9,1349,1349,9101,1,1,1,0,1,1,2D/3D problem,9101 -HB,jgl009,9,9,50,1,1,0,0,0.4761904761904762,0.4761904761904762,counter-example problem,50 -HB,jgl011,11,11,76,1,1,0,0,0.5151515151515151,0.5151515151515151,counter-example problem,76 -HB,jpwh_991,991,991,6027,1,0,1,0,0.9364575059571089,0.9364575059571089,semiconductor device problem,6027 -HB,lns_131,131,131,536,1,0,1,0,0.7028301886792453,0.01886792452830189,computational fluid dynamics problem,536 -HB,lns_3937,3937,3937,25407,1,0,1,0,0.849666726715907,0.001351107908484958,computational fluid dynamics problem,25407 -HB,lns_511,511,511,2796,1,0,1,0,0.7994945240101096,0.02021903959561921,computational fluid dynamics problem,2796 -HB,lnsp3937,3937,3937,25407,1,0,1,0,0.849666726715907,0.001351107908484958,computational fluid dynamics problem,25407 -HB,lnsp_131,131,131,536,1,0,1,0,0.7028301886792453,0.01886792452830189,computational fluid dynamics problem,536 -HB,lnsp_511,511,511,2796,1,0,1,0,0.7994945240101096,0.02021903959561921,computational fluid dynamics problem,2796 -HB,lshp1009,1009,1009,6865,1,1,1,0,1,1,duplicate thermal problem,6865 -HB,lshp1270,1270,1270,8668,1,1,1,0,1,1,thermal problem,8668 -HB,lshp1561,1561,1561,10681,1,1,1,0,1,1,thermal problem,10681 -HB,lshp1882,1882,1882,12904,1,1,1,0,1,1,thermal problem,12904 -HB,lshp2233,2233,2233,15337,1,1,1,0,1,1,thermal problem,15337 -HB,lshp2614,2614,2614,17980,1,1,1,0,1,1,thermal problem,17980 -HB,lshp3025,3025,3025,20833,1,1,1,0,1,1,thermal problem,20833 -HB,lshp3466,3466,3466,23896,1,1,1,0,1,1,thermal problem,23896 -HB,lshp_265,265,265,1753,1,1,1,0,1,1,thermal problem,1753 -HB,lshp_406,406,406,2716,1,1,1,0,1,1,thermal problem,2716 -HB,lshp_577,577,577,3889,1,1,1,0,1,1,thermal problem,3889 -HB,lshp_778,778,778,5272,1,1,1,0,1,1,thermal problem,5272 -HB,lund_a,147,147,2449,1,0,1,1,1,1,structural problem,2449 -HB,lund_b,147,147,2441,1,0,1,1,1,1,structural problem,2441 -HB,mahindas,1258,1258,7682,1,0,0,0,0.01663146779303062,0,economic problem,7682 -HB,mbeacxc,496,496,49920,1,0,0,0,0.3162614104531868,0,economic problem,49920 -HB,mbeaflw,496,496,49920,1,0,0,0,0.3162614104531868,0,economic problem,49920 -HB,mbeause,496,496,41063,1,0,0,0,0.2190689324447286,0.007771192484568281,economic problem,41063 -HB,mcca,180,180,2659,1,0,1,0,0.6446147640177491,0,2D/3D problem,2659 -HB,mcfe,765,765,24382,1,0,1,0,0.699411440911208,0,2D/3D problem,24382 -HB,nnc1374,1374,1374,8588,1,0,1,0,0.8169596690796277,0.5918631769888573,2D/3D problem,8606 -HB,nnc261,261,261,1500,1,0,1,0,0.8237037037037037,0.6133333333333333,2D/3D problem,1500 -HB,nnc666,666,666,4032,1,0,1,0,0.817831590533847,0.5919381557150746,2D/3D problem,4044 -HB,nos1,237,237,1017,1,0,1,1,1,1,structural problem,1017 -HB,nos2,957,957,4137,1,0,1,1,1,1,structural problem,4137 -HB,nos3,960,960,15844,1,0,1,1,1,1,structural problem,15844 -HB,nos4,100,100,594,1,0,1,1,1,1,structural problem,594 -HB,nos5,468,468,5172,1,0,1,1,1,1,structural problem,5172 -HB,nos6,675,675,3255,1,0,1,1,1,1,2D/3D problem,3255 -HB,nos7,729,729,4617,1,0,1,1,1,1,2D/3D problem,4617 -HB,orani678,2529,2529,90158,1,0,0,0,0.07068607068607069,2.223531635296341e-05,economic problem,90158 -HB,orsirr_1,1030,1030,6858,1,0,1,0,1,0.4094028826355525,computational fluid dynamics problem,6858 -HB,orsirr_2,886,886,5970,1,0,1,0,1,0.4118804091266719,computational fluid dynamics problem,5970 -HB,orsreg_1,2205,2205,14133,1,0,1,0,1,0.4119718309859155,computational fluid dynamics problem,14133 -HB,plat1919,1919,1919,32399,1,0,1,1,1,1,2D/3D problem,32399 -HB,plat362,362,362,5786,1,0,1,1,1,1,2D/3D problem,5786 -HB,plsk1919,1919,1919,9662,1,0,1,0,1,0,2D/3D problem,9662 -HB,plskz362,362,362,1760,1,0,1,0,1,0,2D/3D problem,1760 -HB,pores_1,30,30,180,1,0,1,0,0.6266666666666667,0.2933333333333333,computational fluid dynamics problem,180 -HB,pores_2,1224,1224,9613,1,0,1,0,0.6119918941470974,0.3914650137084277,computational fluid dynamics problem,9613 -HB,pores_3,532,532,3474,1,0,1,0,0.7423521414004078,0.3052345343303875,computational fluid dynamics problem,3474 -HB,psmigr_1,3140,3140,543160,1,0,0,0,0.4786508697793794,0.01044772250019444,economic problem,543162 -HB,psmigr_2,3140,3140,540022,1,0,0,0,0.4786508697793794,0,economic problem,540022 -HB,psmigr_3,3140,3140,543160,1,0,0,0,0.4786508697793794,0,economic problem,543162 -HB,rgg010,10,10,76,1,1,0,0,0.6363636363636364,0.6363636363636364,counter-example problem,76 -HB,saylr1,238,238,1128,1,0,1,0,1,0.647191011235955,computational fluid dynamics problem,1128 -HB,saylr3,1000,1000,3750,1,0,1,0,1,1,computational fluid dynamics problem,3750 -HB,saylr4,3564,3564,22316,1,0,1,0,1,1,computational fluid dynamics problem,22316 -HB,sherman1,1000,1000,3750,1,0,1,0,1,1,computational fluid dynamics problem,3750 -HB,sherman2,1080,1080,23094,1,0,1,0,0.6708458253838466,0.004905968928863451,computational fluid dynamics problem,23094 -HB,sherman3,5005,5005,20033,1,0,1,0,1,0.4532871972318339,computational fluid dynamics problem,20033 -HB,sherman4,1104,1104,3786,1,0,1,0,1,0,computational fluid dynamics problem,3786 -HB,sherman5,3312,3312,20793,1,0,1,0,0.7386305131285396,0.1534237171786511,computational fluid dynamics problem,20793 -HB,shl_0,663,663,1687,1,0,0,0,0,0,optimization problem sequence,1687 -HB,shl_200,663,663,1726,1,0,0,0,0,0,subsequent optimization problem,1726 -HB,shl_400,663,663,1712,1,0,0,0,0,0,subsequent optimization problem,1712 -HB,sstmodel,3345,3345,22749,1,1,1,0,1,1,structural problem,22749 -HB,steam1,240,240,2248,1,0,1,0,1,0.2729083665338645,computational fluid dynamics problem,3762 -HB,steam2,600,600,5660,1,0,1,0,1,0.1877470355731225,computational fluid dynamics problem,13760 -HB,steam3,80,80,314,1,0,1,0,1,0.3247863247863248,computational fluid dynamics problem,928 -HB,str_0,363,363,2454,1,0,0,0,0.004893964110929853,0,optimization problem sequence,2454 -HB,str_200,363,363,3068,1,0,0,0,0.01108936725375081,0,subsequent optimization problem,3068 -HB,str_400,363,363,3157,1,0,0,0,0.0196513470681458,0,subsequent optimization problem,3157 -HB,str_600,363,363,3279,1,0,0,0,0.01893129770992366,0,subsequent optimization problem,3279 -HB,watt_1,1856,1856,11360,1,0,1,0,0.9865319865319865,0.9865319865319865,computational fluid dynamics problem,11360 -HB,watt_2,1856,1856,11550,1,0,1,0,0.9804002475758201,0.9671962038374252,computational fluid dynamics problem,11550 -HB,well1033,1033,320,4732,1,0,0,0,0,0,least squares problem,4732 -HB,well1850,1850,712,8755,1,0,0,0,0,0,least squares problem,8758 -HB,west0067,67,67,294,1,0,0,0,0.03424657534246575,0,chemical process simulation problem,294 -HB,west0132,132,132,413,1,0,0,0,0.0196078431372549,0.004914004914004914,chemical process simulation problem,414 -HB,west0156,156,156,362,1,0,0,0,0,0,chemical process simulation problem,371 -HB,west0167,167,167,506,1,0,0,0,0.01622718052738337,0,chemical process simulation problem,507 -HB,west0381,381,381,2134,1,0,0,0,0.005565862708719851,0.0009376465072667605,chemical process simulation problem,2157 -HB,west0479,479,479,1888,1,0,0,0,0.01366982124079916,0,chemical process simulation problem,1910 -HB,west0497,497,497,1721,1,0,0,0,0.006972690296339338,0,chemical process simulation problem,1727 -HB,west0655,655,655,2808,1,0,0,0,0.005616005616005616,0,chemical process simulation problem,2854 -HB,west0989,989,989,3518,1,0,0,0,0.01812004530011325,0.003985197836606889,chemical process simulation problem,3537 -HB,west1505,1505,1505,5414,1,0,0,0,0.001102941176470588,0,chemical process simulation problem,5445 -HB,west2021,2021,2021,7310,1,0,0,0,0.00326619488296135,0,chemical process simulation problem,7353 -HB,will199,199,199,701,1,1,1,0,0.05596465390279824,0.05596465390279824,structural problem,701 -HB,will57,57,57,281,1,1,1,0,0.8660714285714286,0.8660714285714286,semiconductor device problem,281 -HB,wm1,207,277,2909,1,0,0,0,0,0,economic problem,2909 -HB,wm2,207,260,2942,1,0,0,0,0,0,economic problem,2942 -HB,wm3,207,260,2948,1,0,0,0,0,0,economic problem,2948 -HB,young1c,841,841,4089,0,0,1,0,1,0.8479064039408867,acoustics problem,4089 -HB,young2c,841,841,4089,0,0,1,0,1,0.8479064039408867,duplicate acoustics problem,4089 -HB,young3c,841,841,3988,1,0,1,0,0.9380362249761678,0.8071178900540197,acoustics problem,3988 -HB,young4c,841,841,4089,0,0,1,0,1,0.8004926108374384,acoustics problem,4089 -HB,zenios,2873,2873,1314,1,0,0,0,1,1,optimization problem,27191 -ATandT,onetone1,36057,36057,335552,1,0,0,0,0.0742956282869174,0.04331285111284008,frequency-domain circuit simulation problem,341088 -ATandT,onetone2,36057,36057,222596,1,0,0,0,0.1128682425939654,0.06622764699821082,frequency-domain circuit simulation problem,227628 -ATandT,pre2,659033,659033,5834044,1,0,0,0,0.3317760910564062,0.06536187196755584,frequency-domain circuit simulation problem,5959282 -ATandT,twotone,120750,120750,1206265,1,0,0,0,0.2445668624008717,0.1058542372000797,frequency-domain circuit simulation problem,1224224 -Averous,epb0,1794,1794,7764,1,0,1,0,0.5008375209380235,0,thermal problem,7764 -Averous,epb1,14734,14734,95053,1,0,1,0,0.7293915511896314,0,thermal problem,95053 -Averous,epb2,25228,25228,175027,1,0,1,0,0.6705385216189694,0,thermal problem,175027 -Averous,epb3,84617,84617,463625,1,0,1,0,0.6674107142857143,0.03155606214116852,thermal problem,463625 -Bai,af23560,23560,23560,460598,1,0,1,0,0.9973084203031934,0,computational fluid dynamics problem,484256 -Bai,bfwa398,398,398,3678,1,0,1,0,0.9890243902439024,0.9432926829268292,electromagnetics problem,3678 -Bai,bfwa62,62,62,450,1,0,1,0,0.9690721649484536,0.8144329896907216,electromagnetics problem,450 -Bai,bfwa782,782,782,7514,1,0,1,0,0.9916815210932858,0.9604872251931076,electromagnetics problem,7514 -Bai,bfwb398,398,398,2910,1,0,1,0,1,1,electromagnetics problem,2910 -Bai,bfwb62,62,62,342,1,0,1,0,1,1,electromagnetics problem,342 -Bai,bfwb782,782,782,5982,1,0,1,0,1,1,electromagnetics problem,5982 -Bai,bwm200,200,200,796,1,0,0,0,1,0.6644295302013423,chemical process simulation problem,796 -Bai,bwm2000,2000,2000,7996,1,0,0,0,1,0.6664442961974649,chemical process simulation problem,7996 -Bai,cdde1,961,961,4681,1,0,1,0,1,0,computational fluid dynamics problem sequence,4681 -Bai,cdde2,961,961,4681,1,0,1,0,1,0,subsequent computational fluid dynamics problem,4681 -Bai,cdde3,961,961,4681,1,0,1,0,1,0,subsequent computational fluid dynamics problem,4681 -Bai,cdde4,961,961,4681,1,0,1,0,1,0,subsequent computational fluid dynamics problem,4681 -Bai,cdde5,961,961,4681,1,0,1,0,1,0,subsequent computational fluid dynamics problem,4681 -Bai,cdde6,961,961,4681,1,0,1,0,1,0,subsequent computational fluid dynamics problem,4681 -Bai,ck104,104,104,992,1,0,1,0,1,0,2D/3D problem,992 -Bai,ck400,400,400,2860,1,0,1,0,0.9886178861788618,0.001626016260162602,2D/3D problem,2860 -Bai,ck656,656,656,3884,1,0,1,0,0.9913258983890955,0,2D/3D problem,3884 -Bai,dw1024,2048,2048,10114,1,0,1,0,0.9848747830399207,0.9481775353334987,electromagnetics problem,10114 -Bai,dw256A,512,512,2480,1,0,1,0,0.9796747967479674,0.9146341463414634,electromagnetics problem,2480 -Bai,dw256B,512,512,2500,1,0,1,0,0.9698189134808853,0.8832997987927566,electromagnetics problem,2500 -Bai,dw4096,8192,8192,41746,1,0,1,0,0.9626274065685164,0.9154199201287477,electromagnetics problem,41746 -Bai,lop163,163,163,935,1,0,0,0,0.4507772020725389,0,statistical/mathematical problem,935 -Bai,mhda416,416,416,8562,1,0,1,0,0.7675276752767528,0.2675871920009523,electromagnetics problem,8562 -Bai,mhdb416,416,416,2312,1,0,1,1,1,1,electromagnetics problem,2312 -Bai,odepa400,400,400,1201,1,0,1,0,0.9962546816479401,0.9937578027465668,2D/3D problem,1201 -Bai,odepb400,400,400,399,1,0,1,0,1,1,2D/3D problem,399 -Bai,olm100,100,100,396,1,0,1,0,0.668918918918919,0.3310810810810811,computational fluid dynamics problem,396 -Bai,olm1000,1000,1000,3996,1,0,1,0,0.6668891855807744,0.3331108144192256,computational fluid dynamics problem,3996 -Bai,olm2000,2000,2000,7996,1,0,1,0,0.6667778519012675,0.3332221480987325,computational fluid dynamics problem,7996 -Bai,olm500,500,500,1996,1,0,1,0,0.6671122994652406,0.3328877005347594,computational fluid dynamics problem,1996 -Bai,olm5000,5000,5000,19996,1,0,1,0,0.6667111229661243,0.3332888770338757,computational fluid dynamics problem,19996 -Bai,pde225,225,225,1065,1,0,1,0,1,0.5,2D/3D problem,1065 -Bai,pde2961,2961,2961,14585,1,0,1,0,1,0.5013764624913971,2D/3D problem,14585 -Bai,pde900,900,900,4380,1,0,1,0,1,0.5,2D/3D problem,4380 -Bai,qc2534,2534,2534,463360,0,0,1,0,1,0,electromagnetics problem,463360 -Bai,qc324,324,324,26730,0,0,1,0,1,0,electromagnetics problem,26730 -Bai,qh882,882,882,3354,1,0,0,0,0.9264705882352942,0,power network problem,3354 -Bai,rbsa480,480,480,17088,1,0,1,0,0.07485627126598615,0.0007039774727208729,robotics problem,17088 -Bai,rbsb480,480,480,17088,1,0,1,0,0.07568201818715166,0.0008213552361396304,robotics problem,17088 -Bai,rdb2048,2048,2048,12032,1,0,1,0,1,0.7948717948717948,computational fluid dynamics problem,12032 -Bai,rdb5000,5000,5000,29600,1,0,1,0,1,0.7967479674796748,computational fluid dynamics problem,29600 -Bai,rdb968,968,968,5632,1,0,1,0,1,0.7924528301886793,computational fluid dynamics problem,5632 -Bai,rw136,136,136,479,1,0,0,0,0.44258872651357,0.02922755741127349,statistical/mathematical problem,479 -Bai,rw496,496,496,1859,1,0,0,0,0.4690693921463152,0,statistical/mathematical problem,1859 -Bai,rw5151,5151,5151,20199,1,0,0,0,0.4902222882320907,0,statistical/mathematical problem,20199 -Bai,tub100,100,100,396,1,0,1,0,1,0,computational fluid dynamics problem,396 -Bai,tub1000,1000,1000,3996,1,0,1,0,1,0,computational fluid dynamics problem,3996 -Boeing,bcsstk34,588,588,21418,1,0,1,1,1,1,structural problem,21418 -Boeing,bcsstk35,30237,30237,1450163,1,0,1,0,1,1,structural problem,1450163 -Boeing,bcsstk36,23052,23052,1143140,1,0,1,1,1,1,structural problem,1143140 -Boeing,bcsstk37,25503,25503,1140977,1,0,1,0,1,1,structural problem,1140977 -Boeing,bcsstk38,8032,8032,355460,1,0,1,1,1,1,structural problem,355460 -Boeing,bcsstm34,588,588,24270,1,0,1,0,1,1,structural problem,24270 -Boeing,bcsstm35,30237,30237,20619,1,0,1,0,1,1,structural problem,20619 -Boeing,bcsstm36,23052,23052,320606,1,0,1,0,1,1,structural problem,320606 -Boeing,bcsstm37,25503,25503,15525,1,0,1,0,1,1,structural problem,15525 -Boeing,bcsstm38,8032,8032,10485,1,0,1,0,1,1,structural problem,10485 -Boeing,bcsstm39,46772,46772,46772,1,0,1,1,1,1,structural problem,46772 -Boeing,crystk01,4875,4875,315891,1,0,1,0,1,1,materials problem,315891 -Boeing,crystk02,13965,13965,968583,1,0,1,0,1,1,materials problem,968583 -Boeing,crystk03,24696,24696,1751178,1,0,1,0,1,1,materials problem,1751178 -Boeing,crystm01,4875,4875,105339,1,0,1,1,1,1,materials problem,105339 -Boeing,crystm02,13965,13965,322905,1,0,1,1,1,1,materials problem,322905 -Boeing,crystm03,24696,24696,583770,1,0,1,1,1,1,materials problem,583770 -Boeing,ct20stif,52329,52329,2600295,1,0,1,1,1,1,structural problem,2698463 -Boeing,msc00726,726,726,34518,1,0,1,1,1,1,structural problem,34518 -Boeing,msc01050,1050,1050,26198,1,0,1,1,1,1,structural problem,29156 -Boeing,msc01440,1440,1440,44998,1,0,1,1,1,1,structural problem,46270 -Boeing,msc04515,4515,4515,97707,1,0,1,1,1,1,structural problem,97707 -Boeing,msc10848,10848,10848,1229776,1,0,1,1,1,1,structural problem,1229778 -Boeing,msc23052,23052,23052,1142686,1,0,1,1,1,1,structural problem,1154814 -Boeing,nasa1824,1824,1824,39208,1,0,1,0,1,1,duplicate structural problem,39208 -Boeing,nasa2910,2910,2910,174296,1,1,1,0,1,1,duplicate structural problem,174296 -Boeing,nasa4704,4704,4704,104756,1,1,1,0,1,1,duplicate structural problem,104756 -Boeing,pcrystk02,13965,13965,968583,1,1,1,0,1,1,duplicate materials problem,968583 -Boeing,pcrystk03,24696,24696,1751178,1,1,1,0,1,1,duplicate materials problem,1751178 -Boeing,pct20stif,52329,52329,2698463,1,1,1,0,1,1,structural problem,2698463 -Boeing,pwtk,217918,217918,11524432,1,0,1,1,1,1,structural problem,11634424 -Bomhof,circuit_1,2624,2624,35823,1,0,0,0,0.999548641410646,0.2060602413263924,circuit simulation problem,35823 -Bomhof,circuit_2,4510,4510,21199,1,0,0,0,0.8065636603186011,0.4152593124925141,circuit simulation problem,21199 -Bomhof,circuit_3,12127,12127,48137,1,0,0,0,0.7700717835450027,0.3001104362230811,circuit simulation problem,48137 -Bomhof,circuit_4,80209,80209,307604,1,0,0,0,0.8292028705104302,0.3635452210926232,circuit simulation problem,307604 -Bova,rma10,46835,46835,2329092,1,0,1,0,1,0.2355549300354827,computational fluid dynamics problem,2374001 -Brethour,coater1,1348,1348,19457,1,0,1,0,0.5922916666666667,0.395,computational fluid dynamics problem,19457 -Brethour,coater2,9540,9540,207308,1,0,1,0,0.5941884735804569,0.1280874338061923,computational fluid dynamics problem,207308 -Brunetiere,thermal,3456,3456,66528,1,0,1,0,1,0.0730593607305936,thermal problem,66528 -Cote,mplate,5962,5962,142190,0,0,1,0,1,0.05311683354376486,acoustics problem,142190 -Cote,vibrobox,12328,12328,301700,1,0,1,0,1,1,acoustics problem,342828 -DRIVCAV,cavity01,317,317,7280,1,0,1,0,0.8037831733483908,0.5198237885462555,computational fluid dynamics problem sequence,7327 -DRIVCAV,cavity02,317,317,5923,1,0,1,0,0.8037831733483908,0,subsequent computational fluid dynamics problem,7327 -DRIVCAV,cavity03,317,317,7311,1,0,1,0,0.8037831733483908,0,subsequent computational fluid dynamics problem,7327 -DRIVCAV,cavity04,317,317,5923,1,0,1,0,0.8037831733483908,0,subsequent computational fluid dynamics problem,7327 -DRIVCAV,cavity05,1182,1182,32632,1,0,1,0,0.9049083605322621,0.6103499322813317,computational fluid dynamics problem sequence,32747 -DRIVCAV,cavity06,1182,1182,29675,1,0,1,0,0.9049083605322621,0,subsequent computational fluid dynamics problem,32747 -DRIVCAV,cavity07,1182,1182,32702,1,0,1,0,0.9049083605322621,0,subsequent computational fluid dynamics problem,32747 -DRIVCAV,cavity08,1182,1182,29675,1,0,1,0,0.9049083605322621,0,subsequent computational fluid dynamics problem,32747 -DRIVCAV,cavity09,1182,1182,32702,1,0,1,0,0.9049083605322621,0,subsequent computational fluid dynamics problem,32747 -DRIVCAV,cavity10,2597,2597,76171,1,0,1,0,0.9372682821986997,0.6400172395216033,computational fluid dynamics problem sequence,76367 -DRIVCAV,cavity11,2597,2597,71601,1,0,1,0,0.9372682821986997,0,subsequent computational fluid dynamics problem,76367 -DRIVCAV,cavity12,2597,2597,76258,1,0,1,0,0.9372682821986997,0,subsequent computational fluid dynamics problem,76367 -DRIVCAV,cavity13,2597,2597,71601,1,0,1,0,0.9372682821986997,0,subsequent computational fluid dynamics problem,76367 -DRIVCAV,cavity14,2597,2597,76258,1,0,1,0,0.9372682821986997,0,subsequent computational fluid dynamics problem,76367 -DRIVCAV,cavity15,2597,2597,71601,1,0,1,0,0.9372682821986997,0,subsequent computational fluid dynamics problem,76367 -DRIVCAV,cavity16,4562,4562,137887,1,0,1,0,0.9531982436361479,0.6542773036781541,computational fluid dynamics problem sequence,138187 -DRIVCAV,cavity17,4562,4562,131735,1,0,1,0,0.9531982436361479,0,subsequent computational fluid dynamics problem,138187 -DRIVCAV,cavity18,4562,4562,138040,1,0,1,0,0.9531982436361479,0,subsequent computational fluid dynamics problem,138187 -DRIVCAV,cavity19,4562,4562,131735,1,0,1,0,0.9531982436361479,0,subsequent computational fluid dynamics problem,138187 -DRIVCAV,cavity20,4562,4562,138040,1,0,1,0,0.9531982436361479,0,subsequent computational fluid dynamics problem,138187 -DRIVCAV,cavity21,4562,4562,131735,1,0,1,0,0.9531982436361479,0,subsequent computational fluid dynamics problem,138187 -DRIVCAV,cavity22,4562,4562,138040,1,0,1,0,0.9531982436361479,0,subsequent computational fluid dynamics problem,138187 -DRIVCAV,cavity23,4562,4562,131735,1,0,1,0,0.9531982436361479,0,subsequent computational fluid dynamics problem,138187 -DRIVCAV,cavity24,4562,4562,138040,1,0,1,0,0.9531982436361479,0,subsequent computational fluid dynamics problem,138187 -DRIVCAV,cavity25,4562,4562,131735,1,0,1,0,0.9531982436361479,0,subsequent computational fluid dynamics problem,138187 -DRIVCAV,cavity26,4562,4562,138040,1,0,1,0,0.9531982436361479,0,subsequent computational fluid dynamics problem,138187 -FIDAP,ex1,216,216,4317,1,0,1,0,1,0.9980657640232108,computational fluid dynamics problem,4352 -FIDAP,ex10,2410,2410,54840,1,0,1,1,1,1,computational fluid dynamics problem,54840 -FIDAP,ex10hs,2548,2548,57308,1,0,1,1,1,1,computational fluid dynamics problem,57308 -FIDAP,ex11,16614,16614,1096948,1,0,1,0,1,0.9999981487206734,computational fluid dynamics problem,1096948 -FIDAP,ex12,3973,3973,79077,1,0,1,0,1,1,computational fluid dynamics problem,80211 -FIDAP,ex13,2568,2568,75628,1,0,1,1,1,1,computational fluid dynamics problem,75628 -FIDAP,ex14,3251,3251,65875,1,0,1,0,1,1,computational fluid dynamics problem,66775 -FIDAP,ex15,6867,6867,98671,1,0,1,1,1,1,computational fluid dynamics problem,98671 -FIDAP,ex18,5773,5773,71701,1,0,1,0,1,0.4973910933139182,computational fluid dynamics problem,71805 -FIDAP,ex19,12005,12005,259577,1,0,1,0,1,0.4284006268883396,computational fluid dynamics problem,259879 -FIDAP,ex2,441,441,26839,1,0,1,0,1,1,computational fluid dynamics problem,26839 -FIDAP,ex20,2203,2203,67830,1,0,1,0,1,0.9705407160221662,computational fluid dynamics problem,69981 -FIDAP,ex21,656,656,18964,1,0,1,0,1,0.9422327996538296,computational fluid dynamics problem,19144 -FIDAP,ex22,839,839,22460,1,0,1,0,1,0.88617551561714,computational fluid dynamics problem,22715 -FIDAP,ex23,1409,1409,42760,1,0,1,0,1,0.9386855719486492,computational fluid dynamics problem,43703 -FIDAP,ex24,2283,2283,47901,1,0,1,0,1,0.862361129122898,computational fluid dynamics problem,48737 -FIDAP,ex25,848,848,24369,1,0,1,0,1,0.9755481671646816,computational fluid dynamics problem,24612 -FIDAP,ex26,2163,2163,74464,1,0,1,0,1,0.7325380026938618,computational fluid dynamics problem,94033 -FIDAP,ex27,974,974,37652,1,0,1,0,1,0.5304041225928939,computational fluid dynamics problem,40782 -FIDAP,ex28,2603,2603,77031,1,0,1,0,1,0.9877623772912288,computational fluid dynamics problem,77781 -FIDAP,ex29,2870,2870,23754,1,0,1,0,1,0.9963608504117986,computational fluid dynamics problem,23754 -FIDAP,ex3,1821,1821,52685,1,0,1,1,1,1,computational fluid dynamics problem,52685 -FIDAP,ex31,3909,3909,91223,1,0,1,0,1,0.7266897116346767,computational fluid dynamics problem,115357 -FIDAP,ex32,1159,1159,11047,1,0,1,0,1,1,computational fluid dynamics problem,11343 -FIDAP,ex33,1733,1733,22189,1,0,1,1,1,1,computational fluid dynamics problem,22189 -FIDAP,ex35,19716,19716,227872,1,0,1,0,1,0.4608754972232364,computational fluid dynamics problem,228208 -FIDAP,ex36,3079,3079,53099,1,0,1,0,1,0.9952497822816879,computational fluid dynamics problem,53843 -FIDAP,ex37,3565,3565,67591,1,0,1,0,1,0.9536750695030144,computational fluid dynamics problem,67591 -FIDAP,ex4,1601,1601,31849,1,0,1,0,1,1,computational fluid dynamics problem,32299 -FIDAP,ex40,7740,7740,456188,1,0,1,0,1,0.7709917560940942,computational fluid dynamics problem,458012 -FIDAP,ex5,27,27,279,1,0,1,1,1,1,computational fluid dynamics problem,279 -FIDAP,ex6,1651,1651,49062,1,0,1,0,1,0.9792406332233408,computational fluid dynamics problem,49533 -FIDAP,ex7,1633,1633,46626,1,0,1,0,1,0.8240392950014447,computational fluid dynamics problem,54543 -FIDAP,ex8,3096,3096,90841,1,0,1,0,1,0.4310900906034532,computational fluid dynamics problem,106344 -FIDAP,ex9,3363,3363,99471,1,0,1,1,1,1,computational fluid dynamics problem,99471 -Gaertner,big,13209,13209,91465,1,0,0,0,1,0.999974442854222,directed weighted graph,91465 -Gaertner,nopoly,10774,10774,70842,1,0,0,0,1,1,undirected weighted graph,70842 -Gaertner,pesa,11738,11738,79566,1,0,0,0,1,0.999970513652179,directed weighted graph,79566 -Garon,garon1,3175,3175,84723,1,0,1,0,1,0.6720847366622736,computational fluid dynamics problem,88927 -Garon,garon2,13535,13535,373235,1,0,1,0,1,0.6726819817326322,computational fluid dynamics problem,390607 -Goodwin,goodwin,7320,7320,324772,1,0,1,0,0.6353114022282706,0.001199255331506196,computational fluid dynamics problem,324784 -Goodwin,rim,22560,22560,1014951,1,0,1,0,0.6390640220938991,0,computational fluid dynamics problem,1014951 -Graham,graham1,9035,9035,335472,1,0,1,0,0.718197839525901,0.0004322586733311619,computational fluid dynamics problem,335504 -Grund,b1_ss,7,7,15,1,0,0,0,0,0,chemical process simulation problem,15 -Grund,b2_ss,1089,1089,3895,1,0,0,0,0.0146710837671557,0.0005136106831022085,chemical process simulation problem,4228 -Grund,b_dyn,1089,1089,4144,1,0,0,0,0.01454716095729704,0.0004828585224529213,chemical process simulation problem,4264 -Grund,bayer01,57735,57735,275094,1,0,0,0,0.0002304047924196823,2.908118011428904e-05,chemical process simulation problem,277774 -Grund,bayer02,13935,13935,63307,1,0,0,0,0.001884570082449941,0.000284346713425904,chemical process simulation problem,63679 -Grund,bayer03,6747,6747,29195,1,0,0,0,0.003132397173723459,0.0002740852405097985,chemical process simulation problem,56196 -Grund,bayer04,20545,20545,85537,1,0,0,0,0.001584088708967702,0.0002104352502425851,chemical process simulation problem,159082 -Grund,bayer05,3268,3268,20712,1,0,0,0,0.006682474671265359,0.00135200386286818,chemical process simulation problem,27836 -Grund,bayer06,3008,3008,20715,1,0,0,0,0.006745484877058098,0.001351808043257857,chemical process simulation problem,27576 -Grund,bayer07,3268,3268,20963,1,0,0,0,0.006682474671265359,0.0013358141310052,chemical process simulation problem,27836 -Grund,bayer08,3008,3008,20698,1,0,0,0,0.006745484877058098,0.001352918438345574,chemical process simulation problem,27576 -Grund,bayer09,3083,3083,11767,1,0,0,0,0.02112012068640392,0.0001700246535747683,chemical process simulation problem,21216 -Grund,bayer10,13436,13436,71594,1,0,0,0,0.002528365095919851,0.0002234917796929782,chemical process simulation problem,94926 -Grund,d_dyn,87,87,230,1,0,0,0,0.07692307692307693,0.01769911504424779,chemical process simulation problem,238 -Grund,d_dyn1,87,87,232,1,0,0,0,0.07692307692307693,0.01754385964912281,chemical process simulation problem,238 -Grund,d_ss,53,53,144,1,0,0,0,0.04137931034482759,0,chemical process simulation problem,149 -Grund,meg1,2904,2904,58142,1,0,0,0,0.002374602082078637,0.001996042329863202,circuit simulation problem,58142 -Grund,meg4,5860,5860,25258,1,0,0,0,1,1,circuit simulation problem,46842 -Grund,poli,4008,4008,8188,1,0,0,0,0.0291866028708134,0.002392344497607655,economic problem,8188 -Grund,poli_large,15575,15575,33033,1,0,0,0,0.003543059603405909,0.0004582426394776034,economic problem,33074 -Gset,G1,800,800,38352,1,1,0,0,1,1,undirected random graph,38352 -Gset,G10,800,800,38352,1,0,0,0,1,1,undirected weighted random graph,38352 -Gset,G11,800,800,3200,1,0,0,0,1,1,undirected weighted random graph,3200 -Gset,G12,800,800,3200,1,0,0,0,1,1,undirected weighted random graph,3200 -Gset,G13,800,800,3200,1,0,0,0,1,1,undirected weighted random graph,3200 -Gset,G14,800,800,9388,1,1,0,0,1,1,duplicate undirected random graph,9388 -Gset,G15,800,800,9322,1,1,0,0,1,1,duplicate undirected random graph,9322 -Gset,G16,800,800,9344,1,1,0,0,1,1,duplicate undirected random graph,9344 -Gset,G17,800,800,9334,1,1,0,0,1,1,duplicate undirected random graph,9334 -Gset,G18,800,800,9388,1,0,0,0,1,1,undirected weighted random graph,9388 -Gset,G19,800,800,9322,1,0,0,0,1,1,undirected weighted random graph,9322 -Gset,G2,800,800,38352,1,1,0,0,1,1,undirected random graph,38352 -Gset,G20,800,800,9344,1,0,0,0,1,1,undirected weighted random graph,9344 -Gset,G21,800,800,9334,1,0,0,0,1,1,undirected weighted random graph,9334 -Gset,G22,2000,2000,39980,1,1,0,0,1,1,undirected random graph,39980 -Gset,G23,2000,2000,39980,1,1,0,0,1,1,undirected random graph,39980 -Gset,G24,2000,2000,39980,1,1,0,0,1,1,undirected random graph,39980 -Gset,G25,2000,2000,39980,1,1,0,0,1,1,undirected random graph,39980 -Gset,G26,2000,2000,39980,1,1,0,0,1,1,undirected random graph,39980 -Gset,G27,2000,2000,39980,1,0,0,0,1,1,undirected weighted random graph,39980 -Gset,G28,2000,2000,39980,1,0,0,0,1,1,undirected weighted random graph,39980 -Gset,G29,2000,2000,39980,1,0,0,0,1,1,undirected weighted random graph,39980 -Gset,G3,800,800,38352,1,1,0,0,1,1,undirected random graph,38352 -Gset,G30,2000,2000,39980,1,0,0,0,1,1,undirected weighted random graph,39980 -Gset,G31,2000,2000,39980,1,0,0,0,1,1,undirected weighted random graph,39980 -Gset,G32,2000,2000,8000,1,0,0,0,1,1,undirected weighted random graph,8000 -Gset,G33,2000,2000,8000,1,0,0,0,1,1,undirected weighted random graph,8000 -Gset,G34,2000,2000,8000,1,0,0,0,1,1,undirected weighted random graph,8000 -Gset,G35,2000,2000,23556,1,1,0,0,1,1,duplicate undirected random graph,23556 -Gset,G36,2000,2000,23532,1,1,0,0,1,1,duplicate undirected random graph,23532 -Gset,G37,2000,2000,23570,1,1,0,0,1,1,duplicate undirected random graph,23570 -Gset,G38,2000,2000,23558,1,1,0,0,1,1,duplicate undirected random graph,23558 -Gset,G39,2000,2000,23556,1,0,0,0,1,1,undirected weighted random graph,23556 -Gset,G4,800,800,38352,1,1,0,0,1,1,undirected random graph,38352 -Gset,G40,2000,2000,23532,1,0,0,0,1,1,undirected weighted random graph,23532 -Gset,G41,2000,2000,23570,1,0,0,0,1,1,undirected weighted random graph,23570 -Gset,G42,2000,2000,23558,1,0,0,0,1,1,undirected weighted random graph,23558 -Gset,G43,1000,1000,19980,1,1,0,0,1,1,undirected random graph,19980 -Gset,G44,1000,1000,19980,1,1,0,0,1,1,undirected random graph,19980 -Gset,G45,1000,1000,19980,1,1,0,0,1,1,undirected random graph,19980 -Gset,G46,1000,1000,19980,1,1,0,0,1,1,undirected random graph,19980 -Gset,G47,1000,1000,19980,1,1,0,0,1,1,undirected random graph,19980 -Gset,G48,3000,3000,12000,1,1,0,0,1,1,undirected random graph,12000 -Gset,G49,3000,3000,12000,1,1,0,0,1,1,undirected random graph,12000 -Gset,G5,800,800,38352,1,1,0,0,1,1,undirected random graph,38352 -Gset,G50,3000,3000,12000,1,1,0,0,1,1,undirected random graph,12000 -Gset,G51,1000,1000,11818,1,1,0,0,1,1,undirected random graph,11818 -Gset,G52,1000,1000,11832,1,1,0,0,1,1,undirected random graph,11832 -Gset,G53,1000,1000,11828,1,1,0,0,1,1,undirected random graph,11828 -Gset,G54,1000,1000,11832,1,1,0,0,1,1,undirected random graph,11832 -Gset,G55,5000,5000,24996,1,1,0,0,1,1,duplicate undirected random graph,24996 -Gset,G56,5000,5000,24996,1,0,0,0,1,1,undirected weighted random graph,24996 -Gset,G57,5000,5000,20000,1,0,0,0,1,1,undirected weighted random graph,20000 -Gset,G58,5000,5000,59140,1,1,0,0,1,1,duplicate undirected random graph,59140 -Gset,G59,5000,5000,59140,1,0,0,0,1,1,undirected weighted random graph,59140 -Gset,G6,800,800,38352,1,0,0,0,1,1,undirected weighted random graph,38352 -Gset,G60,7000,7000,34296,1,1,0,0,1,1,duplicate undirected random graph,34296 -Gset,G61,7000,7000,34296,1,0,0,0,1,1,undirected weighted random graph,34296 -Gset,G62,7000,7000,28000,1,0,0,0,1,1,undirected weighted random graph,28000 -Gset,G63,7000,7000,82918,1,1,0,0,1,1,duplicate undirected random graph,82918 -Gset,G64,7000,7000,82918,1,0,0,0,1,1,undirected weighted random graph,82918 -Gset,G65,8000,8000,32000,1,0,0,0,1,1,undirected weighted random graph,32000 -Gset,G66,9000,9000,36000,1,0,0,0,1,1,undirected weighted random graph,36000 -Gset,G67,10000,10000,40000,1,0,0,0,1,1,undirected weighted random graph,40000 -Gset,G7,800,800,38352,1,0,0,0,1,1,undirected weighted random graph,38352 -Gset,G8,800,800,38352,1,0,0,0,1,1,undirected weighted random graph,38352 -Gset,G9,800,800,38352,1,0,0,0,1,1,undirected weighted random graph,38352 -Gupta,gupta1,31802,31802,2164210,1,1,0,0,1,1,optimization problem,2164210 -Gupta,gupta2,62064,62064,4248286,1,1,0,0,1,1,optimization problem,4248286 -Gupta,gupta3,16783,16783,9323427,1,1,0,0,1,1,optimization problem,9323427 -Hamm,add20,2395,2395,13151,1,0,0,0,1,0.5265898103384158,circuit simulation problem,17319 -Hamm,add32,4960,4960,19848,1,0,0,0,1,0.3052122514777001,circuit simulation problem,23884 -Hamm,bcircuit,68902,68902,375558,1,0,0,0,1,0.9084315976207868,circuit simulation problem,375558 -Hamm,hcircuit,105676,105676,513072,1,0,0,0,0.9999410961997232,0.1951139297670355,circuit simulation problem,513072 -Hamm,memplus,17758,17758,99147,1,0,0,0,1,0.4962095614886533,circuit simulation problem,126150 -Hamm,scircuit,170998,170998,958936,1,0,0,0,0.9999467019956295,0.8008355096685118,circuit simulation problem,958936 -Hollinger,g7jac010,2880,2880,18229,1,0,0,0,0.07102357505222322,0,economic problem,19635 -Hollinger,g7jac010sc,2880,2880,18229,1,0,0,0,0.07102357505222322,0,economic problem,19635 -Hollinger,g7jac020,5850,5850,42568,1,0,0,0,0.06184526063359838,0,economic problem,45465 -Hollinger,g7jac020sc,5850,5850,42568,1,0,0,0,0.06184526063359838,0,economic problem,45465 -Hollinger,g7jac040,11790,11790,107383,1,0,0,0,0.04830823961664447,0,economic problem,114671 -Hollinger,g7jac040sc,11790,11790,107383,1,0,0,0,0.04830823961664447,0,economic problem,114671 -Hollinger,g7jac050sc,14760,14760,145157,1,0,0,0,0.04349647420233192,0,economic problem,157990 -Hollinger,g7jac060,17730,17730,183325,1,0,0,0,0.04035864774282543,0,economic problem,203316 -Hollinger,g7jac060sc,17730,17730,183325,1,0,0,0,0.04035864774282543,0,economic problem,203316 -Hollinger,g7jac080,23670,23670,259648,1,0,0,0,0.03703210435580416,0,economic problem,293976 -Hollinger,g7jac080sc,23670,23670,259648,1,0,0,0,0.03703210435580416,0,economic problem,293976 -Hollinger,g7jac100,29610,29610,335972,1,0,0,0,0.03529318979455026,0,economic problem,384636 -Hollinger,g7jac100sc,29610,29610,335972,1,0,0,0,0.03529318979455026,0,economic problem,384636 -Hollinger,g7jac120,35550,35550,412306,1,0,0,0,0.0342243022108217,0,economic problem,475296 -Hollinger,g7jac120sc,35550,35550,412306,1,0,0,0,0.0342243022108217,0,economic problem,475296 -Hollinger,g7jac140,41490,41490,488633,1,0,0,0,0.03350074170680273,0,economic problem,565956 -Hollinger,g7jac140sc,41490,41490,488633,1,0,0,0,0.03350074170680273,0,economic problem,565956 -Hollinger,g7jac160,47430,47430,564952,1,0,0,0,0.03297843351620031,0,economic problem,656616 -Hollinger,g7jac160sc,47430,47430,564952,1,0,0,0,0.03297843351620031,0,economic problem,656616 -Hollinger,g7jac180,53370,53370,641290,1,0,0,0,0.03258366406977314,0,economic problem,747276 -Hollinger,g7jac180sc,53370,53370,641290,1,0,0,0,0.03258366406977314,0,economic problem,747276 -Hollinger,g7jac200,59310,59310,717620,1,0,0,0,0.03227480202305086,0,economic problem,837936 -Hollinger,g7jac200sc,59310,59310,717620,1,0,0,0,0.03227480202305086,0,economic problem,837936 -Hollinger,jan99jac020,6774,6774,33744,1,0,0,0,0.003759634062284604,0,economic problem,38692 -Hollinger,jan99jac020sc,6774,6774,33744,1,0,0,0,0.003759634062284604,0,economic problem,38692 -Hollinger,jan99jac040,13694,13694,72734,1,0,0,0,0.003470816220281136,0,economic problem,82842 -Hollinger,jan99jac040sc,13694,13694,72734,1,0,0,0,0.003470816220281136,0,economic problem,82842 -Hollinger,jan99jac060,20614,20614,111903,1,0,0,0,0.003378124765408002,0,economic problem,127182 -Hollinger,jan99jac060sc,20614,20614,111903,1,0,0,0,0.003378124765408002,0,economic problem,127182 -Hollinger,jan99jac080,27534,27534,151063,1,0,0,0,0.003333611134261188,0,economic problem,171522 -Hollinger,jan99jac080sc,27534,27534,151063,1,0,0,0,0.003333611134261188,0,economic problem,171522 -Hollinger,jan99jac100,34454,34454,190224,1,0,0,0,0.003307461633445052,0,economic problem,215862 -Hollinger,jan99jac100sc,34454,34454,190224,1,0,0,0,0.003307461633445052,0,economic problem,215862 -Hollinger,jan99jac120,41374,41374,229385,1,0,0,0,0.003290255360374358,0,economic problem,260202 -Hollinger,jan99jac120sc,41374,41374,229385,1,0,0,0,0.003290255360374358,0,economic problem,260202 -Hollinger,mark3jac020,9129,9129,52883,1,0,0,0,0.07363006419249245,0.01351166293878669,economic problem,56175 -Hollinger,mark3jac020sc,9129,9129,52883,1,0,0,0,0.07363006419249245,0.007349249098461679,economic problem,56175 -Hollinger,mark3jac040,18289,18289,106803,1,0,0,0,0.07382338721543732,0.01389985784236298,economic problem,113435 -Hollinger,mark3jac040sc,18289,18289,106803,1,0,0,0,0.07382338721543732,0.01173364623056615,economic problem,113435 -Hollinger,mark3jac060,27449,27449,160723,1,0,0,0,0.07388687991287715,0.01402730528871371,economic problem,170695 -Hollinger,mark3jac060sc,27449,27449,160723,1,0,0,0,0.07388687991287715,0.0121839735039789,economic problem,170695 -Hollinger,mark3jac080,36609,36609,214643,1,0,0,0,0.07391845139171971,0.01409066941898426,economic problem,227955 -Hollinger,mark3jac080sc,36609,36609,214643,1,0,0,0,0.07391845139171971,0.0125985842018466,economic problem,227955 -Hollinger,mark3jac100,45769,45769,268563,1,0,0,0,0.07393733869014309,0.01412857360574466,economic problem,285215 -Hollinger,mark3jac100sc,45769,45769,268563,1,0,0,0,0.07393733869014309,0.01261351717213372,economic problem,285215 -Hollinger,mark3jac120,54929,54929,322483,1,0,0,0,0.07394990714529154,0.01415379562098285,economic problem,342475 -Hollinger,mark3jac120sc,54929,54929,322483,1,0,0,0,0.07394990714529154,0.01276529035436745,economic problem,342475 -Hollinger,mark3jac140,64089,64089,376395,1,0,0,0,0.07395887333678935,0.01417215070061332,economic problem,399735 -Hollinger,mark3jac140sc,64089,64089,376395,1,0,0,0,0.07395887333678935,0.0128291219789848,economic problem,399735 -LPnetlib,lp_25fv47,821,1876,10705,1,0,0,0,0,0,linear programming problem,10705 -LPnetlib,lp_80bau3b,2262,12061,23264,1,0,0,0,0,0,linear programming problem,23264 -LPnetlib,lp_adlittle,56,138,424,1,0,0,0,0,0,linear programming problem,424 -LPnetlib,lp_afiro,27,51,102,1,0,0,0,0,0,linear programming problem,102 -LPnetlib,lp_agg,488,615,2862,1,0,0,0,0,0,linear programming problem,2862 -LPnetlib,lp_agg2,516,758,4740,1,0,0,0,0,0,linear programming problem,4740 -LPnetlib,lp_agg3,516,758,4756,1,0,0,0,0,0,linear programming problem,4756 -LPnetlib,lp_bandm,305,472,2494,1,0,0,0,0,0,linear programming problem,2494 -LPnetlib,lp_beaconfd,173,295,3408,1,0,0,0,0,0,linear programming problem,3408 -LPnetlib,lp_blend,74,114,522,1,0,0,0,0,0,linear programming problem,522 -LPnetlib,lp_bnl1,643,1586,5532,1,0,0,0,0,0,linear programming problem,5532 -LPnetlib,lp_bnl2,2324,4486,14996,1,0,0,0,0,0,linear programming problem,14996 -LPnetlib,lp_bore3d,233,334,1448,1,0,0,0,0,0,linear programming problem,1448 -LPnetlib,lp_brandy,220,303,2202,1,0,0,0,0,0,linear programming problem,2202 -LPnetlib,lp_capri,271,482,1896,1,0,0,0,0,0,linear programming problem,1896 -LPnetlib,lp_cre_a,3516,7248,18168,1,0,0,0,0,0,linear programming problem,18168 -LPnetlib,lp_cre_b,9648,77137,260785,1,0,0,0,0,0,linear programming problem,260785 -LPnetlib,lp_cre_c,3068,6411,15977,1,0,0,0,0,0,linear programming problem,15977 -LPnetlib,lp_cre_d,8926,73948,246614,1,0,0,0,0,0,linear programming problem,246614 -LPnetlib,lp_cycle,1903,3371,21234,1,0,0,0,0,0,linear programming problem,21234 -LPnetlib,lp_czprob,929,3562,10708,1,0,0,0,0,0,linear programming problem,10708 -LPnetlib,lp_d2q06c,2171,5831,33081,1,0,0,0,0,0,linear programming problem,33081 -LPnetlib,lp_d6cube,415,6184,37704,1,0,0,0,0,0,linear programming problem,37704 -LPnetlib,lp_degen2,444,757,4201,1,0,0,0,0,0,linear programming problem,4201 -LPnetlib,lp_degen3,1503,2604,25432,1,0,0,0,0,0,linear programming problem,25432 -LPnetlib,lp_dfl001,6071,12230,35632,1,0,0,0,0,0,linear programming problem,35632 -LPnetlib,lp_e226,223,472,2768,1,0,0,0,0,0,linear programming problem,2768 -LPnetlib,lp_etamacro,400,816,2537,1,0,0,0,0,0,linear programming problem,2537 -LPnetlib,lp_fffff800,524,1028,6401,1,0,0,0,0,0,linear programming problem,6401 -LPnetlib,lp_finnis,497,1064,2760,1,0,0,0,0,0,linear programming problem,2760 -LPnetlib,lp_fit1d,24,1049,13427,1,0,0,0,0,0,linear programming problem,13427 -LPnetlib,lp_fit1p,627,1677,9868,1,0,0,0,0,0,linear programming problem,9868 -LPnetlib,lp_fit2d,25,10524,129042,1,0,0,0,0,0,linear programming problem,129042 -LPnetlib,lp_fit2p,3000,13525,50284,1,0,0,0,0,0,linear programming problem,50284 -LPnetlib,lp_ganges,1309,1706,6937,1,0,0,0,0,0,linear programming problem,6937 -LPnetlib,lp_gfrd_pnc,616,1160,2445,1,0,0,0,0,0,linear programming problem,2445 -LPnetlib,lp_greenbea,2392,5598,31070,1,0,0,0,0,0,linear programming problem,31070 -LPnetlib,lp_greenbeb,2392,5598,31070,1,0,0,0,0,0,linear programming problem,31070 -LPnetlib,lp_grow15,300,645,5620,1,0,0,0,0,0,linear programming problem,5620 -LPnetlib,lp_grow22,440,946,8252,1,0,0,0,0,0,linear programming problem,8252 -LPnetlib,lp_grow7,140,301,2612,1,0,0,0,0,0,linear programming problem,2612 -LPnetlib,lp_israel,174,316,2443,1,0,0,0,0,0,linear programming problem,2443 -LPnetlib,lp_kb2,43,68,313,1,0,0,0,0,0,linear programming problem,313 -LPnetlib,lp_ken_07,2426,3602,8404,1,0,0,0,0,0,linear programming problem,8404 -LPnetlib,lp_ken_11,14694,21349,49058,1,0,0,0,0,0,linear programming problem,49058 -LPnetlib,lp_ken_13,28632,42659,97246,1,0,0,0,0,0,linear programming problem,97246 -LPnetlib,lp_ken_18,105127,154699,358171,1,0,0,0,0,0,linear programming problem,358171 -LPnetlib,lp_lotfi,153,366,1136,1,0,0,0,0,0,linear programming problem,1136 -LPnetlib,lp_maros,846,1966,10137,1,0,0,0,0,0,linear programming problem,10137 -LPnetlib,lp_maros_r7,3136,9408,144848,1,0,0,0,0,0,linear programming problem,144848 -LPnetlib,lp_modszk1,687,1620,3168,1,0,0,0,0,0,linear programming problem,3168 -LPnetlib,lp_osa_07,1118,25067,144812,1,0,0,0,0,0,linear programming problem,144812 -LPnetlib,lp_osa_14,2337,54797,317097,1,0,0,0,0,0,linear programming problem,317097 -LPnetlib,lp_osa_30,4350,104374,604488,1,0,0,0,0,0,linear programming problem,604488 -LPnetlib,lp_osa_60,10280,243246,1408073,1,0,0,0,0,0,linear programming problem,1408073 -LPnetlib,lp_pds_02,2953,7716,16571,1,0,0,0,0,0,linear programming problem,16571 -LPnetlib,lp_pds_06,9881,29351,63220,1,0,0,0,0,0,linear programming problem,63220 -LPnetlib,lp_pds_10,16558,49932,107605,1,0,0,0,0,0,linear programming problem,107605 -LPnetlib,lp_pds_20,33874,108175,232647,1,0,0,0,0,0,linear programming problem,232647 -LPnetlib,lp_perold,625,1506,6148,1,0,0,0,0,0,linear programming problem,6148 -LPnetlib,lp_pilot,1441,4860,44375,1,0,0,0,0,0,linear programming problem,44375 -LPnetlib,lp_pilot4,410,1123,5264,1,0,0,0,0,0,linear programming problem,5264 -LPnetlib,lp_pilot87,2030,6680,74949,1,0,0,0,0,0,linear programming problem,74949 -LPnetlib,lp_pilot_ja,940,2267,14977,1,0,0,0,0,0,linear programming problem,14977 -LPnetlib,lp_pilot_we,722,2928,9265,1,0,0,0,0,0,linear programming problem,9265 -LPnetlib,lp_pilotnov,975,2446,13331,1,0,0,0,0,0,linear programming problem,13331 -LPnetlib,lp_qap12,3192,8856,38304,1,0,0,0,0,0,linear programming problem,38304 -LPnetlib,lp_qap15,6330,22275,94950,1,0,0,0,0,0,linear programming problem,94950 -LPnetlib,lp_qap8,912,1632,7296,1,0,0,0,0,0,linear programming problem,7296 -LPnetlib,lp_recipe,91,204,687,1,0,0,0,0,0,linear programming problem,687 -LPnetlib,lp_sc105,105,163,340,1,0,0,0,0,0,linear programming problem,340 -LPnetlib,lp_sc205,205,317,665,1,0,0,0,0,0,linear programming problem,665 -LPnetlib,lp_sc50a,50,78,160,1,0,0,0,0,0,linear programming problem,160 -LPnetlib,lp_sc50b,50,78,148,1,0,0,0,0,0,linear programming problem,148 -LPnetlib,lp_scagr25,471,671,1725,1,0,0,0,0,0,linear programming problem,1725 -LPnetlib,lp_scagr7,129,185,465,1,0,0,0,0,0,linear programming problem,465 -LPnetlib,lp_scfxm1,330,600,2732,1,0,0,0,0,0,linear programming problem,2732 -LPnetlib,lp_scfxm2,660,1200,5469,1,0,0,0,0,0,linear programming problem,5469 -LPnetlib,lp_scfxm3,990,1800,8206,1,0,0,0,0,0,linear programming problem,8206 -LPnetlib,lp_scorpion,388,466,1534,1,0,0,0,0,0,linear programming problem,1534 -LPnetlib,lp_scrs8,490,1275,3288,1,0,0,0,0,0,linear programming problem,3288 -LPnetlib,lp_scsd1,77,760,2388,1,0,0,0,0,0,linear programming problem,2388 -LPnetlib,lp_scsd6,147,1350,4316,1,0,0,0,0,0,linear programming problem,4316 -LPnetlib,lp_scsd8,397,2750,8584,1,0,0,0,0,0,linear programming problem,8584 -LPnetlib,lp_sctap1,300,660,1872,1,0,0,0,0,0,linear programming problem,1872 -LPnetlib,lp_sctap2,1090,2500,7334,1,0,0,0,0,0,linear programming problem,7334 -LPnetlib,lp_sctap3,1480,3340,9734,1,0,0,0,0,0,linear programming problem,9734 -LPnetlib,lp_share1b,117,253,1179,1,0,0,0,0,0,linear programming problem,1179 -LPnetlib,lp_share2b,96,162,777,1,0,0,0,0,0,linear programming problem,777 -LPnetlib,lp_shell,536,1777,3558,1,0,0,0,0,0,linear programming problem,3558 -LPnetlib,lp_ship04l,402,2166,6380,1,0,0,0,0,0,linear programming problem,6380 -LPnetlib,lp_ship04s,402,1506,4400,1,0,0,0,0,0,linear programming problem,4400 -LPnetlib,lp_ship08l,778,4363,12882,1,0,0,0,0,0,linear programming problem,12882 -LPnetlib,lp_ship08s,778,2467,7194,1,0,0,0,0,0,linear programming problem,7194 -LPnetlib,lp_ship12l,1151,5533,16276,1,0,0,0,0,0,linear programming problem,16276 -LPnetlib,lp_ship12s,1151,2869,8284,1,0,0,0,0,0,linear programming problem,8284 -LPnetlib,lp_sierra,1227,2735,8001,1,0,0,0,0,0,linear programming problem,8001 -LPnetlib,lp_stair,356,614,4003,1,0,0,0,0,0,linear programming problem,4003 -LPnetlib,lp_standata,359,1274,3230,1,0,0,0,0,0,linear programming problem,3230 -LPnetlib,lp_standgub,361,1383,3338,1,0,0,0,0,0,linear programming problem,3339 -LPnetlib,lp_standmps,467,1274,3878,1,0,0,0,0,0,linear programming problem,3878 -LPnetlib,lp_stocfor1,117,165,501,1,0,0,0,0,0,linear programming problem,501 -LPnetlib,lp_stocfor2,2157,3045,9357,1,0,0,0,0,0,linear programming problem,9357 -LPnetlib,lp_stocfor3,16675,23541,72721,1,0,0,0,0,0,linear programming problem,76473 -LPnetlib,lp_truss,1000,8806,27836,1,0,0,0,0,0,linear programming problem,27836 -LPnetlib,lp_tuff,333,628,4561,1,0,0,0,0,0,linear programming problem,4561 -LPnetlib,lp_vtp_base,198,346,1051,1,0,0,0,0,0,linear programming problem,1051 -LPnetlib,lp_wood1p,244,2595,70216,1,0,0,0,0,0,linear programming problem,70216 -LPnetlib,lp_woodw,1098,8418,37487,1,0,0,0,0,0,linear programming problem,37487 -LPnetlib,lpi_bgdbg1,348,629,1662,1,0,0,0,0,0,linear programming problem,1662 -LPnetlib,lpi_bgetam,400,816,2537,1,0,0,0,0,0,linear programming problem,2537 -LPnetlib,lpi_bgindy,2671,10880,66266,1,0,0,0,0,0,linear programming problem,66266 -LPnetlib,lpi_bgprtr,20,40,70,1,0,0,0,0,0,linear programming problem,70 -LPnetlib,lpi_box1,231,261,651,1,0,0,0,0,0,linear programming problem,651 -LPnetlib,lpi_ceria3d,3576,4400,21178,1,0,0,0,0,0,linear programming problem,21178 -LPnetlib,lpi_chemcom,288,744,1590,1,0,0,0,0,0,linear programming problem,1590 -LPnetlib,lpi_cplex1,3005,5224,10947,1,0,0,0,0,0,linear programming problem,10947 -LPnetlib,lpi_cplex2,224,378,1215,1,0,0,0,0,0,linear programming problem,1215 -LPnetlib,lpi_ex72a,197,215,467,1,0,0,0,0,0,linear programming problem,467 -LPnetlib,lpi_ex73a,193,211,457,1,0,0,0,0,0,linear programming problem,457 -LPnetlib,lpi_forest6,66,131,246,1,0,0,0,0,0,linear programming problem,246 -LPnetlib,lpi_galenet,8,14,22,1,0,0,0,0,0,linear programming problem,22 -LPnetlib,lpi_gosh,3792,13455,99953,1,0,0,0,0,0,linear programming problem,99953 -LPnetlib,lpi_gran,2658,2525,20111,1,0,0,0,0,0,linear programming problem,20111 -LPnetlib,lpi_greenbea,2393,5596,31074,1,0,0,0,0,0,linear programming problem,31074 -LPnetlib,lpi_itest2,9,13,26,1,0,0,0,0,0,linear programming problem,26 -LPnetlib,lpi_itest6,11,17,29,1,0,0,0,0,0,linear programming problem,29 -LPnetlib,lpi_klein1,54,108,750,1,0,0,0,0,0,linear programming problem,750 -LPnetlib,lpi_klein2,477,531,5062,1,0,0,0,0,0,linear programming problem,5062 -LPnetlib,lpi_klein3,994,1082,13101,1,0,0,0,0,0,linear programming problem,13101 -LPnetlib,lpi_mondou2,312,604,1208,1,0,0,0,0,0,linear programming problem,1208 -LPnetlib,lpi_pang,361,741,2933,1,0,0,0,0,0,linear programming problem,2933 -LPnetlib,lpi_pilot4i,410,1123,5264,1,0,0,0,0,0,linear programming problem,5264 -LPnetlib,lpi_qual,323,464,1646,1,0,0,0,0,0,linear programming problem,1646 -LPnetlib,lpi_reactor,318,808,2591,1,0,0,0,0,0,linear programming problem,2591 -LPnetlib,lpi_refinery,323,464,1626,1,0,0,0,0,0,linear programming problem,1626 -LPnetlib,lpi_vol1,323,464,1646,1,0,0,0,0,0,linear programming problem,1646 -LPnetlib,lpi_woodinfe,35,89,140,1,0,0,0,0,0,linear programming problem,140 -Li,li,22695,22695,1215181,1,0,1,0,1,0.8685670104303111,electromagnetics problem,1350309 -Li,pli,22695,22695,1350309,1,1,1,0,1,1,duplicate electromagnetics problem,1350309 -Mallya,lhr01,1477,1477,18427,1,0,0,0,0.007324823611784348,0,chemical process simulation problem,18592 -Mallya,lhr02,2954,2954,36875,1,0,0,0,0.007320486597050275,0,chemical process simulation problem,37206 -Mallya,lhr04,4101,4101,81057,1,0,0,0,0.01515885315769082,0,chemical process simulation problem,82682 -Mallya,lhr04c,4101,4101,82682,1,0,0,0,0.01515885315769082,0,chemical process simulation problem,82682 -Mallya,lhr07,7337,7337,154660,1,0,0,0,0.0173680609288971,0,chemical process simulation problem,156508 -Mallya,lhr07c,7337,7337,156508,1,0,0,0,0.0173680609288971,0,chemical process simulation problem,156508 -Mallya,lhr10,10672,10672,228395,1,0,0,0,0.008791586915847653,1.752395305332977e-05,chemical process simulation problem,232633 -Mallya,lhr10c,10672,10672,232633,1,0,0,0,0.008791586915847653,1.720467106819501e-05,chemical process simulation problem,232633 -Mallya,lhr11,10964,10964,231806,1,0,0,0,0.008200580375103791,3.452591601570929e-05,chemical process simulation problem,233741 -Mallya,lhr11c,10964,10964,233741,1,0,0,0,0.008200580375103791,4.280052387841227e-05,chemical process simulation problem,233741 -Mallya,lhr14,14270,14270,305750,1,0,0,0,0.006622946983926842,0,chemical process simulation problem,307858 -Mallya,lhr14c,14270,14270,307858,1,0,0,0,0.006622946983926842,0,chemical process simulation problem,307858 -Mallya,lhr17,17576,17576,379761,1,0,0,0,0.001513390098553639,0.0001527473453564807,chemical process simulation problem,381975 -Mallya,lhr17c,17576,17576,381975,1,0,0,0,0.001513390098553639,5.236643939631969e-06,chemical process simulation problem,381975 -Mallya,lhr34,35152,35152,746972,1,0,0,0,0.00151326330781556,0.0001553140154832006,chemical process simulation problem,764014 -Mallya,lhr34c,35152,35152,764014,1,0,0,0,0.00151326330781556,5.236205217354879e-06,chemical process simulation problem,764014 -Mallya,lhr71,70304,70304,1494006,1,0,0,0,0.001513199920413014,0.0001553075692357127,chemical process simulation problem,1528092 -Mallya,lhr71c,70304,70304,1528092,1,0,0,0,0.001513199920413014,5.235985883782057e-06,chemical process simulation problem,1528092 -Mulvey,finan512,74752,74752,596992,1,0,0,1,1,1,economic problem,596992 -Mulvey,pfinan512,74752,74752,596992,1,1,0,0,1,1,duplicate economic problem,596992 -Nasa,barth,6691,6691,26439,1,1,1,0,0,0,duplicate structural problem,26439 -Nasa,barth4,6019,6019,23492,1,1,1,0,0,0,duplicate structural problem,23492 -Nasa,barth5,15606,15606,61484,1,1,1,0,0,0,duplicate structural problem,61484 -Nasa,nasa1824,1824,1824,39208,1,0,1,1,1,1,structural problem,39208 -Nasa,nasa2146,2146,2146,72250,1,0,1,1,1,1,structural problem,72250 -Nasa,nasa2910,2910,2910,174296,1,0,1,1,1,1,structural problem,174296 -Nasa,nasa4704,4704,4704,104756,1,0,1,1,1,1,structural problem,104756 -Nasa,nasasrb,54870,54870,2677324,1,0,1,1,1,1,structural problem,2677324 -Nasa,pwt,36519,36519,326107,1,1,1,0,1,1,duplicate structural problem,326107 -Nasa,shuttle_eddy,10429,10429,103599,1,1,1,0,1,1,duplicate structural problem,103599 -Nasa,skirt,12598,12598,196520,1,1,1,0,1,1,duplicate structural problem,196520 -Nemeth,nemeth01,9506,9506,725054,1,0,0,0,1,1,theoretical/quantum chemistry problem sequence,725054 -Nemeth,nemeth02,9506,9506,394808,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,394808 -Nemeth,nemeth03,9506,9506,394808,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,394808 -Nemeth,nemeth04,9506,9506,394808,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,394808 -Nemeth,nemeth05,9506,9506,394808,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,394808 -Nemeth,nemeth06,9506,9506,394808,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,394808 -Nemeth,nemeth07,9506,9506,394812,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,394812 -Nemeth,nemeth08,9506,9506,394816,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,394816 -Nemeth,nemeth09,9506,9506,395506,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,395506 -Nemeth,nemeth10,9506,9506,401448,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,401448 -Nemeth,nemeth11,9506,9506,408264,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,408264 -Nemeth,nemeth12,9506,9506,446818,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,446818 -Nemeth,nemeth13,9506,9506,474472,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,474472 -Nemeth,nemeth14,9506,9506,496144,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,496144 -Nemeth,nemeth15,9506,9506,539802,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,539802 -Nemeth,nemeth16,9506,9506,587012,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,587012 -Nemeth,nemeth17,9506,9506,629620,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,629620 -Nemeth,nemeth18,9506,9506,695234,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,695234 -Nemeth,nemeth19,9506,9506,818302,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,818302 -Nemeth,nemeth20,9506,9506,971870,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,971870 -Nemeth,nemeth21,9506,9506,1173746,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,1173746 -Nemeth,nemeth22,9506,9506,1358832,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,1358832 -Nemeth,nemeth23,9506,9506,1506810,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,1506810 -Nemeth,nemeth24,9506,9506,1506550,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,1506550 -Nemeth,nemeth25,9506,9506,1511758,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,1511758 -Nemeth,nemeth26,9506,9506,1511760,1,0,0,0,1,1,subsequent theoretical/quantum chemistry problem,1511760 -Okunbor,aft01,8205,8205,125567,1,0,1,1,1,1,acoustics problem,125567 -Okunbor,aft02,8184,8184,127762,0,0,1,0,1,0.9963538443526401,acoustics problem,127762 -Qaplib,lp_nug05,210,225,1050,1,0,0,0,0,0,linear programming problem,1050 -Qaplib,lp_nug06,372,486,2232,1,0,0,0,0,0,linear programming problem,2232 -Qaplib,lp_nug07,602,931,4214,1,0,0,0,0,0,linear programming problem,4214 -Qaplib,lp_nug08,912,1632,7296,1,0,0,0,0,0,linear programming problem,7296 -Qaplib,lp_nug12,3192,8856,38304,1,0,0,0,0,0,linear programming problem,38304 -Qaplib,lp_nug15,6330,22275,94950,1,0,0,0,0,0,linear programming problem,94950 -Qaplib,lp_nug20,15240,72600,304800,1,0,0,0,0,0,linear programming problem,304800 -Qaplib,lp_nug30,52260,379350,1567800,1,0,0,0,0,0,linear programming problem,1567800 -Ronis,xenon1,48600,48600,1181120,1,0,1,0,1,0.775813230671423,materials problem,1181120 -Ronis,xenon2,157464,157464,3866688,1,0,1,0,1,0.7756792256277864,materials problem,3866688 -Rothberg,3dtube,45330,45330,3213618,1,1,1,0,1,1,computational fluid dynamics problem,3213618 -Rothberg,cfd1,70656,70656,1825580,1,0,1,1,1,1,computational fluid dynamics problem,1828364 -Rothberg,cfd2,123440,123440,3085406,1,0,1,1,1,1,computational fluid dynamics problem,3087898 -Rothberg,gearbox,153746,153746,9080404,1,1,1,0,1,1,structural problem,9080404 -Rothberg,struct3,53570,53570,1173694,1,1,1,0,1,1,structural problem,1173694 -Rothberg,struct4,4350,4350,237798,1,1,1,0,1,1,structural problem,237798 -Shyy,shyy161,76480,76480,329762,1,0,1,0,0.7263358696509635,0.1824427535660244,computational fluid dynamics problem,329762 -Shyy,shyy41,4720,4720,20042,1,0,1,0,0.7234369459874719,0.1843753693416854,computational fluid dynamics problem,20042 -Simon,appu,14000,14000,1853104,1,0,0,0,1,0.9429754924136972,directed weighted random graph,1853104 -Simon,bbmat,38744,38744,1771722,1,0,1,0,0.5295358625441292,0.0005851199495896658,computational fluid dynamics problem,1771722 -Simon,olafu,16146,16146,1015156,1,0,1,1,1,1,structural problem,1015156 -Simon,raefsky1,3242,3242,293409,1,0,1,0,1,0.09040311269027836,computational fluid dynamics problem sequence,294276 -Simon,raefsky2,3242,3242,293551,1,0,1,0,1,0.09753745147411895,subsequent computational fluid dynamics problem,294276 -Simon,raefsky3,21200,21200,1488768,1,0,1,0,1,0.4763745189320018,computational fluid dynamics problem,1488768 -Simon,raefsky4,19779,19779,1316789,1,0,1,1,1,1,structural problem,1328611 -Simon,raefsky5,6316,6316,167178,1,0,1,0,0,0,structural problem,168658 -Simon,raefsky6,3402,3402,130371,1,0,1,0,0,0,structural problem,137845 -Simon,venkat01,62424,62424,1717792,1,0,1,0,1,0.05542936676316082,computational fluid dynamics problem sequence,1717792 -Simon,venkat25,62424,62424,1717763,1,0,1,0,1,0.05539650790563141,subsequent computational fluid dynamics problem,1717792 -Simon,venkat50,62424,62424,1717777,1,0,1,0,1,0.05539603939461855,subsequent computational fluid dynamics problem,1717792 -TOKAMAK,utm1700b,1700,1700,21509,1,0,1,0,0.5232974910394266,0,electromagnetics problem,21509 -TOKAMAK,utm300,300,300,3155,1,0,1,0,0.4651488616462347,0,electromagnetics problem,3155 -TOKAMAK,utm3060,3060,3060,42211,1,0,1,0,0.524686470332814,0,electromagnetics problem,42211 -TOKAMAK,utm5940,5940,5940,83842,1,0,1,0,0.5290493183743677,0,electromagnetics problem,83842 -Vavasis,av41092,41092,41092,1683902,1,0,1,0,0.0009359570219938022,0,2D/3D problem,1683902 -Wang,swang1,3169,3169,20841,1,0,1,0,1,0,semiconductor device problem sequence,20841 -Wang,swang2,3169,3169,20841,1,0,1,0,1,0,subsequent semiconductor device problem,20841 -Wang,wang1,2903,2903,19093,1,0,1,0,1,0.808153180975911,semiconductor device problem sequence,19093 -Wang,wang2,2903,2903,19093,1,0,1,0,1,0.808153180975911,subsequent semiconductor device problem,19093 -Wang,wang3,26064,26064,177168,1,0,1,0,1,0.9780019059720457,semiconductor device problem,177168 -Wang,wang4,26068,26068,177196,1,0,1,0,1,0.04647715843523371,semiconductor device problem,177196 -Zhao,Zhao1,33861,33861,166453,1,0,1,0,0.9224990949680222,0,electromagnetics problem,166453 -Zhao,Zhao2,33861,33861,166453,1,0,1,0,0.9224990949680222,0,electromagnetics problem,166453 -Zitney,extr1,2837,2837,10967,1,0,0,0,0.004209049456331112,0.000364797081623347,chemical process simulation problem,11407 -Zitney,hydr1,5308,5308,22680,1,0,0,0,0.004126315789473685,0.0006173383896287151,chemical process simulation problem,23752 -Zitney,radfr1,1048,1048,13299,1,0,0,0,0.05374309287714783,0,chemical process simulation problem,13299 -Zitney,rdist1,4134,4134,94408,1,0,0,0,0.05878038669628716,0,chemical process simulation problem,94408 -Zitney,rdist2,3198,3198,56834,1,0,0,0,0.04562603575332323,0,chemical process simulation problem,56934 -Zitney,rdist3a,2398,2398,61896,1,0,0,0,0.1404154186743065,0,chemical process simulation problem,61896 -Cunningham,k3plates,11107,11107,378927,1,0,1,0,1,0.999260507857104,acoustics problem,378927 -Cunningham,m3plates,11107,11107,6639,1,0,1,0,1,1,acoustics problem,6639 -Cunningham,qa8fk,66127,66127,1660579,1,0,1,0,1,1,acoustics problem,1660579 -Cunningham,qa8fm,66127,66127,1660579,1,0,1,1,1,1,acoustics problem,1660579 -Boeing,bcsstk39,46772,46772,2060662,1,0,1,0,1,1,structural problem,2089294 -Chen,pkustk01,22044,22044,979380,1,1,1,0,1,1,structural problem,979380 -Chen,pkustk02,10800,10800,810000,1,1,1,0,1,1,structural problem,810000 -Chen,pkustk03,63336,63336,3130416,1,1,1,0,1,1,structural problem,3130416 -Chen,pkustk04,55590,55590,4218660,1,1,1,0,1,1,structural problem,4218660 -Chen,pkustk05,37164,37164,2205144,1,1,1,0,1,1,structural problem,2205144 -Chen,pkustk06,43164,43164,2571768,1,1,1,0,1,1,structural problem,2571768 -Chen,pkustk07,16860,16860,2418804,1,1,1,0,1,1,structural problem,2418804 -Chen,pkustk08,22209,22209,3226671,1,1,1,0,1,1,structural problem,3226671 -Chen,pkustk09,33960,33960,1583640,1,1,1,0,1,1,structural problem,1583640 -Chen,pkustk10,80676,80676,4308984,1,1,1,0,1,1,structural problem,4308984 -Chen,pkustk11,87804,87804,5217912,1,1,1,0,1,1,structural problem,5217912 -Chen,pkustk12,94653,94653,7512317,1,1,1,0,1,1,structural problem,7512317 -Chen,pkustk13,94893,94893,6616827,1,1,1,0,1,1,structural problem,6616827 -Chen,pkustk14,151926,151926,14836504,1,1,1,0,1,1,structural problem,14836504 -MathWorks,pivtol,102,102,306,1,0,0,0,0.9901960784313726,0,statistical/mathematical problem,306 -Kim,kim1,38415,38415,933195,0,0,1,0,0.9897360244976419,0,2D/3D problem,933195 -Kim,kim2,456976,456976,11330020,0,0,1,0,0.9970363405132914,0,2D/3D problem,11330020 -Langemyr,comsol,1500,1500,97645,1,0,1,0,0.9990327110094128,0.0008944822923708982,structural problem,97645 -Pothen,barth,6691,6691,46187,1,1,1,0,1,1,structural problem,46187 -Pothen,barth4,6019,6019,40965,1,1,1,0,1,1,structural problem,40965 -Pothen,barth5,15606,15606,107362,1,1,1,0,1,1,structural problem,107362 -Pothen,bodyy4,17546,17546,121550,1,0,1,1,1,1,structural problem,121938 -Pothen,bodyy5,18589,18589,128853,1,0,1,1,1,1,structural problem,129281 -Pothen,bodyy6,19366,19366,134208,1,0,1,1,1,1,structural problem,134748 -Pothen,commanche_dual,7920,7920,31680,1,1,1,0,1,1,structural problem,31680 -Pothen,mesh1e1,48,48,306,1,0,1,1,1,1,structural problem,306 -Pothen,mesh1em1,48,48,306,1,0,1,1,1,1,structural problem,306 -Pothen,mesh1em6,48,48,306,1,0,1,1,1,1,structural problem,306 -Pothen,mesh2e1,306,306,2018,1,0,1,1,1,1,structural problem,2018 -Pothen,mesh2em5,306,306,2018,1,0,1,1,1,1,structural problem,2018 -Pothen,mesh3e1,289,289,1377,1,0,1,1,1,1,structural problem,1889 -Pothen,mesh3em5,289,289,1377,1,0,1,1,1,1,structural problem,1889 -Pothen,onera_dual,85567,85567,419201,1,1,1,0,1,1,structural problem,419201 -Pothen,pwt,36519,36519,326107,1,1,1,0,1,1,structural problem,326107 -Pothen,shuttle_eddy,10429,10429,103599,1,1,1,0,1,1,structural problem,103599 -Pothen,skirt,12598,12598,196520,1,1,1,0,1,1,structural problem,196520 -Pothen,sphere2,66,66,450,1,1,1,0,1,1,structural problem,450 -Pothen,sphere3,258,258,1794,1,1,1,0,1,1,structural problem,1794 -Pothen,tandem_dual,94069,94069,460493,1,1,1,0,1,1,structural problem,460493 -Pothen,tandem_vtx,18454,18454,253350,1,1,1,0,1,1,structural problem,253350 -Norris,fv1,9604,9604,85264,1,0,1,1,1,1,2D/3D problem,85264 -Norris,fv2,9801,9801,87025,1,0,1,1,1,1,2D/3D problem,87025 -Norris,fv3,9801,9801,87025,1,0,1,1,1,1,2D/3D problem,87025 -Norris,heart1,3557,3557,1385317,1,0,1,0,1,0,2D/3D problem,1387773 -Norris,heart2,2339,2339,680341,1,0,1,0,1,0,2D/3D problem,682797 -Norris,heart3,2339,2339,680341,1,0,1,0,1,0,2D/3D problem,682797 -Norris,lung1,1650,1650,7419,1,0,1,0,0.5713295198474606,0,computational fluid dynamics problem,7419 -Norris,lung2,109460,109460,492564,1,0,1,0,0.5714270798529903,0,computational fluid dynamics problem,492564 -Norris,stomach,213360,213360,3021648,1,0,1,0,0.8474914253808726,0,2D/3D problem,3021648 -Norris,torso1,116158,116158,8516500,1,0,1,0,0.4243874832715144,0,2D/3D problem,8516500 -Norris,torso2,115967,115967,1033473,1,0,1,0,0.9916360219987662,0,2D/3D problem,1033473 -Norris,torso3,259156,259156,4429042,1,0,1,0,0.9513722917125312,0,2D/3D problem,4429042 -Shen,e40r0100,17281,17281,553562,1,0,1,0,0.3079877899832364,0.001965387548691824,2D/3D problem,553562 -Shen,shermanACa,3432,3432,25220,1,0,1,0,0.163759867817147,0.02157150725169818,2D/3D problem,25220 -Shen,shermanACb,18510,18510,145149,1,0,1,0,0.1493062958488302,0.02665845434660729,2D/3D problem,145149 -Shen,shermanACd,6136,6136,53329,1,0,1,0,0.1520140698832454,0.01665501239590617,2D/3D problem,53329 -Pereyra,landmark,71952,2704,1146848,1,0,0,0,0,0,least squares problem,1151232 -vanHeukelum,cage3,5,5,19,1,0,0,0,1,0,directed weighted graph,19 -vanHeukelum,cage4,9,9,49,1,0,0,0,1,0,directed weighted graph,49 -vanHeukelum,cage5,37,37,233,1,0,0,0,1,0.04081632653061224,directed weighted graph,233 -vanHeukelum,cage6,93,93,785,1,0,0,0,1,0.09248554913294797,directed weighted graph,785 -vanHeukelum,cage7,340,340,3084,1,0,0,0,1,0.1020408163265306,directed weighted graph,3084 -vanHeukelum,cage8,1015,1015,11003,1,0,0,0,1,0.144973968762515,directed weighted graph,11003 -vanHeukelum,cage9,3534,3534,41594,1,0,0,0,1,0.1535470310036784,directed weighted graph,41594 -vanHeukelum,cage10,11397,11397,150645,1,0,0,0,1,0.1743077099850626,directed weighted graph,150645 -vanHeukelum,cage11,39082,39082,559722,1,0,0,0,1,0.1821988322065151,directed weighted graph,559722 -vanHeukelum,cage12,130228,130228,2032536,1,0,0,0,1,0.1935123018985359,directed weighted graph,2032536 -vanHeukelum,cage13,445315,445315,7479343,1,0,0,0,1,0.2004785878020389,directed weighted graph,7479343 -vanHeukelum,cage14,1505785,1505785,27130349,1,0,0,0,1,0.2073316837703073,directed weighted graph,27130349 -vanHeukelum,cage15,5154859,5154859,99199551,1,0,0,0,1,0.2131193964673732,directed weighted graph,99199551 -Hohn,fd12,7500,7500,28462,1,0,1,0,0.0002810765230834094,0,materials problem,28462 -Hohn,fd15,11532,11532,44206,1,0,1,0,0,0,materials problem,44206 -Hohn,fd18,16428,16428,63406,1,0,1,0,3.154275620603729e-05,0,materials problem,63406 -Hohn,sinc12,7500,7500,283992,1,0,1,0,0.01755337541442645,0,materials problem,294986 -Hohn,sinc15,11532,11532,551184,1,0,1,0,0.01378652867239141,0,materials problem,568526 -Hohn,sinc18,16428,16428,948696,1,0,1,0,0.01157085557378834,0,materials problem,973826 -Alemdar,Alemdar,6245,6245,42581,1,0,1,0,1,1,2D/3D problem,42581 -Andrews,Andrews,60000,60000,760154,1,0,1,1,1,1,computer graphics/vision problem,760154 -FEMLAB,ns3Da,20414,20414,1679599,1,0,1,0,0.9999825339398674,0,computational fluid dynamics problem,1679599 -FEMLAB,poisson2D,367,367,2417,1,0,1,0,1,0,computational fluid dynamics problem,2417 -FEMLAB,poisson3Da,13514,13514,352762,1,0,1,0,1,0,computational fluid dynamics problem,352762 -FEMLAB,poisson3Db,85623,85623,2374949,1,0,1,0,1,0,computational fluid dynamics problem,2374949 -FEMLAB,problem1,415,415,2779,1,0,1,0,1,0.6548223350253807,2D/3D problem,2779 -FEMLAB,sme3Da,12504,12504,874887,1,0,1,0,0.9999710105602732,0.4370888572710733,structural problem,874887 -FEMLAB,sme3Db,29067,29067,2081063,1,0,1,0,0.999978557463075,0.4406373111838425,structural problem,2081063 -FEMLAB,sme3Dc,42930,42930,3148656,1,0,1,0,0.9999793929020139,0.4391179389295772,structural problem,3148656 -FEMLAB,waveguide3D,21036,21036,303468,0,0,1,0,1,0,electromagnetics problem,303468 -Lucifora,cell1,7055,7055,30082,1,0,0,0,0.9970503597122302,0,directed weighted graph,34855 -Lucifora,cell2,7055,7055,30082,1,0,0,0,0.9970503597122302,0.004342728101793546,directed weighted graph,34855 -ND,nd3k,9000,9000,3279690,1,0,1,1,1,1,2D/3D problem,3279690 -ND,nd6k,18000,18000,6897316,1,0,1,1,1,1,2D/3D problem,6897316 -ND,nd12k,36000,36000,14220946,1,0,1,1,1,1,2D/3D problem,14220946 -ND,nd24k,72000,72000,28715634,1,0,1,1,1,1,2D/3D problem,28715634 -Schenk_AFE,af_shell1,504855,504855,17562051,1,0,1,0,1,1,structural problem sequence,17588875 -Schenk_AFE,af_shell2,504855,504855,17562051,1,0,1,0,1,1,subsequent structural problem,17588875 -Schenk_AFE,af_shell3,504855,504855,17562051,1,0,1,1,1,1,subsequent structural problem,17588875 -Schenk_AFE,af_shell4,504855,504855,17562051,1,0,1,1,1,1,subsequent structural problem,17588875 -Schenk_AFE,af_shell5,504855,504855,17579155,1,0,1,0,1,1,subsequent structural problem,17588875 -Schenk_AFE,af_shell6,504855,504855,17579155,1,0,1,0,1,1,subsequent structural problem,17588875 -Schenk_AFE,af_shell7,504855,504855,17579155,1,0,1,1,1,1,subsequent structural problem,17588875 -Schenk_AFE,af_shell8,504855,504855,17579155,1,0,1,1,1,1,subsequent structural problem,17588875 -Schenk_AFE,af_shell9,504855,504855,17588845,1,0,1,0,1,1,subsequent structural problem,17588875 -Schenk_IBMNA,c-62,41731,41731,559341,1,0,0,0,1,1,optimization problem sequence,559343 -Schenk_IBMNA,c-64,51035,51035,707985,1,0,0,0,1,1,optimization problem sequence,717841 -Schenk_IBMNA,c-66,49989,49989,444853,1,0,0,0,1,1,optimization problem sequence,499007 -Schenk_IBMNA,c-67,57975,57975,530229,1,0,0,0,1,1,optimization problem sequence,531935 -Schenk_IBMNA,c-73,169422,169422,1279274,1,0,0,0,1,1,optimization problem sequence,1279274 -Schenk_IBMSDS,2D_27628_bjtcai,27628,27628,206670,1,0,1,0,0.9995761793531919,0.2185967538342959,semiconductor device problem,442898 -Schenk_IBMSDS,2D_54019_highK,54019,54019,486129,1,0,1,0,0.9980889117620531,0.189734095484946,semiconductor device problem,996414 -Schenk_IBMSDS,3D_28984_Tetra,28984,28984,285092,1,0,1,0,0.9874041102377119,0.3613007012666531,semiconductor device problem,599170 -Schenk_IBMSDS,3D_51448_3D,51448,51448,537038,1,0,1,0,0.9920908271502504,0.1870343293725159,semiconductor device problem,1056610 -Schenk_IBMSDS,ibm_matrix_2,51448,51448,537038,1,0,1,0,0.9920908271502504,0.185666920653226,semiconductor device problem,1056610 -Schenk_IBMSDS,matrix_9,103430,103430,1205518,1,0,1,0,0.9966503478484926,0.1744833443427385,semiconductor device problem,2121550 -Schenk_IBMSDS,matrix-new_3,125329,125329,893984,1,0,1,0,0.9871118002084263,0.2826079320371298,semiconductor device problem,2678750 -Schenk_ISEI,barrier2-10,115625,115625,2158759,1,0,1,0,1,0.1989334147759642,subsequent semiconductor device problem,3897557 -Schenk_ISEI,barrier2-11,115625,115625,2158759,1,0,1,0,1,0.1988805553760946,subsequent semiconductor device problem,3897557 -Schenk_ISEI,barrier2-12,115625,115625,2158759,1,0,1,0,1,0.1983617501551521,subsequent semiconductor device problem,3897557 -Schenk_ISEI,barrier2-1,113076,113076,2129496,1,0,1,0,1,0.1972208170916773,semiconductor device problem sequence,3805068 -Schenk_ISEI,barrier2-2,113076,113076,2129496,1,0,1,0,1,0.1971642812509299,subsequent semiconductor device problem,3805068 -Schenk_ISEI,barrier2-3,113076,113076,2129496,1,0,1,0,1,0.1980361234266671,subsequent semiconductor device problem,3805068 -Schenk_ISEI,barrier2-4,113076,113076,2129496,1,0,1,0,1,0.189249263546285,subsequent semiconductor device problem,3805068 -Schenk_ISEI,barrier2-9,115625,115625,2158759,1,0,1,0,1,0.2036036406422221,semiconductor device problem sequence,3897557 -Schenk_ISEI,igbt3,10938,10938,130500,1,0,1,0,1,0.1720279018417223,semiconductor device problem,234006 -Schenk_ISEI,nmos3,18588,18588,237130,1,0,1,0,1,0.1670891636390259,semiconductor device problem,386594 -Schenk_ISEI,ohne2,181343,181343,6869939,1,0,1,0,1,0.09493801090692276,semiconductor device problem,11063545 -Schenk_ISEI,para-10,155924,155924,2094873,1,0,1,0,1,0.1980866954210761,subsequent semiconductor device problem,5416358 -Schenk_ISEI,para-4,153226,153226,2930882,1,0,1,0,1,0.2900222345747638,semiconductor device problem,5326228 -Schenk_ISEI,para-5,155924,155924,2094873,1,0,1,0,1,0.3692588097985042,semiconductor device problem sequence,5416358 -Schenk_ISEI,para-6,155924,155924,2094873,1,0,1,0,1,0.3692588097985042,subsequent semiconductor device problem,5416358 -Schenk_ISEI,para-7,155924,155924,2094873,1,0,1,0,1,0.3692588097985042,subsequent semiconductor device problem,5416358 -Schenk_ISEI,para-8,155924,155924,2094873,1,0,1,0,1,0.1779654854253516,subsequent semiconductor device problem,5416358 -Schenk_ISEI,para-9,155924,155924,2094873,1,0,1,0,1,0.1810599453621524,subsequent semiconductor device problem,5416358 -Kamvar,Stanford,281903,281903,2312497,1,1,0,0,0.2766368994208425,0.2766368994208425,directed graph,2312497 -Kamvar,Stanford_Berkeley,683446,683446,7583376,1,1,0,0,0.2502051856587356,0.2502051856587356,directed graph,7583376 -Sumner,graphics,29493,11822,117954,1,0,1,0,0,0,computer graphics/vision problem,117954 -Tromble,language,399130,399130,1216334,1,0,0,0,0.05790231080611451,0,directed weighted graph,1216334 -Sanghavi,ecl32,51993,51993,380415,1,0,1,0,0.9219175329301935,0.6030290297239527,semiconductor device problem,380415 -Sandia,adder_dcop_01,1813,1813,11156,1,0,0,0,0.6544094067343666,0.01582041688936398,subsequent circuit simulation problem,11156 -Sandia,adder_dcop_02,1813,1813,11246,1,0,0,0,0.6481736368448915,0.02604552673372154,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_03,1813,1813,11148,1,0,0,0,0.6429870546699475,0.03509147319995721,subsequent circuit simulation problem,11148 -Sandia,adder_dcop_04,1813,1813,11107,1,0,0,0,0.6505480335267569,0.003868471953578337,subsequent circuit simulation problem,11107 -Sandia,adder_dcop_05,1813,1813,11097,1,0,0,0,0.6473752151462995,0.01333907056798623,subsequent circuit simulation problem,11097 -Sandia,adder_dcop_06,1813,1813,11224,1,0,0,0,0.6486257030669638,0.01464501751034702,subsequent circuit simulation problem,11224 -Sandia,adder_dcop_07,1813,1813,11226,1,0,0,0,0.6484880636604774,0.005941644562334218,subsequent circuit simulation problem,11226 -Sandia,adder_dcop_08,1813,1813,11242,1,0,0,0,0.6478127317021501,0.005296049147336087,subsequent circuit simulation problem,11242 -Sandia,adder_dcop_09,1813,1813,11239,1,0,0,0,0.6484424666242848,0.006145369781733418,subsequent circuit simulation problem,11239 -Sandia,adder_dcop_10,1813,1813,11232,1,0,0,0,0.6478634291167427,0.00572579790054077,subsequent circuit simulation problem,11232 -Sandia,adder_dcop_11,1813,1813,11243,1,0,0,0,0.6481677610675705,0.00487184918449481,subsequent circuit simulation problem,11243 -Sandia,adder_dcop_12,1813,1813,11246,1,0,0,0,0.6481736368448915,0.004446797247220751,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_13,1813,1813,11245,1,0,0,0,0.6482422702244811,0.002753070732740364,subsequent circuit simulation problem,11245 -Sandia,adder_dcop_14,1813,1813,11246,1,0,0,0,0.6481736368448915,0.008258337744838539,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_15,1813,1813,11246,1,0,0,0,0.6481736368448915,0.005929062996294336,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_16,1813,1813,11246,1,0,0,0,0.6481736368448915,0.008258337744838539,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_17,1813,1813,11246,1,0,0,0,0.6481736368448915,0.009105346744309158,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_18,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_19,1813,1813,11245,1,0,0,0,0.6482422702244811,0.007623888182973317,subsequent circuit simulation problem,11245 -Sandia,adder_dcop_20,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_21,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_22,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_23,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_24,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_25,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_26,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_27,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_28,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_29,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_30,1813,1813,11246,1,0,0,0,0.6481736368448915,0.008046585494970884,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_31,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_32,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_33,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_34,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_35,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_36,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_37,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_38,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_39,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_40,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_41,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_42,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_43,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_44,1813,1813,11245,1,0,0,0,0.6482422702244811,0.007623888182973317,subsequent circuit simulation problem,11245 -Sandia,adder_dcop_45,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_46,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_47,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_48,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_49,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_50,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_51,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_52,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_53,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_54,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_55,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_56,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_57,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_58,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_59,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_60,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_61,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_62,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_63,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_64,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_65,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_66,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_67,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_68,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_dcop_69,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246 -Sandia,adder_trans_01,1814,1814,14579,1,0,0,0,1,0.03115703773289494,circuit simulation problem sequence,14579 -Sandia,adder_trans_02,1814,1814,14579,1,0,0,0,1,0.03115703773289494,subsequent circuit simulation problem,14579 -Sandia,fpga_dcop_01,1220,1220,5892,1,0,0,0,0.8183347350714887,0.2733389402859546,circuit simulation problem sequence,5892 -Sandia,fpga_dcop_02,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3271656854499579,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_03,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3448275862068966,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_04,1220,1220,5884,1,0,0,0,0.818871103622578,0.3546756529064869,subsequent circuit simulation problem,5884 -Sandia,fpga_dcop_05,1220,1220,5852,1,0,0,0,0.8210347752332485,0.359626802374894,subsequent circuit simulation problem,5852 -Sandia,fpga_dcop_06,1220,1220,5860,1,0,0,0,0.8204911092294666,0.3539373412362405,subsequent circuit simulation problem,5860 -Sandia,fpga_dcop_07,1220,1220,5855,1,0,0,0,0.8205128205128205,0.3432930705657978,subsequent circuit simulation problem,5855 -Sandia,fpga_dcop_08,1220,1220,5888,1,0,0,0,0.8186026936026936,0.3425925925925926,subsequent circuit simulation problem,5888 -Sandia,fpga_dcop_09,1220,1220,5888,1,0,0,0,0.8186026936026936,0.3337542087542087,subsequent circuit simulation problem,5888 -Sandia,fpga_dcop_10,1220,1220,5884,1,0,0,0,0.818871103622578,0.3323504633529907,subsequent circuit simulation problem,5884 -Sandia,fpga_dcop_11,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3301093355761144,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_12,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3216989066442388,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_13,1220,1220,5892,1,0,0,0,0.8183347350714887,0.325063078216989,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_14,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3187552565180824,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_15,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3221194280908327,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_16,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3221194280908327,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_17,1220,1220,5892,1,0,0,0,0.8183347350714887,0.31959629941127,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_18,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3191757779646762,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_19,1220,1220,5892,1,0,0,0,0.8183347350714887,0.31959629941127,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_20,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3212783851976451,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_21,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3216989066442388,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_22,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3263246425567704,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_23,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3216989066442388,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_24,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3254835996635829,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_25,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3259041211101766,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_26,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3254835996635829,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_27,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3271656854499579,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_28,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3284272497897393,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_29,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3280067283431455,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_30,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3292682926829268,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_31,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3284272497897393,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_32,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3280067283431455,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_33,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3280067283431455,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_34,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3275862068965517,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_35,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3288477712363331,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_36,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3288477712363331,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_37,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3296888141295206,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_38,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3296888141295206,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_39,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3296888141295206,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_40,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3313708999158957,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_41,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3301093355761144,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_42,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3313708999158957,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_43,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3317914213624895,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_44,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3322119428090833,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_45,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3322119428090833,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_46,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3317914213624895,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_47,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3317914213624895,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_48,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3317914213624895,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_49,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3317914213624895,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_50,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3317914213624895,subsequent circuit simulation problem,5892 -Sandia,fpga_dcop_51,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3322119428090833,subsequent circuit simulation problem,5892 -Sandia,fpga_trans_01,1220,1220,7382,1,0,0,0,1,0.214193962748876,circuit simulation problem sequence,7382 -Sandia,fpga_trans_02,1220,1220,7382,1,0,0,0,1,0.2138728323699422,subsequent circuit simulation problem,7382 -Sandia,init_adder1,1813,1813,11156,1,0,0,0,0.6544094067343666,0.01582041688936398,circuit simulation problem sequence,11156 -Sandia,mult_dcop_01,25187,25187,193276,1,0,0,0,0.6144759258929472,0.003383612629779353,circuit simulation problem sequence,193276 -Sandia,mult_dcop_02,25187,25187,193276,1,0,0,0,0.6144759258929472,0.003383612629779353,subsequent circuit simulation problem,193276 -Sandia,mult_dcop_03,25187,25187,193216,1,0,0,0,0.6142197994049846,0.01294544504421048,subsequent circuit simulation problem,193216 -Sandia,oscil_dcop_01,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,circuit simulation problem sequence,1544 -Sandia,oscil_dcop_02,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_03,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_04,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_05,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_06,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_07,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_08,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_09,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_10,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_11,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_12,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_13,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_14,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_15,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_16,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_17,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_18,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_19,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_20,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_21,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_22,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_23,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_24,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_25,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_26,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_27,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_28,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_29,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_30,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_31,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_32,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_33,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_34,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_35,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_36,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_37,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_38,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_39,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_40,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_41,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_42,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_43,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_44,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_45,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_46,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_47,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_48,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_49,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_50,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_51,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_52,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_53,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_54,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_55,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_56,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_dcop_57,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544 -Sandia,oscil_trans_01,430,430,1614,1,0,0,0,0.9737274220032841,0.7175697865353038,circuit simulation problem,1614 -MathWorks,Pd,8081,8081,13036,1,0,0,0,0,0,counter-example problem,13036 -MathWorks,Pd_rhs,8081,12406,6323,1,0,0,0,0,0,counter-example problem,6323 -MathWorks,Harvard500,500,500,2636,1,1,0,0,0.4057744830277019,0.4057744830277019,directed graph,2636 -HB,cegb2802,2802,2802,277362,1,1,1,0,1,1,structural problem,277362 -HB,cegb2919,2919,2919,321543,1,1,1,0,1,1,structural problem,321543 -HB,cegb3024,3024,3024,79848,1,1,1,0,1,1,structural problem,79848 -HB,cegb3306,3306,3306,74916,1,1,1,0,1,1,structural problem,74916 -HB,lap_25,25,25,169,1,1,1,0,1,1,structural problem,169 -HB,lock1074,1074,1074,51588,1,1,1,0,1,1,structural problem,51588 -HB,lock2232,2232,2232,80352,1,1,1,0,1,1,structural problem,80352 -HB,lock3491,3491,3491,160444,1,1,1,0,1,1,structural problem,160444 -HB,lock_700,700,700,22175,1,1,1,0,1,1,structural problem,22175 -HB,man_5976,5976,5976,225046,1,1,1,0,1,1,structural problem,225046 -IBM_Austin,coupled,11341,11341,97193,1,0,0,0,1,0.7823373235828404,circuit simulation problem,98523 -ACUSIM,Pres_Poisson,14822,14822,715804,1,0,1,1,1,1,computational fluid dynamics problem,715804 -Rajat,rajat01,6833,6833,43250,1,1,0,0,0.9957479284779764,0.9957479284779764,circuit simulation problem,43250 -Rajat,rajat02,1960,1960,11187,1,1,0,0,1,1,circuit simulation problem,11187 -Rajat,rajat03,7602,7602,32653,1,0,0,0,1,0.4024380527448012,circuit simulation problem,32653 -Rajat,rajat04,1041,1041,8725,1,0,0,0,1,0.03991188285603214,circuit simulation problem,9642 -Rajat,rajat05,301,301,1250,1,0,0,0,0.7697974217311234,0.7058823529411765,circuit simulation problem,1384 -Rajat,rajat06,10922,10922,46983,1,1,0,0,1,1,circuit simulation problem,46983 -Rajat,rajat07,14842,14842,63913,1,1,0,0,1,1,circuit simulation problem,63913 -Rajat,rajat08,19362,19362,83443,1,1,0,0,1,1,circuit simulation problem,83443 -Rajat,rajat09,24482,24482,105573,1,1,0,0,1,1,circuit simulation problem,105573 -Rajat,rajat10,30202,30202,130303,1,1,0,0,1,1,circuit simulation problem,130303 -Rajat,rajat11,135,135,665,1,0,0,0,0.8911764705882353,0.6303939962476548,circuit simulation problem,812 -Rajat,rajat12,1879,1879,12818,1,0,0,0,1,0.4548776032151991,circuit simulation problem,12926 -Rajat,rajat13,7598,7598,48762,1,0,0,0,1,0.2965265970366772,circuit simulation problem,48922 -Rajat,rajat14,180,180,1475,1,0,0,0,1,0.0245398773006135,circuit simulation problem,1503 -Hamrle,Hamrle1,32,32,98,1,0,0,0,0.06451612903225806,0,circuit simulation problem,98 -Hamrle,Hamrle2,5952,5952,22162,1,0,0,0,0.0009930486593843098,0.0001805543017062382,circuit simulation problem,22162 -Hamrle,Hamrle3,1447360,1447360,5514242,1,0,0,0,2.538882993038383e-06,0,circuit simulation problem,5514242 -Oberwolfach,gyro_k,17361,17361,1021159,1,0,1,1,1,1,duplicate model reduction problem,1021159 -Oberwolfach,gyro_m,17361,17361,340431,1,0,1,1,1,1,duplicate model reduction problem,340431 -Oberwolfach,t2dah_a,11445,11445,176117,1,0,1,0,1,1,duplicate model reduction problem,176117 -Oberwolfach,t2dah_e,11445,11445,176117,1,0,1,1,1,1,duplicate model reduction problem,176117 -Oberwolfach,t2dal_a,4257,4257,37465,1,0,1,0,1,1,duplicate model reduction problem,37465 -Oberwolfach,t2dal_e,4257,4257,4257,1,0,1,1,1,1,duplicate model reduction problem,4257 -Oberwolfach,t3dh_a,79171,79171,4352105,1,0,1,0,1,1,duplicate model reduction problem,4352105 -Oberwolfach,t3dh_e,79171,79171,4352105,1,0,1,0,1,1,duplicate model reduction problem,4352105 -Oberwolfach,t3dl_a,20360,20360,509866,1,0,1,0,1,1,duplicate model reduction problem,509866 -Oberwolfach,t3dl_e,20360,20360,20360,1,0,1,1,1,1,duplicate model reduction problem,20360 -LiuWenzhuo,powersim,15838,15838,64424,1,0,0,0,0.5913695769855386,0.5337339974478245,power network problem,67562 -Lin,Lin,256000,256000,1766400,1,0,1,0,1,1,structural problem,1766400 -Cannizzo,sts4098,4098,4098,72356,1,0,1,1,1,1,structural problem,72356 -GHS_indef,aug2d,29008,29008,76832,1,0,1,0,1,1,2D/3D problem,76832 -GHS_indef,aug2dc,30200,30200,80000,1,0,1,0,1,1,2D/3D problem,80000 -GHS_indef,aug3d,24300,24300,69984,1,0,1,0,1,1,2D/3D problem,69984 -GHS_indef,aug3dcqp,35543,35543,128115,1,0,1,0,1,1,2D/3D problem,128115 -GHS_indef,bmw3_2,227362,227362,11288630,1,0,1,0,1,1,structural problem,11288630 -GHS_indef,c-55,32780,32780,403450,1,0,0,0,1,1,optimization problem,403450 -GHS_indef,c-58,37595,37595,552551,1,0,0,0,1,1,optimization problem,552557 -GHS_indef,c-59,41282,41282,480536,1,0,0,0,1,1,optimization problem,480536 -GHS_indef,c-62ghs,41731,41731,559339,1,0,0,0,1,1,subsequent optimization problem,559343 -GHS_indef,c-63,44234,44234,434704,1,0,0,0,1,1,optimization problem,434704 -GHS_indef,c-68,64810,64810,565996,1,0,0,0,1,1,optimization problem,566006 -GHS_indef,c-69,67458,67458,623914,1,0,0,0,1,1,optimization problem,623970 -GHS_indef,c-70,68924,68924,658986,1,0,0,0,1,1,optimization problem,658986 -GHS_indef,c-71,76638,76638,859520,1,0,0,0,1,1,optimization problem,859554 -GHS_indef,c-72,84064,84064,707546,1,0,0,0,1,1,optimization problem,707558 -GHS_indef,copter2,55476,55476,759952,1,0,1,0,1,1,computational fluid dynamics problem,759952 -GHS_indef,d_pretok,182730,182730,1641672,1,0,1,0,1,1,2D/3D problem,1641672 -GHS_indef,darcy003,389874,389874,2097566,1,0,1,0,1,1,2D/3D problem,2101242 -GHS_indef,dawson5,51537,51537,1010777,1,0,1,0,1,1,structural problem,1010777 -GHS_indef,dtoc,24993,24993,69972,1,0,0,0,1,1,optimization problem,69972 -GHS_indef,exdata_1,6001,6001,2269500,1,0,0,0,1,1,optimization problem,2269501 -GHS_indef,helm2d03,392257,392257,2741935,1,0,1,0,1,1,2D/3D problem,2741935 -GHS_indef,helm3d01,32226,32226,428444,1,0,1,0,1,1,2D/3D problem,428444 -GHS_indef,k1_san,67759,67759,559774,1,0,1,0,1,1,2D/3D problem,559774 -GHS_indef,laser,3002,3002,9000,1,0,1,0,1,1,materials problem,9000 -GHS_indef,mario001,38434,38434,204912,1,0,1,0,1,1,2D/3D problem,206156 -GHS_indef,mario002,389874,389874,2097566,1,0,1,0,1,1,duplicate 2D/3D problem,2101242 -GHS_indef,ncvxqp1,12111,12111,73963,1,0,0,0,1,1,optimization problem,73963 -GHS_indef,ncvxqp9,16554,16554,54040,1,0,0,0,1,1,optimization problem,54040 -GHS_indef,olesnik0,88263,88263,744216,1,0,1,0,1,1,2D/3D problem,744216 -GHS_indef,sit100,10262,10262,61046,1,0,1,0,1,1,2D/3D problem,61046 -GHS_indef,stokes128,49666,49666,558594,1,0,1,0,1,1,computational fluid dynamics problem,558594 -GHS_indef,stokes64,12546,12546,140034,1,0,1,0,1,1,computational fluid dynamics problem,140034 -GHS_indef,stokes64s,12546,12546,140034,1,0,1,0,1,1,computational fluid dynamics problem,140034 -GHS_indef,tuma1,22967,22967,87760,1,0,1,0,1,1,2D/3D problem,87760 -GHS_indef,tuma2,12992,12992,49365,1,0,1,0,1,1,2D/3D problem,49365 -GHS_indef,turon_m,189924,189924,1690876,1,0,1,0,1,1,2D/3D problem,1690876 -GHS_psdef,audikw_1,943695,943695,77651847,1,0,1,1,1,1,structural problem,77651847 -GHS_psdef,bmw7st_1,141347,141347,7318399,1,0,1,1,1,1,structural problem,7339667 -GHS_psdef,bmwcra_1,148770,148770,10641602,1,0,1,1,1,1,structural problem,10644002 -GHS_psdef,copter1,17222,17222,211064,1,1,1,0,1,1,computational fluid dynamics problem,211064 -GHS_psdef,copter2,55476,55476,759952,1,1,1,0,1,1,duplicate computational fluid dynamics problem,759952 -GHS_psdef,crankseg_1,52804,52804,10614210,1,0,1,1,1,1,structural problem,10614210 -GHS_psdef,crankseg_2,63838,63838,14148858,1,0,1,1,1,1,structural problem,14148858 -DNVS,crplat2,18010,18010,960946,1,1,1,0,1,1,structural problem,960946 -DNVS,fcondp2,201822,201822,11294316,1,1,1,0,1,1,structural problem,11294316 -GHS_psdef,finance256,37376,37376,298496,1,1,0,0,1,1,optimization problem,298496 -GHS_psdef,ford1,18728,18728,101576,1,1,1,0,1,1,structural problem,101576 -GHS_psdef,ford2,100196,100196,544688,1,1,1,0,1,1,structural problem,544688 -DNVS,fullb,199187,199187,11708077,1,1,1,0,1,1,structural problem,11708077 -DNVS,halfb,224617,224617,12387821,1,1,1,0,1,1,structural problem,12387821 -GHS_psdef,hood,220542,220542,9895422,1,0,1,1,1,1,structural problem,10768436 -GHS_psdef,inline_1,503712,503712,36816170,1,0,1,1,1,1,structural problem,36816342 -GHS_psdef,ldoor,952203,952203,42493817,1,0,1,1,1,1,structural problem,46522475 -DNVS,m_t1,97578,97578,9753570,1,0,1,1,1,1,structural problem,9753570 -GHS_psdef,oilpan,73752,73752,2148558,1,0,1,1,1,1,structural problem,3597188 -GHS_psdef,opt1,15449,15449,1930655,1,1,1,0,1,1,structural problem,1930655 -GHS_psdef,pds10,16558,16558,149658,1,1,0,0,1,1,optimization problem,149658 -GHS_psdef,pwt,36519,36519,326107,1,1,1,0,1,1,duplicate structural problem,326107 -GHS_psdef,ramage02,16830,16830,2866352,1,1,1,0,1,1,computational fluid dynamics problem,2866352 -GHS_psdef,s3dkq4m2,90449,90449,4427725,1,0,1,1,1,1,structural problem,4820891 -GHS_psdef,s3dkt3m2,90449,90449,3686223,1,0,1,1,1,1,structural problem,3753461 -DNVS,ship_001,34920,34920,3896496,1,0,1,1,1,1,structural problem,4644230 -DNVS,ship_003,121728,121728,3777036,1,0,1,1,1,1,structural problem,8086034 -DNVS,shipsec1,140874,140874,3568176,1,0,1,1,1,1,structural problem,7813404 -DNVS,shipsec5,179860,179860,4598604,1,0,1,1,1,1,structural problem,10113096 -DNVS,shipsec8,114919,114919,3303553,1,0,1,1,1,1,structural problem,6653399 -GHS_psdef,srb1,54924,54924,2962152,1,1,1,0,1,1,structural problem,2962152 -DNVS,thread,29736,29736,4444880,1,0,1,1,1,1,structural problem,4470048 -DNVS,trdheim,22098,22098,1935324,1,1,1,0,1,1,structural problem,1935324 -DNVS,troll,213453,213453,11985111,1,1,1,0,1,1,structural problem,11985111 -DNVS,tsyl201,20685,20685,2454957,1,1,1,0,1,1,structural problem,2454957 -GHS_psdef,vanbody,47072,47072,2329056,1,0,1,1,1,1,structural problem,2336898 -GHS_psdef,wathen100,30401,30401,471601,1,0,1,1,1,1,random 2D/3D problem,471601 -GHS_psdef,wathen120,36441,36441,565761,1,0,1,1,1,1,random 2D/3D problem,565761 -DNVS,x104,108384,108384,8713602,1,0,1,1,1,1,structural problem,10167624 -GHS_indef,a0nsdsil,80016,80016,355034,1,0,0,0,1,1,optimization problem,355034 -GHS_indef,a2nnsnsl,80016,80016,347222,1,0,0,0,1,1,optimization problem,347222 -GHS_indef,a5esindl,60008,60008,255004,1,0,0,0,1,1,optimization problem,255004 -GHS_indef,blockqp1,60012,60012,640033,1,0,0,0,1,1,optimization problem,640033 -GHS_indef,bloweya,30004,30004,150009,1,0,1,0,1,1,materials problem,150009 -GHS_indef,boyd1,93279,93279,1211231,1,0,0,0,1,1,optimization problem,1211231 -GHS_indef,boyd2,466316,466316,1500397,1,0,0,0,1,1,optimization problem,1500397 -GHS_indef,brainpc2,27607,27607,179395,1,0,0,0,1,1,optimization problem,179395 -GHS_indef,bratu3d,27792,27792,173796,1,0,1,0,1,1,2D/3D problem,173796 -GHS_indef,cont-201,80595,80595,438795,1,0,0,0,1,1,optimization problem,438795 -GHS_indef,cont-300,180895,180895,988195,1,0,0,0,1,1,optimization problem,988195 -GHS_indef,dixmaanl,60000,60000,299998,1,0,0,0,1,1,optimization problem,299998 -GHS_indef,linverse,11999,11999,95977,1,0,0,0,1,1,statistical/mathematical problem,95977 -GHS_indef,ncvxbqp1,50000,50000,349968,1,0,0,0,1,1,optimization problem,349968 -GHS_indef,ncvxqp3,75000,75000,499964,1,0,0,0,1,1,optimization problem,499964 -GHS_indef,ncvxqp5,62500,62500,424966,1,0,0,0,1,1,optimization problem,424966 -GHS_indef,ncvxqp7,87500,87500,574962,1,0,0,0,1,1,optimization problem,574962 -GHS_indef,sparsine,50000,50000,1548988,1,0,1,0,1,1,structural problem,1548988 -GHS_indef,spmsrtls,29995,29995,229947,1,0,0,0,1,1,statistical/mathematical problem,229947 -GHS_psdef,cvxbqp1,50000,50000,349968,1,0,0,1,1,1,optimization problem,349968 -GHS_psdef,gridgena,48962,48962,512084,1,0,0,1,1,1,optimization problem,512084 -GHS_psdef,jnlbrng1,40000,40000,199200,1,0,0,1,1,1,optimization problem,199200 -GHS_psdef,minsurfo,40806,40806,203622,1,0,0,1,1,1,optimization problem,203622 -GHS_psdef,obstclae,40000,40000,197608,1,0,0,1,1,1,optimization problem,197608 -GHS_psdef,torsion1,40000,40000,197608,1,0,0,1,1,1,duplicate optimization problem,197608 -Rajat,rajat15,37261,37261,443573,1,0,0,0,1,0.943060217954145,circuit simulation problem,443573 -Bydder,mri1,65536,147456,589824,1,0,1,0,0,0,computer graphics/vision problem,589824 -Bydder,mri2,63240,147456,569160,1,0,1,0,0,0,computer graphics/vision problem,569160 -Engwirda,airfoil_2d,14214,14214,259688,1,0,1,0,0.981668119637925,0,computational fluid dynamics problem,259688 -IBM_EDA,dc1,116835,116835,766396,1,0,0,0,0.8542477149952045,0.2426284829292399,circuit simulation problem sequence,766396 -IBM_EDA,dc2,116835,116835,766396,1,0,0,0,0.8542477149952045,0.2426284829292399,subsequent circuit simulation problem,766396 -IBM_EDA,dc3,116835,116835,766396,1,0,0,0,0.8542477149952045,0.2426407989395915,subsequent circuit simulation problem,766396 -IBM_EDA,trans4,116835,116835,749800,1,0,0,0,0.8542477149952045,0.3020988522272163,circuit simulation problem sequence,766396 -IBM_EDA,trans5,116835,116835,749800,1,0,0,0,0.8542477149952045,0.3018997890878642,subsequent circuit simulation problem,766396 -Morandini,robot,120,120,870,1,0,1,0,0.2177215189873418,0.05316455696202532,robotics problem,870 -Morandini,rotor1,100,100,708,1,0,1,0,0.514018691588785,0.07165109034267912,structural problem,708 -Morandini,rotor2,791,791,10685,1,0,1,0,0.2341063574570172,0.1155537784886046,structural problem,10685 -MathWorks,tomography,500,500,28726,1,0,1,0,1,0.9331113158081201,computer graphics/vision problem,28726 -Kemelmacher,Kemelmacher,28452,9693,100875,1,0,1,0,0,0,computer graphics/vision problem,100875 -MathWorks,Kuu,7102,7102,340200,1,0,1,1,1,1,structural problem,340200 -MathWorks,Muu,7102,7102,170134,1,0,1,1,1,1,structural problem,170134 -Toledo,deltaX,68600,21961,247424,1,0,0,0,0,0,counter-example problem,247424 -VanVelzen,std1_Jac2_db,21982,21982,498771,1,0,0,0,0.3261820218167785,8.389455293641422e-06,chemical process simulation problem,498771 -VanVelzen,std1_Jac2,21982,21982,1248213,1,0,0,0,1.441471454060305e-05,0,chemical process simulation problem,1248731 -VanVelzen,std1_Jac3_db,21982,21982,531826,1,0,0,0,0.3593020610225873,7.84553706623987e-06,chemical process simulation problem,531826 -VanVelzen,std1_Jac3,21982,21982,1455374,1,0,0,0,1.37377639453759e-05,0,chemical process simulation problem,1455848 -VanVelzen,Zd_Jac2_db,22835,22835,676439,1,0,0,0,0.3038965489807284,3.059956793410077e-06,chemical process simulation problem,676439 -VanVelzen,Zd_Jac2,22835,22835,1642407,1,0,0,0,0.000249569948369456,0,chemical process simulation problem,1642833 -VanVelzen,Zd_Jac3_db,22835,22835,713907,1,0,0,0,0.3299540424152621,2.894054454528616e-06,chemical process simulation problem,713907 -VanVelzen,Zd_Jac3,22835,22835,1915726,1,0,0,0,0.0002609405864378739,0,chemical process simulation problem,1916152 -VanVelzen,Zd_Jac6_db,22835,22835,663643,1,0,0,0,0.3245527521504101,3.121059662176502e-06,chemical process simulation problem,663643 -VanVelzen,Zd_Jac6,22835,22835,1711557,1,0,0,0,0.0002161245251101651,0,chemical process simulation problem,1711983 -Rajat,rajat16,94294,94294,476766,1,0,0,0,0.9900237892855388,0.6363336955309653,circuit simulation problem,641159 -Rajat,rajat17,94294,94294,479246,1,0,0,0,0.9900237892855388,0.2705152684049277,circuit simulation problem,641159 -Rajat,rajat18,94294,94294,479151,1,0,0,0,0.626033640830624,0.2849580148146621,circuit simulation problem,485143 -Rajat,rajat19,1157,1157,3699,1,0,0,0,0.9041281299345816,0.9193154034229829,circuit simulation problem,5399 -Lourakis,bundle1,10581,10581,770811,1,0,1,1,1,1,computer graphics/vision problem,770901 -PARSEC,benzene,8219,8219,242669,1,0,0,0,1,1,theoretical/quantum chemistry problem,242669 -PARSEC,CO,221119,221119,7666057,1,0,0,0,1,1,theoretical/quantum chemistry problem,7666057 -PARSEC,Ga10As10H30,113081,113081,6115633,1,0,0,0,1,1,theoretical/quantum chemistry problem,6115633 -PARSEC,Ga19As19H42,133123,133123,8884839,1,0,0,0,1,1,theoretical/quantum chemistry problem,8884839 -PARSEC,Ga3As3H12,61349,61349,5970947,1,0,0,0,1,1,theoretical/quantum chemistry problem,5970947 -PARSEC,Ga41As41H72,268096,268096,18488476,1,0,0,0,1,1,theoretical/quantum chemistry problem,18488476 -PARSEC,GaAsH6,61349,61349,3381809,1,0,0,0,1,1,theoretical/quantum chemistry problem,3381809 -PARSEC,Ge87H76,112985,112985,7892195,1,0,0,0,1,1,theoretical/quantum chemistry problem,7892195 -PARSEC,Ge99H100,112985,112985,8451395,1,0,0,0,1,1,theoretical/quantum chemistry problem,8451395 -PARSEC,H2O,67024,67024,2216736,1,0,0,0,1,1,theoretical/quantum chemistry problem,2216736 -PARSEC,Na5,5832,5832,305630,1,0,0,0,1,1,theoretical/quantum chemistry problem,305630 -PARSEC,Si10H16,17077,17077,875923,1,0,0,0,1,1,theoretical/quantum chemistry problem,875923 -PARSEC,Si2,769,769,17801,1,0,0,0,1,1,theoretical/quantum chemistry problem,17801 -PARSEC,Si34H36,97569,97569,5156379,1,0,0,0,1,1,theoretical/quantum chemistry problem,5156379 -PARSEC,Si41Ge41H72,185639,185639,15011265,1,0,0,0,1,1,theoretical/quantum chemistry problem,15011265 -PARSEC,Si5H12,19896,19896,738598,1,0,0,0,1,1,theoretical/quantum chemistry problem,738598 -PARSEC,Si87H76,240369,240369,10661631,1,0,0,0,1,1,theoretical/quantum chemistry problem,10661631 -PARSEC,SiH4,5041,5041,171903,1,0,0,0,1,1,theoretical/quantum chemistry problem,171903 -PARSEC,SiNa,5743,5743,198787,1,0,0,0,1,1,theoretical/quantum chemistry problem,198787 -PARSEC,SiO2,155331,155331,11283503,1,0,0,0,1,1,theoretical/quantum chemistry problem,11283503 -PARSEC,SiO,33401,33401,1317655,1,0,0,0,1,1,theoretical/quantum chemistry problem,1317655 -Rajat,rajat20,86916,86916,604299,1,0,0,0,0.9906228863063395,0.1122042623922186,circuit simulation problem,605045 -Rajat,rajat21,411676,411676,1876011,1,0,0,0,0.7647530446640561,0.6145057622905241,circuit simulation problem,1893370 -Rajat,rajat22,39899,39899,195429,1,0,0,0,0.9800702079331211,0.3342735182643515,circuit simulation problem,197264 -Rajat,rajat23,110355,110355,555441,1,0,0,0,0.9891581094231137,0.3324988529606978,circuit simulation problem,556938 -Rajat,rajat24,358172,358172,1946979,1,0,0,0,0.996935347128458,0.2716106951495262,circuit simulation problem,1948235 -Rajat,rajat25,87190,87190,606489,1,0,0,0,0.9906572831072957,0.1118151951820715,circuit simulation problem,607235 -Rajat,rajat26,51032,51032,247528,1,0,0,0,0.9853537517311467,0.3271800826884766,circuit simulation problem,249302 -Rajat,rajat27,20640,20640,97353,1,0,0,0,0.9645908149382882,0.3035747337767303,circuit simulation problem,99777 -Rajat,rajat28,87190,87190,606489,1,0,0,0,0.9906572831072957,0.1072533437604223,circuit simulation problem,607235 -MathWorks,Sieber,2290,2290,14873,1,0,0,0,0.7272727272727273,0,counter-example problem,14873 -Andrianov,ex3sta1,16782,16782,678998,1,1,0,0,1,1,optimization problem,678998 -Andrianov,fxm3_6,5026,5026,94026,1,1,0,0,1,1,optimization problem,94026 -Andrianov,fxm4_6,18892,18892,497844,1,1,0,0,1,1,optimization problem,497844 -Andrianov,ins2,309412,309412,2751484,1,1,0,0,1,1,optimization problem,2751484 -Andrianov,lp1,534388,534388,1643420,1,1,0,0,1,1,optimization problem,1643420 -Andrianov,lpl1,32460,32460,328036,1,1,0,0,1,1,optimization problem,328036 -Andrianov,mip1,66463,66463,10352819,1,1,0,0,1,1,optimization problem,10352819 -Andrianov,net100,29920,29920,2033200,1,1,0,0,1,1,optimization problem,2033200 -Andrianov,net125,36720,36720,2577200,1,1,0,0,1,1,optimization problem,2577200 -Andrianov,net150,43520,43520,3121200,1,1,0,0,1,1,optimization problem,3121200 -Andrianov,net25,9520,9520,401200,1,1,0,0,1,1,optimization problem,401200 -Andrianov,net4-1,88343,88343,2441727,1,1,0,0,1,1,optimization problem,2441727 -Andrianov,net50,16320,16320,945200,1,1,0,0,1,1,optimization problem,945200 -Andrianov,net75,23120,23120,1489200,1,1,0,0,1,1,optimization problem,1489200 -Andrianov,pattern1,19242,19242,9323432,1,1,0,0,1,1,optimization problem,9323432 -Andrianov,pf2177,9728,9728,725144,1,1,0,0,1,1,optimization problem,725144 -MKS,fp,7548,7548,834222,1,0,1,0,0.7575174939506899,0.002912877385765126,electromagnetics problem,848553 -Rajat,rajat29,643994,643994,3760246,1,0,0,0,0.6856406275660725,0.6910598008173955,circuit simulation problem,4866270 -Rajat,rajat30,643994,643994,6175244,1,0,0,0,0.9945912407336421,0.009194565053000587,circuit simulation problem,6175377 -Rajat,rajat31,4690002,4690002,20316253,1,0,0,0,1,0.4000960614178773,circuit simulation problem,20316253 -Raju,laminar_duct3D,67173,67173,3788857,1,0,1,0,0.9022908019592145,0.5518386226500883,computational fluid dynamics problem,3833077 -Bates,Chem97Zt,2541,31022,62044,1,1,0,0,0,0,statistical/mathematical problem,62044 -Bates,Chem97ZtZ,2541,2541,7361,1,0,0,1,1,1,statistical/mathematical problem,7361 -Schmid,thermal1,82654,82654,574458,1,0,1,1,1,1,thermal problem,574458 -Schmid,thermal2,1228045,1228045,8580313,1,0,1,1,1,1,thermal problem,8580313 -MathWorks,Kaufhold,8765,8765,42471,1,0,0,0,1,0.778437073518068,counter-example problem,42471 -Bindel,ted_A,10605,10605,424587,1,0,1,0,0.5657922767749357,0.1057079149564699,thermal problem,424587 -Bindel,ted_B,10605,10605,144579,1,0,1,1,1,1,thermal problem,144579 -Bindel,ted_AB,10605,10605,522387,0,0,1,0,0.6415700434950818,0,thermal problem,522387 -Bindel,ted_A_unscaled,10605,10605,424587,1,0,1,0,0.5657922767749357,0.1057079149564699,thermal problem,424587 -Bindel,ted_B_unscaled,10605,10605,144579,1,0,1,1,1,1,thermal problem,144579 -Bindel,ted_AB_unscaled,10605,10605,522387,0,0,1,0,0.6415700434950818,0,thermal problem,522387 -Koutsovasilis,F1,343791,343791,26837113,1,0,1,0,1,1,structural problem,26837113 -AMD,G2_circuit,150102,150102,726674,1,0,0,1,1,1,circuit simulation problem,726674 -IBM_EDA,ckt11752_dc_1,49702,49702,333029,1,0,0,0,0.9838102263462359,0.7355387943965807,circuit simulation problem,333029 -IBM_EDA,ckt11752_tr_0,49702,49702,332807,1,0,0,0,0.9838102263462359,0.7360237367761078,circuit simulation problem,333029 -Sandia,ASIC_100k,99340,99340,940621,1,0,0,0,1,0.002631466347182405,circuit simulation problem,954163 -Sandia,ASIC_100ks,99190,99190,578890,1,0,0,0,1,0.0007546383156139254,circuit simulation problem,578890 -Sandia,ASIC_320k,321821,321821,1931828,1,0,0,0,1,0.3578041366837217,circuit simulation problem,2635364 -Sandia,ASIC_320ks,321671,321671,1316085,1,0,0,0,1,0.2891089626654492,circuit simulation problem,1827807 -Sandia,ASIC_680k,682862,682862,2638997,1,0,0,0,1,0.004444308126428144,circuit simulation problem,3871773 -Sandia,ASIC_680ks,682712,682712,1693767,1,0,0,0,1,0.005801860432914134,circuit simulation problem,2329176 -AMD,G3_circuit,1585478,1585478,7660826,1,0,0,1,1,1,circuit simulation problem,7660826 -GHS_psdef,apache1,80800,80800,542184,1,0,1,1,1,1,structural problem,542184 -GHS_psdef,apache2,715176,715176,4817870,1,0,1,1,1,1,structural problem,4817870 -GHS_indef,bloweybl,30003,30003,109999,1,0,1,0,1,1,materials problem,120000 -GHS_indef,bloweybq,10001,10001,49999,1,0,1,1,1,1,materials problem,69991 -GHS_indef,cvxqp3,17500,17500,114962,1,0,0,0,1,1,optimization problem,122462 -GHS_indef,qpband,20000,20000,45000,1,0,0,0,1,1,optimization problem,45000 -Oberwolfach,chipcool0,20082,20082,281150,1,0,1,0,1,0.8688617524936032,model reduction problem,281150 -Oberwolfach,chipcool1,20082,20082,281150,1,0,1,0,1,0.08556391438245974,model reduction problem,281150 -Oberwolfach,filter2D,1668,1668,10750,1,0,1,0,1,1,model reduction problem,10750 -Oberwolfach,filter3D,106437,106437,2707179,1,0,1,0,1,1,model reduction problem,2707179 -Oberwolfach,flowmeter0,9669,9669,67391,1,0,1,0,1,1,model reduction problem,67391 -Oberwolfach,flowmeter5,9669,9669,67391,1,0,1,0,1,0.06222930598385364,model reduction problem,67391 -Oberwolfach,gas_sensor,66917,66917,1703365,1,0,1,0,1,1,model reduction problem,1703365 -Oberwolfach,gyro,17361,17361,1021159,1,0,1,1,1,1,model reduction problem,1021159 -Oberwolfach,inlet,11730,11730,328323,1,0,1,0,0.6083330964361183,9.475888601453601e-05,model reduction problem,328323 -Oberwolfach,LF10000,19998,19998,99982,1,0,1,1,1,1,model reduction problem,99982 -Oberwolfach,LF10,18,18,82,1,0,1,1,1,1,model reduction problem,82 -Oberwolfach,LFAT5000,19994,19994,79966,1,0,1,1,1,1,model reduction problem,79966 -Oberwolfach,LFAT5,14,14,46,1,0,1,1,1,1,model reduction problem,46 -Oberwolfach,piston,2025,2025,100015,1,0,1,0,1,0.03275844473925911,model reduction problem,100015 -Oberwolfach,rail_1357,1357,1357,8985,1,0,1,0,1,1,model reduction problem,8985 -Oberwolfach,rail_20209,20209,20209,139233,1,0,1,0,1,1,model reduction problem,139233 -Oberwolfach,rail_5177,5177,5177,35185,1,0,1,0,1,1,model reduction problem,35185 -Oberwolfach,rail_79841,79841,79841,553921,1,0,1,0,1,1,model reduction problem,553921 -Oberwolfach,spiral,1434,1434,18228,1,0,1,0,1,1,model reduction problem,18228 -Oberwolfach,t2dah,11445,11445,176117,1,0,1,0,1,1,model reduction problem,176117 -Oberwolfach,t2dal_bci,4257,4257,37465,1,0,1,0,1,1,model reduction problem,37465 -Oberwolfach,t2dal,4257,4257,37465,1,0,1,0,1,1,model reduction problem,37465 -Oberwolfach,t3dh,79171,79171,4352105,1,0,1,0,1,1,model reduction problem,4352105 -Oberwolfach,t3dl,20360,20360,509866,1,0,1,0,1,1,model reduction problem,509866 -Oberwolfach,windscreen,22692,22692,1482390,0,0,1,0,1,0,model reduction problem,1482390 -Oberwolfach,bone010,986703,986703,47851783,1,0,1,1,1,1,model reduction problem,71666325 -Oberwolfach,boneS01,127224,127224,5516602,1,0,1,1,1,1,model reduction problem,6715152 -Oberwolfach,boneS10,914898,914898,40878708,1,0,1,1,1,1,model reduction problem,55468422 -Pajek,California,9664,9664,16150,1,1,0,0,0.02241486068111455,0.02241486068111455,directed graph,16150 -Pajek,Cities,55,46,1342,1,0,0,0,0,0,weighted bipartite graph,1342 -Pajek,CSphd,1882,1882,1740,1,1,0,0,0,0,directed graph,1740 -Pajek,dictionary28,52652,52652,178076,1,1,0,0,1,1,undirected graph,178076 -Pajek,divorce,50,9,225,1,1,0,0,0,0,bipartite graph,225 -Pajek,EAT_RS,23219,23219,325592,1,0,0,0,0.1236262721980876,0.03102501938294547,directed weighted graph,325592 -Pajek,EAT_SR,23219,23219,325589,1,0,0,0,0.1236274132759018,0.03102530574571187,directed weighted graph,325589 -Pajek,EPA,4772,4772,8965,1,1,0,0,0.01249302844394869,0.01249302844394869,directed graph,8965 -Pajek,Erdos02,6927,6927,16944,1,1,0,0,1,1,undirected graph,16944 -Pajek,Erdos971,472,472,2628,1,1,0,0,1,1,undirected graph,2628 -Pajek,Erdos972,5488,5488,14170,1,1,0,0,1,1,undirected graph,14170 -Pajek,Erdos981,485,485,2762,1,1,0,0,1,1,undirected graph,2762 -Pajek,Erdos982,5822,5822,14750,1,1,0,0,1,1,undirected graph,14750 -Pajek,Erdos991,492,492,2834,1,1,0,0,1,1,undirected graph,2834 -Pajek,Erdos992,6100,6100,15030,1,1,0,0,1,1,undirected graph,15030 -Pajek,EVA,8497,8497,6726,1,1,0,0,0.003866745984533016,0.003866745984533016,directed graph,6726 -Pajek,FA,10617,10617,72176,1,0,0,0,0.232333869090506,0.005847142936318794,directed weighted graph,72176 -Pajek,foldoc,13356,13356,120238,1,0,0,0,0.4785009730700777,0.459472047106572,directed weighted graph,120238 -Pajek,football,35,35,118,1,0,0,0,0,0,directed weighted graph,118 -Pajek,GD00_a,352,352,458,1,1,0,0,0,0,directed graph,458 -Pajek,GD00_c,638,638,1041,1,0,0,0,0.02133850630455868,0.01551891367604268,directed multigraph,1041 -Pajek,GD01_a,311,311,645,1,0,0,0,0.01550387596899225,0.01550387596899225,directed weighted graph,645 -Pajek,GD01_Acap,953,953,645,1,0,0,0,0.01550387596899225,0.01550387596899225,directed multigraph,645 -Pajek,GD01_b,18,18,37,1,1,0,0,0.5142857142857142,0.5142857142857142,directed graph,37 -Pajek,GD01_c,33,33,135,1,0,0,0,0,0,directed multigraph,135 -Pajek,GD02_a,23,23,87,1,1,0,0,0.6436781609195402,0.6436781609195402,directed graph,87 -Pajek,GD02_b,80,80,232,1,1,0,0,0,0,directed graph,232 -Pajek,GD06_Java,1538,1538,8032,1,1,0,0,0.05353585657370518,0.05353585657370518,directed graph,8032 -Pajek,GD06_theory,101,101,380,1,1,0,0,1,1,undirected graph,380 -Pajek,GD95_a,36,36,57,1,1,0,0,0.03508771929824561,0.03508771929824561,directed graph,57 -Pajek,GD95_b,73,73,96,1,1,0,0,0,0,directed graph,96 -Pajek,GD95_c,62,62,287,1,1,0,0,0.9965156794425087,0.9965156794425087,directed graph,287 -Pajek,GD96_a,1096,1096,1677,1,0,0,0,0,0,directed multigraph,1677 -Pajek,GD96_b,111,111,193,1,1,0,0,0,0,directed graph,193 -Pajek,GD96_c,65,65,250,1,1,0,0,1,1,undirected graph,250 -Pajek,GD96_d,180,180,229,1,1,0,0,0.008733624454148471,0.008733624454148471,directed graph,229 -Pajek,GD97_a,84,84,332,1,1,0,0,1,1,directed graph,332 -Pajek,GD97_b,47,47,264,1,0,0,0,1,1,undirected weighted graph,264 -Pajek,GD97_c,452,452,460,1,0,0,0,0,0,directed multigraph,460 -Pajek,GD98_a,38,38,50,1,1,0,0,0.16,0.16,directed graph,50 -Pajek,GD98_b,121,121,207,1,1,0,0,0.7246376811594203,0.7246376811594203,directed graph,207 -Pajek,GD98_c,112,112,336,1,1,0,0,1,1,undirected graph,336 -Pajek,GD99_b,64,64,252,1,0,0,0,1,1,undirected multigraph,252 -Pajek,GD99_c,105,105,149,1,1,0,0,0.3892617449664429,0.3892617449664429,directed graph,149 -Pajek,geom,7343,7343,23796,1,0,0,0,1,1,undirected weighted graph,23796 -Pajek,GlossGT,72,72,122,1,1,0,0,0.06557377049180328,0.06557377049180328,directed graph,122 -Pajek,HEP-th,27240,27240,342437,1,1,0,0,0.002774548916757691,0.002774548916757691,directed graph,342437 -Pajek,HEP-th-new,27770,27770,352807,1,1,0,0,0.002738343613933237,0.002738343613933237,directed graph,352807 -Pajek,IMDB,428440,896308,3782463,1,1,0,0,0,0,bipartite graph,3782463 -Pajek,internet,124651,124651,207214,1,0,0,0,0.01444913320583093,0.002245819852432203,directed weighted graph,207214 -Pajek,Journals,124,124,12068,1,0,0,1,1,1,undirected weighted graph,12068 -Pajek,Kohonen,4470,4470,12731,1,1,0,0,0.001728336868567837,0.001728336868567837,directed graph,12731 -Pajek,Lederberg,8843,8843,41601,1,0,0,0,0.003029210241615579,0.003029210241615579,directed multigraph,41601 -Barabasi,NotreDame_actors,392400,127823,1470404,1,1,0,0,0,0,bipartite graph,1470404 -Barabasi,NotreDame_www,325729,325729,929849,1,1,0,0,0.3315875213545298,0.3315875213545298,directed graph,929849 -Barabasi,NotreDame_yeast,2114,2114,4480,1,1,0,0,1,1,undirected graph,4480 -Pajek,ODLIS,2909,2909,18246,1,0,0,0,0.2043747601556932,0.1997697494654898,directed multigraph,18246 -Pajek,patents_main,240547,240547,560943,1,0,0,0,0,0,directed weighted graph,560943 -Pajek,patents,3774768,3774768,14970767,1,1,0,0,0,0,directed graph,14970767 -Pajek,Ragusa16,24,24,81,1,0,0,0,0.3661971830985916,0.1971830985915493,directed weighted graph,81 -Pajek,Ragusa18,23,23,64,1,0,0,0,0.3278688524590164,0.1967213114754098,directed weighted graph,64 -Pajek,Reuters911,13332,13332,296076,1,0,0,0,1,1,undirected weighted graph sequence,296076 -Pajek,Roget,1022,1022,5075,1,1,0,0,0.5620811982656682,0.5620811982656682,directed graph,5075 -Pajek,Sandi_authors,86,86,248,1,0,0,0,1,1,undirected weighted graph,248 -Pajek,Sandi_sandi,314,360,613,1,1,0,0,0,0,bipartite graph,613 -Pajek,SciMet,3084,3084,10413,1,0,0,0,0.002497118709181713,0.002497118709181713,directed multigraph,10413 -Pajek,SmaGri,1059,1059,4919,1,0,0,0,0.0008133387555917039,0.0008133387555917039,directed multigraph,4919 -Pajek,SmallW,396,396,994,1,0,0,0,0,0,directed multigraph,994 -Pajek,Stranke94,10,10,90,1,0,0,0,1,1,undirected weighted graph,90 -Pajek,Tina_AskCal,11,11,29,1,1,0,0,0.2758620689655172,0.2758620689655172,directed graph,29 -Pajek,Tina_AskCog,11,11,36,1,1,0,0,0.5,0.5,directed graph,36 -Pajek,Tina_DisCal,11,11,41,1,1,0,0,0.4390243902439024,0.4390243902439024,directed graph,41 -Pajek,Tina_DisCog,11,11,48,1,1,0,0,0.5,0.5,directed graph,48 -Pajek,USAir97,332,332,4252,1,0,0,0,1,1,undirected weighted graph,4252 -Pajek,USpowerGrid,4941,4941,13188,1,1,0,0,1,1,undirected graph,13188 -Pajek,Wordnet3,82670,82670,132964,1,0,0,0,0.1765463136851033,0.1741230992912098,directed weighted graph,132964 -Pajek,WorldCities,315,100,7518,1,0,0,0,0,0,weighted bipartite graph,7518 -Pajek,yeast,2361,2361,13828,1,1,0,0,1,1,undirected graph,13828 -Pajek,Zewail,6752,6752,54233,1,0,0,0,0.001843929783153858,0.001843929783153858,directed multigraph,54233 -Zitney,extr1b,2836,2836,10965,1,0,0,0,0.003859310586790632,0,chemical process simulation problem sequence,11404 -Zitney,hydr1c,5308,5308,22592,1,0,0,0,0.004126315789473685,0.0006197432492253209,chemical process simulation problem sequence,23752 -Schenk_IBMNA,c-18,2169,2169,15145,1,0,0,0,1,1,optimization problem,15145 -Schenk_IBMNA,c-19,2327,2327,21817,1,0,0,0,1,1,optimization problem,21817 -Schenk_IBMNA,c-20,2921,2921,20445,1,0,0,0,1,1,optimization problem,22685 -Schenk_IBMNA,c-21,3509,3509,32145,1,0,0,0,1,1,optimization problem,32157 -Schenk_IBMNA,c-22,3792,3792,28870,1,0,0,0,1,1,optimization problem,28872 -Schenk_IBMNA,c-23,3969,3969,31079,1,0,0,0,1,1,optimization problem,31079 -Schenk_IBMNA,c-24,4119,4119,35699,1,0,0,0,1,1,optimization problem,35699 -Schenk_IBMNA,c-25,3797,3797,49635,1,0,0,0,1,1,optimization problem,49635 -Schenk_IBMNA,c-26,4307,4307,34537,1,0,0,0,1,1,optimization problem,34537 -Schenk_IBMNA,c-27,4563,4563,30927,1,0,0,0,1,1,optimization problem,31375 -Schenk_IBMNA,c-28,4598,4598,30590,1,0,0,0,1,1,optimization problem,30590 -Schenk_IBMNA,c-29,5033,5033,43731,1,0,0,0,1,1,optimization problem,43731 -Schenk_IBMNA,c-30,5321,5321,65693,1,0,0,0,1,1,optimization problem,65693 -Schenk_IBMNA,c-31,5339,5339,78571,1,0,0,0,1,1,optimization problem,78571 -Schenk_IBMNA,c-32,5975,5975,54471,1,0,0,0,1,1,optimization problem,54471 -Schenk_IBMNA,c-33,6317,6317,56123,1,0,0,0,1,1,optimization problem,56123 -Schenk_IBMNA,c-34,6611,6611,64333,1,0,0,0,1,1,optimization problem,64333 -Schenk_IBMNA,c-35,6537,6537,62891,1,0,0,0,1,1,optimization problem,62891 -Schenk_IBMNA,c-36,7479,7479,65941,1,0,0,0,1,1,optimization problem,65941 -Schenk_IBMNA,c-37,8204,8204,74676,1,0,0,0,1,1,optimization problem,74676 -Schenk_IBMNA,c-38,8127,8127,77689,1,0,0,0,1,1,optimization problem,77689 -Schenk_IBMNA,c-39,9271,9271,116587,1,0,0,0,1,1,optimization problem,136811 -Schenk_IBMNA,c-40,9941,9941,81501,1,0,0,0,1,1,optimization problem,81501 -Schenk_IBMNA,c-41,9769,9769,101635,1,0,0,0,1,1,optimization problem,101745 -Schenk_IBMNA,c-42,10471,10471,110285,1,0,0,0,1,1,optimization problem,110285 -Schenk_IBMNA,c-43,11125,11125,123659,1,0,0,0,1,1,optimization problem,123675 -Schenk_IBMNA,c-44,10728,10728,85000,1,0,0,0,1,1,optimization problem,85000 -Schenk_IBMNA,c-45,13206,13206,174452,1,0,0,0,1,1,optimization problem,174452 -Schenk_IBMNA,c-46,14913,14913,130397,1,0,0,0,1,1,optimization problem,130397 -Schenk_IBMNA,c-47,15343,15343,211401,1,0,0,0,1,1,optimization problem,211401 -Schenk_IBMNA,c-48,18354,18354,166080,1,0,0,0,1,1,optimization problem,166080 -Schenk_IBMNA,c-49,21132,21132,157040,1,0,0,0,1,1,optimization problem,157042 -Schenk_IBMNA,c-50,22401,22401,180245,1,0,0,0,1,1,optimization problem,193625 -Schenk_IBMNA,c-51,23196,23196,203048,1,0,0,0,1,1,optimization problem,203050 -Schenk_IBMNA,c-52,23948,23948,202708,1,0,0,0,1,1,optimization problem,202716 -Schenk_IBMNA,c-53,30235,30235,355139,1,0,0,0,1,1,optimization problem,372213 -Schenk_IBMNA,c-54,31793,31793,385987,1,0,0,0,1,1,optimization problem,391693 -Schenk_IBMNA,c-56,35910,35910,380240,1,0,0,0,1,1,optimization problem,380900 -Schenk_IBMNA,c-57,37833,37833,403373,1,0,0,0,1,1,optimization problem,405197 -Schenk_IBMNA,c-60,43640,43640,298570,1,0,0,0,1,1,optimization problem,298578 -Schenk_IBMNA,c-61,43618,43618,310016,1,0,0,0,1,1,optimization problem,310016 -Schenk_IBMNA,c-65,48066,48066,360428,1,0,0,0,1,1,optimization problem,360528 -Schenk_IBMNA,c-big,345241,345241,2340859,1,0,0,0,1,1,optimization problem,2341011 -Schenk_AFE,af_0_k101,503625,503625,17550675,1,0,1,1,1,1,structural problem,17550675 -Schenk_AFE,af_1_k101,503625,503625,17550675,1,0,1,1,1,1,structural problem,17550675 -Schenk_AFE,af_2_k101,503625,503625,17550675,1,0,1,1,1,1,structural problem,17550675 -Schenk_AFE,af_3_k101,503625,503625,17550675,1,0,1,1,1,1,structural problem,17550675 -Schenk_AFE,af_4_k101,503625,503625,17550675,1,0,1,1,1,1,structural problem,17550675 -Schenk_AFE,af_5_k101,503625,503625,17550675,1,0,1,1,1,1,structural problem,17550675 -Schenk_AFE,af_shell10,1508065,1508065,52259885,1,0,1,0,1,1,structural problem,52672325 -Schenk_IBMNA,c-64b,51035,51035,707601,1,0,0,0,1,1,subsequent optimization problem,717841 -Schenk_IBMNA,c-66b,49989,49989,444851,1,0,0,0,1,1,subsequent optimization problem,499007 -Schenk_IBMNA,c-67b,57975,57975,530583,1,0,0,0,1,1,subsequent optimization problem,531935 -Schenk_IBMNA,c-73b,169422,169422,1279274,1,0,0,0,1,1,subsequent optimization problem,1279274 -QCD,conf5_0-4x4-10,3072,3072,119808,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,119808 -QCD,conf5_0-4x4-14,3072,3072,119808,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,119808 -QCD,conf5_0-4x4-18,3072,3072,119808,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,119808 -QCD,conf5_0-4x4-22,3072,3072,119808,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,119808 -QCD,conf5_0-4x4-26,3072,3072,119808,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,119808 -QCD,conf6_0-4x4-20,3072,3072,119808,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,119808 -QCD,conf6_0-4x4-30,3072,3072,119808,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,119808 -QCD,conf5_4-8x8-05,49152,49152,1916928,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,1916928 -QCD,conf5_4-8x8-10,49152,49152,1916928,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,1916928 -QCD,conf5_4-8x8-15,49152,49152,1916928,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,1916928 -QCD,conf5_4-8x8-20,49152,49152,1916928,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,1916928 -QCD,conf6_0-8x8-20,49152,49152,1916928,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,1916928 -QCD,conf6_0-8x8-30,49152,49152,1916928,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,1916928 -QCD,conf6_0-8x8-80,49152,49152,1916928,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,1916928 -Cylshell,s1rmq4m1,5489,5489,262411,1,0,1,1,1,1,structural problem,281111 -Cylshell,s2rmq4m1,5489,5489,263351,1,0,1,1,1,1,structural problem,281111 -Cylshell,s3rmq4m1,5489,5489,262943,1,0,1,1,1,1,structural problem,281111 -Cylshell,s1rmt3m1,5489,5489,217651,1,0,1,1,1,1,structural problem,219521 -Cylshell,s2rmt3m1,5489,5489,217681,1,0,1,1,1,1,structural problem,219521 -Cylshell,s3rmt3m1,5489,5489,217669,1,0,1,1,1,1,structural problem,219521 -Cylshell,s3rmt3m3,5357,5357,207123,1,0,1,1,1,1,structural problem,207695 -Bai,cryg10000,10000,10000,49699,1,0,1,0,0.9974558553112169,0,materials problem,49699 -Bai,cryg2500,2500,2500,12349,1,0,1,0,0.9948218093207433,0,materials problem,12349 -Bai,dw2048,2048,2048,10114,1,0,1,0,0.9848747830399207,0.9481775353334987,electromagnetics problem,10114 -Bai,dw8192,8192,8192,41746,1,0,1,0,0.9626274065685164,0.9154199201287477,electromagnetics problem,41746 -Bai,dwa512,512,512,2480,1,0,1,0,0.9796747967479674,0.9146341463414634,electromagnetics problem,2480 -Bai,dwb512,512,512,2500,1,0,1,0,0.9698189134808853,0.8832997987927566,electromagnetics problem,2500 -Bai,dwg961a,961,961,3405,0,0,1,0,1,0.36,electromagnetics problem,3405 -Bai,dwg961b,961,961,10591,0,0,1,0,1,0.3476635514018692,electromagnetics problem,10591 -Bai,mhd1280a,1280,1280,47906,0,0,1,0,0.8978403914290535,0.3060992070187278,electromagnetics problem,47906 -Bai,mhd1280b,1280,1280,22778,0,0,1,1,1,1,electromagnetics problem,22778 -Bai,mhd3200a,3200,3200,68026,1,0,1,0,0.7676029990571827,0.2674009667619461,electromagnetics problem,68026 -Bai,mhd3200b,3200,3200,18316,1,0,1,1,1,1,electromagnetics problem,18316 -Bai,mhd4800a,4800,4800,102252,1,0,1,0,0.7677879876950033,0.2677033659541848,electromagnetics problem,102252 -Bai,mhd4800b,4800,4800,27520,1,0,1,1,1,1,electromagnetics problem,27520 -Bai,qh1484,1484,1484,6110,1,0,0,0,1,0.05377207062600321,power network problem,6110 -Bai,qh768,768,768,2934,1,0,0,0,0.9347300564061241,0,power network problem,2934 -Bai,rdb1250,1250,1250,7300,1,0,1,0,1,0.7933884297520661,computational fluid dynamics problem,7300 -Bai,rdb1250l,1250,1250,7300,1,0,1,0,1,0.7933884297520661,computational fluid dynamics problem,7300 -Bai,rdb200,200,200,1120,1,0,1,0,1,0.7826086956521739,computational fluid dynamics problem,1120 -Bai,rdb200l,200,200,1120,1,0,1,0,1,0.7826086956521739,computational fluid dynamics problem,1120 -Bai,rdb2048_noL,2048,2048,12032,1,0,1,0,1,0.7948717948717948,computational fluid dynamics problem,12032 -Bai,rdb3200l,3200,3200,18880,1,0,1,0,1,0.7959183673469388,computational fluid dynamics problem,18880 -Bai,rdb450,450,450,2580,1,0,1,0,1,0.7887323943661971,computational fluid dynamics problem,2580 -Bai,rdb450l,450,450,2580,1,0,1,0,1,0.7887323943661971,computational fluid dynamics problem,2580 -Bai,rdb800l,800,800,4640,1,0,1,0,1,0.7916666666666666,computational fluid dynamics problem,4640 -Bai,tols1090,1090,1090,3546,1,0,1,0,0.3178758414360509,0,computational fluid dynamics problem,3546 -Bai,tols2000,2000,2000,5184,1,0,1,0,0.3387276785714285,0,computational fluid dynamics problem,5184 -Bai,tols340,340,340,2196,1,0,1,0,0.2858627858627859,0,computational fluid dynamics problem,2196 -Bai,tols4000,4000,4000,8784,1,0,1,0,0.3606733524355301,0,computational fluid dynamics problem,8784 -Bai,tols90,90,90,1746,1,0,1,0,0.2688172043010753,0,computational fluid dynamics problem,1746 -POLYFLOW,invextr1_new,30412,30412,1793881,1,0,1,0,0.971442083756505,0.7213486599424203,computational fluid dynamics problem,1793881 -POLYFLOW,mixtank_new,29957,29957,1990919,1,0,1,0,1,0.9890905426943581,computational fluid dynamics problem,1995041 -INPRO,msdoor,415863,415863,19173163,1,0,1,1,1,1,structural problem,20240935 -Mittelmann,nug08-3rd,19728,29856,148416,1,0,0,0,0,0,linear programming problem,148416 -Mittelmann,pds-30,49944,158489,340635,1,0,0,0,0,0,linear programming problem,340635 -Mittelmann,pds-40,66844,217531,466800,1,0,0,0,0,0,linear programming problem,466800 -Mittelmann,pds-50,83060,275814,590833,1,0,0,0,0,0,linear programming problem,590833 -Mittelmann,pds-60,99431,336421,719557,1,0,0,0,0,0,linear programming problem,719557 -Mittelmann,pds-70,114944,390005,833465,1,0,0,0,0,0,linear programming problem,833465 -Mittelmann,pds-80,129181,434580,927826,1,0,0,0,0,0,linear programming problem,927826 -Mittelmann,pds-90,142823,475448,1014136,1,0,0,0,0,0,linear programming problem,1014136 -Mittelmann,pds-100,156243,514577,1096002,1,0,0,0,0,0,linear programming problem,1096002 -Mittelmann,rail507,507,63516,409856,1,0,0,0,0,0,linear programming problem,409856 -Mittelmann,rail516,516,47827,315412,1,0,0,0,0,0,linear programming problem,315412 -Mittelmann,rail582,582,56097,402290,1,0,0,0,0,0,linear programming problem,402290 -Mittelmann,rail2586,2586,923269,8011362,1,0,0,0,0,0,linear programming problem,8011362 -Mittelmann,rail4284,4284,1096894,11284032,1,0,0,0,0,0,linear programming problem,11284032 -Mittelmann,sgpf5y6,246077,312540,831976,1,0,0,0,0,0,linear programming problem,831976 -Mittelmann,stormG2_1000,528185,1377306,3459881,1,0,0,0,0,0,linear programming problem,3459881 -Mittelmann,watson_1,201155,386992,1055093,1,0,0,0,0,0,linear programming problem,1055093 -Mittelmann,watson_2,352013,677224,1846391,1,0,0,0,0,0,linear programming problem,1846391 -Mittelmann,cont11_l,1468599,1961394,5382999,1,0,0,0,0,0,linear programming problem,5382999 -Mittelmann,cont1_l,1918399,1921596,7031999,1,0,0,0,0,0,linear programming problem,7031999 -Mittelmann,fome11,12142,24460,71264,1,0,0,0,0,0,linear programming problem,71264 -Mittelmann,fome12,24284,48920,142528,1,0,0,0,0,0,linear programming problem,142528 -Mittelmann,fome13,48568,97840,285056,1,0,0,0,0,0,linear programming problem,285056 -Mittelmann,fome20,33874,108175,232647,1,0,0,0,0,0,linear programming problem,232647 -Mittelmann,fome21,67748,216350,465294,1,0,0,0,0,0,linear programming problem,465294 -Mittelmann,neos,479119,515905,1526794,1,0,0,0,0,0,linear programming problem,1526794 -Mittelmann,neos1,131581,133473,599590,1,0,0,0,0,0,linear programming problem,599590 -Mittelmann,neos2,132568,134128,685087,1,0,0,0,0,0,linear programming problem,685087 -Mittelmann,neos3,512209,518832,2055024,1,0,0,0,0,0,linear programming problem,2055024 -Mittelmann,spal_004,10203,321696,46168124,1,0,0,0,0,0,linear programming problem,46168124 -Meszaros,gams10am,114,171,407,1,0,0,0,0,0,linear programming problem,407 -Meszaros,gams30am,354,531,1287,1,0,0,0,0,0,linear programming problem,1287 -Meszaros,gams60am,714,1071,2607,1,0,0,0,0,0,linear programming problem,2607 -Meszaros,gas11,459,862,2166,1,0,0,0,0,0,linear programming problem,2166 -Meszaros,aa01,823,8904,72965,1,1,0,0,0,0,linear programming problem,72965 -Meszaros,aa03,825,8627,70806,1,1,0,0,0,0,linear programming problem,70806 -Meszaros,aa3,825,8627,70806,1,1,0,0,0,0,linear programming problem,70806 -Meszaros,aa4,426,7195,52121,1,1,0,0,0,0,linear programming problem,52121 -Meszaros,aa5,801,8308,65953,1,1,0,0,0,0,linear programming problem,65953 -Meszaros,aa6,646,7292,51728,1,1,0,0,0,0,linear programming problem,51728 -Meszaros,air02,50,6774,61555,1,1,0,0,0,0,linear programming problem,61555 -Meszaros,air03,124,10757,91028,1,1,0,0,0,0,linear programming problem,91028 -Meszaros,air04,823,8904,72965,1,1,0,0,0,0,linear programming problem,72965 -Meszaros,air05,426,7195,52121,1,1,0,0,0,0,linear programming problem,52121 -Meszaros,air06,825,8627,70806,1,1,0,0,0,0,linear programming problem,70806 -Meszaros,aircraft,3754,7517,20267,1,0,0,0,0,0,linear programming problem,20267 -Meszaros,bas1lp,5411,9825,587775,1,0,0,0,0,0,linear programming problem,587775 -Meszaros,baxter,27441,30733,111576,1,0,0,0,0,0,linear programming problem,111576 -Meszaros,car4,16384,33052,63724,1,0,0,0,0,0,linear programming problem,63724 -Meszaros,cari,400,1200,152800,1,0,0,0,0,0,linear programming problem,152800 -Meszaros,ch,3700,8291,24102,1,0,0,0,0,0,linear programming problem,24102 -Meszaros,co5,5774,12325,57993,1,0,0,0,0,0,linear programming problem,57993 -Meszaros,co9,10789,22924,109651,1,0,0,0,0,0,linear programming problem,109651 -Meszaros,complex,1023,1408,46463,1,0,0,0,0,0,linear programming problem,46463 -Meszaros,cq5,5048,11748,51571,1,0,0,0,0,0,linear programming problem,51571 -Meszaros,cq9,9278,21534,96653,1,0,0,0,0,0,linear programming problem,96653 -Meszaros,cr42,905,1513,6614,1,0,0,0,0,0,linear programming problem,6614 -Meszaros,crew1,135,6469,46950,1,1,0,0,0,0,linear programming problem,46950 -Meszaros,dano3mip,3202,15851,81633,1,0,0,0,0,0,linear programming problem,81633 -Meszaros,dbic1,43200,226317,1081843,1,0,0,0,0,0,linear programming problem,1081843 -Meszaros,dbir1,18804,45775,1077025,1,0,0,0,0,0,linear programming problem,1077025 -Meszaros,dbir2,18906,45877,1158159,1,0,0,0,0,0,linear programming problem,1158159 -Meszaros,df2177,630,10358,22336,1,1,0,0,0,0,linear programming problem,22336 -Meszaros,e18,24617,38602,156466,1,0,0,0,0,0,linear programming problem,156466 -Meszaros,ex3sta1,17443,17516,68779,1,0,0,0,0,0,linear programming problem,68779 -Meszaros,farm,7,17,41,1,0,0,0,0,0,linear programming problem,41 -Meszaros,gams10a,114,171,407,1,0,0,0,0,0,linear programming problem,407 -Meszaros,gams30a,354,531,1287,1,0,0,0,0,0,linear programming problem,1287 -Meszaros,ge,10099,16369,44825,1,0,0,0,0,0,linear programming problem,44825 -Meszaros,iiasa,669,3639,7317,1,0,0,0,0,0,linear programming problem,7317 -Meszaros,jendrec1,2109,4228,89608,1,0,0,0,0,0,linear programming problem,89608 -Meszaros,kl02,71,36699,212536,1,1,0,0,0,0,linear programming problem,212536 -Meszaros,l9,244,1483,4659,1,0,0,0,0,0,linear programming problem,4659 -Meszaros,lp22,2958,16392,68518,1,0,0,0,0,0,linear programming problem,68518 -Meszaros,lpl2,3294,10881,32232,1,0,0,0,0,0,linear programming problem,32232 -Meszaros,lpl3,10828,33686,100525,1,0,0,0,0,0,linear programming problem,100525 -Meszaros,mod2,34774,66409,199810,1,0,0,0,0,0,linear programming problem,199810 -Meszaros,model1,362,798,3028,1,0,0,0,0,0,linear programming problem,3028 -Meszaros,model2,379,1321,7607,1,0,0,0,0,0,linear programming problem,7607 -Meszaros,model3,1609,4578,23974,1,0,0,0,0,0,linear programming problem,23974 -Meszaros,model4,1337,4962,45753,1,0,0,0,0,0,linear programming problem,45753 -Meszaros,model5,1888,11802,89925,1,0,0,0,0,0,linear programming problem,89925 -Meszaros,model6,2096,5289,27628,1,0,0,0,0,0,linear programming problem,27628 -Meszaros,model7,3358,9582,51027,1,0,0,0,0,0,linear programming problem,51027 -Meszaros,model8,2896,6464,25277,1,0,0,0,0,0,linear programming problem,25277 -Meszaros,model9,2879,10939,55956,1,0,0,0,0,0,linear programming problem,55956 -Meszaros,model10,4400,16819,150372,1,0,0,0,0,0,linear programming problem,150372 -Meszaros,nemsafm,334,2348,2826,1,0,0,0,0,0,linear programming problem,2826 -Meszaros,nemscem,651,1712,3840,1,0,0,0,0,0,linear programming problem,3840 -Meszaros,nemsemm1,3945,75352,1053986,1,0,0,0,0,0,linear programming problem,1053986 -Meszaros,nemsemm2,6943,48878,182012,1,0,0,0,0,0,linear programming problem,182012 -Meszaros,nemspmm1,2372,8903,55867,1,0,0,0,0,0,linear programming problem,55867 -Meszaros,nemspmm2,2301,8734,68225,1,0,0,0,0,0,linear programming problem,68225 -Meszaros,nemswrld,7138,28550,192283,1,0,0,0,0,0,linear programming problem,192283 -Meszaros,nl,7039,15325,47035,1,0,0,0,0,0,linear programming problem,47035 -Meszaros,nw14,73,123409,904910,1,1,0,0,0,0,linear programming problem,904910 -Meszaros,p0033,15,48,113,1,0,0,0,0,0,linear programming problem,113 -Meszaros,p0040,23,63,133,1,0,0,0,0,0,linear programming problem,133 -Meszaros,p010,10090,19090,118000,1,0,0,0,0,0,linear programming problem,118000 -Meszaros,p0201,133,334,2056,1,0,0,0,0,0,linear programming problem,2056 -Meszaros,p0282,241,523,2207,1,0,0,0,0,0,linear programming problem,2207 -Meszaros,p0291,252,543,2283,1,0,0,0,0,0,linear programming problem,2283 -Meszaros,p0548,176,724,1887,1,0,0,0,0,0,linear programming problem,1887 -Meszaros,p05,5090,9590,59045,1,0,0,0,0,0,linear programming problem,59045 -Meszaros,p2756,755,3511,9692,1,0,0,0,0,0,linear programming problem,9692 -Meszaros,p6000,2095,7967,19826,1,0,0,0,0,0,linear programming problem,19826 -Meszaros,pcb1000,1565,2820,20463,1,0,0,0,0,0,linear programming problem,20463 -Meszaros,pcb3000,3960,7732,57479,1,0,0,0,0,0,linear programming problem,57479 -Meszaros,pf2177,9728,10178,30984,1,1,0,0,0,0,linear programming problem,30984 -Meszaros,primagaz,1554,10836,21665,1,0,0,0,0,0,linear programming problem,21665 -Meszaros,problem,12,46,86,1,0,0,0,0,0,linear programming problem,86 -Meszaros,progas,1650,1900,8897,1,0,0,0,0,0,linear programming problem,8897 -Meszaros,qiulp,1192,1900,4492,1,0,0,0,0,0,linear programming problem,4492 -Meszaros,r05,5190,9690,104145,1,0,0,0,0,0,linear programming problem,104145 -Meszaros,refine,29,62,153,1,0,0,0,0,0,linear programming problem,153 -Meszaros,rlfddd,4050,61521,264627,1,0,0,0,0,0,linear programming problem,264627 -Meszaros,rlfdual,8052,74970,282031,1,0,0,0,0,0,linear programming problem,282031 -Meszaros,rlfprim,58866,62716,320591,1,0,0,0,0,0,linear programming problem,320591 -Meszaros,rosen1,520,1544,23794,1,0,0,0,0,0,linear programming problem,23794 -Meszaros,rosen2,1032,3080,47536,1,0,0,0,0,0,linear programming problem,47536 -Meszaros,rosen7,264,776,8034,1,0,0,0,0,0,linear programming problem,8034 -Meszaros,rosen8,520,1544,16058,1,0,0,0,0,0,linear programming problem,16058 -Meszaros,rosen10,2056,6152,64192,1,0,0,0,0,0,linear programming problem,64192 -Meszaros,route,20894,43019,206782,1,0,0,0,0,0,linear programming problem,206782 -Meszaros,seymourl,4944,6316,38493,1,0,0,0,0,0,linear programming problem,38493 -Meszaros,slptsk,2861,3347,72465,1,0,0,0,0,0,linear programming problem,72465 -Meszaros,south31,18425,36321,112398,1,0,0,0,0,0,linear programming problem,112398 -Meszaros,stat96v1,5995,197472,588798,1,0,0,0,0,0,linear programming problem,588798 -Meszaros,stat96v2,29089,957432,2852184,1,0,0,0,0,0,linear programming problem,2852184 -Meszaros,stat96v3,33841,1113780,3317736,1,0,0,0,0,0,linear programming problem,3317736 -Meszaros,stat96v4,3173,63076,491336,1,0,0,0,0,0,linear programming problem,491336 -Meszaros,stat96v5,2307,75779,233921,1,0,0,0,0,0,linear programming problem,233921 -Meszaros,t0331-4l,664,46915,430982,1,1,0,0,0,0,linear programming problem,430982 -Meszaros,testbig,17613,31223,61639,1,0,0,0,0,0,linear programming problem,61639 -Meszaros,ulevimin,6590,46937,164538,1,0,0,0,0,0,linear programming problem,164538 -Meszaros,us04,163,28016,297538,1,1,0,0,0,0,linear programming problem,297538 -Meszaros,world,34506,67147,198883,1,0,0,0,0,0,linear programming problem,198883 -Meszaros,zed,116,142,666,1,0,0,0,0,0,linear programming problem,666 -Meszaros,de063155,852,1848,4913,1,0,0,0,0,0,linear programming problem,4913 -Meszaros,de063157,936,1908,5119,1,0,0,0,0,0,linear programming problem,5119 -Meszaros,de080285,936,1908,5082,1,0,0,0,0,0,linear programming problem,5082 -Meszaros,gen1,769,2561,63086,1,0,0,0,0,0,linear programming problem,63086 -Meszaros,gen2,1121,3264,81855,1,0,0,0,0,0,linear programming problem,81855 -Meszaros,gen4,1537,4298,107103,1,0,0,0,0,0,linear programming problem,107103 -Meszaros,gen,769,2561,63086,1,0,0,0,0,0,linear programming problem,63086 -Meszaros,iprob,3001,3001,9000,1,0,0,0,1,0.5,linear programming problem,9000 -Meszaros,l30,2701,16281,52070,1,0,0,0,0,0,linear programming problem,52070 -Meszaros,stoch_aircraft,3754,7517,20267,1,0,0,0,0,0,linear programming problem,20267 -Meszaros,cep1,1521,4769,8233,1,0,0,0,0,0,linear programming problem,8233 -Meszaros,deter0,1923,5468,11173,1,0,0,0,0,0,linear programming problem,11173 -Meszaros,deter1,5527,15737,32187,1,0,0,0,0,0,linear programming problem,32187 -Meszaros,deter2,6095,17313,35731,1,0,0,0,0,0,linear programming problem,35731 -Meszaros,deter3,7647,21777,44547,1,0,0,0,0,0,linear programming problem,44547 -Meszaros,deter4,3235,9133,19231,1,0,0,0,0,0,linear programming problem,19231 -Meszaros,deter5,5103,14529,29715,1,0,0,0,0,0,linear programming problem,29715 -Meszaros,deter6,4255,12113,24771,1,0,0,0,0,0,linear programming problem,24771 -Meszaros,deter7,6375,18153,37131,1,0,0,0,0,0,linear programming problem,37131 -Meszaros,deter8,3831,10905,22299,1,0,0,0,0,0,linear programming problem,22299 -Meszaros,fxm2-6,1520,2845,12812,1,0,0,0,0,0,linear programming problem,12812 -Meszaros,fxm2-16,3900,7335,32972,1,0,0,0,0,0,linear programming problem,32972 -Meszaros,fxm3_6,6200,12625,57722,1,0,0,0,0,0,linear programming problem,57722 -Meszaros,fxm3_16,41340,85575,392252,1,0,0,0,0,0,linear programming problem,392252 -Meszaros,fxm4_6,22400,47185,265442,1,0,0,0,0,0,linear programming problem,265442 -Meszaros,pgp2,4034,13254,22474,1,0,0,0,0,0,linear programming problem,22474 -Meszaros,stormg2-125,66185,172431,433256,1,0,0,0,0,0,linear programming problem,433256 -Meszaros,stormg2-27,14441,37485,94274,1,0,0,0,0,0,linear programming problem,94274 -Meszaros,stormg2-8,4409,11322,28553,1,0,0,0,0,0,linear programming problem,28553 -Meszaros,degme,185501,659415,8127528,1,0,0,0,0,0,linear programming problem,8127528 -Meszaros,karted,46502,133115,1770349,1,0,0,0,0,0,linear programming problem,1770349 -Meszaros,tp-6,142752,1014301,11537419,1,0,0,0,0,0,linear programming problem,11537419 -Meszaros,ts-palko,22002,47235,1076903,1,0,0,0,0,0,linear programming problem,1076903 -Meszaros,delf,3170,6654,15397,1,0,0,0,0,0,linear programming problem sequence,15397 -Meszaros,kleemin,8,16,44,1,0,0,0,0,0,linear programming problem sequence,44 -Meszaros,large,4282,8617,20635,1,0,0,0,0,0,linear programming problem sequence,20635 -Meszaros,nsct,23003,37563,697738,1,0,0,0,0,0,linear programming problem sequence,697738 -Meszaros,nsic,465,897,3449,1,0,0,0,0,0,linear programming problem sequence,3449 -Meszaros,nsir,4453,10057,154939,1,0,0,0,0,0,linear programming problem sequence,154939 -Meszaros,plddb,3069,5049,10839,1,0,0,0,0,0,linear programming problem sequence,10839 -Meszaros,rat,3136,9408,268908,1,0,0,0,0,0,linear programming problem sequence,268908 -Meszaros,small,677,1400,3207,1,0,0,0,0,0,linear programming problem sequence,3207 -Meszaros,pltexpa,26894,70364,143059,1,0,0,0,0,0,linear programming problem sequence,143059 -Meszaros,sc205-2r,35213,62423,123239,1,0,0,0,0,0,linear programming problem sequence,123239 -Meszaros,scagr7-2b,9743,13847,35885,1,0,0,0,0,0,linear programming problem sequence,35885 -Meszaros,scagr7-2c,2447,3479,9005,1,0,0,0,0,0,linear programming problem sequence,9005 -Meszaros,scagr7-2r,32847,46679,120141,1,0,0,0,0,0,linear programming problem sequence,120141 -Meszaros,scfxm1-2b,19036,33047,111052,1,0,0,0,0,0,linear programming problem sequence,111052 -Meszaros,scfxm1-2r,37980,65943,221388,1,0,0,0,0,0,linear programming problem sequence,221388 -Meszaros,scrs8-2b,1820,3499,7367,1,0,0,0,0,0,linear programming problem sequence,7367 -Meszaros,scrs8-2c,1820,3499,7367,1,0,0,0,0,0,linear programming problem sequence,7367 -Meszaros,scrs8-2r,14364,27691,58439,1,0,0,0,0,0,linear programming problem sequence,58439 -Meszaros,scsd8-2b,5130,35910,112770,1,0,0,0,0,0,linear programming problem sequence,112770 -Meszaros,scsd8-2c,5130,35910,112770,1,0,0,0,0,0,linear programming problem sequence,112770 -Meszaros,scsd8-2r,8650,60550,190210,1,0,0,0,0,0,linear programming problem sequence,190210 -Meszaros,sctap1-2b,15390,33858,99454,1,0,0,0,0,0,linear programming problem sequence,99454 -Meszaros,sctap1-2c,3390,7458,21854,1,0,0,0,0,0,linear programming problem sequence,21854 -Meszaros,sctap1-2r,28830,63426,186366,1,0,0,0,0,0,linear programming problem sequence,186366 -Gleich,wb-cs-stanford,9914,9914,36854,1,1,0,0,0.4572071438616228,0.4572071438616228,directed graph,36854 -Gleich,wb-edu,9845725,9845725,57156537,1,1,0,0,0.3281596169311674,0.3281596169311674,directed graph,57156537 -Gleich,wikipedia-20051105,1634989,1634989,19753078,1,1,0,0,0.1211282592272251,0.1211282592272251,directed graph,19753078 -Gleich,wikipedia-20060925,2983494,2983494,37269096,1,1,0,0,0.1182841447575249,0.1182841447575249,directed graph,37269096 -Gleich,wikipedia-20061104,3148440,3148440,39383235,1,1,0,0,0.1179357318284389,0.1179357318284389,directed graph,39383235 -Gleich,wikipedia-20070206,3566907,3566907,45030389,1,1,0,0,0.1171832378930545,0.1171832378930545,directed graph,45030389 -UTEP,Dubcova1,16129,16129,253009,1,0,1,1,1,1,2D/3D problem,253009 -UTEP,Dubcova2,65025,65025,1030225,1,0,1,1,1,1,2D/3D problem,1030225 -UTEP,Dubcova3,146689,146689,3636643,1,0,1,1,1,1,2D/3D problem,3636649 -BenElechi,BenElechi1,245874,245874,13150496,1,0,1,1,1,1,2D/3D problem,13150496 -Botonakis,FEM_3D_thermal1,17880,17880,430740,1,0,1,0,1,0.950733904955675,thermal problem,430740 -Botonakis,FEM_3D_thermal2,147900,147900,3489300,1,0,1,0,1,0.9502082959238642,thermal problem,3489300 -Wissgott,parabolic_fem,525825,525825,3674625,1,0,1,1,1,1,computational fluid dynamics problem,3674625 -Watson,chem_master1,40401,40401,201201,1,0,1,0,1,0,2D/3D problem,201201 -Watson,Baumann,112211,112211,748331,1,0,1,0,1,0,2D/3D problem,760631 -Sinclair,3Dspectralwave,680943,680943,30290827,0,0,1,0,1,1,materials problem,33650589 -Sinclair,3Dspectralwave2,292008,292008,12935272,0,0,1,0,1,1,materials problem,14322744 -QLi,crashbasis,160000,160000,1750416,1,0,0,0,0.5503013048158469,0.0002439613283568576,optimization problem,1750416 -QLi,majorbasis,160000,160000,1750416,1,0,0,0,0.5503013048158469,0.0002376736652548767,optimization problem,1750416 -Springer,ESOC,327062,37830,6019939,1,0,0,0,0,0,least squares problem,6019939 -Koutsovasilis,F2,71505,71505,5294285,1,0,1,0,1,1,structural problem,5294285 -Szczerba,Ill_Stokes,20896,20896,191368,1,0,1,0,0.9897436481194791,0.3299904380293234,computational fluid dynamics problem,191368 -Rajat,Raj1,263743,263743,1300261,1,0,0,0,0.998585875116119,0.5762090211971218,circuit simulation problem,1302464 -Muite,Chebyshev1,261,261,2319,1,0,1,0,0.5014577259475219,0,structural problem,2319 -Muite,Chebyshev2,2053,2053,18447,1,0,1,0,0.5001829937782115,0,structural problem,18447 -Muite,Chebyshev3,4101,4101,36879,1,0,1,0,0.5000915248032217,0,structural problem,36879 -Muite,Chebyshev4,68121,68121,5377761,1,0,1,0,0.3020408163265306,0.0002369275506437348,structural problem,5377761 -Quaglino,viscoplastic1,4326,4326,61166,1,0,1,0,0.7408163265306122,0,materials problem,61166 -Quaglino,viscoplastic2,32769,32769,381326,1,0,1,0,0.5699899872904575,0,materials problem,381326 -YCheng,psse0,26722,11028,102432,1,0,0,0,0,0,power network problem,102432 -YCheng,psse1,14318,11028,57376,1,0,0,0,0,0,power network problem,57376 -YCheng,psse2,28634,11028,115262,1,0,0,0,0,0,power network problem,115262 -Dehghani,light_in_tissue,29282,29282,406084,0,0,1,0,1,0,electromagnetics problem,406084 -HVDC,hvdc1,24842,24842,158426,1,0,0,0,0.9817003233707516,0.09899271099935641,power network problem,159981 -HVDC,hvdc2,189860,189860,1339638,1,0,0,0,0.9885955341075688,0.06323365458033522,power network problem,1347273 -Zaoui,kkt_power,2063494,2063494,12771361,1,0,0,0,1,1,optimization problem,14612663 -Rost,RFdevice,74104,74104,365580,0,0,1,0,0.0001203589980660497,0.0001148881345175929,semiconductor device problem,365580 -Lee,fem_filter,74062,74062,1731206,0,0,1,0,1,0.6131971633123011,electromagnetics problem,1731206 -Mancktelow,viscorocks,37762,37762,1133641,1,0,1,0,1,0.279507135368047,materials problem,1162244 -Rudnyi,water_tank,60740,60740,2035281,1,0,1,0,0.9691204183655847,0.9180067671423384,computational fluid dynamics problem,2035281 -Rucci,Rucci1,1977885,109900,7791168,1,0,0,0,0,0,least squares problem,7791168 -McRae,ecology1,1000000,1000000,4996000,1,0,1,0,1,1,2D/3D problem,4996000 -McRae,ecology2,999999,999999,4995991,1,0,1,1,1,1,2D/3D problem,4995991 -NYPA,Maragal_1,32,14,234,1,0,0,0,0,0,least squares problem,234 -NYPA,Maragal_2,555,350,4357,1,0,0,0,0,0,least squares problem,4357 -NYPA,Maragal_3,1690,860,18391,1,0,0,0,0,0,least squares problem,18391 -NYPA,Maragal_4,1964,1034,26719,1,0,0,0,0,0,least squares problem,26719 -NYPA,Maragal_5,4654,3320,93091,1,0,0,0,0,0,least squares problem,93091 -NYPA,Maragal_6,21255,10152,537694,1,0,0,0,0,0,least squares problem,537694 -NYPA,Maragal_7,46845,26564,1200537,1,0,0,0,0,0,least squares problem,1200537 -Marini,eurqsa,7245,7245,46142,1,0,0,0,1,1,economic problem,46142 -Castrillon,denormal,89400,89400,1156224,1,0,0,1,1,1,counter-example problem,1156224 -QLi,largebasis,440020,440020,5240084,1,0,0,0,1,0,optimization problem,5560100 -MathWorks,QRpivot,660,749,3808,1,0,0,0,0,0,counter-example problem,3808 -Luong,photogrammetry,1388,390,11816,1,0,1,0,0,0,computer graphics/vision problem,11816 -YZhou,circuit204,1020,1020,5883,1,0,0,0,0.4379791625712601,0.3727147631216827,circuit simulation problem,5883 -CEMW,t2em,921632,921632,4590832,1,0,1,0,0.9988182710127548,0.9988182710127548,electromagnetics problem,4590832 -CEMW,tmt_unsym,917825,917825,4584801,1,0,1,0,1,0,electromagnetics problem,4584801 -CEMW,tmt_sym,726713,726713,5080961,1,0,1,1,1,1,electromagnetics problem,5080961 -CEMW,vfem,93476,93476,1434636,0,0,1,0,1,0.8209624504160578,electromagnetics problem,1434636 -Schenk,nlpkkt80,1062400,1062400,28192672,1,0,0,0,1,1,optimization problem,28704672 -Schenk,nlpkkt120,3542400,3542400,95117792,1,0,0,0,1,1,optimization problem,96845792 -Schenk,nlpkkt160,8345600,8345600,225422112,1,0,0,0,1,1,optimization problem,229518112 -Schenk,nlpkkt200,16240000,16240000,440225632,1,0,0,0,1,1,optimization problem,448225632 -Schenk,nlpkkt240,27993600,27993600,760648352,1,0,0,0,1,1,optimization problem,774472352 -TKK,s4dkt3m2,90449,90449,3753461,1,1,1,0,1,1,structural problem,3753461 -TKK,g3rmt3m3,5357,5357,207695,1,1,1,0,1,1,structural problem,207695 -TKK,t520,5563,5563,286341,1,0,1,0,1,1,structural problem,286341 -TKK,smt,25710,25710,3749582,1,0,1,1,1,1,structural problem,3753184 -TKK,engine,143571,143571,4706073,1,0,1,0,1,1,structural problem,4706073 -TKK,plbuckle,1282,1282,30644,1,0,1,1,1,1,structural problem,30644 -TKK,cbuckle,13681,13681,676515,1,0,1,1,1,1,structural problem,676515 -TKK,cyl6,13681,13681,714241,1,0,1,0,1,1,structural problem,714241 -TKK,tube1,21498,21498,897056,1,1,1,0,1,1,structural problem,897056 -TKK,tube2,21498,21498,897056,1,0,1,0,1,1,structural problem,897056 -TKK,t2d_q4,9801,9801,87025,1,0,1,0,1,0.6937221589143272,structural problem sequence,87025 -TKK,t2d_q9,9801,9801,87025,1,0,1,0,1,0.6937221589143272,structural problem sequence,87025 -Luong,photogrammetry2,4472,936,37056,1,0,1,0,0,0,computer graphics/vision problem,37056 -Um,2cubes_sphere,101492,101492,1647264,1,0,1,1,1,1,electromagnetics problem,1647264 -JGD_BIBD,bibd_9_3,36,84,252,1,1,0,0,0,0,combinatorial problem,252 -JGD_BIBD,bibd_9_5,36,126,1260,1,1,0,0,0,0,combinatorial problem,1260 -JGD_BIBD,bibd_11_5,55,462,4620,1,1,0,0,0,0,combinatorial problem,4620 -JGD_BIBD,bibd_12_4,66,495,2970,1,1,0,0,0,0,combinatorial problem,2970 -JGD_BIBD,bibd_12_5,66,792,7920,1,1,0,0,0,0,combinatorial problem,7920 -JGD_BIBD,bibd_13_6,78,1716,25740,1,1,0,0,0,0,combinatorial problem,25740 -JGD_BIBD,bibd_14_7,91,3432,72072,1,1,0,0,0,0,combinatorial problem,72072 -JGD_BIBD,bibd_15_3,105,455,1365,1,1,0,0,0,0,combinatorial problem,1365 -JGD_BIBD,bibd_15_7,105,6435,135135,1,1,0,0,0,0,combinatorial problem,135135 -JGD_BIBD,bibd_16_8,120,12870,360360,1,1,0,0,0,0,combinatorial problem,360360 -JGD_BIBD,bibd_17_3,136,680,2040,1,1,0,0,0,0,combinatorial problem,2040 -JGD_BIBD,bibd_17_4b,136,2380,14280,1,1,0,0,0,0,combinatorial problem,14280 -JGD_BIBD,bibd_17_4,136,2380,14280,1,1,0,0,0,0,combinatorial problem,14280 -JGD_BIBD,bibd_17_8,136,24310,680680,1,1,0,0,0,0,combinatorial problem,680680 -JGD_BIBD,bibd_18_9,153,48620,1750320,1,1,0,0,0,0,combinatorial problem,1750320 -JGD_BIBD,bibd_19_9,171,92378,3325608,1,1,0,0,0,0,combinatorial problem,3325608 -JGD_BIBD,bibd_20_10,190,184756,8314020,1,1,0,0,0,0,combinatorial problem,8314020 -JGD_BIBD,bibd_22_8,231,319770,8953560,1,1,0,0,0,0,combinatorial problem,8953560 -JGD_BIBD,bibd_49_3,1176,18424,55272,1,1,0,0,0,0,combinatorial problem,55272 -JGD_BIBD,bibd_81_2,3240,3240,3240,1,1,0,1,1,1,combinatorial problem,3240 -JGD_BIBD,bibd_81_3,3240,85320,255960,1,1,0,0,0,0,combinatorial problem,255960 -JGD_CAG,CAG_mat1916,1916,1916,195985,1,0,0,0,0.3002540333592691,0.2119864584245809,combinatorial problem,195985 -JGD_CAG,CAG_mat364,364,364,13585,1,0,0,0,0.4155510173209288,0.2804628999319265,combinatorial problem,13585 -JGD_CAG,CAG_mat72,72,72,1012,1,0,0,0,0.5574468085106383,0.3340425531914893,combinatorial problem,1012 -JGD_Forest,TF10,99,107,622,1,0,0,0,0,0,combinatorial problem,622 -JGD_Forest,TF11,216,236,1607,1,0,0,0,0,0,combinatorial problem,1607 -JGD_Forest,TF12,488,552,4231,1,0,0,0,0,0,combinatorial problem,4231 -JGD_Forest,TF13,1121,1302,11185,1,0,0,0,0,0,combinatorial problem,11185 -JGD_Forest,TF14,2644,3160,29862,1,0,0,0,0,0,combinatorial problem,29862 -JGD_Forest,TF15,6334,7742,80057,1,0,0,0,0,0,combinatorial problem,80057 -JGD_Forest,TF16,15437,19321,216173,1,0,0,0,0,0,combinatorial problem,216173 -JGD_Forest,TF17,38132,48630,586218,1,0,0,0,0,0,combinatorial problem,586218 -JGD_Forest,TF18,95368,123867,1597545,1,0,0,0,0,0,combinatorial problem,1597545 -JGD_Forest,TF19,241029,317955,4370721,1,0,0,0,0,0,combinatorial problem,4370721 -JGD_Franz,Franz1,2240,768,5120,1,0,0,0,0,0,combinatorial problem,5120 -JGD_Franz,Franz2,4032,4480,21504,1,0,0,0,0,0,combinatorial problem,21504 -JGD_Franz,Franz3,1280,2800,11520,1,0,0,0,0,0,combinatorial problem,11520 -JGD_Franz,Franz4,6784,5252,46528,1,0,0,0,0,0,combinatorial problem,46528 -JGD_Franz,Franz5,7382,2882,44056,1,0,0,0,0,0,combinatorial problem,44056 -JGD_Franz,Franz6,7576,3016,45456,1,0,0,0,0,0,combinatorial problem,45456 -JGD_Franz,Franz7,10164,1740,40424,1,0,0,0,0,0,combinatorial problem,40424 -JGD_Franz,Franz8,16728,7176,100368,1,0,0,0,0,0,combinatorial problem,100368 -JGD_Franz,Franz9,19588,4164,97508,1,0,0,0,0,0,combinatorial problem,97508 -JGD_Franz,Franz10,19588,4164,97508,1,0,0,0,0,0,combinatorial problem,97508 -JGD_Franz,Franz11,47104,30144,329728,1,0,0,0,0,0,combinatorial problem,329728 -JGD_G5,IG5-6,30,77,251,1,0,0,0,0,0,combinatorial problem,251 -JGD_G5,IG5-7,62,150,549,1,0,0,0,0,0,combinatorial problem,549 -JGD_G5,IG5-8,156,292,1711,1,0,0,0,0,0,combinatorial problem,1711 -JGD_G5,IG5-9,342,540,4570,1,0,0,0,0,0,combinatorial problem,4570 -JGD_G5,IG5-10,652,976,10273,1,0,0,0,0,0,combinatorial problem,10273 -JGD_G5,IG5-11,1227,1692,22110,1,0,0,0,0,0,combinatorial problem,22110 -JGD_G5,IG5-12,2296,2875,46260,1,0,0,0,0,0,combinatorial problem,46260 -JGD_G5,IG5-13,3994,4731,91209,1,0,0,0,0,0,combinatorial problem,91209 -JGD_G5,IG5-14,6735,7621,173337,1,0,0,0,0,0,combinatorial problem,173337 -JGD_G5,IG5-15,11369,11987,323509,1,0,0,0,0,0,combinatorial problem,323509 -JGD_G5,IG5-16,18846,18485,588326,1,0,0,0,0,0,combinatorial problem,588326 -JGD_G5,IG5-17,30162,27944,1035008,1,0,0,0,0,0,combinatorial problem,1035008 -JGD_G5,IG5-18,47894,41550,1790490,1,0,0,0,0,0,combinatorial problem,1790490 -JGD_GL6,GL6_D_6,469,201,2526,1,0,0,0,0,0,combinatorial problem,2526 -JGD_GL6,GL6_D_7,636,470,5378,1,0,0,0,0,0,combinatorial problem,5378 -JGD_GL6,GL6_D_8,544,637,6153,1,0,0,0,0,0,combinatorial problem,6153 -JGD_GL6,GL6_D_9,340,545,4349,1,0,0,0,0,0,combinatorial problem,4349 -JGD_GL6,GL6_D_10,163,341,2053,1,0,0,0,0,0,combinatorial problem,2053 -JGD_GL7d,GL7d10,1,60,8,1,0,0,0,0,0,combinatorial problem,8 -JGD_GL7d,GL7d11,1019,60,1513,1,0,0,0,0,0,combinatorial problem,1513 -JGD_GL7d,GL7d12,8899,1019,37519,1,0,0,0,0,0,combinatorial problem,37519 -JGD_GL7d,GL7d13,47271,8899,356232,1,0,0,0,0,0,combinatorial problem,356232 -JGD_GL7d,GL7d14,171375,47271,1831183,1,0,0,0,0,0,combinatorial problem,1831183 -JGD_GL7d,GL7d15,460261,171375,6080381,1,0,0,0,0,0,combinatorial problem,6080381 -JGD_GL7d,GL7d16,955128,460261,14488881,1,0,0,0,0,0,combinatorial problem,14488881 -JGD_GL7d,GL7d17,1548650,955128,25978098,1,0,0,0,0,0,combinatorial problem,25978098 -JGD_GL7d,GL7d18,1955309,1548650,35590540,1,0,0,0,0,0,combinatorial problem,35590540 -JGD_GL7d,GL7d19,1911130,1955309,37322725,1,0,0,0,0,0,combinatorial problem,37322725 -JGD_GL7d,GL7d20,1437547,1911130,29893084,1,0,0,0,0,0,combinatorial problem,29893084 -JGD_GL7d,GL7d21,822922,1437547,18174775,1,0,0,0,0,0,combinatorial problem,18174775 -JGD_GL7d,GL7d22,349443,822922,8251000,1,0,0,0,0,0,combinatorial problem,8251000 -JGD_GL7d,GL7d23,105054,349443,2695430,1,0,0,0,0,0,combinatorial problem,2695430 -JGD_GL7d,GL7d24,21074,105054,593892,1,0,0,0,0,0,combinatorial problem,593892 -JGD_GL7d,GL7d25,2798,21074,81671,1,0,0,0,0,0,combinatorial problem,81671 -JGD_GL7d,GL7d26,305,2798,7412,1,0,0,0,0,0,combinatorial problem,7412 -JGD_Groebner,c8_mat11,4562,5761,2462970,1,0,0,0,0,0,combinatorial problem,2462970 -JGD_Groebner,c8_mat11_I,4562,5761,2462970,1,0,0,0,0,0,combinatorial problem,2462970 -JGD_Groebner,f855_mat9,2456,2511,171214,1,0,0,0,0,0,combinatorial problem,171214 -JGD_Groebner,f855_mat9_I,2456,2511,171214,1,0,0,0,0,0,combinatorial problem,171214 -JGD_Groebner,HFE18_96_in,2372,4096,933343,1,1,0,0,0,0,combinatorial problem,933343 -JGD_Groebner,rkat7_mat5,694,738,38114,1,0,0,0,0,0,combinatorial problem,38114 -JGD_Groebner,robot24c1_mat5,404,302,15118,1,0,0,0,0,0,combinatorial problem,15118 -JGD_Groebner,robot24c1_mat5_J,302,404,15118,1,0,0,0,0,0,combinatorial problem,15118 -JGD_Homology,ch3-3-b1,18,9,36,1,0,0,0,0,0,combinatorial problem,36 -JGD_Homology,ch3-3-b2,6,18,18,1,0,0,0,0,0,combinatorial problem,18 -JGD_Homology,ch4-4-b1,72,16,144,1,0,0,0,0,0,combinatorial problem,144 -JGD_Homology,ch4-4-b2,96,72,288,1,0,0,0,0,0,combinatorial problem,288 -JGD_Homology,ch4-4-b3,24,96,96,1,0,0,0,0,0,combinatorial problem,96 -JGD_Homology,ch5-5-b1,200,25,400,1,0,0,0,0,0,combinatorial problem,400 -JGD_Homology,ch5-5-b2,600,200,1800,1,0,0,0,0,0,combinatorial problem,1800 -JGD_Homology,ch5-5-b3,600,600,2400,1,0,0,0,0.02168473728106756,0.01417848206839033,combinatorial problem,2400 -JGD_Homology,ch5-5-b4,120,600,600,1,0,0,0,0,0,combinatorial problem,600 -JGD_Homology,ch6-6-b1,450,36,900,1,0,0,0,0,0,combinatorial problem,900 -JGD_Homology,ch6-6-b2,2400,450,7200,1,0,0,0,0,0,combinatorial problem,7200 -JGD_Homology,ch6-6-b3,5400,2400,21600,1,0,0,0,0,0,combinatorial problem,21600 -JGD_Homology,ch6-6-b4,4320,5400,21600,1,0,0,0,0,0,combinatorial problem,21600 -JGD_Homology,ch6-6-b5,720,4320,4320,1,0,0,0,0,0,combinatorial problem,4320 -JGD_Homology,ch7-6-b1,630,42,1260,1,0,0,0,0,0,combinatorial problem,1260 -JGD_Homology,ch7-6-b2,4200,630,12600,1,0,0,0,0,0,combinatorial problem,12600 -JGD_Homology,ch7-6-b3,12600,4200,50400,1,0,0,0,0,0,combinatorial problem,50400 -JGD_Homology,ch7-6-b4,15120,12600,75600,1,0,0,0,0,0,combinatorial problem,75600 -JGD_Homology,ch7-6-b5,5040,15120,30240,1,0,0,0,0,0,combinatorial problem,30240 -JGD_Homology,ch7-7-b1,882,49,1764,1,0,0,0,0,0,combinatorial problem,1764 -JGD_Homology,ch7-7-b2,7350,882,22050,1,0,0,0,0,0,combinatorial problem,22050 -JGD_Homology,ch7-7-b5,35280,52920,211680,1,0,0,0,0,0,combinatorial problem,211680 -JGD_Homology,ch7-8-b1,1176,56,2352,1,0,0,0,0,0,combinatorial problem,2352 -JGD_Homology,ch7-8-b2,11760,1176,35280,1,0,0,0,0,0,combinatorial problem,35280 -JGD_Homology,ch7-8-b3,58800,11760,235200,1,0,0,0,0,0,combinatorial problem,235200 -JGD_Homology,ch7-8-b4,141120,58800,705600,1,0,0,0,0,0,combinatorial problem,705600 -JGD_Homology,ch7-8-b5,141120,141120,846720,1,0,0,0,0.0003732057190233348,0.0002054993516141147,combinatorial problem,846720 -JGD_Homology,ch7-9-b1,1512,63,3024,1,0,0,0,0,0,combinatorial problem,3024 -JGD_Homology,ch7-9-b2,17640,1512,52920,1,0,0,0,0,0,combinatorial problem,52920 -JGD_Homology,ch7-9-b3,105840,17640,423360,1,0,0,0,0,0,combinatorial problem,423360 -JGD_Homology,ch7-9-b4,317520,105840,1587600,1,0,0,0,0,0,combinatorial problem,1587600 -JGD_Homology,ch7-9-b5,423360,317520,2540160,1,0,0,0,0,0,combinatorial problem,2540160 -JGD_Homology,ch8-8-b1,1568,64,3136,1,0,0,0,0,0,combinatorial problem,3136 -JGD_Homology,ch8-8-b2,18816,1568,56448,1,0,0,0,0,0,combinatorial problem,56448 -JGD_Homology,ch8-8-b3,117600,18816,470400,1,0,0,0,0,0,combinatorial problem,470400 -JGD_Homology,ch8-8-b4,376320,117600,1881600,1,0,0,0,0,0,combinatorial problem,1881600 -JGD_Homology,ch8-8-b5,564480,376320,3386880,1,0,0,0,0,0,combinatorial problem,3386880 -JGD_Homology,cis-n4c6-b1,210,21,420,1,0,0,0,0,0,combinatorial problem,420 -JGD_Homology,cis-n4c6-b13,6300,25605,88200,1,0,0,0,0,0,combinatorial problem,88200 -JGD_Homology,cis-n4c6-b14,920,6300,13800,1,0,0,0,0,0,combinatorial problem,13800 -JGD_Homology,cis-n4c6-b15,60,920,960,1,0,0,0,0,0,combinatorial problem,960 -JGD_Homology,cis-n4c6-b2,1330,210,3990,1,0,0,0,0,0,combinatorial problem,3990 -JGD_Homology,cis-n4c6-b3,5970,1330,23880,1,0,0,0,0,0,combinatorial problem,23880 -JGD_Homology,cis-n4c6-b4,20058,5970,100290,1,0,0,0,0,0,combinatorial problem,100290 -JGD_Homology,D6-6,120576,23740,146520,1,0,0,0,0,0,combinatorial problem,146880 -JGD_Homology,klein-b1,30,10,60,1,0,0,0,0,0,combinatorial problem,60 -JGD_Homology,klein-b2,20,30,60,1,0,0,0,0,0,combinatorial problem,60 -JGD_Homology,lutz30-23-b6,1716,3003,12012,1,0,0,0,0,0,combinatorial problem,12012 -JGD_Homology,m133-b3,200200,200200,800800,1,0,0,0,4.495504495504496e-05,2.747252747252747e-05,combinatorial problem,800800 -JGD_Homology,mk10-b1,630,45,1260,1,0,0,0,0,0,combinatorial problem,1260 -JGD_Homology,mk10-b2,3150,630,9450,1,0,0,0,0,0,combinatorial problem,9450 -JGD_Homology,mk10-b3,4725,3150,18900,1,0,0,0,0,0,combinatorial problem,18900 -JGD_Homology,mk10-b4,945,4725,4725,1,0,0,0,0,0,combinatorial problem,4725 -JGD_Homology,mk11-b1,990,55,1980,1,0,0,0,0,0,combinatorial problem,1980 -JGD_Homology,mk11-b2,6930,990,20790,1,0,0,0,0,0,combinatorial problem,20790 -JGD_Homology,mk11-b3,17325,6930,69300,1,0,0,0,0,0,combinatorial problem,69300 -JGD_Homology,mk11-b4,10395,17325,51975,1,0,0,0,0,0,combinatorial problem,51975 -JGD_Homology,mk11-b4b,9450,17325,47250,1,0,0,0,0,0,combinatorial problem,47250 -JGD_Homology,mk12-b1,1485,66,2970,1,0,0,0,0,0,combinatorial problem,2970 -JGD_Homology,mk12-b2,13860,1485,41580,1,0,0,0,0,0,combinatorial problem,41580 -JGD_Homology,mk12-b3,51975,13860,207900,1,0,0,0,0,0,combinatorial problem,207900 -JGD_Homology,mk12-b4,62370,51975,311850,1,0,0,0,0,0,combinatorial problem,311850 -JGD_Homology,mk12-b5,10395,62370,62370,1,0,0,0,0,0,combinatorial problem,62370 -JGD_Homology,mk13-b5,135135,270270,810810,1,0,0,0,0,0,combinatorial problem,810810 -JGD_Homology,mk9-b1,378,36,756,1,0,0,0,0,0,combinatorial problem,756 -JGD_Homology,mk9-b2,1260,378,3780,1,0,0,0,0,0,combinatorial problem,3780 -JGD_Homology,mk9-b3,945,1260,3780,1,0,0,0,0,0,combinatorial problem,3780 -JGD_Homology,n2c6-b10,30,306,330,1,0,0,0,0,0,combinatorial problem,330 -JGD_Homology,n2c6-b1,105,15,210,1,0,0,0,0,0,combinatorial problem,210 -JGD_Homology,n2c6-b2,455,105,1365,1,0,0,0,0,0,combinatorial problem,1365 -JGD_Homology,n2c6-b3,1365,455,5460,1,0,0,0,0,0,combinatorial problem,5460 -JGD_Homology,n2c6-b4,3003,1365,15015,1,0,0,0,0,0,combinatorial problem,15015 -JGD_Homology,n2c6-b5,4945,3003,29670,1,0,0,0,0,0,combinatorial problem,29670 -JGD_Homology,n2c6-b6,5715,4945,40005,1,0,0,0,0,0,combinatorial problem,40005 -JGD_Homology,n2c6-b7,3990,5715,31920,1,0,0,0,0,0,combinatorial problem,31920 -JGD_Homology,n2c6-b8,1470,3990,13230,1,0,0,0,0,0,combinatorial problem,13230 -JGD_Homology,n2c6-b9,306,1470,3060,1,0,0,0,0,0,combinatorial problem,3060 -JGD_Homology,n3c4-b1,15,6,30,1,0,0,0,0,0,combinatorial problem,30 -JGD_Homology,n3c4-b2,20,15,60,1,0,0,0,0,0,combinatorial problem,60 -JGD_Homology,n3c4-b3,15,20,60,1,0,0,0,0,0,combinatorial problem,60 -JGD_Homology,n3c4-b4,6,15,30,1,0,0,0,0,0,combinatorial problem,30 -JGD_Homology,n3c5-b1,45,10,90,1,0,0,0,0,0,combinatorial problem,90 -JGD_Homology,n3c5-b2,120,45,360,1,0,0,0,0,0,combinatorial problem,360 -JGD_Homology,n3c5-b3,210,120,840,1,0,0,0,0,0,combinatorial problem,840 -JGD_Homology,n3c5-b4,252,210,1260,1,0,0,0,0,0,combinatorial problem,1260 -JGD_Homology,n3c5-b5,210,252,1260,1,0,0,0,0,0,combinatorial problem,1260 -JGD_Homology,n3c5-b6,120,210,840,1,0,0,0,0,0,combinatorial problem,840 -JGD_Homology,n3c5-b7,30,120,240,1,0,0,0,0,0,combinatorial problem,240 -JGD_Homology,n3c6-b10,675,2511,7425,1,0,0,0,0,0,combinatorial problem,7425 -JGD_Homology,n3c6-b1,105,105,210,1,0,0,0,0.08571428571428572,0.0761904761904762,combinatorial problem,210 -JGD_Homology,n3c6-b11,60,675,720,1,0,0,0,0,0,combinatorial problem,720 -JGD_Homology,n3c6-b2,455,105,1365,1,0,0,0,0,0,combinatorial problem,1365 -JGD_Homology,n3c6-b3,1365,455,5460,1,0,0,0,0,0,combinatorial problem,5460 -JGD_Homology,n3c6-b4,3003,1365,15015,1,0,0,0,0,0,combinatorial problem,15015 -JGD_Homology,n3c6-b5,5005,3003,30030,1,0,0,0,0,0,combinatorial problem,30030 -JGD_Homology,n3c6-b6,6435,5005,45045,1,0,0,0,0,0,combinatorial problem,45045 -JGD_Homology,n3c6-b7,6435,6435,51480,1,0,0,0,1,0.5333333333333333,combinatorial problem,51480 -JGD_Homology,n3c6-b8,4935,6435,44415,1,0,0,0,0,0,combinatorial problem,44415 -JGD_Homology,n3c6-b9,2511,4935,25110,1,0,0,0,0,0,combinatorial problem,25110 -JGD_Homology,n4c5-b10,120,630,1320,1,0,0,0,0,0,combinatorial problem,1320 -JGD_Homology,n4c5-b1,105,15,210,1,0,0,0,0,0,combinatorial problem,210 -JGD_Homology,n4c5-b11,10,120,120,1,0,0,0,0,0,combinatorial problem,120 -JGD_Homology,n4c5-b2,455,105,1365,1,0,0,0,0,0,combinatorial problem,1365 -JGD_Homology,n4c5-b3,1350,455,5400,1,0,0,0,0,0,combinatorial problem,5400 -JGD_Homology,n4c5-b4,2852,1350,14260,1,0,0,0,0,0,combinatorial problem,14260 -JGD_Homology,n4c5-b5,4340,2852,26040,1,0,0,0,0,0,combinatorial problem,26040 -JGD_Homology,n4c5-b6,4735,4340,33145,1,0,0,0,0,0,combinatorial problem,33145 -JGD_Homology,n4c5-b7,3635,4735,29080,1,0,0,0,0,0,combinatorial problem,29080 -JGD_Homology,n4c5-b8,1895,3635,17055,1,0,0,0,0,0,combinatorial problem,17055 -JGD_Homology,n4c5-b9,630,1895,6300,1,0,0,0,0,0,combinatorial problem,6300 -JGD_Homology,n4c6-b10,132402,186558,1456422,1,0,0,0,0,0,combinatorial problem,1456422 -JGD_Homology,n4c6-b11,69235,132402,830820,1,0,0,0,0,0,combinatorial problem,830820 -JGD_Homology,n4c6-b1,210,21,420,1,0,0,0,0,0,combinatorial problem,420 -JGD_Homology,n4c6-b12,25605,69235,332865,1,0,0,0,0,0,combinatorial problem,332865 -JGD_Homology,n4c6-b13,6300,25605,88200,1,0,0,0,0,0,combinatorial problem,88200 -JGD_Homology,n4c6-b14,920,6300,13800,1,0,0,0,0,0,combinatorial problem,13800 -JGD_Homology,n4c6-b15,60,920,960,1,0,0,0,0,0,combinatorial problem,960 -JGD_Homology,n4c6-b2,1330,210,3990,1,0,0,0,0,0,combinatorial problem,3990 -JGD_Homology,n4c6-b3,5970,1330,23880,1,0,0,0,0,0,combinatorial problem,23880 -JGD_Homology,n4c6-b4,20058,5970,100290,1,0,0,0,0,0,combinatorial problem,100290 -JGD_Homology,n4c6-b5,51813,20058,310878,1,0,0,0,0,0,combinatorial problem,310878 -JGD_Homology,n4c6-b6,104115,51813,728805,1,0,0,0,0,0,combinatorial problem,728805 -JGD_Homology,n4c6-b7,163215,104115,1305720,1,0,0,0,0,0,combinatorial problem,1305720 -JGD_Homology,n4c6-b8,198895,163215,1790055,1,0,0,0,0,0,combinatorial problem,1790055 -JGD_Homology,n4c6-b9,186558,198895,1865580,1,0,0,0,0,0,combinatorial problem,1865580 -JGD_Homology,shar_te2-b1,17160,286,34320,1,0,0,0,0,0,combinatorial problem,34320 -JGD_Homology,shar_te2-b2,200200,17160,600600,1,0,0,0,0,0,combinatorial problem,600600 -JGD_Homology,shar_te2-b3,200200,200200,800800,1,0,0,0,7.492516848797264e-05,2.997006739518906e-05,combinatorial problem,800800 -QY,case39,40216,40216,1042160,1,0,0,0,1,1,power network problem sequence,1042160 -JGD_Kocay,Trec3,1,2,1,1,1,0,0,0,0,combinatorial problem,1 -JGD_Kocay,Trec4,2,3,3,1,0,0,0,0,0,combinatorial problem,3 -JGD_Kocay,Trec5,3,7,12,1,0,0,0,0,0,combinatorial problem,12 -JGD_Kocay,Trec6,6,15,40,1,0,0,0,0,0,combinatorial problem,40 -JGD_Kocay,Trec7,11,36,147,1,0,0,0,0,0,combinatorial problem,147 -JGD_Kocay,Trec8,23,84,549,1,0,0,0,0,0,combinatorial problem,549 -JGD_Kocay,Trec9,47,201,2147,1,0,0,0,0,0,combinatorial problem,2147 -JGD_Kocay,Trec10,106,478,8612,1,0,0,0,0,0,combinatorial problem,8612 -JGD_Kocay,Trec11,235,1138,35705,1,0,0,0,0,0,combinatorial problem,35705 -JGD_Kocay,Trec12,551,2726,151219,1,0,0,0,0,0,combinatorial problem,151219 -JGD_Kocay,Trec13,1301,6561,654517,1,0,0,0,0,0,combinatorial problem,654517 -JGD_Kocay,Trec14,3159,15905,2872265,1,0,0,0,0,0,combinatorial problem,2872265 -JGD_Margulies,cat_ears_2_1,85,85,254,1,1,0,0,0.03252032520325204,0.03252032520325204,combinatorial problem,254 -JGD_Margulies,cat_ears_2_4,1009,2689,7982,1,1,0,0,0,0,combinatorial problem,7982 -JGD_Margulies,cat_ears_3_1,204,181,542,1,1,0,0,0,0,combinatorial problem,542 -JGD_Margulies,cat_ears_3_4,5226,13271,39592,1,1,0,0,0,0,combinatorial problem,39592 -JGD_Margulies,cat_ears_4_1,377,313,938,1,1,0,0,0,0,combinatorial problem,938 -JGD_Margulies,cat_ears_4_4,19020,44448,132888,1,1,0,0,0,0,combinatorial problem,132888 -JGD_Margulies,flower_4_1,121,129,386,1,1,0,0,0,0,combinatorial problem,386 -JGD_Margulies,flower_4_4,1837,5529,16466,1,1,0,0,0,0,combinatorial problem,16466 -JGD_Margulies,flower_5_1,211,201,602,1,1,0,0,0,0,combinatorial problem,602 -JGD_Margulies,flower_5_4,5226,14721,43942,1,1,0,0,0,0,combinatorial problem,43942 -JGD_Margulies,flower_7_1,463,393,1178,1,1,0,0,0,0,combinatorial problem,1178 -JGD_Margulies,flower_7_4,27693,67593,202218,1,1,0,0,0,0,combinatorial problem,202218 -JGD_Margulies,flower_8_1,625,513,1538,1,1,0,0,0,0,combinatorial problem,1538 -JGD_Margulies,flower_8_4,55081,125361,375266,1,1,0,0,0,0,combinatorial problem,375266 -JGD_Margulies,kneser_10_4_1,349651,330751,992252,1,1,0,0,0,0,combinatorial problem,992252 -JGD_Margulies,kneser_6_2_1,601,676,2027,1,1,0,0,0,0,combinatorial problem,2027 -JGD_Margulies,kneser_8_3_1,15737,15681,47042,1,1,0,0,0,0,combinatorial problem,47042 -JGD_Margulies,wheel_3_1,21,25,74,1,1,0,0,0,0,combinatorial problem,74 -JGD_Margulies,wheel_4_1,36,41,122,1,1,0,0,0,0,combinatorial problem,122 -JGD_Margulies,wheel_5_1,57,61,182,1,1,0,0,0,0,combinatorial problem,182 -JGD_Margulies,wheel_601,902103,723605,2170814,1,1,0,0,0,0,combinatorial problem,2170814 -JGD_Margulies,wheel_6_1,83,85,254,1,1,0,0,0,0,combinatorial problem,254 -JGD_Margulies,wheel_7_1,114,113,338,1,1,0,0,0,0,combinatorial problem,338 -JGD_Relat,rel3,12,5,18,1,0,0,0,0,0,combinatorial problem,18 -JGD_Relat,rel4,66,12,104,1,0,0,0,0,0,combinatorial problem,104 -JGD_Relat,rel5,340,35,656,1,0,0,0,0,0,combinatorial problem,656 -JGD_Relat,rel6,2340,157,5101,1,0,0,0,0,0,combinatorial problem,5101 -JGD_Relat,rel7,21924,1045,50636,1,0,0,0,0,0,combinatorial problem,50636 -JGD_Relat,rel8,345688,12347,821839,1,0,0,0,0,0,combinatorial problem,821839 -JGD_Relat,rel9,9888048,274669,23667183,1,0,0,0,0,0,combinatorial problem,23667183 -JGD_Relat,relat3,12,5,24,1,0,0,0,0,0,combinatorial problem,24 -JGD_Relat,relat4,66,12,172,1,0,0,0,0,0,combinatorial problem,172 -JGD_Relat,relat5,340,35,1058,1,0,0,0,0,0,combinatorial problem,1058 -JGD_Relat,relat6,2340,157,8108,1,0,0,0,0,0,combinatorial problem,8108 -JGD_Relat,relat7b,21924,1045,81355,1,0,0,0,0,0,combinatorial problem,81355 -JGD_Relat,relat7,21924,1045,81355,1,0,0,0,0,0,combinatorial problem,81355 -JGD_Relat,relat8,345688,12347,1334038,1,0,0,0,0,0,combinatorial problem,1334038 -JGD_Relat,relat9,12360060,549336,38955420,1,0,0,0,0,0,combinatorial problem,38955420 -JGD_SL6,D_5,434,115,1832,1,0,0,0,0,0,combinatorial problem,1832 -JGD_SL6,D_6,970,435,6491,1,0,0,0,0,0,combinatorial problem,6491 -JGD_SL6,D_7,1270,971,12714,1,0,0,0,0,0,combinatorial problem,12714 -JGD_SL6,D_8,1132,1271,14966,1,0,0,0,0,0,combinatorial problem,14966 -JGD_SL6,D_9,815,1133,12395,1,0,0,0,0,0,combinatorial problem,12395 -JGD_SL6,D_10,460,816,7614,1,0,0,0,0,0,combinatorial problem,7614 -JGD_SL6,D_11,169,461,2952,1,0,0,0,0,0,combinatorial problem,2952 -JGD_SPG,08blocks,300,300,592,1,0,0,0,0.0273972602739726,0,combinatorial problem,592 -JGD_SPG,EX1,560,560,8736,1,1,0,0,1,1,combinatorial problem,8736 -JGD_SPG,EX2,560,560,8736,1,1,0,0,1,1,combinatorial problem,8736 -JGD_SPG,EX3,2600,2600,71760,1,1,0,0,1,1,combinatorial problem,71760 -JGD_SPG,EX4,2600,2600,71760,1,1,0,0,1,1,combinatorial problem,71760 -JGD_SPG,EX5,6545,6545,295680,1,1,0,0,1,1,combinatorial problem,295680 -JGD_SPG,EX6,6545,6545,295680,1,1,0,0,1,1,combinatorial problem,295680 -JGD_Taha,abtaha2,37932,331,137228,1,0,0,0,0,0,combinatorial problem,137228 -JGD_Taha,abtaha1,14596,209,51307,1,0,0,0,0,0,combinatorial problem,51307 -JGD_Trefethen,Trefethen_20b,19,19,147,1,0,0,1,1,1,combinatorial problem,147 -JGD_Trefethen,Trefethen_20,20,20,158,1,0,0,1,1,1,combinatorial problem,158 -JGD_Trefethen,Trefethen_150,150,150,2040,1,0,0,1,1,1,combinatorial problem,2040 -JGD_Trefethen,Trefethen_200b,199,199,2873,1,0,0,1,1,1,combinatorial problem,2873 -JGD_Trefethen,Trefethen_200,200,200,2890,1,0,0,1,1,1,combinatorial problem,2890 -JGD_Trefethen,Trefethen_300,300,300,4678,1,0,0,1,1,1,combinatorial problem,4678 -JGD_Trefethen,Trefethen_500,500,500,8478,1,0,0,1,1,1,combinatorial problem,8478 -JGD_Trefethen,Trefethen_700,700,700,12654,1,0,0,1,1,1,combinatorial problem,12654 -JGD_Trefethen,Trefethen_2000,2000,2000,41906,1,0,0,1,1,1,combinatorial problem,41906 -JGD_Trefethen,Trefethen_20000b,19999,19999,554435,1,0,0,1,1,1,combinatorial problem,554435 -JGD_Trefethen,Trefethen_20000,20000,20000,554466,1,0,0,1,1,1,combinatorial problem,554466 -QY,case9,14454,14454,147972,1,0,0,0,1,1,power network problem sequence,147972 -FreeFieldTechnologies,mono_500Hz,169410,169410,5033796,0,0,1,0,1,0.820796293715178,acoustics problem,5036288 -Freescale,Freescale1,3428755,3428755,17052626,1,0,0,0,0.9061379876258038,0.3945743467477048,circuit simulation problem,18920347 -NYPA,Maragal_8,33212,75077,1308415,1,0,0,0,0,0,least squares problem,1308415 -Bates,sls,1748122,62729,6804304,1,0,0,0,0,0,least squares problem,6804304 -TSOPF,TSOPF_RS_b2383,38120,38120,16171169,1,0,0,0,0.002485718029041526,0.0003350401831305099,power network problem,16171169 -TSOPF,TSOPF_FS_b300,29214,29214,4400122,1,0,0,0,1,1,power network problem,4400122 -TSOPF,TSOPF_FS_b162_c1,10798,10798,608540,1,0,0,0,1,1,power network problem,608540 -TSOPF,TSOPF_FS_b162_c3,30798,30798,1801300,1,0,0,0,1,1,power network problem,1801300 -TSOPF,TSOPF_FS_b162_c4,40798,40798,2398220,1,0,0,0,1,1,power network problem,2398220 -TSOPF,TSOPF_FS_b300_c1,29214,29214,4400122,1,0,0,0,1,1,power network problem,4400122 -TSOPF,TSOPF_FS_b300_c2,56814,56814,8767466,1,0,0,0,1,1,power network problem,8767466 -TSOPF,TSOPF_FS_b300_c3,84414,84414,13135930,1,0,0,0,1,1,power network problem,13135930 -TSOPF,TSOPF_FS_b39_c19,76216,76216,1977600,1,0,0,0,1,1,power network problem,1977600 -TSOPF,TSOPF_FS_b39_c30,120216,120216,3121160,1,0,0,0,1,1,power network problem,3121160 -TSOPF,TSOPF_FS_b39_c7,28216,28216,730080,1,0,0,0,1,1,power network problem,730080 -TSOPF,TSOPF_FS_b9_c1,2454,2454,25032,1,0,0,0,1,1,power network problem,25032 -TSOPF,TSOPF_FS_b9_c6,14454,14454,147972,1,0,0,0,1,1,power network problem,147972 -TSOPF,TSOPF_RS_b162_c1,5374,5374,205399,1,0,0,0,0.02981942459702805,0.002418331351367556,power network problem,205399 -TSOPF,TSOPF_RS_b162_c3,15374,15374,610299,1,0,0,0,0.02683526094131803,0.0008133934303355415,power network problem,610299 -TSOPF,TSOPF_RS_b162_c4,20374,20374,812749,1,0,0,0,0.02645844479664045,0.0006107347997698388,power network problem,812749 -TSOPF,TSOPF_RS_b2052_c1,25626,25626,6761100,1,0,0,0,0.003886561090908282,0.0002050869579089441,power network problem,6761100 -TSOPF,TSOPF_RS_b2383_c1,38120,38120,16171169,1,0,0,0,0.002485718029041526,0.0003350401831305099,power network problem,16171169 -TSOPF,TSOPF_RS_b300_c1,14538,14538,1474325,1,0,0,0,0.01034334678703541,0.0003506683588241378,power network problem,1474325 -TSOPF,TSOPF_RS_b300_c2,28338,28338,2943887,1,0,0,0,0.00991209710027776,0.0001755931670937033,power network problem,2943887 -TSOPF,TSOPF_RS_b300_c3,42138,42138,4413449,1,0,0,0,0.009768063952888569,0.0001171197776188222,power network problem,4413449 -TSOPF,TSOPF_RS_b39_c19,38098,38098,684206,1,0,0,0,0.05914025923776359,0.0001300058038305281,power network problem,684206 -TSOPF,TSOPF_RS_b39_c30,60098,60098,1079986,1,0,0,0,0.05903687108113011,8.236061201778597e-05,power network problem,1079986 -TSOPF,TSOPF_RS_b39_c7,14098,14098,252446,1,0,0,0,0.05962284731399325,0.0003524007299729407,power network problem,252446 -TSOPF,TSOPF_RS_b678_c1,18696,18696,4396289,1,0,0,0,0.004448301852317109,0.0003256971884064591,power network problem,4396289 -TSOPF,TSOPF_RS_b678_c2,35696,35696,8781949,1,0,0,0,0.004170134538170976,0.0001630280691806068,power network problem,8781949 -TSOPF,TSOPF_RS_b9_c6,7224,7224,54082,1,0,0,0,0.1544436146377894,0.0002560546249866638,power network problem,54082 -Lee,fem_hifreq_circuit,491100,491100,20239237,0,0,1,0,0.999999037883928,0.4990933575151925,electromagnetics problem,20239237 -Buss,connectus,512,394792,1127525,1,1,0,0,0,0,undirected bipartite graph,1127525 -Yoshiyasu,image_interp,240000,120000,711683,1,0,1,0,0,0,computer graphics/vision problem,711683 -Yoshiyasu,mesh_deform,234023,9393,853829,1,0,1,0,0,0,computer graphics/vision problem,853829 -Priebel,130bit,584,575,6120,1,1,0,0,0,0,combinatorial problem,6120 -Priebel,145bit,1002,993,11315,1,1,0,0,0,0,combinatorial problem,11315 -Priebel,162bit,3606,3597,37118,1,1,0,0,0,0,combinatorial problem,37118 -Priebel,176bit,7441,7431,82270,1,1,0,0,0,0,combinatorial problem,82270 -Priebel,192bit,13691,13682,154303,1,1,0,0,0,0,combinatorial problem,154303 -Priebel,208bit,24430,24421,299756,1,1,0,0,0,0,combinatorial problem,299756 -MathWorks,TS,2142,2142,45262,1,0,0,0,0.9525732031943213,0.04130434782608695,counter-example problem,45262 -Botonakis,thermomech_TC,102158,102158,711558,1,0,1,1,1,1,thermal problem,711558 -Botonakis,thermomech_TK,102158,102158,711558,1,0,1,1,1,1,thermal problem,711558 -Botonakis,thermomech_dM,204316,204316,1423116,1,0,1,1,1,1,thermal problem,1423116 -Botonakis,thermomech_dK,204316,204316,2846228,1,0,1,0,1,0.6652727267221619,thermal problem,2846228 -MaxPlanck,shallow_water1,81920,81920,327680,1,0,1,1,1,1,computational fluid dynamics problem,327680 -MaxPlanck,shallow_water2,81920,81920,327680,1,0,1,1,1,1,computational fluid dynamics problem,327680 -Buss,12month1,12471,872622,22624727,1,1,0,0,0,0,undirected bipartite graph,22624727 -Clark,tomographic1,73159,59498,647495,1,0,1,0,0,0,computer graphics/vision problem,647495 -Bourchtein,atmosmodd,1270432,1270432,8814880,1,0,1,0,1,0.6690207156308852,computational fluid dynamics problem,8814880 -Bourchtein,atmosmodj,1270432,1270432,8814880,1,0,1,0,1,0.6690207156308852,computational fluid dynamics problem,8814880 -Bourchtein,atmosmodl,1489752,1489752,10319760,1,0,1,0,1,0.6714503542918647,computational fluid dynamics problem,10319760 -Bourchtein,atmosmodm,1489752,1489752,10319760,1,0,1,0,1,0.6714503542918647,computational fluid dynamics problem,10319760 -Puri,ABACUS_shell_ud,23412,23412,218484,1,0,1,0,1,0.9753731955380578,model reduction problem,218484 -Puri,ABACUS_shell_ld,23412,23412,218484,0,0,1,0,1,0.8850885826771654,model reduction problem,218484 -Puri,ABACUS_shell_md,23412,23412,218484,0,0,1,0,1,0.8850885826771654,model reduction problem,218484 -Puri,ABACUS_shell_hd,23412,23412,218484,0,0,1,0,1,0.8850885826771654,model reduction problem,218484 -Stevenson,LargeRegFile,2111154,801374,4944201,1,0,0,0,0,0,circuit simulation problem,4944201 -Freescale,memchip,2707524,2707524,13343948,1,0,0,0,0.9058013004223939,0.4015860962358702,circuit simulation problem,14810202 -Freescale,transient,178866,178866,961368,1,0,0,0,0.9996742983044353,0.243597788123496,circuit simulation problem,961790 -Freescale,circuit5M,5558326,5558326,59524291,1,0,0,0,0.9999998517635799,0.4198865531852634,circuit simulation problem,59524291 -Freescale,circuit5M_dc,3523317,3523317,14865409,1,0,0,0,0.9072118240231114,0.7150571517141635,circuit simulation problem,19194193 -Grund,poli3,16955,16955,37849,1,0,0,0,0.00507322676366421,0.0005743275581506652,economic problem,37849 -Grund,poli4,33833,33833,73249,1,0,0,0,0.003551857113862391,0.0002029632636492795,economic problem,73249 -Belcastro,human_gene1,22283,22283,24669643,1,0,0,0,1,1,undirected weighted graph,24669643 -Belcastro,human_gene2,14340,14340,18068388,1,0,0,0,1,1,undirected weighted graph,18068388 -Belcastro,mouse_gene,45101,45101,28967291,1,0,0,0,1,1,undirected weighted graph,28967291 -Um,offshore,259789,259789,4242673,1,0,1,1,1,1,electromagnetics problem,4242673 -SNAP,soc-Epinions1,75888,75888,508837,1,1,0,0,0.405226035056413,0.405226035056413,directed graph,508837 -SNAP,soc-LiveJournal1,4847571,4847571,68993773,1,1,0,0,0.7484193555024753,0.7484193555024753,directed graph,68993773 -SNAP,soc-Slashdot0811,77360,77360,905468,1,1,0,0,0.8669352939826918,0.8669352939826918,directed graph,905468 -SNAP,soc-Slashdot0902,82168,82168,948464,1,1,0,0,0.8410650442849082,0.8410650442849082,directed graph,948464 -SNAP,wiki-Vote,8297,8297,103689,1,1,0,0,0.0564572905515532,0.0564572905515532,directed graph,103689 -SNAP,email-EuAll,265214,265214,420045,1,1,0,0,0.2600511748250413,0.2600511748250413,directed graph,420045 -SNAP,email-Enron,36692,36692,367662,1,1,0,0,1,1,directed graph,367662 -SNAP,wiki-Talk,2394385,2394385,5021410,1,1,0,0,0.1441208744157517,0.1441208744157517,directed graph,5021410 -SNAP,cit-HepPh,34546,34546,421578,1,1,0,0,0.003117186276789061,0.003117186276789061,directed graph,421578 -SNAP,cit-HepTh,27770,27770,352807,1,1,0,0,0.002738343613933237,0.002738343613933237,directed graph,352807 -SNAP,cit-Patents,3774768,3774768,16518948,1,1,0,0,0,0,directed graph,16518948 -SNAP,ca-AstroPh,18772,18772,396160,1,1,0,0,1,1,undirected graph,396160 -SNAP,ca-CondMat,23133,23133,186936,1,1,0,0,1,1,undirected graph,186936 -SNAP,ca-GrQc,5242,5242,28980,1,1,0,0,1,1,undirected graph,28980 -SNAP,ca-HepPh,12008,12008,237010,1,1,0,0,1,1,undirected graph,237010 -SNAP,ca-HepTh,9877,9877,51971,1,1,0,0,1,1,undirected graph,51971 -SNAP,web-BerkStan,685230,685230,7600595,1,1,0,0,0.2502764586193581,0.2502764586193581,directed graph,7600595 -SNAP,web-Google,916428,916428,5105039,1,1,0,0,0.3067510355944391,0.3067510355944391,directed graph,5105039 -SNAP,web-NotreDame,325729,325729,1497134,1,1,0,0,0.5165359238309862,0.5165359238309862,directed graph,1497134 -SNAP,web-Stanford,281903,281903,2312497,1,1,0,0,0.2766368994208425,0.2766368994208425,directed graph,2312497 -SNAP,amazon0302,262111,262111,1234877,1,1,0,0,0.5427018237443891,0.5427018237443891,directed graph,1234877 -SNAP,amazon0312,400727,400727,3200440,1,1,0,0,0.5315337891039982,0.5315337891039982,directed graph,3200440 -SNAP,amazon0505,410236,410236,3356824,1,1,0,0,0.5465803390347542,0.5465803390347542,directed graph,3356824 -SNAP,amazon0601,403394,403394,3387388,1,1,0,0,0.5573497928197183,0.5573497928197183,directed graph,3387388 -SNAP,p2p-Gnutella04,10879,10879,39994,1,1,0,0,0,0,directed graph,39994 -SNAP,p2p-Gnutella05,8846,8846,31839,1,1,0,0,0,0,directed graph,31839 -SNAP,p2p-Gnutella06,8717,8717,31525,1,1,0,0,0,0,directed graph,31525 -SNAP,p2p-Gnutella08,6301,6301,20777,1,1,0,0,0,0,directed graph,20777 -SNAP,p2p-Gnutella09,8114,8114,26013,1,1,0,0,0,0,directed graph,26013 -SNAP,p2p-Gnutella24,26518,26518,65369,1,1,0,0,0,0,directed graph,65369 -SNAP,p2p-Gnutella25,22687,22687,54705,1,1,0,0,0,0,directed graph,54705 -SNAP,p2p-Gnutella30,36682,36682,88328,1,1,0,0,0,0,directed graph,88328 -SNAP,p2p-Gnutella31,62586,62586,147892,1,1,0,0,0,0,directed graph,147892 -SNAP,roadNet-CA,1971281,1971281,5533214,1,1,0,0,1,1,undirected graph,5533214 -SNAP,roadNet-PA,1090920,1090920,3083796,1,1,0,0,1,1,undirected graph,3083796 -SNAP,roadNet-TX,1393383,1393383,3843320,1,1,0,0,1,1,undirected graph,3843320 -SNAP,as-735,7716,7716,26467,1,1,0,0,1,1,undirected graph sequence,26467 -SNAP,as-Skitter,1696415,1696415,22190596,1,1,0,0,1,1,undirected graph,22190596 -SNAP,as-caida,31379,31379,106762,1,0,0,0,1,0.07869841329311927,directed weighted graph sequence,106762 -SNAP,Oregon-1,11492,11492,46818,1,1,0,0,1,1,undirected graph sequence,46818 -SNAP,Oregon-2,11806,11806,65460,1,1,0,0,1,1,undirected graph sequence,65460 -SNAP,soc-sign-epinions,131828,131828,841372,1,0,0,0,0.3082520316984202,0.3018224331855771,directed weighted graph,841372 -SNAP,soc-sign-Slashdot081106,77357,77357,516575,1,0,0,0,0.1859207278710739,0.1786110438948846,directed weighted graph,516575 -SNAP,soc-sign-Slashdot090216,81871,81871,545671,1,0,0,0,0.175926519826049,0.168574104176326,directed weighted graph,545671 -SNAP,soc-sign-Slashdot090221,82144,82144,549202,1,0,0,0,0.1774246998372184,0.1703271291801559,directed weighted graph,549202 -Dziekonski,gsm_106857,589446,589446,21758924,1,0,1,0,1,1,electromagnetics problem,21758924 -Gleich,flickr,820878,820878,9837214,1,1,0,0,0.653017002578169,0.653017002578169,directed graph,9837214 -Gleich,minnesota,2642,2642,6606,1,1,0,0,1,1,undirected graph,6606 -Gleich,usroads-48,126146,126146,323900,1,1,0,0,1,1,undirected graph,323900 -Gleich,usroads,129164,129164,330870,1,1,0,0,1,1,undirected graph,330870 -Fluorem,DK01R,903,903,11766,1,0,1,0,0.9614287029365737,0,computational fluid dynamics problem,11766 -Fluorem,GT01R,7980,7980,430909,1,0,1,0,0.8811455350661695,9.457852263618717e-06,computational fluid dynamics problem,430909 -Fluorem,PR02R,161070,161070,8185136,1,0,1,0,0.9476492341912441,0.0001697393815055858,computational fluid dynamics problem,8185136 -Fluorem,RM07R,381689,381689,37464962,1,0,1,0,0.9261667922354103,4.260681089287885e-06,computational fluid dynamics problem,37464962 -Rommes,ww_36_pmec_36,66,66,1194,1,0,1,0,0.3339191564147627,0,eigenvalue/model reduction problem,1194 -Rommes,ww_vref_6405,13251,13251,48737,1,0,1,0,0.707540090603374,0.5856980072816208,eigenvalue/model reduction problem,48737 -Rommes,xingo_afonso_itaipu,13250,13250,48735,1,0,1,0,0.7075597554196776,0.5857142857142857,eigenvalue/model reduction problem,48735 -Rommes,mimo8x8_system,13309,13309,48872,1,0,1,0,0.7060291752177048,0.5844472793832159,eigenvalue/model reduction problem,48872 -Rommes,mimo28x28_system,13251,13251,48737,1,0,1,0,0.707540090603374,0.5856980072816208,eigenvalue/model reduction problem,48737 -Rommes,mimo46x46_system,13250,13250,48735,1,0,1,0,0.7075597554196776,0.5857142857142857,eigenvalue/model reduction problem,48735 -Rommes,juba40k,40337,40337,144945,1,0,1,0,0.6934462083788981,0.5830536596402579,eigenvalue/model reduction problem,144945 -Rommes,bauru5727,40366,40366,145019,1,0,1,0,0.6931521565005984,0.5828064190876452,eigenvalue/model reduction problem,145019 -Rommes,zeros_nopss_13k,13296,13296,48827,1,0,1,0,0.7083056110002218,0.5842204479929031,eigenvalue/model reduction problem,48827 -Rommes,descriptor_xingo6u,20738,20738,73916,1,0,1,0,0.6848202865775669,0.5745268504272713,eigenvalue/model reduction problem,73916 -Rommes,nopss_11k,11685,11685,44941,1,0,1,0,0.7502077151335311,0.6233827893175075,eigenvalue/model reduction problem,44941 -Rommes,xingo3012,20944,20944,74386,1,0,1,0,0.681002526603102,0.5712336093539642,eigenvalue/model reduction problem,74386 -Rommes,bips98_606,7135,7135,34738,1,0,1,0,0.9086859688195991,0.7583878152166104,eigenvalue/model reduction problem,34738 -Rommes,bips98_1142,9735,9735,40983,1,0,1,0,0.8020552507215579,0.6694154587839767,eigenvalue/model reduction problem,40983 -Rommes,bips98_1450,11305,11305,44678,1,0,1,0,0.7593684272891549,0.6277809047012471,eigenvalue/model reduction problem,44678 -Rommes,bips07_1693,13275,13275,49044,1,0,1,0,0.706761938919083,0.5845449278560985,eigenvalue/model reduction problem,49044 -Rommes,bips07_1998,15066,15066,62198,1,0,1,0,0.7978660841804587,0.6687809795849786,eigenvalue/model reduction problem,62198 -Rommes,bips07_2476,16861,16861,66498,1,0,1,0,0.7574573297176583,0.6349098739830914,eigenvalue/model reduction problem,66498 -Rommes,bips07_3078,21128,21128,75729,1,0,1,0,0.6861363964744979,0.5750975292587777,eigenvalue/model reduction problem,75729 -Rommes,M10PI_n1,528,528,1317,1,0,1,0,1,0,eigenvalue/model reduction problem,1317 -Rommes,M20PI_n1,1028,1028,2547,1,0,1,0,1,0,eigenvalue/model reduction problem,2547 -Rommes,M40PI_n1,2028,2028,5007,1,0,1,0,1,0,eigenvalue/model reduction problem,5007 -Rommes,M80PI_n1,4028,4028,9927,1,0,1,0,1,0,eigenvalue/model reduction problem,9927 -Rommes,M10PI_n,682,682,1633,1,0,1,0,1,0.1132075471698113,eigenvalue/model reduction problem,1633 -Rommes,M20PI_n,1182,1182,2881,1,0,1,0,1,0.04173622704507512,eigenvalue/model reduction problem,2881 -Rommes,M40PI_n,2182,2182,5341,1,0,1,0,1,0.02274795268425842,eigenvalue/model reduction problem,5341 -Rommes,M80PI_n,4182,4182,10261,1,0,1,0,1,0.01191043353978085,eigenvalue/model reduction problem,10261 -Rommes,S10PI_n1,528,528,1317,1,0,1,0,1,0,eigenvalue/model reduction problem,1317 -Rommes,S20PI_n1,1028,1028,2547,1,0,1,0,1,0,eigenvalue/model reduction problem,2547 -Rommes,S40PI_n1,2028,2028,5007,1,0,1,0,1,0,eigenvalue/model reduction problem,5007 -Rommes,S80PI_n1,4028,4028,9927,1,0,1,0,1,0,eigenvalue/model reduction problem,9927 -Rommes,S10PI_n,682,682,1633,1,0,1,0,1,0.1132075471698113,eigenvalue/model reduction problem,1633 -Rommes,S20PI_n,1182,1182,2881,1,0,1,0,1,0.04173622704507512,eigenvalue/model reduction problem,2881 -Rommes,S40PI_n,2182,2182,5341,1,0,1,0,1,0.02274795268425842,eigenvalue/model reduction problem,5341 -Rommes,S80PI_n,4182,4182,10261,1,0,1,0,1,0.01191043353978085,eigenvalue/model reduction problem,10261 -Williams,pdb1HYS,36417,36417,4344765,1,0,0,1,1,1,weighted undirected graph,4344765 -Williams,consph,83334,83334,6010480,1,0,1,1,1,1,2D/3D problem,6010480 -Williams,cant,62451,62451,4007383,1,0,1,1,1,1,2D/3D problem,4007383 -Williams,mac_econ_fwd500,206500,206500,1273389,1,0,0,0,0.05995735832675801,0.005599217836274841,economic problem,1273389 -Williams,mc2depi,525825,525825,2100225,1,0,1,0,0,0,2D/3D problem,2100225 -Williams,cop20k_A,121192,121192,2624331,1,0,1,0,1,1,2D/3D problem,2624331 -Williams,webbase-1M,1000005,1000005,3105536,1,0,0,0,0.09552554676231316,0.01588957844838191,weighted directed graph,3105536 -Freescale,FullChip,2987012,2987012,26621983,1,0,0,0,0.9999996192108144,0.005614865134161801,circuit simulation problem,26621990 -Dattorro,EternityII_E,11077,262144,1503732,1,0,0,0,0,0,optimization problem,1503732 -Dattorro,EternityII_Etilde,10054,204304,1170516,1,0,0,0,0,0,optimization problem,1170516 -Dattorro,EternityII_A,7362,150638,782087,1,0,0,0,0,0,optimization problem,782087 -Fluorem,HV15R,2017169,2017169,283073458,1,0,1,0,0.8446615474952066,0.0007082780488857875,computational fluid dynamics problem,283073458 -Dziekonski,dielFilterV2clx,607232,607232,25309272,0,0,1,0,1,0.9712899015627859,electromagnetics problem,25309272 -Dziekonski,dielFilterV2real,1157456,1157456,48538952,1,0,1,0,1,1,electromagnetics problem,48538952 -Dziekonski,dielFilterV3clx,420408,420408,32886208,0,0,1,0,1,0.8873627016737613,electromagnetics problem,32886208 -Dziekonski,dielFilterV3real,1102824,1102824,89306020,1,0,1,0,1,1,electromagnetics problem,89306020 -Newman,adjnoun,112,112,850,1,1,0,0,1,1,undirected graph,850 -Newman,as-22july06,22963,22963,96872,1,1,0,0,1,1,undirected graph,96872 -Newman,astro-ph,16706,16706,242502,1,0,0,0,1,1,undirected weighted graph,242502 -Newman,celegansneural,297,297,2345,1,0,0,0,0.1680170575692964,0.04946695095948828,directed weighted graph,2345 -Newman,cond-mat,16726,16726,95188,1,0,0,0,1,1,undirected weighted graph,95188 -Newman,cond-mat-2003,31163,31163,240058,1,0,0,0,1,1,undirected weighted graph,240058 -Newman,cond-mat-2005,40421,40421,351382,1,0,0,0,1,1,undirected weighted graph,351386 -Newman,dolphins,62,62,318,1,1,0,0,1,1,undirected graph,318 -Newman,football,115,115,1226,1,0,0,0,1,1,undirected multigraph,1226 -Newman,hep-th,8361,8361,31502,1,0,0,0,1,1,undirected weighted graph,31502 -Newman,karate,34,34,156,1,1,0,0,1,1,undirected graph,156 -Newman,lesmis,77,77,508,1,0,0,0,1,1,undirected weighted graph,508 -Newman,netscience,1589,1589,5484,1,0,0,0,1,1,undirected weighted graph,5484 -Newman,polblogs,1490,1490,19025,1,0,0,0,0.242561244874356,0.2414046893071181,directed multigraph,19025 -Newman,polbooks,105,105,882,1,1,0,0,1,1,undirected graph,882 -Newman,power,4941,4941,13188,1,1,0,0,1,1,undirected graph,13188 -Arenas,PGPgiantcompo,10680,10680,48632,1,0,0,0,1,1,undirected multigraph,48632 -Arenas,jazz,198,198,5484,1,0,0,0,1,1,undirected multigraph,5484 -Arenas,celegans_metabolic,453,453,4065,1,0,0,0,1,1,undirected multigraph,4065 -Arenas,email,1133,1133,10902,1,1,0,0,1,1,undirected graph,10902 -AG-Monien,3elt,4720,4720,27444,1,1,1,0,1,1,2D/3D problem,27444 -AG-Monien,3elt_dual,9000,9000,26556,1,1,1,0,1,1,2D/3D problem,26556 -AG-Monien,airfoil1,4253,4253,24578,1,1,1,0,1,1,2D/3D problem,24578 -AG-Monien,airfoil1_dual,8034,8034,23626,1,1,1,0,1,1,2D/3D problem,23626 -AG-Monien,big_dual,30269,30269,89858,1,1,1,0,1,1,2D/3D problem,89858 -AG-Monien,crack,10240,10240,60760,1,1,1,0,1,1,2D/3D problem,60760 -AG-Monien,crack_dual,20141,20141,60086,1,1,1,0,1,1,2D/3D problem,60086 -AG-Monien,grid1,252,252,952,1,1,1,0,1,1,2D/3D problem,952 -AG-Monien,grid1_dual,224,224,840,1,1,1,0,1,1,2D/3D problem,840 -AG-Monien,grid2,3296,3296,12864,1,1,1,0,1,1,2D/3D problem,12864 -AG-Monien,grid2_dual,3136,3136,12224,1,1,1,0,1,1,2D/3D problem,12224 -AG-Monien,netz4504,1961,1961,5156,1,1,1,0,1,1,2D/3D problem,5156 -AG-Monien,netz4504_dual,615,615,2342,1,1,1,0,1,1,2D/3D problem,2342 -AG-Monien,ukerbe1,5981,5981,15704,1,1,1,0,1,1,2D/3D problem,15704 -AG-Monien,ukerbe1_dual,1866,1866,7076,1,1,1,0,1,1,2D/3D problem,7076 -AG-Monien,whitaker3,9800,9800,57978,1,1,1,0,1,1,2D/3D problem,57978 -AG-Monien,whitaker3_dual,19190,19190,57162,1,1,1,0,1,1,2D/3D problem,57162 -AG-Monien,brack2,62631,62631,733118,1,1,1,0,1,1,2D/3D problem,733118 -AG-Monien,wave,156317,156317,2118662,1,1,1,0,1,1,2D/3D problem,2118662 -AG-Monien,diag,2559,2559,8184,1,1,1,0,1,1,2D/3D problem,8184 -AG-Monien,L,956,956,3640,1,1,1,0,1,1,2D/3D problem,3640 -AG-Monien,L-9,17983,17983,71192,1,1,1,0,1,1,2D/3D problem,71192 -AG-Monien,stufe,1036,1036,3736,1,1,1,0,1,1,2D/3D problem,3736 -AG-Monien,stufe-10,24010,24010,92828,1,1,1,0,1,1,2D/3D problem,92828 -AG-Monien,biplane-9,21701,21701,84076,1,1,1,0,1,1,2D/3D problem,84076 -AG-Monien,shock-9,36476,36476,142580,1,1,1,0,1,1,2D/3D problem,142580 -AG-Monien,bfly,49152,49152,196608,1,1,0,0,1,1,undirected graph sequence,196608 -AG-Monien,cage,366,366,5124,1,1,0,0,1,1,undirected graph sequence,5124 -AG-Monien,cca,49152,49152,139264,1,1,0,0,1,1,undirected graph sequence,139264 -AG-Monien,ccc,49152,49152,147456,1,1,0,0,1,1,undirected graph sequence,147456 -AG-Monien,debr,1048576,1048576,4194298,1,1,0,0,1,1,undirected graph sequence,4194298 -AG-Monien,se,32768,32768,98300,1,1,0,0,1,1,undirected graph sequence,98300 -LAW,cnr-2000,325557,325557,3216152,1,1,0,0,0.2491384628169437,0.2491384628169437,directed graph,3216152 -LAW,in-2004,1382908,1382908,16917053,1,1,0,0,0.3564974165403691,0.3564974165403691,directed graph,16917053 -LAW,eu-2005,862664,862664,19235140,1,1,0,0,0.277066804642518,0.277066804642518,directed graph,19235140 -LAW,enron,69244,69244,276143,1,1,0,0,0.1468201946046729,0.1468201946046729,directed graph,276143 -LAW,amazon-2008,735323,735323,5158388,1,1,0,0,0.6338864001699756,0.6338864001699756,directed graph,5158388 -LAW,ljournal-2008,5363260,5363260,79023142,1,1,0,0,0.7302651670539438,0.7302651670539438,directed graph,79023142 -LAW,hollywood-2009,1139905,1139905,113891327,1,1,0,0,1,1,undirected graph,113891327 -LAW,dblp-2010,326186,326186,1615400,1,1,0,0,1,1,undirected graph,1615400 -LAW,webbase-2001,118142155,118142155,1019903190,1,1,0,0,0.2780598082364509,0.2780598082364509,directed graph,1019903190 -LAW,uk-2002,18520486,18520486,298113762,1,1,0,0,0.2084315853924949,0.2084315853924949,directed graph,298113762 -LAW,indochina-2004,7414866,7414866,194109311,1,1,0,0,0.4240142027924715,0.4240142027924715,directed graph,194109311 -LAW,it-2004,41291594,41291594,1150725436,1,1,0,0,0.1906175218924703,0.1906175218924703,directed graph,1150725436 -LAW,arabic-2005,22744080,22744080,639999458,1,1,0,0,0.2447917196533005,0.2447917196533005,directed graph,639999458 -LAW,sk-2005,50636154,50636154,1949412601,1,1,0,0,0.1245713694644861,0.1245713694644861,directed graph,1949412601 -LAW,uk-2005,39459925,39459925,936364282,1,1,0,0,0.3002522210250522,0.3002522210250522,directed graph,936364282 -DIMACS10,caidaRouterLevel,192244,192244,1218132,1,1,0,0,1,1,undirected graph,1218132 -DIMACS10,chesapeake,39,39,340,1,1,0,0,1,1,undirected graph,340 -DIMACS10,road_central,14081816,14081816,33866826,1,1,0,0,1,1,undirected graph,33866826 -DIMACS10,road_usa,23947347,23947347,57708624,1,1,0,0,1,1,undirected graph,57708624 -DIMACS10,citationCiteseer,268495,268495,2313294,1,1,0,0,1,1,undirected graph,2313294 -DIMACS10,coAuthorsCiteseer,227320,227320,1628268,1,1,0,0,1,1,undirected graph,1628268 -DIMACS10,coAuthorsDBLP,299067,299067,1955352,1,1,0,0,1,1,undirected graph,1955352 -DIMACS10,coPapersCiteseer,434102,434102,32073440,1,1,0,0,1,1,undirected graph,32073440 -DIMACS10,coPapersDBLP,540486,540486,30491458,1,1,0,0,1,1,undirected graph,30491458 -DIMACS10,delaunay_n10,1024,1024,6112,1,1,0,0,1,1,undirected graph,6112 -DIMACS10,delaunay_n11,2048,2048,12254,1,1,0,0,1,1,undirected graph,12254 -DIMACS10,delaunay_n12,4096,4096,24528,1,1,0,0,1,1,undirected graph,24528 -DIMACS10,delaunay_n13,8192,8192,49094,1,1,0,0,1,1,undirected graph,49094 -DIMACS10,delaunay_n14,16384,16384,98244,1,1,0,0,1,1,undirected graph,98244 -DIMACS10,delaunay_n15,32768,32768,196548,1,1,0,0,1,1,undirected graph,196548 -DIMACS10,delaunay_n16,65536,65536,393150,1,1,0,0,1,1,undirected graph,393150 -DIMACS10,delaunay_n17,131072,131072,786352,1,1,0,0,1,1,undirected graph,786352 -DIMACS10,delaunay_n18,262144,262144,1572792,1,1,0,0,1,1,undirected graph,1572792 -DIMACS10,delaunay_n19,524288,524288,3145646,1,1,0,0,1,1,undirected graph,3145646 -DIMACS10,delaunay_n20,1048576,1048576,6291372,1,1,0,0,1,1,undirected graph,6291372 -DIMACS10,delaunay_n21,2097152,2097152,12582816,1,1,0,0,1,1,undirected graph,12582816 -DIMACS10,delaunay_n22,4194304,4194304,25165738,1,1,0,0,1,1,undirected graph,25165738 -DIMACS10,delaunay_n23,8388608,8388608,50331568,1,1,0,0,1,1,undirected graph,50331568 -DIMACS10,delaunay_n24,16777216,16777216,100663202,1,1,0,0,1,1,undirected graph,100663202 -DIMACS10,hugebubbles-00000,18318143,18318143,54940162,1,1,0,0,1,1,undirected graph,54940162 -DIMACS10,hugebubbles-00010,19458087,19458087,58359528,1,1,0,0,1,1,undirected graph,58359528 -DIMACS10,hugebubbles-00020,21198119,21198119,63580358,1,1,0,0,1,1,undirected graph,63580358 -DIMACS10,hugetrace-00000,4588484,4588484,13758266,1,1,0,0,1,1,undirected graph,13758266 -DIMACS10,hugetrace-00010,12057441,12057441,36164358,1,1,0,0,1,1,undirected graph,36164358 -DIMACS10,hugetrace-00020,16002413,16002413,47997626,1,1,0,0,1,1,undirected graph,47997626 -DIMACS10,hugetric-00000,5824554,5824554,17467046,1,1,0,0,1,1,undirected graph,17467046 -DIMACS10,hugetric-00010,6592765,6592765,19771708,1,1,0,0,1,1,undirected graph,19771708 -DIMACS10,hugetric-00020,7122792,7122792,21361554,1,1,0,0,1,1,undirected graph,21361554 -DIMACS10,kron_g500-logn16,65536,65536,4912469,1,0,0,0,1,1,undirected multigraph,4912469 -DIMACS10,kron_g500-logn17,131072,131072,10228360,1,0,0,0,1,1,undirected multigraph,10228360 -DIMACS10,kron_g500-logn18,262144,262144,21165908,1,0,0,0,1,1,undirected multigraph,21165908 -DIMACS10,kron_g500-logn19,524288,524288,43562265,1,0,0,0,1,1,undirected multigraph,43562265 -DIMACS10,kron_g500-logn20,1048576,1048576,89239674,1,0,0,0,1,1,undirected multigraph,89239674 -DIMACS10,kron_g500-logn21,2097152,2097152,182082942,1,0,0,0,1,1,undirected multigraph,182082942 -DIMACS10,adaptive,6815744,6815744,27248640,1,1,0,0,1,1,undirected graph,27248640 -DIMACS10,channel-500x100x100-b050,4802000,4802000,85362744,1,1,0,0,1,1,undirected graph,85362744 -DIMACS10,packing-500x100x100-b050,2145852,2145852,34976486,1,1,0,0,1,1,undirected graph,34976486 -DIMACS10,venturiLevel3,4026819,4026819,16108474,1,1,0,0,1,1,undirected graph,16108474 -DIMACS10,rgg_n_2_15_s0,32768,32768,320480,1,1,0,0,1,1,undirected random graph,320480 -DIMACS10,rgg_n_2_16_s0,65536,65536,684254,1,1,0,0,1,1,undirected random graph,684254 -DIMACS10,rgg_n_2_17_s0,131072,131072,1457506,1,1,0,0,1,1,undirected random graph,1457506 -DIMACS10,rgg_n_2_18_s0,262144,262144,3094566,1,1,0,0,1,1,undirected random graph,3094566 -DIMACS10,rgg_n_2_19_s0,524288,524288,6539532,1,1,0,0,1,1,undirected random graph,6539532 -DIMACS10,rgg_n_2_20_s0,1048576,1048576,13783240,1,1,0,0,1,1,undirected random graph,13783240 -DIMACS10,rgg_n_2_21_s0,2097152,2097152,28975990,1,1,0,0,1,1,undirected random graph,28975990 -DIMACS10,rgg_n_2_22_s0,4194304,4194304,60718396,1,1,0,0,1,1,undirected random graph,60718396 -DIMACS10,rgg_n_2_23_s0,8388608,8388608,127002786,1,1,0,0,1,1,undirected random graph,127002786 -DIMACS10,rgg_n_2_24_s0,16777216,16777216,265114400,1,1,0,0,1,1,undirected random graph,265114400 -DIMACS10,asia_osm,11950757,11950757,25423206,1,1,0,0,1,1,undirected graph,25423206 -DIMACS10,belgium_osm,1441295,1441295,3099940,1,1,0,0,1,1,undirected graph,3099940 -DIMACS10,europe_osm,50912018,50912018,108109320,1,1,0,0,1,1,undirected graph,108109320 -DIMACS10,germany_osm,11548845,11548845,24738362,1,1,0,0,1,1,undirected graph,24738362 -DIMACS10,great-britain_osm,7733822,7733822,16313034,1,1,0,0,1,1,undirected graph,16313034 -DIMACS10,italy_osm,6686493,6686493,14027956,1,1,0,0,1,1,undirected graph,14027956 -DIMACS10,luxembourg_osm,114599,114599,239332,1,1,0,0,1,1,undirected graph,239332 -DIMACS10,netherlands_osm,2216688,2216688,4882476,1,1,0,0,1,1,undirected graph,4882476 -DIMACS10,144,144649,144649,2148786,1,1,0,0,1,1,undirected graph,2148786 -DIMACS10,598a,110971,110971,1483868,1,1,0,0,1,1,undirected graph,1483868 -DIMACS10,auto,448695,448695,6629222,1,1,0,0,1,1,undirected graph,6629222 -DIMACS10,cs4,22499,22499,87716,1,1,0,0,1,1,undirected graph,87716 -DIMACS10,cti,16840,16840,96464,1,1,0,0,1,1,undirected graph,96464 -DIMACS10,data,2851,2851,30186,1,1,0,0,1,1,undirected graph,30186 -DIMACS10,fe_4elt2,11143,11143,65636,1,1,0,0,1,1,undirected graph,65636 -DIMACS10,fe_body,45087,45087,327468,1,1,0,0,1,1,undirected graph,327468 -DIMACS10,fe_ocean,143437,143437,819186,1,1,0,0,1,1,undirected graph,819186 -DIMACS10,fe_rotor,99617,99617,1324862,1,1,0,0,1,1,undirected graph,1324862 -DIMACS10,fe_sphere,16386,16386,98304,1,1,0,0,1,1,undirected graph,98304 -DIMACS10,fe_tooth,78136,78136,905182,1,1,0,0,1,1,undirected graph,905182 -DIMACS10,m14b,214765,214765,3358036,1,1,0,0,1,1,undirected graph,3358036 -DIMACS10,t60k,60005,60005,178880,1,1,0,0,1,1,undirected graph,178880 -DIMACS10,uk,4824,4824,13674,1,1,0,0,1,1,undirected graph,13674 -DIMACS10,wing,62032,62032,243088,1,1,0,0,1,1,undirected graph,243088 -DIMACS10,wing_nodal,10937,10937,150976,1,1,0,0,1,1,undirected graph,150976 -IPSO,HTC_336_4438,226340,226340,783496,1,0,0,0,1,1,power network problem,904522 -IPSO,HTC_336_9129,226340,226340,762969,1,0,0,0,1,1,power network problem,883422 -IPSO,OPF_10000,43887,43887,426898,1,0,0,0,1,1,power network problem,467711 -IPSO,OPF_3754,15435,15435,141478,1,0,0,0,1,1,power network problem,158273 -IPSO,OPF_6000,29902,29902,274697,1,0,0,0,1,1,power network problem,302418 -IPSO,TSC_OPF_1047,8140,8140,2012833,1,0,0,0,1,1,power network problem,2016902 -IPSO,TSC_OPF_300,9774,9774,820783,1,0,0,0,1,1,power network problem,820804 -Janna,Serena,1391349,1391349,64131971,1,0,1,1,1,1,structural problem,64531701 -Janna,Emilia_923,923136,923136,40373538,1,0,1,1,1,1,structural problem,41005206 -Janna,Fault_639,638802,638802,27245944,1,0,1,1,1,1,structural problem,28614564 -Janna,Flan_1565,1564794,1564794,114165372,1,0,1,1,1,1,structural problem,117406044 -Janna,Geo_1438,1437960,1437960,60236322,1,0,1,1,1,1,structural problem,63156690 -Janna,Hook_1498,1498023,1498023,59374451,1,0,1,1,1,1,structural problem,60917445 -Janna,StocF-1465,1465137,1465137,21005389,1,0,1,1,1,1,computational fluid dynamics problem,21005389 -Janna,Cube_Coup_dt0,2164760,2164760,124406070,1,0,1,0,1,1,structural problem,127206144 -Janna,Cube_Coup_dt6,2164760,2164760,124406070,1,0,1,0,1,1,structural problem,127206144 -Janna,Long_Coup_dt0,1470152,1470152,84422970,1,0,1,0,1,1,structural problem,87088992 -Janna,Long_Coup_dt6,1470152,1470152,84422970,1,0,1,0,1,1,structural problem,87088992 -Schulthess,N_biocarta,1922,1996,4335,1,0,0,0,0,0,biochemical network,4335 -Schulthess,N_pid,3625,3923,8054,1,0,0,0,0,0,biochemical network,8054 -Schulthess,N_reactome,10204,16559,43816,1,0,0,0,0,0,biochemical network,43816 -Chevron,Chevron1,37365,37365,330633,0,0,0,0,0.994503321194266,0.7103673090824775,other problem,330633 -Chevron,Chevron2,90249,90249,803173,0,0,0,0,0.9963698795383519,0.6400065084076283,other problem,803173 -Chevron,Chevron3,381381,381381,3413113,0,0,0,0,0.998180577966654,0.5728441696033818,other problem,3413113 -Chevron,Chevron4,711450,711450,6376412,0,0,0,0,0.9986570077610406,0.5561276492234193,other problem,6376412 -CPM,cz148,148,148,1527,1,0,1,0,0.4263959390862944,0.2306018854242204,2D/3D problem,1527 -CPM,cz308,308,308,3182,1,0,1,0,0.4342379958246347,0.2219902574808629,2D/3D problem,3182 -CPM,cz628,628,628,6346,1,0,1,0,0.441413081497027,0.2308499475341028,2D/3D problem,6346 -CPM,cz1268,1268,1268,12786,1,0,1,0,0.4386178155929849,0.2340684146553221,2D/3D problem,12786 -CPM,cz2548,2548,2548,25674,1,0,1,0,0.4322407679667906,0.2349736227622589,2D/3D problem,25674 -CPM,cz5108,5108,5108,51412,1,0,1,0,0.433914996544575,0.2362214927436075,2D/3D problem,51412 -CPM,cz10228,10228,10228,102876,1,0,1,0,0.4358431914342458,0.2371988602020551,2D/3D problem,102876 -CPM,cz20468,20468,20468,206076,1,0,1,0,0.4354769190983147,0.2377052713245119,2D/3D problem,206076 -CPM,cz40948,40948,40948,412148,1,0,1,0,0.4349030172413793,0.2372683189655172,2D/3D problem,412148 -Davis,FX_March2010,1319,9498,301899,1,0,0,0,0,0,term/document graph,301899 -Bodendiek,CurlCurl_0,11083,11083,113343,1,0,1,0,1,1,model reduction problem,113343 -Bodendiek,CurlCurl_1,226451,226451,2472071,1,0,1,0,1,1,model reduction problem,2472071 -Bodendiek,CurlCurl_2,806529,806529,8921789,1,0,1,0,1,1,model reduction problem,8921789 -Bodendiek,CurlCurl_3,1219574,1219574,13544618,1,0,1,0,1,1,model reduction problem,13544618 -Bodendiek,CurlCurl_4,2380515,2380515,26515867,1,0,1,0,1,1,model reduction problem,26515867 -DIMACS10,G_n_pin_pout,100000,100000,1002396,1,1,0,0,1,1,random undirected graph,1002396 -DIMACS10,preferentialAttachment,100000,100000,999970,1,1,0,0,1,1,random undirected graph,999970 -DIMACS10,smallworld,100000,100000,999996,1,1,0,0,1,1,random undirected graph,999996 -DIMACS10,333SP,3712815,3712815,22217266,1,1,0,0,1,1,undirected graph,22217266 -DIMACS10,AS365,3799275,3799275,22736152,1,1,0,0,1,1,undirected graph,22736152 -DIMACS10,M6,3501776,3501776,21003872,1,1,0,0,1,1,undirected graph,21003872 -DIMACS10,NACA0015,1039183,1039183,6229636,1,1,0,0,1,1,undirected graph,6229636 -DIMACS10,NLR,4163763,4163763,24975952,1,1,0,0,1,1,undirected graph,24975952 -DIMACS10,ak2010,45292,45292,217098,1,0,0,0,1,1,undirected weighted graph,217098 -DIMACS10,al2010,252266,252266,1230482,1,0,0,0,1,1,undirected weighted graph,1230482 -DIMACS10,ar2010,186211,186211,904310,1,0,0,0,1,1,undirected weighted graph,904310 -DIMACS10,az2010,241666,241666,1196094,1,0,0,0,1,1,undirected weighted graph,1196094 -DIMACS10,ca2010,710145,710145,3489366,1,0,0,0,1,1,undirected weighted graph,3489366 -DIMACS10,co2010,201062,201062,974574,1,0,0,0,1,1,undirected weighted graph,974574 -DIMACS10,ct2010,67578,67578,336352,1,0,0,0,1,1,undirected weighted graph,336352 -DIMACS10,de2010,24115,24115,116056,1,0,0,0,1,1,undirected weighted graph,116056 -DIMACS10,fl2010,484481,484481,2346294,1,0,0,0,1,1,undirected weighted graph,2346294 -DIMACS10,ga2010,291086,291086,1418056,1,0,0,0,1,1,undirected weighted graph,1418056 -DIMACS10,hi2010,25016,25016,124126,1,0,0,0,1,1,undirected weighted graph,124126 -DIMACS10,ia2010,216007,216007,1021170,1,0,0,0,1,1,undirected weighted graph,1021170 -DIMACS10,id2010,149842,149842,728264,1,0,0,0,1,1,undirected weighted graph,728264 -DIMACS10,il2010,451554,451554,2164464,1,0,0,0,1,1,undirected weighted graph,2164464 -DIMACS10,in2010,267071,267071,1281716,1,0,0,0,1,1,undirected weighted graph,1281716 -DIMACS10,ks2010,238600,238600,1121798,1,0,0,0,1,1,undirected weighted graph,1121798 -DIMACS10,ky2010,161672,161672,787778,1,0,0,0,1,1,undirected weighted graph,787778 -DIMACS10,la2010,204447,204447,980634,1,0,0,0,1,1,undirected weighted graph,980634 -DIMACS10,ma2010,157508,157508,776610,1,0,0,0,1,1,undirected weighted graph,776610 -DIMACS10,md2010,145247,145247,700378,1,0,0,0,1,1,undirected weighted graph,700378 -DIMACS10,me2010,69518,69518,335476,1,0,0,0,1,1,undirected weighted graph,335476 -DIMACS10,mi2010,329885,329885,1578090,1,0,0,0,1,1,undirected weighted graph,1578090 -DIMACS10,mn2010,259777,259777,1227102,1,0,0,0,1,1,undirected weighted graph,1227102 -DIMACS10,mo2010,343565,343565,1656568,1,0,0,0,1,1,undirected weighted graph,1656568 -DIMACS10,ms2010,171778,171778,839980,1,0,0,0,1,1,undirected weighted graph,839980 -DIMACS10,mt2010,132288,132288,638668,1,0,0,0,1,1,undirected weighted graph,638668 -DIMACS10,nc2010,288987,288987,1416620,1,0,0,0,1,1,undirected weighted graph,1416620 -DIMACS10,nd2010,133769,133769,625946,1,0,0,0,1,1,undirected weighted graph,625946 -DIMACS10,ne2010,193352,193352,913854,1,0,0,0,1,1,undirected weighted graph,913854 -DIMACS10,nh2010,48837,48837,234550,1,0,0,0,1,1,undirected weighted graph,234550 -DIMACS10,nj2010,169588,169588,829912,1,0,0,0,1,1,undirected weighted graph,829912 -DIMACS10,nm2010,168609,168609,830970,1,0,0,0,1,1,undirected weighted graph,830970 -DIMACS10,nv2010,84538,84538,416998,1,0,0,0,1,1,undirected weighted graph,416998 -DIMACS10,ny2010,350169,350169,1709544,1,0,0,0,1,1,undirected weighted graph,1709544 -DIMACS10,oh2010,365344,365344,1768240,1,0,0,0,1,1,undirected weighted graph,1768240 -DIMACS10,ok2010,269118,269118,1274148,1,0,0,0,1,1,undirected weighted graph,1274148 -DIMACS10,or2010,196621,196621,979512,1,0,0,0,1,1,undirected weighted graph,979512 -DIMACS10,pa2010,421545,421545,2058462,1,0,0,0,1,1,undirected weighted graph,2058462 -DIMACS10,ri2010,25181,25181,125750,1,0,0,0,1,1,undirected weighted graph,125750 -DIMACS10,sc2010,181908,181908,893160,1,0,0,0,1,1,undirected weighted graph,893160 -DIMACS10,sd2010,88360,88360,410722,1,0,0,0,1,1,undirected weighted graph,410722 -DIMACS10,tn2010,240116,240116,1193966,1,0,0,0,1,1,undirected weighted graph,1193966 -DIMACS10,tx2010,914231,914231,4456272,1,0,0,0,1,1,undirected weighted graph,4456272 -DIMACS10,ut2010,115406,115406,572066,1,0,0,0,1,1,undirected weighted graph,572066 -DIMACS10,va2010,285762,285762,1402128,1,0,0,0,1,1,undirected weighted graph,1402128 -DIMACS10,vt2010,32580,32580,155598,1,0,0,0,1,1,undirected weighted graph,155598 -DIMACS10,wa2010,195574,195574,947432,1,0,0,0,1,1,undirected weighted graph,947432 -DIMACS10,wi2010,253096,253096,1209404,1,0,0,0,1,1,undirected weighted graph,1209404 -DIMACS10,wv2010,135218,135218,662922,1,0,0,0,1,1,undirected weighted graph,662922 -DIMACS10,wy2010,86204,86204,427586,1,0,0,0,1,1,undirected weighted graph,427586 -DIMACS10,vsp_barth5_1Ksep_50in_5Kout,32212,32212,203610,1,1,0,0,1,1,random unweighted graph,203610 -DIMACS10,vsp_bcsstk30_500sep_10in_1Kout,58348,58348,4033156,1,1,0,0,1,1,random unweighted graph,4033156 -DIMACS10,vsp_befref_fxm_2_4_air02,14109,14109,196448,1,1,0,0,1,1,random unweighted graph,196448 -DIMACS10,vsp_bump2_e18_aa01_model1_crew1,56438,56438,601602,1,1,0,0,1,1,random unweighted graph,601602 -DIMACS10,vsp_c-30_data_data,11023,11023,124368,1,1,0,0,1,1,random unweighted graph,124368 -DIMACS10,vsp_c-60_data_cti_cs4,85830,85830,482160,1,1,0,0,1,1,random unweighted graph,482160 -DIMACS10,vsp_data_and_seymourl,9167,9167,111732,1,1,0,0,1,1,random unweighted graph,111732 -DIMACS10,vsp_finan512_scagr7-2c_rlfddd,139752,139752,1104040,1,1,0,0,1,1,random unweighted graph,1104040 -DIMACS10,vsp_mod2_pgp2_slptsk,101364,101364,778736,1,1,0,0,1,1,random unweighted graph,778736 -DIMACS10,vsp_model1_crew1_cr42_south31,45101,45101,379952,1,1,0,0,1,1,random unweighted graph,379952 -DIMACS10,vsp_msc10848_300sep_100in_1Kout,21996,21996,2442056,1,1,0,0,1,1,random unweighted graph,2442056 -DIMACS10,vsp_p0291_seymourl_iiasa,10498,10498,107736,1,1,0,0,1,1,random unweighted graph,107736 -DIMACS10,vsp_sctap1-2b_and_seymourl,40174,40174,281662,1,1,0,0,1,1,random unweighted graph,281662 -DIMACS10,vsp_south31_slptsk,39668,39668,379828,1,1,0,0,1,1,random unweighted graph,379828 -DIMACS10,vsp_vibrobox_scagr7-2c_rlfddd,77328,77328,871172,1,1,0,0,1,1,random unweighted graph,871172 -Janna,CoupCons3D,416800,416800,17277420,1,0,1,0,1,0.008881642549324996,structural problem,22322336 -Janna,ML_Laplace,377002,377002,27582698,1,0,1,0,1,7.351401706466176e-08,structural problem,27689972 -Janna,Transport,1602111,1602111,23487281,1,0,1,0,1,0,structural problem,23500731 -Janna,ML_Geer,1504002,1504002,110686677,1,0,1,0,1,2.381330188145693e-07,structural problem,110879972 -ANSYS,Delor64K,64719,1785345,652140,1,0,0,0,0,0,least squares problem,652140 -ANSYS,Delor295K,295734,1823928,2401323,1,0,0,0,0,0,least squares problem,2401323 -ANSYS,Delor338K,343236,887058,4211599,1,0,0,0,0,0,least squares problem,4211599 -Sorensen,Linux_call_graph,324085,324085,1208908,1,0,0,0,0.0006324084497715383,0.0006125422157473015,directed weighted graph,1208908 -LeGresley,LeGresley_2508,2508,2508,16727,1,0,0,0,0.8376116463886349,0.2557141852450946,power network problem,16727 -LeGresley,LeGresley_4908,4908,4908,30482,1,0,0,0,0.9773607038123168,0.02995112414467253,power network problem,30482 -LeGresley,LeGresley_87936,87936,87936,593276,1,0,0,0,0.9792020608292309,0.03427471928313486,power network problem,593276 -Brogan,specular,477976,1600,7647040,1,0,1,0,0,0,computer vision problem,7647616 -Janna,Bump_2911,2911419,2911419,127729899,1,0,1,1,1,1,2D/3D problem,127729899 -Janna,Queen_4147,4147110,4147110,316548962,1,0,1,1,1,1,2D/3D problem,329499284 -Janna,PFlow_742,742793,742793,37138461,1,0,1,1,1,1,2D/3D problem,37138461 -Freescale,Freescale2,2999349,2999349,14313235,1,0,0,0,0.9853083953765489,0.5600838032920651,circuit simulation matrix,23042677 -Moqri,MISKnowledgeMap,2427,2427,57022,1,0,0,0,1,1,undirected weighted graph,57022 -Mazaheri,bundle_adj,513351,513351,20207907,1,0,1,1,1,1,computer vision problem,20208051 -VDOL,dynamicSoaringProblem_1,647,647,5367,1,0,0,0,1,1,optimal control problem,5367 -VDOL,dynamicSoaringProblem_2,1591,1591,15588,1,0,0,0,1,1,optimal control problem,15588 -VDOL,dynamicSoaringProblem_3,2871,2871,32022,1,0,0,0,1,1,optimal control problem,32022 -VDOL,dynamicSoaringProblem_4,3191,3191,36516,1,0,0,0,1,1,optimal control problem,36516 -VDOL,dynamicSoaringProblem_5,3271,3271,36789,1,0,0,0,1,1,optimal control problem,36789 -VDOL,dynamicSoaringProblem_6,3431,3431,36741,1,0,0,0,1,1,optimal control problem,36741 -VDOL,dynamicSoaringProblem_7,3511,3511,37680,1,0,0,0,1,1,optimal control problem,37680 -VDOL,dynamicSoaringProblem_8,3543,3543,38136,1,0,0,0,1,1,optimal control problem,38136 -VDOL,freeFlyingRobot_1,798,798,5246,1,0,0,0,1,1,optimal control problem,5246 -VDOL,freeFlyingRobot_2,1338,1338,11600,1,0,0,0,1,1,optimal control problem,11600 -VDOL,freeFlyingRobot_3,1718,1718,12922,1,0,0,0,1,1,optimal control problem,12922 -VDOL,freeFlyingRobot_4,2358,2358,18218,1,0,0,0,1,1,optimal control problem,18218 -VDOL,freeFlyingRobot_5,2878,2878,24582,1,0,0,0,1,1,optimal control problem,24582 -VDOL,freeFlyingRobot_6,3358,3358,27030,1,0,0,0,1,1,optimal control problem,27030 -VDOL,freeFlyingRobot_7,3918,3918,31046,1,0,0,0,1,1,optimal control problem,31046 -VDOL,freeFlyingRobot_8,4398,4398,34958,1,0,0,0,1,1,optimal control problem,34958 -VDOL,freeFlyingRobot_9,4778,4778,39964,1,0,0,0,1,1,optimal control problem,39964 -VDOL,freeFlyingRobot_10,5218,5218,40080,1,0,0,0,1,1,optimal control problem,40080 -VDOL,freeFlyingRobot_11,5438,5438,40054,1,0,0,0,1,1,optimal control problem,40054 -VDOL,freeFlyingRobot_12,5578,5578,41940,1,0,0,0,1,1,optimal control problem,41940 -VDOL,freeFlyingRobot_13,5718,5718,43994,1,0,0,0,1,1,optimal control problem,43994 -VDOL,freeFlyingRobot_14,5958,5958,43298,1,0,0,0,1,1,optimal control problem,43298 -VDOL,freeFlyingRobot_15,6038,6038,43378,1,0,0,0,1,1,optimal control problem,43378 -VDOL,freeFlyingRobot_16,6078,6078,44006,1,0,0,0,1,1,optimal control problem,44006 -VDOL,goddardRocketProblem_1,831,831,8498,1,0,0,0,1,1,optimal control problem,8498 -VDOL,goddardRocketProblem_2,867,867,9058,1,0,0,0,1,1,optimal control problem,9058 -VDOL,hangGlider_1,360,360,3477,1,0,0,0,1,1,optimal control problem,3477 -VDOL,hangGlider_2,1647,1647,14754,1,0,0,0,1,1,optimal control problem,14754 -VDOL,hangGlider_3,10260,10260,92703,1,0,0,0,1,1,optimal control problem,92703 -VDOL,hangGlider_4,15561,15561,149532,1,0,0,0,1,1,optimal control problem,149532 -VDOL,hangGlider_5,16011,16011,155246,1,0,0,0,1,1,optimal control problem,155246 -VDOL,kineticBatchReactor_1,2052,2052,20600,1,0,0,0,1,1,optimal control problem,20600 -VDOL,kineticBatchReactor_2,4361,4361,44840,1,0,0,0,1,1,optimal control problem,44840 -VDOL,kineticBatchReactor_3,5082,5082,53219,1,0,0,0,1,1,optimal control problem,53219 -VDOL,kineticBatchReactor_4,7105,7105,74869,1,0,0,0,1,1,optimal control problem,74869 -VDOL,kineticBatchReactor_5,7641,7641,80767,1,0,0,0,1,1,optimal control problem,80767 -VDOL,kineticBatchReactor_6,7773,7773,82500,1,0,0,0,1,1,optimal control problem,82500 -VDOL,kineticBatchReactor_7,7968,7968,84508,1,0,0,0,1,1,optimal control problem,84508 -VDOL,kineticBatchReactor_8,8052,8052,85194,1,0,0,0,1,1,optimal control problem,85194 -VDOL,kineticBatchReactor_9,8115,8115,86183,1,0,0,0,1,1,optimal control problem,86183 -VDOL,lowThrust_1,584,584,6133,1,0,0,0,1,1,optimal control problem,6133 -VDOL,lowThrust_2,2312,2312,26261,1,0,0,0,1,1,optimal control problem,26261 -VDOL,lowThrust_3,7064,7064,80645,1,0,0,0,1,1,optimal control problem,80645 -VDOL,lowThrust_4,13562,13562,160947,1,0,0,0,1,1,optimal control problem,160947 -VDOL,lowThrust_5,16262,16262,198369,1,0,0,0,1,1,optimal control problem,198369 -VDOL,lowThrust_6,16928,16928,207349,1,0,0,0,1,1,optimal control problem,207349 -VDOL,lowThrust_7,17378,17378,211561,1,0,0,0,1,1,optimal control problem,211561 -VDOL,lowThrust_8,17702,17702,216445,1,0,0,0,1,1,optimal control problem,216445 -VDOL,lowThrust_9,18044,18044,219589,1,0,0,0,1,1,optimal control problem,219589 -VDOL,lowThrust_10,18260,18260,222005,1,0,0,0,1,1,optimal control problem,222005 -VDOL,lowThrust_11,18368,18368,223801,1,0,0,0,1,1,optimal control problem,223801 -VDOL,lowThrust_12,18458,18458,224593,1,0,0,0,1,1,optimal control problem,224593 -VDOL,lowThrust_13,18476,18476,224897,1,0,0,0,1,1,optimal control problem,224897 -VDOL,orbitRaising_1,442,442,2906,1,0,0,0,1,1,optimal control problem,2906 -VDOL,orbitRaising_2,739,739,6526,1,0,0,0,1,1,optimal control problem,6526 -VDOL,orbitRaising_3,761,761,6098,1,0,0,0,1,1,optimal control problem,6098 -VDOL,orbitRaising_4,915,915,7790,1,0,0,0,1,1,optimal control problem,7790 -VDOL,reorientation_1,677,677,7326,1,0,0,0,1,1,optimal control problem,7326 -VDOL,reorientation_2,1544,1544,17910,1,0,0,0,1,1,optimal control problem,17910 -VDOL,reorientation_3,2513,2513,32166,1,0,0,0,1,1,optimal control problem,32166 -VDOL,reorientation_4,2717,2717,33630,1,0,0,0,1,1,optimal control problem,33630 -VDOL,reorientation_5,2904,2904,35590,1,0,0,0,1,1,optimal control problem,35590 -VDOL,reorientation_6,3006,3006,37342,1,0,0,0,1,1,optimal control problem,37342 -VDOL,reorientation_7,3057,3057,37150,1,0,0,0,1,1,optimal control problem,37150 -VDOL,reorientation_8,3108,3108,37894,1,0,0,0,1,1,optimal control problem,37894 -VDOL,spaceShuttleEntry_1,560,560,6891,1,0,0,0,1,1,optimal control problem,6891 -VDOL,spaceShuttleEntry_2,1428,1428,24073,1,0,0,0,1,1,optimal control problem,24073 -VDOL,spaceShuttleEntry_3,1834,1834,28757,1,0,0,0,1,1,optimal control problem,28757 -VDOL,spaceShuttleEntry_4,2450,2450,39669,1,0,0,0,1,1,optimal control problem,39669 -VDOL,spaceStation_1,99,99,927,1,0,0,0,1,1,optimal control problem,927 -VDOL,spaceStation_2,329,329,5817,1,0,0,0,1,1,optimal control problem,5817 -VDOL,spaceStation_3,467,467,5103,1,0,0,0,1,1,optimal control problem,5103 -VDOL,spaceStation_4,950,950,13626,1,0,0,0,1,1,optimal control problem,13626 -VDOL,spaceStation_5,1019,1019,15219,1,0,0,0,1,1,optimal control problem,15219 -VDOL,spaceStation_6,1111,1111,17595,1,0,0,0,1,1,optimal control problem,17595 -VDOL,spaceStation_7,1134,1134,18252,1,0,0,0,1,1,optimal control problem,18252 -VDOL,spaceStation_8,1157,1157,18945,1,0,0,0,1,1,optimal control problem,18945 -VDOL,spaceStation_9,1180,1180,19674,1,0,0,0,1,1,optimal control problem,19674 -VDOL,spaceStation_10,1272,1272,17478,1,0,0,0,1,1,optimal control problem,17478 -VDOL,spaceStation_11,1364,1364,18954,1,0,0,0,1,1,optimal control problem,18954 -VDOL,spaceStation_12,1410,1410,19728,1,0,0,0,1,1,optimal control problem,19728 -VDOL,spaceStation_13,1617,1617,23649,1,0,0,0,1,1,optimal control problem,23649 -VDOL,spaceStation_14,1640,1640,24324,1,0,0,0,1,1,optimal control problem,24324 -VDOL,tumorAntiAngiogenesis_1,205,205,1783,1,0,0,0,1,1,optimal control problem,1783 -VDOL,tumorAntiAngiogenesis_2,305,305,2699,1,0,0,0,1,1,optimal control problem,2699 -VDOL,tumorAntiAngiogenesis_3,410,410,3952,1,0,0,0,1,1,optimal control problem,3952 -VDOL,tumorAntiAngiogenesis_4,455,455,4593,1,0,0,0,1,1,optimal control problem,4593 -VDOL,tumorAntiAngiogenesis_5,460,460,4690,1,0,0,0,1,1,optimal control problem,4690 -VDOL,tumorAntiAngiogenesis_6,465,465,4795,1,0,0,0,1,1,optimal control problem,4795 -VDOL,tumorAntiAngiogenesis_7,475,475,4597,1,0,0,0,1,1,optimal control problem,4597 -VDOL,tumorAntiAngiogenesis_8,490,490,4776,1,0,0,0,1,1,optimal control problem,4776 -Meng,iChem_Jacobian,274087,274087,4137369,0,0,0,0,0.9801722990969854,0.6457048695901567,computational chemistry problem,4137369 -Harvard_Seismology,JP,87616,67320,13734559,1,0,0,0,0,0,tomography problem,13734559 -Mycielski,mycielskian2,2,2,2,1,1,0,0,1,1,undirected graph,2 -Mycielski,mycielskian3,5,5,10,1,1,0,0,1,1,undirected graph,10 -Mycielski,mycielskian4,11,11,40,1,1,0,0,1,1,undirected graph,40 -Mycielski,mycielskian5,23,23,142,1,1,0,0,1,1,undirected graph,142 -Mycielski,mycielskian6,47,47,472,1,1,0,0,1,1,undirected graph,472 -Mycielski,mycielskian7,95,95,1510,1,1,0,0,1,1,undirected graph,1510 -Mycielski,mycielskian8,191,191,4720,1,1,0,0,1,1,undirected graph,4720 -Mycielski,mycielskian9,383,383,14542,1,1,0,0,1,1,undirected graph,14542 -Mycielski,mycielskian10,767,767,44392,1,1,0,0,1,1,undirected graph,44392 -Mycielski,mycielskian11,1535,1535,134710,1,1,0,0,1,1,undirected graph,134710 -Mycielski,mycielskian12,3071,3071,407200,1,1,0,0,1,1,undirected graph,407200 -Mycielski,mycielskian13,6143,6143,1227742,1,1,0,0,1,1,undirected graph,1227742 -Mycielski,mycielskian14,12287,12287,3695512,1,1,0,0,1,1,undirected graph,3695512 -Mycielski,mycielskian15,24575,24575,11111110,1,1,0,0,1,1,undirected graph,11111110 -Mycielski,mycielskian16,49151,49151,33382480,1,1,0,0,1,1,undirected graph,33382480 -Mycielski,mycielskian17,98303,98303,100245742,1,1,0,0,1,1,undirected graph,100245742 -Mycielski,mycielskian18,196607,196607,300933832,1,1,0,0,1,1,undirected graph,300933832 -Mycielski,mycielskian19,393215,393215,903194710,1,1,0,0,1,1,undirected graph,903194710 -Mycielski,mycielskian20,786431,786431,2710370560,1,1,0,0,1,1,undirected graph,2710370560 -SNAP,CollegeMsg,1899,1899,20296,1,0,0,0,0.6363815530153725,0.263204572329523,directed temporal multigraph,20296 -SNAP,com-Amazon,334863,334863,1851744,1,1,0,0,1,1,undirected graph with communities,1851744 -SNAP,com-DBLP,317080,317080,2099732,1,1,0,0,1,1,undirected graph with communities,2099732 -SNAP,com-Friendster,65608366,65608366,3612134270,1,1,0,0,1,1,undirected graph with communities,3612134270 -SNAP,com-LiveJournal,3997962,3997962,69362378,1,1,0,0,1,1,undirected graph with communities,69362378 -SNAP,com-Orkut,3072441,3072441,234370166,1,1,0,0,1,1,undirected graph with communities,234370166 -SNAP,com-Youtube,1134890,1134890,5975248,1,1,0,0,1,1,undirected graph with communities,5975248 -SNAP,email-Eu-core,1005,1005,25571,1,1,0,0,0.7112198644149385,0.7112198644149385,directed graph with communities,25571 -SNAP,email-Eu-core-temporal,1005,1005,24929,1,0,0,0,0.7112198644149385,0.1435276184363592,directed temporal multigraph,24929 -SNAP,higgs-twitter,456626,456626,14855842,1,1,0,0,0.3160251211999823,0.3160251211999823,directed temporal multigraph,14855842 -SNAP,loc-Brightkite,58228,58228,428156,1,1,0,0,1,1,undirected graph,428156 -SNAP,loc-Gowalla,196591,196591,1900654,1,1,0,0,1,1,undirected graph,1900654 -SNAP,soc-Pokec,1632803,1632803,30622564,1,1,0,0,0.5434293483720044,0.5434293483720044,directed graph,30622564 -SNAP,soc-sign-bitcoin-alpha,3783,3783,24186,1,0,0,0,0.832051600099231,0.5957165302240965,directed weighted temporal graph,24186 -SNAP,soc-sign-bitcoin-otc,5881,5881,35592,1,0,0,0,0.7923128792987189,0.5689480782198246,directed weighted temporal graph,35592 -SNAP,sx-askubuntu,159316,159316,596933,1,0,0,0,0.3265757288095759,0.1849102403322678,directed temporal multigraph,596933 -SNAP,sx-mathoverflow,24818,24818,239978,1,0,0,0,0.3509349053252102,0.1609975832379348,directed temporal multigraph,239978 -SNAP,sx-stackoverflow,2601977,2601977,36233450,1,0,0,0,0.3837726021373516,0.2153393177894375,directed temporal multigraph,36233450 -SNAP,sx-superuser,194085,194085,924886,1,0,0,0,0.3272723867800749,0.197851768013418,directed temporal multigraph,924886 -SNAP,twitter7,41652230,41652230,1468365182,1,1,0,0,0.3621059600332965,0.3621059600332965,directed graph,1468365182 -SNAP,wiki-RfA,11380,11380,188077,1,0,0,0,0.07451698016852498,0.06452676823559451,directed weighted temporal multigraph,188077 -SNAP,wiki-talk-temporal,1140149,1140149,3309592,1,0,0,0,0.2189736490582744,0.07817954976807415,directed temporal multigraph,3309592 -SNAP,wiki-topcats,1791489,1791489,28511807,1,1,0,0,0.2149515115699757,0.2149515115699757,directed graph with communities,28511807 -MAWI,mawi_201512012345,18571154,18571154,38040320,1,0,0,0,1,1,undirected weighted graph,38040320 -MAWI,mawi_201512020000,35991342,35991342,74485420,1,0,0,0,1,1,undirected weighted graph,74485420 -MAWI,mawi_201512020030,68863315,68863315,143414960,1,0,0,0,1,1,undirected weighted graph,143414960 -MAWI,mawi_201512020130,128568730,128568730,270234840,1,0,0,0,1,1,undirected weighted graph,270234840 -MAWI,mawi_201512020330,226196185,226196185,480047894,1,0,0,0,1,1,undirected weighted graph,480047894 -GenBank,kmer_A2a,170728175,170728175,360585172,1,1,0,0,1,1,undirected graph,360585172 -GenBank,kmer_P1a,139353211,139353211,297829984,1,1,0,0,1,1,undirected graph,297829984 -GenBank,kmer_U1a,67716231,67716231,138778562,1,1,0,0,1,1,undirected graph,138778562 -GenBank,kmer_V1r,214005017,214005017,465410904,1,1,0,0,1,1,undirected graph,465410904 -GenBank,kmer_V2a,55042369,55042369,117217600,1,1,0,0,1,1,undirected graph,117217600 -Sybrandt,MOLIERE_2016,30239687,30239687,6669254694,1,0,0,0,1,1,undirected weighted graph,6677301366 -Guettel,TEM27623,27623,27623,439469,1,0,1,0,1,1,electromagnetics problem,439469 -Guettel,TEM152078,152078,152078,6459326,1,0,1,0,1,1,electromagnetics problem,6459326 -Guettel,TEM181302,181302,181302,7839010,1,0,1,0,1,1,electromagnetics problem,7839010 -Grueninger,windtunnel_evap2d,8256,8256,109368,1,0,1,0,1,0,computational fluid dynamics problem,233468 -Grueninger,windtunnel_evap3d,40816,40816,803978,1,0,1,0,1,0,computational fluid dynamics problem,2730600 -PowerSystem,power197k,197156,197156,805839,1,0,0,0,0.2139400937122758,0.02415229830828888,power network problem,3698357 -Goodwin,Goodwin_010,1182,1182,32282,1,0,1,0,0.9018121596229179,0.05496990350011147,computational fluid dynamics,32282 -Goodwin,Goodwin_013,1965,1965,56069,1,0,1,0,0.9250320454129281,0.05779161325764512,computational fluid dynamics,56069 -Goodwin,Goodwin_017,3317,3317,97773,1,0,1,0,0.9419021841757411,0.06036381947504249,computational fluid dynamics,97773 -Goodwin,Goodwin_023,6005,6005,182168,1,0,1,0,0.9572036973485083,0.06214380952916754,computational fluid dynamics,182168 -Goodwin,Goodwin_030,10142,10142,312814,1,0,1,0,0.966280360610536,0.06332624905442888,computational fluid dynamics,312814 -Goodwin,Goodwin_040,17922,17922,561677,1,0,1,0,0.9746314856878265,0.06442027585251406,computational fluid dynamics,561677 -Goodwin,Goodwin_054,32510,32510,1030878,1,0,1,0,0.9804679703906704,0.06537882962720246,computational fluid dynamics,1030878 -Goodwin,Goodwin_071,56021,56021,1797934,1,0,1,0,0.9849308636424431,0.06556841497181332,computational fluid dynamics,1797934 -Goodwin,Goodwin_095,100037,100037,3226066,1,0,1,0,0.9880933163299772,0.06443620776105316,computational fluid dynamics,3226066 -Goodwin,Goodwin_127,178437,178437,5778545,1,0,1,0,0.9902002197399873,0.06589012929818107,computational fluid dynamics,5778545 -TAMU_SmartGridCenter,ACTIVSg2000,4000,4000,28505,1,0,0,0,1,0.007345439706182412,power network problem,29336 -TAMU_SmartGridCenter,ACTIVSg10K,20000,20000,135888,1,0,0,0,1,0.02328109899213033,power network problem,137736 -TAMU_SmartGridCenter,ACTIVSg70K,69999,69999,238627,1,0,0,0,1,1,power network problem,238627 -Embree,ifiss_mat,96307,96307,3599932,1,0,1,0,1,0.2845118352876235,computational fluid dynamics,3599932 -Hardesty,Hardesty1,938905,938905,12143314,1,0,1,0,1,1,computer graphics/vision problem,12143314 -Hardesty,Hardesty2,929901,303645,4020731,1,0,1,0,0,0,computer graphics/vision problem,4020731 -Hardesty,Hardesty3,8217820,7591564,40451632,1,0,1,0,0,0,computer graphics/vision problem,40451632 -VLSI,nv1,75468,75468,1635003,1,0,1,0,1,0.2136393219773843,semiconductor device problem,2449194 -VLSI,nv2,1453908,1453908,37475646,1,0,1,0,1,0.1982255825635065,semiconductor device problem,52728362 -VLSI,imagesensor,118758,118758,1446396,1,0,1,0,1,0.2826177015120085,semiconductor device problem,1893170 -VLSI,mosfet2,46994,46994,1013062,1,0,1,0,1,0.1797368301196189,semiconductor device problem,1499460 -VLSI,power9,155376,155376,1887730,1,0,1,0,1,0.3018551635520223,semiconductor device problem,2512300 -VLSI,radiation,223104,223104,5526637,1,0,1,0,1,0.1890109856957617,semiconductor device problem,7637688 -VLSI,test1,392908,392908,9447535,1,0,1,0,1,0.2651539373184561,semiconductor device problem,12968200 -VLSI,dgreen,1200611,1200611,26606169,1,0,1,0,1,0.1732208361650628,semiconductor device problem,38259877 -VLSI,vas_stokes_1M,1090664,1090664,34767207,1,0,1,0,0.420954312323566,0.1713153870930279,semiconductor process problem,34767207 -VLSI,vas_stokes_2M,2146677,2146677,65129037,1,0,1,0,0.464592974921867,0.2072952172640085,semiconductor process problem,65129037 -VLSI,vas_stokes_4M,4382246,4382246,131577616,1,0,1,0,0.4636465777016884,0.1138555436412505,semiconductor process problem,131577616 -VLSI,stokes,11449533,11449533,349321980,1,0,1,0,0.4729070672045655,0.1293167418294988,semiconductor process problem,349321980 -VLSI,ss1,205282,205282,845089,1,0,1,0,0.9936840328411537,0.04200954350296261,semiconductor process problem,845089 -VLSI,ss,1652680,1652680,34753577,1,0,1,0,0.6780372145201986,0.2889301761218133,semiconductor process problem,34753577 -Freescale,nxp1,414604,414604,2655880,1,0,0,0,0.9998398784682034,0.8220579996645768,circuit simulation problem,2656177 -Martin,marine1,400320,400320,6226538,1,0,0,0,0.922267240944297,0,chemical oceanography problem,6226538 -Negre,dendrimer,730,730,63024,1,0,0,0,1,1,computational chemistry problem,63024 -Precima,analytics,303813,303813,2006126,1,0,0,0,1,1,data analytics problem,2006126 -GAP,GAP-twitter,61578415,61578415,1468364884,1,0,0,0,0.3621059600332965,0.001422518355458029,directed weighted graph,1468364884 -GAP,GAP-web,50636151,50636151,1930292948,1,0,0,0,0.1245713694644861,0.0004892386935249789,directed weighted graph,1930292948 -GAP,GAP-road,23947347,23947347,57708624,1,0,0,0,1,1,directed weighted graph,57708624 -GAP,GAP-kron,134217726,134217726,4223264644,1,0,0,0,1,1,random undirected weighted graph,4223264644 -GAP,GAP-urand,134217728,134217728,4294966740,1,0,0,0,1,1,random undirected weighted graph,4294966740 -ML_Graph,Binaryalphadigs_10NN,1404,1404,19392,1,0,0,0,1,1,undirected weighted graph,19392 -ML_Graph,breasttissue_10NN,106,106,1412,1,0,0,0,1,1,undirected weighted graph,1412 -ML_Graph,cnae9_10NN,1080,1080,18278,1,0,0,0,1,1,undirected weighted graph,18278 -ML_Graph,collins_15NN,1000,1000,16492,1,0,0,0,1,1,undirected weighted graph,16492 -ML_Graph,dataset12mfeatfactors_10NN,2000,2000,27442,1,0,0,0,1,1,undirected weighted graph,27442 -ML_Graph,dataset16mfeatkarhunen_10NN,2000,2000,27668,1,0,0,0,1,1,undirected weighted graph,27668 -ML_Graph,dataset18mfeatmorphological_10NN,2000,2000,22832,1,0,0,0,1,1,undirected weighted graph,22832 -ML_Graph,dataset20mfeatpixel_10NN,2000,2000,27932,1,0,0,0,1,1,undirected weighted graph,27932 -ML_Graph,dataset22mfeatzernike_10NN,2000,2000,27414,1,0,0,0,1,1,undirected weighted graph,27414 -ML_Graph,dermatology_5NN,366,366,2440,1,0,0,0,1,1,undirected weighted graph,2440 -ML_Graph,Ecoli_10NN,336,336,4560,1,0,0,0,1,1,undirected weighted graph,4560 -ML_Graph,Fashion_MNIST_norm_10NN,10000,10000,158304,1,0,0,0,1,1,undirected weighted graph,158304 -ML_Graph,Glass_10NN,214,214,2986,1,0,0,0,1,1,undirected weighted graph,2986 -ML_Graph,har_10NN,10299,10299,151736,1,0,0,0,1,1,undirected weighted graph,151736 -ML_Graph,indianpines_10NN,9144,9144,124656,1,0,0,0,1,1,undirected weighted graph,124656 -ML_Graph,iris_dataset_30NN,150,150,5518,1,0,0,0,1,1,undirected weighted graph,5518 -ML_Graph,JapaneseVowelsSmall_10NN,9961,9961,131144,1,0,0,0,1,1,undirected weighted graph,131144 -ML_Graph,k49_norm_10NN,38547,38547,618158,1,0,0,0,1,1,undirected weighted graph,618158 -ML_Graph,kmnist_norm_10NN,10000,10000,156932,1,0,0,0,1,1,undirected weighted graph,156932 -ML_Graph,mice_10NN,1077,1077,13484,1,0,0,0,1,1,undirected weighted graph,13484 -ML_Graph,micromass_10NN,571,571,9668,1,0,0,0,1,1,undirected weighted graph,9668 -ML_Graph,mnist_test_norm_10NN,10000,10000,145600,1,0,0,0,1,1,undirected weighted graph,145600 -ML_Graph,Olivetti_norm_10NN,400,400,5656,1,0,0,0,1,1,undirected weighted graph,5656 -ML_Graph,optdigits_10NN,5620,5620,79650,1,0,0,0,1,1,undirected weighted graph,79650 -ML_Graph,Plants_10NN,1600,1600,21930,1,0,0,0,1,1,undirected weighted graph,21930 -ML_Graph,plantsmargin_12NN,1600,1600,25482,1,0,0,0,1,1,undirected weighted graph,25482 -ML_Graph,plantstexture_10NN,1599,1599,21204,1,0,0,0,1,1,undirected weighted graph,21204 -ML_Graph,semeion_10NN,1593,1593,22226,1,0,0,0,1,1,undirected weighted graph,22226 -ML_Graph,Spectro_10NN,531,531,7422,1,0,0,0,1,1,undirected weighted graph,7422 -ML_Graph,umistfacesnorm_10NN,575,575,6990,1,0,0,0,1,1,undirected weighted graph,6990 -ML_Graph,usps_norm_5NN,11000,11000,81112,1,0,0,0,1,1,undirected weighted graph,81112 -ML_Graph,Vehicle_10NN,846,846,10894,1,0,0,0,1,1,undirected weighted graph,10894 -ML_Graph,worms20_10NN,20055,20055,240826,1,0,0,0,1,1,undirected weighted graph,240826 -ML_Graph,YaleA_10NN,165,165,2268,1,0,0,0,1,1,undirected weighted graph,2268 -ML_Graph,YaleB_10NN,2414,2414,34808,1,0,0,0,1,1,undirected weighted graph,34808 -ML_Graph,yeast_30NN,1484,1484,62350,1,0,0,0,1,1,undirected weighted graph,62350 -Sybrandt,AGATHA_2015,183964077,183964077,11588725964,1,1,0,0,1,1,undirected graph,11588725964 -FlowIPM22,Spielman_k100,338402,338402,1025404,1,0,0,1,1,1,undirected weighted graph,1025404 -FlowIPM22,Spielman_k200,2686802,2686802,8100804,1,0,0,0,1,1,undirected weighted graph,8100804 -FlowIPM22,Spielman_k300,9045202,9045202,27226204,1,0,0,0,1,1,undirected weighted graph,27226204 -FlowIPM22,Spielman_k400,21413602,21413602,64401604,1,0,0,0,1,1,undirected weighted graph,64401604 -FlowIPM22,Spielman_k500,41792002,41792002,125627004,1,0,0,0,1,1,undirected weighted graph,125627004 -FlowIPM22,Spielman_k600,72180402,72180402,216902404,1,0,0,0,1,1,undirected weighted graph,216902404 -FlowIPM22,uni_chimera_i1,100000,100000,1100592,1,0,0,1,1,1,undirected weighted graph,1100592 -FlowIPM22,uni_chimera_i2,100000,100000,797974,1,0,0,1,1,1,undirected weighted graph,797974 -FlowIPM22,uni_chimera_i3,100000,100000,499696,1,0,0,0,1,1,undirected weighted graph,499696 -FlowIPM22,uni_chimera_i4,100000,100000,814436,1,0,0,0,1,1,undirected weighted graph,814436 -FlowIPM22,uni_chimera_i5,100000,100000,499982,1,0,0,1,1,1,undirected weighted graph,499982 diff --git a/reports/suitesparse-groups/output/collection_attribute_distributions.csv b/reports/suitesparse-groups/output/collection_attribute_distributions.csv deleted file mode 100644 index cb6fa8e..0000000 --- a/reports/suitesparse-groups/output/collection_attribute_distributions.csv +++ /dev/null @@ -1,124 +0,0 @@ -attribute,value,count,share -shape,rectangular,678,0.23347107 -shape,square,2226,0.76652893 -website_structure,Hermitian,3,0.00103306 -website_structure,rectangular,678,0.23347107 -website_structure,skew-symmetric,2,0.00068871 -website_structure,symmetric,1201,0.41356749 -website_structure,unsymmetric,1020,0.35123967 -entry_type,binary,601,0.20695592 -entry_type,complex,49,0.01687328 -entry_type,integer,522,0.17975207 -entry_type,real,1732,0.59641873 -numeric_domain_isReal_flag,complex,49,0.01687328 -numeric_domain_isReal_flag,real_or_noncomplex,2855,0.98312672 -binary_isBinary_flag,binary,601,0.20695592 -binary_isBinary_flag,not_binary,2303,0.79304408 -exact_symmetry_class,exact_numerical_or_hermitian,1196,0.41184573 -exact_symmetry_class,partially_pattern_symmetric,768,0.26446281 -exact_symmetry_class,pattern_symmetric_only,222,0.07644628 -exact_symmetry_class,pattern_unsymmetric,40,0.0137741 -exact_symmetry_class,rectangular,678,0.23347107 -positive_definite,no,2664,0.91735537 -positive_definite,yes,240,0.08264463 -is_2d3d,0,2005,0.690427 -is_2d3d,1,899,0.309573 -structural_full_rank,false,212,0.07300275 -structural_full_rank,missing,532,0.18319559 -structural_full_rank,true,2160,0.74380165 -kind,2D/3D problem,119,0.04097796 -kind,2D/3D problem sequence,4,0.00137741 -kind,acoustics problem,12,0.00413223 -kind,biochemical network,3,0.00103306 -kind,bipartite graph,4,0.00137741 -kind,chemical oceanography problem,1,0.00034435 -kind,chemical process simulation problem,68,0.02341598 -kind,chemical process simulation problem sequence,2,0.00068871 -kind,circuit simulation matrix,1,0.00034435 -kind,circuit simulation problem,68,0.02341598 -kind,circuit simulation problem sequence,8,0.00275482 -kind,combinatorial problem,299,0.10296143 -kind,computational chemistry problem,2,0.00068871 -kind,computational fluid dynamics,11,0.00378788 -kind,computational fluid dynamics problem,136,0.04683196 -kind,computational fluid dynamics problem sequence,7,0.00241047 -kind,computer graphics/vision problem,15,0.00516529 -kind,computer vision problem,2,0.00068871 -kind,counter-example problem,11,0.00378788 -kind,data analytics problem,1,0.00034435 -kind,directed graph,83,0.02858127 -kind,directed graph with communities,2,0.00068871 -kind,directed multigraph,12,0.00413223 -kind,directed temporal multigraph,8,0.00275482 -kind,directed weighted graph,46,0.01584022 -kind,directed weighted graph sequence,1,0.00034435 -kind,directed weighted random graph,1,0.00034435 -kind,directed weighted temporal graph,2,0.00068871 -kind,directed weighted temporal multigraph,1,0.00034435 -kind,duplicate 2D/3D problem,1,0.00034435 -kind,duplicate acoustics problem,1,0.00034435 -kind,duplicate computational fluid dynamics problem,1,0.00034435 -kind,duplicate economic problem,1,0.00034435 -kind,duplicate electromagnetics problem,1,0.00034435 -kind,duplicate materials problem,2,0.00068871 -kind,duplicate model reduction problem,10,0.00344353 -kind,duplicate optimization problem,1,0.00034435 -kind,duplicate structural problem,12,0.00413223 -kind,duplicate thermal problem,1,0.00034435 -kind,duplicate undirected random graph,12,0.00413223 -kind,economic problem,70,0.02410468 -kind,eigenvalue/model reduction problem,35,0.01205234 -kind,electromagnetics problem,52,0.01790634 -kind,frequency-domain circuit simulation problem,4,0.00137741 -kind,least squares problem,26,0.00895317 -kind,linear programming problem,317,0.10915978 -kind,linear programming problem sequence,25,0.00860882 -kind,materials problem,26,0.00895317 -kind,model reduction problem,37,0.01274105 -kind,optimal control problem,91,0.03133609 -kind,optimization problem,111,0.03822314 -kind,optimization problem sequence,8,0.00275482 -kind,other problem,4,0.00137741 -kind,power network problem,66,0.02272727 -kind,power network problem sequence,3,0.00103306 -kind,random 2D/3D problem,2,0.00068871 -kind,random undirected graph,3,0.00103306 -kind,random undirected weighted graph,2,0.00068871 -kind,random unweighted graph,15,0.00516529 -kind,robotics problem,3,0.00103306 -kind,semiconductor device problem,25,0.00860882 -kind,semiconductor device problem sequence,5,0.00172176 -kind,semiconductor process problem,6,0.00206612 -kind,statistical/mathematical problem,10,0.00344353 -kind,structural problem,279,0.09607438 -kind,structural problem sequence,3,0.00103306 -kind,subsequent 2D/3D problem,10,0.00344353 -kind,subsequent circuit simulation problem,182,0.06267218 -kind,subsequent computational fluid dynamics problem,30,0.01033058 -kind,subsequent optimization problem,18,0.00619835 -kind,subsequent power network problem,1,0.00034435 -kind,subsequent semiconductor device problem,13,0.00447658 -kind,subsequent structural problem,8,0.00275482 -kind,subsequent theoretical/quantum chemistry problem,25,0.00860882 -kind,term/document graph,1,0.00034435 -kind,theoretical/quantum chemistry problem,35,0.01205234 -kind,theoretical/quantum chemistry problem sequence,1,0.00034435 -kind,thermal problem,30,0.01033058 -kind,tomography problem,1,0.00034435 -kind,undirected bipartite graph,2,0.00068871 -kind,undirected graph,129,0.04442149 -kind,undirected graph sequence,9,0.00309917 -kind,undirected graph with communities,6,0.00206612 -kind,undirected multigraph,11,0.00378788 -kind,undirected random graph,32,0.01101928 -kind,undirected weighted graph,121,0.04166667 -kind,undirected weighted graph sequence,1,0.00034435 -kind,undirected weighted random graph,33,0.01136364 -kind,weighted bipartite graph,2,0.00068871 -kind,weighted directed graph,1,0.00034435 -kind,weighted undirected graph,1,0.00034435 -size_band_by_max_dimension,100k_to_1m,358,0.12327824 -size_band_by_max_dimension,10k_to_100k,769,0.26480716 -size_band_by_max_dimension,1k_to_10k,948,0.32644628 -size_band_by_max_dimension,ge_1m,163,0.05612948 -size_band_by_max_dimension,lt_1k,666,0.22933884 diff --git a/reports/suitesparse-groups/output/collection_summary.json b/reports/suitesparse-groups/output/collection_summary.json deleted file mode 100644 index b1894af..0000000 --- a/reports/suitesparse-groups/output/collection_summary.json +++ /dev/null @@ -1,76 +0,0 @@ -{ - "collection": { - "declared_matrix_count": 2904, - "explicit_zero_count_total": 259828688, - "group_count": 203, - "group_size": { - "groups_lt_5": 114, - "max": 292, - "mean": 14.305418719211822, - "median": 4, - "min": 1, - "singleton_groups": 51 - }, - "matrices_with_explicit_zeros": 432, - "ncols": { - "max": 226196185, - "median": 6139.5, - "min": 2 - }, - "nentries": { - "max": 11588725964, - "median": 57235, - "min": 1 - }, - "nnz": { - "max": 11588725964, - "median": 57092, - "min": 1 - }, - "nrows": { - "max": 226196185, - "median": 4942.5, - "min": 1 - }, - "numerical_symmetry": { - "max": 1.0, - "median": 0.4398776250567098, - "min": 0.0 - }, - "numerical_symmetry_missing_count": 0, - "parsed_matrix_count": 2904, - "pattern_symmetry": { - "max": 1.0, - "median": 0.992887429995702, - "min": 0.0 - }, - "pattern_symmetry_missing_count": 0, - "ssstats_distinct_kind_count": 91, - "ssstats_vs_website_kind_mismatch_count": 4, - "website_distinct_kind_count": 90 - }, - "definitions_and_cautions": { - "categorical_diversity_score": "Unofficial descriptive metric: the mean fraction outside the modal category across shape, website structure, entry type, exact symmetry class, positive-definite status, and problem kind.", - "diversity_label": "Unofficial screening rule. Groups with fewer than five matrices are not classified; heterogeneous means at least three attributes have a modal share below 90%; homogeneous means none do.", - "entry_type": "The website's mutually exclusive Rutherford-Boeing entry-type label: real, complex, integer, or binary. In the ssstats flags, integer and binary matrices are also real/non-complex; isBinary is a separate flag.", - "group": "A provenance/source label, typically a person, organization, or earlier collection; it is not a homogeneity guarantee.", - "kind_source_discrepancy": "Group statistics use the ssstats.csv kind. Four Chevron matrices are 'other problem' in ssstats.csv but '2D/3D problem' in the website repository; see kind_source_mismatches.csv.", - "nentries": "All stored pattern entries, equal to nnz plus explicit zeros supplied by the matrix author.", - "nnz": "Numerically nonzero entries in A; explicit stored zeros are excluded.", - "numerical_symmetry": "Matched conjugate off-diagonal values divided by all off-diagonal entries; this measures Hermitian symmetry and rectangular matrices are assigned 0.", - "pattern_symmetry": "Matched off-diagonal structural entries divided by all off-diagonal entries; rectangular matrices are assigned 0.", - "posdef_index": "1 known positive definite; 0 known not positive definite; -1 symmetric/Hermitian with unknown definiteness. No -1 values occur in this snapshot." - }, - "source": { - "ss_index_url": "https://sparse.tamu.edu/files/ss_index.mat", - "ssstats_revision_embedded": "31-Oct-2023 18:12:37", - "ssstats_url": "https://sparse.tamu.edu/files/ssstats.csv", - "website_metadata": { - "available": true, - "checkout": "/private/tmp/ssweb.o4KLNI", - "commit": "0316f796684820b319a8601110e1ce51617c9ee0", - "metadata_records": 2904, - "repository": "https://github.com/ScottKolo/suitesparse-matrix-collection-website" - } - } -} diff --git a/reports/suitesparse-groups/output/group_catalogue.csv b/reports/suitesparse-groups/output/group_catalogue.csv deleted file mode 100644 index 2f6ce23..0000000 --- a/reports/suitesparse-groups/output/group_catalogue.csv +++ /dev/null @@ -1,204 +0,0 @@ -group,matrix_count,source_or_meaning,normalized_kind_counts,dimension_min,dimension_max,size_profile,shape_counts,structure_counts,entry_type_counts,known_positive_definite,assessment,categorical_diversity_score,source_url -ACUSIM,1,"Computational fluid dynamics matrices from ACUSIM, Inc.",computational fluid dynamics problem (1),14822,14822,one dimension,square (1),symmetric (1),real (1),1,singleton; one dimension,0.0,https://sparse.tamu.edu/ACUSIM -AG-Monien,32,"AG-Monien Graph Collection, Ralf Diekmann and Robert Preis",2D/3D problem (26); undirected graph (6),224,1048576,wide (>100×),square (32),symmetric (32),binary (32),0,some categorical mix; wide (>100×),0.03125,https://sparse.tamu.edu/AG-Monien -AMD,2,"Circuit simulation matrices from Ufuk Okuyucu, AMD, Inc.",circuit simulation problem (2),150102,1585478,moderate (4–100×),square (2),symmetric (2),real (2),2,small; uniform observed types; moderate (4–100×),0.0,https://sparse.tamu.edu/AMD -ANSYS,3,Underdetermined systems needing well-conditioned bases to be found.,least squares problem (3),887058,1823928,narrow (≤4×),rectangular (3),rectangular (3),real (3),0,small; uniform observed types; narrow (≤4×),0.0,https://sparse.tamu.edu/ANSYS -ATandT,4,"Frequency domain analysis of large nonlinear analog circuits, AT&T.",frequency-domain circuit simulation problem (4),36057,659033,moderate (4–100×),square (4),unsymmetric (4),real (4),0,small; uniform observed types; moderate (4–100×),0.0,https://sparse.tamu.edu/ATandT -Alemdar,1,"A finite-element matrix from Bulent Alemdar, RAM International.",2D/3D problem (1),6245,6245,one dimension,square (1),symmetric (1),integer (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Alemdar -Andrews,1,"Large eigenvalue problem from Stuart Andrews, Brown University, Rhode Island.",computer graphics/vision problem (1),60000,60000,one dimension,square (1),symmetric (1),integer (1),1,singleton; one dimension,0.0,https://sparse.tamu.edu/Andrews -Andrianov,16,"Matrices from Alexander Andrianov, SAS Institute Inc.",optimization problem (16),5026,534388,wide (>100×),square (16),symmetric (16),binary (16),0,categorically uniform; wide (>100×),0.0,https://sparse.tamu.edu/Andrianov -Arenas,4,"Networks from Alex Arenas, Univeristy Rovira i Virgili, Tarragona, Spain",undirected multigraph (3); undirected graph (1),198,10680,moderate (4–100×),square (4),symmetric (4),integer (3); binary (1),0,small; observed mix; moderate (4–100×),0.083333,https://sparse.tamu.edu/Arenas -Averous,4,"Matrices from David Averous, Institut National Polytechnique de Toulouse (INPT).",thermal problem (4),1794,84617,moderate (4–100×),square (4),unsymmetric (4),real (4),0,small; uniform observed types; moderate (4–100×),0.0,https://sparse.tamu.edu/Averous -Bai,78,"Matrices from Z. Bai, D. Day, J. Demmel, and J. Dongarra.",computational fluid dynamics problem (31); electromagnetics problem (26); 2D/3D problem (8); statistical/mathematical problem (4); power network problem (3); chemical process simulation problem (2); materials problem (2); robotics problem (2),62,23560,wide (>100×),square (78),unsymmetric (66); symmetric (11); Hermitian (1),real (70); complex (6); integer (2),4,high categorical mix; wide (>100×),0.245726,https://sparse.tamu.edu/Bai -Barabasi,3,"Notre Dame networks, A. Barabasi.",bipartite graph (1); directed graph (1); undirected graph (1),2114,392400,wide (>100×),square (2); rectangular (1),rectangular (1); symmetric (1); unsymmetric (1),binary (3),0,small; observed mix; wide (>100×),0.388889,https://sparse.tamu.edu/Barabasi -Bates,3,"Statistical matrices, D. Bates, Univ. Wisconsin",statistical/mathematical problem (2); least squares problem (1),2541,1748122,wide (>100×),rectangular (2); square (1),rectangular (2); symmetric (1),real (2); binary (1),1,small; observed mix; wide (>100×),0.333333,https://sparse.tamu.edu/Bates -Belcastro,3,"Gene networks from V. Belcastro, TIGEM, Naples, Italy",undirected weighted graph (3),14340,45101,narrow (≤4×),square (3),symmetric (3),real (3),0,small; uniform observed types; narrow (≤4×),0.0,https://sparse.tamu.edu/Belcastro -BenElechi,1,"Matrix from S. Ben Elechi, Arcelor, Inc. with many sparse right-hand sides.",2D/3D problem (1),245874,245874,one dimension,square (1),symmetric (1),real (1),1,singleton; one dimension,0.0,https://sparse.tamu.edu/BenElechi -Bindel,6,"Coupled linear thermoeslasticity equations. D Bindel, UC Berkeley",thermal problem (6),10605,10605,one dimension,square (6),unsymmetric (4); symmetric (2),real (4); complex (2),2,high categorical mix; one dimension,0.222222,https://sparse.tamu.edu/Bindel -Bodendiek,5,"Curl-Curl operator of 2nd order Maxwell's equations, A. Bodendiek",model reduction problem (5),11083,2380515,wide (>100×),square (5),symmetric (5),real (5),0,categorically uniform; wide (>100×),0.0,https://sparse.tamu.edu/Bodendiek -Boeing,32,"Structural engineering matrices from Roger Grimes, Boeing.",structural problem (24); materials problem (8),588,217918,wide (>100×),square (32),symmetric (32),real (27); binary (5),15,high categorical mix; wide (>100×),0.161458,https://sparse.tamu.edu/Boeing -Bomhof,4,"Matrices from circuit simulation problems. Wim Bomhof, Univ. Utrecht.",circuit simulation problem (4),2624,80209,moderate (4–100×),square (4),unsymmetric (4),real (4),0,small; uniform observed types; moderate (4–100×),0.0,https://sparse.tamu.edu/Bomhof -Botonakis,6,"FEM thermal/mechanical problems from Ioannis Botonakis, Germany.",thermal problem (6),17880,204316,moderate (4–100×),square (6),symmetric (3); unsymmetric (3),real (6),3,high categorical mix; moderate (4–100×),0.25,https://sparse.tamu.edu/Botonakis -Bourchtein,4,Atmospheric modeling problems from Andrei Bourchtein,computational fluid dynamics problem (4),1270432,1489752,narrow (≤4×),square (4),unsymmetric (4),real (3); integer (1),0,small; observed mix; narrow (≤4×),0.041667,https://sparse.tamu.edu/Bourchtein -Bova,1,"Model of Charleston Harbor. Steve Bova, Nichols Research Corporation.",computational fluid dynamics problem (1),46835,46835,one dimension,square (1),unsymmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Bova -Brethour,2,"Numerical simulation of coating flows, from Jim Brethour.",computational fluid dynamics problem (2),1348,9540,moderate (4–100×),square (2),unsymmetric (2),real (2),0,small; uniform observed types; moderate (4–100×),0.0,https://sparse.tamu.edu/Brethour -Brogan,1,"specular surface reconstruction problem, Submitted by Joel Brogan, Notre Dame, July 2014",computer vision problem (1),477976,477976,one dimension,rectangular (1),rectangular (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Brogan -Brunetiere,1,A matrix from Noel Brunetiere. Finite-element discretization of thermal problem.,thermal problem (1),3456,3456,one dimension,square (1),unsymmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Brunetiere -Buss,2,"Website recommendation system, Steven Buss, Univ. Florida",undirected bipartite graph (2),394792,872622,narrow (≤4×),rectangular (2),rectangular (2),binary (2),0,small; uniform observed types; narrow (≤4×),0.0,https://sparse.tamu.edu/Buss -Bydder,2,"MRI reconstruction matrices from Mark Bydder, UCSD.",computer graphics/vision problem (2),147456,147456,one dimension,rectangular (2),rectangular (2),real (2),0,small; uniform observed types; one dimension,0.0,https://sparse.tamu.edu/Bydder -CEMW,4,"Electromagnetic problems from D. Isaak, Computational_EM_Works",electromagnetics problem (4),93476,921632,moderate (4–100×),square (4),unsymmetric (3); symmetric (1),real (2); complex (1); integer (1),1,small; observed mix; moderate (4–100×),0.25,https://sparse.tamu.edu/CEMW -CPM,9,"Closest Point Method, Yujia Chen, Andy Wathen, Shengxin Zhu",2D/3D problem (9),148,40948,wide (>100×),square (9),unsymmetric (9),real (9),0,categorically uniform; wide (>100×),0.0,https://sparse.tamu.edu/CPM -Cannizzo,1,Structural engineering matrix (finite-element) from Fabio Cannizzo.,structural problem (1),4098,4098,one dimension,square (1),symmetric (1),real (1),1,singleton; one dimension,0.0,https://sparse.tamu.edu/Cannizzo -Castrillon,1,"Counter-example matrix (denormal in A+alpha*I), J. Castrillon, Teledyne",counter-example problem (1),89400,89400,one dimension,square (1),symmetric (1),real (1),1,singleton; one dimension,0.0,https://sparse.tamu.edu/Castrillon -Chen,14,"Finite element matrices from Pu Chen, Peking University.",structural problem (14),10800,151926,moderate (4–100×),square (14),symmetric (14),binary (14),0,categorically uniform; moderate (4–100×),0.0,https://sparse.tamu.edu/Chen -Chevron,4,"2D Helmholtz; Temporal freq. domain seismic modeling. J. Washbourne, Chevron",2D/3D problem (4),37365,711450,moderate (4–100×),square (4),unsymmetric (4),complex (4),0,small; uniform observed types; moderate (4–100×),0.0,https://sparse.tamu.edu/Chevron -Clark,1,"Tomographic reconstruction of a particle field in fluid flow, T. Clark",computer graphics/vision problem (1),73159,73159,one dimension,rectangular (1),rectangular (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Clark -Cote,2,"Vibroacoustic matrices from Andre Cote, Univ. Sherbrooke, Quebec.",acoustics problem (2),5962,12328,narrow (≤4×),square (2),symmetric (2),complex (1); real (1),0,small; observed mix; narrow (≤4×),0.166667,https://sparse.tamu.edu/Cote -Cunningham,4,"Finite element matrices from Andrew Cunningham, Vibro-Acoustic Sciences, Inc.",acoustics problem (4),11107,66127,moderate (4–100×),square (4),symmetric (3); unsymmetric (1),real (4),1,small; observed mix; moderate (4–100×),0.125,https://sparse.tamu.edu/Cunningham -Cylshell,7,"Finite element analysis of cylindrical shells, R. Kouhia, Helsinki.",structural problem (7),5357,5489,narrow (≤4×),square (7),symmetric (7),real (7),7,categorically uniform; narrow (≤4×),0.0,https://sparse.tamu.edu/Cylshell -DIMACS10,151,10th DIMACS Implementation Challenge,undirected graph (67); undirected weighted graph (50); random unweighted graph (15); undirected random graph (10); undirected multigraph (6); random undirected graph (3),39,50912018,wide (>100×),square (151),symmetric (151),binary (95); integer (56),0,some categorical mix; wide (>100×),0.154525,https://sparse.tamu.edu/DIMACS10 -DNVS,15,"Positive definite matrices from Christian Damhaug, DNV Software.",structural problem (15),18010,224617,moderate (4–100×),square (15),symmetric (15),real (8); binary (7),8,some categorical mix; moderate (4–100×),0.155556,https://sparse.tamu.edu/DNVS -DRIVCAV,26,"Driven cavity, finite element matrices from Yousef Saad (SPARSKIT2 collection).",computational fluid dynamics problem (26),317,4562,moderate (4–100×),square (26),unsymmetric (26),real (26),0,some categorical mix; moderate (4–100×),0.025641,https://sparse.tamu.edu/DRIVCAV -Dattorro,3,"Dattorro Convex Optimization of Eternity II, Jon Dattoro",optimization problem (3),150638,262144,narrow (≤4×),rectangular (3),rectangular (3),integer (3),0,small; uniform observed types; narrow (≤4×),0.0,https://sparse.tamu.edu/Dattorro -Davis,1,"Analysis of the MATLAB FileExchange, March 2010",term/document graph (1),9498,9498,one dimension,rectangular (1),rectangular (1),integer (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Davis -Dehghani,1,"Light transport in soft tissue, Hamid Dehghani, Univ. of Exeter, UK",electromagnetics problem (1),29282,29282,one dimension,square (1),unsymmetric (1),complex (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Dehghani -Dziekonski,5,High order vector finite element method in electromagnetics,electromagnetics problem (5),420408,1157456,narrow (≤4×),square (5),symmetric (5),real (3); complex (2),0,some categorical mix; narrow (≤4×),0.133333,https://sparse.tamu.edu/Dziekonski -Embree,1,"Fluid stability problem from IFISS package, Mark Embree, Virginia Tech",computational fluid dynamics (1),96307,96307,one dimension,square (1),unsymmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Embree -Engwirda,1,Unstructured 2D mesh (an airfoil) from Darren Engwirda.,computational fluid dynamics problem (1),14214,14214,one dimension,square (1),unsymmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Engwirda -FEMLAB,9,"Test matrices from FEMLAB, a finite-element method toolbox for MATLAB.",computational fluid dynamics problem (4); structural problem (3); 2D/3D problem (1); electromagnetics problem (1),367,85623,wide (>100×),square (9),unsymmetric (9),real (8); complex (1),0,high categorical mix; wide (>100×),0.185185,https://sparse.tamu.edu/FEMLAB -FIDAP,35,"Fluid dynamics, finite element matrices from Yousef Saad (SPARSKIT2 collection).",computational fluid dynamics problem (35),27,19716,wide (>100×),square (35),unsymmetric (22); symmetric (13),real (35),8,high categorical mix; wide (>100×),0.161905,https://sparse.tamu.edu/FIDAP -FlowIPM22,11,FlowIPM22: Laplacians from Newton-Steps of Maxflow Short-step IPM on Chimera Graphs,undirected weighted graph (11),100000,72180402,wide (>100×),square (11),symmetric (11),real (11),4,some categorical mix; wide (>100×),0.060606,https://sparse.tamu.edu/FlowIPM22 -Fluorem,5,"CFD matrices from Francois Pacull, FLUOREM, in Lyon, France",computational fluid dynamics problem (5),903,2017169,wide (>100×),square (5),unsymmetric (5),real (5),0,categorically uniform; wide (>100×),0.0,https://sparse.tamu.edu/Fluorem -FreeFieldTechnologies,1,"3D vibro-acoustic problem, aircraft engine nacelle, from M. Gontier",acoustics problem (1),169410,169410,one dimension,square (1),unsymmetric (1),complex (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/FreeFieldTechnologies -Freescale,8,"Circuit simulation matrices K. Gullapalli, Freescale Semiconductor",circuit simulation problem (7); circuit simulation matrix (1),178866,5558326,moderate (4–100×),square (8),unsymmetric (8),real (8),0,some categorical mix; moderate (4–100×),0.020833,https://sparse.tamu.edu/Freescale -GAP,5,"GAP Benchmark matrices: Scott Beamer, Krste Asanovic', and David Patterson.",directed weighted graph (3); random undirected weighted graph (2),23947347,134217728,moderate (4–100×),square (5),symmetric (3); unsymmetric (2),integer (5),0,high categorical mix; moderate (4–100×),0.2,https://sparse.tamu.edu/GAP -GHS_indef,60,"Indefinite matrices collected by Nick Gould, Yifan Hu, and Jennifer Scott.",optimization problem (30); 2D/3D problem (17); computational fluid dynamics problem (4); materials problem (4); structural problem (3); statistical/mathematical problem (2),3002,466316,wide (>100×),square (60),symmetric (60),real (57); integer (3),1,some categorical mix; wide (>100×),0.097222,https://sparse.tamu.edu/GHS_indef -GHS_psdef,32,"Positive definite matrices collected by Nick Gould, Yifan Hu, & Jennifer Scott.",structural problem (19); optimization problem (8); computational fluid dynamics problem (3); random 2D/3D problem (2),15449,952203,moderate (4–100×),square (32),symmetric (32),real (22); binary (10),22,high categorical mix; moderate (4–100×),0.177083,https://sparse.tamu.edu/GHS_psdef -Gaertner,3,"Matrices from Dr. Klaus Gaertner, Weierstrass Institute Berlin.",directed weighted graph (2); undirected weighted graph (1),10774,13209,narrow (≤4×),square (3),unsymmetric (2); symmetric (1),integer (3),0,small; observed mix; narrow (≤4×),0.166667,https://sparse.tamu.edu/Gaertner -Garon,2,"Matrices from Andre Garon, Univ. of Montreal. 2D Navier-Stokes.",computational fluid dynamics problem (2),3175,13535,moderate (4–100×),square (2),unsymmetric (2),real (2),0,small; uniform observed types; moderate (4–100×),0.0,https://sparse.tamu.edu/Garon -GenBank,5,Protein k-mer graphs generated using data from GenBank,undirected graph (5),55042369,214005017,narrow (≤4×),square (5),symmetric (5),binary (5),0,categorically uniform; narrow (≤4×),0.0,https://sparse.tamu.edu/GenBank -Gleich,10,"Web matrices and wikipedia matrices from David Gleich, Stanford.",directed graph (7); undirected graph (3),2642,9845725,wide (>100×),square (10),unsymmetric (7); symmetric (3),binary (10),0,high categorical mix; wide (>100×),0.15,https://sparse.tamu.edu/Gleich -Goodwin,12,Fluid mechanics matrices from Ralph Goodwin,computational fluid dynamics (10); computational fluid dynamics problem (2),1182,178437,wide (>100×),square (12),unsymmetric (12),real (12),0,some categorical mix; wide (>100×),0.027778,https://sparse.tamu.edu/Goodwin -Graham,1,"Finite element matrix from Dave Graham, Univ. of Illinois.",computational fluid dynamics problem (1),9035,9035,one dimension,square (1),unsymmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Graham -Grueninger,2,"Wind tunnel evaporation, Christoph Grueninger, Univ. Stuttgart",computational fluid dynamics problem (2),8256,40816,moderate (4–100×),square (2),unsymmetric (2),real (2),0,small; uniform observed types; moderate (4–100×),0.0,https://sparse.tamu.edu/Grueninger -Grund,22,"Matrices from Friedrich Grund, WIAS Berlin. Chemical process simulation.",chemical process simulation problem (16); economic problem (4); circuit simulation problem (2),7,57735,wide (>100×),square (22),unsymmetric (21); symmetric (1),real (22),0,some categorical mix; wide (>100×),0.068182,https://sparse.tamu.edu/Grund -Gset,67,"Random graphs from Yinyu Ye, Univ. of Iowa.",undirected random graph (34); undirected weighted random graph (33),800,10000,moderate (4–100×),square (67),symmetric (67),binary (34); integer (33),0,some categorical mix; moderate (4–100×),0.166667,https://sparse.tamu.edu/Gset -Guettel,3,"3D Transient Electromagnetic Modelleing, Stefan Guettel, Univ Manchester",electromagnetics problem (3),27623,181302,moderate (4–100×),square (3),symmetric (3),real (3),0,small; uniform observed types; moderate (4–100×),0.0,https://sparse.tamu.edu/Guettel -Gupta,3,"Linear programming matrices from Anshul Gupta, IBM T. J. Watson Research Center.",optimization problem (3),16783,62064,narrow (≤4×),square (3),symmetric (3),binary (3),0,small; uniform observed types; narrow (≤4×),0.0,https://sparse.tamu.edu/Gupta -HB,292,"The original Harwell-Boeing collection. Iain Duff, Roger Grimes, and John Lewis.",structural problem (121); 2D/3D problem (36); computational fluid dynamics problem (27); power network problem (18); optimization problem (17); chemical process simulation problem (16); economic problem (14); thermal problem (12); least squares problem (11); directed weighted graph (8); acoustics problem (4); counter-example problem (3); semiconductor device problem (2); directed graph (1); materials problem (1); statistical/mathematical problem (1),9,44609,wide (>100×),square (276); rectangular (16),symmetric (169); unsymmetric (105); rectangular (16); skew-symmetric (2),real (170); binary (112); integer (7); complex (3),60,high categorical mix; wide (>100×),0.352169,https://sparse.tamu.edu/HB -HVDC,2,"Power simulation matrices from Alex Wang, Manitoba HVDC, Canada.",power network problem (2),24842,189860,moderate (4–100×),square (2),unsymmetric (2),real (2),0,small; uniform observed types; moderate (4–100×),0.0,https://sparse.tamu.edu/HVDC -Hamm,6,"Semiconductor simulation matrices from Steve Hamm, Motorola, Inc.",circuit simulation problem (6),2395,170998,moderate (4–100×),square (6),unsymmetric (6),real (6),0,some categorical mix; moderate (4–100×),0.055556,https://sparse.tamu.edu/Hamm -Hamrle,3,"Circuit simulation matrices from Jaroslav Hamrle, RIKEN, Japan.",circuit simulation problem (3),32,1447360,wide (>100×),square (3),unsymmetric (3),real (3),0,small; uniform observed types; wide (>100×),0.0,https://sparse.tamu.edu/Hamrle -Hardesty,3,"Surface fitting problem for visualization, Sean Hardesty",computer graphics/vision problem (3),929901,8217820,moderate (4–100×),rectangular (2); square (1),rectangular (2); symmetric (1),real (2); integer (1),0,small; observed mix; moderate (4–100×),0.222222,https://sparse.tamu.edu/Hardesty -Harvard_Seismology,1,"Earth's crust underneath Japan, Harvard Seismology Group.",tomography problem (1),87616,87616,one dimension,rectangular (1),rectangular (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Harvard_Seismology -Hohn,6,"Single-material crack problem, Michael Hohn, Univ. of Utah.",materials problem (6),7500,16428,narrow (≤4×),square (6),unsymmetric (6),real (6),0,some categorical mix; narrow (≤4×),0.027778,https://sparse.tamu.edu/Hohn -Hollinger,49,"Economic models from Peter Holinger, Intex Solutions, Inc.",economic problem (49),2880,64089,moderate (4–100×),square (49),unsymmetric (49),real (49),0,categorically uniform; moderate (4–100×),0.0,https://sparse.tamu.edu/Hollinger -IBM_Austin,1,"Circuit simulation matrix from Emrah Acar, IBM Austin. High degree of coupling.",circuit simulation problem (1),11341,11341,one dimension,square (1),unsymmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/IBM_Austin -IBM_EDA,7,"Circuit simulation matrices from Tim Lehner, IBM EDA.",circuit simulation problem (7),49702,116835,narrow (≤4×),square (7),unsymmetric (7),real (7),0,some categorical mix; narrow (≤4×),0.095238,https://sparse.tamu.edu/IBM_EDA -INPRO,1,"Parasol matrices, collected by Jacko Koster",structural problem (1),415863,415863,one dimension,square (1),symmetric (1),real (1),1,singleton; one dimension,0.0,https://sparse.tamu.edu/INPRO -IPSO,7,"Power simulation matrices, Li Peijie, Inst. of Power Systems, Guangxi Univ.",power network problem (7),8140,226340,moderate (4–100×),square (7),symmetric (7),real (7),0,categorically uniform; moderate (4–100×),0.0,https://sparse.tamu.edu/IPSO -JGD_BIBD,21,"Balanced Incomplete Block Design from Mark Giesbrecht, Univ. of Waterloo",combinatorial problem (21),84,319770,wide (>100×),rectangular (20); square (1),rectangular (20); symmetric (1),binary (21),1,categorically uniform; wide (>100×),0.031746,https://sparse.tamu.edu/JGD_BIBD -JGD_CAG,3,"CAG matrix set from Michael Monagan, Simon Fraser Univ., Canada",combinatorial problem (3),72,1916,moderate (4–100×),square (3),unsymmetric (3),integer (3),0,small; uniform observed types; moderate (4–100×),0.0,https://sparse.tamu.edu/JGD_CAG -JGD_Forest,10,Forests and Trees from Nicolas Thiery,combinatorial problem (10),107,317955,wide (>100×),rectangular (10),rectangular (10),integer (10),0,categorically uniform; wide (>100×),0.0,https://sparse.tamu.edu/JGD_Forest -JGD_Franz,11,"Cohomology of various rings, from Matthias Franz, Univ. Konstanz, Germany",combinatorial problem (11),2240,47104,moderate (4–100×),rectangular (11),rectangular (11),integer (11),0,categorically uniform; moderate (4–100×),0.0,https://sparse.tamu.edu/JGD_Franz -JGD_G5,13,"Decomposable subspaces at degree d of the invariant ring of G5, Nicolas Thiery.",combinatorial problem (13),77,47894,wide (>100×),rectangular (13),rectangular (13),integer (13),0,categorically uniform; wide (>100×),0.0,https://sparse.tamu.edu/JGD_G5 -JGD_GL6,5,"Differentials of the Voronoi complex of perfect forms of rank 6 mod GL_6(Z), from Philippe Elbaz-Vincent, Institut Fourier,…",combinatorial problem (5),341,637,narrow (≤4×),rectangular (5),rectangular (5),integer (5),0,categorically uniform; narrow (≤4×),0.0,https://sparse.tamu.edu/JGD_GL6 -JGD_GL7d,17,Differentials of the Voronoi complex of perfect forms of rank 7 mod GL_7(Z),combinatorial problem (17),60,1955309,wide (>100×),rectangular (17),rectangular (17),integer (17),0,categorically uniform; wide (>100×),0.0,https://sparse.tamu.edu/JGD_GL7d -JGD_Groebner,8,"Gro""bner basis from Jean-Charles Fauge`res, From Jean-Guillaume Dumas' Sparse Integer Matrix Collection,",combinatorial problem (8),404,5761,moderate (4–100×),rectangular (8),rectangular (8),integer (7); binary (1),0,some categorical mix; moderate (4–100×),0.020833,https://sparse.tamu.edu/JGD_Groebner -JGD_Homology,128,Simplicial complexes from Homology from Volkmar Welker.,combinatorial problem (128),15,564480,wide (>100×),rectangular (122); square (6),rectangular (122); unsymmetric (6),integer (128),0,categorically uniform; wide (>100×),0.023438,https://sparse.tamu.edu/JGD_Homology -JGD_Kocay,12,"Brute force disjoint product matrices in tree algebra on n nodes, Nicolas Thiery",combinatorial problem (12),2,15905,wide (>100×),rectangular (12),rectangular (12),integer (11); binary (1),0,categorically uniform; wide (>100×),0.013889,https://sparse.tamu.edu/JGD_Kocay -JGD_Margulies,23,"Combinatorial optimization as polynomial eqns, Susan Margulies, UC Davis",combinatorial problem (23),25,902103,wide (>100×),rectangular (22); square (1),rectangular (22); unsymmetric (1),binary (23),0,categorically uniform; wide (>100×),0.021739,https://sparse.tamu.edu/JGD_Margulies -JGD_Relat,15,"Relations from Florent Hiver, Univ. Paris-Est Marne-La-Valle'e",combinatorial problem (15),12,12360060,wide (>100×),rectangular (15),rectangular (15),integer (15),0,categorically uniform; wide (>100×),0.0,https://sparse.tamu.edu/JGD_Relat -JGD_SL6,7,Differentials of the Voronoi complex of perfect forms,combinatorial problem (7),434,1271,narrow (≤4×),rectangular (7),rectangular (7),integer (7),0,categorically uniform; narrow (≤4×),0.0,https://sparse.tamu.edu/JGD_SL6 -JGD_SPG,7,"Symmetric powers of graphs from Gordon Royle, Univ Western Australia",combinatorial problem (7),300,6545,moderate (4–100×),square (7),symmetric (6); unsymmetric (1),binary (6); integer (1),0,high categorical mix; moderate (4–100×),0.071429,https://sparse.tamu.edu/JGD_SPG -JGD_Taha,2,"Taha matrix set from Abdul Salam Jarrah, New Mexico State Univ.",combinatorial problem (2),14596,37932,narrow (≤4×),rectangular (2),rectangular (2),integer (2),0,small; uniform observed types; narrow (≤4×),0.0,https://sparse.tamu.edu/JGD_Taha -JGD_Trefethen,11,"Diagonal matrices with primes, Nick Trefethen, Oxford Univ.",combinatorial problem (11),19,20000,wide (>100×),square (11),symmetric (11),integer (11),11,categorically uniform; wide (>100×),0.0,https://sparse.tamu.edu/JGD_Trefethen -Janna,18,Finite element simulations: gas reservoir and structural problems. Univ Padova.,structural problem (14); 2D/3D problem (3); computational fluid dynamics problem (1),377002,4147110,moderate (4–100×),square (18),symmetric (14); unsymmetric (4),real (18),10,high categorical mix; moderate (4–100×),0.185185,https://sparse.tamu.edu/Janna -Kamvar,2,"Web matrices from Sep Kamvar, Stanford University.",directed graph (2),281903,683446,narrow (≤4×),square (2),unsymmetric (2),binary (2),0,small; uniform observed types; narrow (≤4×),0.0,https://sparse.tamu.edu/Kamvar -Kemelmacher,1,"Computer vision problem from Ira Kemelmacher, Weizmann Inst., Israel.",computer graphics/vision problem (1),28452,28452,one dimension,rectangular (1),rectangular (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Kemelmacher -Kim,2,"2D complex meshes, from Wonsik Kim, Korea.",2D/3D problem (2),38415,456976,moderate (4–100×),square (2),unsymmetric (2),complex (2),0,small; uniform observed types; moderate (4–100×),0.0,https://sparse.tamu.edu/Kim -Koutsovasilis,2,"Symmetric indefinite matrix from P. Koutsovasilis, Tech. Univ. Dresden",structural problem (2),71505,343791,moderate (4–100×),square (2),symmetric (2),real (2),0,small; uniform observed types; moderate (4–100×),0.0,https://sparse.tamu.edu/Koutsovasilis -LAW,15,"Laboratory for Web Algorithmics (LAW), Università degli Studi di Milano",directed graph (13); undirected graph (2),69244,118142155,wide (>100×),square (15),unsymmetric (13); symmetric (2),binary (15),0,high categorical mix; wide (>100×),0.066667,https://sparse.tamu.edu/LAW -LPnetlib,138,"NETLIB Linear Programming problems, www.netlib.org.",linear programming problem (138),13,243246,wide (>100×),rectangular (138),rectangular (138),real (108); integer (30),0,some categorical mix; wide (>100×),0.036232,https://sparse.tamu.edu/LPnetlib -Langemyr,1,"FEM structural mechanics matrix from Lars Langemyr, COMSOL.",structural problem (1),1500,1500,one dimension,square (1),unsymmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Langemyr -LeGresley,3,Power flow analyses of electrical grids.,power network problem (3),2508,87936,moderate (4–100×),square (3),unsymmetric (3),real (3),0,small; uniform observed types; moderate (4–100×),0.0,https://sparse.tamu.edu/LeGresley -Lee,2,"FEM, Electromagnetics, Center for Computational Electromagnetics, UIUC",electromagnetics problem (2),74062,491100,moderate (4–100×),square (2),unsymmetric (2),complex (2),0,small; observed mix; moderate (4–100×),0.083333,https://sparse.tamu.edu/Lee -Li,2,"3D magnetohydrodynamics finite element matrix from Ben Li, Washington State Univ.",electromagnetics problem (2),22695,22695,one dimension,square (2),symmetric (1); unsymmetric (1),binary (1); real (1),0,small; observed mix; one dimension,0.333333,https://sparse.tamu.edu/Li -Lin,1,"Large sparse eigenvalue problem from Chun-hung (Martin) Lin. NCHC, Taiwan.",structural problem (1),256000,256000,one dimension,square (1),symmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Lin -LiuWenzhuo,1,"Power system simulation matrix from Wenzhou Liu, EPRI, China.",power network problem (1),15838,15838,one dimension,square (1),unsymmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/LiuWenzhuo -Lourakis,1,"3D vision matrices, M. Lourakis, Foundation for Research & Technology - Hellas.",computer graphics/vision problem (1),10581,10581,one dimension,square (1),symmetric (1),real (1),1,singleton; one dimension,0.0,https://sparse.tamu.edu/Lourakis -Lucifora,2,"GSM cell traffic matrices from Salvatore Lucifora, Telecom Italia Mobile.",directed weighted graph (2),7055,7055,one dimension,square (2),unsymmetric (2),real (2),0,small; uniform observed types; one dimension,0.0,https://sparse.tamu.edu/Lucifora -Luong,2,"Photogrammetry problem from Bruno Luong, FOGALE nanotech, France.",computer graphics/vision problem (2),1388,4472,narrow (≤4×),rectangular (2),rectangular (2),real (2),0,small; uniform observed types; narrow (≤4×),0.0,https://sparse.tamu.edu/Luong -MAWI,5,MAWI Working Group Traffic Archive (http://mawi.wide.ad.jp/mawi/): The MAWI (Measurement and Analysis on the WIDE Internet)…,undirected weighted graph (5),18571154,226196185,moderate (4–100×),square (5),symmetric (5),integer (5),0,categorically uniform; moderate (4–100×),0.0,https://sparse.tamu.edu/MAWI -MKS,1,"2-D Fokker Planck equations, electron dynamics. Dan Smith, MKS Inst.",electromagnetics problem (1),7548,7548,one dimension,square (1),unsymmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/MKS -ML_Graph,36,"ML_Graph: adjacency matrices from machine learning datasets, Olaf Schenk.",undirected weighted graph (36),106,38547,wide (>100×),square (36),symmetric (36),real (36),0,categorically uniform; wide (>100×),0.0,https://sparse.tamu.edu/ML_Graph -Mallya,18,"Chemical process simulation matrices, J. Mallya, S. Zitney, M. Stadtherr.",chemical process simulation problem (18),1477,70304,moderate (4–100×),square (18),unsymmetric (18),real (18),0,categorically uniform; moderate (4–100×),0.0,https://sparse.tamu.edu/Mallya -Mancktelow,1,"FEM viscoelastic behavior of rocks, Neil Mancktelow, ETH-Zentrum",materials problem (1),37762,37762,one dimension,square (1),unsymmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Mancktelow -Marini,1,"Economic Time Series, T. Di Fonzo (Univ Padua) and M. Marini, (ISTAT, Italy)",economic problem (1),7245,7245,one dimension,square (1),symmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Marini -Martin,1,Martin/marine1: chemical oceanography; a marine nitrogen cycle inverse model,chemical oceanography problem (1),400320,400320,one dimension,square (1),unsymmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Martin -MathWorks,11,"Matrices contributed by The Mathworks, Inc.",counter-example problem (6); structural problem (2); computer graphics/vision problem (1); directed graph (1); statistical/mathematical problem (1),102,12406,wide (>100×),square (9); rectangular (2),unsymmetric (7); rectangular (2); symmetric (2),real (10); binary (1),2,high categorical mix; wide (>100×),0.318182,https://sparse.tamu.edu/MathWorks -MaxPlanck,2,"Weather shallow water equations, Max-Planck Institute of Meteoroloy",computational fluid dynamics problem (2),81920,81920,one dimension,square (2),symmetric (2),real (2),2,small; uniform observed types; one dimension,0.0,https://sparse.tamu.edu/MaxPlanck -Mazaheri,1,"Sparse bundle adjustment problem from Mehdi Mazaheri, University of Calgary",computer vision problem (1),513351,513351,one dimension,square (1),symmetric (1),real (1),1,singleton; one dimension,0.0,https://sparse.tamu.edu/Mazaheri -McRae,2,"circuitscape: circuit theory applied to animal/gene flow. B. McRae, UCSB",2D/3D problem (2),999999,1000000,narrow (≤4×),square (2),symmetric (2),real (2),1,small; observed mix; narrow (≤4×),0.083333,https://sparse.tamu.edu/McRae -Meng,1,"Jacobian matrix from a computational chemistry problem, iChem, China'",computational chemistry problem (1),274087,274087,one dimension,square (1),unsymmetric (1),complex (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Meng -Meszaros,166,Linear programming problems from C. Me'sza'ros,linear programming problem (166),16,1113780,wide (>100×),rectangular (165); square (1),rectangular (165); unsymmetric (1),real (107); integer (41); binary (18),0,some categorical mix; wide (>100×),0.087349,https://sparse.tamu.edu/Meszaros -Mittelmann,30,Linear programming problems from H. Mittelmann,linear programming problem (30),24460,1961394,moderate (4–100×),rectangular (30),rectangular (30),integer (20); real (10),0,some categorical mix; moderate (4–100×),0.055556,https://sparse.tamu.edu/Mittelmann -Moqri,1,"Document similarity graph for MIS articles, including metadata",undirected weighted graph (1),2427,2427,one dimension,square (1),symmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Moqri -Morandini,3,Multibody simulation problems from Marco Morandini.,structural problem (2); robotics problem (1),100,791,moderate (4–100×),square (3),unsymmetric (3),real (3),0,small; observed mix; moderate (4–100×),0.055556,https://sparse.tamu.edu/Morandini -Muite,4,"Ill-conditioned Chebyshev integration matrices from Benson Muite, Oxford.",structural problem (4),261,68121,wide (>100×),square (4),unsymmetric (4),real (4),0,small; uniform observed types; wide (>100×),0.0,https://sparse.tamu.edu/Muite -Mulvey,2,"Multistage stochastic financial modelling. J. Mulvey, Princeton.",economic problem (2),74752,74752,one dimension,square (2),symmetric (2),binary (1); real (1),1,small; observed mix; one dimension,0.25,https://sparse.tamu.edu/Mulvey -Mycielski,19,Mycielski graph adjacency matrices,undirected graph (19),2,786431,wide (>100×),square (19),symmetric (19),binary (19),0,categorically uniform; wide (>100×),0.0,https://sparse.tamu.edu/Mycielski -ND,4,ND problem set. 3D mesh problems.,2D/3D problem (4),9000,72000,moderate (4–100×),square (4),symmetric (4),real (4),4,small; uniform observed types; moderate (4–100×),0.0,https://sparse.tamu.edu/ND -NYPA,8,"Rank-deficient least squares problems. D. Maragal, New York Power Authority",least squares problem (8),32,75077,wide (>100×),rectangular (8),rectangular (8),real (8),0,categorically uniform; wide (>100×),0.0,https://sparse.tamu.edu/NYPA -Nasa,11,"NASA matrices obtained from Alex Pothen, ICASE and Old Dominion Univ.",structural problem (11),1824,54870,moderate (4–100×),square (11),symmetric (8); unsymmetric (3),binary (6); real (5),5,high categorical mix; moderate (4–100×),0.318182,https://sparse.tamu.edu/Nasa -Negre,1,"Negre: phenyl dendrimer density matrix, for graph partitioning",computational chemistry problem (1),730,730,one dimension,square (1),symmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Negre -Nemeth,26,"A set of matrices from Karoly Nemeth, theoretical chemistry.",theoretical/quantum chemistry problem (26),9506,9506,one dimension,square (26),symmetric (26),real (26),0,categorically uniform; one dimension,0.00641,https://sparse.tamu.edu/Nemeth -Newman,16,"Network graph collection from Mark Newman, University of Michigan",undirected weighted graph (7); undirected graph (6); directed multigraph (1); directed weighted graph (1); undirected multigraph (1),34,40421,wide (>100×),square (16),symmetric (14); unsymmetric (2),binary (6); real (6); integer (4),0,high categorical mix; wide (>100×),0.239583,https://sparse.tamu.edu/Newman -Norris,12,"Finite element modelling of human body parts. Stuart Norris, Univ. Aukland.",2D/3D problem (10); computational fluid dynamics problem (2),1650,259156,wide (>100×),square (12),unsymmetric (9); symmetric (3),real (12),3,high categorical mix; wide (>100×),0.194444,https://sparse.tamu.edu/Norris -Oberwolfach,38,Oberwolfach model reduction benchmark collection,model reduction problem (38),14,986703,wide (>100×),square (38),symmetric (33); unsymmetric (5),real (37); complex (1),13,high categorical mix; wide (>100×),0.153509,https://sparse.tamu.edu/Oberwolfach -Okunbor,2,Acoustic radiation around aft duct fan. Walter Eversman and Daniel Okunbor.,acoustics problem (2),8184,8205,narrow (≤4×),square (2),symmetric (1); unsymmetric (1),complex (1); real (1),1,small; observed mix; narrow (≤4×),0.333333,https://sparse.tamu.edu/Okunbor -PARSEC,21,"PARSEC matrices, density functional theory. Zhou, Saad, Tiago, Chelikowsky, UMN.",theoretical/quantum chemistry problem (21),769,268096,wide (>100×),square (21),symmetric (21),real (21),0,categorically uniform; wide (>100×),0.0,https://sparse.tamu.edu/PARSEC -POLYFLOW,2,"Parasol matrices, collected by Jacko Koster",computational fluid dynamics problem (2),29957,30412,narrow (≤4×),square (2),unsymmetric (2),real (2),0,small; observed mix; narrow (≤4×),0.083333,https://sparse.tamu.edu/POLYFLOW -Pajek,76,"Pajek data sets, V. Batagelj and A. Mrvar.",directed graph (28); undirected graph (13); directed multigraph (11); directed weighted graph (11); undirected weighted graph (7); bipartite graph (3); weighted bipartite graph (2); undirected multigraph (1),10,3774768,wide (>100×),square (71); rectangular (5),unsymmetric (49); symmetric (22); rectangular (5),binary (44); integer (28); real (4),1,high categorical mix; wide (>100×),0.333333,https://sparse.tamu.edu/Pajek -Pereyra,1,"From Victor Pereyra, Stanford University.",least squares problem (1),71952,71952,one dimension,rectangular (1),rectangular (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Pereyra -Pothen,22,"Structrual engineering matrices from NASA, collected by Alex Pothen.",structural problem (22),48,94069,wide (>100×),square (22),symmetric (22),binary (12); real (10),10,some categorical mix; wide (>100×),0.151515,https://sparse.tamu.edu/Pothen -PowerSystem,1,Power system matrix,power network problem (1),197156,197156,one dimension,square (1),unsymmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/PowerSystem -Precima,1,Precima: data analytics problem,data analytics problem (1),303813,303813,one dimension,square (1),symmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Precima -Priebel,6,"Quadratic sieve matrices from David Priebel, Tenn. Tech. Univ.",combinatorial problem (6),584,24430,moderate (4–100×),rectangular (6),rectangular (6),binary (6),0,categorically uniform; moderate (4–100×),0.0,https://sparse.tamu.edu/Priebel -Puri,4,ABAQUS benchmarks: pt.loaded fluid-filled shell. Srinivasan Puri,model reduction problem (4),23412,23412,one dimension,square (4),unsymmetric (4),complex (3); real (1),0,small; observed mix; one dimension,0.041667,https://sparse.tamu.edu/Puri -QCD,14,"Quantum Chromodynamics, B. Medeke, Univ. Wuppertal, Germany",theoretical/quantum chemistry problem (14),3072,49152,moderate (4–100×),square (14),unsymmetric (14),complex (14),0,categorically uniform; moderate (4–100×),0.0,https://sparse.tamu.edu/QCD -QLi,3,Mixed complementarity optimization problems from Qaun Li and Michael Ferris.,optimization problem (3),160000,440020,narrow (≤4×),square (3),unsymmetric (3),real (3),0,small; observed mix; narrow (≤4×),0.055556,https://sparse.tamu.edu/QLi -QY,2,"Transient stabilty constrained interior pt. optimal power flow, J. Quanyuan",power network problem (2),14454,40216,narrow (≤4×),square (2),symmetric (2),real (2),0,small; uniform observed types; narrow (≤4×),0.0,https://sparse.tamu.edu/QY -Qaplib,8,"Linear programming, lower bounds for quadratic assignment problem, M. Resende.",linear programming problem (8),225,379350,wide (>100×),rectangular (8),rectangular (8),integer (8),0,categorically uniform; wide (>100×),0.0,https://sparse.tamu.edu/Qaplib -Quaglino,2,"FEM discretization of a viscoplastic collision problem, Alessio Quaglino.",materials problem (2),4326,32769,moderate (4–100×),square (2),unsymmetric (2),real (2),0,small; uniform observed types; moderate (4–100×),0.0,https://sparse.tamu.edu/Quaglino -Rajat,32,Circuit simulation matrices from Rajat and Raj.,circuit simulation problem (32),135,4690002,wide (>100×),square (32),unsymmetric (26); symmetric (6),real (25); binary (7),0,high categorical mix; wide (>100×),0.135417,https://sparse.tamu.edu/Rajat -Raju,1,"3D laminar flow, M. Raju, Case Western Univ.",computational fluid dynamics problem (1),67173,67173,one dimension,square (1),unsymmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Raju -Rommes,35,"Power system models from Joost Rommes, Nelson Martins, Francisco Freitas",eigenvalue/model reduction problem (35),66,40366,wide (>100×),square (35),unsymmetric (35),real (35),0,some categorical mix; wide (>100×),0.07619,https://sparse.tamu.edu/Rommes -Ronis,2,"Crystalline compounds (zeolites,sodalites), D. Ronis, Univ. of McGill.",materials problem (2),48600,157464,narrow (≤4×),square (2),unsymmetric (2),real (2),0,small; uniform observed types; narrow (≤4×),0.0,https://sparse.tamu.edu/Ronis -Rost,1,"semiconductor device simulation, Johann Rost, Politechnia Univ., Bucharest.",semiconductor device problem (1),74104,74104,one dimension,square (1),unsymmetric (1),complex (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Rost -Rothberg,6,"Structural engineering matrices from Ed Rothberg, Silicon Graphics, Inc.",computational fluid dynamics problem (3); structural problem (3),4350,153746,moderate (4–100×),square (6),symmetric (6),binary (4); real (2),2,high categorical mix; moderate (4–100×),0.194444,https://sparse.tamu.edu/Rothberg -Rucci,1,"ill-conditioned least-squares problem, A. Rucci",least squares problem (1),1977885,1977885,one dimension,rectangular (1),rectangular (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Rucci -Rudnyi,1,"3D fluid flow: water in a tank. Evgenii Rudnyi, IMTEK",computational fluid dynamics problem (1),60740,60740,one dimension,square (1),unsymmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Rudnyi -SNAP,68,"Networks from SNAP (Stanford Network Analysis Platform) Network Data Sets, Jure Leskovec…",directed graph (30); undirected graph (14); directed temporal multigraph (8); undirected graph with communities (6); directed weighted graph (5); directed graph with communities (2); directed weighted temporal graph (2); directed weighted temporal multigraph (1),1005,65608366,wide (>100×),square (68),unsymmetric (47); symmetric (21),binary (53); integer (15),0,high categorical mix; wide (>100×),0.259804,https://sparse.tamu.edu/SNAP -Sandia,192,Circuit simulation matrices from Sandia National Laboratory.,circuit simulation problem (192),430,682862,wide (>100×),square (192),unsymmetric (192),real (192),0,categorically uniform; wide (>100×),0.019965,https://sparse.tamu.edu/Sandia -Sanghavi,1,"ECL32, a semiconductor device simulation matrix. Jagesh Sanghavi, UC Berkeley.",semiconductor device problem (1),51993,51993,one dimension,square (1),unsymmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Sanghavi -Schenk,5,"Symmetric indefinite KKT matrices, O. Schenk, Univ. of Basel, Switzerland",optimization problem (5),1062400,27993600,moderate (4–100×),square (5),symmetric (5),real (5),0,categorically uniform; moderate (4–100×),0.0,https://sparse.tamu.edu/Schenk -Schenk_AFE,16,"Sheet metal forming matrices, Olaf Schenk, Univ. of Basel.",structural problem (16),503625,1508065,narrow (≤4×),square (16),symmetric (16),real (16),10,some categorical mix; narrow (≤4×),0.145833,https://sparse.tamu.edu/Schenk_AFE -Schenk_IBMNA,52,"Non-linear optimization matrices, Olaf Schenk, Univ. of Basel.",optimization problem (52),2169,345241,wide (>100×),square (52),symmetric (52),real (52),0,some categorical mix; wide (>100×),0.028846,https://sparse.tamu.edu/Schenk_IBMNA -Schenk_IBMSDS,7,"Semiconductor device simulation matrices, Olaf Schenk, Univ. of Basel.",semiconductor device problem (7),27628,125329,moderate (4–100×),square (7),unsymmetric (7),real (7),0,categorically uniform; moderate (4–100×),0.0,https://sparse.tamu.edu/Schenk_IBMSDS -Schenk_ISEI,18,"Semiconductor device simulation matrices, Olaf Schenk, Univ. of Basel.",semiconductor device problem (18),10938,181343,moderate (4–100×),square (18),unsymmetric (18),real (18),0,some categorical mix; moderate (4–100×),0.064815,https://sparse.tamu.edu/Schenk_ISEI -Schmid,2,"Unstructured FEM, steady state thermal problem. D. Schmid, Univ. Oslo",thermal problem (2),82654,1228045,moderate (4–100×),square (2),symmetric (2),real (2),2,small; uniform observed types; moderate (4–100×),0.0,https://sparse.tamu.edu/Schmid -Schulthess,3,"Biochemical networks from Pascal Schulthess, Inst. Pathology, Berlin",biochemical network (3),1996,16559,moderate (4–100×),rectangular (3),rectangular (3),integer (3),0,small; uniform observed types; moderate (4–100×),0.0,https://sparse.tamu.edu/Schulthess -Shen,4,"Matrices from Kai Shen, UCSB (now at Univ. of Rochester, NY).",2D/3D problem (4),3432,18510,moderate (4–100×),square (4),unsymmetric (4),real (4),0,small; uniform observed types; moderate (4–100×),0.0,https://sparse.tamu.edu/Shen -Shyy,2,"Computational fluid dynamics, viscous flow, Wei Shyy, Univ. Florida.",computational fluid dynamics problem (2),4720,76480,moderate (4–100×),square (2),unsymmetric (2),real (2),0,small; uniform observed types; moderate (4–100×),0.0,https://sparse.tamu.edu/Shyy -Simon,12,"Structural engineering, computational fluid dynamics. Horst Simon.",computational fluid dynamics problem (7); structural problem (4); directed weighted random graph (1),3242,62424,moderate (4–100×),square (12),unsymmetric (10); symmetric (2),real (12),2,high categorical mix; moderate (4–100×),0.236111,https://sparse.tamu.edu/Simon -Sinclair,2,"3-D spectral-element elastic wave modelling in freq. domain, C. Sinclair, Univ. Adelaide, Australia.",materials problem (2),292008,680943,narrow (≤4×),square (2),Hermitian (2),complex (2),0,small; uniform observed types; narrow (≤4×),0.0,https://sparse.tamu.edu/Sinclair -Sorensen,1,Call graph of the Linux kernel.,directed weighted graph (1),324085,324085,one dimension,square (1),unsymmetric (1),integer (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Sorensen -Springer,1,"Tim Springer, Solenix (www.solenix.ch), Navigation Support Office, OPS-GN (www.nng.esoc.de), European Space Operations…",least squares problem (1),327062,327062,one dimension,rectangular (1),rectangular (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Springer -Stevenson,1,"Circuit optimization for digital circuits. John Peter Stevenson, Stanford.",circuit simulation problem (1),2111154,2111154,one dimension,rectangular (1),rectangular (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Stevenson -Sumner,1,"A computer graphics problem from Bob Sumner, PhD student at MIT (as of 2003).",computer graphics/vision problem (1),29493,29493,one dimension,rectangular (1),rectangular (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Sumner -Sybrandt,2,Sybrandt: machine learning graphs from document analysis,undirected graph (1); undirected weighted graph (1),30239687,183964077,moderate (4–100×),square (2),symmetric (2),binary (1); real (1),0,small; observed mix; moderate (4–100×),0.166667,https://sparse.tamu.edu/Sybrandt -Szczerba,1,"Ill-conditioned CFD matrix from D. Szczerba, ETH Computer Vision Lab, Zurich.",computational fluid dynamics problem (1),20896,20896,one dimension,square (1),unsymmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Szczerba -TAMU_SmartGridCenter,3,Texas A&M Smart Grid Center: synthetic (yet realistic) power system grids,power network problem (3),4000,69999,moderate (4–100×),square (3),unsymmetric (2); symmetric (1),real (3),0,small; observed mix; moderate (4–100×),0.111111,https://sparse.tamu.edu/TAMU_SmartGridCenter -TKK,12,"Matrices from Reijo Kouhia, Structural Mechanics, Helsinki Univ. of Technology",structural problem (12),1282,143571,wide (>100×),square (12),symmetric (10); unsymmetric (2),real (5); integer (4); binary (3),3,high categorical mix; wide (>100×),0.222222,https://sparse.tamu.edu/TKK -TOKAMAK,4,"TOKAMAK test matrices from the SPARSKIT2 collection, Yousef Saad, Univ. MN.",electromagnetics problem (4),300,5940,moderate (4–100×),square (4),unsymmetric (4),real (4),0,small; uniform observed types; moderate (4–100×),0.0,https://sparse.tamu.edu/TOKAMAK -TSOPF,27,"Transient stability-constrained optimal power flow, G. Geng, Zhejiang Univ.",power network problem (27),2454,120216,moderate (4–100×),square (27),unsymmetric (15); symmetric (12),real (27),0,some categorical mix; moderate (4–100×),0.148148,https://sparse.tamu.edu/TSOPF -Toledo,1,"Null space problem, computer graphics domain. Matrices from Sivan Toledo.",counter-example problem (1),68600,68600,one dimension,rectangular (1),rectangular (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Toledo -Tromble,1,Finite-state machine model for natural language processing. Roy Tromble.,directed weighted graph (1),399130,399130,one dimension,square (1),unsymmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Tromble -UTEP,3,"Matrices from Lenka Dubcova, Jakub Cerveny, and Pavel Solin",2D/3D problem (3),16129,146689,moderate (4–100×),square (3),symmetric (3),real (3),3,small; uniform observed types; moderate (4–100×),0.0,https://sparse.tamu.edu/UTEP -Um,2,"FEM, electromagnetics, 2 cubes in a sphere. Evan Um, Geophysics, Stanford",electromagnetics problem (2),101492,259789,narrow (≤4×),square (2),symmetric (2),real (2),2,small; uniform observed types; narrow (≤4×),0.0,https://sparse.tamu.edu/Um -VDOL,91,"Optimal control problems, Vehicle Dynamics & Optimization Lab, UF",optimal control problem (91),99,18476,wide (>100×),square (91),symmetric (91),real (91),0,categorically uniform; wide (>100×),0.0,https://sparse.tamu.edu/VDOL -VLSI,14,VLSI: semiconductor device and process simulation,semiconductor device problem (8); semiconductor process problem (6),46994,11449533,wide (>100×),square (14),unsymmetric (14),real (14),0,some categorical mix; wide (>100×),0.142857,https://sparse.tamu.edu/VLSI -VanVelzen,10,"Chemical process simulation matrices from Nils van Velzen, VORtech Computing.",chemical process simulation problem (10),21982,22835,narrow (≤4×),square (10),unsymmetric (10),real (10),0,categorically uniform; narrow (≤4×),0.0,https://sparse.tamu.edu/VanVelzen -Vavasis,1,"Irregular finite-element matrix from Steve Vavasis, Cornell.",2D/3D problem (1),41092,41092,one dimension,square (1),unsymmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Vavasis -Wang,6,"3D semiconductor device, 2D incompressible flow. S. Wang, Univ. New South Wales.",semiconductor device problem (6),2903,26068,moderate (4–100×),square (6),unsymmetric (6),real (6),0,some categorical mix; moderate (4–100×),0.111111,https://sparse.tamu.edu/Wang -Watson,2,Chemical reaction simulation matrices from Layne Watson and Jingwei Zhang.,2D/3D problem (2),40401,112211,narrow (≤4×),square (2),unsymmetric (2),real (2),0,small; uniform observed types; narrow (≤4×),0.0,https://sparse.tamu.edu/Watson -Williams,7,Matrices used by S. Williams et al for sparse matrix multiplication on GPUs.,2D/3D problem (4); economic problem (1); weighted directed graph (1); weighted undirected graph (1),36417,1000005,moderate (4–100×),square (7),symmetric (4); unsymmetric (3),real (6); integer (1),3,high categorical mix; moderate (4–100×),0.309524,https://sparse.tamu.edu/Williams -Wissgott,1,"Parabolic FEM problem from Phillip Wissgott, TU Vienna.",computational fluid dynamics problem (1),525825,525825,one dimension,square (1),symmetric (1),real (1),1,singleton; one dimension,0.0,https://sparse.tamu.edu/Wissgott -YCheng,3,"Power system state simulation matrices Yunzhi Cheng, UT Arlington.",power network problem (3),14318,28634,narrow (≤4×),rectangular (3),rectangular (3),real (3),0,small; uniform observed types; narrow (≤4×),0.0,https://sparse.tamu.edu/YCheng -YZhou,1,"Circuit simulation problem, Y. Zhou, Carleton Univ, Canada",circuit simulation problem (1),1020,1020,one dimension,square (1),unsymmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/YZhou -Yoshiyasu,2,"Image editting and mesh deformation problem from Y. Yoshiyasu, Keio Univ., Japan",computer graphics/vision problem (2),234023,240000,narrow (≤4×),rectangular (2),rectangular (2),real (2),0,small; uniform observed types; narrow (≤4×),0.0,https://sparse.tamu.edu/Yoshiyasu -Zaoui,1,"Optimal power flow, nonlinear optimization (KKT), Fabrice Zaoui, RTE, France",optimization problem (1),2063494,2063494,one dimension,square (1),symmetric (1),real (1),0,singleton; one dimension,0.0,https://sparse.tamu.edu/Zaoui -Zhao,2,"Reduced-order modeling, electromagnetics. A. Cangellaris and L. Zhao.",electromagnetics problem (2),33861,33861,one dimension,square (2),unsymmetric (2),real (2),0,small; uniform observed types; one dimension,0.0,https://sparse.tamu.edu/Zhao -Zitney,8,"Chemical engineering applications. S. Zitney, Cray Research.",chemical process simulation problem (8),1048,5308,moderate (4–100×),square (8),unsymmetric (8),real (8),0,some categorical mix; moderate (4–100×),0.041667,https://sparse.tamu.edu/Zitney -vanHeukelum,13,"DNA electrophoresis model. Alexander van Heukelum, Utrect Univ, The Netherlands.",directed weighted graph (13),5,5154859,wide (>100×),square (13),unsymmetric (13),real (13),0,categorically uniform; wide (>100×),0.0,https://sparse.tamu.edu/vanHeukelum diff --git a/reports/suitesparse-groups/output/group_diversity_ranking.csv b/reports/suitesparse-groups/output/group_diversity_ranking.csv deleted file mode 100644 index d77eba0..0000000 --- a/reports/suitesparse-groups/output/group_diversity_ranking.csv +++ /dev/null @@ -1,204 +0,0 @@ -group,matrix_count,diversity_label,categorical_diversity_score,mixed_attribute_count_dominant_share_lt_0_90,distinct_kinds,dimension_log10_span,nnz_log10_span,nentries_log10_span,matrices_with_explicit_zeros,explicit_zero_count_total,nrows_min,nrows_median,nrows_max,ncols_min,ncols_median,ncols_max,nnz_min,nnz_median,nnz_max,nentries_min,nentries_median,nentries_max,pattern_symmetry_min,pattern_symmetry_median,pattern_symmetry_max,numerical_symmetry_min,numerical_symmetry_median,numerical_symmetry_max,shape_counts,dominant_shape,dominant_shape_share,structure_counts,dominant_structure,dominant_structure_share,entry_type_counts,dominant_entry_type,dominant_entry_type_share,exact_symmetry_counts,dominant_exact_symmetry,dominant_exact_symmetry_share,positive_definite_counts,dominant_positive_definite,dominant_positive_definite_share,kind_counts,dominant_kind,dominant_kind_share,structural_full_rank_counts,dominant_structural_full_rank,dominant_structural_full_rank_share,is_2d3d_counts,dominant_is_2d3d,dominant_is_2d3d_share,cholesky_candidate_counts,dominant_cholesky_candidate,dominant_cholesky_candidate_share,size_band_counts,dominant_size_band,dominant_size_band_share,smallest_nrows_matrices,largest_nrows_matrices,smallest_ncols_matrices,largest_ncols_matrices,smallest_nnz_matrices,largest_nnz_matrices,smallest_nentries_matrices,largest_nentries_matrices -Barabasi,3,small_group_lt5,0.388889,4,3,2.268624,2.516159,2.516159,0,0,2114,325729,392400,2114,127823,325729,4480,929849,1470404,4480,929849,1470404,0.0,0.3315875213545298,1.0,0.0,0.3315875213545298,1.0,rectangular:1; square:2,square,0.666667,rectangular:1; symmetric:1; unsymmetric:1,rectangular,0.333333,binary:3,binary,1.0,exact_numerical_or_hermitian:1; partially_pattern_symmetric:1; rectangular:1,exact_numerical_or_hermitian,0.333333,no:3,no,1.0,bipartite graph:1; directed graph:1; undirected graph:1,bipartite graph,0.333333,false:1; missing:2,missing,0.666667,no:3,no,1.0,no:3,no,1.0,100k_to_1m:2; 1k_to_10k:1,100k_to_1m,0.666667,Barabasi/NotreDame_yeast=2114,Barabasi/NotreDame_actors=392400,Barabasi/NotreDame_yeast=2114,Barabasi/NotreDame_www=325729,Barabasi/NotreDame_yeast=4480,Barabasi/NotreDame_actors=1470404,Barabasi/NotreDame_yeast=4480,Barabasi/NotreDame_actors=1470404 -HB,292,categorically_heterogeneous,0.352169,5,25,3.69518,4.930162,4.629132,51,41359,9,680,44609,9,670.5,44609,24,4089,2043492,48,4089,2043492,0.0,1,1.0,0.0,1,1.0,rectangular:16; square:276,square,0.945205,rectangular:16; skew-symmetric:2; symmetric:169; unsymmetric:105,symmetric,0.578767,binary:112; complex:3; integer:7; real:170,real,0.582192,exact_numerical_or_hermitian:169; partially_pattern_symmetric:83; pattern_symmetric_only:15; pattern_unsymmetric:9; rectangular:16,exact_numerical_or_hermitian,0.578767,no:232; yes:60,no,0.794521,2D/3D problem:22; 2D/3D problem sequence:4; acoustics problem:3; chemical process simulation problem:16; computational fluid dynamics problem:27; counter-example problem:3; directed graph:1; directed weighted graph:8; duplicate acoustics problem:1; duplicate structural problem:2; duplicate thermal problem:1; economic problem:14; least squares problem:11; materials problem:1; optimization problem:1; optimization problem sequence:3; power network problem:16; power network problem sequence:1; semiconductor device problem:2; statistical/mathematical problem:1; structural problem:119; subsequent 2D/3D problem:10; subsequent optimization problem:13; subsequent power network problem:1; thermal problem:11,structural problem,0.407534,false:20; missing:9; true:263,true,0.900685,no:89; yes:203,yes,0.695205,no:140; yes:152,yes,0.520548,10k_to_100k:9; 1k_to_10k:98; lt_1k:185,lt_1k,0.633562,HB/jgl009=9,HB/bcsstk32=44609,HB/jgl009=9,HB/bcsstk32=44609,HB/bcsstm01=24,HB/bcsstk30=2043492,HB/bcsstm01=48,HB/bcsstk30=2043492 -Pajek,76,categorically_heterogeneous,0.333333,4,9,5.57689,5.712846,5.712846,0,0,10,462,3774768,9,462,3774768,29,1708.5,14970767,29,1708.5,14970767,0.0,0.16827315684255165,1.0,0.0,0.11278688524590164,1.0,rectangular:5; square:71,square,0.934211,rectangular:5; symmetric:22; unsymmetric:49,unsymmetric,0.644737,binary:44; integer:28; real:4,binary,0.578947,exact_numerical_or_hermitian:22; partially_pattern_symmetric:37; pattern_unsymmetric:12; rectangular:5,partially_pattern_symmetric,0.486842,no:75; yes:1,no,0.986842,bipartite graph:3; directed graph:28; directed multigraph:11; directed weighted graph:11; undirected graph:13; undirected multigraph:1; undirected weighted graph:6; undirected weighted graph sequence:1; weighted bipartite graph:2,directed graph,0.368421,false:2; missing:71; true:3,missing,0.934211,no:76,no,1.0,no:75; yes:1,no,0.986842,100k_to_1m:3; 10k_to_100k:9; 1k_to_10k:20; ge_1m:1; lt_1k:43,lt_1k,0.565789,Pajek/Stranke94=10,Pajek/patents=3774768,Pajek/divorce=9,Pajek/patents=3774768,Pajek/Tina_AskCal=29,Pajek/patents=14970767,Pajek/Tina_AskCal=29,Pajek/patents=14970767 -Bates,3,small_group_lt5,0.333333,6,2,2.837567,2.965847,2.965847,0,0,2541,2541,1748122,2541,31022,62729,7361,62044,6804304,7361,62044,6804304,0.0,0,1.0,0.0,0,1.0,rectangular:2; square:1,rectangular,0.666667,rectangular:2; symmetric:1,rectangular,0.666667,binary:1; real:2,real,0.666667,exact_numerical_or_hermitian:1; rectangular:2,rectangular,0.666667,no:2; yes:1,no,0.666667,least squares problem:1; statistical/mathematical problem:2,statistical/mathematical problem,0.666667,true:3,true,1.0,no:3,no,1.0,no:2; yes:1,no,0.666667,10k_to_100k:1; 1k_to_10k:1; ge_1m:1,10k_to_100k,0.333333,Bates/Chem97Zt=2541; Bates/Chem97ZtZ=2541,Bates/sls=1748122,Bates/Chem97ZtZ=2541,Bates/sls=62729,Bates/Chem97ZtZ=7361,Bates/sls=6804304,Bates/Chem97ZtZ=7361,Bates/sls=6804304 -Li,2,small_group_lt5,0.333333,4,2,0.0,0.045792,0.0,1,135128,22695,22695,22695,22695,22695,22695,1215181,1282745,1350309,1350309,1350309,1350309,1.0,1,1.0,0.8685670104303111,0.9342835052151556,1.0,square:2,square,1.0,symmetric:1; unsymmetric:1,symmetric,0.5,binary:1; real:1,binary,0.5,exact_numerical_or_hermitian:1; pattern_symmetric_only:1,exact_numerical_or_hermitian,0.5,no:2,no,1.0,duplicate electromagnetics problem:1; electromagnetics problem:1,duplicate electromagnetics problem,0.5,true:2,true,1.0,yes:2,yes,1.0,no:1; yes:1,no,0.5,10k_to_100k:2,10k_to_100k,1.0,Li/li=22695; Li/pli=22695,Li/li=22695; Li/pli=22695,Li/li=22695; Li/pli=22695,Li/li=22695; Li/pli=22695,Li/li=1215181,Li/pli=1350309,Li/li=1350309; Li/pli=1350309,Li/li=1350309; Li/pli=1350309 -Okunbor,2,small_group_lt5,0.333333,4,1,0.001113,0.007526,0.007526,0,0,8184,8194.5,8205,8184,8194.5,8205,125567,126664.5,127762,125567,126664.5,127762,1.0,1,1.0,0.9963538443526401,0.99817692217632,1.0,square:2,square,1.0,symmetric:1; unsymmetric:1,symmetric,0.5,complex:1; real:1,complex,0.5,exact_numerical_or_hermitian:1; pattern_symmetric_only:1,exact_numerical_or_hermitian,0.5,no:1; yes:1,no,0.5,acoustics problem:2,acoustics problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:1; yes:1,no,0.5,1k_to_10k:2,1k_to_10k,1.0,Okunbor/aft02=8184,Okunbor/aft01=8205,Okunbor/aft02=8184,Okunbor/aft01=8205,Okunbor/aft01=125567,Okunbor/aft02=127762,Okunbor/aft01=125567,Okunbor/aft02=127762 -MathWorks,11,categorically_heterogeneous,0.318182,5,5,2.085032,3.046013,3.046013,0,0,102,2290,8765,102,2290,12406,306,14873,340200,306,14873,340200,0.0,0.9525732031943213,1.0,0.0,0.04130434782608695,1.0,rectangular:2; square:9,square,0.818182,rectangular:2; symmetric:2; unsymmetric:7,unsymmetric,0.636364,binary:1; real:10,real,0.909091,exact_numerical_or_hermitian:2; partially_pattern_symmetric:4; pattern_symmetric_only:2; pattern_unsymmetric:1; rectangular:2,partially_pattern_symmetric,0.363636,no:9; yes:2,no,0.818182,computer graphics/vision problem:1; counter-example problem:6; directed graph:1; statistical/mathematical problem:1; structural problem:2,counter-example problem,0.545455,false:2; missing:1; true:8,true,0.727273,no:8; yes:3,no,0.727273,no:9; yes:2,no,0.818182,10k_to_100k:1; 1k_to_10k:6; lt_1k:4,1k_to_10k,0.545455,MathWorks/pivtol=102,MathWorks/Kaufhold=8765,MathWorks/pivtol=102,MathWorks/Pd_rhs=12406,MathWorks/pivtol=306,MathWorks/Kuu=340200,MathWorks/pivtol=306,MathWorks/Kuu=340200 -Nasa,11,categorically_heterogeneous,0.318182,5,2,1.47831,2.056781,2.056781,0,0,1824,6691,54870,1824,6691,54870,23492,103599,2677324,23492,103599,2677324,0.0,1,1.0,0.0,1,1.0,square:11,square,1.0,symmetric:8; unsymmetric:3,symmetric,0.727273,binary:6; real:5,binary,0.545455,exact_numerical_or_hermitian:8; pattern_unsymmetric:3,exact_numerical_or_hermitian,0.727273,no:6; yes:5,no,0.545455,duplicate structural problem:6; structural problem:5,duplicate structural problem,0.545455,true:11,true,1.0,yes:11,yes,1.0,no:3; yes:8,yes,0.727273,10k_to_100k:5; 1k_to_10k:6,1k_to_10k,0.545455,Nasa/nasa1824=1824,Nasa/nasasrb=54870,Nasa/nasa1824=1824,Nasa/nasasrb=54870,Nasa/barth4=23492,Nasa/nasasrb=2677324,Nasa/barth4=23492,Nasa/nasasrb=2677324 -Williams,7,categorically_heterogeneous,0.309524,5,4,1.438698,0.673948,0.673948,0,0,36417,121192,1000005,36417,121192,1000005,1273389,3105536,6010480,1273389,3105536,6010480,0.0,1,1.0,0.0,1,1.0,square:7,square,1.0,symmetric:4; unsymmetric:3,symmetric,0.571429,integer:1; real:6,real,0.857143,exact_numerical_or_hermitian:4; partially_pattern_symmetric:2; pattern_unsymmetric:1,exact_numerical_or_hermitian,0.571429,no:4; yes:3,no,0.571429,2D/3D problem:4; economic problem:1; weighted directed graph:1; weighted undirected graph:1,2D/3D problem,0.571429,false:1; missing:2; true:4,true,0.571429,no:3; yes:4,yes,0.571429,no:4; yes:3,no,0.571429,100k_to_1m:3; 10k_to_100k:3; ge_1m:1,100k_to_1m,0.428571,Williams/pdb1HYS=36417,Williams/webbase-1M=1000005,Williams/pdb1HYS=36417,Williams/webbase-1M=1000005,Williams/mac_econ_fwd500=1273389,Williams/consph=6010480,Williams/mac_econ_fwd500=1273389,Williams/consph=6010480 -SNAP,68,categorically_heterogeneous,0.259804,4,10,4.814793,5.250353,5.250353,0,0,1005,82007.5,65608366,1005,82007.5,65608366,20296,547436.5,3612134270,20296,547436.5,3612134270,0.0,0.5371178064241937,1.0,0.0,0.38366599754485475,1.0,square:68,square,1.0,symmetric:21; unsymmetric:47,unsymmetric,0.691176,binary:53; integer:15,binary,0.779412,exact_numerical_or_hermitian:21; partially_pattern_symmetric:36; pattern_symmetric_only:1; pattern_unsymmetric:10,partially_pattern_symmetric,0.529412,no:68,no,1.0,directed graph:30; directed graph with communities:2; directed temporal multigraph:8; directed weighted graph:4; directed weighted graph sequence:1; directed weighted temporal graph:2; directed weighted temporal multigraph:1; undirected graph:11; undirected graph sequence:3; undirected graph with communities:6,directed graph,0.441176,missing:68,missing,1.0,no:68,no,1.0,no:68,no,1.0,100k_to_1m:16; 10k_to_100k:23; 1k_to_10k:13; ge_1m:16,10k_to_100k,0.338235,SNAP/email-Eu-core=1005; SNAP/email-Eu-core-temporal=1005,SNAP/com-Friendster=65608366,SNAP/email-Eu-core=1005; SNAP/email-Eu-core-temporal=1005,SNAP/com-Friendster=65608366,SNAP/CollegeMsg=20296,SNAP/com-Friendster=3612134270,SNAP/CollegeMsg=20296,SNAP/com-Friendster=3612134270 -Botonakis,6,categorically_heterogeneous,0.25,3,1,1.057935,0.908523,0.908523,0,0,17880,125029,204316,17880,125029,204316,430740,1067337,3489300,430740,1067337,3489300,1.0,1,1.0,0.6652727267221619,0.9753669524778374,1.0,square:6,square,1.0,symmetric:3; unsymmetric:3,symmetric,0.5,real:6,real,1.0,exact_numerical_or_hermitian:3; pattern_symmetric_only:3,exact_numerical_or_hermitian,0.5,no:3; yes:3,no,0.5,thermal problem:6,thermal problem,1.0,true:6,true,1.0,yes:6,yes,1.0,no:3; yes:3,no,0.5,100k_to_1m:5; 10k_to_100k:1,100k_to_1m,0.833333,Botonakis/FEM_3D_thermal1=17880,Botonakis/thermomech_dM=204316; Botonakis/thermomech_dK=204316,Botonakis/FEM_3D_thermal1=17880,Botonakis/thermomech_dM=204316; Botonakis/thermomech_dK=204316,Botonakis/FEM_3D_thermal1=430740,Botonakis/FEM_3D_thermal2=3489300,Botonakis/FEM_3D_thermal1=430740,Botonakis/FEM_3D_thermal2=3489300 -CEMW,4,small_group_lt5,0.25,4,1,0.993857,0.549204,0.549204,0,0,93476,822269,921632,93476,822269,921632,1434636,4587816.5,5080961,1434636,4587816.5,5080961,0.9988182710127548,1,1.0,0.0,0.9098903607144062,1.0,square:4,square,1.0,symmetric:1; unsymmetric:3,unsymmetric,0.75,complex:1; integer:1; real:2,real,0.5,exact_numerical_or_hermitian:1; partially_pattern_symmetric:1; pattern_symmetric_only:2,pattern_symmetric_only,0.5,no:3; yes:1,no,0.75,electromagnetics problem:4,electromagnetics problem,1.0,true:4,true,1.0,yes:4,yes,1.0,no:3; yes:1,no,0.75,100k_to_1m:3; 10k_to_100k:1,100k_to_1m,0.75,CEMW/vfem=93476,CEMW/t2em=921632,CEMW/vfem=93476,CEMW/t2em=921632,CEMW/vfem=1434636,CEMW/tmt_sym=5080961,CEMW/vfem=1434636,CEMW/tmt_sym=5080961 -Mulvey,2,small_group_lt5,0.25,3,2,0.0,0.0,0.0,0,0,74752,74752,74752,74752,74752,74752,596992,596992,596992,596992,596992,596992,1.0,1,1.0,1.0,1,1.0,square:2,square,1.0,symmetric:2,symmetric,1.0,binary:1; real:1,binary,0.5,exact_numerical_or_hermitian:2,exact_numerical_or_hermitian,1.0,no:1; yes:1,no,0.5,duplicate economic problem:1; economic problem:1,duplicate economic problem,0.5,true:2,true,1.0,no:2,no,1.0,yes:2,yes,1.0,10k_to_100k:2,10k_to_100k,1.0,Mulvey/finan512=74752; Mulvey/pfinan512=74752,Mulvey/finan512=74752; Mulvey/pfinan512=74752,Mulvey/finan512=74752; Mulvey/pfinan512=74752,Mulvey/finan512=74752; Mulvey/pfinan512=74752,Mulvey/finan512=596992; Mulvey/pfinan512=596992,Mulvey/finan512=596992; Mulvey/pfinan512=596992,Mulvey/finan512=596992; Mulvey/pfinan512=596992,Mulvey/finan512=596992; Mulvey/pfinan512=596992 -Bai,78,categorically_heterogeneous,0.245726,4,10,2.579784,3.131892,3.151049,1,23658,62,961,23560,62,961,23560,342,4681,463360,342,4681,484256,0.07485627126598615,0.9987279276556085,1.0,0.0,0.34047621421787244,1.0,square:78,square,1.0,Hermitian:1; symmetric:11; unsymmetric:66,unsymmetric,0.846154,complex:6; integer:2; real:70,real,0.897436,exact_numerical_or_hermitian:8; partially_pattern_symmetric:39; pattern_symmetric_only:31,partially_pattern_symmetric,0.5,no:74; yes:4,no,0.948718,2D/3D problem:8; chemical process simulation problem:2; computational fluid dynamics problem:25; computational fluid dynamics problem sequence:1; electromagnetics problem:26; materials problem:2; power network problem:3; robotics problem:2; statistical/mathematical problem:4; subsequent computational fluid dynamics problem:5,electromagnetics problem,0.333333,false:2; true:76,true,0.974359,no:9; yes:69,yes,0.884615,no:74; yes:4,no,0.948718,10k_to_100k:2; 1k_to_10k:29; lt_1k:47,lt_1k,0.602564,Bai/bfwa62=62; Bai/bfwb62=62,Bai/af23560=23560,Bai/bfwa62=62; Bai/bfwb62=62,Bai/af23560=23560,Bai/bfwb62=342,Bai/qc2534=463360,Bai/bfwb62=342,Bai/af23560=484256 -Newman,16,categorically_heterogeneous,0.239583,4,5,3.075128,3.352655,3.35266,1,4,34,1539.5,40421,34,1539.5,40421,156,9336,351382,156,9336,351386,0.1680170575692964,1,1.0,0.04946695095948828,1,1.0,square:16,square,1.0,symmetric:14; unsymmetric:2,symmetric,0.875,binary:6; integer:4; real:6,binary,0.375,exact_numerical_or_hermitian:14; partially_pattern_symmetric:2,exact_numerical_or_hermitian,0.875,no:16,no,1.0,directed multigraph:1; directed weighted graph:1; undirected graph:6; undirected multigraph:1; undirected weighted graph:7,undirected weighted graph,0.4375,missing:16,missing,1.0,no:16,no,1.0,no:16,no,1.0,10k_to_100k:5; 1k_to_10k:4; lt_1k:7,lt_1k,0.4375,Newman/karate=34,Newman/cond-mat-2005=40421,Newman/karate=34,Newman/cond-mat-2005=40421,Newman/karate=156,Newman/cond-mat-2005=351382,Newman/karate=156,Newman/cond-mat-2005=351386 -Simon,12,categorically_heterogeneous,0.236111,4,5,1.284539,1.152719,1.128509,7,22412,3242,17962.5,62424,3242,17962.5,62424,130371,1402778.5,1853104,137845,1408689.5,1853104,0.0,1,1.0,0.0,0.0729162397267196,1.0,square:12,square,1.0,symmetric:2; unsymmetric:10,unsymmetric,0.833333,real:12,real,1.0,exact_numerical_or_hermitian:2; partially_pattern_symmetric:1; pattern_symmetric_only:7; pattern_unsymmetric:2,pattern_symmetric_only,0.583333,no:10; yes:2,no,0.833333,computational fluid dynamics problem:2; computational fluid dynamics problem sequence:2; directed weighted random graph:1; structural problem:4; subsequent computational fluid dynamics problem:3,structural problem,0.333333,missing:1; true:11,true,0.916667,no:1; yes:11,yes,0.916667,no:10; yes:2,no,0.833333,10k_to_100k:8; 1k_to_10k:4,10k_to_100k,0.666667,Simon/raefsky1=3242; Simon/raefsky2=3242,Simon/venkat01=62424; Simon/venkat25=62424; Simon/venkat50=62424,Simon/raefsky1=3242; Simon/raefsky2=3242,Simon/venkat01=62424; Simon/venkat25=62424; Simon/venkat50=62424,Simon/raefsky6=130371,Simon/appu=1853104,Simon/raefsky6=137845,Simon/appu=1853104 -TKK,12,categorically_heterogeneous,0.222222,5,2,2.049179,2.186313,2.186313,1,3602,1282,13681,143571,1282,13681,143571,30644,695378,4706073,30644,695378,4706073,1.0,1,1.0,0.6937221589143272,1,1.0,square:12,square,1.0,symmetric:10; unsymmetric:2,symmetric,0.833333,binary:3; integer:4; real:5,real,0.416667,exact_numerical_or_hermitian:10; pattern_symmetric_only:2,exact_numerical_or_hermitian,0.833333,no:9; yes:3,no,0.75,structural problem:10; structural problem sequence:2,structural problem,0.833333,true:12,true,1.0,yes:12,yes,1.0,no:2; yes:10,yes,0.833333,100k_to_1m:1; 10k_to_100k:6; 1k_to_10k:5,10k_to_100k,0.5,TKK/plbuckle=1282,TKK/engine=143571,TKK/plbuckle=1282,TKK/engine=143571,TKK/plbuckle=30644,TKK/engine=4706073,TKK/plbuckle=30644,TKK/engine=4706073 -Bindel,6,categorically_heterogeneous,0.222222,4,1,0.0,0.557887,0.557887,0,0,10605,10605,10605,10605,10605,10605,144579,424587,522387,144579,424587,522387,0.5657922767749357,0.6415700434950818,1.0,0.0,0.1057079149564699,1.0,square:6,square,1.0,symmetric:2; unsymmetric:4,unsymmetric,0.666667,complex:2; real:4,real,0.666667,exact_numerical_or_hermitian:2; partially_pattern_symmetric:4,partially_pattern_symmetric,0.666667,no:4; yes:2,no,0.666667,thermal problem:6,thermal problem,1.0,true:6,true,1.0,yes:6,yes,1.0,no:4; yes:2,no,0.666667,10k_to_100k:6,10k_to_100k,1.0,Bindel/ted_A=10605; Bindel/ted_B=10605; Bindel/ted_AB=10605; Bindel/ted_A_unscaled=10605; Bindel/ted_B_unscaled=10605; Bindel/ted_AB_unscaled=10605,Bindel/ted_A=10605; Bindel/ted_B=10605; Bindel/ted_AB=10605; Bindel/ted_A_unscaled=10605; Bindel/ted_B_unscaled=10605; Bindel/ted_AB_unscaled=10605,Bindel/ted_A=10605; Bindel/ted_B=10605; Bindel/ted_AB=10605; Bindel/ted_A_unscaled=10605; Bindel/ted_B_unscaled=10605; Bindel/ted_AB_unscaled=10605,Bindel/ted_A=10605; Bindel/ted_B=10605; Bindel/ted_AB=10605; Bindel/ted_A_unscaled=10605; Bindel/ted_B_unscaled=10605; Bindel/ted_AB_unscaled=10605,Bindel/ted_B=144579; Bindel/ted_B_unscaled=144579,Bindel/ted_AB=522387; Bindel/ted_AB_unscaled=522387,Bindel/ted_B=144579; Bindel/ted_B_unscaled=144579,Bindel/ted_AB=522387; Bindel/ted_AB_unscaled=522387 -Hardesty,3,small_group_lt5,0.222222,4,1,0.94632,1.002631,1.002631,0,0,929901,938905,8217820,303645,938905,7591564,4020731,12143314,40451632,4020731,12143314,40451632,0.0,0,1.0,0.0,0,1.0,rectangular:2; square:1,rectangular,0.666667,rectangular:2; symmetric:1,rectangular,0.666667,integer:1; real:2,real,0.666667,exact_numerical_or_hermitian:1; rectangular:2,rectangular,0.666667,no:3,no,1.0,computer graphics/vision problem:3,computer graphics/vision problem,1.0,true:3,true,1.0,yes:3,yes,1.0,no:3,no,1.0,100k_to_1m:2; ge_1m:1,100k_to_1m,0.666667,Hardesty/Hardesty2=929901,Hardesty/Hardesty3=8217820,Hardesty/Hardesty2=303645,Hardesty/Hardesty3=7591564,Hardesty/Hardesty2=4020731,Hardesty/Hardesty3=40451632,Hardesty/Hardesty2=4020731,Hardesty/Hardesty3=40451632 -GAP,5,categorically_heterogeneous,0.2,3,2,0.748552,1.871719,1.871719,0,0,23947347,61578415,134217728,23947347,61578415,134217728,57708624,1930292948,4294966740,57708624,1930292948,4294966740,0.1245713694644861,1,1.0,0.0004892386935249789,1,1.0,square:5,square,1.0,symmetric:3; unsymmetric:2,symmetric,0.6,integer:5,integer,1.0,exact_numerical_or_hermitian:3; partially_pattern_symmetric:2,exact_numerical_or_hermitian,0.6,no:5,no,1.0,directed weighted graph:3; random undirected weighted graph:2,directed weighted graph,0.6,missing:5,missing,1.0,no:5,no,1.0,no:5,no,1.0,ge_1m:5,ge_1m,1.0,GAP/GAP-road=23947347,GAP/GAP-urand=134217728,GAP/GAP-road=23947347,GAP/GAP-urand=134217728,GAP/GAP-road=57708624,GAP/GAP-urand=4294966740,GAP/GAP-road=57708624,GAP/GAP-urand=4294966740 -Norris,12,categorically_heterogeneous,0.194444,4,2,2.196077,3.059916,3.059916,3,7368,1650,9801,259156,1650,9801,259156,7419,680341,8516500,7419,682797,8516500,0.4243874832715144,0.995818010999383,1.0,0.0,0,1.0,square:12,square,1.0,symmetric:3; unsymmetric:9,unsymmetric,0.75,real:12,real,1.0,exact_numerical_or_hermitian:3; partially_pattern_symmetric:6; pattern_symmetric_only:3,partially_pattern_symmetric,0.5,no:9; yes:3,no,0.75,2D/3D problem:10; computational fluid dynamics problem:2,2D/3D problem,0.833333,true:12,true,1.0,yes:12,yes,1.0,no:9; yes:3,no,0.75,100k_to_1m:5; 1k_to_10k:7,1k_to_10k,0.583333,Norris/lung1=1650,Norris/torso3=259156,Norris/lung1=1650,Norris/torso3=259156,Norris/lung1=7419,Norris/torso1=8516500,Norris/lung1=7419,Norris/torso1=8516500 -Rothberg,6,categorically_heterogeneous,0.194444,3,2,1.548315,1.581897,1.581897,2,5276,4350,62113,153746,4350,62113,153746,237798,2455493,9080404,237798,2458131,9080404,1.0,1,1.0,1.0,1,1.0,square:6,square,1.0,symmetric:6,symmetric,1.0,binary:4; real:2,binary,0.666667,exact_numerical_or_hermitian:6,exact_numerical_or_hermitian,1.0,no:4; yes:2,no,0.666667,computational fluid dynamics problem:3; structural problem:3,computational fluid dynamics problem,0.5,true:6,true,1.0,yes:6,yes,1.0,yes:6,yes,1.0,100k_to_1m:2; 10k_to_100k:3; 1k_to_10k:1,10k_to_100k,0.5,Rothberg/struct4=4350,Rothberg/gearbox=153746,Rothberg/struct4=4350,Rothberg/gearbox=153746,Rothberg/struct4=237798,Rothberg/gearbox=9080404,Rothberg/struct4=237798,Rothberg/gearbox=9080404 -Janna,18,categorically_heterogeneous,0.185185,4,3,1.041402,1.262962,1.195524,15,39345501,377002,1470152,4147110,377002,1470152,4147110,17277420,62184146.5,316548962,21005389,63844195.5,329499284,1.0,1,1.0,0.0,1,1.0,square:18,square,1.0,symmetric:14; unsymmetric:4,symmetric,0.777778,real:18,real,1.0,exact_numerical_or_hermitian:14; pattern_symmetric_only:4,exact_numerical_or_hermitian,0.777778,no:8; yes:10,yes,0.555556,2D/3D problem:3; computational fluid dynamics problem:1; structural problem:14,structural problem,0.777778,true:18,true,1.0,yes:18,yes,1.0,no:8; yes:10,yes,0.555556,100k_to_1m:5; ge_1m:13,ge_1m,0.722222,Janna/ML_Laplace=377002,Janna/Queen_4147=4147110,Janna/ML_Laplace=377002,Janna/Queen_4147=4147110,Janna/CoupCons3D=17277420,Janna/Queen_4147=316548962,Janna/StocF-1465=21005389,Janna/Queen_4147=329499284 -FEMLAB,9,categorically_heterogeneous,0.185185,3,4,2.367924,3.114849,3.114849,0,0,367,20414,85623,367,20414,85623,2417,874887,3148656,2417,874887,3148656,0.9999710105602732,1,1.0,0.0,0,0.6548223350253807,square:9,square,1.0,unsymmetric:9,unsymmetric,1.0,complex:1; real:8,real,0.888889,partially_pattern_symmetric:4; pattern_symmetric_only:5,pattern_symmetric_only,0.555556,no:9,no,1.0,2D/3D problem:1; computational fluid dynamics problem:4; electromagnetics problem:1; structural problem:3,computational fluid dynamics problem,0.444444,true:9,true,1.0,yes:9,yes,1.0,no:9,no,1.0,10k_to_100k:7; lt_1k:2,10k_to_100k,0.777778,FEMLAB/poisson2D=367,FEMLAB/poisson3Db=85623,FEMLAB/poisson2D=367,FEMLAB/poisson3Db=85623,FEMLAB/poisson2D=2417,FEMLAB/sme3Dc=3148656,FEMLAB/poisson2D=2417,FEMLAB/sme3Dc=3148656 -GHS_psdef,32,categorically_heterogeneous,0.177083,3,7,1.789829,2.883361,2.883361,9,6842388,15449,51402,952203,15449,51402,952203,101576,1345303.5,77651847,101576,1345303.5,77651847,1.0,1,1.0,1.0,1,1.0,square:32,square,1.0,symmetric:32,symmetric,1.0,binary:10; real:22,real,0.6875,exact_numerical_or_hermitian:32,exact_numerical_or_hermitian,1.0,no:10; yes:22,yes,0.6875,computational fluid dynamics problem:2; duplicate computational fluid dynamics problem:1; duplicate optimization problem:1; duplicate structural problem:1; optimization problem:7; random 2D/3D problem:2; structural problem:18,structural problem,0.5625,true:32,true,1.0,no:8; yes:24,yes,0.75,yes:32,yes,1.0,100k_to_1m:8; 10k_to_100k:24,10k_to_100k,0.75,GHS_psdef/opt1=15449,GHS_psdef/ldoor=952203,GHS_psdef/opt1=15449,GHS_psdef/ldoor=952203,GHS_psdef/ford1=101576,GHS_psdef/audikw_1=77651847,GHS_psdef/ford1=101576,GHS_psdef/audikw_1=77651847 -Gset,67,mixed_some_attributes,0.166667,2,3,1.09691,1.413499,1.413499,0,0,800,2000,10000,800,2000,10000,3200,23570,82918,3200,23570,82918,1.0,1,1.0,1.0,1,1.0,square:67,square,1.0,symmetric:67,symmetric,1.0,binary:34; integer:33,binary,0.507463,exact_numerical_or_hermitian:67,exact_numerical_or_hermitian,1.0,no:67,no,1.0,duplicate undirected random graph:12; undirected random graph:22; undirected weighted random graph:33,undirected weighted random graph,0.492537,missing:67,missing,1.0,no:67,no,1.0,no:67,no,1.0,10k_to_100k:1; 1k_to_10k:45; lt_1k:21,1k_to_10k,0.671642,Gset/G1=800; Gset/G10=800; Gset/G11=800; Gset/G12=800; Gset/G13=800; Gset/G14=800; Gset/G15=800; Gset/G16=800; Gset/G17=800; Gset/G18=800; Gset/G19=800; Gset/G2=800; Gset/G20=800; Gset/G21=800; Gset/G3=800; Gset/G4=800; Gset/G5=800; Gset/G6=800; Gset/G7=800; Gset/G8=800; Gset/G9=800,Gset/G67=10000,Gset/G1=800; Gset/G10=800; Gset/G11=800; Gset/G12=800; Gset/G13=800; Gset/G14=800; Gset/G15=800; Gset/G16=800; Gset/G17=800; Gset/G18=800; Gset/G19=800; Gset/G2=800; Gset/G20=800; Gset/G21=800; Gset/G3=800; Gset/G4=800; Gset/G5=800; Gset/G6=800; Gset/G7=800; Gset/G8=800; Gset/G9=800,Gset/G67=10000,Gset/G11=3200; Gset/G12=3200; Gset/G13=3200,Gset/G63=82918; Gset/G64=82918,Gset/G11=3200; Gset/G12=3200; Gset/G13=3200,Gset/G63=82918; Gset/G64=82918 -Gaertner,3,small_group_lt5,0.166667,3,2,0.088493,0.110964,0.110964,0,0,10774,11738,13209,10774,11738,13209,70842,79566,91465,70842,79566,91465,1.0,1,1.0,0.999970513652179,0.999974442854222,1.0,square:3,square,1.0,symmetric:1; unsymmetric:2,unsymmetric,0.666667,integer:3,integer,1.0,exact_numerical_or_hermitian:1; pattern_symmetric_only:2,pattern_symmetric_only,0.666667,no:3,no,1.0,directed weighted graph:2; undirected weighted graph:1,directed weighted graph,0.666667,missing:3,missing,1.0,no:3,no,1.0,no:2; yes:1,no,0.666667,10k_to_100k:3,10k_to_100k,1.0,Gaertner/nopoly=10774,Gaertner/big=13209,Gaertner/nopoly=10774,Gaertner/big=13209,Gaertner/nopoly=70842,Gaertner/big=91465,Gaertner/nopoly=70842,Gaertner/big=91465 -Cote,2,small_group_lt5,0.166667,2,1,0.315501,0.326706,0.382207,1,41128,5962,9145,12328,5962,9145,12328,142190,221945,301700,142190,242509,342828,1.0,1,1.0,0.05311683354376486,0.5265584167718824,1.0,square:2,square,1.0,symmetric:2,symmetric,1.0,complex:1; real:1,complex,0.5,exact_numerical_or_hermitian:1; pattern_symmetric_only:1,exact_numerical_or_hermitian,0.5,no:2,no,1.0,acoustics problem:2,acoustics problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:1; yes:1,no,0.5,10k_to_100k:1; 1k_to_10k:1,10k_to_100k,0.5,Cote/mplate=5962,Cote/vibrobox=12328,Cote/mplate=5962,Cote/vibrobox=12328,Cote/mplate=142190,Cote/vibrobox=301700,Cote/mplate=142190,Cote/vibrobox=342828 -Sybrandt,2,small_group_lt5,0.166667,2,2,0.784156,0.239958,0.239435,1,8046672,30239687,107101882,183964077,30239687,107101882,183964077,6669254694,9128990329,11588725964,6677301366,9133013665,11588725964,1.0,1,1.0,1.0,1,1.0,square:2,square,1.0,symmetric:2,symmetric,1.0,binary:1; real:1,binary,0.5,exact_numerical_or_hermitian:2,exact_numerical_or_hermitian,1.0,no:2,no,1.0,undirected graph:1; undirected weighted graph:1,undirected graph,0.5,missing:2,missing,1.0,no:2,no,1.0,no:2,no,1.0,ge_1m:2,ge_1m,1.0,Sybrandt/MOLIERE_2016=30239687,Sybrandt/AGATHA_2015=183964077,Sybrandt/MOLIERE_2016=30239687,Sybrandt/AGATHA_2015=183964077,Sybrandt/MOLIERE_2016=6669254694,Sybrandt/AGATHA_2015=11588725964,Sybrandt/MOLIERE_2016=6677301366,Sybrandt/AGATHA_2015=11588725964 -FIDAP,35,categorically_heterogeneous,0.161905,3,1,2.863455,3.594582,3.594582,23,82207,27,2410,19716,27,2410,19716,279,54840,1096948,279,54840,1096948,1.0,1,1.0,0.4284006268883396,0.9877623772912288,1.0,square:35,square,1.0,symmetric:13; unsymmetric:22,unsymmetric,0.628571,real:35,real,1.0,exact_numerical_or_hermitian:13; pattern_symmetric_only:22,pattern_symmetric_only,0.628571,no:27; yes:8,no,0.771429,computational fluid dynamics problem:35,computational fluid dynamics problem,1.0,true:35,true,1.0,yes:35,yes,1.0,no:27; yes:8,no,0.771429,10k_to_100k:3; 1k_to_10k:25; lt_1k:7,1k_to_10k,0.714286,FIDAP/ex5=27,FIDAP/ex35=19716,FIDAP/ex5=27,FIDAP/ex35=19716,FIDAP/ex5=279,FIDAP/ex11=1096948,FIDAP/ex5=279,FIDAP/ex11=1096948 -Boeing,32,categorically_heterogeneous,0.161458,3,4,2.568916,3.041051,3.045176,7,253152,588,13965,217918,588,13965,217918,10485,321755.5,11524432,10485,321755.5,11634424,1.0,1,1.0,1.0,1,1.0,square:32,square,1.0,symmetric:32,symmetric,1.0,binary:5; real:27,real,0.84375,exact_numerical_or_hermitian:32,exact_numerical_or_hermitian,1.0,no:17; yes:15,no,0.53125,duplicate materials problem:2; duplicate structural problem:3; materials problem:6; structural problem:21,structural problem,0.65625,false:4; true:28,true,0.875,yes:32,yes,1.0,no:5; yes:27,yes,0.84375,100k_to_1m:1; 10k_to_100k:18; 1k_to_10k:10; lt_1k:3,10k_to_100k,0.5625,Boeing/bcsstk34=588; Boeing/bcsstm34=588,Boeing/pwtk=217918,Boeing/bcsstk34=588; Boeing/bcsstm34=588,Boeing/pwtk=217918,Boeing/bcsstm38=10485,Boeing/pwtk=11524432,Boeing/bcsstm38=10485,Boeing/pwtk=11634424 -DNVS,15,mixed_some_attributes,0.155556,2,1,1.095929,1.110296,1.110296,7,19645488,18010,114919,224617,18010,114919,224617,960946,4444880,12387821,960946,8086034,12387821,1.0,1,1.0,1.0,1,1.0,square:15,square,1.0,symmetric:15,symmetric,1.0,binary:7; real:8,real,0.533333,exact_numerical_or_hermitian:15,exact_numerical_or_hermitian,1.0,no:7; yes:8,yes,0.533333,structural problem:15,structural problem,1.0,true:15,true,1.0,yes:15,yes,1.0,yes:15,yes,1.0,100k_to_1m:9; 10k_to_100k:6,100k_to_1m,0.6,DNVS/crplat2=18010,DNVS/halfb=224617,DNVS/crplat2=18010,DNVS/halfb=224617,DNVS/crplat2=960946,DNVS/halfb=12387821,DNVS/crplat2=960946,DNVS/halfb=12387821 -DIMACS10,151,mixed_some_attributes,0.154525,2,6,6.115756,5.891954,5.891954,0,0,39,196621,50912018,39,196621,50912018,340,1227102,265114400,340,1227102,265114400,1.0,1,1.0,1.0,1,1.0,square:151,square,1.0,symmetric:151,symmetric,1.0,binary:95; integer:56,binary,0.629139,exact_numerical_or_hermitian:151,exact_numerical_or_hermitian,1.0,no:151,no,1.0,random undirected graph:3; random unweighted graph:15; undirected graph:67; undirected multigraph:6; undirected random graph:10; undirected weighted graph:50,undirected graph,0.443709,missing:151,missing,1.0,no:151,no,1.0,no:151,no,1.0,100k_to_1m:65; 10k_to_100k:39; 1k_to_10k:7; ge_1m:39; lt_1k:1,100k_to_1m,0.430464,DIMACS10/chesapeake=39,DIMACS10/europe_osm=50912018,DIMACS10/chesapeake=39,DIMACS10/europe_osm=50912018,DIMACS10/chesapeake=340,DIMACS10/rgg_n_2_24_s0=265114400,DIMACS10/chesapeake=340,DIMACS10/rgg_n_2_24_s0=265114400 -Oberwolfach,38,categorically_heterogeneous,0.153509,4,2,4.848058,6.01714,6.192557,3,39602806,14,17361,986703,14,17361,986703,46,176117,47851783,46,176117,71666325,0.6083330964361183,1,1.0,0.0,1,1.0,square:38,square,1.0,symmetric:33; unsymmetric:5,symmetric,0.868421,complex:1; real:37,real,0.973684,exact_numerical_or_hermitian:32; partially_pattern_symmetric:1; pattern_symmetric_only:5,exact_numerical_or_hermitian,0.842105,no:25; yes:13,no,0.657895,duplicate model reduction problem:10; model reduction problem:28,model reduction problem,0.736842,true:38,true,1.0,yes:38,yes,1.0,no:24; yes:14,no,0.631579,100k_to_1m:4; 10k_to_100k:21; 1k_to_10k:11; lt_1k:2,10k_to_100k,0.552632,Oberwolfach/LFAT5=14,Oberwolfach/bone010=986703,Oberwolfach/LFAT5=14,Oberwolfach/bone010=986703,Oberwolfach/LFAT5=46,Oberwolfach/bone010=47851783,Oberwolfach/LFAT5=46,Oberwolfach/bone010=71666325 -Pothen,22,mixed_some_attributes,0.151515,2,1,3.292205,3.177502,3.177502,5,2380,48,7305.5,94069,48,7305.5,94069,306,43576,460493,306,43576,460493,1.0,1,1.0,1.0,1,1.0,square:22,square,1.0,symmetric:22,symmetric,1.0,binary:12; real:10,binary,0.545455,exact_numerical_or_hermitian:22,exact_numerical_or_hermitian,1.0,no:12; yes:10,no,0.545455,structural problem:22,structural problem,1.0,true:22,true,1.0,yes:22,yes,1.0,yes:22,yes,1.0,10k_to_100k:10; 1k_to_10k:3; lt_1k:9,10k_to_100k,0.454545,Pothen/mesh1e1=48; Pothen/mesh1em1=48; Pothen/mesh1em6=48,Pothen/tandem_dual=94069,Pothen/mesh1e1=48; Pothen/mesh1em1=48; Pothen/mesh1em6=48,Pothen/tandem_dual=94069,Pothen/mesh1e1=306; Pothen/mesh1em1=306; Pothen/mesh1em6=306,Pothen/tandem_dual=460493,Pothen/mesh1e1=306; Pothen/mesh1em1=306; Pothen/mesh1em6=306,Pothen/tandem_dual=460493 -Gleich,10,categorically_heterogeneous,0.15,3,2,3.571315,3.937127,3.937127,0,0,2642,1227933.5,9845725,2642,1227933.5,9845725,6606,14795146,57156537,6606,14795146,57156537,0.1171832378930545,0.3926833803963951,1.0,0.1171832378930545,0.3926833803963951,1.0,square:10,square,1.0,symmetric:3; unsymmetric:7,unsymmetric,0.7,binary:10,binary,1.0,exact_numerical_or_hermitian:3; partially_pattern_symmetric:7,partially_pattern_symmetric,0.7,no:10,no,1.0,directed graph:7; undirected graph:3,directed graph,0.7,missing:10,missing,1.0,no:10,no,1.0,no:10,no,1.0,100k_to_1m:3; 1k_to_10k:2; ge_1m:5,ge_1m,0.5,Gleich/minnesota=2642,Gleich/wb-edu=9845725,Gleich/minnesota=2642,Gleich/wb-edu=9845725,Gleich/minnesota=6606,Gleich/wb-edu=57156537,Gleich/minnesota=6606,Gleich/wb-edu=57156537 -TSOPF,27,mixed_some_attributes,0.148148,2,1,1.690088,2.810246,2.810246,0,0,2454,29214,120216,2454,29214,120216,25032,1977600,16171169,25032,1977600,16171169,0.002485718029041526,0.05962284731399325,1.0,8.236061201778597e-05,0.0008133934303355415,1.0,square:27,square,1.0,symmetric:12; unsymmetric:15,unsymmetric,0.555556,real:27,real,1.0,exact_numerical_or_hermitian:12; partially_pattern_symmetric:15,partially_pattern_symmetric,0.555556,no:27,no,1.0,power network problem:27,power network problem,1.0,true:27,true,1.0,no:27,no,1.0,no:27,no,1.0,100k_to_1m:1; 10k_to_100k:23; 1k_to_10k:3,10k_to_100k,0.851852,TSOPF/TSOPF_FS_b9_c1=2454,TSOPF/TSOPF_FS_b39_c30=120216,TSOPF/TSOPF_FS_b9_c1=2454,TSOPF/TSOPF_FS_b39_c30=120216,TSOPF/TSOPF_FS_b9_c1=25032,TSOPF/TSOPF_RS_b2383=16171169; TSOPF/TSOPF_RS_b2383_c1=16171169,TSOPF/TSOPF_FS_b9_c1=25032,TSOPF/TSOPF_RS_b2383=16171169; TSOPF/TSOPF_RS_b2383_c1=16171169 -Schenk_AFE,16,mixed_some_attributes,0.145833,2,3,0.476313,0.473875,0.477289,10,558646,503625,504855,1508065,503625,504855,1508065,17550675,17562051,52259885,17550675,17588875,52672325,1.0,1,1.0,1.0,1,1.0,square:16,square,1.0,symmetric:16,symmetric,1.0,real:16,real,1.0,exact_numerical_or_hermitian:16,exact_numerical_or_hermitian,1.0,no:6; yes:10,yes,0.625,structural problem:7; structural problem sequence:1; subsequent structural problem:8,subsequent structural problem,0.5,true:16,true,1.0,yes:16,yes,1.0,yes:16,yes,1.0,100k_to_1m:15; ge_1m:1,100k_to_1m,0.9375,Schenk_AFE/af_0_k101=503625; Schenk_AFE/af_1_k101=503625; Schenk_AFE/af_2_k101=503625; Schenk_AFE/af_3_k101=503625; Schenk_AFE/af_4_k101=503625; Schenk_AFE/af_5_k101=503625,Schenk_AFE/af_shell10=1508065,Schenk_AFE/af_0_k101=503625; Schenk_AFE/af_1_k101=503625; Schenk_AFE/af_2_k101=503625; Schenk_AFE/af_3_k101=503625; Schenk_AFE/af_4_k101=503625; Schenk_AFE/af_5_k101=503625,Schenk_AFE/af_shell10=1508065,Schenk_AFE/af_0_k101=17550675; Schenk_AFE/af_1_k101=17550675; Schenk_AFE/af_2_k101=17550675; Schenk_AFE/af_3_k101=17550675; Schenk_AFE/af_4_k101=17550675; Schenk_AFE/af_5_k101=17550675,Schenk_AFE/af_shell10=52259885,Schenk_AFE/af_0_k101=17550675; Schenk_AFE/af_1_k101=17550675; Schenk_AFE/af_2_k101=17550675; Schenk_AFE/af_3_k101=17550675; Schenk_AFE/af_4_k101=17550675; Schenk_AFE/af_5_k101=17550675,Schenk_AFE/af_shell10=52672325 -VLSI,14,mixed_some_attributes,0.142857,2,2,2.386745,2.616323,2.616323,8,34910073,46994,741786,11449533,46994,741786,11449533,845089,18026852,349321980,845089,23860888.5,349321980,0.420954312323566,1,1.0,0.04200954350296261,0.1936182841296341,0.3018551635520223,square:14,square,1.0,unsymmetric:14,unsymmetric,1.0,real:14,real,1.0,partially_pattern_symmetric:6; pattern_symmetric_only:8,pattern_symmetric_only,0.571429,no:14,no,1.0,semiconductor device problem:8; semiconductor process problem:6,semiconductor device problem,0.571429,true:14,true,1.0,yes:14,yes,1.0,no:14,no,1.0,100k_to_1m:5; 10k_to_100k:2; ge_1m:7,ge_1m,0.5,VLSI/mosfet2=46994,VLSI/stokes=11449533,VLSI/mosfet2=46994,VLSI/stokes=11449533,VLSI/ss1=845089,VLSI/stokes=349321980,VLSI/ss1=845089,VLSI/stokes=349321980 -Rajat,32,categorically_heterogeneous,0.135417,3,1,4.540839,4.485022,4.398288,22,1472235,135,33731.5,4690002,135,33731.5,4690002,665,162866,20316253,812,163783.5,20316253,0.626033640830624,0.9951695846058093,1.0,0.009194565053000587,0.4286578279800002,1.0,square:32,square,1.0,symmetric:6; unsymmetric:26,unsymmetric,0.8125,binary:7; real:25,real,0.78125,exact_numerical_or_hermitian:6; partially_pattern_symmetric:19; pattern_symmetric_only:7,partially_pattern_symmetric,0.59375,no:32,no,1.0,circuit simulation problem:32,circuit simulation problem,1.0,true:32,true,1.0,no:32,no,1.0,no:32,no,1.0,100k_to_1m:6; 10k_to_100k:15; 1k_to_10k:7; ge_1m:1; lt_1k:3,10k_to_100k,0.46875,Rajat/rajat11=135,Rajat/rajat31=4690002,Rajat/rajat11=135,Rajat/rajat31=4690002,Rajat/rajat11=665,Rajat/rajat31=20316253,Rajat/rajat11=812,Rajat/rajat31=20316253 -Dziekonski,5,mixed_some_attributes,0.133333,2,1,0.439834,0.613243,0.613243,0,0,420408,607232,1157456,420408,607232,1157456,21758924,32886208,89306020,21758924,32886208,89306020,1.0,1,1.0,0.8873627016737613,1,1.0,square:5,square,1.0,symmetric:5,symmetric,1.0,complex:2; real:3,real,0.6,exact_numerical_or_hermitian:3; pattern_symmetric_only:2,exact_numerical_or_hermitian,0.6,no:5,no,1.0,electromagnetics problem:5,electromagnetics problem,1.0,true:5,true,1.0,yes:5,yes,1.0,no:5,no,1.0,100k_to_1m:3; ge_1m:2,100k_to_1m,0.6,Dziekonski/dielFilterV3clx=420408,Dziekonski/dielFilterV2real=1157456,Dziekonski/dielFilterV3clx=420408,Dziekonski/dielFilterV2real=1157456,Dziekonski/gsm_106857=21758924,Dziekonski/dielFilterV3real=89306020,Dziekonski/gsm_106857=21758924,Dziekonski/dielFilterV3real=89306020 -Cunningham,4,small_group_lt5,0.125,3,1,0.774782,2.398157,2.398157,0,0,11107,38617,66127,11107,38617,66127,6639,1019753,1660579,6639,1019753,1660579,1.0,1,1.0,0.999260507857104,1,1.0,square:4,square,1.0,symmetric:3; unsymmetric:1,symmetric,0.75,real:4,real,1.0,exact_numerical_or_hermitian:3; pattern_symmetric_only:1,exact_numerical_or_hermitian,0.75,no:3; yes:1,no,0.75,acoustics problem:4,acoustics problem,1.0,false:1; true:3,true,0.75,yes:4,yes,1.0,no:2; yes:2,no,0.5,10k_to_100k:4,10k_to_100k,1.0,Cunningham/k3plates=11107; Cunningham/m3plates=11107,Cunningham/qa8fk=66127; Cunningham/qa8fm=66127,Cunningham/k3plates=11107; Cunningham/m3plates=11107,Cunningham/qa8fk=66127; Cunningham/qa8fm=66127,Cunningham/m3plates=6639,Cunningham/qa8fk=1660579; Cunningham/qa8fm=1660579,Cunningham/m3plates=6639,Cunningham/qa8fk=1660579; Cunningham/qa8fm=1660579 -Wang,6,mixed_some_attributes,0.111111,1,3,0.953261,0.96758,0.96758,0,0,2903,3169,26068,2903,3169,26068,19093,20841,177196,19093,20841,177196,1.0,1,1.0,0.0,0.4273151697055724,0.9780019059720457,square:6,square,1.0,unsymmetric:6,unsymmetric,1.0,real:6,real,1.0,pattern_symmetric_only:6,pattern_symmetric_only,1.0,no:6,no,1.0,semiconductor device problem:2; semiconductor device problem sequence:2; subsequent semiconductor device problem:2,semiconductor device problem,0.333333,true:6,true,1.0,yes:6,yes,1.0,no:6,no,1.0,10k_to_100k:2; 1k_to_10k:4,1k_to_10k,0.666667,Wang/wang1=2903; Wang/wang2=2903,Wang/wang4=26068,Wang/wang1=2903; Wang/wang2=2903,Wang/wang4=26068,Wang/wang1=19093; Wang/wang2=19093,Wang/wang4=177196,Wang/wang1=19093; Wang/wang2=19093,Wang/wang4=177196 -TAMU_SmartGridCenter,3,small_group_lt5,0.111111,2,1,1.243032,0.922799,0.910319,2,2679,4000,20000,69999,4000,20000,69999,28505,135888,238627,29336,137736,238627,1.0,1,1.0,0.007345439706182412,0.02328109899213033,1.0,square:3,square,1.0,symmetric:1; unsymmetric:2,unsymmetric,0.666667,real:3,real,1.0,exact_numerical_or_hermitian:1; pattern_symmetric_only:2,pattern_symmetric_only,0.666667,no:3,no,1.0,power network problem:3,power network problem,1.0,true:3,true,1.0,no:3,no,1.0,no:3,no,1.0,10k_to_100k:2; 1k_to_10k:1,10k_to_100k,0.666667,TAMU_SmartGridCenter/ACTIVSg2000=4000,TAMU_SmartGridCenter/ACTIVSg70K=69999,TAMU_SmartGridCenter/ACTIVSg2000=4000,TAMU_SmartGridCenter/ACTIVSg70K=69999,TAMU_SmartGridCenter/ACTIVSg2000=28505,TAMU_SmartGridCenter/ACTIVSg70K=238627,TAMU_SmartGridCenter/ACTIVSg2000=29336,TAMU_SmartGridCenter/ACTIVSg70K=238627 -GHS_indef,60,mixed_some_attributes,0.097222,1,8,2.19127,3.098399,3.098399,13,46212,3002,46950,466316,3002,46950,466316,9000,426705,11288630,9000,426705,11288630,1.0,1,1.0,1.0,1,1.0,square:60,square,1.0,symmetric:60,symmetric,1.0,integer:3; real:57,real,0.95,exact_numerical_or_hermitian:60,exact_numerical_or_hermitian,1.0,no:59; yes:1,no,0.983333,2D/3D problem:16; computational fluid dynamics problem:4; duplicate 2D/3D problem:1; materials problem:4; optimization problem:29; statistical/mathematical problem:2; structural problem:3; subsequent optimization problem:1,optimization problem,0.483333,false:7; true:53,true,0.883333,no:32; yes:28,no,0.533333,no:55; yes:5,no,0.916667,100k_to_1m:8; 10k_to_100k:50; 1k_to_10k:2,10k_to_100k,0.833333,GHS_indef/laser=3002,GHS_indef/boyd2=466316,GHS_indef/laser=3002,GHS_indef/boyd2=466316,GHS_indef/laser=9000,GHS_indef/bmw3_2=11288630,GHS_indef/laser=9000,GHS_indef/bmw3_2=11288630 -IBM_EDA,7,mixed_some_attributes,0.095238,1,3,0.371199,0.362261,0.361971,3,33414,49702,116835,116835,49702,116835,116835,332807,749800,766396,333029,766396,766396,0.8542477149952045,0.8542477149952045,0.9838102263462359,0.2426284829292399,0.3018997890878642,0.7360237367761078,square:7,square,1.0,unsymmetric:7,unsymmetric,1.0,real:7,real,1.0,partially_pattern_symmetric:7,partially_pattern_symmetric,1.0,no:7,no,1.0,circuit simulation problem:2; circuit simulation problem sequence:2; subsequent circuit simulation problem:3,subsequent circuit simulation problem,0.428571,true:7,true,1.0,no:7,no,1.0,no:7,no,1.0,100k_to_1m:5; 10k_to_100k:2,100k_to_1m,0.714286,IBM_EDA/ckt11752_dc_1=49702; IBM_EDA/ckt11752_tr_0=49702,IBM_EDA/dc1=116835; IBM_EDA/dc2=116835; IBM_EDA/dc3=116835; IBM_EDA/trans4=116835; IBM_EDA/trans5=116835,IBM_EDA/ckt11752_dc_1=49702; IBM_EDA/ckt11752_tr_0=49702,IBM_EDA/dc1=116835; IBM_EDA/dc2=116835; IBM_EDA/dc3=116835; IBM_EDA/trans4=116835; IBM_EDA/trans5=116835,IBM_EDA/ckt11752_tr_0=332807,IBM_EDA/dc1=766396; IBM_EDA/dc2=766396; IBM_EDA/dc3=766396,IBM_EDA/ckt11752_dc_1=333029; IBM_EDA/ckt11752_tr_0=333029,IBM_EDA/dc1=766396; IBM_EDA/dc2=766396; IBM_EDA/dc3=766396; IBM_EDA/trans4=766396; IBM_EDA/trans5=766396 -Meszaros,166,mixed_some_attributes,0.087349,2,2,4.842679,5.449325,5.449325,0,0,7,3035,185501,16,9495,1113780,41,53994,11537419,41,53994,11537419,0.0,0,1.0,0.0,0,0.5,rectangular:165; square:1,rectangular,0.993976,rectangular:165; unsymmetric:1,rectangular,0.993976,binary:18; integer:41; real:107,real,0.644578,pattern_symmetric_only:1; rectangular:165,rectangular,0.993976,no:166,no,1.0,linear programming problem:141; linear programming problem sequence:25,linear programming problem,0.849398,false:18; true:148,true,0.891566,no:166,no,1.0,no:166,no,1.0,100k_to_1m:7; 10k_to_100k:70; 1k_to_10k:68; ge_1m:2; lt_1k:19,10k_to_100k,0.421687,Meszaros/farm=7,Meszaros/degme=185501,Meszaros/kleemin=16,Meszaros/stat96v3=1113780,Meszaros/farm=41,Meszaros/tp-6=11537419,Meszaros/farm=41,Meszaros/tp-6=11537419 -Arenas,4,small_group_lt5,0.083333,2,2,1.731906,1.077862,1.077862,0,0,198,793,10680,198,793,10680,4065,8193,48632,4065,8193,48632,1.0,1,1.0,1.0,1,1.0,square:4,square,1.0,symmetric:4,symmetric,1.0,binary:1; integer:3,integer,0.75,exact_numerical_or_hermitian:4,exact_numerical_or_hermitian,1.0,no:4,no,1.0,undirected graph:1; undirected multigraph:3,undirected multigraph,0.75,missing:4,missing,1.0,no:4,no,1.0,no:4,no,1.0,10k_to_100k:1; 1k_to_10k:1; lt_1k:2,lt_1k,0.5,Arenas/jazz=198,Arenas/PGPgiantcompo=10680,Arenas/jazz=198,Arenas/PGPgiantcompo=10680,Arenas/celegans_metabolic=4065,Arenas/PGPgiantcompo=48632,Arenas/celegans_metabolic=4065,Arenas/PGPgiantcompo=48632 -Lee,2,small_group_lt5,0.083333,1,1,0.821574,1.067845,1.067845,0,0,74062,282581,491100,74062,282581,491100,1731206,10985221.5,20239237,1731206,10985221.5,20239237,0.999999037883928,0.9999995189419639,1.0,0.4990933575151925,0.5561452604137468,0.6131971633123011,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,complex:2,complex,1.0,partially_pattern_symmetric:1; pattern_symmetric_only:1,partially_pattern_symmetric,0.5,no:2,no,1.0,electromagnetics problem:2,electromagnetics problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,100k_to_1m:1; 10k_to_100k:1,100k_to_1m,0.5,Lee/fem_filter=74062,Lee/fem_hifreq_circuit=491100,Lee/fem_filter=74062,Lee/fem_hifreq_circuit=491100,Lee/fem_filter=1731206,Lee/fem_hifreq_circuit=20239237,Lee/fem_filter=1731206,Lee/fem_hifreq_circuit=20239237 -McRae,2,small_group_lt5,0.083333,1,1,0.0,1e-06,1e-06,0,0,999999,999999.5,1000000,999999,999999.5,1000000,4995991,4995995.5,4996000,4995991,4995995.5,4996000,1.0,1,1.0,1.0,1,1.0,square:2,square,1.0,symmetric:2,symmetric,1.0,real:2,real,1.0,exact_numerical_or_hermitian:2,exact_numerical_or_hermitian,1.0,no:1; yes:1,no,0.5,2D/3D problem:2,2D/3D problem,1.0,true:2,true,1.0,yes:2,yes,1.0,yes:2,yes,1.0,100k_to_1m:1; ge_1m:1,100k_to_1m,0.5,McRae/ecology2=999999,McRae/ecology1=1000000,McRae/ecology2=999999,McRae/ecology1=1000000,McRae/ecology2=4995991,McRae/ecology1=4996000,McRae/ecology2=4995991,McRae/ecology1=4996000 -POLYFLOW,2,small_group_lt5,0.083333,1,1,0.006547,0.04526,0.046158,1,4122,29957,30184.5,30412,29957,30184.5,30412,1793881,1892400,1990919,1793881,1894461,1995041,0.971442083756505,0.9857210418782525,1.0,0.7213486599424203,0.8552196013183893,0.9890905426943581,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,real:2,real,1.0,partially_pattern_symmetric:1; pattern_symmetric_only:1,partially_pattern_symmetric,0.5,no:2,no,1.0,computational fluid dynamics problem:2,computational fluid dynamics problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,10k_to_100k:2,10k_to_100k,1.0,POLYFLOW/mixtank_new=29957,POLYFLOW/invextr1_new=30412,POLYFLOW/mixtank_new=29957,POLYFLOW/invextr1_new=30412,POLYFLOW/invextr1_new=1793881,POLYFLOW/mixtank_new=1990919,POLYFLOW/invextr1_new=1793881,POLYFLOW/mixtank_new=1995041 -Rommes,35,mixed_some_attributes,0.07619,1,1,2.786472,2.084421,2.084421,0,0,66,7135,40366,66,7135,40366,1194,34738,145019,1194,34738,145019,0.3339191564147627,0.8020552507215579,1.0,0.0,0.5712336093539642,0.7583878152166104,square:35,square,1.0,unsymmetric:35,unsymmetric,1.0,real:35,real,1.0,partially_pattern_symmetric:19; pattern_symmetric_only:16,partially_pattern_symmetric,0.542857,no:35,no,1.0,eigenvalue/model reduction problem:35,eigenvalue/model reduction problem,1.0,false:22; true:13,false,0.628571,yes:35,yes,1.0,no:35,no,1.0,10k_to_100k:16; 1k_to_10k:14; lt_1k:5,10k_to_100k,0.457143,Rommes/ww_36_pmec_36=66,Rommes/bauru5727=40366,Rommes/ww_36_pmec_36=66,Rommes/bauru5727=40366,Rommes/ww_36_pmec_36=1194,Rommes/bauru5727=145019,Rommes/ww_36_pmec_36=1194,Rommes/bauru5727=145019 -JGD_SPG,7,categorically_heterogeneous,0.071429,3,1,1.338788,2.6985,2.6985,0,0,300,2600,6545,300,2600,6545,592,71760,295680,592,71760,295680,0.0273972602739726,1,1.0,0.0,1,1.0,square:7,square,1.0,symmetric:6; unsymmetric:1,symmetric,0.857143,binary:6; integer:1,binary,0.857143,exact_numerical_or_hermitian:6; partially_pattern_symmetric:1,exact_numerical_or_hermitian,0.857143,no:7,no,1.0,combinatorial problem:7,combinatorial problem,1.0,true:7,true,1.0,no:7,no,1.0,no:7,no,1.0,1k_to_10k:4; lt_1k:3,1k_to_10k,0.571429,JGD_SPG/08blocks=300,JGD_SPG/EX5=6545; JGD_SPG/EX6=6545,JGD_SPG/08blocks=300,JGD_SPG/EX5=6545; JGD_SPG/EX6=6545,JGD_SPG/08blocks=592,JGD_SPG/EX5=295680; JGD_SPG/EX6=295680,JGD_SPG/08blocks=592,JGD_SPG/EX5=295680; JGD_SPG/EX6=295680 -Grund,22,mixed_some_attributes,0.068182,1,3,3.916341,4.26339,4.2676,17,186212,7,3268,57735,7,3268,57735,15,20839,275094,15,27836,277774,0.0,0.006682474671265359,1.0,0.0,0.0004982346027775649,1.0,square:22,square,1.0,symmetric:1; unsymmetric:21,unsymmetric,0.954545,real:22,real,1.0,exact_numerical_or_hermitian:1; partially_pattern_symmetric:20; pattern_unsymmetric:1,partially_pattern_symmetric,0.909091,no:22,no,1.0,chemical process simulation problem:16; circuit simulation problem:2; economic problem:4,chemical process simulation problem,0.727273,true:22,true,1.0,no:22,no,1.0,no:22,no,1.0,10k_to_100k:7; 1k_to_10k:11; lt_1k:4,1k_to_10k,0.5,Grund/b1_ss=7,Grund/bayer01=57735,Grund/b1_ss=7,Grund/bayer01=57735,Grund/b1_ss=15,Grund/bayer01=275094,Grund/b1_ss=15,Grund/bayer01=277774 -LAW,15,categorically_heterogeneous,0.066667,3,2,3.232023,3.84877,3.84877,0,0,69244,5363260,118142155,69244,5363260,118142155,276143,113891327,1949412601,276143,113891327,1949412601,0.1245713694644861,0.2780598082364509,1.0,0.1245713694644861,0.2780598082364509,1.0,square:15,square,1.0,symmetric:2; unsymmetric:13,unsymmetric,0.866667,binary:15,binary,1.0,exact_numerical_or_hermitian:2; partially_pattern_symmetric:13,partially_pattern_symmetric,0.866667,no:15,no,1.0,directed graph:13; undirected graph:2,directed graph,0.866667,missing:15,missing,1.0,no:15,no,1.0,no:14; yes:1,no,0.933333,100k_to_1m:4; 10k_to_100k:1; ge_1m:10,ge_1m,0.666667,LAW/enron=69244,LAW/webbase-2001=118142155,LAW/enron=69244,LAW/webbase-2001=118142155,LAW/enron=276143,LAW/sk-2005=1949412601,LAW/enron=276143,LAW/sk-2005=1949412601 -Schenk_ISEI,18,mixed_some_attributes,0.064815,1,3,1.219563,1.721342,1.674667,18,40428312,10938,115625,181343,10938,115625,181343,130500,2129496,6869939,234006,3897557,11063545,1.0,1,1.0,0.09493801090692276,0.1980614094238716,0.3692588097985042,square:18,square,1.0,unsymmetric:18,unsymmetric,1.0,real:18,real,1.0,pattern_symmetric_only:18,pattern_symmetric_only,1.0,no:18,no,1.0,semiconductor device problem:4; semiconductor device problem sequence:3; subsequent semiconductor device problem:11,subsequent semiconductor device problem,0.611111,true:18,true,1.0,yes:18,yes,1.0,no:18,no,1.0,100k_to_1m:16; 10k_to_100k:2,100k_to_1m,0.888889,Schenk_ISEI/igbt3=10938,Schenk_ISEI/ohne2=181343,Schenk_ISEI/igbt3=10938,Schenk_ISEI/ohne2=181343,Schenk_ISEI/igbt3=130500,Schenk_ISEI/ohne2=6869939,Schenk_ISEI/igbt3=234006,Schenk_ISEI/ohne2=11063545 -FlowIPM22,11,mixed_some_attributes,0.060606,1,1,2.858419,2.637558,2.637558,0,0,100000,338402,72180402,100000,338402,72180402,499696,1100592,216902404,499696,1100592,216902404,1.0,1,1.0,1.0,1,1.0,square:11,square,1.0,symmetric:11,symmetric,1.0,real:11,real,1.0,exact_numerical_or_hermitian:11,exact_numerical_or_hermitian,1.0,no:7; yes:4,no,0.636364,undirected weighted graph:11,undirected weighted graph,1.0,missing:11,missing,1.0,no:11,no,1.0,yes:11,yes,1.0,100k_to_1m:6; ge_1m:5,100k_to_1m,0.545455,FlowIPM22/uni_chimera_i1=100000; FlowIPM22/uni_chimera_i2=100000; FlowIPM22/uni_chimera_i3=100000; FlowIPM22/uni_chimera_i4=100000; FlowIPM22/uni_chimera_i5=100000,FlowIPM22/Spielman_k600=72180402,FlowIPM22/uni_chimera_i1=100000; FlowIPM22/uni_chimera_i2=100000; FlowIPM22/uni_chimera_i3=100000; FlowIPM22/uni_chimera_i4=100000; FlowIPM22/uni_chimera_i5=100000,FlowIPM22/Spielman_k600=72180402,FlowIPM22/uni_chimera_i3=499696,FlowIPM22/Spielman_k600=216902404,FlowIPM22/uni_chimera_i3=499696,FlowIPM22/Spielman_k600=216902404 -Mittelmann,30,mixed_some_attributes,0.055556,1,1,1.904108,2.811472,2.811472,0,0,507,91245.5,1918399,24460,317118,1961394,71264,775766.5,46168124,71264,775766.5,46168124,0.0,0,0.0,0.0,0,0.0,rectangular:30,rectangular,1.0,rectangular:30,rectangular,1.0,integer:20; real:10,integer,0.666667,rectangular:30,rectangular,1.0,no:30,no,1.0,linear programming problem:30,linear programming problem,1.0,false:11; true:19,true,0.633333,no:30,no,1.0,no:30,no,1.0,100k_to_1m:19; 10k_to_100k:7; ge_1m:4,100k_to_1m,0.633333,Mittelmann/rail507=507,Mittelmann/cont1_l=1918399,Mittelmann/fome11=24460,Mittelmann/cont11_l=1961394,Mittelmann/fome11=71264,Mittelmann/spal_004=46168124,Mittelmann/fome11=71264,Mittelmann/spal_004=46168124 -Hamm,6,mixed_some_attributes,0.055556,1,1,1.853686,1.862831,1.743267,3,35207,2395,43330,170998,2395,43330,170998,13151,237352.5,958936,17319,250854,958936,0.9999410961997232,1,1.0,0.1951139297670355,0.5113996859135346,0.9084315976207868,square:6,square,1.0,unsymmetric:6,unsymmetric,1.0,real:6,real,1.0,partially_pattern_symmetric:2; pattern_symmetric_only:4,pattern_symmetric_only,0.666667,no:6,no,1.0,circuit simulation problem:6,circuit simulation problem,1.0,true:6,true,1.0,no:6,no,1.0,no:6,no,1.0,100k_to_1m:2; 10k_to_100k:2; 1k_to_10k:2,100k_to_1m,0.333333,Hamm/add20=2395,Hamm/scircuit=170998,Hamm/add20=2395,Hamm/scircuit=170998,Hamm/add20=13151,Hamm/scircuit=958936,Hamm/add20=17319,Hamm/scircuit=958936 -Morandini,3,small_group_lt5,0.055556,1,2,0.898176,1.178741,1.178741,0,0,100,120,791,100,120,791,708,870,10685,708,870,10685,0.2177215189873418,0.2341063574570172,0.514018691588785,0.05316455696202532,0.07165109034267912,0.1155537784886046,square:3,square,1.0,unsymmetric:3,unsymmetric,1.0,real:3,real,1.0,partially_pattern_symmetric:3,partially_pattern_symmetric,1.0,no:3,no,1.0,robotics problem:1; structural problem:2,structural problem,0.666667,true:3,true,1.0,yes:3,yes,1.0,no:3,no,1.0,lt_1k:3,lt_1k,1.0,Morandini/rotor1=100,Morandini/rotor2=791,Morandini/rotor1=100,Morandini/rotor2=791,Morandini/rotor1=708,Morandini/rotor2=10685,Morandini/rotor1=708,Morandini/rotor2=10685 -QLi,3,small_group_lt5,0.055556,1,1,0.439352,0.476197,0.501941,1,320016,160000,160000,440020,160000,160000,440020,1750416,1750416,5240084,1750416,1750416,5560100,0.5503013048158469,0.5503013048158469,1.0,0.0,0.0002376736652548767,0.0002439613283568576,square:3,square,1.0,unsymmetric:3,unsymmetric,1.0,real:3,real,1.0,partially_pattern_symmetric:2; pattern_symmetric_only:1,partially_pattern_symmetric,0.666667,no:3,no,1.0,optimization problem:3,optimization problem,1.0,true:3,true,1.0,no:3,no,1.0,no:3,no,1.0,100k_to_1m:3,100k_to_1m,1.0,QLi/crashbasis=160000; QLi/majorbasis=160000,QLi/largebasis=440020,QLi/crashbasis=160000; QLi/majorbasis=160000,QLi/largebasis=440020,QLi/crashbasis=1750416; QLi/majorbasis=1750416,QLi/largebasis=5240084,QLi/crashbasis=1750416; QLi/majorbasis=1750416,QLi/largebasis=5560100 -Zitney,8,mixed_some_attributes,0.041667,1,2,0.70457,0.935,0.917952,5,3211,1048,3017.5,5308,1048,3017.5,5308,10965,22636,94408,11404,23752,94408,0.003859310586790632,0.024917542604827172,0.1404154186743065,0.0,0,0.0006197432492253209,square:8,square,1.0,unsymmetric:8,unsymmetric,1.0,real:8,real,1.0,partially_pattern_symmetric:8,partially_pattern_symmetric,1.0,no:8,no,1.0,chemical process simulation problem:6; chemical process simulation problem sequence:2,chemical process simulation problem,0.75,true:8,true,1.0,no:8,no,1.0,no:8,no,1.0,1k_to_10k:8,1k_to_10k,1.0,Zitney/radfr1=1048,Zitney/hydr1=5308; Zitney/hydr1c=5308,Zitney/radfr1=1048,Zitney/hydr1=5308; Zitney/hydr1c=5308,Zitney/extr1b=10965,Zitney/rdist1=94408,Zitney/extr1b=11404,Zitney/rdist1=94408 -Bourchtein,4,small_group_lt5,0.041667,1,1,0.069163,0.068453,0.068453,0,0,1270432,1380092,1489752,1270432,1380092,1489752,8814880,9567320,10319760,8814880,9567320,10319760,1.0,1,1.0,0.6690207156308852,0.6702355349613749,0.6714503542918647,square:4,square,1.0,unsymmetric:4,unsymmetric,1.0,integer:1; real:3,real,0.75,pattern_symmetric_only:4,pattern_symmetric_only,1.0,no:4,no,1.0,computational fluid dynamics problem:4,computational fluid dynamics problem,1.0,true:4,true,1.0,yes:4,yes,1.0,no:4,no,1.0,ge_1m:4,ge_1m,1.0,Bourchtein/atmosmodd=1270432; Bourchtein/atmosmodj=1270432,Bourchtein/atmosmodl=1489752; Bourchtein/atmosmodm=1489752,Bourchtein/atmosmodd=1270432; Bourchtein/atmosmodj=1270432,Bourchtein/atmosmodl=1489752; Bourchtein/atmosmodm=1489752,Bourchtein/atmosmodd=8814880; Bourchtein/atmosmodj=8814880,Bourchtein/atmosmodl=10319760; Bourchtein/atmosmodm=10319760,Bourchtein/atmosmodd=8814880; Bourchtein/atmosmodj=8814880,Bourchtein/atmosmodl=10319760; Bourchtein/atmosmodm=10319760 -Puri,4,small_group_lt5,0.041667,1,1,0.0,0.0,0.0,0,0,23412,23412,23412,23412,23412,23412,218484,218484,218484,218484,218484,218484,1.0,1,1.0,0.8850885826771654,0.8850885826771654,0.9753731955380578,square:4,square,1.0,unsymmetric:4,unsymmetric,1.0,complex:3; real:1,complex,0.75,pattern_symmetric_only:4,pattern_symmetric_only,1.0,no:4,no,1.0,model reduction problem:4,model reduction problem,1.0,true:4,true,1.0,yes:4,yes,1.0,no:4,no,1.0,10k_to_100k:4,10k_to_100k,1.0,Puri/ABACUS_shell_ud=23412; Puri/ABACUS_shell_ld=23412; Puri/ABACUS_shell_md=23412; Puri/ABACUS_shell_hd=23412,Puri/ABACUS_shell_ud=23412; Puri/ABACUS_shell_ld=23412; Puri/ABACUS_shell_md=23412; Puri/ABACUS_shell_hd=23412,Puri/ABACUS_shell_ud=23412; Puri/ABACUS_shell_ld=23412; Puri/ABACUS_shell_md=23412; Puri/ABACUS_shell_hd=23412,Puri/ABACUS_shell_ud=23412; Puri/ABACUS_shell_ld=23412; Puri/ABACUS_shell_md=23412; Puri/ABACUS_shell_hd=23412,Puri/ABACUS_shell_ud=218484; Puri/ABACUS_shell_ld=218484; Puri/ABACUS_shell_md=218484; Puri/ABACUS_shell_hd=218484,Puri/ABACUS_shell_ud=218484; Puri/ABACUS_shell_ld=218484; Puri/ABACUS_shell_md=218484; Puri/ABACUS_shell_hd=218484,Puri/ABACUS_shell_ud=218484; Puri/ABACUS_shell_ld=218484; Puri/ABACUS_shell_md=218484; Puri/ABACUS_shell_hd=218484,Puri/ABACUS_shell_ud=218484; Puri/ABACUS_shell_ld=218484; Puri/ABACUS_shell_md=218484; Puri/ABACUS_shell_hd=218484 -LPnetlib,138,mixed_some_attributes,0.036232,1,1,4.272102,4.806202,4.806202,2,3753,8,489,105127,13,1312.5,243246,22,5500.5,1408073,22,5500.5,1408073,0.0,0,0.0,0.0,0,0.0,rectangular:138,rectangular,1.0,rectangular:138,rectangular,1.0,integer:30; real:108,real,0.782609,rectangular:138,rectangular,1.0,no:138,no,1.0,linear programming problem:138,linear programming problem,1.0,false:25; true:113,true,0.818841,no:138,no,1.0,no:138,no,1.0,100k_to_1m:4; 10k_to_100k:16; 1k_to_10k:60; lt_1k:58,1k_to_10k,0.434783,LPnetlib/lpi_galenet=8,LPnetlib/lp_ken_18=105127,LPnetlib/lpi_itest2=13,LPnetlib/lp_osa_60=243246,LPnetlib/lpi_galenet=22,LPnetlib/lp_osa_60=1408073,LPnetlib/lpi_galenet=22,LPnetlib/lp_osa_60=1408073 -JGD_BIBD,21,type_homogeneous_size_diverse,0.031746,0,1,3.580558,4.550595,4.550595,0,0,36,136,3240,84,3240,319770,252,25740,8953560,252,25740,8953560,0.0,0,1.0,0.0,0,1.0,rectangular:20; square:1,rectangular,0.952381,rectangular:20; symmetric:1,rectangular,0.952381,binary:21,binary,1.0,exact_numerical_or_hermitian:1; rectangular:20,rectangular,0.952381,no:20; yes:1,no,0.952381,combinatorial problem:21,combinatorial problem,1.0,true:21,true,1.0,no:21,no,1.0,no:20; yes:1,no,0.952381,100k_to_1m:2; 10k_to_100k:6; 1k_to_10k:6; lt_1k:7,lt_1k,0.333333,JGD_BIBD/bibd_9_3=36; JGD_BIBD/bibd_9_5=36,JGD_BIBD/bibd_81_2=3240; JGD_BIBD/bibd_81_3=3240,JGD_BIBD/bibd_9_3=84,JGD_BIBD/bibd_22_8=319770,JGD_BIBD/bibd_9_3=252,JGD_BIBD/bibd_22_8=8953560,JGD_BIBD/bibd_9_3=252,JGD_BIBD/bibd_22_8=8953560 -AG-Monien,32,mixed_some_attributes,0.03125,1,2,3.670352,3.69838,3.69838,0,0,224,9400,1048576,224,9400,1048576,840,42303,4194298,840,42303,4194298,1.0,1,1.0,1.0,1,1.0,square:32,square,1.0,symmetric:32,symmetric,1.0,binary:32,binary,1.0,exact_numerical_or_hermitian:32,exact_numerical_or_hermitian,1.0,no:32,no,1.0,2D/3D problem:26; undirected graph sequence:6,2D/3D problem,0.8125,false:7; missing:6; true:19,true,0.59375,no:6; yes:26,yes,0.8125,no:32,no,1.0,100k_to_1m:1; 10k_to_100k:13; 1k_to_10k:12; ge_1m:1; lt_1k:5,10k_to_100k,0.40625,AG-Monien/grid1_dual=224,AG-Monien/debr=1048576,AG-Monien/grid1_dual=224,AG-Monien/debr=1048576,AG-Monien/grid1_dual=840,AG-Monien/debr=4194298,AG-Monien/grid1_dual=840,AG-Monien/debr=4194298 -Schenk_IBMNA,52,mixed_some_attributes,0.028846,1,3,2.201863,2.189106,2.189134,25,193434,2169,10926.5,345241,2169,10926.5,345241,15145,120123,2340859,15145,127036,2341011,1.0,1,1.0,1.0,1,1.0,square:52,square,1.0,symmetric:52,symmetric,1.0,real:52,real,1.0,exact_numerical_or_hermitian:52,exact_numerical_or_hermitian,1.0,no:52,no,1.0,optimization problem:43; optimization problem sequence:5; subsequent optimization problem:4,optimization problem,0.826923,true:52,true,1.0,no:52,no,1.0,no:52,no,1.0,100k_to_1m:3; 10k_to_100k:25; 1k_to_10k:24,10k_to_100k,0.480769,Schenk_IBMNA/c-18=2169,Schenk_IBMNA/c-big=345241,Schenk_IBMNA/c-18=2169,Schenk_IBMNA/c-big=345241,Schenk_IBMNA/c-18=15145,Schenk_IBMNA/c-big=2340859,Schenk_IBMNA/c-18=15145,Schenk_IBMNA/c-big=2341011 -Goodwin,12,mixed_some_attributes,0.027778,1,2,2.178867,2.252858,2.252858,1,12,1182,14032,178437,1182,14032,178437,32282,443224.5,5778545,32282,443230.5,5778545,0.6353114022282706,0.9617420289795222,0.9902002197399873,0.0,0.06273502929179821,0.06589012929818107,square:12,square,1.0,unsymmetric:12,unsymmetric,1.0,real:12,real,1.0,partially_pattern_symmetric:12,partially_pattern_symmetric,1.0,no:12,no,1.0,computational fluid dynamics:10; computational fluid dynamics problem:2,computational fluid dynamics,0.833333,true:12,true,1.0,yes:12,yes,1.0,no:12,no,1.0,100k_to_1m:2; 10k_to_100k:5; 1k_to_10k:5,10k_to_100k,0.416667,Goodwin/Goodwin_010=1182,Goodwin/Goodwin_127=178437,Goodwin/Goodwin_010=1182,Goodwin/Goodwin_127=178437,Goodwin/Goodwin_010=32282,Goodwin/Goodwin_127=5778545,Goodwin/Goodwin_010=32282,Goodwin/Goodwin_127=5778545 -Hohn,6,mixed_some_attributes,0.027778,1,1,0.340523,1.522862,1.534216,3,53466,7500,11532,16428,7500,11532,16428,28462,173699,948696,28462,179196,973826,0.0,0.005925966048435875,0.01755337541442645,0.0,0,0.0,square:6,square,1.0,unsymmetric:6,unsymmetric,1.0,real:6,real,1.0,partially_pattern_symmetric:5; pattern_unsymmetric:1,partially_pattern_symmetric,0.833333,no:6,no,1.0,materials problem:6,materials problem,1.0,true:6,true,1.0,yes:6,yes,1.0,no:6,no,1.0,10k_to_100k:4; 1k_to_10k:2,10k_to_100k,0.666667,Hohn/fd12=7500; Hohn/sinc12=7500,Hohn/fd18=16428; Hohn/sinc18=16428,Hohn/fd12=7500; Hohn/sinc12=7500,Hohn/fd18=16428; Hohn/sinc18=16428,Hohn/fd12=28462,Hohn/sinc18=948696,Hohn/fd12=28462,Hohn/sinc18=973826 -DRIVCAV,26,mixed_some_attributes,0.025641,1,2,1.158096,1.367463,1.275541,26,57227,317,2597,4562,317,2597,4562,5923,76214.5,138040,7327,76367,138187,0.8037831733483908,0.9372682821986997,0.9531982436361479,0.0,0,0.6542773036781541,square:26,square,1.0,unsymmetric:26,unsymmetric,1.0,real:26,real,1.0,partially_pattern_symmetric:26,partially_pattern_symmetric,1.0,no:26,no,1.0,computational fluid dynamics problem sequence:4; subsequent computational fluid dynamics problem:22,subsequent computational fluid dynamics problem,0.846154,true:26,true,1.0,yes:26,yes,1.0,no:26,no,1.0,1k_to_10k:22; lt_1k:4,1k_to_10k,0.846154,DRIVCAV/cavity01=317; DRIVCAV/cavity02=317; DRIVCAV/cavity03=317; DRIVCAV/cavity04=317,DRIVCAV/cavity16=4562; DRIVCAV/cavity17=4562; DRIVCAV/cavity18=4562; DRIVCAV/cavity19=4562; DRIVCAV/cavity20=4562; DRIVCAV/cavity21=4562; DRIVCAV/cavity22=4562; DRIVCAV/cavity23=4562; DRIVCAV/cavity24=4562; DRIVCAV/cavity25=4562; DRIVCAV/cavity26=4562,DRIVCAV/cavity01=317; DRIVCAV/cavity02=317; DRIVCAV/cavity03=317; DRIVCAV/cavity04=317,DRIVCAV/cavity16=4562; DRIVCAV/cavity17=4562; DRIVCAV/cavity18=4562; DRIVCAV/cavity19=4562; DRIVCAV/cavity20=4562; DRIVCAV/cavity21=4562; DRIVCAV/cavity22=4562; DRIVCAV/cavity23=4562; DRIVCAV/cavity24=4562; DRIVCAV/cavity25=4562; DRIVCAV/cavity26=4562,DRIVCAV/cavity02=5923; DRIVCAV/cavity04=5923,DRIVCAV/cavity18=138040; DRIVCAV/cavity20=138040; DRIVCAV/cavity22=138040; DRIVCAV/cavity24=138040; DRIVCAV/cavity26=138040,DRIVCAV/cavity01=7327; DRIVCAV/cavity02=7327; DRIVCAV/cavity03=7327; DRIVCAV/cavity04=7327,DRIVCAV/cavity16=138187; DRIVCAV/cavity17=138187; DRIVCAV/cavity18=138187; DRIVCAV/cavity19=138187; DRIVCAV/cavity20=138187; DRIVCAV/cavity21=138187; DRIVCAV/cavity22=138187; DRIVCAV/cavity23=138187; DRIVCAV/cavity24=138187; DRIVCAV/cavity25=138187; DRIVCAV/cavity26=138187 -JGD_Homology,128,type_homogeneous_size_diverse,0.023438,0,1,4.575557,5.274527,5.274527,1,360,6,2455.5,564480,6,1365,376320,18,13800,3386880,18,13800,3386880,0.0,0,1.0,0.0,0,0.5333333333333333,rectangular:122; square:6,rectangular,0.953125,rectangular:122; unsymmetric:6,rectangular,0.953125,integer:128,integer,1.0,partially_pattern_symmetric:5; pattern_symmetric_only:1; rectangular:122,rectangular,0.953125,no:128,no,1.0,combinatorial problem:128,combinatorial problem,1.0,false:2; true:126,true,0.984375,no:128,no,1.0,no:128,no,1.0,100k_to_1m:19; 10k_to_100k:22; 1k_to_10k:45; lt_1k:42,1k_to_10k,0.351562,JGD_Homology/ch3-3-b2=6; JGD_Homology/n3c4-b4=6,JGD_Homology/ch8-8-b5=564480,JGD_Homology/n3c4-b1=6,JGD_Homology/ch8-8-b5=376320,JGD_Homology/ch3-3-b2=18,JGD_Homology/ch8-8-b5=3386880,JGD_Homology/ch3-3-b2=18,JGD_Homology/ch8-8-b5=3386880 -JGD_Margulies,23,type_homogeneous_size_diverse,0.021739,0,1,4.557316,4.467391,4.467391,0,0,21,601,902103,25,513,723605,74,1538,2170814,74,1538,2170814,0.0,0,0.03252032520325204,0.0,0,0.03252032520325204,rectangular:22; square:1,rectangular,0.956522,rectangular:22; unsymmetric:1,rectangular,0.956522,binary:23,binary,1.0,partially_pattern_symmetric:1; rectangular:22,rectangular,0.956522,no:23,no,1.0,combinatorial problem:23,combinatorial problem,1.0,false:11; true:12,true,0.521739,no:23,no,1.0,no:23,no,1.0,100k_to_1m:3; 10k_to_100k:5; 1k_to_10k:2; lt_1k:13,lt_1k,0.565217,JGD_Margulies/wheel_3_1=21,JGD_Margulies/wheel_601=902103,JGD_Margulies/wheel_3_1=25,JGD_Margulies/wheel_601=723605,JGD_Margulies/wheel_3_1=74,JGD_Margulies/wheel_601=2170814,JGD_Margulies/wheel_3_1=74,JGD_Margulies/wheel_601=2170814 -Freescale,8,mixed_some_attributes,0.020833,1,2,1.492416,1.791805,1.791614,7,16392927,178866,2993180.5,5558326,178866,2993180.5,5558326,961368,14589322,59524291,961790,19057270,59524291,0.9058013004223939,0.9924913468404921,0.9999998517635799,0.005614865134161801,0.4107363247105668,0.8220579996645768,square:8,square,1.0,unsymmetric:8,unsymmetric,1.0,real:8,real,1.0,partially_pattern_symmetric:8,partially_pattern_symmetric,1.0,no:8,no,1.0,circuit simulation matrix:1; circuit simulation problem:7,circuit simulation problem,0.875,true:8,true,1.0,no:8,no,1.0,no:8,no,1.0,100k_to_1m:2; ge_1m:6,ge_1m,0.75,Freescale/transient=178866,Freescale/circuit5M=5558326,Freescale/transient=178866,Freescale/circuit5M=5558326,Freescale/transient=961368,Freescale/circuit5M=59524291,Freescale/transient=961790,Freescale/circuit5M=59524291 -JGD_Groebner,8,mixed_some_attributes,0.020833,1,1,1.154117,2.211965,2.211965,0,0,302,2414,4562,302,2511,5761,15118,171214,2462970,15118,171214,2462970,0.0,0,0.0,0.0,0,0.0,rectangular:8,rectangular,1.0,rectangular:8,rectangular,1.0,binary:1; integer:7,integer,0.875,rectangular:8,rectangular,1.0,no:8,no,1.0,combinatorial problem:8,combinatorial problem,1.0,false:1; true:7,true,0.875,no:8,no,1.0,no:8,no,1.0,1k_to_10k:5; lt_1k:3,1k_to_10k,0.625,JGD_Groebner/robot24c1_mat5_J=302,JGD_Groebner/c8_mat11=4562; JGD_Groebner/c8_mat11_I=4562,JGD_Groebner/robot24c1_mat5=302,JGD_Groebner/c8_mat11=5761; JGD_Groebner/c8_mat11_I=5761,JGD_Groebner/robot24c1_mat5=15118; JGD_Groebner/robot24c1_mat5_J=15118,JGD_Groebner/c8_mat11=2462970; JGD_Groebner/c8_mat11_I=2462970,JGD_Groebner/robot24c1_mat5=15118; JGD_Groebner/robot24c1_mat5_J=15118,JGD_Groebner/c8_mat11=2462970; JGD_Groebner/c8_mat11_I=2462970 -Sandia,192,type_homogeneous_size_diverse,0.019965,0,3,3.200864,3.232792,3.399263,5,3096985,430,1220,682862,430,1220,682862,1544,5892,2638997,1544,5892,3871773,0.6142197994049846,0.8183347350714887,1.0,0.0007546383156139254,0.32569386038687975,0.7175697865353038,square:192,square,1.0,unsymmetric:192,unsymmetric,1.0,real:192,real,1.0,partially_pattern_symmetric:182; pattern_symmetric_only:10,partially_pattern_symmetric,0.947917,no:192,no,1.0,circuit simulation problem:7; circuit simulation problem sequence:6; subsequent circuit simulation problem:179,subsequent circuit simulation problem,0.932292,true:192,true,1.0,no:192,no,1.0,no:192,no,1.0,100k_to_1m:4; 10k_to_100k:5; 1k_to_10k:125; lt_1k:58,1k_to_10k,0.651042,Sandia/oscil_dcop_01=430; Sandia/oscil_dcop_02=430; Sandia/oscil_dcop_03=430; Sandia/oscil_dcop_04=430; Sandia/oscil_dcop_05=430; Sandia/oscil_dcop_06=430; Sandia/oscil_dcop_07=430; Sandia/oscil_dcop_08=430; Sandia/oscil_dcop_09=430; Sandia/oscil_dcop_10=430; Sandia/oscil_dcop_11=430; Sandia/oscil_dcop_12=430; Sandia/oscil_dcop_13=430; Sandia/oscil_dcop_14=430; Sandia/oscil_dcop_15=430; Sandia/oscil_dcop_16=430; Sandia/oscil_dcop_17=430; Sandia/oscil_dcop_18=430; Sandia/oscil_dcop_19=430; Sandia/oscil_dcop_20=430; Sandia/oscil_dcop_21=430; Sandia/oscil_dcop_22=430; Sandia/oscil_dcop_23=430; Sandia/oscil_dcop_24=430; Sandia/oscil_dcop_25=430; Sandia/oscil_dcop_26=430; Sandia/oscil_dcop_27=430; Sandia/oscil_dcop_28=430; Sandia/oscil_dcop_29=430; Sandia/oscil_dcop_30=430; Sandia/oscil_dcop_31=430; Sandia/oscil_dcop_32=430; Sandia/oscil_dcop_33=430; Sandia/oscil_dcop_34=430; Sandia/oscil_dcop_35=430; Sandia/oscil_dcop_36=430; Sandia/oscil_dcop_37=430; Sandia/oscil_dcop_38=430; Sandia/oscil_dcop_39=430; Sandia/oscil_dcop_40=430; Sandia/oscil_dcop_41=430; Sandia/oscil_dcop_42=430; Sandia/oscil_dcop_43=430; Sandia/oscil_dcop_44=430; Sandia/oscil_dcop_45=430; Sandia/oscil_dcop_46=430; Sandia/oscil_dcop_47=430; Sandia/oscil_dcop_48=430; Sandia/oscil_dcop_49=430; Sandia/oscil_dcop_50=430; Sandia/oscil_dcop_51=430; Sandia/oscil_dcop_52=430; Sandia/oscil_dcop_53=430; Sandia/oscil_dcop_54=430; Sandia/oscil_dcop_55=430; Sandia/oscil_dcop_56=430; Sandia/oscil_dcop_57=430; Sandia/oscil_trans_01=430,Sandia/ASIC_680k=682862,Sandia/oscil_dcop_01=430; Sandia/oscil_dcop_02=430; Sandia/oscil_dcop_03=430; Sandia/oscil_dcop_04=430; Sandia/oscil_dcop_05=430; Sandia/oscil_dcop_06=430; Sandia/oscil_dcop_07=430; Sandia/oscil_dcop_08=430; Sandia/oscil_dcop_09=430; Sandia/oscil_dcop_10=430; Sandia/oscil_dcop_11=430; Sandia/oscil_dcop_12=430; Sandia/oscil_dcop_13=430; Sandia/oscil_dcop_14=430; Sandia/oscil_dcop_15=430; Sandia/oscil_dcop_16=430; Sandia/oscil_dcop_17=430; Sandia/oscil_dcop_18=430; Sandia/oscil_dcop_19=430; Sandia/oscil_dcop_20=430; Sandia/oscil_dcop_21=430; Sandia/oscil_dcop_22=430; Sandia/oscil_dcop_23=430; Sandia/oscil_dcop_24=430; Sandia/oscil_dcop_25=430; Sandia/oscil_dcop_26=430; Sandia/oscil_dcop_27=430; Sandia/oscil_dcop_28=430; Sandia/oscil_dcop_29=430; Sandia/oscil_dcop_30=430; Sandia/oscil_dcop_31=430; Sandia/oscil_dcop_32=430; Sandia/oscil_dcop_33=430; Sandia/oscil_dcop_34=430; Sandia/oscil_dcop_35=430; Sandia/oscil_dcop_36=430; Sandia/oscil_dcop_37=430; Sandia/oscil_dcop_38=430; Sandia/oscil_dcop_39=430; Sandia/oscil_dcop_40=430; Sandia/oscil_dcop_41=430; Sandia/oscil_dcop_42=430; Sandia/oscil_dcop_43=430; Sandia/oscil_dcop_44=430; Sandia/oscil_dcop_45=430; Sandia/oscil_dcop_46=430; Sandia/oscil_dcop_47=430; Sandia/oscil_dcop_48=430; Sandia/oscil_dcop_49=430; Sandia/oscil_dcop_50=430; Sandia/oscil_dcop_51=430; Sandia/oscil_dcop_52=430; Sandia/oscil_dcop_53=430; Sandia/oscil_dcop_54=430; Sandia/oscil_dcop_55=430; Sandia/oscil_dcop_56=430; Sandia/oscil_dcop_57=430; Sandia/oscil_trans_01=430,Sandia/ASIC_680k=682862,Sandia/oscil_dcop_01=1544; Sandia/oscil_dcop_02=1544; Sandia/oscil_dcop_03=1544; Sandia/oscil_dcop_04=1544; Sandia/oscil_dcop_05=1544; Sandia/oscil_dcop_06=1544; Sandia/oscil_dcop_07=1544; Sandia/oscil_dcop_08=1544; Sandia/oscil_dcop_09=1544; Sandia/oscil_dcop_10=1544; Sandia/oscil_dcop_11=1544; Sandia/oscil_dcop_12=1544; Sandia/oscil_dcop_13=1544; Sandia/oscil_dcop_14=1544; Sandia/oscil_dcop_15=1544; Sandia/oscil_dcop_16=1544; Sandia/oscil_dcop_17=1544; Sandia/oscil_dcop_18=1544; Sandia/oscil_dcop_19=1544; Sandia/oscil_dcop_20=1544; Sandia/oscil_dcop_21=1544; Sandia/oscil_dcop_22=1544; Sandia/oscil_dcop_23=1544; Sandia/oscil_dcop_24=1544; Sandia/oscil_dcop_25=1544; Sandia/oscil_dcop_26=1544; Sandia/oscil_dcop_27=1544; Sandia/oscil_dcop_28=1544; Sandia/oscil_dcop_29=1544; Sandia/oscil_dcop_30=1544; Sandia/oscil_dcop_31=1544; Sandia/oscil_dcop_32=1544; Sandia/oscil_dcop_33=1544; Sandia/oscil_dcop_34=1544; Sandia/oscil_dcop_35=1544; Sandia/oscil_dcop_36=1544; Sandia/oscil_dcop_37=1544; Sandia/oscil_dcop_38=1544; Sandia/oscil_dcop_39=1544; Sandia/oscil_dcop_40=1544; Sandia/oscil_dcop_41=1544; Sandia/oscil_dcop_42=1544; Sandia/oscil_dcop_43=1544; Sandia/oscil_dcop_44=1544; Sandia/oscil_dcop_45=1544; Sandia/oscil_dcop_46=1544; Sandia/oscil_dcop_47=1544; Sandia/oscil_dcop_48=1544; Sandia/oscil_dcop_49=1544; Sandia/oscil_dcop_50=1544; Sandia/oscil_dcop_51=1544; Sandia/oscil_dcop_52=1544; Sandia/oscil_dcop_53=1544; Sandia/oscil_dcop_54=1544; Sandia/oscil_dcop_55=1544; Sandia/oscil_dcop_56=1544; Sandia/oscil_dcop_57=1544,Sandia/ASIC_680k=2638997,Sandia/oscil_dcop_01=1544; Sandia/oscil_dcop_02=1544; Sandia/oscil_dcop_03=1544; Sandia/oscil_dcop_04=1544; Sandia/oscil_dcop_05=1544; Sandia/oscil_dcop_06=1544; Sandia/oscil_dcop_07=1544; Sandia/oscil_dcop_08=1544; Sandia/oscil_dcop_09=1544; Sandia/oscil_dcop_10=1544; Sandia/oscil_dcop_11=1544; Sandia/oscil_dcop_12=1544; Sandia/oscil_dcop_13=1544; Sandia/oscil_dcop_14=1544; Sandia/oscil_dcop_15=1544; Sandia/oscil_dcop_16=1544; Sandia/oscil_dcop_17=1544; Sandia/oscil_dcop_18=1544; Sandia/oscil_dcop_19=1544; Sandia/oscil_dcop_20=1544; Sandia/oscil_dcop_21=1544; Sandia/oscil_dcop_22=1544; Sandia/oscil_dcop_23=1544; Sandia/oscil_dcop_24=1544; Sandia/oscil_dcop_25=1544; Sandia/oscil_dcop_26=1544; Sandia/oscil_dcop_27=1544; Sandia/oscil_dcop_28=1544; Sandia/oscil_dcop_29=1544; Sandia/oscil_dcop_30=1544; Sandia/oscil_dcop_31=1544; Sandia/oscil_dcop_32=1544; Sandia/oscil_dcop_33=1544; Sandia/oscil_dcop_34=1544; Sandia/oscil_dcop_35=1544; Sandia/oscil_dcop_36=1544; Sandia/oscil_dcop_37=1544; Sandia/oscil_dcop_38=1544; Sandia/oscil_dcop_39=1544; Sandia/oscil_dcop_40=1544; Sandia/oscil_dcop_41=1544; Sandia/oscil_dcop_42=1544; Sandia/oscil_dcop_43=1544; Sandia/oscil_dcop_44=1544; Sandia/oscil_dcop_45=1544; Sandia/oscil_dcop_46=1544; Sandia/oscil_dcop_47=1544; Sandia/oscil_dcop_48=1544; Sandia/oscil_dcop_49=1544; Sandia/oscil_dcop_50=1544; Sandia/oscil_dcop_51=1544; Sandia/oscil_dcop_52=1544; Sandia/oscil_dcop_53=1544; Sandia/oscil_dcop_54=1544; Sandia/oscil_dcop_55=1544; Sandia/oscil_dcop_56=1544; Sandia/oscil_dcop_57=1544,Sandia/ASIC_680k=3871773 -JGD_Kocay,12,type_homogeneous_size_diverse,0.013889,0,1,3.900504,6.458225,6.458225,0,0,1,35,3159,2,142.5,15905,1,1348,2872265,1,1348,2872265,0.0,0,0.0,0.0,0,0.0,rectangular:12,rectangular,1.0,rectangular:12,rectangular,1.0,binary:1; integer:11,integer,0.916667,rectangular:12,rectangular,1.0,no:12,no,1.0,combinatorial problem:12,combinatorial problem,1.0,true:12,true,1.0,no:12,no,1.0,no:12,no,1.0,10k_to_100k:1; 1k_to_10k:3; lt_1k:8,lt_1k,0.666667,JGD_Kocay/Trec3=1,JGD_Kocay/Trec14=3159,JGD_Kocay/Trec3=2,JGD_Kocay/Trec14=15905,JGD_Kocay/Trec3=1,JGD_Kocay/Trec14=2872265,JGD_Kocay/Trec3=1,JGD_Kocay/Trec14=2872265 -Nemeth,26,homogeneous,0.00641,0,2,0.0,0.583097,0.583097,0,0,9506,9506,9506,9506,9506,9506,394808,517973,1511760,394808,517973,1511760,1.0,1,1.0,1.0,1,1.0,square:26,square,1.0,symmetric:26,symmetric,1.0,real:26,real,1.0,exact_numerical_or_hermitian:26,exact_numerical_or_hermitian,1.0,no:26,no,1.0,subsequent theoretical/quantum chemistry problem:25; theoretical/quantum chemistry problem sequence:1,subsequent theoretical/quantum chemistry problem,0.961538,true:26,true,1.0,no:26,no,1.0,no:26,no,1.0,1k_to_10k:26,1k_to_10k,1.0,Nemeth/nemeth01=9506; Nemeth/nemeth02=9506; Nemeth/nemeth03=9506; Nemeth/nemeth04=9506; Nemeth/nemeth05=9506; Nemeth/nemeth06=9506; Nemeth/nemeth07=9506; Nemeth/nemeth08=9506; Nemeth/nemeth09=9506; Nemeth/nemeth10=9506; Nemeth/nemeth11=9506; Nemeth/nemeth12=9506; Nemeth/nemeth13=9506; Nemeth/nemeth14=9506; Nemeth/nemeth15=9506; Nemeth/nemeth16=9506; Nemeth/nemeth17=9506; Nemeth/nemeth18=9506; Nemeth/nemeth19=9506; Nemeth/nemeth20=9506; Nemeth/nemeth21=9506; Nemeth/nemeth22=9506; Nemeth/nemeth23=9506; Nemeth/nemeth24=9506; Nemeth/nemeth25=9506; Nemeth/nemeth26=9506,Nemeth/nemeth01=9506; Nemeth/nemeth02=9506; Nemeth/nemeth03=9506; Nemeth/nemeth04=9506; Nemeth/nemeth05=9506; Nemeth/nemeth06=9506; Nemeth/nemeth07=9506; Nemeth/nemeth08=9506; Nemeth/nemeth09=9506; Nemeth/nemeth10=9506; Nemeth/nemeth11=9506; Nemeth/nemeth12=9506; Nemeth/nemeth13=9506; Nemeth/nemeth14=9506; Nemeth/nemeth15=9506; Nemeth/nemeth16=9506; Nemeth/nemeth17=9506; Nemeth/nemeth18=9506; Nemeth/nemeth19=9506; Nemeth/nemeth20=9506; Nemeth/nemeth21=9506; Nemeth/nemeth22=9506; Nemeth/nemeth23=9506; Nemeth/nemeth24=9506; Nemeth/nemeth25=9506; Nemeth/nemeth26=9506,Nemeth/nemeth01=9506; Nemeth/nemeth02=9506; Nemeth/nemeth03=9506; Nemeth/nemeth04=9506; Nemeth/nemeth05=9506; Nemeth/nemeth06=9506; Nemeth/nemeth07=9506; Nemeth/nemeth08=9506; Nemeth/nemeth09=9506; Nemeth/nemeth10=9506; Nemeth/nemeth11=9506; Nemeth/nemeth12=9506; Nemeth/nemeth13=9506; Nemeth/nemeth14=9506; Nemeth/nemeth15=9506; Nemeth/nemeth16=9506; Nemeth/nemeth17=9506; Nemeth/nemeth18=9506; Nemeth/nemeth19=9506; Nemeth/nemeth20=9506; Nemeth/nemeth21=9506; Nemeth/nemeth22=9506; Nemeth/nemeth23=9506; Nemeth/nemeth24=9506; Nemeth/nemeth25=9506; Nemeth/nemeth26=9506,Nemeth/nemeth01=9506; Nemeth/nemeth02=9506; Nemeth/nemeth03=9506; Nemeth/nemeth04=9506; Nemeth/nemeth05=9506; Nemeth/nemeth06=9506; Nemeth/nemeth07=9506; Nemeth/nemeth08=9506; Nemeth/nemeth09=9506; Nemeth/nemeth10=9506; Nemeth/nemeth11=9506; Nemeth/nemeth12=9506; Nemeth/nemeth13=9506; Nemeth/nemeth14=9506; Nemeth/nemeth15=9506; Nemeth/nemeth16=9506; Nemeth/nemeth17=9506; Nemeth/nemeth18=9506; Nemeth/nemeth19=9506; Nemeth/nemeth20=9506; Nemeth/nemeth21=9506; Nemeth/nemeth22=9506; Nemeth/nemeth23=9506; Nemeth/nemeth24=9506; Nemeth/nemeth25=9506; Nemeth/nemeth26=9506,Nemeth/nemeth02=394808; Nemeth/nemeth03=394808; Nemeth/nemeth04=394808; Nemeth/nemeth05=394808; Nemeth/nemeth06=394808,Nemeth/nemeth26=1511760,Nemeth/nemeth02=394808; Nemeth/nemeth03=394808; Nemeth/nemeth04=394808; Nemeth/nemeth05=394808; Nemeth/nemeth06=394808,Nemeth/nemeth26=1511760 -VDOL,91,homogeneous,0.0,0,1,2.270973,2.384904,2.384904,0,0,99,2513,18476,99,2513,18476,927,28757,224897,927,28757,224897,1.0,1,1.0,1.0,1,1.0,square:91,square,1.0,symmetric:91,symmetric,1.0,real:91,real,1.0,exact_numerical_or_hermitian:91,exact_numerical_or_hermitian,1.0,no:91,no,1.0,optimal control problem:91,optimal control problem,1.0,false:1; true:90,true,0.989011,no:91,no,1.0,no:91,no,1.0,10k_to_100k:13; 1k_to_10k:54; lt_1k:24,1k_to_10k,0.593407,VDOL/spaceStation_1=99,VDOL/lowThrust_13=18476,VDOL/spaceStation_1=99,VDOL/lowThrust_13=18476,VDOL/spaceStation_1=927,VDOL/lowThrust_13=224897,VDOL/spaceStation_1=927,VDOL/lowThrust_13=224897 -Hollinger,49,homogeneous,0.0,0,1,1.347391,1.595132,1.63018,49,1559421,2880,27534,64089,2880,27534,64089,18229,190224,717620,19635,215862,837936,0.003290255360374358,0.03703210435580416,0.07395887333678935,0.0,0,0.01417215070061332,square:49,square,1.0,unsymmetric:49,unsymmetric,1.0,real:49,real,1.0,partially_pattern_symmetric:49,partially_pattern_symmetric,1.0,no:49,no,1.0,economic problem:49,economic problem,1.0,true:49,true,1.0,no:49,no,1.0,no:49,no,1.0,10k_to_100k:41; 1k_to_10k:8,10k_to_100k,0.836735,Hollinger/g7jac010=2880; Hollinger/g7jac010sc=2880,Hollinger/mark3jac140=64089; Hollinger/mark3jac140sc=64089,Hollinger/g7jac010=2880; Hollinger/g7jac010sc=2880,Hollinger/mark3jac140=64089; Hollinger/mark3jac140sc=64089,Hollinger/g7jac010=18229; Hollinger/g7jac010sc=18229,Hollinger/g7jac200=717620; Hollinger/g7jac200sc=717620,Hollinger/g7jac010=19635; Hollinger/g7jac010sc=19635,Hollinger/g7jac200=837936; Hollinger/g7jac200sc=837936 -ML_Graph,36,homogeneous,0.0,0,1,2.560685,2.641265,2.641265,0,0,106,1599.5,38547,106,1599.5,38547,1412,22529,618158,1412,22529,618158,1.0,1,1.0,1.0,1,1.0,square:36,square,1.0,symmetric:36,symmetric,1.0,real:36,real,1.0,exact_numerical_or_hermitian:36,exact_numerical_or_hermitian,1.0,no:36,no,1.0,undirected weighted graph:36,undirected weighted graph,1.0,missing:36,missing,1.0,no:36,no,1.0,no:36,no,1.0,10k_to_100k:7; 1k_to_10k:18; lt_1k:11,1k_to_10k,0.5,ML_Graph/breasttissue_10NN=106,ML_Graph/k49_norm_10NN=38547,ML_Graph/breasttissue_10NN=106,ML_Graph/k49_norm_10NN=38547,ML_Graph/breasttissue_10NN=1412,ML_Graph/k49_norm_10NN=618158,ML_Graph/breasttissue_10NN=1412,ML_Graph/k49_norm_10NN=618158 -PARSEC,21,type_homogeneous_size_diverse,0.0,0,1,2.542364,3.016457,3.016457,0,0,769,67024,268096,769,67024,268096,17801,5156379,18488476,17801,5156379,18488476,1.0,1,1.0,1.0,1,1.0,square:21,square,1.0,symmetric:21,symmetric,1.0,real:21,real,1.0,exact_numerical_or_hermitian:21,exact_numerical_or_hermitian,1.0,no:21,no,1.0,theoretical/quantum chemistry problem:21,theoretical/quantum chemistry problem,1.0,true:21,true,1.0,no:21,no,1.0,yes:21,yes,1.0,100k_to_1m:9; 10k_to_100k:7; 1k_to_10k:4; lt_1k:1,100k_to_1m,0.428571,PARSEC/Si2=769,PARSEC/Ga41As41H72=268096,PARSEC/Si2=769,PARSEC/Ga41As41H72=268096,PARSEC/Si2=17801,PARSEC/Ga41As41H72=18488476,PARSEC/Si2=17801,PARSEC/Ga41As41H72=18488476 -Mycielski,19,type_homogeneous_size_diverse,0.0,0,1,5.594631,9.131999,9.131999,0,0,2,1535,786431,2,1535,786431,2,134710,2710370560,2,134710,2710370560,1.0,1,1.0,1.0,1,1.0,square:19,square,1.0,symmetric:19,symmetric,1.0,binary:19,binary,1.0,exact_numerical_or_hermitian:19,exact_numerical_or_hermitian,1.0,no:19,no,1.0,undirected graph:19,undirected graph,1.0,missing:19,missing,1.0,no:19,no,1.0,no:19,no,1.0,100k_to_1m:3; 10k_to_100k:4; 1k_to_10k:3; lt_1k:9,lt_1k,0.473684,Mycielski/mycielskian2=2,Mycielski/mycielskian20=786431,Mycielski/mycielskian2=2,Mycielski/mycielskian20=786431,Mycielski/mycielskian2=2,Mycielski/mycielskian20=2710370560,Mycielski/mycielskian2=2,Mycielski/mycielskian20=2710370560 -Mallya,18,homogeneous,0.0,0,1,1.6776,1.918695,1.914823,10,65592,1477,10964,70304,1477,10964,70304,18427,233187,1528092,18592,233741,1528092,0.001513199920413014,0.0073226551044173116,0.0173680609288971,0.0,5.236095550568468e-06,0.0001553140154832006,square:18,square,1.0,unsymmetric:18,unsymmetric,1.0,real:18,real,1.0,partially_pattern_symmetric:18,partially_pattern_symmetric,1.0,no:18,no,1.0,chemical process simulation problem:18,chemical process simulation problem,1.0,true:18,true,1.0,no:18,no,1.0,no:18,no,1.0,10k_to_100k:12; 1k_to_10k:6,10k_to_100k,0.666667,Mallya/lhr01=1477,Mallya/lhr71=70304; Mallya/lhr71c=70304,Mallya/lhr01=1477,Mallya/lhr71=70304; Mallya/lhr71c=70304,Mallya/lhr01=18427,Mallya/lhr71c=1528092,Mallya/lhr01=18592,Mallya/lhr71=1528092; Mallya/lhr71c=1528092 -JGD_GL7d,17,type_homogeneous_size_diverse,0.0,0,1,4.513064,6.668883,6.668883,0,0,1,171375,1955309,60,171375,1955309,8,2695430,37322725,8,2695430,37322725,0.0,0,0.0,0.0,0,0.0,rectangular:17,rectangular,1.0,rectangular:17,rectangular,1.0,integer:17,integer,1.0,rectangular:17,rectangular,1.0,no:17,no,1.0,combinatorial problem:17,combinatorial problem,1.0,false:9; true:8,false,0.529412,no:17,no,1.0,no:17,no,1.0,100k_to_1m:6; 10k_to_100k:2; 1k_to_10k:3; ge_1m:5; lt_1k:1,100k_to_1m,0.352941,JGD_GL7d/GL7d10=1,JGD_GL7d/GL7d18=1955309,JGD_GL7d/GL7d10=60; JGD_GL7d/GL7d11=60,JGD_GL7d/GL7d19=1955309,JGD_GL7d/GL7d10=8,JGD_GL7d/GL7d19=37322725,JGD_GL7d/GL7d10=8,JGD_GL7d/GL7d19=37322725 -Andrianov,16,homogeneous,0.0,0,1,2.026634,2.041811,2.041811,0,0,5026,26520,534388,5026,26520,534388,94026,1566310,10352819,94026,1566310,10352819,1.0,1,1.0,1.0,1,1.0,square:16,square,1.0,symmetric:16,symmetric,1.0,binary:16,binary,1.0,exact_numerical_or_hermitian:16,exact_numerical_or_hermitian,1.0,no:16,no,1.0,optimization problem:16,optimization problem,1.0,true:16,true,1.0,no:16,no,1.0,yes:16,yes,1.0,100k_to_1m:2; 10k_to_100k:11; 1k_to_10k:3,10k_to_100k,0.6875,Andrianov/fxm3_6=5026,Andrianov/lp1=534388,Andrianov/fxm3_6=5026,Andrianov/lp1=534388,Andrianov/fxm3_6=94026,Andrianov/mip1=10352819,Andrianov/fxm3_6=94026,Andrianov/mip1=10352819 -JGD_Relat,15,type_homogeneous_size_diverse,0.0,0,1,6.012839,6.335295,6.335295,0,0,12,2340,12360060,5,157,549336,18,8108,38955420,18,8108,38955420,0.0,0,0.0,0.0,0,0.0,rectangular:15,rectangular,1.0,rectangular:15,rectangular,1.0,integer:15,integer,1.0,rectangular:15,rectangular,1.0,no:15,no,1.0,combinatorial problem:15,combinatorial problem,1.0,false:15,false,1.0,no:15,no,1.0,no:15,no,1.0,100k_to_1m:2; 10k_to_100k:3; 1k_to_10k:2; ge_1m:2; lt_1k:6,lt_1k,0.4,JGD_Relat/rel3=12; JGD_Relat/relat3=12,JGD_Relat/relat9=12360060,JGD_Relat/rel3=5; JGD_Relat/relat3=5,JGD_Relat/relat9=549336,JGD_Relat/rel3=18,JGD_Relat/relat9=38955420,JGD_Relat/rel3=18,JGD_Relat/relat9=38955420 -Chen,14,homogeneous,0.0,0,1,1.148208,1.262847,1.262847,0,0,10800,49377,151926,10800,49377,151926,810000,3178543.5,14836504,810000,3178543.5,14836504,1.0,1,1.0,1.0,1,1.0,square:14,square,1.0,symmetric:14,symmetric,1.0,binary:14,binary,1.0,exact_numerical_or_hermitian:14,exact_numerical_or_hermitian,1.0,no:14,no,1.0,structural problem:14,structural problem,1.0,true:14,true,1.0,yes:14,yes,1.0,yes:14,yes,1.0,100k_to_1m:1; 10k_to_100k:13,10k_to_100k,0.928571,Chen/pkustk02=10800,Chen/pkustk14=151926,Chen/pkustk02=10800,Chen/pkustk14=151926,Chen/pkustk02=810000,Chen/pkustk14=14836504,Chen/pkustk02=810000,Chen/pkustk14=14836504 -QCD,14,homogeneous,0.0,0,1,1.20412,1.20412,1.20412,0,0,3072,26112,49152,3072,26112,49152,119808,1018368,1916928,119808,1018368,1916928,0.9230769230769231,0.9230769230769231,0.9230769230769231,0.4615384615384616,0.4615384615384616,0.4615384615384616,square:14,square,1.0,unsymmetric:14,unsymmetric,1.0,complex:14,complex,1.0,partially_pattern_symmetric:14,partially_pattern_symmetric,1.0,no:14,no,1.0,theoretical/quantum chemistry problem:14,theoretical/quantum chemistry problem,1.0,true:14,true,1.0,no:14,no,1.0,no:14,no,1.0,10k_to_100k:7; 1k_to_10k:7,10k_to_100k,0.5,QCD/conf5_0-4x4-10=3072; QCD/conf5_0-4x4-14=3072; QCD/conf5_0-4x4-18=3072; QCD/conf5_0-4x4-22=3072; QCD/conf5_0-4x4-26=3072; QCD/conf6_0-4x4-20=3072; QCD/conf6_0-4x4-30=3072,QCD/conf5_4-8x8-05=49152; QCD/conf5_4-8x8-10=49152; QCD/conf5_4-8x8-15=49152; QCD/conf5_4-8x8-20=49152; QCD/conf6_0-8x8-20=49152; QCD/conf6_0-8x8-30=49152; QCD/conf6_0-8x8-80=49152,QCD/conf5_0-4x4-10=3072; QCD/conf5_0-4x4-14=3072; QCD/conf5_0-4x4-18=3072; QCD/conf5_0-4x4-22=3072; QCD/conf5_0-4x4-26=3072; QCD/conf6_0-4x4-20=3072; QCD/conf6_0-4x4-30=3072,QCD/conf5_4-8x8-05=49152; QCD/conf5_4-8x8-10=49152; QCD/conf5_4-8x8-15=49152; QCD/conf5_4-8x8-20=49152; QCD/conf6_0-8x8-20=49152; QCD/conf6_0-8x8-30=49152; QCD/conf6_0-8x8-80=49152,QCD/conf5_0-4x4-10=119808; QCD/conf5_0-4x4-14=119808; QCD/conf5_0-4x4-18=119808; QCD/conf5_0-4x4-22=119808; QCD/conf5_0-4x4-26=119808; QCD/conf6_0-4x4-20=119808; QCD/conf6_0-4x4-30=119808,QCD/conf5_4-8x8-05=1916928; QCD/conf5_4-8x8-10=1916928; QCD/conf5_4-8x8-15=1916928; QCD/conf5_4-8x8-20=1916928; QCD/conf6_0-8x8-20=1916928; QCD/conf6_0-8x8-30=1916928; QCD/conf6_0-8x8-80=1916928,QCD/conf5_0-4x4-10=119808; QCD/conf5_0-4x4-14=119808; QCD/conf5_0-4x4-18=119808; QCD/conf5_0-4x4-22=119808; QCD/conf5_0-4x4-26=119808; QCD/conf6_0-4x4-20=119808; QCD/conf6_0-4x4-30=119808,QCD/conf5_4-8x8-05=1916928; QCD/conf5_4-8x8-10=1916928; QCD/conf5_4-8x8-15=1916928; QCD/conf5_4-8x8-20=1916928; QCD/conf6_0-8x8-20=1916928; QCD/conf6_0-8x8-30=1916928; QCD/conf6_0-8x8-80=1916928 -JGD_G5,13,type_homogeneous_size_diverse,0.0,0,1,2.79379,3.853298,3.853298,0,0,30,2296,47894,77,2875,41550,251,46260,1790490,251,46260,1790490,0.0,0,0.0,0.0,0,0.0,rectangular:13,rectangular,1.0,rectangular:13,rectangular,1.0,integer:13,integer,1.0,rectangular:13,rectangular,1.0,no:13,no,1.0,combinatorial problem:13,combinatorial problem,1.0,false:10; true:3,false,0.769231,no:13,no,1.0,no:13,no,1.0,10k_to_100k:4; 1k_to_10k:4; lt_1k:5,lt_1k,0.384615,JGD_G5/IG5-6=30,JGD_G5/IG5-18=47894,JGD_G5/IG5-6=77,JGD_G5/IG5-18=41550,JGD_G5/IG5-6=251,JGD_G5/IG5-18=1790490,JGD_G5/IG5-6=251,JGD_G5/IG5-18=1790490 -vanHeukelum,13,type_homogeneous_size_diverse,0.0,0,1,6.013247,6.717756,6.717756,0,0,5,3534,5154859,5,3534,5154859,19,41594,99199551,19,41594,99199551,1.0,1,1.0,0.0,0.1535470310036784,0.2131193964673732,square:13,square,1.0,unsymmetric:13,unsymmetric,1.0,real:13,real,1.0,pattern_symmetric_only:13,pattern_symmetric_only,1.0,no:13,no,1.0,directed weighted graph:13,directed weighted graph,1.0,missing:12; true:1,missing,0.923077,no:13,no,1.0,no:13,no,1.0,100k_to_1m:2; 10k_to_100k:2; 1k_to_10k:2; ge_1m:2; lt_1k:5,lt_1k,0.384615,vanHeukelum/cage3=5,vanHeukelum/cage15=5154859,vanHeukelum/cage3=5,vanHeukelum/cage15=5154859,vanHeukelum/cage3=19,vanHeukelum/cage15=99199551,vanHeukelum/cage3=19,vanHeukelum/cage15=99199551 -JGD_Franz,11,homogeneous,0.0,0,1,1.32281,1.808886,1.808886,0,0,1280,7576,47104,768,4164,30144,5120,45456,329728,5120,45456,329728,0.0,0,0.0,0.0,0,0.0,rectangular:11,rectangular,1.0,rectangular:11,rectangular,1.0,integer:11,integer,1.0,rectangular:11,rectangular,1.0,no:11,no,1.0,combinatorial problem:11,combinatorial problem,1.0,true:11,true,1.0,no:11,no,1.0,no:11,no,1.0,10k_to_100k:5; 1k_to_10k:6,1k_to_10k,0.545455,JGD_Franz/Franz3=1280,JGD_Franz/Franz11=47104,JGD_Franz/Franz1=768,JGD_Franz/Franz11=30144,JGD_Franz/Franz1=5120,JGD_Franz/Franz11=329728,JGD_Franz/Franz1=5120,JGD_Franz/Franz11=329728 -JGD_Trefethen,11,type_homogeneous_size_diverse,0.0,0,1,3.022276,3.576558,3.576558,0,0,19,300,20000,19,300,20000,147,4678,554466,147,4678,554466,1.0,1,1.0,1.0,1,1.0,square:11,square,1.0,symmetric:11,symmetric,1.0,integer:11,integer,1.0,exact_numerical_or_hermitian:11,exact_numerical_or_hermitian,1.0,yes:11,yes,1.0,combinatorial problem:11,combinatorial problem,1.0,true:11,true,1.0,no:11,no,1.0,yes:11,yes,1.0,10k_to_100k:2; 1k_to_10k:1; lt_1k:8,lt_1k,0.727273,JGD_Trefethen/Trefethen_20b=19,JGD_Trefethen/Trefethen_20000=20000,JGD_Trefethen/Trefethen_20b=19,JGD_Trefethen/Trefethen_20000=20000,JGD_Trefethen/Trefethen_20b=147,JGD_Trefethen/Trefethen_20000=554466,JGD_Trefethen/Trefethen_20b=147,JGD_Trefethen/Trefethen_20000=554466 -JGD_Forest,10,type_homogeneous_size_diverse,0.0,0,1,3.472982,3.846763,3.846763,0,0,99,4489,241029,107,5451,317955,622,54959.5,4370721,622,54959.5,4370721,0.0,0,0.0,0.0,0,0.0,rectangular:10,rectangular,1.0,rectangular:10,rectangular,1.0,integer:10,integer,1.0,rectangular:10,rectangular,1.0,no:10,no,1.0,combinatorial problem:10,combinatorial problem,1.0,true:10,true,1.0,no:10,no,1.0,no:10,no,1.0,100k_to_1m:2; 10k_to_100k:2; 1k_to_10k:3; lt_1k:3,1k_to_10k,0.3,JGD_Forest/TF10=99,JGD_Forest/TF19=241029,JGD_Forest/TF10=107,JGD_Forest/TF19=317955,JGD_Forest/TF10=622,JGD_Forest/TF19=4370721,JGD_Forest/TF10=622,JGD_Forest/TF19=4370721 -VanVelzen,10,homogeneous,0.0,0,1,0.016534,0.584432,0.584529,5,2270,21982,22835,22835,21982,22835,22835,498771,981060,1915726,498771,981319,1916152,1.37377639453759e-05,0.15207874478358313,0.3593020610225873,0.0,1.447027227264308e-06,8.389455293641422e-06,square:10,square,1.0,unsymmetric:10,unsymmetric,1.0,real:10,real,1.0,partially_pattern_symmetric:10,partially_pattern_symmetric,1.0,no:10,no,1.0,chemical process simulation problem:10,chemical process simulation problem,1.0,true:10,true,1.0,no:10,no,1.0,no:10,no,1.0,10k_to_100k:10,10k_to_100k,1.0,VanVelzen/std1_Jac2_db=21982; VanVelzen/std1_Jac2=21982; VanVelzen/std1_Jac3_db=21982; VanVelzen/std1_Jac3=21982,VanVelzen/Zd_Jac2_db=22835; VanVelzen/Zd_Jac2=22835; VanVelzen/Zd_Jac3_db=22835; VanVelzen/Zd_Jac3=22835; VanVelzen/Zd_Jac6_db=22835; VanVelzen/Zd_Jac6=22835,VanVelzen/std1_Jac2_db=21982; VanVelzen/std1_Jac2=21982; VanVelzen/std1_Jac3_db=21982; VanVelzen/std1_Jac3=21982,VanVelzen/Zd_Jac2_db=22835; VanVelzen/Zd_Jac2=22835; VanVelzen/Zd_Jac3_db=22835; VanVelzen/Zd_Jac3=22835; VanVelzen/Zd_Jac6_db=22835; VanVelzen/Zd_Jac6=22835,VanVelzen/std1_Jac2_db=498771,VanVelzen/Zd_Jac3=1915726,VanVelzen/std1_Jac2_db=498771,VanVelzen/Zd_Jac3=1916152 -CPM,9,homogeneous,0.0,0,1,2.441971,2.431214,2.431214,0,0,148,2548,40948,148,2548,40948,1527,25674,412148,1527,25674,412148,0.4263959390862944,0.4349030172413793,0.441413081497027,0.2219902574808629,0.2349736227622589,0.2377052713245119,square:9,square,1.0,unsymmetric:9,unsymmetric,1.0,real:9,real,1.0,partially_pattern_symmetric:9,partially_pattern_symmetric,1.0,no:9,no,1.0,2D/3D problem:9,2D/3D problem,1.0,true:9,true,1.0,yes:9,yes,1.0,no:9,no,1.0,10k_to_100k:3; 1k_to_10k:3; lt_1k:3,10k_to_100k,0.333333,CPM/cz148=148,CPM/cz40948=40948,CPM/cz148=148,CPM/cz40948=40948,CPM/cz148=1527,CPM/cz40948=412148,CPM/cz148=1527,CPM/cz40948=412148 -NYPA,8,type_homogeneous_size_diverse,0.0,0,1,3.370357,3.74753,3.74753,0,0,32,3309,46845,14,2177,75077,234,59905,1308415,234,59905,1308415,0.0,0,0.0,0.0,0,0.0,rectangular:8,rectangular,1.0,rectangular:8,rectangular,1.0,real:8,real,1.0,rectangular:8,rectangular,1.0,no:8,no,1.0,least squares problem:8,least squares problem,1.0,false:7; true:1,false,0.875,no:8,no,1.0,no:8,no,1.0,10k_to_100k:3; 1k_to_10k:3; lt_1k:2,10k_to_100k,0.375,NYPA/Maragal_1=32,NYPA/Maragal_7=46845,NYPA/Maragal_1=14,NYPA/Maragal_8=75077,NYPA/Maragal_1=234,NYPA/Maragal_8=1308415,NYPA/Maragal_1=234,NYPA/Maragal_8=1308415 -Qaplib,8,type_homogeneous_size_diverse,0.0,0,1,3.226858,3.174101,3.174101,0,0,210,2052,52260,225,5244,379350,1050,22800,1567800,1050,22800,1567800,0.0,0,0.0,0.0,0,0.0,rectangular:8,rectangular,1.0,rectangular:8,rectangular,1.0,integer:8,integer,1.0,rectangular:8,rectangular,1.0,no:8,no,1.0,linear programming problem:8,linear programming problem,1.0,true:8,true,1.0,no:8,no,1.0,no:8,no,1.0,100k_to_1m:1; 10k_to_100k:2; 1k_to_10k:2; lt_1k:3,lt_1k,0.375,Qaplib/lp_nug05=210,Qaplib/lp_nug30=52260,Qaplib/lp_nug05=225,Qaplib/lp_nug30=379350,Qaplib/lp_nug05=1050,Qaplib/lp_nug30=1567800,Qaplib/lp_nug05=1050,Qaplib/lp_nug30=1567800 -Cylshell,7,homogeneous,0.0,0,1,0.010572,0.104307,0.131452,7,60762,5357,5489,5489,5357,5489,5489,207123,217681,263351,207695,219521,281111,1.0,1,1.0,1.0,1,1.0,square:7,square,1.0,symmetric:7,symmetric,1.0,real:7,real,1.0,exact_numerical_or_hermitian:7,exact_numerical_or_hermitian,1.0,yes:7,yes,1.0,structural problem:7,structural problem,1.0,true:7,true,1.0,yes:7,yes,1.0,yes:7,yes,1.0,1k_to_10k:7,1k_to_10k,1.0,Cylshell/s3rmt3m3=5357,Cylshell/s1rmq4m1=5489; Cylshell/s2rmq4m1=5489; Cylshell/s3rmq4m1=5489; Cylshell/s1rmt3m1=5489; Cylshell/s2rmt3m1=5489; Cylshell/s3rmt3m1=5489,Cylshell/s3rmt3m3=5357,Cylshell/s1rmq4m1=5489; Cylshell/s2rmq4m1=5489; Cylshell/s3rmq4m1=5489; Cylshell/s1rmt3m1=5489; Cylshell/s2rmt3m1=5489; Cylshell/s3rmt3m1=5489,Cylshell/s3rmt3m3=207123,Cylshell/s2rmq4m1=263351,Cylshell/s3rmt3m3=207695,Cylshell/s1rmq4m1=281111; Cylshell/s2rmq4m1=281111; Cylshell/s3rmq4m1=281111 -IPSO,7,homogeneous,0.0,0,1,1.444137,1.153119,1.105278,7,330898,8140,29902,226340,8140,29902,226340,141478,762969,2012833,158273,820804,2016902,1.0,1,1.0,1.0,1,1.0,square:7,square,1.0,symmetric:7,symmetric,1.0,real:7,real,1.0,exact_numerical_or_hermitian:7,exact_numerical_or_hermitian,1.0,no:7,no,1.0,power network problem:7,power network problem,1.0,true:7,true,1.0,no:7,no,1.0,no:7,no,1.0,100k_to_1m:2; 10k_to_100k:3; 1k_to_10k:2,10k_to_100k,0.428571,IPSO/TSC_OPF_1047=8140,IPSO/HTC_336_4438=226340; IPSO/HTC_336_9129=226340,IPSO/TSC_OPF_1047=8140,IPSO/HTC_336_4438=226340; IPSO/HTC_336_9129=226340,IPSO/OPF_3754=141478,IPSO/TSC_OPF_1047=2012833,IPSO/OPF_3754=158273,IPSO/TSC_OPF_1047=2016902 -JGD_SL6,7,homogeneous,0.0,0,1,0.466656,0.91218,0.91218,0,0,169,815,1270,115,816,1271,1832,7614,14966,1832,7614,14966,0.0,0,0.0,0.0,0,0.0,rectangular:7,rectangular,1.0,rectangular:7,rectangular,1.0,integer:7,integer,1.0,rectangular:7,rectangular,1.0,no:7,no,1.0,combinatorial problem:7,combinatorial problem,1.0,false:7,false,1.0,no:7,no,1.0,no:7,no,1.0,1k_to_10k:3; lt_1k:4,lt_1k,0.571429,JGD_SL6/D_11=169,JGD_SL6/D_7=1270,JGD_SL6/D_5=115,JGD_SL6/D_8=1271,JGD_SL6/D_5=1832,JGD_SL6/D_8=14966,JGD_SL6/D_5=1832,JGD_SL6/D_8=14966 -Schenk_IBMSDS,7,homogeneous,0.0,0,1,0.656702,0.765896,0.781628,7,4800533,27628,51448,125329,27628,51448,125329,206670,537038,1205518,442898,1056610,2678750,0.9871118002084263,0.9920908271502504,0.9995761793531919,0.1744833443427385,0.189734095484946,0.3613007012666531,square:7,square,1.0,unsymmetric:7,unsymmetric,1.0,real:7,real,1.0,partially_pattern_symmetric:7,partially_pattern_symmetric,1.0,no:7,no,1.0,semiconductor device problem:7,semiconductor device problem,1.0,true:7,true,1.0,yes:7,yes,1.0,no:7,no,1.0,100k_to_1m:2; 10k_to_100k:5,10k_to_100k,0.714286,Schenk_IBMSDS/2D_27628_bjtcai=27628,Schenk_IBMSDS/matrix-new_3=125329,Schenk_IBMSDS/2D_27628_bjtcai=27628,Schenk_IBMSDS/matrix-new_3=125329,Schenk_IBMSDS/2D_27628_bjtcai=206670,Schenk_IBMSDS/matrix_9=1205518,Schenk_IBMSDS/2D_27628_bjtcai=442898,Schenk_IBMSDS/matrix-new_3=2678750 -Priebel,6,homogeneous,0.0,0,1,1.621511,1.690016,1.690016,0,0,584,5523.5,24430,575,5514,24421,6120,59694,299756,6120,59694,299756,0.0,0,0.0,0.0,0,0.0,rectangular:6,rectangular,1.0,rectangular:6,rectangular,1.0,binary:6,binary,1.0,rectangular:6,rectangular,1.0,no:6,no,1.0,combinatorial problem:6,combinatorial problem,1.0,false:6,false,1.0,no:6,no,1.0,no:6,no,1.0,10k_to_100k:2; 1k_to_10k:3; lt_1k:1,1k_to_10k,0.5,Priebel/130bit=584,Priebel/208bit=24430,Priebel/130bit=575,Priebel/208bit=24421,Priebel/130bit=6120,Priebel/208bit=299756,Priebel/130bit=6120,Priebel/208bit=299756 -Bodendiek,5,homogeneous,0.0,0,1,2.332014,2.369111,2.369111,0,0,11083,806529,2380515,11083,806529,2380515,113343,8921789,26515867,113343,8921789,26515867,1.0,1,1.0,1.0,1,1.0,square:5,square,1.0,symmetric:5,symmetric,1.0,real:5,real,1.0,exact_numerical_or_hermitian:5,exact_numerical_or_hermitian,1.0,no:5,no,1.0,model reduction problem:5,model reduction problem,1.0,true:5,true,1.0,yes:5,yes,1.0,yes:5,yes,1.0,100k_to_1m:2; 10k_to_100k:1; ge_1m:2,100k_to_1m,0.4,Bodendiek/CurlCurl_0=11083,Bodendiek/CurlCurl_4=2380515,Bodendiek/CurlCurl_0=11083,Bodendiek/CurlCurl_4=2380515,Bodendiek/CurlCurl_0=113343,Bodendiek/CurlCurl_4=26515867,Bodendiek/CurlCurl_0=113343,Bodendiek/CurlCurl_4=26515867 -Fluorem,5,type_homogeneous_size_diverse,0.0,0,1,3.349055,4.38127,4.38127,0,0,903,161070,2017169,903,161070,2017169,11766,8185136,283073458,11766,8185136,283073458,0.8446615474952066,0.9261667922354103,0.9614287029365737,0.0,9.457852263618717e-06,0.0007082780488857875,square:5,square,1.0,unsymmetric:5,unsymmetric,1.0,real:5,real,1.0,partially_pattern_symmetric:5,partially_pattern_symmetric,1.0,no:5,no,1.0,computational fluid dynamics problem:5,computational fluid dynamics problem,1.0,true:5,true,1.0,yes:5,yes,1.0,no:5,no,1.0,100k_to_1m:2; 1k_to_10k:1; ge_1m:1; lt_1k:1,100k_to_1m,0.4,Fluorem/DK01R=903,Fluorem/HV15R=2017169,Fluorem/DK01R=903,Fluorem/HV15R=2017169,Fluorem/DK01R=11766,Fluorem/HV15R=283073458,Fluorem/DK01R=11766,Fluorem/HV15R=283073458 -GenBank,5,homogeneous,0.0,0,1,0.589727,0.598844,0.598844,0,0,55042369,139353211,214005017,55042369,139353211,214005017,117217600,297829984,465410904,117217600,297829984,465410904,1.0,1,1.0,1.0,1,1.0,square:5,square,1.0,symmetric:5,symmetric,1.0,binary:5,binary,1.0,exact_numerical_or_hermitian:5,exact_numerical_or_hermitian,1.0,no:5,no,1.0,undirected graph:5,undirected graph,1.0,missing:5,missing,1.0,no:5,no,1.0,no:5,no,1.0,ge_1m:5,ge_1m,1.0,GenBank/kmer_V2a=55042369,GenBank/kmer_V1r=214005017,GenBank/kmer_V2a=55042369,GenBank/kmer_V1r=214005017,GenBank/kmer_V2a=117217600,GenBank/kmer_V1r=465410904,GenBank/kmer_V2a=117217600,GenBank/kmer_V1r=465410904 -JGD_GL6,5,homogeneous,0.0,0,1,0.271385,0.476698,0.476698,0,0,163,469,636,201,470,637,2053,4349,6153,2053,4349,6153,0.0,0,0.0,0.0,0,0.0,rectangular:5,rectangular,1.0,rectangular:5,rectangular,1.0,integer:5,integer,1.0,rectangular:5,rectangular,1.0,no:5,no,1.0,combinatorial problem:5,combinatorial problem,1.0,false:5,false,1.0,no:5,no,1.0,no:5,no,1.0,lt_1k:5,lt_1k,1.0,JGD_GL6/GL6_D_10=163,JGD_GL6/GL6_D_7=636,JGD_GL6/GL6_D_6=201,JGD_GL6/GL6_D_8=637,JGD_GL6/GL6_D_10=2053,JGD_GL6/GL6_D_8=6153,JGD_GL6/GL6_D_10=2053,JGD_GL6/GL6_D_8=6153 -MAWI,5,homogeneous,0.0,0,1,1.085646,1.10104,1.10104,0,0,18571154,68863315,226196185,18571154,68863315,226196185,38040320,143414960,480047894,38040320,143414960,480047894,1.0,1,1.0,1.0,1,1.0,square:5,square,1.0,symmetric:5,symmetric,1.0,integer:5,integer,1.0,exact_numerical_or_hermitian:5,exact_numerical_or_hermitian,1.0,no:5,no,1.0,undirected weighted graph:5,undirected weighted graph,1.0,missing:5,missing,1.0,no:5,no,1.0,no:5,no,1.0,ge_1m:5,ge_1m,1.0,MAWI/mawi_201512012345=18571154,MAWI/mawi_201512020330=226196185,MAWI/mawi_201512012345=18571154,MAWI/mawi_201512020330=226196185,MAWI/mawi_201512012345=38040320,MAWI/mawi_201512020330=480047894,MAWI/mawi_201512012345=38040320,MAWI/mawi_201512020330=480047894 -Schenk,5,homogeneous,0.0,0,1,1.420771,1.431048,1.431053,5,28160000,1062400,8345600,27993600,1062400,8345600,27993600,28192672,225422112,760648352,28704672,229518112,774472352,1.0,1,1.0,1.0,1,1.0,square:5,square,1.0,symmetric:5,symmetric,1.0,real:5,real,1.0,exact_numerical_or_hermitian:5,exact_numerical_or_hermitian,1.0,no:5,no,1.0,optimization problem:5,optimization problem,1.0,true:5,true,1.0,no:5,no,1.0,no:5,no,1.0,ge_1m:5,ge_1m,1.0,Schenk/nlpkkt80=1062400,Schenk/nlpkkt240=27993600,Schenk/nlpkkt80=1062400,Schenk/nlpkkt240=27993600,Schenk/nlpkkt80=28192672,Schenk/nlpkkt240=760648352,Schenk/nlpkkt80=28704672,Schenk/nlpkkt240=774472352 -ATandT,4,small_group_lt5,0.0,0,1,1.261918,1.418452,1.417968,4,153765,36057,78403.5,659033,36057,78403.5,659033,222596,770908.5,5834044,227628,782656,5959282,0.0742956282869174,0.17871755249741855,0.3317760910564062,0.04331285111284008,0.06579475948288332,0.1058542372000797,square:4,square,1.0,unsymmetric:4,unsymmetric,1.0,real:4,real,1.0,partially_pattern_symmetric:4,partially_pattern_symmetric,1.0,no:4,no,1.0,frequency-domain circuit simulation problem:4,frequency-domain circuit simulation problem,1.0,true:4,true,1.0,no:4,no,1.0,no:4,no,1.0,100k_to_1m:2; 10k_to_100k:2,100k_to_1m,0.5,ATandT/onetone1=36057; ATandT/onetone2=36057,ATandT/pre2=659033,ATandT/onetone1=36057; ATandT/onetone2=36057,ATandT/pre2=659033,ATandT/onetone2=222596,ATandT/pre2=5834044,ATandT/onetone2=227628,ATandT/pre2=5959282 -Averous,4,small_group_lt5,0.0,0,1,1.673635,1.776081,1.776081,0,0,1794,19981,84617,1794,19981,84617,7764,135040,463625,7764,135040,463625,0.5008375209380235,0.6689746179523419,0.7293915511896314,0.0,0,0.03155606214116852,square:4,square,1.0,unsymmetric:4,unsymmetric,1.0,real:4,real,1.0,partially_pattern_symmetric:4,partially_pattern_symmetric,1.0,no:4,no,1.0,thermal problem:4,thermal problem,1.0,true:4,true,1.0,yes:4,yes,1.0,no:4,no,1.0,10k_to_100k:3; 1k_to_10k:1,10k_to_100k,0.75,Averous/epb0=1794,Averous/epb3=84617,Averous/epb0=1794,Averous/epb3=84617,Averous/epb0=7764,Averous/epb3=463625,Averous/epb0=7764,Averous/epb3=463625 -Bomhof,4,small_group_lt5,0.0,0,1,1.485259,1.161677,1.161677,0,0,2624,8318.5,80209,2624,8318.5,80209,21199,41980,307604,21199,41980,307604,0.7700717835450027,0.8178832654145156,0.999548641410646,0.2060602413263924,0.33182782865785215,0.4152593124925141,square:4,square,1.0,unsymmetric:4,unsymmetric,1.0,real:4,real,1.0,partially_pattern_symmetric:4,partially_pattern_symmetric,1.0,no:4,no,1.0,circuit simulation problem:4,circuit simulation problem,1.0,true:4,true,1.0,no:4,no,1.0,no:4,no,1.0,10k_to_100k:2; 1k_to_10k:2,10k_to_100k,0.5,Bomhof/circuit_1=2624,Bomhof/circuit_4=80209,Bomhof/circuit_1=2624,Bomhof/circuit_4=80209,Bomhof/circuit_2=21199,Bomhof/circuit_4=307604,Bomhof/circuit_2=21199,Bomhof/circuit_4=307604 -Chevron,4,small_group_lt5,0.0,0,1,1.279679,1.28523,1.28523,0,0,37365,235815,711450,37365,235815,711450,330633,2108143,6376412,330633,2108143,6376412,0.994503321194266,0.997275228752503,0.9986570077610406,0.5561276492234193,0.606425339005505,0.7103673090824775,square:4,square,1.0,unsymmetric:4,unsymmetric,1.0,complex:4,complex,1.0,partially_pattern_symmetric:4,partially_pattern_symmetric,1.0,no:4,no,1.0,other problem:4,other problem,1.0,true:4,true,1.0,no:4,no,1.0,no:4,no,1.0,100k_to_1m:2; 10k_to_100k:2,100k_to_1m,0.5,Chevron/Chevron1=37365,Chevron/Chevron4=711450,Chevron/Chevron1=37365,Chevron/Chevron4=711450,Chevron/Chevron1=330633,Chevron/Chevron4=6376412,Chevron/Chevron1=330633,Chevron/Chevron4=6376412 -Muite,4,small_group_lt5,0.0,0,1,2.416641,3.365301,3.365301,0,0,261,3077,68121,261,3077,68121,2319,27663,5377761,2319,27663,5377761,0.3020408163265306,0.5001372592907166,0.5014577259475219,0.0,0,0.0002369275506437348,square:4,square,1.0,unsymmetric:4,unsymmetric,1.0,real:4,real,1.0,partially_pattern_symmetric:4,partially_pattern_symmetric,1.0,no:4,no,1.0,structural problem:4,structural problem,1.0,true:4,true,1.0,yes:4,yes,1.0,no:4,no,1.0,10k_to_100k:1; 1k_to_10k:2; lt_1k:1,1k_to_10k,0.5,Muite/Chebyshev1=261,Muite/Chebyshev4=68121,Muite/Chebyshev1=261,Muite/Chebyshev4=68121,Muite/Chebyshev1=2319,Muite/Chebyshev4=5377761,Muite/Chebyshev1=2319,Muite/Chebyshev4=5377761 -ND,4,small_group_lt5,0.0,0,1,0.90309,0.942286,0.942286,0,0,9000,27000,72000,9000,27000,72000,3279690,10559131,28715634,3279690,10559131,28715634,1.0,1,1.0,1.0,1,1.0,square:4,square,1.0,symmetric:4,symmetric,1.0,real:4,real,1.0,exact_numerical_or_hermitian:4,exact_numerical_or_hermitian,1.0,yes:4,yes,1.0,2D/3D problem:4,2D/3D problem,1.0,true:4,true,1.0,yes:4,yes,1.0,yes:4,yes,1.0,10k_to_100k:3; 1k_to_10k:1,10k_to_100k,0.75,ND/nd3k=9000,ND/nd24k=72000,ND/nd3k=9000,ND/nd24k=72000,ND/nd3k=3279690,ND/nd24k=28715634,ND/nd3k=3279690,ND/nd24k=28715634 -Shen,4,small_group_lt5,0.0,0,1,0.731859,1.341421,1.341421,0,0,3432,11708.5,18510,3432,11708.5,18510,25220,99239,553562,25220,99239,553562,0.1493062958488302,0.1578869688501962,0.3079877899832364,0.001965387548691824,0.019113259823802173,0.02665845434660729,square:4,square,1.0,unsymmetric:4,unsymmetric,1.0,real:4,real,1.0,partially_pattern_symmetric:4,partially_pattern_symmetric,1.0,no:4,no,1.0,2D/3D problem:4,2D/3D problem,1.0,true:4,true,1.0,yes:4,yes,1.0,no:4,no,1.0,10k_to_100k:2; 1k_to_10k:2,10k_to_100k,0.5,Shen/shermanACa=3432,Shen/shermanACb=18510,Shen/shermanACa=3432,Shen/shermanACb=18510,Shen/shermanACa=25220,Shen/e40r0100=553562,Shen/shermanACa=25220,Shen/e40r0100=553562 -TOKAMAK,4,small_group_lt5,0.0,0,1,1.296665,1.424462,1.424462,0,0,300,2380,5940,300,2380,5940,3155,31860,83842,3155,31860,83842,0.4651488616462347,0.5239919806861203,0.5290493183743677,0.0,0,0.0,square:4,square,1.0,unsymmetric:4,unsymmetric,1.0,real:4,real,1.0,partially_pattern_symmetric:4,partially_pattern_symmetric,1.0,no:4,no,1.0,electromagnetics problem:4,electromagnetics problem,1.0,true:4,true,1.0,yes:4,yes,1.0,no:4,no,1.0,1k_to_10k:3; lt_1k:1,1k_to_10k,0.75,TOKAMAK/utm300=300,TOKAMAK/utm5940=5940,TOKAMAK/utm300=300,TOKAMAK/utm5940=5940,TOKAMAK/utm300=3155,TOKAMAK/utm5940=83842,TOKAMAK/utm300=3155,TOKAMAK/utm5940=83842 -ANSYS,3,small_group_lt5,0.0,0,1,0.313056,0.810106,0.810106,0,0,64719,295734,343236,887058,1785345,1823928,652140,2401323,4211599,652140,2401323,4211599,0.0,0,0.0,0.0,0,0.0,rectangular:3,rectangular,1.0,rectangular:3,rectangular,1.0,real:3,real,1.0,rectangular:3,rectangular,1.0,no:3,no,1.0,least squares problem:3,least squares problem,1.0,true:3,true,1.0,no:3,no,1.0,no:3,no,1.0,100k_to_1m:1; ge_1m:2,ge_1m,0.666667,ANSYS/Delor64K=64719,ANSYS/Delor338K=343236,ANSYS/Delor338K=887058,ANSYS/Delor295K=1823928,ANSYS/Delor64K=652140,ANSYS/Delor338K=4211599,ANSYS/Delor64K=652140,ANSYS/Delor338K=4211599 -Belcastro,3,small_group_lt5,0.0,0,1,0.497637,0.204988,0.204988,0,0,14340,22283,45101,14340,22283,45101,18068388,24669643,28967291,18068388,24669643,28967291,1.0,1,1.0,1.0,1,1.0,square:3,square,1.0,symmetric:3,symmetric,1.0,real:3,real,1.0,exact_numerical_or_hermitian:3,exact_numerical_or_hermitian,1.0,no:3,no,1.0,undirected weighted graph:3,undirected weighted graph,1.0,missing:3,missing,1.0,no:3,no,1.0,yes:3,yes,1.0,10k_to_100k:3,10k_to_100k,1.0,Belcastro/human_gene2=14340,Belcastro/mouse_gene=45101,Belcastro/human_gene2=14340,Belcastro/mouse_gene=45101,Belcastro/human_gene2=18068388,Belcastro/mouse_gene=28967291,Belcastro/human_gene2=18068388,Belcastro/mouse_gene=28967291 -Dattorro,3,small_group_lt5,0.0,0,1,0.240605,0.283915,0.283915,0,0,7362,10054,11077,150638,204304,262144,782087,1170516,1503732,782087,1170516,1503732,0.0,0,0.0,0.0,0,0.0,rectangular:3,rectangular,1.0,rectangular:3,rectangular,1.0,integer:3,integer,1.0,rectangular:3,rectangular,1.0,no:3,no,1.0,optimization problem:3,optimization problem,1.0,true:3,true,1.0,no:3,no,1.0,no:3,no,1.0,100k_to_1m:3,100k_to_1m,1.0,Dattorro/EternityII_A=7362,Dattorro/EternityII_E=11077,Dattorro/EternityII_A=150638,Dattorro/EternityII_E=262144,Dattorro/EternityII_A=782087,Dattorro/EternityII_E=1503732,Dattorro/EternityII_A=782087,Dattorro/EternityII_E=1503732 -Guettel,3,small_group_lt5,0.0,0,1,0.817132,1.251333,1.251333,0,0,27623,152078,181302,27623,152078,181302,439469,6459326,7839010,439469,6459326,7839010,1.0,1,1.0,1.0,1,1.0,square:3,square,1.0,symmetric:3,symmetric,1.0,real:3,real,1.0,exact_numerical_or_hermitian:3,exact_numerical_or_hermitian,1.0,no:3,no,1.0,electromagnetics problem:3,electromagnetics problem,1.0,true:3,true,1.0,yes:3,yes,1.0,yes:3,yes,1.0,100k_to_1m:2; 10k_to_100k:1,100k_to_1m,0.666667,Guettel/TEM27623=27623,Guettel/TEM181302=181302,Guettel/TEM27623=27623,Guettel/TEM181302=181302,Guettel/TEM27623=439469,Guettel/TEM181302=7839010,Guettel/TEM27623=439469,Guettel/TEM181302=7839010 -Gupta,3,small_group_lt5,0.0,0,1,0.56797,0.634276,0.634276,0,0,16783,31802,62064,16783,31802,62064,2164210,4248286,9323427,2164210,4248286,9323427,1.0,1,1.0,1.0,1,1.0,square:3,square,1.0,symmetric:3,symmetric,1.0,binary:3,binary,1.0,exact_numerical_or_hermitian:3,exact_numerical_or_hermitian,1.0,no:3,no,1.0,optimization problem:3,optimization problem,1.0,true:3,true,1.0,no:3,no,1.0,yes:3,yes,1.0,10k_to_100k:3,10k_to_100k,1.0,Gupta/gupta3=16783,Gupta/gupta2=62064,Gupta/gupta3=16783,Gupta/gupta2=62064,Gupta/gupta1=2164210,Gupta/gupta3=9323427,Gupta/gupta1=2164210,Gupta/gupta3=9323427 -Hamrle,3,small_group_lt5,0.0,0,1,4.655427,4.75026,4.75026,0,0,32,5952,1447360,32,5952,1447360,98,22162,5514242,98,22162,5514242,2.538882993038383e-06,0.0009930486593843098,0.06451612903225806,0.0,0,0.0001805543017062382,square:3,square,1.0,unsymmetric:3,unsymmetric,1.0,real:3,real,1.0,partially_pattern_symmetric:3,partially_pattern_symmetric,1.0,no:3,no,1.0,circuit simulation problem:3,circuit simulation problem,1.0,true:3,true,1.0,no:3,no,1.0,no:3,no,1.0,1k_to_10k:1; ge_1m:1; lt_1k:1,1k_to_10k,0.333333,Hamrle/Hamrle1=32,Hamrle/Hamrle3=1447360,Hamrle/Hamrle1=32,Hamrle/Hamrle3=1447360,Hamrle/Hamrle1=98,Hamrle/Hamrle3=5514242,Hamrle/Hamrle1=98,Hamrle/Hamrle3=5514242 -JGD_CAG,3,small_group_lt5,0.0,0,1,1.425063,2.287042,2.287042,0,0,72,364,1916,72,364,1916,1012,13585,195985,1012,13585,195985,0.3002540333592691,0.4155510173209288,0.5574468085106383,0.2119864584245809,0.2804628999319265,0.3340425531914893,square:3,square,1.0,unsymmetric:3,unsymmetric,1.0,integer:3,integer,1.0,partially_pattern_symmetric:3,partially_pattern_symmetric,1.0,no:3,no,1.0,combinatorial problem:3,combinatorial problem,1.0,true:3,true,1.0,no:3,no,1.0,no:3,no,1.0,1k_to_10k:1; lt_1k:2,lt_1k,0.666667,JGD_CAG/CAG_mat72=72,JGD_CAG/CAG_mat1916=1916,JGD_CAG/CAG_mat72=72,JGD_CAG/CAG_mat1916=1916,JGD_CAG/CAG_mat72=1012,JGD_CAG/CAG_mat1916=195985,JGD_CAG/CAG_mat72=1012,JGD_CAG/CAG_mat1916=195985 -LeGresley,3,small_group_lt5,0.0,0,1,1.544839,1.549839,1.549839,0,0,2508,4908,87936,2508,4908,87936,16727,30482,593276,16727,30482,593276,0.837611646388635,0.9773607038123168,0.9792020608292309,0.02995112414467253,0.03427471928313486,0.2557141852450946,square:3,square,1.0,unsymmetric:3,unsymmetric,1.0,real:3,real,1.0,partially_pattern_symmetric:3,partially_pattern_symmetric,1.0,no:3,no,1.0,power network problem:3,power network problem,1.0,true:3,true,1.0,no:3,no,1.0,no:3,no,1.0,10k_to_100k:1; 1k_to_10k:2,1k_to_10k,0.666667,LeGresley/LeGresley_2508=2508,LeGresley/LeGresley_87936=87936,LeGresley/LeGresley_2508=2508,LeGresley/LeGresley_87936=87936,LeGresley/LeGresley_2508=16727,LeGresley/LeGresley_87936=593276,LeGresley/LeGresley_2508=16727,LeGresley/LeGresley_87936=593276 -Schulthess,3,small_group_lt5,0.0,0,1,0.918874,1.004644,1.004644,0,0,1922,3625,10204,1996,3923,16559,4335,8054,43816,4335,8054,43816,0.0,0,0.0,0.0,0,0.0,rectangular:3,rectangular,1.0,rectangular:3,rectangular,1.0,integer:3,integer,1.0,rectangular:3,rectangular,1.0,no:3,no,1.0,biochemical network:3,biochemical network,1.0,false:3,false,1.0,no:3,no,1.0,no:3,no,1.0,10k_to_100k:1; 1k_to_10k:2,1k_to_10k,0.666667,Schulthess/N_biocarta=1922,Schulthess/N_reactome=10204,Schulthess/N_biocarta=1996,Schulthess/N_reactome=16559,Schulthess/N_biocarta=4335,Schulthess/N_reactome=43816,Schulthess/N_biocarta=4335,Schulthess/N_reactome=43816 -UTEP,3,small_group_lt5,0.0,0,1,0.95879,1.157565,1.157565,1,6,16129,65025,146689,16129,65025,146689,253009,1030225,3636643,253009,1030225,3636649,1.0,1,1.0,1.0,1,1.0,square:3,square,1.0,symmetric:3,symmetric,1.0,real:3,real,1.0,exact_numerical_or_hermitian:3,exact_numerical_or_hermitian,1.0,yes:3,yes,1.0,2D/3D problem:3,2D/3D problem,1.0,true:3,true,1.0,yes:3,yes,1.0,yes:3,yes,1.0,100k_to_1m:1; 10k_to_100k:2,10k_to_100k,0.666667,UTEP/Dubcova1=16129,UTEP/Dubcova3=146689,UTEP/Dubcova1=16129,UTEP/Dubcova3=146689,UTEP/Dubcova1=253009,UTEP/Dubcova3=3636643,UTEP/Dubcova1=253009,UTEP/Dubcova3=3636649 -YCheng,3,small_group_lt5,0.0,0,1,0.301,0.302956,0.302956,0,0,14318,26722,28634,11028,11028,11028,57376,102432,115262,57376,102432,115262,0.0,0,0.0,0.0,0,0.0,rectangular:3,rectangular,1.0,rectangular:3,rectangular,1.0,real:3,real,1.0,rectangular:3,rectangular,1.0,no:3,no,1.0,power network problem:3,power network problem,1.0,true:3,true,1.0,no:3,no,1.0,no:3,no,1.0,10k_to_100k:3,10k_to_100k,1.0,YCheng/psse1=14318,YCheng/psse2=28634,YCheng/psse0=11028; YCheng/psse1=11028; YCheng/psse2=11028,YCheng/psse0=11028; YCheng/psse1=11028; YCheng/psse2=11028,YCheng/psse1=57376,YCheng/psse2=115262,YCheng/psse1=57376,YCheng/psse2=115262 -AMD,2,small_group_lt5,0.0,0,1,1.023774,1.022936,1.022936,0,0,150102,867790,1585478,150102,867790,1585478,726674,4193750,7660826,726674,4193750,7660826,1.0,1,1.0,1.0,1,1.0,square:2,square,1.0,symmetric:2,symmetric,1.0,real:2,real,1.0,exact_numerical_or_hermitian:2,exact_numerical_or_hermitian,1.0,yes:2,yes,1.0,circuit simulation problem:2,circuit simulation problem,1.0,true:2,true,1.0,no:2,no,1.0,yes:2,yes,1.0,100k_to_1m:1; ge_1m:1,100k_to_1m,0.5,AMD/G2_circuit=150102,AMD/G3_circuit=1585478,AMD/G2_circuit=150102,AMD/G3_circuit=1585478,AMD/G2_circuit=726674,AMD/G3_circuit=7660826,AMD/G2_circuit=726674,AMD/G3_circuit=7660826 -Brethour,2,small_group_lt5,0.0,0,1,0.849858,1.02754,1.02754,0,0,1348,5444,9540,1348,5444,9540,19457,113382.5,207308,19457,113382.5,207308,0.5922916666666667,0.5932400701235618,0.5941884735804569,0.1280874338061923,0.2615437169030962,0.395,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,real:2,real,1.0,partially_pattern_symmetric:2,partially_pattern_symmetric,1.0,no:2,no,1.0,computational fluid dynamics problem:2,computational fluid dynamics problem,1.0,false:2,false,1.0,yes:2,yes,1.0,no:2,no,1.0,1k_to_10k:2,1k_to_10k,1.0,Brethour/coater1=1348,Brethour/coater2=9540,Brethour/coater1=1348,Brethour/coater2=9540,Brethour/coater1=19457,Brethour/coater2=207308,Brethour/coater1=19457,Brethour/coater2=207308 -Buss,2,small_group_lt5,0.0,0,1,0.344458,1.302457,1.302457,0,0,512,6491.5,12471,394792,633707,872622,1127525,11876126,22624727,1127525,11876126,22624727,0.0,0,0.0,0.0,0,0.0,rectangular:2,rectangular,1.0,rectangular:2,rectangular,1.0,binary:2,binary,1.0,rectangular:2,rectangular,1.0,no:2,no,1.0,undirected bipartite graph:2,undirected bipartite graph,1.0,false:2,false,1.0,no:2,no,1.0,no:2,no,1.0,100k_to_1m:2,100k_to_1m,1.0,Buss/connectus=512,Buss/12month1=12471,Buss/connectus=394792,Buss/12month1=872622,Buss/connectus=1127525,Buss/12month1=22624727,Buss/connectus=1127525,Buss/12month1=22624727 -Bydder,2,small_group_lt5,0.0,0,1,0.0,0.015488,0.015488,0,0,63240,64388,65536,147456,147456,147456,569160,579492,589824,569160,579492,589824,0.0,0,0.0,0.0,0,0.0,rectangular:2,rectangular,1.0,rectangular:2,rectangular,1.0,real:2,real,1.0,rectangular:2,rectangular,1.0,no:2,no,1.0,computer graphics/vision problem:2,computer graphics/vision problem,1.0,false:2,false,1.0,yes:2,yes,1.0,no:2,no,1.0,100k_to_1m:2,100k_to_1m,1.0,Bydder/mri2=63240,Bydder/mri1=65536,Bydder/mri1=147456; Bydder/mri2=147456,Bydder/mri1=147456; Bydder/mri2=147456,Bydder/mri2=569160,Bydder/mri1=589824,Bydder/mri2=569160,Bydder/mri1=589824 -Garon,2,small_group_lt5,0.0,0,1,0.629715,0.643981,0.642706,2,21576,3175,8355,13535,3175,8355,13535,84723,228979,373235,88927,239767,390607,1.0,1,1.0,0.6720847366622736,0.6723833591974528,0.6726819817326322,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,real:2,real,1.0,pattern_symmetric_only:2,pattern_symmetric_only,1.0,no:2,no,1.0,computational fluid dynamics problem:2,computational fluid dynamics problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,10k_to_100k:1; 1k_to_10k:1,10k_to_100k,0.5,Garon/garon1=3175,Garon/garon2=13535,Garon/garon1=3175,Garon/garon2=13535,Garon/garon1=84723,Garon/garon2=373235,Garon/garon1=88927,Garon/garon2=390607 -Grueninger,2,small_group_lt5,0.0,0,1,0.694061,0.866354,1.068031,2,2050722,8256,24536,40816,8256,24536,40816,109368,456673,803978,233468,1482034,2730600,1.0,1,1.0,0.0,0,0.0,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,real:2,real,1.0,pattern_symmetric_only:2,pattern_symmetric_only,1.0,no:2,no,1.0,computational fluid dynamics problem:2,computational fluid dynamics problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,10k_to_100k:1; 1k_to_10k:1,10k_to_100k,0.5,Grueninger/windtunnel_evap2d=8256,Grueninger/windtunnel_evap3d=40816,Grueninger/windtunnel_evap2d=8256,Grueninger/windtunnel_evap3d=40816,Grueninger/windtunnel_evap2d=109368,Grueninger/windtunnel_evap3d=803978,Grueninger/windtunnel_evap2d=233468,Grueninger/windtunnel_evap3d=2730600 -HVDC,2,small_group_lt5,0.0,0,1,0.883247,0.927161,0.925387,2,9190,24842,107351,189860,24842,107351,189860,158426,749032,1339638,159981,753627,1347273,0.9817003233707516,0.9851479287391602,0.9885955341075688,0.06323365458033522,0.08111318278984582,0.09899271099935641,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,real:2,real,1.0,partially_pattern_symmetric:2,partially_pattern_symmetric,1.0,no:2,no,1.0,power network problem:2,power network problem,1.0,true:2,true,1.0,no:2,no,1.0,no:2,no,1.0,100k_to_1m:1; 10k_to_100k:1,100k_to_1m,0.5,HVDC/hvdc1=24842,HVDC/hvdc2=189860,HVDC/hvdc1=24842,HVDC/hvdc2=189860,HVDC/hvdc1=158426,HVDC/hvdc2=1339638,HVDC/hvdc1=159981,HVDC/hvdc2=1347273 -JGD_Taha,2,small_group_lt5,0.0,0,1,0.414772,0.427266,0.427266,0,0,14596,26264,37932,209,270,331,51307,94267.5,137228,51307,94267.5,137228,0.0,0,0.0,0.0,0,0.0,rectangular:2,rectangular,1.0,rectangular:2,rectangular,1.0,integer:2,integer,1.0,rectangular:2,rectangular,1.0,no:2,no,1.0,combinatorial problem:2,combinatorial problem,1.0,true:2,true,1.0,no:2,no,1.0,no:2,no,1.0,10k_to_100k:2,10k_to_100k,1.0,JGD_Taha/abtaha1=14596,JGD_Taha/abtaha2=37932,JGD_Taha/abtaha1=209,JGD_Taha/abtaha2=331,JGD_Taha/abtaha1=51307,JGD_Taha/abtaha2=137228,JGD_Taha/abtaha1=51307,JGD_Taha/abtaha2=137228 -Kamvar,2,small_group_lt5,0.0,0,1,0.384605,0.515781,0.515781,0,0,281903,482674.5,683446,281903,482674.5,683446,2312497,4947936.5,7583376,2312497,4947936.5,7583376,0.2502051856587356,0.26342104253978904,0.2766368994208425,0.2502051856587356,0.26342104253978904,0.2766368994208425,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,binary:2,binary,1.0,partially_pattern_symmetric:2,partially_pattern_symmetric,1.0,no:2,no,1.0,directed graph:2,directed graph,1.0,missing:2,missing,1.0,no:2,no,1.0,no:2,no,1.0,100k_to_1m:2,100k_to_1m,1.0,Kamvar/Stanford=281903,Kamvar/Stanford_Berkeley=683446,Kamvar/Stanford=281903,Kamvar/Stanford_Berkeley=683446,Kamvar/Stanford=2312497,Kamvar/Stanford_Berkeley=7583376,Kamvar/Stanford=2312497,Kamvar/Stanford_Berkeley=7583376 -Kim,2,small_group_lt5,0.0,0,1,1.075393,1.084258,1.084258,0,0,38415,247695.5,456976,38415,247695.5,456976,933195,6131607.5,11330020,933195,6131607.5,11330020,0.9897360244976419,0.9933861825054666,0.9970363405132914,0.0,0,0.0,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,complex:2,complex,1.0,partially_pattern_symmetric:2,partially_pattern_symmetric,1.0,no:2,no,1.0,2D/3D problem:2,2D/3D problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,100k_to_1m:1; 10k_to_100k:1,100k_to_1m,0.5,Kim/kim1=38415,Kim/kim2=456976,Kim/kim1=38415,Kim/kim2=456976,Kim/kim1=933195,Kim/kim2=11330020,Kim/kim1=933195,Kim/kim2=11330020 -Koutsovasilis,2,small_group_lt5,0.0,0,1,0.681958,0.704928,0.704928,0,0,71505,207648,343791,71505,207648,343791,5294285,16065699,26837113,5294285,16065699,26837113,1.0,1,1.0,1.0,1,1.0,square:2,square,1.0,symmetric:2,symmetric,1.0,real:2,real,1.0,exact_numerical_or_hermitian:2,exact_numerical_or_hermitian,1.0,no:2,no,1.0,structural problem:2,structural problem,1.0,true:2,true,1.0,yes:2,yes,1.0,yes:2,yes,1.0,100k_to_1m:1; 10k_to_100k:1,100k_to_1m,0.5,Koutsovasilis/F2=71505,Koutsovasilis/F1=343791,Koutsovasilis/F2=71505,Koutsovasilis/F1=343791,Koutsovasilis/F2=5294285,Koutsovasilis/F1=26837113,Koutsovasilis/F2=5294285,Koutsovasilis/F1=26837113 -Lucifora,2,small_group_lt5,0.0,0,1,0.0,0.0,0.0,2,9546,7055,7055,7055,7055,7055,7055,30082,30082,30082,34855,34855,34855,0.9970503597122302,0.9970503597122302,0.9970503597122302,0.0,0.002171364050896773,0.004342728101793546,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,real:2,real,1.0,partially_pattern_symmetric:2,partially_pattern_symmetric,1.0,no:2,no,1.0,directed weighted graph:2,directed weighted graph,1.0,missing:2,missing,1.0,no:2,no,1.0,no:2,no,1.0,1k_to_10k:2,1k_to_10k,1.0,Lucifora/cell1=7055; Lucifora/cell2=7055,Lucifora/cell1=7055; Lucifora/cell2=7055,Lucifora/cell1=7055; Lucifora/cell2=7055,Lucifora/cell1=7055; Lucifora/cell2=7055,Lucifora/cell1=30082; Lucifora/cell2=30082,Lucifora/cell1=30082; Lucifora/cell2=30082,Lucifora/cell1=34855; Lucifora/cell2=34855,Lucifora/cell1=34855; Lucifora/cell2=34855 -Luong,2,small_group_lt5,0.0,0,1,0.508112,0.496388,0.496388,0,0,1388,2930,4472,390,663,936,11816,24436,37056,11816,24436,37056,0.0,0,0.0,0.0,0,0.0,rectangular:2,rectangular,1.0,rectangular:2,rectangular,1.0,real:2,real,1.0,rectangular:2,rectangular,1.0,no:2,no,1.0,computer graphics/vision problem:2,computer graphics/vision problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,1k_to_10k:2,1k_to_10k,1.0,Luong/photogrammetry=1388,Luong/photogrammetry2=4472,Luong/photogrammetry=390,Luong/photogrammetry2=936,Luong/photogrammetry=11816,Luong/photogrammetry2=37056,Luong/photogrammetry=11816,Luong/photogrammetry2=37056 -MaxPlanck,2,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,81920,81920,81920,81920,81920,81920,327680,327680,327680,327680,327680,327680,1.0,1,1.0,1.0,1,1.0,square:2,square,1.0,symmetric:2,symmetric,1.0,real:2,real,1.0,exact_numerical_or_hermitian:2,exact_numerical_or_hermitian,1.0,yes:2,yes,1.0,computational fluid dynamics problem:2,computational fluid dynamics problem,1.0,true:2,true,1.0,yes:2,yes,1.0,yes:2,yes,1.0,10k_to_100k:2,10k_to_100k,1.0,MaxPlanck/shallow_water1=81920; MaxPlanck/shallow_water2=81920,MaxPlanck/shallow_water1=81920; MaxPlanck/shallow_water2=81920,MaxPlanck/shallow_water1=81920; MaxPlanck/shallow_water2=81920,MaxPlanck/shallow_water1=81920; MaxPlanck/shallow_water2=81920,MaxPlanck/shallow_water1=327680; MaxPlanck/shallow_water2=327680,MaxPlanck/shallow_water1=327680; MaxPlanck/shallow_water2=327680,MaxPlanck/shallow_water1=327680; MaxPlanck/shallow_water2=327680,MaxPlanck/shallow_water1=327680; MaxPlanck/shallow_water2=327680 -QY,2,small_group_lt5,0.0,0,1,0.444411,0.847755,0.847755,0,0,14454,27335,40216,14454,27335,40216,147972,595066,1042160,147972,595066,1042160,1.0,1,1.0,1.0,1,1.0,square:2,square,1.0,symmetric:2,symmetric,1.0,real:2,real,1.0,exact_numerical_or_hermitian:2,exact_numerical_or_hermitian,1.0,no:2,no,1.0,power network problem sequence:2,power network problem sequence,1.0,true:2,true,1.0,no:2,no,1.0,no:2,no,1.0,10k_to_100k:2,10k_to_100k,1.0,QY/case9=14454,QY/case39=40216,QY/case9=14454,QY/case39=40216,QY/case9=147972,QY/case39=1042160,QY/case9=147972,QY/case39=1042160 -Quaglino,2,small_group_lt5,0.0,0,1,0.879377,0.794786,0.794786,0,0,4326,18547.5,32769,4326,18547.5,32769,61166,221246,381326,61166,221246,381326,0.5699899872904575,0.6554031569105349,0.7408163265306122,0.0,0,0.0,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,real:2,real,1.0,partially_pattern_symmetric:2,partially_pattern_symmetric,1.0,no:2,no,1.0,materials problem:2,materials problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,10k_to_100k:1; 1k_to_10k:1,10k_to_100k,0.5,Quaglino/viscoplastic1=4326,Quaglino/viscoplastic2=32769,Quaglino/viscoplastic1=4326,Quaglino/viscoplastic2=32769,Quaglino/viscoplastic1=61166,Quaglino/viscoplastic2=381326,Quaglino/viscoplastic1=61166,Quaglino/viscoplastic2=381326 -Ronis,2,small_group_lt5,0.0,0,1,0.510545,0.515045,0.515045,0,0,48600,103032,157464,48600,103032,157464,1181120,2523904,3866688,1181120,2523904,3866688,1.0,1,1.0,0.7756792256277864,0.7757462281496047,0.775813230671423,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,real:2,real,1.0,pattern_symmetric_only:2,pattern_symmetric_only,1.0,no:2,no,1.0,materials problem:2,materials problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,100k_to_1m:1; 10k_to_100k:1,100k_to_1m,0.5,Ronis/xenon1=48600,Ronis/xenon2=157464,Ronis/xenon1=48600,Ronis/xenon2=157464,Ronis/xenon1=1181120,Ronis/xenon2=3866688,Ronis/xenon1=1181120,Ronis/xenon2=3866688 -Schmid,2,small_group_lt5,0.0,0,1,1.17195,1.174245,1.174245,0,0,82654,655349.5,1228045,82654,655349.5,1228045,574458,4577385.5,8580313,574458,4577385.5,8580313,1.0,1,1.0,1.0,1,1.0,square:2,square,1.0,symmetric:2,symmetric,1.0,real:2,real,1.0,exact_numerical_or_hermitian:2,exact_numerical_or_hermitian,1.0,yes:2,yes,1.0,thermal problem:2,thermal problem,1.0,true:2,true,1.0,yes:2,yes,1.0,yes:2,yes,1.0,10k_to_100k:1; ge_1m:1,10k_to_100k,0.5,Schmid/thermal1=82654,Schmid/thermal2=1228045,Schmid/thermal1=82654,Schmid/thermal2=1228045,Schmid/thermal1=574458,Schmid/thermal2=8580313,Schmid/thermal1=574458,Schmid/thermal2=8580313 -Shyy,2,small_group_lt5,0.0,0,1,1.209606,1.21626,1.21626,0,0,4720,40600,76480,4720,40600,76480,20042,174902,329762,20042,174902,329762,0.7234369459874719,0.7248864078192176,0.7263358696509635,0.1824427535660244,0.1834090614538549,0.1843753693416854,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,real:2,real,1.0,partially_pattern_symmetric:2,partially_pattern_symmetric,1.0,no:2,no,1.0,computational fluid dynamics problem:2,computational fluid dynamics problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,10k_to_100k:1; 1k_to_10k:1,10k_to_100k,0.5,Shyy/shyy41=4720,Shyy/shyy161=76480,Shyy/shyy41=4720,Shyy/shyy161=76480,Shyy/shyy41=20042,Shyy/shyy161=329762,Shyy/shyy41=20042,Shyy/shyy161=329762 -Sinclair,2,small_group_lt5,0.0,0,1,0.367716,0.369536,0.370966,2,4747234,292008,486475.5,680943,292008,486475.5,680943,12935272,21613049.5,30290827,14322744,23986666.5,33650589,1.0,1,1.0,1.0,1,1.0,square:2,square,1.0,Hermitian:2,Hermitian,1.0,complex:2,complex,1.0,exact_numerical_or_hermitian:2,exact_numerical_or_hermitian,1.0,no:2,no,1.0,materials problem:2,materials problem,1.0,true:2,true,1.0,yes:2,yes,1.0,yes:2,yes,1.0,100k_to_1m:2,100k_to_1m,1.0,Sinclair/3Dspectralwave2=292008,Sinclair/3Dspectralwave=680943,Sinclair/3Dspectralwave2=292008,Sinclair/3Dspectralwave=680943,Sinclair/3Dspectralwave2=12935272,Sinclair/3Dspectralwave=30290827,Sinclair/3Dspectralwave2=14322744,Sinclair/3Dspectralwave=33650589 -Um,2,small_group_lt5,0.0,0,1,0.408189,0.410876,0.410876,0,0,101492,180640.5,259789,101492,180640.5,259789,1647264,2944968.5,4242673,1647264,2944968.5,4242673,1.0,1,1.0,1.0,1,1.0,square:2,square,1.0,symmetric:2,symmetric,1.0,real:2,real,1.0,exact_numerical_or_hermitian:2,exact_numerical_or_hermitian,1.0,yes:2,yes,1.0,electromagnetics problem:2,electromagnetics problem,1.0,true:2,true,1.0,yes:2,yes,1.0,yes:2,yes,1.0,100k_to_1m:2,100k_to_1m,1.0,Um/2cubes_sphere=101492,Um/offshore=259789,Um/2cubes_sphere=101492,Um/offshore=259789,Um/2cubes_sphere=1647264,Um/offshore=4242673,Um/2cubes_sphere=1647264,Um/offshore=4242673 -Watson,2,small_group_lt5,0.0,0,1,0.443643,0.570464,0.577544,1,12300,40401,76306,112211,40401,76306,112211,201201,474766,748331,201201,480916,760631,1.0,1,1.0,0.0,0,0.0,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,real:2,real,1.0,pattern_symmetric_only:2,pattern_symmetric_only,1.0,no:2,no,1.0,2D/3D problem:2,2D/3D problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,100k_to_1m:1; 10k_to_100k:1,100k_to_1m,0.5,Watson/chem_master1=40401,Watson/Baumann=112211,Watson/chem_master1=40401,Watson/Baumann=112211,Watson/chem_master1=201201,Watson/Baumann=748331,Watson/chem_master1=201201,Watson/Baumann=760631 -Yoshiyasu,2,small_group_lt5,0.0,0,1,0.010953,0.079084,0.079084,0,0,234023,237011.5,240000,9393,64696.5,120000,711683,782756,853829,711683,782756,853829,0.0,0,0.0,0.0,0,0.0,rectangular:2,rectangular,1.0,rectangular:2,rectangular,1.0,real:2,real,1.0,rectangular:2,rectangular,1.0,no:2,no,1.0,computer graphics/vision problem:2,computer graphics/vision problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,100k_to_1m:2,100k_to_1m,1.0,Yoshiyasu/mesh_deform=234023,Yoshiyasu/image_interp=240000,Yoshiyasu/mesh_deform=9393,Yoshiyasu/image_interp=120000,Yoshiyasu/image_interp=711683,Yoshiyasu/mesh_deform=853829,Yoshiyasu/image_interp=711683,Yoshiyasu/mesh_deform=853829 -Zhao,2,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,33861,33861,33861,33861,33861,33861,166453,166453,166453,166453,166453,166453,0.9224990949680222,0.9224990949680222,0.9224990949680222,0.0,0,0.0,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,real:2,real,1.0,partially_pattern_symmetric:2,partially_pattern_symmetric,1.0,no:2,no,1.0,electromagnetics problem:2,electromagnetics problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,10k_to_100k:2,10k_to_100k,1.0,Zhao/Zhao1=33861; Zhao/Zhao2=33861,Zhao/Zhao1=33861; Zhao/Zhao2=33861,Zhao/Zhao1=33861; Zhao/Zhao2=33861,Zhao/Zhao1=33861; Zhao/Zhao2=33861,Zhao/Zhao1=166453; Zhao/Zhao2=166453,Zhao/Zhao1=166453; Zhao/Zhao2=166453,Zhao/Zhao1=166453; Zhao/Zhao2=166453,Zhao/Zhao1=166453; Zhao/Zhao2=166453 -ACUSIM,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,14822,14822,14822,14822,14822,14822,715804,715804,715804,715804,715804,715804,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,yes:1,yes,1.0,computational fluid dynamics problem:1,computational fluid dynamics problem,1.0,true:1,true,1.0,yes:1,yes,1.0,yes:1,yes,1.0,10k_to_100k:1,10k_to_100k,1.0,ACUSIM/Pres_Poisson=14822,ACUSIM/Pres_Poisson=14822,ACUSIM/Pres_Poisson=14822,ACUSIM/Pres_Poisson=14822,ACUSIM/Pres_Poisson=715804,ACUSIM/Pres_Poisson=715804,ACUSIM/Pres_Poisson=715804,ACUSIM/Pres_Poisson=715804 -Alemdar,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,6245,6245,6245,6245,6245,6245,42581,42581,42581,42581,42581,42581,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,integer:1,integer,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,no:1,no,1.0,2D/3D problem:1,2D/3D problem,1.0,true:1,true,1.0,yes:1,yes,1.0,yes:1,yes,1.0,1k_to_10k:1,1k_to_10k,1.0,Alemdar/Alemdar=6245,Alemdar/Alemdar=6245,Alemdar/Alemdar=6245,Alemdar/Alemdar=6245,Alemdar/Alemdar=42581,Alemdar/Alemdar=42581,Alemdar/Alemdar=42581,Alemdar/Alemdar=42581 -Andrews,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,60000,60000,60000,60000,60000,60000,760154,760154,760154,760154,760154,760154,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,integer:1,integer,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,yes:1,yes,1.0,computer graphics/vision problem:1,computer graphics/vision problem,1.0,true:1,true,1.0,yes:1,yes,1.0,yes:1,yes,1.0,10k_to_100k:1,10k_to_100k,1.0,Andrews/Andrews=60000,Andrews/Andrews=60000,Andrews/Andrews=60000,Andrews/Andrews=60000,Andrews/Andrews=760154,Andrews/Andrews=760154,Andrews/Andrews=760154,Andrews/Andrews=760154 -BenElechi,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,245874,245874,245874,245874,245874,245874,13150496,13150496,13150496,13150496,13150496,13150496,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,yes:1,yes,1.0,2D/3D problem:1,2D/3D problem,1.0,true:1,true,1.0,yes:1,yes,1.0,yes:1,yes,1.0,100k_to_1m:1,100k_to_1m,1.0,BenElechi/BenElechi1=245874,BenElechi/BenElechi1=245874,BenElechi/BenElechi1=245874,BenElechi/BenElechi1=245874,BenElechi/BenElechi1=13150496,BenElechi/BenElechi1=13150496,BenElechi/BenElechi1=13150496,BenElechi/BenElechi1=13150496 -Bova,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,44909,46835,46835,46835,46835,46835,46835,2329092,2329092,2329092,2374001,2374001,2374001,1.0,1,1.0,0.2355549300354827,0.2355549300354827,0.2355549300354827,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,pattern_symmetric_only:1,pattern_symmetric_only,1.0,no:1,no,1.0,computational fluid dynamics problem:1,computational fluid dynamics problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Bova/rma10=46835,Bova/rma10=46835,Bova/rma10=46835,Bova/rma10=46835,Bova/rma10=2329092,Bova/rma10=2329092,Bova/rma10=2374001,Bova/rma10=2374001 -Brogan,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,576,477976,477976,477976,1600,1600,1600,7647040,7647040,7647040,7647616,7647616,7647616,0.0,0,0.0,0.0,0,0.0,rectangular:1,rectangular,1.0,rectangular:1,rectangular,1.0,real:1,real,1.0,rectangular:1,rectangular,1.0,no:1,no,1.0,computer vision problem:1,computer vision problem,1.0,false:1,false,1.0,yes:1,yes,1.0,no:1,no,1.0,100k_to_1m:1,100k_to_1m,1.0,Brogan/specular=477976,Brogan/specular=477976,Brogan/specular=1600,Brogan/specular=1600,Brogan/specular=7647040,Brogan/specular=7647040,Brogan/specular=7647616,Brogan/specular=7647616 -Brunetiere,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,3456,3456,3456,3456,3456,3456,66528,66528,66528,66528,66528,66528,1.0,1,1.0,0.0730593607305936,0.0730593607305936,0.0730593607305936,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,pattern_symmetric_only:1,pattern_symmetric_only,1.0,no:1,no,1.0,thermal problem:1,thermal problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,1k_to_10k:1,1k_to_10k,1.0,Brunetiere/thermal=3456,Brunetiere/thermal=3456,Brunetiere/thermal=3456,Brunetiere/thermal=3456,Brunetiere/thermal=66528,Brunetiere/thermal=66528,Brunetiere/thermal=66528,Brunetiere/thermal=66528 -Cannizzo,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,4098,4098,4098,4098,4098,4098,72356,72356,72356,72356,72356,72356,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,yes:1,yes,1.0,structural problem:1,structural problem,1.0,true:1,true,1.0,yes:1,yes,1.0,yes:1,yes,1.0,1k_to_10k:1,1k_to_10k,1.0,Cannizzo/sts4098=4098,Cannizzo/sts4098=4098,Cannizzo/sts4098=4098,Cannizzo/sts4098=4098,Cannizzo/sts4098=72356,Cannizzo/sts4098=72356,Cannizzo/sts4098=72356,Cannizzo/sts4098=72356 -Castrillon,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,89400,89400,89400,89400,89400,89400,1156224,1156224,1156224,1156224,1156224,1156224,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,yes:1,yes,1.0,counter-example problem:1,counter-example problem,1.0,true:1,true,1.0,no:1,no,1.0,yes:1,yes,1.0,10k_to_100k:1,10k_to_100k,1.0,Castrillon/denormal=89400,Castrillon/denormal=89400,Castrillon/denormal=89400,Castrillon/denormal=89400,Castrillon/denormal=1156224,Castrillon/denormal=1156224,Castrillon/denormal=1156224,Castrillon/denormal=1156224 -Clark,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,73159,73159,73159,59498,59498,59498,647495,647495,647495,647495,647495,647495,0.0,0,0.0,0.0,0,0.0,rectangular:1,rectangular,1.0,rectangular:1,rectangular,1.0,real:1,real,1.0,rectangular:1,rectangular,1.0,no:1,no,1.0,computer graphics/vision problem:1,computer graphics/vision problem,1.0,false:1,false,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Clark/tomographic1=73159,Clark/tomographic1=73159,Clark/tomographic1=59498,Clark/tomographic1=59498,Clark/tomographic1=647495,Clark/tomographic1=647495,Clark/tomographic1=647495,Clark/tomographic1=647495 -Davis,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,1319,1319,1319,9498,9498,9498,301899,301899,301899,301899,301899,301899,0.0,0,0.0,0.0,0,0.0,rectangular:1,rectangular,1.0,rectangular:1,rectangular,1.0,integer:1,integer,1.0,rectangular:1,rectangular,1.0,no:1,no,1.0,term/document graph:1,term/document graph,1.0,false:1,false,1.0,no:1,no,1.0,no:1,no,1.0,1k_to_10k:1,1k_to_10k,1.0,Davis/FX_March2010=1319,Davis/FX_March2010=1319,Davis/FX_March2010=9498,Davis/FX_March2010=9498,Davis/FX_March2010=301899,Davis/FX_March2010=301899,Davis/FX_March2010=301899,Davis/FX_March2010=301899 -Dehghani,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,29282,29282,29282,29282,29282,29282,406084,406084,406084,406084,406084,406084,1.0,1,1.0,0.0,0,0.0,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,complex:1,complex,1.0,pattern_symmetric_only:1,pattern_symmetric_only,1.0,no:1,no,1.0,electromagnetics problem:1,electromagnetics problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Dehghani/light_in_tissue=29282,Dehghani/light_in_tissue=29282,Dehghani/light_in_tissue=29282,Dehghani/light_in_tissue=29282,Dehghani/light_in_tissue=406084,Dehghani/light_in_tissue=406084,Dehghani/light_in_tissue=406084,Dehghani/light_in_tissue=406084 -Embree,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,96307,96307,96307,96307,96307,96307,3599932,3599932,3599932,3599932,3599932,3599932,1.0,1,1.0,0.2845118352876235,0.2845118352876235,0.2845118352876235,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,pattern_symmetric_only:1,pattern_symmetric_only,1.0,no:1,no,1.0,computational fluid dynamics:1,computational fluid dynamics,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Embree/ifiss_mat=96307,Embree/ifiss_mat=96307,Embree/ifiss_mat=96307,Embree/ifiss_mat=96307,Embree/ifiss_mat=3599932,Embree/ifiss_mat=3599932,Embree/ifiss_mat=3599932,Embree/ifiss_mat=3599932 -Engwirda,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,14214,14214,14214,14214,14214,14214,259688,259688,259688,259688,259688,259688,0.981668119637925,0.981668119637925,0.981668119637925,0.0,0,0.0,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,computational fluid dynamics problem:1,computational fluid dynamics problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Engwirda/airfoil_2d=14214,Engwirda/airfoil_2d=14214,Engwirda/airfoil_2d=14214,Engwirda/airfoil_2d=14214,Engwirda/airfoil_2d=259688,Engwirda/airfoil_2d=259688,Engwirda/airfoil_2d=259688,Engwirda/airfoil_2d=259688 -FreeFieldTechnologies,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,2492,169410,169410,169410,169410,169410,169410,5033796,5033796,5033796,5036288,5036288,5036288,1.0,1,1.0,0.820796293715178,0.820796293715178,0.820796293715178,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,complex:1,complex,1.0,pattern_symmetric_only:1,pattern_symmetric_only,1.0,no:1,no,1.0,acoustics problem:1,acoustics problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,100k_to_1m:1,100k_to_1m,1.0,FreeFieldTechnologies/mono_500Hz=169410,FreeFieldTechnologies/mono_500Hz=169410,FreeFieldTechnologies/mono_500Hz=169410,FreeFieldTechnologies/mono_500Hz=169410,FreeFieldTechnologies/mono_500Hz=5033796,FreeFieldTechnologies/mono_500Hz=5033796,FreeFieldTechnologies/mono_500Hz=5036288,FreeFieldTechnologies/mono_500Hz=5036288 -Graham,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,32,9035,9035,9035,9035,9035,9035,335472,335472,335472,335504,335504,335504,0.718197839525901,0.718197839525901,0.718197839525901,0.0004322586733311619,0.0004322586733311619,0.0004322586733311619,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,computational fluid dynamics problem:1,computational fluid dynamics problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,1k_to_10k:1,1k_to_10k,1.0,Graham/graham1=9035,Graham/graham1=9035,Graham/graham1=9035,Graham/graham1=9035,Graham/graham1=335472,Graham/graham1=335472,Graham/graham1=335504,Graham/graham1=335504 -Harvard_Seismology,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,87616,87616,87616,67320,67320,67320,13734559,13734559,13734559,13734559,13734559,13734559,0.0,0,0.0,0.0,0,0.0,rectangular:1,rectangular,1.0,rectangular:1,rectangular,1.0,real:1,real,1.0,rectangular:1,rectangular,1.0,no:1,no,1.0,tomography problem:1,tomography problem,1.0,false:1,false,1.0,no:1,no,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Harvard_Seismology/JP=87616,Harvard_Seismology/JP=87616,Harvard_Seismology/JP=67320,Harvard_Seismology/JP=67320,Harvard_Seismology/JP=13734559,Harvard_Seismology/JP=13734559,Harvard_Seismology/JP=13734559,Harvard_Seismology/JP=13734559 -IBM_Austin,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,1330,11341,11341,11341,11341,11341,11341,97193,97193,97193,98523,98523,98523,1.0,1,1.0,0.7823373235828404,0.7823373235828404,0.7823373235828404,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,pattern_symmetric_only:1,pattern_symmetric_only,1.0,no:1,no,1.0,circuit simulation problem:1,circuit simulation problem,1.0,true:1,true,1.0,no:1,no,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,IBM_Austin/coupled=11341,IBM_Austin/coupled=11341,IBM_Austin/coupled=11341,IBM_Austin/coupled=11341,IBM_Austin/coupled=97193,IBM_Austin/coupled=97193,IBM_Austin/coupled=98523,IBM_Austin/coupled=98523 -INPRO,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,1067772,415863,415863,415863,415863,415863,415863,19173163,19173163,19173163,20240935,20240935,20240935,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,yes:1,yes,1.0,structural problem:1,structural problem,1.0,true:1,true,1.0,yes:1,yes,1.0,yes:1,yes,1.0,100k_to_1m:1,100k_to_1m,1.0,INPRO/msdoor=415863,INPRO/msdoor=415863,INPRO/msdoor=415863,INPRO/msdoor=415863,INPRO/msdoor=19173163,INPRO/msdoor=19173163,INPRO/msdoor=20240935,INPRO/msdoor=20240935 -Kemelmacher,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,28452,28452,28452,9693,9693,9693,100875,100875,100875,100875,100875,100875,0.0,0,0.0,0.0,0,0.0,rectangular:1,rectangular,1.0,rectangular:1,rectangular,1.0,real:1,real,1.0,rectangular:1,rectangular,1.0,no:1,no,1.0,computer graphics/vision problem:1,computer graphics/vision problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Kemelmacher/Kemelmacher=28452,Kemelmacher/Kemelmacher=28452,Kemelmacher/Kemelmacher=9693,Kemelmacher/Kemelmacher=9693,Kemelmacher/Kemelmacher=100875,Kemelmacher/Kemelmacher=100875,Kemelmacher/Kemelmacher=100875,Kemelmacher/Kemelmacher=100875 -Langemyr,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,1500,1500,1500,1500,1500,1500,97645,97645,97645,97645,97645,97645,0.9990327110094128,0.9990327110094128,0.9990327110094128,0.0008944822923708982,0.0008944822923708982,0.0008944822923708982,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,structural problem:1,structural problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,1k_to_10k:1,1k_to_10k,1.0,Langemyr/comsol=1500,Langemyr/comsol=1500,Langemyr/comsol=1500,Langemyr/comsol=1500,Langemyr/comsol=97645,Langemyr/comsol=97645,Langemyr/comsol=97645,Langemyr/comsol=97645 -Lin,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,256000,256000,256000,256000,256000,256000,1766400,1766400,1766400,1766400,1766400,1766400,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,no:1,no,1.0,structural problem:1,structural problem,1.0,true:1,true,1.0,yes:1,yes,1.0,yes:1,yes,1.0,100k_to_1m:1,100k_to_1m,1.0,Lin/Lin=256000,Lin/Lin=256000,Lin/Lin=256000,Lin/Lin=256000,Lin/Lin=1766400,Lin/Lin=1766400,Lin/Lin=1766400,Lin/Lin=1766400 -LiuWenzhuo,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,3138,15838,15838,15838,15838,15838,15838,64424,64424,64424,67562,67562,67562,0.5913695769855386,0.5913695769855386,0.5913695769855386,0.5337339974478245,0.5337339974478245,0.5337339974478245,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,power network problem:1,power network problem,1.0,true:1,true,1.0,no:1,no,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,LiuWenzhuo/powersim=15838,LiuWenzhuo/powersim=15838,LiuWenzhuo/powersim=15838,LiuWenzhuo/powersim=15838,LiuWenzhuo/powersim=64424,LiuWenzhuo/powersim=64424,LiuWenzhuo/powersim=67562,LiuWenzhuo/powersim=67562 -Lourakis,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,90,10581,10581,10581,10581,10581,10581,770811,770811,770811,770901,770901,770901,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,yes:1,yes,1.0,computer graphics/vision problem:1,computer graphics/vision problem,1.0,true:1,true,1.0,yes:1,yes,1.0,yes:1,yes,1.0,10k_to_100k:1,10k_to_100k,1.0,Lourakis/bundle1=10581,Lourakis/bundle1=10581,Lourakis/bundle1=10581,Lourakis/bundle1=10581,Lourakis/bundle1=770811,Lourakis/bundle1=770811,Lourakis/bundle1=770901,Lourakis/bundle1=770901 -MKS,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,14331,7548,7548,7548,7548,7548,7548,834222,834222,834222,848553,848553,848553,0.7575174939506899,0.7575174939506899,0.7575174939506899,0.002912877385765126,0.002912877385765126,0.002912877385765126,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,electromagnetics problem:1,electromagnetics problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,1k_to_10k:1,1k_to_10k,1.0,MKS/fp=7548,MKS/fp=7548,MKS/fp=7548,MKS/fp=7548,MKS/fp=834222,MKS/fp=834222,MKS/fp=848553,MKS/fp=848553 -Mancktelow,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,28603,37762,37762,37762,37762,37762,37762,1133641,1133641,1133641,1162244,1162244,1162244,1.0,1,1.0,0.279507135368047,0.279507135368047,0.279507135368047,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,pattern_symmetric_only:1,pattern_symmetric_only,1.0,no:1,no,1.0,materials problem:1,materials problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Mancktelow/viscorocks=37762,Mancktelow/viscorocks=37762,Mancktelow/viscorocks=37762,Mancktelow/viscorocks=37762,Mancktelow/viscorocks=1133641,Mancktelow/viscorocks=1133641,Mancktelow/viscorocks=1162244,Mancktelow/viscorocks=1162244 -Marini,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,7245,7245,7245,7245,7245,7245,46142,46142,46142,46142,46142,46142,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,no:1,no,1.0,economic problem:1,economic problem,1.0,true:1,true,1.0,no:1,no,1.0,no:1,no,1.0,1k_to_10k:1,1k_to_10k,1.0,Marini/eurqsa=7245,Marini/eurqsa=7245,Marini/eurqsa=7245,Marini/eurqsa=7245,Marini/eurqsa=46142,Marini/eurqsa=46142,Marini/eurqsa=46142,Marini/eurqsa=46142 -Martin,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,400320,400320,400320,400320,400320,400320,6226538,6226538,6226538,6226538,6226538,6226538,0.922267240944297,0.922267240944297,0.922267240944297,0.0,0,0.0,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,chemical oceanography problem:1,chemical oceanography problem,1.0,missing:1,missing,1.0,no:1,no,1.0,no:1,no,1.0,100k_to_1m:1,100k_to_1m,1.0,Martin/marine1=400320,Martin/marine1=400320,Martin/marine1=400320,Martin/marine1=400320,Martin/marine1=6226538,Martin/marine1=6226538,Martin/marine1=6226538,Martin/marine1=6226538 -Mazaheri,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,144,513351,513351,513351,513351,513351,513351,20207907,20207907,20207907,20208051,20208051,20208051,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,yes:1,yes,1.0,computer vision problem:1,computer vision problem,1.0,true:1,true,1.0,yes:1,yes,1.0,yes:1,yes,1.0,100k_to_1m:1,100k_to_1m,1.0,Mazaheri/bundle_adj=513351,Mazaheri/bundle_adj=513351,Mazaheri/bundle_adj=513351,Mazaheri/bundle_adj=513351,Mazaheri/bundle_adj=20207907,Mazaheri/bundle_adj=20207907,Mazaheri/bundle_adj=20208051,Mazaheri/bundle_adj=20208051 -Meng,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,274087,274087,274087,274087,274087,274087,4137369,4137369,4137369,4137369,4137369,4137369,0.9801722990969854,0.9801722990969854,0.9801722990969854,0.6457048695901567,0.6457048695901567,0.6457048695901567,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,complex:1,complex,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,computational chemistry problem:1,computational chemistry problem,1.0,true:1,true,1.0,no:1,no,1.0,no:1,no,1.0,100k_to_1m:1,100k_to_1m,1.0,Meng/iChem_Jacobian=274087,Meng/iChem_Jacobian=274087,Meng/iChem_Jacobian=274087,Meng/iChem_Jacobian=274087,Meng/iChem_Jacobian=4137369,Meng/iChem_Jacobian=4137369,Meng/iChem_Jacobian=4137369,Meng/iChem_Jacobian=4137369 -Moqri,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,2427,2427,2427,2427,2427,2427,57022,57022,57022,57022,57022,57022,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,no:1,no,1.0,undirected weighted graph:1,undirected weighted graph,1.0,missing:1,missing,1.0,no:1,no,1.0,no:1,no,1.0,1k_to_10k:1,1k_to_10k,1.0,Moqri/MISKnowledgeMap=2427,Moqri/MISKnowledgeMap=2427,Moqri/MISKnowledgeMap=2427,Moqri/MISKnowledgeMap=2427,Moqri/MISKnowledgeMap=57022,Moqri/MISKnowledgeMap=57022,Moqri/MISKnowledgeMap=57022,Moqri/MISKnowledgeMap=57022 -Negre,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,730,730,730,730,730,730,63024,63024,63024,63024,63024,63024,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,no:1,no,1.0,computational chemistry problem:1,computational chemistry problem,1.0,true:1,true,1.0,no:1,no,1.0,yes:1,yes,1.0,lt_1k:1,lt_1k,1.0,Negre/dendrimer=730,Negre/dendrimer=730,Negre/dendrimer=730,Negre/dendrimer=730,Negre/dendrimer=63024,Negre/dendrimer=63024,Negre/dendrimer=63024,Negre/dendrimer=63024 -Pereyra,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,4384,71952,71952,71952,2704,2704,2704,1146848,1146848,1146848,1151232,1151232,1151232,0.0,0,0.0,0.0,0,0.0,rectangular:1,rectangular,1.0,rectangular:1,rectangular,1.0,real:1,real,1.0,rectangular:1,rectangular,1.0,no:1,no,1.0,least squares problem:1,least squares problem,1.0,false:1,false,1.0,no:1,no,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Pereyra/landmark=71952,Pereyra/landmark=71952,Pereyra/landmark=2704,Pereyra/landmark=2704,Pereyra/landmark=1146848,Pereyra/landmark=1146848,Pereyra/landmark=1151232,Pereyra/landmark=1151232 -PowerSystem,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,2892518,197156,197156,197156,197156,197156,197156,805839,805839,805839,3698357,3698357,3698357,0.2139400937122758,0.2139400937122758,0.2139400937122758,0.02415229830828888,0.02415229830828888,0.02415229830828888,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,power network problem:1,power network problem,1.0,true:1,true,1.0,no:1,no,1.0,no:1,no,1.0,100k_to_1m:1,100k_to_1m,1.0,PowerSystem/power197k=197156,PowerSystem/power197k=197156,PowerSystem/power197k=197156,PowerSystem/power197k=197156,PowerSystem/power197k=805839,PowerSystem/power197k=805839,PowerSystem/power197k=3698357,PowerSystem/power197k=3698357 -Precima,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,303813,303813,303813,303813,303813,303813,2006126,2006126,2006126,2006126,2006126,2006126,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,no:1,no,1.0,data analytics problem:1,data analytics problem,1.0,true:1,true,1.0,no:1,no,1.0,no:1,no,1.0,100k_to_1m:1,100k_to_1m,1.0,Precima/analytics=303813,Precima/analytics=303813,Precima/analytics=303813,Precima/analytics=303813,Precima/analytics=2006126,Precima/analytics=2006126,Precima/analytics=2006126,Precima/analytics=2006126 -Raju,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,44220,67173,67173,67173,67173,67173,67173,3788857,3788857,3788857,3833077,3833077,3833077,0.9022908019592145,0.9022908019592145,0.9022908019592145,0.5518386226500883,0.5518386226500883,0.5518386226500883,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,computational fluid dynamics problem:1,computational fluid dynamics problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Raju/laminar_duct3D=67173,Raju/laminar_duct3D=67173,Raju/laminar_duct3D=67173,Raju/laminar_duct3D=67173,Raju/laminar_duct3D=3788857,Raju/laminar_duct3D=3788857,Raju/laminar_duct3D=3833077,Raju/laminar_duct3D=3833077 -Rost,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,74104,74104,74104,74104,74104,74104,365580,365580,365580,365580,365580,365580,0.0001203589980660497,0.0001203589980660497,0.0001203589980660497,0.0001148881345175929,0.0001148881345175929,0.0001148881345175929,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,complex:1,complex,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,semiconductor device problem:1,semiconductor device problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Rost/RFdevice=74104,Rost/RFdevice=74104,Rost/RFdevice=74104,Rost/RFdevice=74104,Rost/RFdevice=365580,Rost/RFdevice=365580,Rost/RFdevice=365580,Rost/RFdevice=365580 -Rucci,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,1977885,1977885,1977885,109900,109900,109900,7791168,7791168,7791168,7791168,7791168,7791168,0.0,0,0.0,0.0,0,0.0,rectangular:1,rectangular,1.0,rectangular:1,rectangular,1.0,real:1,real,1.0,rectangular:1,rectangular,1.0,no:1,no,1.0,least squares problem:1,least squares problem,1.0,true:1,true,1.0,no:1,no,1.0,no:1,no,1.0,ge_1m:1,ge_1m,1.0,Rucci/Rucci1=1977885,Rucci/Rucci1=1977885,Rucci/Rucci1=109900,Rucci/Rucci1=109900,Rucci/Rucci1=7791168,Rucci/Rucci1=7791168,Rucci/Rucci1=7791168,Rucci/Rucci1=7791168 -Rudnyi,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,60740,60740,60740,60740,60740,60740,2035281,2035281,2035281,2035281,2035281,2035281,0.9691204183655847,0.9691204183655847,0.9691204183655847,0.9180067671423384,0.9180067671423384,0.9180067671423384,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,computational fluid dynamics problem:1,computational fluid dynamics problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Rudnyi/water_tank=60740,Rudnyi/water_tank=60740,Rudnyi/water_tank=60740,Rudnyi/water_tank=60740,Rudnyi/water_tank=2035281,Rudnyi/water_tank=2035281,Rudnyi/water_tank=2035281,Rudnyi/water_tank=2035281 -Sanghavi,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,51993,51993,51993,51993,51993,51993,380415,380415,380415,380415,380415,380415,0.9219175329301935,0.9219175329301935,0.9219175329301935,0.6030290297239527,0.6030290297239527,0.6030290297239527,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,semiconductor device problem:1,semiconductor device problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Sanghavi/ecl32=51993,Sanghavi/ecl32=51993,Sanghavi/ecl32=51993,Sanghavi/ecl32=51993,Sanghavi/ecl32=380415,Sanghavi/ecl32=380415,Sanghavi/ecl32=380415,Sanghavi/ecl32=380415 -Sorensen,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,324085,324085,324085,324085,324085,324085,1208908,1208908,1208908,1208908,1208908,1208908,0.0006324084497715383,0.0006324084497715383,0.0006324084497715383,0.0006125422157473015,0.0006125422157473015,0.0006125422157473015,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,integer:1,integer,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,directed weighted graph:1,directed weighted graph,1.0,missing:1,missing,1.0,no:1,no,1.0,no:1,no,1.0,100k_to_1m:1,100k_to_1m,1.0,Sorensen/Linux_call_graph=324085,Sorensen/Linux_call_graph=324085,Sorensen/Linux_call_graph=324085,Sorensen/Linux_call_graph=324085,Sorensen/Linux_call_graph=1208908,Sorensen/Linux_call_graph=1208908,Sorensen/Linux_call_graph=1208908,Sorensen/Linux_call_graph=1208908 -Springer,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,327062,327062,327062,37830,37830,37830,6019939,6019939,6019939,6019939,6019939,6019939,0.0,0,0.0,0.0,0,0.0,rectangular:1,rectangular,1.0,rectangular:1,rectangular,1.0,real:1,real,1.0,rectangular:1,rectangular,1.0,no:1,no,1.0,least squares problem:1,least squares problem,1.0,false:1,false,1.0,no:1,no,1.0,no:1,no,1.0,100k_to_1m:1,100k_to_1m,1.0,Springer/ESOC=327062,Springer/ESOC=327062,Springer/ESOC=37830,Springer/ESOC=37830,Springer/ESOC=6019939,Springer/ESOC=6019939,Springer/ESOC=6019939,Springer/ESOC=6019939 -Stevenson,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,2111154,2111154,2111154,801374,801374,801374,4944201,4944201,4944201,4944201,4944201,4944201,0.0,0,0.0,0.0,0,0.0,rectangular:1,rectangular,1.0,rectangular:1,rectangular,1.0,real:1,real,1.0,rectangular:1,rectangular,1.0,no:1,no,1.0,circuit simulation problem:1,circuit simulation problem,1.0,true:1,true,1.0,no:1,no,1.0,no:1,no,1.0,ge_1m:1,ge_1m,1.0,Stevenson/LargeRegFile=2111154,Stevenson/LargeRegFile=2111154,Stevenson/LargeRegFile=801374,Stevenson/LargeRegFile=801374,Stevenson/LargeRegFile=4944201,Stevenson/LargeRegFile=4944201,Stevenson/LargeRegFile=4944201,Stevenson/LargeRegFile=4944201 -Sumner,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,29493,29493,29493,11822,11822,11822,117954,117954,117954,117954,117954,117954,0.0,0,0.0,0.0,0,0.0,rectangular:1,rectangular,1.0,rectangular:1,rectangular,1.0,real:1,real,1.0,rectangular:1,rectangular,1.0,no:1,no,1.0,computer graphics/vision problem:1,computer graphics/vision problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Sumner/graphics=29493,Sumner/graphics=29493,Sumner/graphics=11822,Sumner/graphics=11822,Sumner/graphics=117954,Sumner/graphics=117954,Sumner/graphics=117954,Sumner/graphics=117954 -Szczerba,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,20896,20896,20896,20896,20896,20896,191368,191368,191368,191368,191368,191368,0.9897436481194791,0.9897436481194791,0.9897436481194791,0.3299904380293234,0.3299904380293234,0.3299904380293234,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,computational fluid dynamics problem:1,computational fluid dynamics problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Szczerba/Ill_Stokes=20896,Szczerba/Ill_Stokes=20896,Szczerba/Ill_Stokes=20896,Szczerba/Ill_Stokes=20896,Szczerba/Ill_Stokes=191368,Szczerba/Ill_Stokes=191368,Szczerba/Ill_Stokes=191368,Szczerba/Ill_Stokes=191368 -Toledo,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,68600,68600,68600,21961,21961,21961,247424,247424,247424,247424,247424,247424,0.0,0,0.0,0.0,0,0.0,rectangular:1,rectangular,1.0,rectangular:1,rectangular,1.0,real:1,real,1.0,rectangular:1,rectangular,1.0,no:1,no,1.0,counter-example problem:1,counter-example problem,1.0,true:1,true,1.0,no:1,no,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Toledo/deltaX=68600,Toledo/deltaX=68600,Toledo/deltaX=21961,Toledo/deltaX=21961,Toledo/deltaX=247424,Toledo/deltaX=247424,Toledo/deltaX=247424,Toledo/deltaX=247424 -Tromble,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,399130,399130,399130,399130,399130,399130,1216334,1216334,1216334,1216334,1216334,1216334,0.05790231080611451,0.05790231080611451,0.05790231080611451,0.0,0,0.0,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,directed weighted graph:1,directed weighted graph,1.0,missing:1,missing,1.0,no:1,no,1.0,no:1,no,1.0,100k_to_1m:1,100k_to_1m,1.0,Tromble/language=399130,Tromble/language=399130,Tromble/language=399130,Tromble/language=399130,Tromble/language=1216334,Tromble/language=1216334,Tromble/language=1216334,Tromble/language=1216334 -Vavasis,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,41092,41092,41092,41092,41092,41092,1683902,1683902,1683902,1683902,1683902,1683902,0.0009359570219938022,0.0009359570219938022,0.0009359570219938022,0.0,0,0.0,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,2D/3D problem:1,2D/3D problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Vavasis/av41092=41092,Vavasis/av41092=41092,Vavasis/av41092=41092,Vavasis/av41092=41092,Vavasis/av41092=1683902,Vavasis/av41092=1683902,Vavasis/av41092=1683902,Vavasis/av41092=1683902 -Wissgott,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,525825,525825,525825,525825,525825,525825,3674625,3674625,3674625,3674625,3674625,3674625,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,yes:1,yes,1.0,computational fluid dynamics problem:1,computational fluid dynamics problem,1.0,true:1,true,1.0,yes:1,yes,1.0,yes:1,yes,1.0,100k_to_1m:1,100k_to_1m,1.0,Wissgott/parabolic_fem=525825,Wissgott/parabolic_fem=525825,Wissgott/parabolic_fem=525825,Wissgott/parabolic_fem=525825,Wissgott/parabolic_fem=3674625,Wissgott/parabolic_fem=3674625,Wissgott/parabolic_fem=3674625,Wissgott/parabolic_fem=3674625 -YZhou,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,1020,1020,1020,1020,1020,1020,5883,5883,5883,5883,5883,5883,0.4379791625712601,0.4379791625712601,0.4379791625712601,0.3727147631216827,0.3727147631216827,0.3727147631216827,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,circuit simulation problem:1,circuit simulation problem,1.0,true:1,true,1.0,no:1,no,1.0,no:1,no,1.0,1k_to_10k:1,1k_to_10k,1.0,YZhou/circuit204=1020,YZhou/circuit204=1020,YZhou/circuit204=1020,YZhou/circuit204=1020,YZhou/circuit204=5883,YZhou/circuit204=5883,YZhou/circuit204=5883,YZhou/circuit204=5883 -Zaoui,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,1841302,2063494,2063494,2063494,2063494,2063494,2063494,12771361,12771361,12771361,14612663,14612663,14612663,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,no:1,no,1.0,optimization problem:1,optimization problem,1.0,true:1,true,1.0,no:1,no,1.0,no:1,no,1.0,ge_1m:1,ge_1m,1.0,Zaoui/kkt_power=2063494,Zaoui/kkt_power=2063494,Zaoui/kkt_power=2063494,Zaoui/kkt_power=2063494,Zaoui/kkt_power=12771361,Zaoui/kkt_power=12771361,Zaoui/kkt_power=14612663,Zaoui/kkt_power=14612663 diff --git a/reports/suitesparse-groups/output/group_size_distribution.csv b/reports/suitesparse-groups/output/group_size_distribution.csv deleted file mode 100644 index 422fec5..0000000 --- a/reports/suitesparse-groups/output/group_size_distribution.csv +++ /dev/null @@ -1,44 +0,0 @@ -group_size,number_of_groups,number_of_matrices,share_of_groups,share_of_matrices -1,51,51,0.25123153,0.01756198 -2,32,64,0.15763547,0.02203857 -3,18,54,0.08866995,0.01859504 -4,13,52,0.06403941,0.01790634 -5,8,40,0.03940887,0.0137741 -6,7,42,0.03448276,0.01446281 -7,7,49,0.03448276,0.01687328 -8,5,40,0.02463054,0.0137741 -9,2,18,0.00985222,0.00619835 -10,3,30,0.01477833,0.01033058 -11,5,55,0.02463054,0.01893939 -12,5,60,0.02463054,0.02066116 -13,2,26,0.00985222,0.00895317 -14,3,42,0.01477833,0.01446281 -15,3,45,0.01477833,0.01549587 -16,3,48,0.01477833,0.01652893 -17,1,17,0.00492611,0.00585399 -18,3,54,0.01477833,0.01859504 -19,1,19,0.00492611,0.0065427 -21,2,42,0.00985222,0.01446281 -22,2,44,0.00985222,0.01515152 -23,1,23,0.00492611,0.00792011 -26,2,52,0.00985222,0.01790634 -27,1,27,0.00492611,0.00929752 -30,1,30,0.00492611,0.01033058 -32,4,128,0.01970443,0.04407713 -35,2,70,0.00985222,0.02410468 -36,1,36,0.00492611,0.01239669 -38,1,38,0.00492611,0.0130854 -49,1,49,0.00492611,0.01687328 -52,1,52,0.00492611,0.01790634 -60,1,60,0.00492611,0.02066116 -67,1,67,0.00492611,0.02307163 -68,1,68,0.00492611,0.02341598 -76,1,76,0.00492611,0.0261708 -78,1,78,0.00492611,0.0268595 -91,1,91,0.00492611,0.03133609 -128,1,128,0.00492611,0.04407713 -138,1,138,0.00492611,0.04752066 -151,1,151,0.00492611,0.05199725 -166,1,166,0.00492611,0.05716253 -192,1,192,0.00492611,0.0661157 -292,1,292,0.00492611,0.10055096 diff --git a/reports/suitesparse-groups/output/group_summary.csv b/reports/suitesparse-groups/output/group_summary.csv deleted file mode 100644 index 2c51f66..0000000 --- a/reports/suitesparse-groups/output/group_summary.csv +++ /dev/null @@ -1,204 +0,0 @@ -group,matrix_count,diversity_label,categorical_diversity_score,mixed_attribute_count_dominant_share_lt_0_90,distinct_kinds,dimension_log10_span,nnz_log10_span,nentries_log10_span,matrices_with_explicit_zeros,explicit_zero_count_total,nrows_min,nrows_median,nrows_max,ncols_min,ncols_median,ncols_max,nnz_min,nnz_median,nnz_max,nentries_min,nentries_median,nentries_max,pattern_symmetry_min,pattern_symmetry_median,pattern_symmetry_max,numerical_symmetry_min,numerical_symmetry_median,numerical_symmetry_max,shape_counts,dominant_shape,dominant_shape_share,structure_counts,dominant_structure,dominant_structure_share,entry_type_counts,dominant_entry_type,dominant_entry_type_share,exact_symmetry_counts,dominant_exact_symmetry,dominant_exact_symmetry_share,positive_definite_counts,dominant_positive_definite,dominant_positive_definite_share,kind_counts,dominant_kind,dominant_kind_share,structural_full_rank_counts,dominant_structural_full_rank,dominant_structural_full_rank_share,is_2d3d_counts,dominant_is_2d3d,dominant_is_2d3d_share,cholesky_candidate_counts,dominant_cholesky_candidate,dominant_cholesky_candidate_share,size_band_counts,dominant_size_band,dominant_size_band_share,smallest_nrows_matrices,largest_nrows_matrices,smallest_ncols_matrices,largest_ncols_matrices,smallest_nnz_matrices,largest_nnz_matrices,smallest_nentries_matrices,largest_nentries_matrices -ACUSIM,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,14822,14822,14822,14822,14822,14822,715804,715804,715804,715804,715804,715804,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,yes:1,yes,1.0,computational fluid dynamics problem:1,computational fluid dynamics problem,1.0,true:1,true,1.0,yes:1,yes,1.0,yes:1,yes,1.0,10k_to_100k:1,10k_to_100k,1.0,ACUSIM/Pres_Poisson=14822,ACUSIM/Pres_Poisson=14822,ACUSIM/Pres_Poisson=14822,ACUSIM/Pres_Poisson=14822,ACUSIM/Pres_Poisson=715804,ACUSIM/Pres_Poisson=715804,ACUSIM/Pres_Poisson=715804,ACUSIM/Pres_Poisson=715804 -AG-Monien,32,mixed_some_attributes,0.03125,1,2,3.670352,3.69838,3.69838,0,0,224,9400,1048576,224,9400,1048576,840,42303,4194298,840,42303,4194298,1.0,1,1.0,1.0,1,1.0,square:32,square,1.0,symmetric:32,symmetric,1.0,binary:32,binary,1.0,exact_numerical_or_hermitian:32,exact_numerical_or_hermitian,1.0,no:32,no,1.0,2D/3D problem:26; undirected graph sequence:6,2D/3D problem,0.8125,false:7; missing:6; true:19,true,0.59375,no:6; yes:26,yes,0.8125,no:32,no,1.0,100k_to_1m:1; 10k_to_100k:13; 1k_to_10k:12; ge_1m:1; lt_1k:5,10k_to_100k,0.40625,AG-Monien/grid1_dual=224,AG-Monien/debr=1048576,AG-Monien/grid1_dual=224,AG-Monien/debr=1048576,AG-Monien/grid1_dual=840,AG-Monien/debr=4194298,AG-Monien/grid1_dual=840,AG-Monien/debr=4194298 -AMD,2,small_group_lt5,0.0,0,1,1.023774,1.022936,1.022936,0,0,150102,867790,1585478,150102,867790,1585478,726674,4193750,7660826,726674,4193750,7660826,1.0,1,1.0,1.0,1,1.0,square:2,square,1.0,symmetric:2,symmetric,1.0,real:2,real,1.0,exact_numerical_or_hermitian:2,exact_numerical_or_hermitian,1.0,yes:2,yes,1.0,circuit simulation problem:2,circuit simulation problem,1.0,true:2,true,1.0,no:2,no,1.0,yes:2,yes,1.0,100k_to_1m:1; ge_1m:1,100k_to_1m,0.5,AMD/G2_circuit=150102,AMD/G3_circuit=1585478,AMD/G2_circuit=150102,AMD/G3_circuit=1585478,AMD/G2_circuit=726674,AMD/G3_circuit=7660826,AMD/G2_circuit=726674,AMD/G3_circuit=7660826 -ANSYS,3,small_group_lt5,0.0,0,1,0.313056,0.810106,0.810106,0,0,64719,295734,343236,887058,1785345,1823928,652140,2401323,4211599,652140,2401323,4211599,0.0,0,0.0,0.0,0,0.0,rectangular:3,rectangular,1.0,rectangular:3,rectangular,1.0,real:3,real,1.0,rectangular:3,rectangular,1.0,no:3,no,1.0,least squares problem:3,least squares problem,1.0,true:3,true,1.0,no:3,no,1.0,no:3,no,1.0,100k_to_1m:1; ge_1m:2,ge_1m,0.666667,ANSYS/Delor64K=64719,ANSYS/Delor338K=343236,ANSYS/Delor338K=887058,ANSYS/Delor295K=1823928,ANSYS/Delor64K=652140,ANSYS/Delor338K=4211599,ANSYS/Delor64K=652140,ANSYS/Delor338K=4211599 -ATandT,4,small_group_lt5,0.0,0,1,1.261918,1.418452,1.417968,4,153765,36057,78403.5,659033,36057,78403.5,659033,222596,770908.5,5834044,227628,782656,5959282,0.0742956282869174,0.17871755249741855,0.3317760910564062,0.04331285111284008,0.06579475948288332,0.1058542372000797,square:4,square,1.0,unsymmetric:4,unsymmetric,1.0,real:4,real,1.0,partially_pattern_symmetric:4,partially_pattern_symmetric,1.0,no:4,no,1.0,frequency-domain circuit simulation problem:4,frequency-domain circuit simulation problem,1.0,true:4,true,1.0,no:4,no,1.0,no:4,no,1.0,100k_to_1m:2; 10k_to_100k:2,100k_to_1m,0.5,ATandT/onetone1=36057; ATandT/onetone2=36057,ATandT/pre2=659033,ATandT/onetone1=36057; ATandT/onetone2=36057,ATandT/pre2=659033,ATandT/onetone2=222596,ATandT/pre2=5834044,ATandT/onetone2=227628,ATandT/pre2=5959282 -Alemdar,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,6245,6245,6245,6245,6245,6245,42581,42581,42581,42581,42581,42581,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,integer:1,integer,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,no:1,no,1.0,2D/3D problem:1,2D/3D problem,1.0,true:1,true,1.0,yes:1,yes,1.0,yes:1,yes,1.0,1k_to_10k:1,1k_to_10k,1.0,Alemdar/Alemdar=6245,Alemdar/Alemdar=6245,Alemdar/Alemdar=6245,Alemdar/Alemdar=6245,Alemdar/Alemdar=42581,Alemdar/Alemdar=42581,Alemdar/Alemdar=42581,Alemdar/Alemdar=42581 -Andrews,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,60000,60000,60000,60000,60000,60000,760154,760154,760154,760154,760154,760154,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,integer:1,integer,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,yes:1,yes,1.0,computer graphics/vision problem:1,computer graphics/vision problem,1.0,true:1,true,1.0,yes:1,yes,1.0,yes:1,yes,1.0,10k_to_100k:1,10k_to_100k,1.0,Andrews/Andrews=60000,Andrews/Andrews=60000,Andrews/Andrews=60000,Andrews/Andrews=60000,Andrews/Andrews=760154,Andrews/Andrews=760154,Andrews/Andrews=760154,Andrews/Andrews=760154 -Andrianov,16,homogeneous,0.0,0,1,2.026634,2.041811,2.041811,0,0,5026,26520,534388,5026,26520,534388,94026,1566310,10352819,94026,1566310,10352819,1.0,1,1.0,1.0,1,1.0,square:16,square,1.0,symmetric:16,symmetric,1.0,binary:16,binary,1.0,exact_numerical_or_hermitian:16,exact_numerical_or_hermitian,1.0,no:16,no,1.0,optimization problem:16,optimization problem,1.0,true:16,true,1.0,no:16,no,1.0,yes:16,yes,1.0,100k_to_1m:2; 10k_to_100k:11; 1k_to_10k:3,10k_to_100k,0.6875,Andrianov/fxm3_6=5026,Andrianov/lp1=534388,Andrianov/fxm3_6=5026,Andrianov/lp1=534388,Andrianov/fxm3_6=94026,Andrianov/mip1=10352819,Andrianov/fxm3_6=94026,Andrianov/mip1=10352819 -Arenas,4,small_group_lt5,0.083333,2,2,1.731906,1.077862,1.077862,0,0,198,793,10680,198,793,10680,4065,8193,48632,4065,8193,48632,1.0,1,1.0,1.0,1,1.0,square:4,square,1.0,symmetric:4,symmetric,1.0,binary:1; integer:3,integer,0.75,exact_numerical_or_hermitian:4,exact_numerical_or_hermitian,1.0,no:4,no,1.0,undirected graph:1; undirected multigraph:3,undirected multigraph,0.75,missing:4,missing,1.0,no:4,no,1.0,no:4,no,1.0,10k_to_100k:1; 1k_to_10k:1; lt_1k:2,lt_1k,0.5,Arenas/jazz=198,Arenas/PGPgiantcompo=10680,Arenas/jazz=198,Arenas/PGPgiantcompo=10680,Arenas/celegans_metabolic=4065,Arenas/PGPgiantcompo=48632,Arenas/celegans_metabolic=4065,Arenas/PGPgiantcompo=48632 -Averous,4,small_group_lt5,0.0,0,1,1.673635,1.776081,1.776081,0,0,1794,19981,84617,1794,19981,84617,7764,135040,463625,7764,135040,463625,0.5008375209380235,0.6689746179523419,0.7293915511896314,0.0,0,0.03155606214116852,square:4,square,1.0,unsymmetric:4,unsymmetric,1.0,real:4,real,1.0,partially_pattern_symmetric:4,partially_pattern_symmetric,1.0,no:4,no,1.0,thermal problem:4,thermal problem,1.0,true:4,true,1.0,yes:4,yes,1.0,no:4,no,1.0,10k_to_100k:3; 1k_to_10k:1,10k_to_100k,0.75,Averous/epb0=1794,Averous/epb3=84617,Averous/epb0=1794,Averous/epb3=84617,Averous/epb0=7764,Averous/epb3=463625,Averous/epb0=7764,Averous/epb3=463625 -Bai,78,categorically_heterogeneous,0.245726,4,10,2.579784,3.131892,3.151049,1,23658,62,961,23560,62,961,23560,342,4681,463360,342,4681,484256,0.07485627126598615,0.9987279276556085,1.0,0.0,0.34047621421787244,1.0,square:78,square,1.0,Hermitian:1; symmetric:11; unsymmetric:66,unsymmetric,0.846154,complex:6; integer:2; real:70,real,0.897436,exact_numerical_or_hermitian:8; partially_pattern_symmetric:39; pattern_symmetric_only:31,partially_pattern_symmetric,0.5,no:74; yes:4,no,0.948718,2D/3D problem:8; chemical process simulation problem:2; computational fluid dynamics problem:25; computational fluid dynamics problem sequence:1; electromagnetics problem:26; materials problem:2; power network problem:3; robotics problem:2; statistical/mathematical problem:4; subsequent computational fluid dynamics problem:5,electromagnetics problem,0.333333,false:2; true:76,true,0.974359,no:9; yes:69,yes,0.884615,no:74; yes:4,no,0.948718,10k_to_100k:2; 1k_to_10k:29; lt_1k:47,lt_1k,0.602564,Bai/bfwa62=62; Bai/bfwb62=62,Bai/af23560=23560,Bai/bfwa62=62; Bai/bfwb62=62,Bai/af23560=23560,Bai/bfwb62=342,Bai/qc2534=463360,Bai/bfwb62=342,Bai/af23560=484256 -Barabasi,3,small_group_lt5,0.388889,4,3,2.268624,2.516159,2.516159,0,0,2114,325729,392400,2114,127823,325729,4480,929849,1470404,4480,929849,1470404,0.0,0.3315875213545298,1.0,0.0,0.3315875213545298,1.0,rectangular:1; square:2,square,0.666667,rectangular:1; symmetric:1; unsymmetric:1,rectangular,0.333333,binary:3,binary,1.0,exact_numerical_or_hermitian:1; partially_pattern_symmetric:1; rectangular:1,exact_numerical_or_hermitian,0.333333,no:3,no,1.0,bipartite graph:1; directed graph:1; undirected graph:1,bipartite graph,0.333333,false:1; missing:2,missing,0.666667,no:3,no,1.0,no:3,no,1.0,100k_to_1m:2; 1k_to_10k:1,100k_to_1m,0.666667,Barabasi/NotreDame_yeast=2114,Barabasi/NotreDame_actors=392400,Barabasi/NotreDame_yeast=2114,Barabasi/NotreDame_www=325729,Barabasi/NotreDame_yeast=4480,Barabasi/NotreDame_actors=1470404,Barabasi/NotreDame_yeast=4480,Barabasi/NotreDame_actors=1470404 -Bates,3,small_group_lt5,0.333333,6,2,2.837567,2.965847,2.965847,0,0,2541,2541,1748122,2541,31022,62729,7361,62044,6804304,7361,62044,6804304,0.0,0,1.0,0.0,0,1.0,rectangular:2; square:1,rectangular,0.666667,rectangular:2; symmetric:1,rectangular,0.666667,binary:1; real:2,real,0.666667,exact_numerical_or_hermitian:1; rectangular:2,rectangular,0.666667,no:2; yes:1,no,0.666667,least squares problem:1; statistical/mathematical problem:2,statistical/mathematical problem,0.666667,true:3,true,1.0,no:3,no,1.0,no:2; yes:1,no,0.666667,10k_to_100k:1; 1k_to_10k:1; ge_1m:1,10k_to_100k,0.333333,Bates/Chem97Zt=2541; Bates/Chem97ZtZ=2541,Bates/sls=1748122,Bates/Chem97ZtZ=2541,Bates/sls=62729,Bates/Chem97ZtZ=7361,Bates/sls=6804304,Bates/Chem97ZtZ=7361,Bates/sls=6804304 -Belcastro,3,small_group_lt5,0.0,0,1,0.497637,0.204988,0.204988,0,0,14340,22283,45101,14340,22283,45101,18068388,24669643,28967291,18068388,24669643,28967291,1.0,1,1.0,1.0,1,1.0,square:3,square,1.0,symmetric:3,symmetric,1.0,real:3,real,1.0,exact_numerical_or_hermitian:3,exact_numerical_or_hermitian,1.0,no:3,no,1.0,undirected weighted graph:3,undirected weighted graph,1.0,missing:3,missing,1.0,no:3,no,1.0,yes:3,yes,1.0,10k_to_100k:3,10k_to_100k,1.0,Belcastro/human_gene2=14340,Belcastro/mouse_gene=45101,Belcastro/human_gene2=14340,Belcastro/mouse_gene=45101,Belcastro/human_gene2=18068388,Belcastro/mouse_gene=28967291,Belcastro/human_gene2=18068388,Belcastro/mouse_gene=28967291 -BenElechi,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,245874,245874,245874,245874,245874,245874,13150496,13150496,13150496,13150496,13150496,13150496,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,yes:1,yes,1.0,2D/3D problem:1,2D/3D problem,1.0,true:1,true,1.0,yes:1,yes,1.0,yes:1,yes,1.0,100k_to_1m:1,100k_to_1m,1.0,BenElechi/BenElechi1=245874,BenElechi/BenElechi1=245874,BenElechi/BenElechi1=245874,BenElechi/BenElechi1=245874,BenElechi/BenElechi1=13150496,BenElechi/BenElechi1=13150496,BenElechi/BenElechi1=13150496,BenElechi/BenElechi1=13150496 -Bindel,6,categorically_heterogeneous,0.222222,4,1,0.0,0.557887,0.557887,0,0,10605,10605,10605,10605,10605,10605,144579,424587,522387,144579,424587,522387,0.5657922767749357,0.6415700434950818,1.0,0.0,0.1057079149564699,1.0,square:6,square,1.0,symmetric:2; unsymmetric:4,unsymmetric,0.666667,complex:2; real:4,real,0.666667,exact_numerical_or_hermitian:2; partially_pattern_symmetric:4,partially_pattern_symmetric,0.666667,no:4; yes:2,no,0.666667,thermal problem:6,thermal problem,1.0,true:6,true,1.0,yes:6,yes,1.0,no:4; yes:2,no,0.666667,10k_to_100k:6,10k_to_100k,1.0,Bindel/ted_A=10605; Bindel/ted_B=10605; Bindel/ted_AB=10605; Bindel/ted_A_unscaled=10605; Bindel/ted_B_unscaled=10605; Bindel/ted_AB_unscaled=10605,Bindel/ted_A=10605; Bindel/ted_B=10605; Bindel/ted_AB=10605; Bindel/ted_A_unscaled=10605; Bindel/ted_B_unscaled=10605; Bindel/ted_AB_unscaled=10605,Bindel/ted_A=10605; Bindel/ted_B=10605; Bindel/ted_AB=10605; Bindel/ted_A_unscaled=10605; Bindel/ted_B_unscaled=10605; Bindel/ted_AB_unscaled=10605,Bindel/ted_A=10605; Bindel/ted_B=10605; Bindel/ted_AB=10605; Bindel/ted_A_unscaled=10605; Bindel/ted_B_unscaled=10605; Bindel/ted_AB_unscaled=10605,Bindel/ted_B=144579; Bindel/ted_B_unscaled=144579,Bindel/ted_AB=522387; Bindel/ted_AB_unscaled=522387,Bindel/ted_B=144579; Bindel/ted_B_unscaled=144579,Bindel/ted_AB=522387; Bindel/ted_AB_unscaled=522387 -Bodendiek,5,homogeneous,0.0,0,1,2.332014,2.369111,2.369111,0,0,11083,806529,2380515,11083,806529,2380515,113343,8921789,26515867,113343,8921789,26515867,1.0,1,1.0,1.0,1,1.0,square:5,square,1.0,symmetric:5,symmetric,1.0,real:5,real,1.0,exact_numerical_or_hermitian:5,exact_numerical_or_hermitian,1.0,no:5,no,1.0,model reduction problem:5,model reduction problem,1.0,true:5,true,1.0,yes:5,yes,1.0,yes:5,yes,1.0,100k_to_1m:2; 10k_to_100k:1; ge_1m:2,100k_to_1m,0.4,Bodendiek/CurlCurl_0=11083,Bodendiek/CurlCurl_4=2380515,Bodendiek/CurlCurl_0=11083,Bodendiek/CurlCurl_4=2380515,Bodendiek/CurlCurl_0=113343,Bodendiek/CurlCurl_4=26515867,Bodendiek/CurlCurl_0=113343,Bodendiek/CurlCurl_4=26515867 -Boeing,32,categorically_heterogeneous,0.161458,3,4,2.568916,3.041051,3.045176,7,253152,588,13965,217918,588,13965,217918,10485,321755.5,11524432,10485,321755.5,11634424,1.0,1,1.0,1.0,1,1.0,square:32,square,1.0,symmetric:32,symmetric,1.0,binary:5; real:27,real,0.84375,exact_numerical_or_hermitian:32,exact_numerical_or_hermitian,1.0,no:17; yes:15,no,0.53125,duplicate materials problem:2; duplicate structural problem:3; materials problem:6; structural problem:21,structural problem,0.65625,false:4; true:28,true,0.875,yes:32,yes,1.0,no:5; yes:27,yes,0.84375,100k_to_1m:1; 10k_to_100k:18; 1k_to_10k:10; lt_1k:3,10k_to_100k,0.5625,Boeing/bcsstk34=588; Boeing/bcsstm34=588,Boeing/pwtk=217918,Boeing/bcsstk34=588; Boeing/bcsstm34=588,Boeing/pwtk=217918,Boeing/bcsstm38=10485,Boeing/pwtk=11524432,Boeing/bcsstm38=10485,Boeing/pwtk=11634424 -Bomhof,4,small_group_lt5,0.0,0,1,1.485259,1.161677,1.161677,0,0,2624,8318.5,80209,2624,8318.5,80209,21199,41980,307604,21199,41980,307604,0.7700717835450027,0.8178832654145156,0.999548641410646,0.2060602413263924,0.33182782865785215,0.4152593124925141,square:4,square,1.0,unsymmetric:4,unsymmetric,1.0,real:4,real,1.0,partially_pattern_symmetric:4,partially_pattern_symmetric,1.0,no:4,no,1.0,circuit simulation problem:4,circuit simulation problem,1.0,true:4,true,1.0,no:4,no,1.0,no:4,no,1.0,10k_to_100k:2; 1k_to_10k:2,10k_to_100k,0.5,Bomhof/circuit_1=2624,Bomhof/circuit_4=80209,Bomhof/circuit_1=2624,Bomhof/circuit_4=80209,Bomhof/circuit_2=21199,Bomhof/circuit_4=307604,Bomhof/circuit_2=21199,Bomhof/circuit_4=307604 -Botonakis,6,categorically_heterogeneous,0.25,3,1,1.057935,0.908523,0.908523,0,0,17880,125029,204316,17880,125029,204316,430740,1067337,3489300,430740,1067337,3489300,1.0,1,1.0,0.6652727267221619,0.9753669524778374,1.0,square:6,square,1.0,symmetric:3; unsymmetric:3,symmetric,0.5,real:6,real,1.0,exact_numerical_or_hermitian:3; pattern_symmetric_only:3,exact_numerical_or_hermitian,0.5,no:3; yes:3,no,0.5,thermal problem:6,thermal problem,1.0,true:6,true,1.0,yes:6,yes,1.0,no:3; yes:3,no,0.5,100k_to_1m:5; 10k_to_100k:1,100k_to_1m,0.833333,Botonakis/FEM_3D_thermal1=17880,Botonakis/thermomech_dM=204316; Botonakis/thermomech_dK=204316,Botonakis/FEM_3D_thermal1=17880,Botonakis/thermomech_dM=204316; Botonakis/thermomech_dK=204316,Botonakis/FEM_3D_thermal1=430740,Botonakis/FEM_3D_thermal2=3489300,Botonakis/FEM_3D_thermal1=430740,Botonakis/FEM_3D_thermal2=3489300 -Bourchtein,4,small_group_lt5,0.041667,1,1,0.069163,0.068453,0.068453,0,0,1270432,1380092,1489752,1270432,1380092,1489752,8814880,9567320,10319760,8814880,9567320,10319760,1.0,1,1.0,0.6690207156308852,0.6702355349613749,0.6714503542918647,square:4,square,1.0,unsymmetric:4,unsymmetric,1.0,integer:1; real:3,real,0.75,pattern_symmetric_only:4,pattern_symmetric_only,1.0,no:4,no,1.0,computational fluid dynamics problem:4,computational fluid dynamics problem,1.0,true:4,true,1.0,yes:4,yes,1.0,no:4,no,1.0,ge_1m:4,ge_1m,1.0,Bourchtein/atmosmodd=1270432; Bourchtein/atmosmodj=1270432,Bourchtein/atmosmodl=1489752; Bourchtein/atmosmodm=1489752,Bourchtein/atmosmodd=1270432; Bourchtein/atmosmodj=1270432,Bourchtein/atmosmodl=1489752; Bourchtein/atmosmodm=1489752,Bourchtein/atmosmodd=8814880; Bourchtein/atmosmodj=8814880,Bourchtein/atmosmodl=10319760; Bourchtein/atmosmodm=10319760,Bourchtein/atmosmodd=8814880; Bourchtein/atmosmodj=8814880,Bourchtein/atmosmodl=10319760; Bourchtein/atmosmodm=10319760 -Bova,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,44909,46835,46835,46835,46835,46835,46835,2329092,2329092,2329092,2374001,2374001,2374001,1.0,1,1.0,0.2355549300354827,0.2355549300354827,0.2355549300354827,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,pattern_symmetric_only:1,pattern_symmetric_only,1.0,no:1,no,1.0,computational fluid dynamics problem:1,computational fluid dynamics problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Bova/rma10=46835,Bova/rma10=46835,Bova/rma10=46835,Bova/rma10=46835,Bova/rma10=2329092,Bova/rma10=2329092,Bova/rma10=2374001,Bova/rma10=2374001 -Brethour,2,small_group_lt5,0.0,0,1,0.849858,1.02754,1.02754,0,0,1348,5444,9540,1348,5444,9540,19457,113382.5,207308,19457,113382.5,207308,0.5922916666666667,0.5932400701235618,0.5941884735804569,0.1280874338061923,0.2615437169030962,0.395,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,real:2,real,1.0,partially_pattern_symmetric:2,partially_pattern_symmetric,1.0,no:2,no,1.0,computational fluid dynamics problem:2,computational fluid dynamics problem,1.0,false:2,false,1.0,yes:2,yes,1.0,no:2,no,1.0,1k_to_10k:2,1k_to_10k,1.0,Brethour/coater1=1348,Brethour/coater2=9540,Brethour/coater1=1348,Brethour/coater2=9540,Brethour/coater1=19457,Brethour/coater2=207308,Brethour/coater1=19457,Brethour/coater2=207308 -Brogan,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,576,477976,477976,477976,1600,1600,1600,7647040,7647040,7647040,7647616,7647616,7647616,0.0,0,0.0,0.0,0,0.0,rectangular:1,rectangular,1.0,rectangular:1,rectangular,1.0,real:1,real,1.0,rectangular:1,rectangular,1.0,no:1,no,1.0,computer vision problem:1,computer vision problem,1.0,false:1,false,1.0,yes:1,yes,1.0,no:1,no,1.0,100k_to_1m:1,100k_to_1m,1.0,Brogan/specular=477976,Brogan/specular=477976,Brogan/specular=1600,Brogan/specular=1600,Brogan/specular=7647040,Brogan/specular=7647040,Brogan/specular=7647616,Brogan/specular=7647616 -Brunetiere,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,3456,3456,3456,3456,3456,3456,66528,66528,66528,66528,66528,66528,1.0,1,1.0,0.0730593607305936,0.0730593607305936,0.0730593607305936,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,pattern_symmetric_only:1,pattern_symmetric_only,1.0,no:1,no,1.0,thermal problem:1,thermal problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,1k_to_10k:1,1k_to_10k,1.0,Brunetiere/thermal=3456,Brunetiere/thermal=3456,Brunetiere/thermal=3456,Brunetiere/thermal=3456,Brunetiere/thermal=66528,Brunetiere/thermal=66528,Brunetiere/thermal=66528,Brunetiere/thermal=66528 -Buss,2,small_group_lt5,0.0,0,1,0.344458,1.302457,1.302457,0,0,512,6491.5,12471,394792,633707,872622,1127525,11876126,22624727,1127525,11876126,22624727,0.0,0,0.0,0.0,0,0.0,rectangular:2,rectangular,1.0,rectangular:2,rectangular,1.0,binary:2,binary,1.0,rectangular:2,rectangular,1.0,no:2,no,1.0,undirected bipartite graph:2,undirected bipartite graph,1.0,false:2,false,1.0,no:2,no,1.0,no:2,no,1.0,100k_to_1m:2,100k_to_1m,1.0,Buss/connectus=512,Buss/12month1=12471,Buss/connectus=394792,Buss/12month1=872622,Buss/connectus=1127525,Buss/12month1=22624727,Buss/connectus=1127525,Buss/12month1=22624727 -Bydder,2,small_group_lt5,0.0,0,1,0.0,0.015488,0.015488,0,0,63240,64388,65536,147456,147456,147456,569160,579492,589824,569160,579492,589824,0.0,0,0.0,0.0,0,0.0,rectangular:2,rectangular,1.0,rectangular:2,rectangular,1.0,real:2,real,1.0,rectangular:2,rectangular,1.0,no:2,no,1.0,computer graphics/vision problem:2,computer graphics/vision problem,1.0,false:2,false,1.0,yes:2,yes,1.0,no:2,no,1.0,100k_to_1m:2,100k_to_1m,1.0,Bydder/mri2=63240,Bydder/mri1=65536,Bydder/mri1=147456; Bydder/mri2=147456,Bydder/mri1=147456; Bydder/mri2=147456,Bydder/mri2=569160,Bydder/mri1=589824,Bydder/mri2=569160,Bydder/mri1=589824 -CEMW,4,small_group_lt5,0.25,4,1,0.993857,0.549204,0.549204,0,0,93476,822269,921632,93476,822269,921632,1434636,4587816.5,5080961,1434636,4587816.5,5080961,0.9988182710127548,1,1.0,0.0,0.9098903607144062,1.0,square:4,square,1.0,symmetric:1; unsymmetric:3,unsymmetric,0.75,complex:1; integer:1; real:2,real,0.5,exact_numerical_or_hermitian:1; partially_pattern_symmetric:1; pattern_symmetric_only:2,pattern_symmetric_only,0.5,no:3; yes:1,no,0.75,electromagnetics problem:4,electromagnetics problem,1.0,true:4,true,1.0,yes:4,yes,1.0,no:3; yes:1,no,0.75,100k_to_1m:3; 10k_to_100k:1,100k_to_1m,0.75,CEMW/vfem=93476,CEMW/t2em=921632,CEMW/vfem=93476,CEMW/t2em=921632,CEMW/vfem=1434636,CEMW/tmt_sym=5080961,CEMW/vfem=1434636,CEMW/tmt_sym=5080961 -CPM,9,homogeneous,0.0,0,1,2.441971,2.431214,2.431214,0,0,148,2548,40948,148,2548,40948,1527,25674,412148,1527,25674,412148,0.4263959390862944,0.4349030172413793,0.441413081497027,0.2219902574808629,0.2349736227622589,0.2377052713245119,square:9,square,1.0,unsymmetric:9,unsymmetric,1.0,real:9,real,1.0,partially_pattern_symmetric:9,partially_pattern_symmetric,1.0,no:9,no,1.0,2D/3D problem:9,2D/3D problem,1.0,true:9,true,1.0,yes:9,yes,1.0,no:9,no,1.0,10k_to_100k:3; 1k_to_10k:3; lt_1k:3,10k_to_100k,0.333333,CPM/cz148=148,CPM/cz40948=40948,CPM/cz148=148,CPM/cz40948=40948,CPM/cz148=1527,CPM/cz40948=412148,CPM/cz148=1527,CPM/cz40948=412148 -Cannizzo,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,4098,4098,4098,4098,4098,4098,72356,72356,72356,72356,72356,72356,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,yes:1,yes,1.0,structural problem:1,structural problem,1.0,true:1,true,1.0,yes:1,yes,1.0,yes:1,yes,1.0,1k_to_10k:1,1k_to_10k,1.0,Cannizzo/sts4098=4098,Cannizzo/sts4098=4098,Cannizzo/sts4098=4098,Cannizzo/sts4098=4098,Cannizzo/sts4098=72356,Cannizzo/sts4098=72356,Cannizzo/sts4098=72356,Cannizzo/sts4098=72356 -Castrillon,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,89400,89400,89400,89400,89400,89400,1156224,1156224,1156224,1156224,1156224,1156224,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,yes:1,yes,1.0,counter-example problem:1,counter-example problem,1.0,true:1,true,1.0,no:1,no,1.0,yes:1,yes,1.0,10k_to_100k:1,10k_to_100k,1.0,Castrillon/denormal=89400,Castrillon/denormal=89400,Castrillon/denormal=89400,Castrillon/denormal=89400,Castrillon/denormal=1156224,Castrillon/denormal=1156224,Castrillon/denormal=1156224,Castrillon/denormal=1156224 -Chen,14,homogeneous,0.0,0,1,1.148208,1.262847,1.262847,0,0,10800,49377,151926,10800,49377,151926,810000,3178543.5,14836504,810000,3178543.5,14836504,1.0,1,1.0,1.0,1,1.0,square:14,square,1.0,symmetric:14,symmetric,1.0,binary:14,binary,1.0,exact_numerical_or_hermitian:14,exact_numerical_or_hermitian,1.0,no:14,no,1.0,structural problem:14,structural problem,1.0,true:14,true,1.0,yes:14,yes,1.0,yes:14,yes,1.0,100k_to_1m:1; 10k_to_100k:13,10k_to_100k,0.928571,Chen/pkustk02=10800,Chen/pkustk14=151926,Chen/pkustk02=10800,Chen/pkustk14=151926,Chen/pkustk02=810000,Chen/pkustk14=14836504,Chen/pkustk02=810000,Chen/pkustk14=14836504 -Chevron,4,small_group_lt5,0.0,0,1,1.279679,1.28523,1.28523,0,0,37365,235815,711450,37365,235815,711450,330633,2108143,6376412,330633,2108143,6376412,0.994503321194266,0.997275228752503,0.9986570077610406,0.5561276492234193,0.606425339005505,0.7103673090824775,square:4,square,1.0,unsymmetric:4,unsymmetric,1.0,complex:4,complex,1.0,partially_pattern_symmetric:4,partially_pattern_symmetric,1.0,no:4,no,1.0,other problem:4,other problem,1.0,true:4,true,1.0,no:4,no,1.0,no:4,no,1.0,100k_to_1m:2; 10k_to_100k:2,100k_to_1m,0.5,Chevron/Chevron1=37365,Chevron/Chevron4=711450,Chevron/Chevron1=37365,Chevron/Chevron4=711450,Chevron/Chevron1=330633,Chevron/Chevron4=6376412,Chevron/Chevron1=330633,Chevron/Chevron4=6376412 -Clark,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,73159,73159,73159,59498,59498,59498,647495,647495,647495,647495,647495,647495,0.0,0,0.0,0.0,0,0.0,rectangular:1,rectangular,1.0,rectangular:1,rectangular,1.0,real:1,real,1.0,rectangular:1,rectangular,1.0,no:1,no,1.0,computer graphics/vision problem:1,computer graphics/vision problem,1.0,false:1,false,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Clark/tomographic1=73159,Clark/tomographic1=73159,Clark/tomographic1=59498,Clark/tomographic1=59498,Clark/tomographic1=647495,Clark/tomographic1=647495,Clark/tomographic1=647495,Clark/tomographic1=647495 -Cote,2,small_group_lt5,0.166667,2,1,0.315501,0.326706,0.382207,1,41128,5962,9145,12328,5962,9145,12328,142190,221945,301700,142190,242509,342828,1.0,1,1.0,0.05311683354376486,0.5265584167718824,1.0,square:2,square,1.0,symmetric:2,symmetric,1.0,complex:1; real:1,complex,0.5,exact_numerical_or_hermitian:1; pattern_symmetric_only:1,exact_numerical_or_hermitian,0.5,no:2,no,1.0,acoustics problem:2,acoustics problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:1; yes:1,no,0.5,10k_to_100k:1; 1k_to_10k:1,10k_to_100k,0.5,Cote/mplate=5962,Cote/vibrobox=12328,Cote/mplate=5962,Cote/vibrobox=12328,Cote/mplate=142190,Cote/vibrobox=301700,Cote/mplate=142190,Cote/vibrobox=342828 -Cunningham,4,small_group_lt5,0.125,3,1,0.774782,2.398157,2.398157,0,0,11107,38617,66127,11107,38617,66127,6639,1019753,1660579,6639,1019753,1660579,1.0,1,1.0,0.999260507857104,1,1.0,square:4,square,1.0,symmetric:3; unsymmetric:1,symmetric,0.75,real:4,real,1.0,exact_numerical_or_hermitian:3; pattern_symmetric_only:1,exact_numerical_or_hermitian,0.75,no:3; yes:1,no,0.75,acoustics problem:4,acoustics problem,1.0,false:1; true:3,true,0.75,yes:4,yes,1.0,no:2; yes:2,no,0.5,10k_to_100k:4,10k_to_100k,1.0,Cunningham/k3plates=11107; Cunningham/m3plates=11107,Cunningham/qa8fk=66127; Cunningham/qa8fm=66127,Cunningham/k3plates=11107; Cunningham/m3plates=11107,Cunningham/qa8fk=66127; Cunningham/qa8fm=66127,Cunningham/m3plates=6639,Cunningham/qa8fk=1660579; Cunningham/qa8fm=1660579,Cunningham/m3plates=6639,Cunningham/qa8fk=1660579; Cunningham/qa8fm=1660579 -Cylshell,7,homogeneous,0.0,0,1,0.010572,0.104307,0.131452,7,60762,5357,5489,5489,5357,5489,5489,207123,217681,263351,207695,219521,281111,1.0,1,1.0,1.0,1,1.0,square:7,square,1.0,symmetric:7,symmetric,1.0,real:7,real,1.0,exact_numerical_or_hermitian:7,exact_numerical_or_hermitian,1.0,yes:7,yes,1.0,structural problem:7,structural problem,1.0,true:7,true,1.0,yes:7,yes,1.0,yes:7,yes,1.0,1k_to_10k:7,1k_to_10k,1.0,Cylshell/s3rmt3m3=5357,Cylshell/s1rmq4m1=5489; Cylshell/s2rmq4m1=5489; Cylshell/s3rmq4m1=5489; Cylshell/s1rmt3m1=5489; Cylshell/s2rmt3m1=5489; Cylshell/s3rmt3m1=5489,Cylshell/s3rmt3m3=5357,Cylshell/s1rmq4m1=5489; Cylshell/s2rmq4m1=5489; Cylshell/s3rmq4m1=5489; Cylshell/s1rmt3m1=5489; Cylshell/s2rmt3m1=5489; Cylshell/s3rmt3m1=5489,Cylshell/s3rmt3m3=207123,Cylshell/s2rmq4m1=263351,Cylshell/s3rmt3m3=207695,Cylshell/s1rmq4m1=281111; Cylshell/s2rmq4m1=281111; Cylshell/s3rmq4m1=281111 -DIMACS10,151,mixed_some_attributes,0.154525,2,6,6.115756,5.891954,5.891954,0,0,39,196621,50912018,39,196621,50912018,340,1227102,265114400,340,1227102,265114400,1.0,1,1.0,1.0,1,1.0,square:151,square,1.0,symmetric:151,symmetric,1.0,binary:95; integer:56,binary,0.629139,exact_numerical_or_hermitian:151,exact_numerical_or_hermitian,1.0,no:151,no,1.0,random undirected graph:3; random unweighted graph:15; undirected graph:67; undirected multigraph:6; undirected random graph:10; undirected weighted graph:50,undirected graph,0.443709,missing:151,missing,1.0,no:151,no,1.0,no:151,no,1.0,100k_to_1m:65; 10k_to_100k:39; 1k_to_10k:7; ge_1m:39; lt_1k:1,100k_to_1m,0.430464,DIMACS10/chesapeake=39,DIMACS10/europe_osm=50912018,DIMACS10/chesapeake=39,DIMACS10/europe_osm=50912018,DIMACS10/chesapeake=340,DIMACS10/rgg_n_2_24_s0=265114400,DIMACS10/chesapeake=340,DIMACS10/rgg_n_2_24_s0=265114400 -DNVS,15,mixed_some_attributes,0.155556,2,1,1.095929,1.110296,1.110296,7,19645488,18010,114919,224617,18010,114919,224617,960946,4444880,12387821,960946,8086034,12387821,1.0,1,1.0,1.0,1,1.0,square:15,square,1.0,symmetric:15,symmetric,1.0,binary:7; real:8,real,0.533333,exact_numerical_or_hermitian:15,exact_numerical_or_hermitian,1.0,no:7; yes:8,yes,0.533333,structural problem:15,structural problem,1.0,true:15,true,1.0,yes:15,yes,1.0,yes:15,yes,1.0,100k_to_1m:9; 10k_to_100k:6,100k_to_1m,0.6,DNVS/crplat2=18010,DNVS/halfb=224617,DNVS/crplat2=18010,DNVS/halfb=224617,DNVS/crplat2=960946,DNVS/halfb=12387821,DNVS/crplat2=960946,DNVS/halfb=12387821 -DRIVCAV,26,mixed_some_attributes,0.025641,1,2,1.158096,1.367463,1.275541,26,57227,317,2597,4562,317,2597,4562,5923,76214.5,138040,7327,76367,138187,0.8037831733483908,0.9372682821986997,0.9531982436361479,0.0,0,0.6542773036781541,square:26,square,1.0,unsymmetric:26,unsymmetric,1.0,real:26,real,1.0,partially_pattern_symmetric:26,partially_pattern_symmetric,1.0,no:26,no,1.0,computational fluid dynamics problem sequence:4; subsequent computational fluid dynamics problem:22,subsequent computational fluid dynamics problem,0.846154,true:26,true,1.0,yes:26,yes,1.0,no:26,no,1.0,1k_to_10k:22; lt_1k:4,1k_to_10k,0.846154,DRIVCAV/cavity01=317; DRIVCAV/cavity02=317; DRIVCAV/cavity03=317; DRIVCAV/cavity04=317,DRIVCAV/cavity16=4562; DRIVCAV/cavity17=4562; DRIVCAV/cavity18=4562; DRIVCAV/cavity19=4562; DRIVCAV/cavity20=4562; DRIVCAV/cavity21=4562; DRIVCAV/cavity22=4562; DRIVCAV/cavity23=4562; DRIVCAV/cavity24=4562; DRIVCAV/cavity25=4562; DRIVCAV/cavity26=4562,DRIVCAV/cavity01=317; DRIVCAV/cavity02=317; DRIVCAV/cavity03=317; DRIVCAV/cavity04=317,DRIVCAV/cavity16=4562; DRIVCAV/cavity17=4562; DRIVCAV/cavity18=4562; DRIVCAV/cavity19=4562; DRIVCAV/cavity20=4562; DRIVCAV/cavity21=4562; DRIVCAV/cavity22=4562; DRIVCAV/cavity23=4562; DRIVCAV/cavity24=4562; DRIVCAV/cavity25=4562; DRIVCAV/cavity26=4562,DRIVCAV/cavity02=5923; DRIVCAV/cavity04=5923,DRIVCAV/cavity18=138040; DRIVCAV/cavity20=138040; DRIVCAV/cavity22=138040; DRIVCAV/cavity24=138040; DRIVCAV/cavity26=138040,DRIVCAV/cavity01=7327; DRIVCAV/cavity02=7327; DRIVCAV/cavity03=7327; DRIVCAV/cavity04=7327,DRIVCAV/cavity16=138187; DRIVCAV/cavity17=138187; DRIVCAV/cavity18=138187; DRIVCAV/cavity19=138187; DRIVCAV/cavity20=138187; DRIVCAV/cavity21=138187; DRIVCAV/cavity22=138187; DRIVCAV/cavity23=138187; DRIVCAV/cavity24=138187; DRIVCAV/cavity25=138187; DRIVCAV/cavity26=138187 -Dattorro,3,small_group_lt5,0.0,0,1,0.240605,0.283915,0.283915,0,0,7362,10054,11077,150638,204304,262144,782087,1170516,1503732,782087,1170516,1503732,0.0,0,0.0,0.0,0,0.0,rectangular:3,rectangular,1.0,rectangular:3,rectangular,1.0,integer:3,integer,1.0,rectangular:3,rectangular,1.0,no:3,no,1.0,optimization problem:3,optimization problem,1.0,true:3,true,1.0,no:3,no,1.0,no:3,no,1.0,100k_to_1m:3,100k_to_1m,1.0,Dattorro/EternityII_A=7362,Dattorro/EternityII_E=11077,Dattorro/EternityII_A=150638,Dattorro/EternityII_E=262144,Dattorro/EternityII_A=782087,Dattorro/EternityII_E=1503732,Dattorro/EternityII_A=782087,Dattorro/EternityII_E=1503732 -Davis,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,1319,1319,1319,9498,9498,9498,301899,301899,301899,301899,301899,301899,0.0,0,0.0,0.0,0,0.0,rectangular:1,rectangular,1.0,rectangular:1,rectangular,1.0,integer:1,integer,1.0,rectangular:1,rectangular,1.0,no:1,no,1.0,term/document graph:1,term/document graph,1.0,false:1,false,1.0,no:1,no,1.0,no:1,no,1.0,1k_to_10k:1,1k_to_10k,1.0,Davis/FX_March2010=1319,Davis/FX_March2010=1319,Davis/FX_March2010=9498,Davis/FX_March2010=9498,Davis/FX_March2010=301899,Davis/FX_March2010=301899,Davis/FX_March2010=301899,Davis/FX_March2010=301899 -Dehghani,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,29282,29282,29282,29282,29282,29282,406084,406084,406084,406084,406084,406084,1.0,1,1.0,0.0,0,0.0,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,complex:1,complex,1.0,pattern_symmetric_only:1,pattern_symmetric_only,1.0,no:1,no,1.0,electromagnetics problem:1,electromagnetics problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Dehghani/light_in_tissue=29282,Dehghani/light_in_tissue=29282,Dehghani/light_in_tissue=29282,Dehghani/light_in_tissue=29282,Dehghani/light_in_tissue=406084,Dehghani/light_in_tissue=406084,Dehghani/light_in_tissue=406084,Dehghani/light_in_tissue=406084 -Dziekonski,5,mixed_some_attributes,0.133333,2,1,0.439834,0.613243,0.613243,0,0,420408,607232,1157456,420408,607232,1157456,21758924,32886208,89306020,21758924,32886208,89306020,1.0,1,1.0,0.8873627016737613,1,1.0,square:5,square,1.0,symmetric:5,symmetric,1.0,complex:2; real:3,real,0.6,exact_numerical_or_hermitian:3; pattern_symmetric_only:2,exact_numerical_or_hermitian,0.6,no:5,no,1.0,electromagnetics problem:5,electromagnetics problem,1.0,true:5,true,1.0,yes:5,yes,1.0,no:5,no,1.0,100k_to_1m:3; ge_1m:2,100k_to_1m,0.6,Dziekonski/dielFilterV3clx=420408,Dziekonski/dielFilterV2real=1157456,Dziekonski/dielFilterV3clx=420408,Dziekonski/dielFilterV2real=1157456,Dziekonski/gsm_106857=21758924,Dziekonski/dielFilterV3real=89306020,Dziekonski/gsm_106857=21758924,Dziekonski/dielFilterV3real=89306020 -Embree,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,96307,96307,96307,96307,96307,96307,3599932,3599932,3599932,3599932,3599932,3599932,1.0,1,1.0,0.2845118352876235,0.2845118352876235,0.2845118352876235,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,pattern_symmetric_only:1,pattern_symmetric_only,1.0,no:1,no,1.0,computational fluid dynamics:1,computational fluid dynamics,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Embree/ifiss_mat=96307,Embree/ifiss_mat=96307,Embree/ifiss_mat=96307,Embree/ifiss_mat=96307,Embree/ifiss_mat=3599932,Embree/ifiss_mat=3599932,Embree/ifiss_mat=3599932,Embree/ifiss_mat=3599932 -Engwirda,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,14214,14214,14214,14214,14214,14214,259688,259688,259688,259688,259688,259688,0.981668119637925,0.981668119637925,0.981668119637925,0.0,0,0.0,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,computational fluid dynamics problem:1,computational fluid dynamics problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Engwirda/airfoil_2d=14214,Engwirda/airfoil_2d=14214,Engwirda/airfoil_2d=14214,Engwirda/airfoil_2d=14214,Engwirda/airfoil_2d=259688,Engwirda/airfoil_2d=259688,Engwirda/airfoil_2d=259688,Engwirda/airfoil_2d=259688 -FEMLAB,9,categorically_heterogeneous,0.185185,3,4,2.367924,3.114849,3.114849,0,0,367,20414,85623,367,20414,85623,2417,874887,3148656,2417,874887,3148656,0.9999710105602732,1,1.0,0.0,0,0.6548223350253807,square:9,square,1.0,unsymmetric:9,unsymmetric,1.0,complex:1; real:8,real,0.888889,partially_pattern_symmetric:4; pattern_symmetric_only:5,pattern_symmetric_only,0.555556,no:9,no,1.0,2D/3D problem:1; computational fluid dynamics problem:4; electromagnetics problem:1; structural problem:3,computational fluid dynamics problem,0.444444,true:9,true,1.0,yes:9,yes,1.0,no:9,no,1.0,10k_to_100k:7; lt_1k:2,10k_to_100k,0.777778,FEMLAB/poisson2D=367,FEMLAB/poisson3Db=85623,FEMLAB/poisson2D=367,FEMLAB/poisson3Db=85623,FEMLAB/poisson2D=2417,FEMLAB/sme3Dc=3148656,FEMLAB/poisson2D=2417,FEMLAB/sme3Dc=3148656 -FIDAP,35,categorically_heterogeneous,0.161905,3,1,2.863455,3.594582,3.594582,23,82207,27,2410,19716,27,2410,19716,279,54840,1096948,279,54840,1096948,1.0,1,1.0,0.4284006268883396,0.9877623772912288,1.0,square:35,square,1.0,symmetric:13; unsymmetric:22,unsymmetric,0.628571,real:35,real,1.0,exact_numerical_or_hermitian:13; pattern_symmetric_only:22,pattern_symmetric_only,0.628571,no:27; yes:8,no,0.771429,computational fluid dynamics problem:35,computational fluid dynamics problem,1.0,true:35,true,1.0,yes:35,yes,1.0,no:27; yes:8,no,0.771429,10k_to_100k:3; 1k_to_10k:25; lt_1k:7,1k_to_10k,0.714286,FIDAP/ex5=27,FIDAP/ex35=19716,FIDAP/ex5=27,FIDAP/ex35=19716,FIDAP/ex5=279,FIDAP/ex11=1096948,FIDAP/ex5=279,FIDAP/ex11=1096948 -FlowIPM22,11,mixed_some_attributes,0.060606,1,1,2.858419,2.637558,2.637558,0,0,100000,338402,72180402,100000,338402,72180402,499696,1100592,216902404,499696,1100592,216902404,1.0,1,1.0,1.0,1,1.0,square:11,square,1.0,symmetric:11,symmetric,1.0,real:11,real,1.0,exact_numerical_or_hermitian:11,exact_numerical_or_hermitian,1.0,no:7; yes:4,no,0.636364,undirected weighted graph:11,undirected weighted graph,1.0,missing:11,missing,1.0,no:11,no,1.0,yes:11,yes,1.0,100k_to_1m:6; ge_1m:5,100k_to_1m,0.545455,FlowIPM22/uni_chimera_i1=100000; FlowIPM22/uni_chimera_i2=100000; FlowIPM22/uni_chimera_i3=100000; FlowIPM22/uni_chimera_i4=100000; FlowIPM22/uni_chimera_i5=100000,FlowIPM22/Spielman_k600=72180402,FlowIPM22/uni_chimera_i1=100000; FlowIPM22/uni_chimera_i2=100000; FlowIPM22/uni_chimera_i3=100000; FlowIPM22/uni_chimera_i4=100000; FlowIPM22/uni_chimera_i5=100000,FlowIPM22/Spielman_k600=72180402,FlowIPM22/uni_chimera_i3=499696,FlowIPM22/Spielman_k600=216902404,FlowIPM22/uni_chimera_i3=499696,FlowIPM22/Spielman_k600=216902404 -Fluorem,5,type_homogeneous_size_diverse,0.0,0,1,3.349055,4.38127,4.38127,0,0,903,161070,2017169,903,161070,2017169,11766,8185136,283073458,11766,8185136,283073458,0.8446615474952066,0.9261667922354103,0.9614287029365737,0.0,9.457852263618717e-06,0.0007082780488857875,square:5,square,1.0,unsymmetric:5,unsymmetric,1.0,real:5,real,1.0,partially_pattern_symmetric:5,partially_pattern_symmetric,1.0,no:5,no,1.0,computational fluid dynamics problem:5,computational fluid dynamics problem,1.0,true:5,true,1.0,yes:5,yes,1.0,no:5,no,1.0,100k_to_1m:2; 1k_to_10k:1; ge_1m:1; lt_1k:1,100k_to_1m,0.4,Fluorem/DK01R=903,Fluorem/HV15R=2017169,Fluorem/DK01R=903,Fluorem/HV15R=2017169,Fluorem/DK01R=11766,Fluorem/HV15R=283073458,Fluorem/DK01R=11766,Fluorem/HV15R=283073458 -FreeFieldTechnologies,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,2492,169410,169410,169410,169410,169410,169410,5033796,5033796,5033796,5036288,5036288,5036288,1.0,1,1.0,0.820796293715178,0.820796293715178,0.820796293715178,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,complex:1,complex,1.0,pattern_symmetric_only:1,pattern_symmetric_only,1.0,no:1,no,1.0,acoustics problem:1,acoustics problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,100k_to_1m:1,100k_to_1m,1.0,FreeFieldTechnologies/mono_500Hz=169410,FreeFieldTechnologies/mono_500Hz=169410,FreeFieldTechnologies/mono_500Hz=169410,FreeFieldTechnologies/mono_500Hz=169410,FreeFieldTechnologies/mono_500Hz=5033796,FreeFieldTechnologies/mono_500Hz=5033796,FreeFieldTechnologies/mono_500Hz=5036288,FreeFieldTechnologies/mono_500Hz=5036288 -Freescale,8,mixed_some_attributes,0.020833,1,2,1.492416,1.791805,1.791614,7,16392927,178866,2993180.5,5558326,178866,2993180.5,5558326,961368,14589322,59524291,961790,19057270,59524291,0.9058013004223939,0.9924913468404921,0.9999998517635799,0.005614865134161801,0.4107363247105668,0.8220579996645768,square:8,square,1.0,unsymmetric:8,unsymmetric,1.0,real:8,real,1.0,partially_pattern_symmetric:8,partially_pattern_symmetric,1.0,no:8,no,1.0,circuit simulation matrix:1; circuit simulation problem:7,circuit simulation problem,0.875,true:8,true,1.0,no:8,no,1.0,no:8,no,1.0,100k_to_1m:2; ge_1m:6,ge_1m,0.75,Freescale/transient=178866,Freescale/circuit5M=5558326,Freescale/transient=178866,Freescale/circuit5M=5558326,Freescale/transient=961368,Freescale/circuit5M=59524291,Freescale/transient=961790,Freescale/circuit5M=59524291 -GAP,5,categorically_heterogeneous,0.2,3,2,0.748552,1.871719,1.871719,0,0,23947347,61578415,134217728,23947347,61578415,134217728,57708624,1930292948,4294966740,57708624,1930292948,4294966740,0.1245713694644861,1,1.0,0.0004892386935249789,1,1.0,square:5,square,1.0,symmetric:3; unsymmetric:2,symmetric,0.6,integer:5,integer,1.0,exact_numerical_or_hermitian:3; partially_pattern_symmetric:2,exact_numerical_or_hermitian,0.6,no:5,no,1.0,directed weighted graph:3; random undirected weighted graph:2,directed weighted graph,0.6,missing:5,missing,1.0,no:5,no,1.0,no:5,no,1.0,ge_1m:5,ge_1m,1.0,GAP/GAP-road=23947347,GAP/GAP-urand=134217728,GAP/GAP-road=23947347,GAP/GAP-urand=134217728,GAP/GAP-road=57708624,GAP/GAP-urand=4294966740,GAP/GAP-road=57708624,GAP/GAP-urand=4294966740 -GHS_indef,60,mixed_some_attributes,0.097222,1,8,2.19127,3.098399,3.098399,13,46212,3002,46950,466316,3002,46950,466316,9000,426705,11288630,9000,426705,11288630,1.0,1,1.0,1.0,1,1.0,square:60,square,1.0,symmetric:60,symmetric,1.0,integer:3; real:57,real,0.95,exact_numerical_or_hermitian:60,exact_numerical_or_hermitian,1.0,no:59; yes:1,no,0.983333,2D/3D problem:16; computational fluid dynamics problem:4; duplicate 2D/3D problem:1; materials problem:4; optimization problem:29; statistical/mathematical problem:2; structural problem:3; subsequent optimization problem:1,optimization problem,0.483333,false:7; true:53,true,0.883333,no:32; yes:28,no,0.533333,no:55; yes:5,no,0.916667,100k_to_1m:8; 10k_to_100k:50; 1k_to_10k:2,10k_to_100k,0.833333,GHS_indef/laser=3002,GHS_indef/boyd2=466316,GHS_indef/laser=3002,GHS_indef/boyd2=466316,GHS_indef/laser=9000,GHS_indef/bmw3_2=11288630,GHS_indef/laser=9000,GHS_indef/bmw3_2=11288630 -GHS_psdef,32,categorically_heterogeneous,0.177083,3,7,1.789829,2.883361,2.883361,9,6842388,15449,51402,952203,15449,51402,952203,101576,1345303.5,77651847,101576,1345303.5,77651847,1.0,1,1.0,1.0,1,1.0,square:32,square,1.0,symmetric:32,symmetric,1.0,binary:10; real:22,real,0.6875,exact_numerical_or_hermitian:32,exact_numerical_or_hermitian,1.0,no:10; yes:22,yes,0.6875,computational fluid dynamics problem:2; duplicate computational fluid dynamics problem:1; duplicate optimization problem:1; duplicate structural problem:1; optimization problem:7; random 2D/3D problem:2; structural problem:18,structural problem,0.5625,true:32,true,1.0,no:8; yes:24,yes,0.75,yes:32,yes,1.0,100k_to_1m:8; 10k_to_100k:24,10k_to_100k,0.75,GHS_psdef/opt1=15449,GHS_psdef/ldoor=952203,GHS_psdef/opt1=15449,GHS_psdef/ldoor=952203,GHS_psdef/ford1=101576,GHS_psdef/audikw_1=77651847,GHS_psdef/ford1=101576,GHS_psdef/audikw_1=77651847 -Gaertner,3,small_group_lt5,0.166667,3,2,0.088493,0.110964,0.110964,0,0,10774,11738,13209,10774,11738,13209,70842,79566,91465,70842,79566,91465,1.0,1,1.0,0.999970513652179,0.999974442854222,1.0,square:3,square,1.0,symmetric:1; unsymmetric:2,unsymmetric,0.666667,integer:3,integer,1.0,exact_numerical_or_hermitian:1; pattern_symmetric_only:2,pattern_symmetric_only,0.666667,no:3,no,1.0,directed weighted graph:2; undirected weighted graph:1,directed weighted graph,0.666667,missing:3,missing,1.0,no:3,no,1.0,no:2; yes:1,no,0.666667,10k_to_100k:3,10k_to_100k,1.0,Gaertner/nopoly=10774,Gaertner/big=13209,Gaertner/nopoly=10774,Gaertner/big=13209,Gaertner/nopoly=70842,Gaertner/big=91465,Gaertner/nopoly=70842,Gaertner/big=91465 -Garon,2,small_group_lt5,0.0,0,1,0.629715,0.643981,0.642706,2,21576,3175,8355,13535,3175,8355,13535,84723,228979,373235,88927,239767,390607,1.0,1,1.0,0.6720847366622736,0.6723833591974528,0.6726819817326322,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,real:2,real,1.0,pattern_symmetric_only:2,pattern_symmetric_only,1.0,no:2,no,1.0,computational fluid dynamics problem:2,computational fluid dynamics problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,10k_to_100k:1; 1k_to_10k:1,10k_to_100k,0.5,Garon/garon1=3175,Garon/garon2=13535,Garon/garon1=3175,Garon/garon2=13535,Garon/garon1=84723,Garon/garon2=373235,Garon/garon1=88927,Garon/garon2=390607 -GenBank,5,homogeneous,0.0,0,1,0.589727,0.598844,0.598844,0,0,55042369,139353211,214005017,55042369,139353211,214005017,117217600,297829984,465410904,117217600,297829984,465410904,1.0,1,1.0,1.0,1,1.0,square:5,square,1.0,symmetric:5,symmetric,1.0,binary:5,binary,1.0,exact_numerical_or_hermitian:5,exact_numerical_or_hermitian,1.0,no:5,no,1.0,undirected graph:5,undirected graph,1.0,missing:5,missing,1.0,no:5,no,1.0,no:5,no,1.0,ge_1m:5,ge_1m,1.0,GenBank/kmer_V2a=55042369,GenBank/kmer_V1r=214005017,GenBank/kmer_V2a=55042369,GenBank/kmer_V1r=214005017,GenBank/kmer_V2a=117217600,GenBank/kmer_V1r=465410904,GenBank/kmer_V2a=117217600,GenBank/kmer_V1r=465410904 -Gleich,10,categorically_heterogeneous,0.15,3,2,3.571315,3.937127,3.937127,0,0,2642,1227933.5,9845725,2642,1227933.5,9845725,6606,14795146,57156537,6606,14795146,57156537,0.1171832378930545,0.3926833803963951,1.0,0.1171832378930545,0.3926833803963951,1.0,square:10,square,1.0,symmetric:3; unsymmetric:7,unsymmetric,0.7,binary:10,binary,1.0,exact_numerical_or_hermitian:3; partially_pattern_symmetric:7,partially_pattern_symmetric,0.7,no:10,no,1.0,directed graph:7; undirected graph:3,directed graph,0.7,missing:10,missing,1.0,no:10,no,1.0,no:10,no,1.0,100k_to_1m:3; 1k_to_10k:2; ge_1m:5,ge_1m,0.5,Gleich/minnesota=2642,Gleich/wb-edu=9845725,Gleich/minnesota=2642,Gleich/wb-edu=9845725,Gleich/minnesota=6606,Gleich/wb-edu=57156537,Gleich/minnesota=6606,Gleich/wb-edu=57156537 -Goodwin,12,mixed_some_attributes,0.027778,1,2,2.178867,2.252858,2.252858,1,12,1182,14032,178437,1182,14032,178437,32282,443224.5,5778545,32282,443230.5,5778545,0.6353114022282706,0.9617420289795222,0.9902002197399873,0.0,0.06273502929179821,0.06589012929818107,square:12,square,1.0,unsymmetric:12,unsymmetric,1.0,real:12,real,1.0,partially_pattern_symmetric:12,partially_pattern_symmetric,1.0,no:12,no,1.0,computational fluid dynamics:10; computational fluid dynamics problem:2,computational fluid dynamics,0.833333,true:12,true,1.0,yes:12,yes,1.0,no:12,no,1.0,100k_to_1m:2; 10k_to_100k:5; 1k_to_10k:5,10k_to_100k,0.416667,Goodwin/Goodwin_010=1182,Goodwin/Goodwin_127=178437,Goodwin/Goodwin_010=1182,Goodwin/Goodwin_127=178437,Goodwin/Goodwin_010=32282,Goodwin/Goodwin_127=5778545,Goodwin/Goodwin_010=32282,Goodwin/Goodwin_127=5778545 -Graham,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,32,9035,9035,9035,9035,9035,9035,335472,335472,335472,335504,335504,335504,0.718197839525901,0.718197839525901,0.718197839525901,0.0004322586733311619,0.0004322586733311619,0.0004322586733311619,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,computational fluid dynamics problem:1,computational fluid dynamics problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,1k_to_10k:1,1k_to_10k,1.0,Graham/graham1=9035,Graham/graham1=9035,Graham/graham1=9035,Graham/graham1=9035,Graham/graham1=335472,Graham/graham1=335472,Graham/graham1=335504,Graham/graham1=335504 -Grueninger,2,small_group_lt5,0.0,0,1,0.694061,0.866354,1.068031,2,2050722,8256,24536,40816,8256,24536,40816,109368,456673,803978,233468,1482034,2730600,1.0,1,1.0,0.0,0,0.0,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,real:2,real,1.0,pattern_symmetric_only:2,pattern_symmetric_only,1.0,no:2,no,1.0,computational fluid dynamics problem:2,computational fluid dynamics problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,10k_to_100k:1; 1k_to_10k:1,10k_to_100k,0.5,Grueninger/windtunnel_evap2d=8256,Grueninger/windtunnel_evap3d=40816,Grueninger/windtunnel_evap2d=8256,Grueninger/windtunnel_evap3d=40816,Grueninger/windtunnel_evap2d=109368,Grueninger/windtunnel_evap3d=803978,Grueninger/windtunnel_evap2d=233468,Grueninger/windtunnel_evap3d=2730600 -Grund,22,mixed_some_attributes,0.068182,1,3,3.916341,4.26339,4.2676,17,186212,7,3268,57735,7,3268,57735,15,20839,275094,15,27836,277774,0.0,0.006682474671265359,1.0,0.0,0.0004982346027775649,1.0,square:22,square,1.0,symmetric:1; unsymmetric:21,unsymmetric,0.954545,real:22,real,1.0,exact_numerical_or_hermitian:1; partially_pattern_symmetric:20; pattern_unsymmetric:1,partially_pattern_symmetric,0.909091,no:22,no,1.0,chemical process simulation problem:16; circuit simulation problem:2; economic problem:4,chemical process simulation problem,0.727273,true:22,true,1.0,no:22,no,1.0,no:22,no,1.0,10k_to_100k:7; 1k_to_10k:11; lt_1k:4,1k_to_10k,0.5,Grund/b1_ss=7,Grund/bayer01=57735,Grund/b1_ss=7,Grund/bayer01=57735,Grund/b1_ss=15,Grund/bayer01=275094,Grund/b1_ss=15,Grund/bayer01=277774 -Gset,67,mixed_some_attributes,0.166667,2,3,1.09691,1.413499,1.413499,0,0,800,2000,10000,800,2000,10000,3200,23570,82918,3200,23570,82918,1.0,1,1.0,1.0,1,1.0,square:67,square,1.0,symmetric:67,symmetric,1.0,binary:34; integer:33,binary,0.507463,exact_numerical_or_hermitian:67,exact_numerical_or_hermitian,1.0,no:67,no,1.0,duplicate undirected random graph:12; undirected random graph:22; undirected weighted random graph:33,undirected weighted random graph,0.492537,missing:67,missing,1.0,no:67,no,1.0,no:67,no,1.0,10k_to_100k:1; 1k_to_10k:45; lt_1k:21,1k_to_10k,0.671642,Gset/G1=800; Gset/G10=800; Gset/G11=800; Gset/G12=800; Gset/G13=800; Gset/G14=800; Gset/G15=800; Gset/G16=800; Gset/G17=800; Gset/G18=800; Gset/G19=800; Gset/G2=800; Gset/G20=800; Gset/G21=800; Gset/G3=800; Gset/G4=800; Gset/G5=800; Gset/G6=800; Gset/G7=800; Gset/G8=800; Gset/G9=800,Gset/G67=10000,Gset/G1=800; Gset/G10=800; Gset/G11=800; Gset/G12=800; Gset/G13=800; Gset/G14=800; Gset/G15=800; Gset/G16=800; Gset/G17=800; Gset/G18=800; Gset/G19=800; Gset/G2=800; Gset/G20=800; Gset/G21=800; Gset/G3=800; Gset/G4=800; Gset/G5=800; Gset/G6=800; Gset/G7=800; Gset/G8=800; Gset/G9=800,Gset/G67=10000,Gset/G11=3200; Gset/G12=3200; Gset/G13=3200,Gset/G63=82918; Gset/G64=82918,Gset/G11=3200; Gset/G12=3200; Gset/G13=3200,Gset/G63=82918; Gset/G64=82918 -Guettel,3,small_group_lt5,0.0,0,1,0.817132,1.251333,1.251333,0,0,27623,152078,181302,27623,152078,181302,439469,6459326,7839010,439469,6459326,7839010,1.0,1,1.0,1.0,1,1.0,square:3,square,1.0,symmetric:3,symmetric,1.0,real:3,real,1.0,exact_numerical_or_hermitian:3,exact_numerical_or_hermitian,1.0,no:3,no,1.0,electromagnetics problem:3,electromagnetics problem,1.0,true:3,true,1.0,yes:3,yes,1.0,yes:3,yes,1.0,100k_to_1m:2; 10k_to_100k:1,100k_to_1m,0.666667,Guettel/TEM27623=27623,Guettel/TEM181302=181302,Guettel/TEM27623=27623,Guettel/TEM181302=181302,Guettel/TEM27623=439469,Guettel/TEM181302=7839010,Guettel/TEM27623=439469,Guettel/TEM181302=7839010 -Gupta,3,small_group_lt5,0.0,0,1,0.56797,0.634276,0.634276,0,0,16783,31802,62064,16783,31802,62064,2164210,4248286,9323427,2164210,4248286,9323427,1.0,1,1.0,1.0,1,1.0,square:3,square,1.0,symmetric:3,symmetric,1.0,binary:3,binary,1.0,exact_numerical_or_hermitian:3,exact_numerical_or_hermitian,1.0,no:3,no,1.0,optimization problem:3,optimization problem,1.0,true:3,true,1.0,no:3,no,1.0,yes:3,yes,1.0,10k_to_100k:3,10k_to_100k,1.0,Gupta/gupta3=16783,Gupta/gupta2=62064,Gupta/gupta3=16783,Gupta/gupta2=62064,Gupta/gupta1=2164210,Gupta/gupta3=9323427,Gupta/gupta1=2164210,Gupta/gupta3=9323427 -HB,292,categorically_heterogeneous,0.352169,5,25,3.69518,4.930162,4.629132,51,41359,9,680,44609,9,670.5,44609,24,4089,2043492,48,4089,2043492,0.0,1,1.0,0.0,1,1.0,rectangular:16; square:276,square,0.945205,rectangular:16; skew-symmetric:2; symmetric:169; unsymmetric:105,symmetric,0.578767,binary:112; complex:3; integer:7; real:170,real,0.582192,exact_numerical_or_hermitian:169; partially_pattern_symmetric:83; pattern_symmetric_only:15; pattern_unsymmetric:9; rectangular:16,exact_numerical_or_hermitian,0.578767,no:232; yes:60,no,0.794521,2D/3D problem:22; 2D/3D problem sequence:4; acoustics problem:3; chemical process simulation problem:16; computational fluid dynamics problem:27; counter-example problem:3; directed graph:1; directed weighted graph:8; duplicate acoustics problem:1; duplicate structural problem:2; duplicate thermal problem:1; economic problem:14; least squares problem:11; materials problem:1; optimization problem:1; optimization problem sequence:3; power network problem:16; power network problem sequence:1; semiconductor device problem:2; statistical/mathematical problem:1; structural problem:119; subsequent 2D/3D problem:10; subsequent optimization problem:13; subsequent power network problem:1; thermal problem:11,structural problem,0.407534,false:20; missing:9; true:263,true,0.900685,no:89; yes:203,yes,0.695205,no:140; yes:152,yes,0.520548,10k_to_100k:9; 1k_to_10k:98; lt_1k:185,lt_1k,0.633562,HB/jgl009=9,HB/bcsstk32=44609,HB/jgl009=9,HB/bcsstk32=44609,HB/bcsstm01=24,HB/bcsstk30=2043492,HB/bcsstm01=48,HB/bcsstk30=2043492 -HVDC,2,small_group_lt5,0.0,0,1,0.883247,0.927161,0.925387,2,9190,24842,107351,189860,24842,107351,189860,158426,749032,1339638,159981,753627,1347273,0.9817003233707516,0.9851479287391602,0.9885955341075688,0.06323365458033522,0.08111318278984582,0.09899271099935641,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,real:2,real,1.0,partially_pattern_symmetric:2,partially_pattern_symmetric,1.0,no:2,no,1.0,power network problem:2,power network problem,1.0,true:2,true,1.0,no:2,no,1.0,no:2,no,1.0,100k_to_1m:1; 10k_to_100k:1,100k_to_1m,0.5,HVDC/hvdc1=24842,HVDC/hvdc2=189860,HVDC/hvdc1=24842,HVDC/hvdc2=189860,HVDC/hvdc1=158426,HVDC/hvdc2=1339638,HVDC/hvdc1=159981,HVDC/hvdc2=1347273 -Hamm,6,mixed_some_attributes,0.055556,1,1,1.853686,1.862831,1.743267,3,35207,2395,43330,170998,2395,43330,170998,13151,237352.5,958936,17319,250854,958936,0.9999410961997232,1,1.0,0.1951139297670355,0.5113996859135346,0.9084315976207868,square:6,square,1.0,unsymmetric:6,unsymmetric,1.0,real:6,real,1.0,partially_pattern_symmetric:2; pattern_symmetric_only:4,pattern_symmetric_only,0.666667,no:6,no,1.0,circuit simulation problem:6,circuit simulation problem,1.0,true:6,true,1.0,no:6,no,1.0,no:6,no,1.0,100k_to_1m:2; 10k_to_100k:2; 1k_to_10k:2,100k_to_1m,0.333333,Hamm/add20=2395,Hamm/scircuit=170998,Hamm/add20=2395,Hamm/scircuit=170998,Hamm/add20=13151,Hamm/scircuit=958936,Hamm/add20=17319,Hamm/scircuit=958936 -Hamrle,3,small_group_lt5,0.0,0,1,4.655427,4.75026,4.75026,0,0,32,5952,1447360,32,5952,1447360,98,22162,5514242,98,22162,5514242,2.538882993038383e-06,0.0009930486593843098,0.06451612903225806,0.0,0,0.0001805543017062382,square:3,square,1.0,unsymmetric:3,unsymmetric,1.0,real:3,real,1.0,partially_pattern_symmetric:3,partially_pattern_symmetric,1.0,no:3,no,1.0,circuit simulation problem:3,circuit simulation problem,1.0,true:3,true,1.0,no:3,no,1.0,no:3,no,1.0,1k_to_10k:1; ge_1m:1; lt_1k:1,1k_to_10k,0.333333,Hamrle/Hamrle1=32,Hamrle/Hamrle3=1447360,Hamrle/Hamrle1=32,Hamrle/Hamrle3=1447360,Hamrle/Hamrle1=98,Hamrle/Hamrle3=5514242,Hamrle/Hamrle1=98,Hamrle/Hamrle3=5514242 -Hardesty,3,small_group_lt5,0.222222,4,1,0.94632,1.002631,1.002631,0,0,929901,938905,8217820,303645,938905,7591564,4020731,12143314,40451632,4020731,12143314,40451632,0.0,0,1.0,0.0,0,1.0,rectangular:2; square:1,rectangular,0.666667,rectangular:2; symmetric:1,rectangular,0.666667,integer:1; real:2,real,0.666667,exact_numerical_or_hermitian:1; rectangular:2,rectangular,0.666667,no:3,no,1.0,computer graphics/vision problem:3,computer graphics/vision problem,1.0,true:3,true,1.0,yes:3,yes,1.0,no:3,no,1.0,100k_to_1m:2; ge_1m:1,100k_to_1m,0.666667,Hardesty/Hardesty2=929901,Hardesty/Hardesty3=8217820,Hardesty/Hardesty2=303645,Hardesty/Hardesty3=7591564,Hardesty/Hardesty2=4020731,Hardesty/Hardesty3=40451632,Hardesty/Hardesty2=4020731,Hardesty/Hardesty3=40451632 -Harvard_Seismology,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,87616,87616,87616,67320,67320,67320,13734559,13734559,13734559,13734559,13734559,13734559,0.0,0,0.0,0.0,0,0.0,rectangular:1,rectangular,1.0,rectangular:1,rectangular,1.0,real:1,real,1.0,rectangular:1,rectangular,1.0,no:1,no,1.0,tomography problem:1,tomography problem,1.0,false:1,false,1.0,no:1,no,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Harvard_Seismology/JP=87616,Harvard_Seismology/JP=87616,Harvard_Seismology/JP=67320,Harvard_Seismology/JP=67320,Harvard_Seismology/JP=13734559,Harvard_Seismology/JP=13734559,Harvard_Seismology/JP=13734559,Harvard_Seismology/JP=13734559 -Hohn,6,mixed_some_attributes,0.027778,1,1,0.340523,1.522862,1.534216,3,53466,7500,11532,16428,7500,11532,16428,28462,173699,948696,28462,179196,973826,0.0,0.005925966048435875,0.01755337541442645,0.0,0,0.0,square:6,square,1.0,unsymmetric:6,unsymmetric,1.0,real:6,real,1.0,partially_pattern_symmetric:5; pattern_unsymmetric:1,partially_pattern_symmetric,0.833333,no:6,no,1.0,materials problem:6,materials problem,1.0,true:6,true,1.0,yes:6,yes,1.0,no:6,no,1.0,10k_to_100k:4; 1k_to_10k:2,10k_to_100k,0.666667,Hohn/fd12=7500; Hohn/sinc12=7500,Hohn/fd18=16428; Hohn/sinc18=16428,Hohn/fd12=7500; Hohn/sinc12=7500,Hohn/fd18=16428; Hohn/sinc18=16428,Hohn/fd12=28462,Hohn/sinc18=948696,Hohn/fd12=28462,Hohn/sinc18=973826 -Hollinger,49,homogeneous,0.0,0,1,1.347391,1.595132,1.63018,49,1559421,2880,27534,64089,2880,27534,64089,18229,190224,717620,19635,215862,837936,0.003290255360374358,0.03703210435580416,0.07395887333678935,0.0,0,0.01417215070061332,square:49,square,1.0,unsymmetric:49,unsymmetric,1.0,real:49,real,1.0,partially_pattern_symmetric:49,partially_pattern_symmetric,1.0,no:49,no,1.0,economic problem:49,economic problem,1.0,true:49,true,1.0,no:49,no,1.0,no:49,no,1.0,10k_to_100k:41; 1k_to_10k:8,10k_to_100k,0.836735,Hollinger/g7jac010=2880; Hollinger/g7jac010sc=2880,Hollinger/mark3jac140=64089; Hollinger/mark3jac140sc=64089,Hollinger/g7jac010=2880; Hollinger/g7jac010sc=2880,Hollinger/mark3jac140=64089; Hollinger/mark3jac140sc=64089,Hollinger/g7jac010=18229; Hollinger/g7jac010sc=18229,Hollinger/g7jac200=717620; Hollinger/g7jac200sc=717620,Hollinger/g7jac010=19635; Hollinger/g7jac010sc=19635,Hollinger/g7jac200=837936; Hollinger/g7jac200sc=837936 -IBM_Austin,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,1330,11341,11341,11341,11341,11341,11341,97193,97193,97193,98523,98523,98523,1.0,1,1.0,0.7823373235828404,0.7823373235828404,0.7823373235828404,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,pattern_symmetric_only:1,pattern_symmetric_only,1.0,no:1,no,1.0,circuit simulation problem:1,circuit simulation problem,1.0,true:1,true,1.0,no:1,no,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,IBM_Austin/coupled=11341,IBM_Austin/coupled=11341,IBM_Austin/coupled=11341,IBM_Austin/coupled=11341,IBM_Austin/coupled=97193,IBM_Austin/coupled=97193,IBM_Austin/coupled=98523,IBM_Austin/coupled=98523 -IBM_EDA,7,mixed_some_attributes,0.095238,1,3,0.371199,0.362261,0.361971,3,33414,49702,116835,116835,49702,116835,116835,332807,749800,766396,333029,766396,766396,0.8542477149952045,0.8542477149952045,0.9838102263462359,0.2426284829292399,0.3018997890878642,0.7360237367761078,square:7,square,1.0,unsymmetric:7,unsymmetric,1.0,real:7,real,1.0,partially_pattern_symmetric:7,partially_pattern_symmetric,1.0,no:7,no,1.0,circuit simulation problem:2; circuit simulation problem sequence:2; subsequent circuit simulation problem:3,subsequent circuit simulation problem,0.428571,true:7,true,1.0,no:7,no,1.0,no:7,no,1.0,100k_to_1m:5; 10k_to_100k:2,100k_to_1m,0.714286,IBM_EDA/ckt11752_dc_1=49702; IBM_EDA/ckt11752_tr_0=49702,IBM_EDA/dc1=116835; IBM_EDA/dc2=116835; IBM_EDA/dc3=116835; IBM_EDA/trans4=116835; IBM_EDA/trans5=116835,IBM_EDA/ckt11752_dc_1=49702; IBM_EDA/ckt11752_tr_0=49702,IBM_EDA/dc1=116835; IBM_EDA/dc2=116835; IBM_EDA/dc3=116835; IBM_EDA/trans4=116835; IBM_EDA/trans5=116835,IBM_EDA/ckt11752_tr_0=332807,IBM_EDA/dc1=766396; IBM_EDA/dc2=766396; IBM_EDA/dc3=766396,IBM_EDA/ckt11752_dc_1=333029; IBM_EDA/ckt11752_tr_0=333029,IBM_EDA/dc1=766396; IBM_EDA/dc2=766396; IBM_EDA/dc3=766396; IBM_EDA/trans4=766396; IBM_EDA/trans5=766396 -INPRO,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,1067772,415863,415863,415863,415863,415863,415863,19173163,19173163,19173163,20240935,20240935,20240935,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,yes:1,yes,1.0,structural problem:1,structural problem,1.0,true:1,true,1.0,yes:1,yes,1.0,yes:1,yes,1.0,100k_to_1m:1,100k_to_1m,1.0,INPRO/msdoor=415863,INPRO/msdoor=415863,INPRO/msdoor=415863,INPRO/msdoor=415863,INPRO/msdoor=19173163,INPRO/msdoor=19173163,INPRO/msdoor=20240935,INPRO/msdoor=20240935 -IPSO,7,homogeneous,0.0,0,1,1.444137,1.153119,1.105278,7,330898,8140,29902,226340,8140,29902,226340,141478,762969,2012833,158273,820804,2016902,1.0,1,1.0,1.0,1,1.0,square:7,square,1.0,symmetric:7,symmetric,1.0,real:7,real,1.0,exact_numerical_or_hermitian:7,exact_numerical_or_hermitian,1.0,no:7,no,1.0,power network problem:7,power network problem,1.0,true:7,true,1.0,no:7,no,1.0,no:7,no,1.0,100k_to_1m:2; 10k_to_100k:3; 1k_to_10k:2,10k_to_100k,0.428571,IPSO/TSC_OPF_1047=8140,IPSO/HTC_336_4438=226340; IPSO/HTC_336_9129=226340,IPSO/TSC_OPF_1047=8140,IPSO/HTC_336_4438=226340; IPSO/HTC_336_9129=226340,IPSO/OPF_3754=141478,IPSO/TSC_OPF_1047=2012833,IPSO/OPF_3754=158273,IPSO/TSC_OPF_1047=2016902 -JGD_BIBD,21,type_homogeneous_size_diverse,0.031746,0,1,3.580558,4.550595,4.550595,0,0,36,136,3240,84,3240,319770,252,25740,8953560,252,25740,8953560,0.0,0,1.0,0.0,0,1.0,rectangular:20; square:1,rectangular,0.952381,rectangular:20; symmetric:1,rectangular,0.952381,binary:21,binary,1.0,exact_numerical_or_hermitian:1; rectangular:20,rectangular,0.952381,no:20; yes:1,no,0.952381,combinatorial problem:21,combinatorial problem,1.0,true:21,true,1.0,no:21,no,1.0,no:20; yes:1,no,0.952381,100k_to_1m:2; 10k_to_100k:6; 1k_to_10k:6; lt_1k:7,lt_1k,0.333333,JGD_BIBD/bibd_9_3=36; JGD_BIBD/bibd_9_5=36,JGD_BIBD/bibd_81_2=3240; JGD_BIBD/bibd_81_3=3240,JGD_BIBD/bibd_9_3=84,JGD_BIBD/bibd_22_8=319770,JGD_BIBD/bibd_9_3=252,JGD_BIBD/bibd_22_8=8953560,JGD_BIBD/bibd_9_3=252,JGD_BIBD/bibd_22_8=8953560 -JGD_CAG,3,small_group_lt5,0.0,0,1,1.425063,2.287042,2.287042,0,0,72,364,1916,72,364,1916,1012,13585,195985,1012,13585,195985,0.3002540333592691,0.4155510173209288,0.5574468085106383,0.2119864584245809,0.2804628999319265,0.3340425531914893,square:3,square,1.0,unsymmetric:3,unsymmetric,1.0,integer:3,integer,1.0,partially_pattern_symmetric:3,partially_pattern_symmetric,1.0,no:3,no,1.0,combinatorial problem:3,combinatorial problem,1.0,true:3,true,1.0,no:3,no,1.0,no:3,no,1.0,1k_to_10k:1; lt_1k:2,lt_1k,0.666667,JGD_CAG/CAG_mat72=72,JGD_CAG/CAG_mat1916=1916,JGD_CAG/CAG_mat72=72,JGD_CAG/CAG_mat1916=1916,JGD_CAG/CAG_mat72=1012,JGD_CAG/CAG_mat1916=195985,JGD_CAG/CAG_mat72=1012,JGD_CAG/CAG_mat1916=195985 -JGD_Forest,10,type_homogeneous_size_diverse,0.0,0,1,3.472982,3.846763,3.846763,0,0,99,4489,241029,107,5451,317955,622,54959.5,4370721,622,54959.5,4370721,0.0,0,0.0,0.0,0,0.0,rectangular:10,rectangular,1.0,rectangular:10,rectangular,1.0,integer:10,integer,1.0,rectangular:10,rectangular,1.0,no:10,no,1.0,combinatorial problem:10,combinatorial problem,1.0,true:10,true,1.0,no:10,no,1.0,no:10,no,1.0,100k_to_1m:2; 10k_to_100k:2; 1k_to_10k:3; lt_1k:3,1k_to_10k,0.3,JGD_Forest/TF10=99,JGD_Forest/TF19=241029,JGD_Forest/TF10=107,JGD_Forest/TF19=317955,JGD_Forest/TF10=622,JGD_Forest/TF19=4370721,JGD_Forest/TF10=622,JGD_Forest/TF19=4370721 -JGD_Franz,11,homogeneous,0.0,0,1,1.32281,1.808886,1.808886,0,0,1280,7576,47104,768,4164,30144,5120,45456,329728,5120,45456,329728,0.0,0,0.0,0.0,0,0.0,rectangular:11,rectangular,1.0,rectangular:11,rectangular,1.0,integer:11,integer,1.0,rectangular:11,rectangular,1.0,no:11,no,1.0,combinatorial problem:11,combinatorial problem,1.0,true:11,true,1.0,no:11,no,1.0,no:11,no,1.0,10k_to_100k:5; 1k_to_10k:6,1k_to_10k,0.545455,JGD_Franz/Franz3=1280,JGD_Franz/Franz11=47104,JGD_Franz/Franz1=768,JGD_Franz/Franz11=30144,JGD_Franz/Franz1=5120,JGD_Franz/Franz11=329728,JGD_Franz/Franz1=5120,JGD_Franz/Franz11=329728 -JGD_G5,13,type_homogeneous_size_diverse,0.0,0,1,2.79379,3.853298,3.853298,0,0,30,2296,47894,77,2875,41550,251,46260,1790490,251,46260,1790490,0.0,0,0.0,0.0,0,0.0,rectangular:13,rectangular,1.0,rectangular:13,rectangular,1.0,integer:13,integer,1.0,rectangular:13,rectangular,1.0,no:13,no,1.0,combinatorial problem:13,combinatorial problem,1.0,false:10; true:3,false,0.769231,no:13,no,1.0,no:13,no,1.0,10k_to_100k:4; 1k_to_10k:4; lt_1k:5,lt_1k,0.384615,JGD_G5/IG5-6=30,JGD_G5/IG5-18=47894,JGD_G5/IG5-6=77,JGD_G5/IG5-18=41550,JGD_G5/IG5-6=251,JGD_G5/IG5-18=1790490,JGD_G5/IG5-6=251,JGD_G5/IG5-18=1790490 -JGD_GL6,5,homogeneous,0.0,0,1,0.271385,0.476698,0.476698,0,0,163,469,636,201,470,637,2053,4349,6153,2053,4349,6153,0.0,0,0.0,0.0,0,0.0,rectangular:5,rectangular,1.0,rectangular:5,rectangular,1.0,integer:5,integer,1.0,rectangular:5,rectangular,1.0,no:5,no,1.0,combinatorial problem:5,combinatorial problem,1.0,false:5,false,1.0,no:5,no,1.0,no:5,no,1.0,lt_1k:5,lt_1k,1.0,JGD_GL6/GL6_D_10=163,JGD_GL6/GL6_D_7=636,JGD_GL6/GL6_D_6=201,JGD_GL6/GL6_D_8=637,JGD_GL6/GL6_D_10=2053,JGD_GL6/GL6_D_8=6153,JGD_GL6/GL6_D_10=2053,JGD_GL6/GL6_D_8=6153 -JGD_GL7d,17,type_homogeneous_size_diverse,0.0,0,1,4.513064,6.668883,6.668883,0,0,1,171375,1955309,60,171375,1955309,8,2695430,37322725,8,2695430,37322725,0.0,0,0.0,0.0,0,0.0,rectangular:17,rectangular,1.0,rectangular:17,rectangular,1.0,integer:17,integer,1.0,rectangular:17,rectangular,1.0,no:17,no,1.0,combinatorial problem:17,combinatorial problem,1.0,false:9; true:8,false,0.529412,no:17,no,1.0,no:17,no,1.0,100k_to_1m:6; 10k_to_100k:2; 1k_to_10k:3; ge_1m:5; lt_1k:1,100k_to_1m,0.352941,JGD_GL7d/GL7d10=1,JGD_GL7d/GL7d18=1955309,JGD_GL7d/GL7d10=60; JGD_GL7d/GL7d11=60,JGD_GL7d/GL7d19=1955309,JGD_GL7d/GL7d10=8,JGD_GL7d/GL7d19=37322725,JGD_GL7d/GL7d10=8,JGD_GL7d/GL7d19=37322725 -JGD_Groebner,8,mixed_some_attributes,0.020833,1,1,1.154117,2.211965,2.211965,0,0,302,2414,4562,302,2511,5761,15118,171214,2462970,15118,171214,2462970,0.0,0,0.0,0.0,0,0.0,rectangular:8,rectangular,1.0,rectangular:8,rectangular,1.0,binary:1; integer:7,integer,0.875,rectangular:8,rectangular,1.0,no:8,no,1.0,combinatorial problem:8,combinatorial problem,1.0,false:1; true:7,true,0.875,no:8,no,1.0,no:8,no,1.0,1k_to_10k:5; lt_1k:3,1k_to_10k,0.625,JGD_Groebner/robot24c1_mat5_J=302,JGD_Groebner/c8_mat11=4562; JGD_Groebner/c8_mat11_I=4562,JGD_Groebner/robot24c1_mat5=302,JGD_Groebner/c8_mat11=5761; JGD_Groebner/c8_mat11_I=5761,JGD_Groebner/robot24c1_mat5=15118; JGD_Groebner/robot24c1_mat5_J=15118,JGD_Groebner/c8_mat11=2462970; JGD_Groebner/c8_mat11_I=2462970,JGD_Groebner/robot24c1_mat5=15118; JGD_Groebner/robot24c1_mat5_J=15118,JGD_Groebner/c8_mat11=2462970; JGD_Groebner/c8_mat11_I=2462970 -JGD_Homology,128,type_homogeneous_size_diverse,0.023438,0,1,4.575557,5.274527,5.274527,1,360,6,2455.5,564480,6,1365,376320,18,13800,3386880,18,13800,3386880,0.0,0,1.0,0.0,0,0.5333333333333333,rectangular:122; square:6,rectangular,0.953125,rectangular:122; unsymmetric:6,rectangular,0.953125,integer:128,integer,1.0,partially_pattern_symmetric:5; pattern_symmetric_only:1; rectangular:122,rectangular,0.953125,no:128,no,1.0,combinatorial problem:128,combinatorial problem,1.0,false:2; true:126,true,0.984375,no:128,no,1.0,no:128,no,1.0,100k_to_1m:19; 10k_to_100k:22; 1k_to_10k:45; lt_1k:42,1k_to_10k,0.351562,JGD_Homology/ch3-3-b2=6; JGD_Homology/n3c4-b4=6,JGD_Homology/ch8-8-b5=564480,JGD_Homology/n3c4-b1=6,JGD_Homology/ch8-8-b5=376320,JGD_Homology/ch3-3-b2=18,JGD_Homology/ch8-8-b5=3386880,JGD_Homology/ch3-3-b2=18,JGD_Homology/ch8-8-b5=3386880 -JGD_Kocay,12,type_homogeneous_size_diverse,0.013889,0,1,3.900504,6.458225,6.458225,0,0,1,35,3159,2,142.5,15905,1,1348,2872265,1,1348,2872265,0.0,0,0.0,0.0,0,0.0,rectangular:12,rectangular,1.0,rectangular:12,rectangular,1.0,binary:1; integer:11,integer,0.916667,rectangular:12,rectangular,1.0,no:12,no,1.0,combinatorial problem:12,combinatorial problem,1.0,true:12,true,1.0,no:12,no,1.0,no:12,no,1.0,10k_to_100k:1; 1k_to_10k:3; lt_1k:8,lt_1k,0.666667,JGD_Kocay/Trec3=1,JGD_Kocay/Trec14=3159,JGD_Kocay/Trec3=2,JGD_Kocay/Trec14=15905,JGD_Kocay/Trec3=1,JGD_Kocay/Trec14=2872265,JGD_Kocay/Trec3=1,JGD_Kocay/Trec14=2872265 -JGD_Margulies,23,type_homogeneous_size_diverse,0.021739,0,1,4.557316,4.467391,4.467391,0,0,21,601,902103,25,513,723605,74,1538,2170814,74,1538,2170814,0.0,0,0.03252032520325204,0.0,0,0.03252032520325204,rectangular:22; square:1,rectangular,0.956522,rectangular:22; unsymmetric:1,rectangular,0.956522,binary:23,binary,1.0,partially_pattern_symmetric:1; rectangular:22,rectangular,0.956522,no:23,no,1.0,combinatorial problem:23,combinatorial problem,1.0,false:11; true:12,true,0.521739,no:23,no,1.0,no:23,no,1.0,100k_to_1m:3; 10k_to_100k:5; 1k_to_10k:2; lt_1k:13,lt_1k,0.565217,JGD_Margulies/wheel_3_1=21,JGD_Margulies/wheel_601=902103,JGD_Margulies/wheel_3_1=25,JGD_Margulies/wheel_601=723605,JGD_Margulies/wheel_3_1=74,JGD_Margulies/wheel_601=2170814,JGD_Margulies/wheel_3_1=74,JGD_Margulies/wheel_601=2170814 -JGD_Relat,15,type_homogeneous_size_diverse,0.0,0,1,6.012839,6.335295,6.335295,0,0,12,2340,12360060,5,157,549336,18,8108,38955420,18,8108,38955420,0.0,0,0.0,0.0,0,0.0,rectangular:15,rectangular,1.0,rectangular:15,rectangular,1.0,integer:15,integer,1.0,rectangular:15,rectangular,1.0,no:15,no,1.0,combinatorial problem:15,combinatorial problem,1.0,false:15,false,1.0,no:15,no,1.0,no:15,no,1.0,100k_to_1m:2; 10k_to_100k:3; 1k_to_10k:2; ge_1m:2; lt_1k:6,lt_1k,0.4,JGD_Relat/rel3=12; JGD_Relat/relat3=12,JGD_Relat/relat9=12360060,JGD_Relat/rel3=5; JGD_Relat/relat3=5,JGD_Relat/relat9=549336,JGD_Relat/rel3=18,JGD_Relat/relat9=38955420,JGD_Relat/rel3=18,JGD_Relat/relat9=38955420 -JGD_SL6,7,homogeneous,0.0,0,1,0.466656,0.91218,0.91218,0,0,169,815,1270,115,816,1271,1832,7614,14966,1832,7614,14966,0.0,0,0.0,0.0,0,0.0,rectangular:7,rectangular,1.0,rectangular:7,rectangular,1.0,integer:7,integer,1.0,rectangular:7,rectangular,1.0,no:7,no,1.0,combinatorial problem:7,combinatorial problem,1.0,false:7,false,1.0,no:7,no,1.0,no:7,no,1.0,1k_to_10k:3; lt_1k:4,lt_1k,0.571429,JGD_SL6/D_11=169,JGD_SL6/D_7=1270,JGD_SL6/D_5=115,JGD_SL6/D_8=1271,JGD_SL6/D_5=1832,JGD_SL6/D_8=14966,JGD_SL6/D_5=1832,JGD_SL6/D_8=14966 -JGD_SPG,7,categorically_heterogeneous,0.071429,3,1,1.338788,2.6985,2.6985,0,0,300,2600,6545,300,2600,6545,592,71760,295680,592,71760,295680,0.0273972602739726,1,1.0,0.0,1,1.0,square:7,square,1.0,symmetric:6; unsymmetric:1,symmetric,0.857143,binary:6; integer:1,binary,0.857143,exact_numerical_or_hermitian:6; partially_pattern_symmetric:1,exact_numerical_or_hermitian,0.857143,no:7,no,1.0,combinatorial problem:7,combinatorial problem,1.0,true:7,true,1.0,no:7,no,1.0,no:7,no,1.0,1k_to_10k:4; lt_1k:3,1k_to_10k,0.571429,JGD_SPG/08blocks=300,JGD_SPG/EX5=6545; JGD_SPG/EX6=6545,JGD_SPG/08blocks=300,JGD_SPG/EX5=6545; JGD_SPG/EX6=6545,JGD_SPG/08blocks=592,JGD_SPG/EX5=295680; JGD_SPG/EX6=295680,JGD_SPG/08blocks=592,JGD_SPG/EX5=295680; JGD_SPG/EX6=295680 -JGD_Taha,2,small_group_lt5,0.0,0,1,0.414772,0.427266,0.427266,0,0,14596,26264,37932,209,270,331,51307,94267.5,137228,51307,94267.5,137228,0.0,0,0.0,0.0,0,0.0,rectangular:2,rectangular,1.0,rectangular:2,rectangular,1.0,integer:2,integer,1.0,rectangular:2,rectangular,1.0,no:2,no,1.0,combinatorial problem:2,combinatorial problem,1.0,true:2,true,1.0,no:2,no,1.0,no:2,no,1.0,10k_to_100k:2,10k_to_100k,1.0,JGD_Taha/abtaha1=14596,JGD_Taha/abtaha2=37932,JGD_Taha/abtaha1=209,JGD_Taha/abtaha2=331,JGD_Taha/abtaha1=51307,JGD_Taha/abtaha2=137228,JGD_Taha/abtaha1=51307,JGD_Taha/abtaha2=137228 -JGD_Trefethen,11,type_homogeneous_size_diverse,0.0,0,1,3.022276,3.576558,3.576558,0,0,19,300,20000,19,300,20000,147,4678,554466,147,4678,554466,1.0,1,1.0,1.0,1,1.0,square:11,square,1.0,symmetric:11,symmetric,1.0,integer:11,integer,1.0,exact_numerical_or_hermitian:11,exact_numerical_or_hermitian,1.0,yes:11,yes,1.0,combinatorial problem:11,combinatorial problem,1.0,true:11,true,1.0,no:11,no,1.0,yes:11,yes,1.0,10k_to_100k:2; 1k_to_10k:1; lt_1k:8,lt_1k,0.727273,JGD_Trefethen/Trefethen_20b=19,JGD_Trefethen/Trefethen_20000=20000,JGD_Trefethen/Trefethen_20b=19,JGD_Trefethen/Trefethen_20000=20000,JGD_Trefethen/Trefethen_20b=147,JGD_Trefethen/Trefethen_20000=554466,JGD_Trefethen/Trefethen_20b=147,JGD_Trefethen/Trefethen_20000=554466 -Janna,18,categorically_heterogeneous,0.185185,4,3,1.041402,1.262962,1.195524,15,39345501,377002,1470152,4147110,377002,1470152,4147110,17277420,62184146.5,316548962,21005389,63844195.5,329499284,1.0,1,1.0,0.0,1,1.0,square:18,square,1.0,symmetric:14; unsymmetric:4,symmetric,0.777778,real:18,real,1.0,exact_numerical_or_hermitian:14; pattern_symmetric_only:4,exact_numerical_or_hermitian,0.777778,no:8; yes:10,yes,0.555556,2D/3D problem:3; computational fluid dynamics problem:1; structural problem:14,structural problem,0.777778,true:18,true,1.0,yes:18,yes,1.0,no:8; yes:10,yes,0.555556,100k_to_1m:5; ge_1m:13,ge_1m,0.722222,Janna/ML_Laplace=377002,Janna/Queen_4147=4147110,Janna/ML_Laplace=377002,Janna/Queen_4147=4147110,Janna/CoupCons3D=17277420,Janna/Queen_4147=316548962,Janna/StocF-1465=21005389,Janna/Queen_4147=329499284 -Kamvar,2,small_group_lt5,0.0,0,1,0.384605,0.515781,0.515781,0,0,281903,482674.5,683446,281903,482674.5,683446,2312497,4947936.5,7583376,2312497,4947936.5,7583376,0.2502051856587356,0.26342104253978904,0.2766368994208425,0.2502051856587356,0.26342104253978904,0.2766368994208425,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,binary:2,binary,1.0,partially_pattern_symmetric:2,partially_pattern_symmetric,1.0,no:2,no,1.0,directed graph:2,directed graph,1.0,missing:2,missing,1.0,no:2,no,1.0,no:2,no,1.0,100k_to_1m:2,100k_to_1m,1.0,Kamvar/Stanford=281903,Kamvar/Stanford_Berkeley=683446,Kamvar/Stanford=281903,Kamvar/Stanford_Berkeley=683446,Kamvar/Stanford=2312497,Kamvar/Stanford_Berkeley=7583376,Kamvar/Stanford=2312497,Kamvar/Stanford_Berkeley=7583376 -Kemelmacher,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,28452,28452,28452,9693,9693,9693,100875,100875,100875,100875,100875,100875,0.0,0,0.0,0.0,0,0.0,rectangular:1,rectangular,1.0,rectangular:1,rectangular,1.0,real:1,real,1.0,rectangular:1,rectangular,1.0,no:1,no,1.0,computer graphics/vision problem:1,computer graphics/vision problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Kemelmacher/Kemelmacher=28452,Kemelmacher/Kemelmacher=28452,Kemelmacher/Kemelmacher=9693,Kemelmacher/Kemelmacher=9693,Kemelmacher/Kemelmacher=100875,Kemelmacher/Kemelmacher=100875,Kemelmacher/Kemelmacher=100875,Kemelmacher/Kemelmacher=100875 -Kim,2,small_group_lt5,0.0,0,1,1.075393,1.084258,1.084258,0,0,38415,247695.5,456976,38415,247695.5,456976,933195,6131607.5,11330020,933195,6131607.5,11330020,0.9897360244976419,0.9933861825054666,0.9970363405132914,0.0,0,0.0,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,complex:2,complex,1.0,partially_pattern_symmetric:2,partially_pattern_symmetric,1.0,no:2,no,1.0,2D/3D problem:2,2D/3D problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,100k_to_1m:1; 10k_to_100k:1,100k_to_1m,0.5,Kim/kim1=38415,Kim/kim2=456976,Kim/kim1=38415,Kim/kim2=456976,Kim/kim1=933195,Kim/kim2=11330020,Kim/kim1=933195,Kim/kim2=11330020 -Koutsovasilis,2,small_group_lt5,0.0,0,1,0.681958,0.704928,0.704928,0,0,71505,207648,343791,71505,207648,343791,5294285,16065699,26837113,5294285,16065699,26837113,1.0,1,1.0,1.0,1,1.0,square:2,square,1.0,symmetric:2,symmetric,1.0,real:2,real,1.0,exact_numerical_or_hermitian:2,exact_numerical_or_hermitian,1.0,no:2,no,1.0,structural problem:2,structural problem,1.0,true:2,true,1.0,yes:2,yes,1.0,yes:2,yes,1.0,100k_to_1m:1; 10k_to_100k:1,100k_to_1m,0.5,Koutsovasilis/F2=71505,Koutsovasilis/F1=343791,Koutsovasilis/F2=71505,Koutsovasilis/F1=343791,Koutsovasilis/F2=5294285,Koutsovasilis/F1=26837113,Koutsovasilis/F2=5294285,Koutsovasilis/F1=26837113 -LAW,15,categorically_heterogeneous,0.066667,3,2,3.232023,3.84877,3.84877,0,0,69244,5363260,118142155,69244,5363260,118142155,276143,113891327,1949412601,276143,113891327,1949412601,0.1245713694644861,0.2780598082364509,1.0,0.1245713694644861,0.2780598082364509,1.0,square:15,square,1.0,symmetric:2; unsymmetric:13,unsymmetric,0.866667,binary:15,binary,1.0,exact_numerical_or_hermitian:2; partially_pattern_symmetric:13,partially_pattern_symmetric,0.866667,no:15,no,1.0,directed graph:13; undirected graph:2,directed graph,0.866667,missing:15,missing,1.0,no:15,no,1.0,no:14; yes:1,no,0.933333,100k_to_1m:4; 10k_to_100k:1; ge_1m:10,ge_1m,0.666667,LAW/enron=69244,LAW/webbase-2001=118142155,LAW/enron=69244,LAW/webbase-2001=118142155,LAW/enron=276143,LAW/sk-2005=1949412601,LAW/enron=276143,LAW/sk-2005=1949412601 -LPnetlib,138,mixed_some_attributes,0.036232,1,1,4.272102,4.806202,4.806202,2,3753,8,489,105127,13,1312.5,243246,22,5500.5,1408073,22,5500.5,1408073,0.0,0,0.0,0.0,0,0.0,rectangular:138,rectangular,1.0,rectangular:138,rectangular,1.0,integer:30; real:108,real,0.782609,rectangular:138,rectangular,1.0,no:138,no,1.0,linear programming problem:138,linear programming problem,1.0,false:25; true:113,true,0.818841,no:138,no,1.0,no:138,no,1.0,100k_to_1m:4; 10k_to_100k:16; 1k_to_10k:60; lt_1k:58,1k_to_10k,0.434783,LPnetlib/lpi_galenet=8,LPnetlib/lp_ken_18=105127,LPnetlib/lpi_itest2=13,LPnetlib/lp_osa_60=243246,LPnetlib/lpi_galenet=22,LPnetlib/lp_osa_60=1408073,LPnetlib/lpi_galenet=22,LPnetlib/lp_osa_60=1408073 -Langemyr,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,1500,1500,1500,1500,1500,1500,97645,97645,97645,97645,97645,97645,0.9990327110094128,0.9990327110094128,0.9990327110094128,0.0008944822923708982,0.0008944822923708982,0.0008944822923708982,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,structural problem:1,structural problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,1k_to_10k:1,1k_to_10k,1.0,Langemyr/comsol=1500,Langemyr/comsol=1500,Langemyr/comsol=1500,Langemyr/comsol=1500,Langemyr/comsol=97645,Langemyr/comsol=97645,Langemyr/comsol=97645,Langemyr/comsol=97645 -LeGresley,3,small_group_lt5,0.0,0,1,1.544839,1.549839,1.549839,0,0,2508,4908,87936,2508,4908,87936,16727,30482,593276,16727,30482,593276,0.837611646388635,0.9773607038123168,0.9792020608292309,0.02995112414467253,0.03427471928313486,0.2557141852450946,square:3,square,1.0,unsymmetric:3,unsymmetric,1.0,real:3,real,1.0,partially_pattern_symmetric:3,partially_pattern_symmetric,1.0,no:3,no,1.0,power network problem:3,power network problem,1.0,true:3,true,1.0,no:3,no,1.0,no:3,no,1.0,10k_to_100k:1; 1k_to_10k:2,1k_to_10k,0.666667,LeGresley/LeGresley_2508=2508,LeGresley/LeGresley_87936=87936,LeGresley/LeGresley_2508=2508,LeGresley/LeGresley_87936=87936,LeGresley/LeGresley_2508=16727,LeGresley/LeGresley_87936=593276,LeGresley/LeGresley_2508=16727,LeGresley/LeGresley_87936=593276 -Lee,2,small_group_lt5,0.083333,1,1,0.821574,1.067845,1.067845,0,0,74062,282581,491100,74062,282581,491100,1731206,10985221.5,20239237,1731206,10985221.5,20239237,0.999999037883928,0.9999995189419639,1.0,0.4990933575151925,0.5561452604137468,0.6131971633123011,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,complex:2,complex,1.0,partially_pattern_symmetric:1; pattern_symmetric_only:1,partially_pattern_symmetric,0.5,no:2,no,1.0,electromagnetics problem:2,electromagnetics problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,100k_to_1m:1; 10k_to_100k:1,100k_to_1m,0.5,Lee/fem_filter=74062,Lee/fem_hifreq_circuit=491100,Lee/fem_filter=74062,Lee/fem_hifreq_circuit=491100,Lee/fem_filter=1731206,Lee/fem_hifreq_circuit=20239237,Lee/fem_filter=1731206,Lee/fem_hifreq_circuit=20239237 -Li,2,small_group_lt5,0.333333,4,2,0.0,0.045792,0.0,1,135128,22695,22695,22695,22695,22695,22695,1215181,1282745,1350309,1350309,1350309,1350309,1.0,1,1.0,0.8685670104303111,0.9342835052151556,1.0,square:2,square,1.0,symmetric:1; unsymmetric:1,symmetric,0.5,binary:1; real:1,binary,0.5,exact_numerical_or_hermitian:1; pattern_symmetric_only:1,exact_numerical_or_hermitian,0.5,no:2,no,1.0,duplicate electromagnetics problem:1; electromagnetics problem:1,duplicate electromagnetics problem,0.5,true:2,true,1.0,yes:2,yes,1.0,no:1; yes:1,no,0.5,10k_to_100k:2,10k_to_100k,1.0,Li/li=22695; Li/pli=22695,Li/li=22695; Li/pli=22695,Li/li=22695; Li/pli=22695,Li/li=22695; Li/pli=22695,Li/li=1215181,Li/pli=1350309,Li/li=1350309; Li/pli=1350309,Li/li=1350309; Li/pli=1350309 -Lin,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,256000,256000,256000,256000,256000,256000,1766400,1766400,1766400,1766400,1766400,1766400,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,no:1,no,1.0,structural problem:1,structural problem,1.0,true:1,true,1.0,yes:1,yes,1.0,yes:1,yes,1.0,100k_to_1m:1,100k_to_1m,1.0,Lin/Lin=256000,Lin/Lin=256000,Lin/Lin=256000,Lin/Lin=256000,Lin/Lin=1766400,Lin/Lin=1766400,Lin/Lin=1766400,Lin/Lin=1766400 -LiuWenzhuo,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,3138,15838,15838,15838,15838,15838,15838,64424,64424,64424,67562,67562,67562,0.5913695769855386,0.5913695769855386,0.5913695769855386,0.5337339974478245,0.5337339974478245,0.5337339974478245,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,power network problem:1,power network problem,1.0,true:1,true,1.0,no:1,no,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,LiuWenzhuo/powersim=15838,LiuWenzhuo/powersim=15838,LiuWenzhuo/powersim=15838,LiuWenzhuo/powersim=15838,LiuWenzhuo/powersim=64424,LiuWenzhuo/powersim=64424,LiuWenzhuo/powersim=67562,LiuWenzhuo/powersim=67562 -Lourakis,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,90,10581,10581,10581,10581,10581,10581,770811,770811,770811,770901,770901,770901,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,yes:1,yes,1.0,computer graphics/vision problem:1,computer graphics/vision problem,1.0,true:1,true,1.0,yes:1,yes,1.0,yes:1,yes,1.0,10k_to_100k:1,10k_to_100k,1.0,Lourakis/bundle1=10581,Lourakis/bundle1=10581,Lourakis/bundle1=10581,Lourakis/bundle1=10581,Lourakis/bundle1=770811,Lourakis/bundle1=770811,Lourakis/bundle1=770901,Lourakis/bundle1=770901 -Lucifora,2,small_group_lt5,0.0,0,1,0.0,0.0,0.0,2,9546,7055,7055,7055,7055,7055,7055,30082,30082,30082,34855,34855,34855,0.9970503597122302,0.9970503597122302,0.9970503597122302,0.0,0.002171364050896773,0.004342728101793546,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,real:2,real,1.0,partially_pattern_symmetric:2,partially_pattern_symmetric,1.0,no:2,no,1.0,directed weighted graph:2,directed weighted graph,1.0,missing:2,missing,1.0,no:2,no,1.0,no:2,no,1.0,1k_to_10k:2,1k_to_10k,1.0,Lucifora/cell1=7055; Lucifora/cell2=7055,Lucifora/cell1=7055; Lucifora/cell2=7055,Lucifora/cell1=7055; Lucifora/cell2=7055,Lucifora/cell1=7055; Lucifora/cell2=7055,Lucifora/cell1=30082; Lucifora/cell2=30082,Lucifora/cell1=30082; Lucifora/cell2=30082,Lucifora/cell1=34855; Lucifora/cell2=34855,Lucifora/cell1=34855; Lucifora/cell2=34855 -Luong,2,small_group_lt5,0.0,0,1,0.508112,0.496388,0.496388,0,0,1388,2930,4472,390,663,936,11816,24436,37056,11816,24436,37056,0.0,0,0.0,0.0,0,0.0,rectangular:2,rectangular,1.0,rectangular:2,rectangular,1.0,real:2,real,1.0,rectangular:2,rectangular,1.0,no:2,no,1.0,computer graphics/vision problem:2,computer graphics/vision problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,1k_to_10k:2,1k_to_10k,1.0,Luong/photogrammetry=1388,Luong/photogrammetry2=4472,Luong/photogrammetry=390,Luong/photogrammetry2=936,Luong/photogrammetry=11816,Luong/photogrammetry2=37056,Luong/photogrammetry=11816,Luong/photogrammetry2=37056 -MAWI,5,homogeneous,0.0,0,1,1.085646,1.10104,1.10104,0,0,18571154,68863315,226196185,18571154,68863315,226196185,38040320,143414960,480047894,38040320,143414960,480047894,1.0,1,1.0,1.0,1,1.0,square:5,square,1.0,symmetric:5,symmetric,1.0,integer:5,integer,1.0,exact_numerical_or_hermitian:5,exact_numerical_or_hermitian,1.0,no:5,no,1.0,undirected weighted graph:5,undirected weighted graph,1.0,missing:5,missing,1.0,no:5,no,1.0,no:5,no,1.0,ge_1m:5,ge_1m,1.0,MAWI/mawi_201512012345=18571154,MAWI/mawi_201512020330=226196185,MAWI/mawi_201512012345=18571154,MAWI/mawi_201512020330=226196185,MAWI/mawi_201512012345=38040320,MAWI/mawi_201512020330=480047894,MAWI/mawi_201512012345=38040320,MAWI/mawi_201512020330=480047894 -MKS,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,14331,7548,7548,7548,7548,7548,7548,834222,834222,834222,848553,848553,848553,0.7575174939506899,0.7575174939506899,0.7575174939506899,0.002912877385765126,0.002912877385765126,0.002912877385765126,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,electromagnetics problem:1,electromagnetics problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,1k_to_10k:1,1k_to_10k,1.0,MKS/fp=7548,MKS/fp=7548,MKS/fp=7548,MKS/fp=7548,MKS/fp=834222,MKS/fp=834222,MKS/fp=848553,MKS/fp=848553 -ML_Graph,36,homogeneous,0.0,0,1,2.560685,2.641265,2.641265,0,0,106,1599.5,38547,106,1599.5,38547,1412,22529,618158,1412,22529,618158,1.0,1,1.0,1.0,1,1.0,square:36,square,1.0,symmetric:36,symmetric,1.0,real:36,real,1.0,exact_numerical_or_hermitian:36,exact_numerical_or_hermitian,1.0,no:36,no,1.0,undirected weighted graph:36,undirected weighted graph,1.0,missing:36,missing,1.0,no:36,no,1.0,no:36,no,1.0,10k_to_100k:7; 1k_to_10k:18; lt_1k:11,1k_to_10k,0.5,ML_Graph/breasttissue_10NN=106,ML_Graph/k49_norm_10NN=38547,ML_Graph/breasttissue_10NN=106,ML_Graph/k49_norm_10NN=38547,ML_Graph/breasttissue_10NN=1412,ML_Graph/k49_norm_10NN=618158,ML_Graph/breasttissue_10NN=1412,ML_Graph/k49_norm_10NN=618158 -Mallya,18,homogeneous,0.0,0,1,1.6776,1.918695,1.914823,10,65592,1477,10964,70304,1477,10964,70304,18427,233187,1528092,18592,233741,1528092,0.001513199920413014,0.0073226551044173116,0.0173680609288971,0.0,5.236095550568468e-06,0.0001553140154832006,square:18,square,1.0,unsymmetric:18,unsymmetric,1.0,real:18,real,1.0,partially_pattern_symmetric:18,partially_pattern_symmetric,1.0,no:18,no,1.0,chemical process simulation problem:18,chemical process simulation problem,1.0,true:18,true,1.0,no:18,no,1.0,no:18,no,1.0,10k_to_100k:12; 1k_to_10k:6,10k_to_100k,0.666667,Mallya/lhr01=1477,Mallya/lhr71=70304; Mallya/lhr71c=70304,Mallya/lhr01=1477,Mallya/lhr71=70304; Mallya/lhr71c=70304,Mallya/lhr01=18427,Mallya/lhr71c=1528092,Mallya/lhr01=18592,Mallya/lhr71=1528092; Mallya/lhr71c=1528092 -Mancktelow,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,28603,37762,37762,37762,37762,37762,37762,1133641,1133641,1133641,1162244,1162244,1162244,1.0,1,1.0,0.279507135368047,0.279507135368047,0.279507135368047,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,pattern_symmetric_only:1,pattern_symmetric_only,1.0,no:1,no,1.0,materials problem:1,materials problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Mancktelow/viscorocks=37762,Mancktelow/viscorocks=37762,Mancktelow/viscorocks=37762,Mancktelow/viscorocks=37762,Mancktelow/viscorocks=1133641,Mancktelow/viscorocks=1133641,Mancktelow/viscorocks=1162244,Mancktelow/viscorocks=1162244 -Marini,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,7245,7245,7245,7245,7245,7245,46142,46142,46142,46142,46142,46142,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,no:1,no,1.0,economic problem:1,economic problem,1.0,true:1,true,1.0,no:1,no,1.0,no:1,no,1.0,1k_to_10k:1,1k_to_10k,1.0,Marini/eurqsa=7245,Marini/eurqsa=7245,Marini/eurqsa=7245,Marini/eurqsa=7245,Marini/eurqsa=46142,Marini/eurqsa=46142,Marini/eurqsa=46142,Marini/eurqsa=46142 -Martin,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,400320,400320,400320,400320,400320,400320,6226538,6226538,6226538,6226538,6226538,6226538,0.922267240944297,0.922267240944297,0.922267240944297,0.0,0,0.0,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,chemical oceanography problem:1,chemical oceanography problem,1.0,missing:1,missing,1.0,no:1,no,1.0,no:1,no,1.0,100k_to_1m:1,100k_to_1m,1.0,Martin/marine1=400320,Martin/marine1=400320,Martin/marine1=400320,Martin/marine1=400320,Martin/marine1=6226538,Martin/marine1=6226538,Martin/marine1=6226538,Martin/marine1=6226538 -MathWorks,11,categorically_heterogeneous,0.318182,5,5,2.085032,3.046013,3.046013,0,0,102,2290,8765,102,2290,12406,306,14873,340200,306,14873,340200,0.0,0.9525732031943213,1.0,0.0,0.04130434782608695,1.0,rectangular:2; square:9,square,0.818182,rectangular:2; symmetric:2; unsymmetric:7,unsymmetric,0.636364,binary:1; real:10,real,0.909091,exact_numerical_or_hermitian:2; partially_pattern_symmetric:4; pattern_symmetric_only:2; pattern_unsymmetric:1; rectangular:2,partially_pattern_symmetric,0.363636,no:9; yes:2,no,0.818182,computer graphics/vision problem:1; counter-example problem:6; directed graph:1; statistical/mathematical problem:1; structural problem:2,counter-example problem,0.545455,false:2; missing:1; true:8,true,0.727273,no:8; yes:3,no,0.727273,no:9; yes:2,no,0.818182,10k_to_100k:1; 1k_to_10k:6; lt_1k:4,1k_to_10k,0.545455,MathWorks/pivtol=102,MathWorks/Kaufhold=8765,MathWorks/pivtol=102,MathWorks/Pd_rhs=12406,MathWorks/pivtol=306,MathWorks/Kuu=340200,MathWorks/pivtol=306,MathWorks/Kuu=340200 -MaxPlanck,2,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,81920,81920,81920,81920,81920,81920,327680,327680,327680,327680,327680,327680,1.0,1,1.0,1.0,1,1.0,square:2,square,1.0,symmetric:2,symmetric,1.0,real:2,real,1.0,exact_numerical_or_hermitian:2,exact_numerical_or_hermitian,1.0,yes:2,yes,1.0,computational fluid dynamics problem:2,computational fluid dynamics problem,1.0,true:2,true,1.0,yes:2,yes,1.0,yes:2,yes,1.0,10k_to_100k:2,10k_to_100k,1.0,MaxPlanck/shallow_water1=81920; MaxPlanck/shallow_water2=81920,MaxPlanck/shallow_water1=81920; MaxPlanck/shallow_water2=81920,MaxPlanck/shallow_water1=81920; MaxPlanck/shallow_water2=81920,MaxPlanck/shallow_water1=81920; MaxPlanck/shallow_water2=81920,MaxPlanck/shallow_water1=327680; MaxPlanck/shallow_water2=327680,MaxPlanck/shallow_water1=327680; MaxPlanck/shallow_water2=327680,MaxPlanck/shallow_water1=327680; MaxPlanck/shallow_water2=327680,MaxPlanck/shallow_water1=327680; MaxPlanck/shallow_water2=327680 -Mazaheri,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,144,513351,513351,513351,513351,513351,513351,20207907,20207907,20207907,20208051,20208051,20208051,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,yes:1,yes,1.0,computer vision problem:1,computer vision problem,1.0,true:1,true,1.0,yes:1,yes,1.0,yes:1,yes,1.0,100k_to_1m:1,100k_to_1m,1.0,Mazaheri/bundle_adj=513351,Mazaheri/bundle_adj=513351,Mazaheri/bundle_adj=513351,Mazaheri/bundle_adj=513351,Mazaheri/bundle_adj=20207907,Mazaheri/bundle_adj=20207907,Mazaheri/bundle_adj=20208051,Mazaheri/bundle_adj=20208051 -McRae,2,small_group_lt5,0.083333,1,1,0.0,1e-06,1e-06,0,0,999999,999999.5,1000000,999999,999999.5,1000000,4995991,4995995.5,4996000,4995991,4995995.5,4996000,1.0,1,1.0,1.0,1,1.0,square:2,square,1.0,symmetric:2,symmetric,1.0,real:2,real,1.0,exact_numerical_or_hermitian:2,exact_numerical_or_hermitian,1.0,no:1; yes:1,no,0.5,2D/3D problem:2,2D/3D problem,1.0,true:2,true,1.0,yes:2,yes,1.0,yes:2,yes,1.0,100k_to_1m:1; ge_1m:1,100k_to_1m,0.5,McRae/ecology2=999999,McRae/ecology1=1000000,McRae/ecology2=999999,McRae/ecology1=1000000,McRae/ecology2=4995991,McRae/ecology1=4996000,McRae/ecology2=4995991,McRae/ecology1=4996000 -Meng,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,274087,274087,274087,274087,274087,274087,4137369,4137369,4137369,4137369,4137369,4137369,0.9801722990969854,0.9801722990969854,0.9801722990969854,0.6457048695901567,0.6457048695901567,0.6457048695901567,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,complex:1,complex,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,computational chemistry problem:1,computational chemistry problem,1.0,true:1,true,1.0,no:1,no,1.0,no:1,no,1.0,100k_to_1m:1,100k_to_1m,1.0,Meng/iChem_Jacobian=274087,Meng/iChem_Jacobian=274087,Meng/iChem_Jacobian=274087,Meng/iChem_Jacobian=274087,Meng/iChem_Jacobian=4137369,Meng/iChem_Jacobian=4137369,Meng/iChem_Jacobian=4137369,Meng/iChem_Jacobian=4137369 -Meszaros,166,mixed_some_attributes,0.087349,2,2,4.842679,5.449325,5.449325,0,0,7,3035,185501,16,9495,1113780,41,53994,11537419,41,53994,11537419,0.0,0,1.0,0.0,0,0.5,rectangular:165; square:1,rectangular,0.993976,rectangular:165; unsymmetric:1,rectangular,0.993976,binary:18; integer:41; real:107,real,0.644578,pattern_symmetric_only:1; rectangular:165,rectangular,0.993976,no:166,no,1.0,linear programming problem:141; linear programming problem sequence:25,linear programming problem,0.849398,false:18; true:148,true,0.891566,no:166,no,1.0,no:166,no,1.0,100k_to_1m:7; 10k_to_100k:70; 1k_to_10k:68; ge_1m:2; lt_1k:19,10k_to_100k,0.421687,Meszaros/farm=7,Meszaros/degme=185501,Meszaros/kleemin=16,Meszaros/stat96v3=1113780,Meszaros/farm=41,Meszaros/tp-6=11537419,Meszaros/farm=41,Meszaros/tp-6=11537419 -Mittelmann,30,mixed_some_attributes,0.055556,1,1,1.904108,2.811472,2.811472,0,0,507,91245.5,1918399,24460,317118,1961394,71264,775766.5,46168124,71264,775766.5,46168124,0.0,0,0.0,0.0,0,0.0,rectangular:30,rectangular,1.0,rectangular:30,rectangular,1.0,integer:20; real:10,integer,0.666667,rectangular:30,rectangular,1.0,no:30,no,1.0,linear programming problem:30,linear programming problem,1.0,false:11; true:19,true,0.633333,no:30,no,1.0,no:30,no,1.0,100k_to_1m:19; 10k_to_100k:7; ge_1m:4,100k_to_1m,0.633333,Mittelmann/rail507=507,Mittelmann/cont1_l=1918399,Mittelmann/fome11=24460,Mittelmann/cont11_l=1961394,Mittelmann/fome11=71264,Mittelmann/spal_004=46168124,Mittelmann/fome11=71264,Mittelmann/spal_004=46168124 -Moqri,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,2427,2427,2427,2427,2427,2427,57022,57022,57022,57022,57022,57022,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,no:1,no,1.0,undirected weighted graph:1,undirected weighted graph,1.0,missing:1,missing,1.0,no:1,no,1.0,no:1,no,1.0,1k_to_10k:1,1k_to_10k,1.0,Moqri/MISKnowledgeMap=2427,Moqri/MISKnowledgeMap=2427,Moqri/MISKnowledgeMap=2427,Moqri/MISKnowledgeMap=2427,Moqri/MISKnowledgeMap=57022,Moqri/MISKnowledgeMap=57022,Moqri/MISKnowledgeMap=57022,Moqri/MISKnowledgeMap=57022 -Morandini,3,small_group_lt5,0.055556,1,2,0.898176,1.178741,1.178741,0,0,100,120,791,100,120,791,708,870,10685,708,870,10685,0.2177215189873418,0.2341063574570172,0.514018691588785,0.05316455696202532,0.07165109034267912,0.1155537784886046,square:3,square,1.0,unsymmetric:3,unsymmetric,1.0,real:3,real,1.0,partially_pattern_symmetric:3,partially_pattern_symmetric,1.0,no:3,no,1.0,robotics problem:1; structural problem:2,structural problem,0.666667,true:3,true,1.0,yes:3,yes,1.0,no:3,no,1.0,lt_1k:3,lt_1k,1.0,Morandini/rotor1=100,Morandini/rotor2=791,Morandini/rotor1=100,Morandini/rotor2=791,Morandini/rotor1=708,Morandini/rotor2=10685,Morandini/rotor1=708,Morandini/rotor2=10685 -Muite,4,small_group_lt5,0.0,0,1,2.416641,3.365301,3.365301,0,0,261,3077,68121,261,3077,68121,2319,27663,5377761,2319,27663,5377761,0.3020408163265306,0.5001372592907166,0.5014577259475219,0.0,0,0.0002369275506437348,square:4,square,1.0,unsymmetric:4,unsymmetric,1.0,real:4,real,1.0,partially_pattern_symmetric:4,partially_pattern_symmetric,1.0,no:4,no,1.0,structural problem:4,structural problem,1.0,true:4,true,1.0,yes:4,yes,1.0,no:4,no,1.0,10k_to_100k:1; 1k_to_10k:2; lt_1k:1,1k_to_10k,0.5,Muite/Chebyshev1=261,Muite/Chebyshev4=68121,Muite/Chebyshev1=261,Muite/Chebyshev4=68121,Muite/Chebyshev1=2319,Muite/Chebyshev4=5377761,Muite/Chebyshev1=2319,Muite/Chebyshev4=5377761 -Mulvey,2,small_group_lt5,0.25,3,2,0.0,0.0,0.0,0,0,74752,74752,74752,74752,74752,74752,596992,596992,596992,596992,596992,596992,1.0,1,1.0,1.0,1,1.0,square:2,square,1.0,symmetric:2,symmetric,1.0,binary:1; real:1,binary,0.5,exact_numerical_or_hermitian:2,exact_numerical_or_hermitian,1.0,no:1; yes:1,no,0.5,duplicate economic problem:1; economic problem:1,duplicate economic problem,0.5,true:2,true,1.0,no:2,no,1.0,yes:2,yes,1.0,10k_to_100k:2,10k_to_100k,1.0,Mulvey/finan512=74752; Mulvey/pfinan512=74752,Mulvey/finan512=74752; Mulvey/pfinan512=74752,Mulvey/finan512=74752; Mulvey/pfinan512=74752,Mulvey/finan512=74752; Mulvey/pfinan512=74752,Mulvey/finan512=596992; Mulvey/pfinan512=596992,Mulvey/finan512=596992; Mulvey/pfinan512=596992,Mulvey/finan512=596992; Mulvey/pfinan512=596992,Mulvey/finan512=596992; Mulvey/pfinan512=596992 -Mycielski,19,type_homogeneous_size_diverse,0.0,0,1,5.594631,9.131999,9.131999,0,0,2,1535,786431,2,1535,786431,2,134710,2710370560,2,134710,2710370560,1.0,1,1.0,1.0,1,1.0,square:19,square,1.0,symmetric:19,symmetric,1.0,binary:19,binary,1.0,exact_numerical_or_hermitian:19,exact_numerical_or_hermitian,1.0,no:19,no,1.0,undirected graph:19,undirected graph,1.0,missing:19,missing,1.0,no:19,no,1.0,no:19,no,1.0,100k_to_1m:3; 10k_to_100k:4; 1k_to_10k:3; lt_1k:9,lt_1k,0.473684,Mycielski/mycielskian2=2,Mycielski/mycielskian20=786431,Mycielski/mycielskian2=2,Mycielski/mycielskian20=786431,Mycielski/mycielskian2=2,Mycielski/mycielskian20=2710370560,Mycielski/mycielskian2=2,Mycielski/mycielskian20=2710370560 -ND,4,small_group_lt5,0.0,0,1,0.90309,0.942286,0.942286,0,0,9000,27000,72000,9000,27000,72000,3279690,10559131,28715634,3279690,10559131,28715634,1.0,1,1.0,1.0,1,1.0,square:4,square,1.0,symmetric:4,symmetric,1.0,real:4,real,1.0,exact_numerical_or_hermitian:4,exact_numerical_or_hermitian,1.0,yes:4,yes,1.0,2D/3D problem:4,2D/3D problem,1.0,true:4,true,1.0,yes:4,yes,1.0,yes:4,yes,1.0,10k_to_100k:3; 1k_to_10k:1,10k_to_100k,0.75,ND/nd3k=9000,ND/nd24k=72000,ND/nd3k=9000,ND/nd24k=72000,ND/nd3k=3279690,ND/nd24k=28715634,ND/nd3k=3279690,ND/nd24k=28715634 -NYPA,8,type_homogeneous_size_diverse,0.0,0,1,3.370357,3.74753,3.74753,0,0,32,3309,46845,14,2177,75077,234,59905,1308415,234,59905,1308415,0.0,0,0.0,0.0,0,0.0,rectangular:8,rectangular,1.0,rectangular:8,rectangular,1.0,real:8,real,1.0,rectangular:8,rectangular,1.0,no:8,no,1.0,least squares problem:8,least squares problem,1.0,false:7; true:1,false,0.875,no:8,no,1.0,no:8,no,1.0,10k_to_100k:3; 1k_to_10k:3; lt_1k:2,10k_to_100k,0.375,NYPA/Maragal_1=32,NYPA/Maragal_7=46845,NYPA/Maragal_1=14,NYPA/Maragal_8=75077,NYPA/Maragal_1=234,NYPA/Maragal_8=1308415,NYPA/Maragal_1=234,NYPA/Maragal_8=1308415 -Nasa,11,categorically_heterogeneous,0.318182,5,2,1.47831,2.056781,2.056781,0,0,1824,6691,54870,1824,6691,54870,23492,103599,2677324,23492,103599,2677324,0.0,1,1.0,0.0,1,1.0,square:11,square,1.0,symmetric:8; unsymmetric:3,symmetric,0.727273,binary:6; real:5,binary,0.545455,exact_numerical_or_hermitian:8; pattern_unsymmetric:3,exact_numerical_or_hermitian,0.727273,no:6; yes:5,no,0.545455,duplicate structural problem:6; structural problem:5,duplicate structural problem,0.545455,true:11,true,1.0,yes:11,yes,1.0,no:3; yes:8,yes,0.727273,10k_to_100k:5; 1k_to_10k:6,1k_to_10k,0.545455,Nasa/nasa1824=1824,Nasa/nasasrb=54870,Nasa/nasa1824=1824,Nasa/nasasrb=54870,Nasa/barth4=23492,Nasa/nasasrb=2677324,Nasa/barth4=23492,Nasa/nasasrb=2677324 -Negre,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,730,730,730,730,730,730,63024,63024,63024,63024,63024,63024,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,no:1,no,1.0,computational chemistry problem:1,computational chemistry problem,1.0,true:1,true,1.0,no:1,no,1.0,yes:1,yes,1.0,lt_1k:1,lt_1k,1.0,Negre/dendrimer=730,Negre/dendrimer=730,Negre/dendrimer=730,Negre/dendrimer=730,Negre/dendrimer=63024,Negre/dendrimer=63024,Negre/dendrimer=63024,Negre/dendrimer=63024 -Nemeth,26,homogeneous,0.00641,0,2,0.0,0.583097,0.583097,0,0,9506,9506,9506,9506,9506,9506,394808,517973,1511760,394808,517973,1511760,1.0,1,1.0,1.0,1,1.0,square:26,square,1.0,symmetric:26,symmetric,1.0,real:26,real,1.0,exact_numerical_or_hermitian:26,exact_numerical_or_hermitian,1.0,no:26,no,1.0,subsequent theoretical/quantum chemistry problem:25; theoretical/quantum chemistry problem sequence:1,subsequent theoretical/quantum chemistry problem,0.961538,true:26,true,1.0,no:26,no,1.0,no:26,no,1.0,1k_to_10k:26,1k_to_10k,1.0,Nemeth/nemeth01=9506; Nemeth/nemeth02=9506; Nemeth/nemeth03=9506; Nemeth/nemeth04=9506; Nemeth/nemeth05=9506; Nemeth/nemeth06=9506; Nemeth/nemeth07=9506; Nemeth/nemeth08=9506; Nemeth/nemeth09=9506; Nemeth/nemeth10=9506; Nemeth/nemeth11=9506; Nemeth/nemeth12=9506; Nemeth/nemeth13=9506; Nemeth/nemeth14=9506; Nemeth/nemeth15=9506; Nemeth/nemeth16=9506; Nemeth/nemeth17=9506; Nemeth/nemeth18=9506; Nemeth/nemeth19=9506; Nemeth/nemeth20=9506; Nemeth/nemeth21=9506; Nemeth/nemeth22=9506; Nemeth/nemeth23=9506; Nemeth/nemeth24=9506; Nemeth/nemeth25=9506; Nemeth/nemeth26=9506,Nemeth/nemeth01=9506; Nemeth/nemeth02=9506; Nemeth/nemeth03=9506; Nemeth/nemeth04=9506; Nemeth/nemeth05=9506; Nemeth/nemeth06=9506; Nemeth/nemeth07=9506; Nemeth/nemeth08=9506; Nemeth/nemeth09=9506; Nemeth/nemeth10=9506; Nemeth/nemeth11=9506; Nemeth/nemeth12=9506; Nemeth/nemeth13=9506; Nemeth/nemeth14=9506; Nemeth/nemeth15=9506; Nemeth/nemeth16=9506; Nemeth/nemeth17=9506; Nemeth/nemeth18=9506; Nemeth/nemeth19=9506; Nemeth/nemeth20=9506; Nemeth/nemeth21=9506; Nemeth/nemeth22=9506; Nemeth/nemeth23=9506; Nemeth/nemeth24=9506; Nemeth/nemeth25=9506; Nemeth/nemeth26=9506,Nemeth/nemeth01=9506; Nemeth/nemeth02=9506; Nemeth/nemeth03=9506; Nemeth/nemeth04=9506; Nemeth/nemeth05=9506; Nemeth/nemeth06=9506; Nemeth/nemeth07=9506; Nemeth/nemeth08=9506; Nemeth/nemeth09=9506; Nemeth/nemeth10=9506; Nemeth/nemeth11=9506; Nemeth/nemeth12=9506; Nemeth/nemeth13=9506; Nemeth/nemeth14=9506; Nemeth/nemeth15=9506; Nemeth/nemeth16=9506; Nemeth/nemeth17=9506; Nemeth/nemeth18=9506; Nemeth/nemeth19=9506; Nemeth/nemeth20=9506; Nemeth/nemeth21=9506; Nemeth/nemeth22=9506; Nemeth/nemeth23=9506; Nemeth/nemeth24=9506; Nemeth/nemeth25=9506; Nemeth/nemeth26=9506,Nemeth/nemeth01=9506; Nemeth/nemeth02=9506; Nemeth/nemeth03=9506; Nemeth/nemeth04=9506; Nemeth/nemeth05=9506; Nemeth/nemeth06=9506; Nemeth/nemeth07=9506; Nemeth/nemeth08=9506; Nemeth/nemeth09=9506; Nemeth/nemeth10=9506; Nemeth/nemeth11=9506; Nemeth/nemeth12=9506; Nemeth/nemeth13=9506; Nemeth/nemeth14=9506; Nemeth/nemeth15=9506; Nemeth/nemeth16=9506; Nemeth/nemeth17=9506; Nemeth/nemeth18=9506; Nemeth/nemeth19=9506; Nemeth/nemeth20=9506; Nemeth/nemeth21=9506; Nemeth/nemeth22=9506; Nemeth/nemeth23=9506; Nemeth/nemeth24=9506; Nemeth/nemeth25=9506; Nemeth/nemeth26=9506,Nemeth/nemeth02=394808; Nemeth/nemeth03=394808; Nemeth/nemeth04=394808; Nemeth/nemeth05=394808; Nemeth/nemeth06=394808,Nemeth/nemeth26=1511760,Nemeth/nemeth02=394808; Nemeth/nemeth03=394808; Nemeth/nemeth04=394808; Nemeth/nemeth05=394808; Nemeth/nemeth06=394808,Nemeth/nemeth26=1511760 -Newman,16,categorically_heterogeneous,0.239583,4,5,3.075128,3.352655,3.35266,1,4,34,1539.5,40421,34,1539.5,40421,156,9336,351382,156,9336,351386,0.1680170575692964,1,1.0,0.04946695095948828,1,1.0,square:16,square,1.0,symmetric:14; unsymmetric:2,symmetric,0.875,binary:6; integer:4; real:6,binary,0.375,exact_numerical_or_hermitian:14; partially_pattern_symmetric:2,exact_numerical_or_hermitian,0.875,no:16,no,1.0,directed multigraph:1; directed weighted graph:1; undirected graph:6; undirected multigraph:1; undirected weighted graph:7,undirected weighted graph,0.4375,missing:16,missing,1.0,no:16,no,1.0,no:16,no,1.0,10k_to_100k:5; 1k_to_10k:4; lt_1k:7,lt_1k,0.4375,Newman/karate=34,Newman/cond-mat-2005=40421,Newman/karate=34,Newman/cond-mat-2005=40421,Newman/karate=156,Newman/cond-mat-2005=351382,Newman/karate=156,Newman/cond-mat-2005=351386 -Norris,12,categorically_heterogeneous,0.194444,4,2,2.196077,3.059916,3.059916,3,7368,1650,9801,259156,1650,9801,259156,7419,680341,8516500,7419,682797,8516500,0.4243874832715144,0.995818010999383,1.0,0.0,0,1.0,square:12,square,1.0,symmetric:3; unsymmetric:9,unsymmetric,0.75,real:12,real,1.0,exact_numerical_or_hermitian:3; partially_pattern_symmetric:6; pattern_symmetric_only:3,partially_pattern_symmetric,0.5,no:9; yes:3,no,0.75,2D/3D problem:10; computational fluid dynamics problem:2,2D/3D problem,0.833333,true:12,true,1.0,yes:12,yes,1.0,no:9; yes:3,no,0.75,100k_to_1m:5; 1k_to_10k:7,1k_to_10k,0.583333,Norris/lung1=1650,Norris/torso3=259156,Norris/lung1=1650,Norris/torso3=259156,Norris/lung1=7419,Norris/torso1=8516500,Norris/lung1=7419,Norris/torso1=8516500 -Oberwolfach,38,categorically_heterogeneous,0.153509,4,2,4.848058,6.01714,6.192557,3,39602806,14,17361,986703,14,17361,986703,46,176117,47851783,46,176117,71666325,0.6083330964361183,1,1.0,0.0,1,1.0,square:38,square,1.0,symmetric:33; unsymmetric:5,symmetric,0.868421,complex:1; real:37,real,0.973684,exact_numerical_or_hermitian:32; partially_pattern_symmetric:1; pattern_symmetric_only:5,exact_numerical_or_hermitian,0.842105,no:25; yes:13,no,0.657895,duplicate model reduction problem:10; model reduction problem:28,model reduction problem,0.736842,true:38,true,1.0,yes:38,yes,1.0,no:24; yes:14,no,0.631579,100k_to_1m:4; 10k_to_100k:21; 1k_to_10k:11; lt_1k:2,10k_to_100k,0.552632,Oberwolfach/LFAT5=14,Oberwolfach/bone010=986703,Oberwolfach/LFAT5=14,Oberwolfach/bone010=986703,Oberwolfach/LFAT5=46,Oberwolfach/bone010=47851783,Oberwolfach/LFAT5=46,Oberwolfach/bone010=71666325 -Okunbor,2,small_group_lt5,0.333333,4,1,0.001113,0.007526,0.007526,0,0,8184,8194.5,8205,8184,8194.5,8205,125567,126664.5,127762,125567,126664.5,127762,1.0,1,1.0,0.9963538443526401,0.99817692217632,1.0,square:2,square,1.0,symmetric:1; unsymmetric:1,symmetric,0.5,complex:1; real:1,complex,0.5,exact_numerical_or_hermitian:1; pattern_symmetric_only:1,exact_numerical_or_hermitian,0.5,no:1; yes:1,no,0.5,acoustics problem:2,acoustics problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:1; yes:1,no,0.5,1k_to_10k:2,1k_to_10k,1.0,Okunbor/aft02=8184,Okunbor/aft01=8205,Okunbor/aft02=8184,Okunbor/aft01=8205,Okunbor/aft01=125567,Okunbor/aft02=127762,Okunbor/aft01=125567,Okunbor/aft02=127762 -PARSEC,21,type_homogeneous_size_diverse,0.0,0,1,2.542364,3.016457,3.016457,0,0,769,67024,268096,769,67024,268096,17801,5156379,18488476,17801,5156379,18488476,1.0,1,1.0,1.0,1,1.0,square:21,square,1.0,symmetric:21,symmetric,1.0,real:21,real,1.0,exact_numerical_or_hermitian:21,exact_numerical_or_hermitian,1.0,no:21,no,1.0,theoretical/quantum chemistry problem:21,theoretical/quantum chemistry problem,1.0,true:21,true,1.0,no:21,no,1.0,yes:21,yes,1.0,100k_to_1m:9; 10k_to_100k:7; 1k_to_10k:4; lt_1k:1,100k_to_1m,0.428571,PARSEC/Si2=769,PARSEC/Ga41As41H72=268096,PARSEC/Si2=769,PARSEC/Ga41As41H72=268096,PARSEC/Si2=17801,PARSEC/Ga41As41H72=18488476,PARSEC/Si2=17801,PARSEC/Ga41As41H72=18488476 -POLYFLOW,2,small_group_lt5,0.083333,1,1,0.006547,0.04526,0.046158,1,4122,29957,30184.5,30412,29957,30184.5,30412,1793881,1892400,1990919,1793881,1894461,1995041,0.971442083756505,0.9857210418782525,1.0,0.7213486599424203,0.8552196013183893,0.9890905426943581,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,real:2,real,1.0,partially_pattern_symmetric:1; pattern_symmetric_only:1,partially_pattern_symmetric,0.5,no:2,no,1.0,computational fluid dynamics problem:2,computational fluid dynamics problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,10k_to_100k:2,10k_to_100k,1.0,POLYFLOW/mixtank_new=29957,POLYFLOW/invextr1_new=30412,POLYFLOW/mixtank_new=29957,POLYFLOW/invextr1_new=30412,POLYFLOW/invextr1_new=1793881,POLYFLOW/mixtank_new=1990919,POLYFLOW/invextr1_new=1793881,POLYFLOW/mixtank_new=1995041 -Pajek,76,categorically_heterogeneous,0.333333,4,9,5.57689,5.712846,5.712846,0,0,10,462,3774768,9,462,3774768,29,1708.5,14970767,29,1708.5,14970767,0.0,0.16827315684255165,1.0,0.0,0.11278688524590164,1.0,rectangular:5; square:71,square,0.934211,rectangular:5; symmetric:22; unsymmetric:49,unsymmetric,0.644737,binary:44; integer:28; real:4,binary,0.578947,exact_numerical_or_hermitian:22; partially_pattern_symmetric:37; pattern_unsymmetric:12; rectangular:5,partially_pattern_symmetric,0.486842,no:75; yes:1,no,0.986842,bipartite graph:3; directed graph:28; directed multigraph:11; directed weighted graph:11; undirected graph:13; undirected multigraph:1; undirected weighted graph:6; undirected weighted graph sequence:1; weighted bipartite graph:2,directed graph,0.368421,false:2; missing:71; true:3,missing,0.934211,no:76,no,1.0,no:75; yes:1,no,0.986842,100k_to_1m:3; 10k_to_100k:9; 1k_to_10k:20; ge_1m:1; lt_1k:43,lt_1k,0.565789,Pajek/Stranke94=10,Pajek/patents=3774768,Pajek/divorce=9,Pajek/patents=3774768,Pajek/Tina_AskCal=29,Pajek/patents=14970767,Pajek/Tina_AskCal=29,Pajek/patents=14970767 -Pereyra,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,4384,71952,71952,71952,2704,2704,2704,1146848,1146848,1146848,1151232,1151232,1151232,0.0,0,0.0,0.0,0,0.0,rectangular:1,rectangular,1.0,rectangular:1,rectangular,1.0,real:1,real,1.0,rectangular:1,rectangular,1.0,no:1,no,1.0,least squares problem:1,least squares problem,1.0,false:1,false,1.0,no:1,no,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Pereyra/landmark=71952,Pereyra/landmark=71952,Pereyra/landmark=2704,Pereyra/landmark=2704,Pereyra/landmark=1146848,Pereyra/landmark=1146848,Pereyra/landmark=1151232,Pereyra/landmark=1151232 -Pothen,22,mixed_some_attributes,0.151515,2,1,3.292205,3.177502,3.177502,5,2380,48,7305.5,94069,48,7305.5,94069,306,43576,460493,306,43576,460493,1.0,1,1.0,1.0,1,1.0,square:22,square,1.0,symmetric:22,symmetric,1.0,binary:12; real:10,binary,0.545455,exact_numerical_or_hermitian:22,exact_numerical_or_hermitian,1.0,no:12; yes:10,no,0.545455,structural problem:22,structural problem,1.0,true:22,true,1.0,yes:22,yes,1.0,yes:22,yes,1.0,10k_to_100k:10; 1k_to_10k:3; lt_1k:9,10k_to_100k,0.454545,Pothen/mesh1e1=48; Pothen/mesh1em1=48; Pothen/mesh1em6=48,Pothen/tandem_dual=94069,Pothen/mesh1e1=48; Pothen/mesh1em1=48; Pothen/mesh1em6=48,Pothen/tandem_dual=94069,Pothen/mesh1e1=306; Pothen/mesh1em1=306; Pothen/mesh1em6=306,Pothen/tandem_dual=460493,Pothen/mesh1e1=306; Pothen/mesh1em1=306; Pothen/mesh1em6=306,Pothen/tandem_dual=460493 -PowerSystem,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,2892518,197156,197156,197156,197156,197156,197156,805839,805839,805839,3698357,3698357,3698357,0.2139400937122758,0.2139400937122758,0.2139400937122758,0.02415229830828888,0.02415229830828888,0.02415229830828888,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,power network problem:1,power network problem,1.0,true:1,true,1.0,no:1,no,1.0,no:1,no,1.0,100k_to_1m:1,100k_to_1m,1.0,PowerSystem/power197k=197156,PowerSystem/power197k=197156,PowerSystem/power197k=197156,PowerSystem/power197k=197156,PowerSystem/power197k=805839,PowerSystem/power197k=805839,PowerSystem/power197k=3698357,PowerSystem/power197k=3698357 -Precima,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,303813,303813,303813,303813,303813,303813,2006126,2006126,2006126,2006126,2006126,2006126,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,no:1,no,1.0,data analytics problem:1,data analytics problem,1.0,true:1,true,1.0,no:1,no,1.0,no:1,no,1.0,100k_to_1m:1,100k_to_1m,1.0,Precima/analytics=303813,Precima/analytics=303813,Precima/analytics=303813,Precima/analytics=303813,Precima/analytics=2006126,Precima/analytics=2006126,Precima/analytics=2006126,Precima/analytics=2006126 -Priebel,6,homogeneous,0.0,0,1,1.621511,1.690016,1.690016,0,0,584,5523.5,24430,575,5514,24421,6120,59694,299756,6120,59694,299756,0.0,0,0.0,0.0,0,0.0,rectangular:6,rectangular,1.0,rectangular:6,rectangular,1.0,binary:6,binary,1.0,rectangular:6,rectangular,1.0,no:6,no,1.0,combinatorial problem:6,combinatorial problem,1.0,false:6,false,1.0,no:6,no,1.0,no:6,no,1.0,10k_to_100k:2; 1k_to_10k:3; lt_1k:1,1k_to_10k,0.5,Priebel/130bit=584,Priebel/208bit=24430,Priebel/130bit=575,Priebel/208bit=24421,Priebel/130bit=6120,Priebel/208bit=299756,Priebel/130bit=6120,Priebel/208bit=299756 -Puri,4,small_group_lt5,0.041667,1,1,0.0,0.0,0.0,0,0,23412,23412,23412,23412,23412,23412,218484,218484,218484,218484,218484,218484,1.0,1,1.0,0.8850885826771654,0.8850885826771654,0.9753731955380578,square:4,square,1.0,unsymmetric:4,unsymmetric,1.0,complex:3; real:1,complex,0.75,pattern_symmetric_only:4,pattern_symmetric_only,1.0,no:4,no,1.0,model reduction problem:4,model reduction problem,1.0,true:4,true,1.0,yes:4,yes,1.0,no:4,no,1.0,10k_to_100k:4,10k_to_100k,1.0,Puri/ABACUS_shell_ud=23412; Puri/ABACUS_shell_ld=23412; Puri/ABACUS_shell_md=23412; Puri/ABACUS_shell_hd=23412,Puri/ABACUS_shell_ud=23412; Puri/ABACUS_shell_ld=23412; Puri/ABACUS_shell_md=23412; Puri/ABACUS_shell_hd=23412,Puri/ABACUS_shell_ud=23412; Puri/ABACUS_shell_ld=23412; Puri/ABACUS_shell_md=23412; Puri/ABACUS_shell_hd=23412,Puri/ABACUS_shell_ud=23412; Puri/ABACUS_shell_ld=23412; Puri/ABACUS_shell_md=23412; Puri/ABACUS_shell_hd=23412,Puri/ABACUS_shell_ud=218484; Puri/ABACUS_shell_ld=218484; Puri/ABACUS_shell_md=218484; Puri/ABACUS_shell_hd=218484,Puri/ABACUS_shell_ud=218484; Puri/ABACUS_shell_ld=218484; Puri/ABACUS_shell_md=218484; Puri/ABACUS_shell_hd=218484,Puri/ABACUS_shell_ud=218484; Puri/ABACUS_shell_ld=218484; Puri/ABACUS_shell_md=218484; Puri/ABACUS_shell_hd=218484,Puri/ABACUS_shell_ud=218484; Puri/ABACUS_shell_ld=218484; Puri/ABACUS_shell_md=218484; Puri/ABACUS_shell_hd=218484 -QCD,14,homogeneous,0.0,0,1,1.20412,1.20412,1.20412,0,0,3072,26112,49152,3072,26112,49152,119808,1018368,1916928,119808,1018368,1916928,0.9230769230769231,0.9230769230769231,0.9230769230769231,0.4615384615384616,0.4615384615384616,0.4615384615384616,square:14,square,1.0,unsymmetric:14,unsymmetric,1.0,complex:14,complex,1.0,partially_pattern_symmetric:14,partially_pattern_symmetric,1.0,no:14,no,1.0,theoretical/quantum chemistry problem:14,theoretical/quantum chemistry problem,1.0,true:14,true,1.0,no:14,no,1.0,no:14,no,1.0,10k_to_100k:7; 1k_to_10k:7,10k_to_100k,0.5,QCD/conf5_0-4x4-10=3072; QCD/conf5_0-4x4-14=3072; QCD/conf5_0-4x4-18=3072; QCD/conf5_0-4x4-22=3072; QCD/conf5_0-4x4-26=3072; QCD/conf6_0-4x4-20=3072; QCD/conf6_0-4x4-30=3072,QCD/conf5_4-8x8-05=49152; QCD/conf5_4-8x8-10=49152; QCD/conf5_4-8x8-15=49152; QCD/conf5_4-8x8-20=49152; QCD/conf6_0-8x8-20=49152; QCD/conf6_0-8x8-30=49152; QCD/conf6_0-8x8-80=49152,QCD/conf5_0-4x4-10=3072; QCD/conf5_0-4x4-14=3072; QCD/conf5_0-4x4-18=3072; QCD/conf5_0-4x4-22=3072; QCD/conf5_0-4x4-26=3072; QCD/conf6_0-4x4-20=3072; QCD/conf6_0-4x4-30=3072,QCD/conf5_4-8x8-05=49152; QCD/conf5_4-8x8-10=49152; QCD/conf5_4-8x8-15=49152; QCD/conf5_4-8x8-20=49152; QCD/conf6_0-8x8-20=49152; QCD/conf6_0-8x8-30=49152; QCD/conf6_0-8x8-80=49152,QCD/conf5_0-4x4-10=119808; QCD/conf5_0-4x4-14=119808; QCD/conf5_0-4x4-18=119808; QCD/conf5_0-4x4-22=119808; QCD/conf5_0-4x4-26=119808; QCD/conf6_0-4x4-20=119808; QCD/conf6_0-4x4-30=119808,QCD/conf5_4-8x8-05=1916928; QCD/conf5_4-8x8-10=1916928; QCD/conf5_4-8x8-15=1916928; QCD/conf5_4-8x8-20=1916928; QCD/conf6_0-8x8-20=1916928; QCD/conf6_0-8x8-30=1916928; QCD/conf6_0-8x8-80=1916928,QCD/conf5_0-4x4-10=119808; QCD/conf5_0-4x4-14=119808; QCD/conf5_0-4x4-18=119808; QCD/conf5_0-4x4-22=119808; QCD/conf5_0-4x4-26=119808; QCD/conf6_0-4x4-20=119808; QCD/conf6_0-4x4-30=119808,QCD/conf5_4-8x8-05=1916928; QCD/conf5_4-8x8-10=1916928; QCD/conf5_4-8x8-15=1916928; QCD/conf5_4-8x8-20=1916928; QCD/conf6_0-8x8-20=1916928; QCD/conf6_0-8x8-30=1916928; QCD/conf6_0-8x8-80=1916928 -QLi,3,small_group_lt5,0.055556,1,1,0.439352,0.476197,0.501941,1,320016,160000,160000,440020,160000,160000,440020,1750416,1750416,5240084,1750416,1750416,5560100,0.5503013048158469,0.5503013048158469,1.0,0.0,0.0002376736652548767,0.0002439613283568576,square:3,square,1.0,unsymmetric:3,unsymmetric,1.0,real:3,real,1.0,partially_pattern_symmetric:2; pattern_symmetric_only:1,partially_pattern_symmetric,0.666667,no:3,no,1.0,optimization problem:3,optimization problem,1.0,true:3,true,1.0,no:3,no,1.0,no:3,no,1.0,100k_to_1m:3,100k_to_1m,1.0,QLi/crashbasis=160000; QLi/majorbasis=160000,QLi/largebasis=440020,QLi/crashbasis=160000; QLi/majorbasis=160000,QLi/largebasis=440020,QLi/crashbasis=1750416; QLi/majorbasis=1750416,QLi/largebasis=5240084,QLi/crashbasis=1750416; QLi/majorbasis=1750416,QLi/largebasis=5560100 -QY,2,small_group_lt5,0.0,0,1,0.444411,0.847755,0.847755,0,0,14454,27335,40216,14454,27335,40216,147972,595066,1042160,147972,595066,1042160,1.0,1,1.0,1.0,1,1.0,square:2,square,1.0,symmetric:2,symmetric,1.0,real:2,real,1.0,exact_numerical_or_hermitian:2,exact_numerical_or_hermitian,1.0,no:2,no,1.0,power network problem sequence:2,power network problem sequence,1.0,true:2,true,1.0,no:2,no,1.0,no:2,no,1.0,10k_to_100k:2,10k_to_100k,1.0,QY/case9=14454,QY/case39=40216,QY/case9=14454,QY/case39=40216,QY/case9=147972,QY/case39=1042160,QY/case9=147972,QY/case39=1042160 -Qaplib,8,type_homogeneous_size_diverse,0.0,0,1,3.226858,3.174101,3.174101,0,0,210,2052,52260,225,5244,379350,1050,22800,1567800,1050,22800,1567800,0.0,0,0.0,0.0,0,0.0,rectangular:8,rectangular,1.0,rectangular:8,rectangular,1.0,integer:8,integer,1.0,rectangular:8,rectangular,1.0,no:8,no,1.0,linear programming problem:8,linear programming problem,1.0,true:8,true,1.0,no:8,no,1.0,no:8,no,1.0,100k_to_1m:1; 10k_to_100k:2; 1k_to_10k:2; lt_1k:3,lt_1k,0.375,Qaplib/lp_nug05=210,Qaplib/lp_nug30=52260,Qaplib/lp_nug05=225,Qaplib/lp_nug30=379350,Qaplib/lp_nug05=1050,Qaplib/lp_nug30=1567800,Qaplib/lp_nug05=1050,Qaplib/lp_nug30=1567800 -Quaglino,2,small_group_lt5,0.0,0,1,0.879377,0.794786,0.794786,0,0,4326,18547.5,32769,4326,18547.5,32769,61166,221246,381326,61166,221246,381326,0.5699899872904575,0.6554031569105349,0.7408163265306122,0.0,0,0.0,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,real:2,real,1.0,partially_pattern_symmetric:2,partially_pattern_symmetric,1.0,no:2,no,1.0,materials problem:2,materials problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,10k_to_100k:1; 1k_to_10k:1,10k_to_100k,0.5,Quaglino/viscoplastic1=4326,Quaglino/viscoplastic2=32769,Quaglino/viscoplastic1=4326,Quaglino/viscoplastic2=32769,Quaglino/viscoplastic1=61166,Quaglino/viscoplastic2=381326,Quaglino/viscoplastic1=61166,Quaglino/viscoplastic2=381326 -Rajat,32,categorically_heterogeneous,0.135417,3,1,4.540839,4.485022,4.398288,22,1472235,135,33731.5,4690002,135,33731.5,4690002,665,162866,20316253,812,163783.5,20316253,0.626033640830624,0.9951695846058093,1.0,0.009194565053000587,0.4286578279800002,1.0,square:32,square,1.0,symmetric:6; unsymmetric:26,unsymmetric,0.8125,binary:7; real:25,real,0.78125,exact_numerical_or_hermitian:6; partially_pattern_symmetric:19; pattern_symmetric_only:7,partially_pattern_symmetric,0.59375,no:32,no,1.0,circuit simulation problem:32,circuit simulation problem,1.0,true:32,true,1.0,no:32,no,1.0,no:32,no,1.0,100k_to_1m:6; 10k_to_100k:15; 1k_to_10k:7; ge_1m:1; lt_1k:3,10k_to_100k,0.46875,Rajat/rajat11=135,Rajat/rajat31=4690002,Rajat/rajat11=135,Rajat/rajat31=4690002,Rajat/rajat11=665,Rajat/rajat31=20316253,Rajat/rajat11=812,Rajat/rajat31=20316253 -Raju,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,44220,67173,67173,67173,67173,67173,67173,3788857,3788857,3788857,3833077,3833077,3833077,0.9022908019592145,0.9022908019592145,0.9022908019592145,0.5518386226500883,0.5518386226500883,0.5518386226500883,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,computational fluid dynamics problem:1,computational fluid dynamics problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Raju/laminar_duct3D=67173,Raju/laminar_duct3D=67173,Raju/laminar_duct3D=67173,Raju/laminar_duct3D=67173,Raju/laminar_duct3D=3788857,Raju/laminar_duct3D=3788857,Raju/laminar_duct3D=3833077,Raju/laminar_duct3D=3833077 -Rommes,35,mixed_some_attributes,0.07619,1,1,2.786472,2.084421,2.084421,0,0,66,7135,40366,66,7135,40366,1194,34738,145019,1194,34738,145019,0.3339191564147627,0.8020552507215579,1.0,0.0,0.5712336093539642,0.7583878152166104,square:35,square,1.0,unsymmetric:35,unsymmetric,1.0,real:35,real,1.0,partially_pattern_symmetric:19; pattern_symmetric_only:16,partially_pattern_symmetric,0.542857,no:35,no,1.0,eigenvalue/model reduction problem:35,eigenvalue/model reduction problem,1.0,false:22; true:13,false,0.628571,yes:35,yes,1.0,no:35,no,1.0,10k_to_100k:16; 1k_to_10k:14; lt_1k:5,10k_to_100k,0.457143,Rommes/ww_36_pmec_36=66,Rommes/bauru5727=40366,Rommes/ww_36_pmec_36=66,Rommes/bauru5727=40366,Rommes/ww_36_pmec_36=1194,Rommes/bauru5727=145019,Rommes/ww_36_pmec_36=1194,Rommes/bauru5727=145019 -Ronis,2,small_group_lt5,0.0,0,1,0.510545,0.515045,0.515045,0,0,48600,103032,157464,48600,103032,157464,1181120,2523904,3866688,1181120,2523904,3866688,1.0,1,1.0,0.7756792256277864,0.7757462281496047,0.775813230671423,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,real:2,real,1.0,pattern_symmetric_only:2,pattern_symmetric_only,1.0,no:2,no,1.0,materials problem:2,materials problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,100k_to_1m:1; 10k_to_100k:1,100k_to_1m,0.5,Ronis/xenon1=48600,Ronis/xenon2=157464,Ronis/xenon1=48600,Ronis/xenon2=157464,Ronis/xenon1=1181120,Ronis/xenon2=3866688,Ronis/xenon1=1181120,Ronis/xenon2=3866688 -Rost,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,74104,74104,74104,74104,74104,74104,365580,365580,365580,365580,365580,365580,0.0001203589980660497,0.0001203589980660497,0.0001203589980660497,0.0001148881345175929,0.0001148881345175929,0.0001148881345175929,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,complex:1,complex,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,semiconductor device problem:1,semiconductor device problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Rost/RFdevice=74104,Rost/RFdevice=74104,Rost/RFdevice=74104,Rost/RFdevice=74104,Rost/RFdevice=365580,Rost/RFdevice=365580,Rost/RFdevice=365580,Rost/RFdevice=365580 -Rothberg,6,categorically_heterogeneous,0.194444,3,2,1.548315,1.581897,1.581897,2,5276,4350,62113,153746,4350,62113,153746,237798,2455493,9080404,237798,2458131,9080404,1.0,1,1.0,1.0,1,1.0,square:6,square,1.0,symmetric:6,symmetric,1.0,binary:4; real:2,binary,0.666667,exact_numerical_or_hermitian:6,exact_numerical_or_hermitian,1.0,no:4; yes:2,no,0.666667,computational fluid dynamics problem:3; structural problem:3,computational fluid dynamics problem,0.5,true:6,true,1.0,yes:6,yes,1.0,yes:6,yes,1.0,100k_to_1m:2; 10k_to_100k:3; 1k_to_10k:1,10k_to_100k,0.5,Rothberg/struct4=4350,Rothberg/gearbox=153746,Rothberg/struct4=4350,Rothberg/gearbox=153746,Rothberg/struct4=237798,Rothberg/gearbox=9080404,Rothberg/struct4=237798,Rothberg/gearbox=9080404 -Rucci,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,1977885,1977885,1977885,109900,109900,109900,7791168,7791168,7791168,7791168,7791168,7791168,0.0,0,0.0,0.0,0,0.0,rectangular:1,rectangular,1.0,rectangular:1,rectangular,1.0,real:1,real,1.0,rectangular:1,rectangular,1.0,no:1,no,1.0,least squares problem:1,least squares problem,1.0,true:1,true,1.0,no:1,no,1.0,no:1,no,1.0,ge_1m:1,ge_1m,1.0,Rucci/Rucci1=1977885,Rucci/Rucci1=1977885,Rucci/Rucci1=109900,Rucci/Rucci1=109900,Rucci/Rucci1=7791168,Rucci/Rucci1=7791168,Rucci/Rucci1=7791168,Rucci/Rucci1=7791168 -Rudnyi,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,60740,60740,60740,60740,60740,60740,2035281,2035281,2035281,2035281,2035281,2035281,0.9691204183655847,0.9691204183655847,0.9691204183655847,0.9180067671423384,0.9180067671423384,0.9180067671423384,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,computational fluid dynamics problem:1,computational fluid dynamics problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Rudnyi/water_tank=60740,Rudnyi/water_tank=60740,Rudnyi/water_tank=60740,Rudnyi/water_tank=60740,Rudnyi/water_tank=2035281,Rudnyi/water_tank=2035281,Rudnyi/water_tank=2035281,Rudnyi/water_tank=2035281 -SNAP,68,categorically_heterogeneous,0.259804,4,10,4.814793,5.250353,5.250353,0,0,1005,82007.5,65608366,1005,82007.5,65608366,20296,547436.5,3612134270,20296,547436.5,3612134270,0.0,0.5371178064241937,1.0,0.0,0.38366599754485475,1.0,square:68,square,1.0,symmetric:21; unsymmetric:47,unsymmetric,0.691176,binary:53; integer:15,binary,0.779412,exact_numerical_or_hermitian:21; partially_pattern_symmetric:36; pattern_symmetric_only:1; pattern_unsymmetric:10,partially_pattern_symmetric,0.529412,no:68,no,1.0,directed graph:30; directed graph with communities:2; directed temporal multigraph:8; directed weighted graph:4; directed weighted graph sequence:1; directed weighted temporal graph:2; directed weighted temporal multigraph:1; undirected graph:11; undirected graph sequence:3; undirected graph with communities:6,directed graph,0.441176,missing:68,missing,1.0,no:68,no,1.0,no:68,no,1.0,100k_to_1m:16; 10k_to_100k:23; 1k_to_10k:13; ge_1m:16,10k_to_100k,0.338235,SNAP/email-Eu-core=1005; SNAP/email-Eu-core-temporal=1005,SNAP/com-Friendster=65608366,SNAP/email-Eu-core=1005; SNAP/email-Eu-core-temporal=1005,SNAP/com-Friendster=65608366,SNAP/CollegeMsg=20296,SNAP/com-Friendster=3612134270,SNAP/CollegeMsg=20296,SNAP/com-Friendster=3612134270 -Sandia,192,type_homogeneous_size_diverse,0.019965,0,3,3.200864,3.232792,3.399263,5,3096985,430,1220,682862,430,1220,682862,1544,5892,2638997,1544,5892,3871773,0.6142197994049846,0.8183347350714887,1.0,0.0007546383156139254,0.32569386038687975,0.7175697865353038,square:192,square,1.0,unsymmetric:192,unsymmetric,1.0,real:192,real,1.0,partially_pattern_symmetric:182; pattern_symmetric_only:10,partially_pattern_symmetric,0.947917,no:192,no,1.0,circuit simulation problem:7; circuit simulation problem sequence:6; subsequent circuit simulation problem:179,subsequent circuit simulation problem,0.932292,true:192,true,1.0,no:192,no,1.0,no:192,no,1.0,100k_to_1m:4; 10k_to_100k:5; 1k_to_10k:125; lt_1k:58,1k_to_10k,0.651042,Sandia/oscil_dcop_01=430; Sandia/oscil_dcop_02=430; Sandia/oscil_dcop_03=430; Sandia/oscil_dcop_04=430; Sandia/oscil_dcop_05=430; Sandia/oscil_dcop_06=430; Sandia/oscil_dcop_07=430; Sandia/oscil_dcop_08=430; Sandia/oscil_dcop_09=430; Sandia/oscil_dcop_10=430; Sandia/oscil_dcop_11=430; Sandia/oscil_dcop_12=430; Sandia/oscil_dcop_13=430; Sandia/oscil_dcop_14=430; Sandia/oscil_dcop_15=430; Sandia/oscil_dcop_16=430; Sandia/oscil_dcop_17=430; Sandia/oscil_dcop_18=430; Sandia/oscil_dcop_19=430; Sandia/oscil_dcop_20=430; Sandia/oscil_dcop_21=430; Sandia/oscil_dcop_22=430; Sandia/oscil_dcop_23=430; Sandia/oscil_dcop_24=430; Sandia/oscil_dcop_25=430; Sandia/oscil_dcop_26=430; Sandia/oscil_dcop_27=430; Sandia/oscil_dcop_28=430; Sandia/oscil_dcop_29=430; Sandia/oscil_dcop_30=430; Sandia/oscil_dcop_31=430; Sandia/oscil_dcop_32=430; Sandia/oscil_dcop_33=430; Sandia/oscil_dcop_34=430; Sandia/oscil_dcop_35=430; Sandia/oscil_dcop_36=430; Sandia/oscil_dcop_37=430; Sandia/oscil_dcop_38=430; Sandia/oscil_dcop_39=430; Sandia/oscil_dcop_40=430; Sandia/oscil_dcop_41=430; Sandia/oscil_dcop_42=430; Sandia/oscil_dcop_43=430; Sandia/oscil_dcop_44=430; Sandia/oscil_dcop_45=430; Sandia/oscil_dcop_46=430; Sandia/oscil_dcop_47=430; Sandia/oscil_dcop_48=430; Sandia/oscil_dcop_49=430; Sandia/oscil_dcop_50=430; Sandia/oscil_dcop_51=430; Sandia/oscil_dcop_52=430; Sandia/oscil_dcop_53=430; Sandia/oscil_dcop_54=430; Sandia/oscil_dcop_55=430; Sandia/oscil_dcop_56=430; Sandia/oscil_dcop_57=430; Sandia/oscil_trans_01=430,Sandia/ASIC_680k=682862,Sandia/oscil_dcop_01=430; Sandia/oscil_dcop_02=430; Sandia/oscil_dcop_03=430; Sandia/oscil_dcop_04=430; Sandia/oscil_dcop_05=430; Sandia/oscil_dcop_06=430; Sandia/oscil_dcop_07=430; Sandia/oscil_dcop_08=430; Sandia/oscil_dcop_09=430; Sandia/oscil_dcop_10=430; Sandia/oscil_dcop_11=430; Sandia/oscil_dcop_12=430; Sandia/oscil_dcop_13=430; Sandia/oscil_dcop_14=430; Sandia/oscil_dcop_15=430; Sandia/oscil_dcop_16=430; Sandia/oscil_dcop_17=430; Sandia/oscil_dcop_18=430; Sandia/oscil_dcop_19=430; Sandia/oscil_dcop_20=430; Sandia/oscil_dcop_21=430; Sandia/oscil_dcop_22=430; Sandia/oscil_dcop_23=430; Sandia/oscil_dcop_24=430; Sandia/oscil_dcop_25=430; Sandia/oscil_dcop_26=430; Sandia/oscil_dcop_27=430; Sandia/oscil_dcop_28=430; Sandia/oscil_dcop_29=430; Sandia/oscil_dcop_30=430; Sandia/oscil_dcop_31=430; Sandia/oscil_dcop_32=430; Sandia/oscil_dcop_33=430; Sandia/oscil_dcop_34=430; Sandia/oscil_dcop_35=430; Sandia/oscil_dcop_36=430; Sandia/oscil_dcop_37=430; Sandia/oscil_dcop_38=430; Sandia/oscil_dcop_39=430; Sandia/oscil_dcop_40=430; Sandia/oscil_dcop_41=430; Sandia/oscil_dcop_42=430; Sandia/oscil_dcop_43=430; Sandia/oscil_dcop_44=430; Sandia/oscil_dcop_45=430; Sandia/oscil_dcop_46=430; Sandia/oscil_dcop_47=430; Sandia/oscil_dcop_48=430; Sandia/oscil_dcop_49=430; Sandia/oscil_dcop_50=430; Sandia/oscil_dcop_51=430; Sandia/oscil_dcop_52=430; Sandia/oscil_dcop_53=430; Sandia/oscil_dcop_54=430; Sandia/oscil_dcop_55=430; Sandia/oscil_dcop_56=430; Sandia/oscil_dcop_57=430; Sandia/oscil_trans_01=430,Sandia/ASIC_680k=682862,Sandia/oscil_dcop_01=1544; Sandia/oscil_dcop_02=1544; Sandia/oscil_dcop_03=1544; Sandia/oscil_dcop_04=1544; Sandia/oscil_dcop_05=1544; Sandia/oscil_dcop_06=1544; Sandia/oscil_dcop_07=1544; Sandia/oscil_dcop_08=1544; Sandia/oscil_dcop_09=1544; Sandia/oscil_dcop_10=1544; Sandia/oscil_dcop_11=1544; Sandia/oscil_dcop_12=1544; Sandia/oscil_dcop_13=1544; Sandia/oscil_dcop_14=1544; Sandia/oscil_dcop_15=1544; Sandia/oscil_dcop_16=1544; Sandia/oscil_dcop_17=1544; Sandia/oscil_dcop_18=1544; Sandia/oscil_dcop_19=1544; Sandia/oscil_dcop_20=1544; Sandia/oscil_dcop_21=1544; Sandia/oscil_dcop_22=1544; Sandia/oscil_dcop_23=1544; Sandia/oscil_dcop_24=1544; Sandia/oscil_dcop_25=1544; Sandia/oscil_dcop_26=1544; Sandia/oscil_dcop_27=1544; Sandia/oscil_dcop_28=1544; Sandia/oscil_dcop_29=1544; Sandia/oscil_dcop_30=1544; Sandia/oscil_dcop_31=1544; Sandia/oscil_dcop_32=1544; Sandia/oscil_dcop_33=1544; Sandia/oscil_dcop_34=1544; Sandia/oscil_dcop_35=1544; Sandia/oscil_dcop_36=1544; Sandia/oscil_dcop_37=1544; Sandia/oscil_dcop_38=1544; Sandia/oscil_dcop_39=1544; Sandia/oscil_dcop_40=1544; Sandia/oscil_dcop_41=1544; Sandia/oscil_dcop_42=1544; Sandia/oscil_dcop_43=1544; Sandia/oscil_dcop_44=1544; Sandia/oscil_dcop_45=1544; Sandia/oscil_dcop_46=1544; Sandia/oscil_dcop_47=1544; Sandia/oscil_dcop_48=1544; Sandia/oscil_dcop_49=1544; Sandia/oscil_dcop_50=1544; Sandia/oscil_dcop_51=1544; Sandia/oscil_dcop_52=1544; Sandia/oscil_dcop_53=1544; Sandia/oscil_dcop_54=1544; Sandia/oscil_dcop_55=1544; Sandia/oscil_dcop_56=1544; Sandia/oscil_dcop_57=1544,Sandia/ASIC_680k=2638997,Sandia/oscil_dcop_01=1544; Sandia/oscil_dcop_02=1544; Sandia/oscil_dcop_03=1544; Sandia/oscil_dcop_04=1544; Sandia/oscil_dcop_05=1544; Sandia/oscil_dcop_06=1544; Sandia/oscil_dcop_07=1544; Sandia/oscil_dcop_08=1544; Sandia/oscil_dcop_09=1544; Sandia/oscil_dcop_10=1544; Sandia/oscil_dcop_11=1544; Sandia/oscil_dcop_12=1544; Sandia/oscil_dcop_13=1544; Sandia/oscil_dcop_14=1544; Sandia/oscil_dcop_15=1544; Sandia/oscil_dcop_16=1544; Sandia/oscil_dcop_17=1544; Sandia/oscil_dcop_18=1544; Sandia/oscil_dcop_19=1544; Sandia/oscil_dcop_20=1544; Sandia/oscil_dcop_21=1544; Sandia/oscil_dcop_22=1544; Sandia/oscil_dcop_23=1544; Sandia/oscil_dcop_24=1544; Sandia/oscil_dcop_25=1544; Sandia/oscil_dcop_26=1544; Sandia/oscil_dcop_27=1544; Sandia/oscil_dcop_28=1544; Sandia/oscil_dcop_29=1544; Sandia/oscil_dcop_30=1544; Sandia/oscil_dcop_31=1544; Sandia/oscil_dcop_32=1544; Sandia/oscil_dcop_33=1544; Sandia/oscil_dcop_34=1544; Sandia/oscil_dcop_35=1544; Sandia/oscil_dcop_36=1544; Sandia/oscil_dcop_37=1544; Sandia/oscil_dcop_38=1544; Sandia/oscil_dcop_39=1544; Sandia/oscil_dcop_40=1544; Sandia/oscil_dcop_41=1544; Sandia/oscil_dcop_42=1544; Sandia/oscil_dcop_43=1544; Sandia/oscil_dcop_44=1544; Sandia/oscil_dcop_45=1544; Sandia/oscil_dcop_46=1544; Sandia/oscil_dcop_47=1544; Sandia/oscil_dcop_48=1544; Sandia/oscil_dcop_49=1544; Sandia/oscil_dcop_50=1544; Sandia/oscil_dcop_51=1544; Sandia/oscil_dcop_52=1544; Sandia/oscil_dcop_53=1544; Sandia/oscil_dcop_54=1544; Sandia/oscil_dcop_55=1544; Sandia/oscil_dcop_56=1544; Sandia/oscil_dcop_57=1544,Sandia/ASIC_680k=3871773 -Sanghavi,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,51993,51993,51993,51993,51993,51993,380415,380415,380415,380415,380415,380415,0.9219175329301935,0.9219175329301935,0.9219175329301935,0.6030290297239527,0.6030290297239527,0.6030290297239527,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,semiconductor device problem:1,semiconductor device problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Sanghavi/ecl32=51993,Sanghavi/ecl32=51993,Sanghavi/ecl32=51993,Sanghavi/ecl32=51993,Sanghavi/ecl32=380415,Sanghavi/ecl32=380415,Sanghavi/ecl32=380415,Sanghavi/ecl32=380415 -Schenk,5,homogeneous,0.0,0,1,1.420771,1.431048,1.431053,5,28160000,1062400,8345600,27993600,1062400,8345600,27993600,28192672,225422112,760648352,28704672,229518112,774472352,1.0,1,1.0,1.0,1,1.0,square:5,square,1.0,symmetric:5,symmetric,1.0,real:5,real,1.0,exact_numerical_or_hermitian:5,exact_numerical_or_hermitian,1.0,no:5,no,1.0,optimization problem:5,optimization problem,1.0,true:5,true,1.0,no:5,no,1.0,no:5,no,1.0,ge_1m:5,ge_1m,1.0,Schenk/nlpkkt80=1062400,Schenk/nlpkkt240=27993600,Schenk/nlpkkt80=1062400,Schenk/nlpkkt240=27993600,Schenk/nlpkkt80=28192672,Schenk/nlpkkt240=760648352,Schenk/nlpkkt80=28704672,Schenk/nlpkkt240=774472352 -Schenk_AFE,16,mixed_some_attributes,0.145833,2,3,0.476313,0.473875,0.477289,10,558646,503625,504855,1508065,503625,504855,1508065,17550675,17562051,52259885,17550675,17588875,52672325,1.0,1,1.0,1.0,1,1.0,square:16,square,1.0,symmetric:16,symmetric,1.0,real:16,real,1.0,exact_numerical_or_hermitian:16,exact_numerical_or_hermitian,1.0,no:6; yes:10,yes,0.625,structural problem:7; structural problem sequence:1; subsequent structural problem:8,subsequent structural problem,0.5,true:16,true,1.0,yes:16,yes,1.0,yes:16,yes,1.0,100k_to_1m:15; ge_1m:1,100k_to_1m,0.9375,Schenk_AFE/af_0_k101=503625; Schenk_AFE/af_1_k101=503625; Schenk_AFE/af_2_k101=503625; Schenk_AFE/af_3_k101=503625; Schenk_AFE/af_4_k101=503625; Schenk_AFE/af_5_k101=503625,Schenk_AFE/af_shell10=1508065,Schenk_AFE/af_0_k101=503625; Schenk_AFE/af_1_k101=503625; Schenk_AFE/af_2_k101=503625; Schenk_AFE/af_3_k101=503625; Schenk_AFE/af_4_k101=503625; Schenk_AFE/af_5_k101=503625,Schenk_AFE/af_shell10=1508065,Schenk_AFE/af_0_k101=17550675; Schenk_AFE/af_1_k101=17550675; Schenk_AFE/af_2_k101=17550675; Schenk_AFE/af_3_k101=17550675; Schenk_AFE/af_4_k101=17550675; Schenk_AFE/af_5_k101=17550675,Schenk_AFE/af_shell10=52259885,Schenk_AFE/af_0_k101=17550675; Schenk_AFE/af_1_k101=17550675; Schenk_AFE/af_2_k101=17550675; Schenk_AFE/af_3_k101=17550675; Schenk_AFE/af_4_k101=17550675; Schenk_AFE/af_5_k101=17550675,Schenk_AFE/af_shell10=52672325 -Schenk_IBMNA,52,mixed_some_attributes,0.028846,1,3,2.201863,2.189106,2.189134,25,193434,2169,10926.5,345241,2169,10926.5,345241,15145,120123,2340859,15145,127036,2341011,1.0,1,1.0,1.0,1,1.0,square:52,square,1.0,symmetric:52,symmetric,1.0,real:52,real,1.0,exact_numerical_or_hermitian:52,exact_numerical_or_hermitian,1.0,no:52,no,1.0,optimization problem:43; optimization problem sequence:5; subsequent optimization problem:4,optimization problem,0.826923,true:52,true,1.0,no:52,no,1.0,no:52,no,1.0,100k_to_1m:3; 10k_to_100k:25; 1k_to_10k:24,10k_to_100k,0.480769,Schenk_IBMNA/c-18=2169,Schenk_IBMNA/c-big=345241,Schenk_IBMNA/c-18=2169,Schenk_IBMNA/c-big=345241,Schenk_IBMNA/c-18=15145,Schenk_IBMNA/c-big=2340859,Schenk_IBMNA/c-18=15145,Schenk_IBMNA/c-big=2341011 -Schenk_IBMSDS,7,homogeneous,0.0,0,1,0.656702,0.765896,0.781628,7,4800533,27628,51448,125329,27628,51448,125329,206670,537038,1205518,442898,1056610,2678750,0.9871118002084263,0.9920908271502504,0.9995761793531919,0.1744833443427385,0.189734095484946,0.3613007012666531,square:7,square,1.0,unsymmetric:7,unsymmetric,1.0,real:7,real,1.0,partially_pattern_symmetric:7,partially_pattern_symmetric,1.0,no:7,no,1.0,semiconductor device problem:7,semiconductor device problem,1.0,true:7,true,1.0,yes:7,yes,1.0,no:7,no,1.0,100k_to_1m:2; 10k_to_100k:5,10k_to_100k,0.714286,Schenk_IBMSDS/2D_27628_bjtcai=27628,Schenk_IBMSDS/matrix-new_3=125329,Schenk_IBMSDS/2D_27628_bjtcai=27628,Schenk_IBMSDS/matrix-new_3=125329,Schenk_IBMSDS/2D_27628_bjtcai=206670,Schenk_IBMSDS/matrix_9=1205518,Schenk_IBMSDS/2D_27628_bjtcai=442898,Schenk_IBMSDS/matrix-new_3=2678750 -Schenk_ISEI,18,mixed_some_attributes,0.064815,1,3,1.219563,1.721342,1.674667,18,40428312,10938,115625,181343,10938,115625,181343,130500,2129496,6869939,234006,3897557,11063545,1.0,1,1.0,0.09493801090692276,0.1980614094238716,0.3692588097985042,square:18,square,1.0,unsymmetric:18,unsymmetric,1.0,real:18,real,1.0,pattern_symmetric_only:18,pattern_symmetric_only,1.0,no:18,no,1.0,semiconductor device problem:4; semiconductor device problem sequence:3; subsequent semiconductor device problem:11,subsequent semiconductor device problem,0.611111,true:18,true,1.0,yes:18,yes,1.0,no:18,no,1.0,100k_to_1m:16; 10k_to_100k:2,100k_to_1m,0.888889,Schenk_ISEI/igbt3=10938,Schenk_ISEI/ohne2=181343,Schenk_ISEI/igbt3=10938,Schenk_ISEI/ohne2=181343,Schenk_ISEI/igbt3=130500,Schenk_ISEI/ohne2=6869939,Schenk_ISEI/igbt3=234006,Schenk_ISEI/ohne2=11063545 -Schmid,2,small_group_lt5,0.0,0,1,1.17195,1.174245,1.174245,0,0,82654,655349.5,1228045,82654,655349.5,1228045,574458,4577385.5,8580313,574458,4577385.5,8580313,1.0,1,1.0,1.0,1,1.0,square:2,square,1.0,symmetric:2,symmetric,1.0,real:2,real,1.0,exact_numerical_or_hermitian:2,exact_numerical_or_hermitian,1.0,yes:2,yes,1.0,thermal problem:2,thermal problem,1.0,true:2,true,1.0,yes:2,yes,1.0,yes:2,yes,1.0,10k_to_100k:1; ge_1m:1,10k_to_100k,0.5,Schmid/thermal1=82654,Schmid/thermal2=1228045,Schmid/thermal1=82654,Schmid/thermal2=1228045,Schmid/thermal1=574458,Schmid/thermal2=8580313,Schmid/thermal1=574458,Schmid/thermal2=8580313 -Schulthess,3,small_group_lt5,0.0,0,1,0.918874,1.004644,1.004644,0,0,1922,3625,10204,1996,3923,16559,4335,8054,43816,4335,8054,43816,0.0,0,0.0,0.0,0,0.0,rectangular:3,rectangular,1.0,rectangular:3,rectangular,1.0,integer:3,integer,1.0,rectangular:3,rectangular,1.0,no:3,no,1.0,biochemical network:3,biochemical network,1.0,false:3,false,1.0,no:3,no,1.0,no:3,no,1.0,10k_to_100k:1; 1k_to_10k:2,1k_to_10k,0.666667,Schulthess/N_biocarta=1922,Schulthess/N_reactome=10204,Schulthess/N_biocarta=1996,Schulthess/N_reactome=16559,Schulthess/N_biocarta=4335,Schulthess/N_reactome=43816,Schulthess/N_biocarta=4335,Schulthess/N_reactome=43816 -Shen,4,small_group_lt5,0.0,0,1,0.731859,1.341421,1.341421,0,0,3432,11708.5,18510,3432,11708.5,18510,25220,99239,553562,25220,99239,553562,0.1493062958488302,0.1578869688501962,0.3079877899832364,0.001965387548691824,0.019113259823802173,0.02665845434660729,square:4,square,1.0,unsymmetric:4,unsymmetric,1.0,real:4,real,1.0,partially_pattern_symmetric:4,partially_pattern_symmetric,1.0,no:4,no,1.0,2D/3D problem:4,2D/3D problem,1.0,true:4,true,1.0,yes:4,yes,1.0,no:4,no,1.0,10k_to_100k:2; 1k_to_10k:2,10k_to_100k,0.5,Shen/shermanACa=3432,Shen/shermanACb=18510,Shen/shermanACa=3432,Shen/shermanACb=18510,Shen/shermanACa=25220,Shen/e40r0100=553562,Shen/shermanACa=25220,Shen/e40r0100=553562 -Shyy,2,small_group_lt5,0.0,0,1,1.209606,1.21626,1.21626,0,0,4720,40600,76480,4720,40600,76480,20042,174902,329762,20042,174902,329762,0.7234369459874719,0.7248864078192176,0.7263358696509635,0.1824427535660244,0.1834090614538549,0.1843753693416854,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,real:2,real,1.0,partially_pattern_symmetric:2,partially_pattern_symmetric,1.0,no:2,no,1.0,computational fluid dynamics problem:2,computational fluid dynamics problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,10k_to_100k:1; 1k_to_10k:1,10k_to_100k,0.5,Shyy/shyy41=4720,Shyy/shyy161=76480,Shyy/shyy41=4720,Shyy/shyy161=76480,Shyy/shyy41=20042,Shyy/shyy161=329762,Shyy/shyy41=20042,Shyy/shyy161=329762 -Simon,12,categorically_heterogeneous,0.236111,4,5,1.284539,1.152719,1.128509,7,22412,3242,17962.5,62424,3242,17962.5,62424,130371,1402778.5,1853104,137845,1408689.5,1853104,0.0,1,1.0,0.0,0.0729162397267196,1.0,square:12,square,1.0,symmetric:2; unsymmetric:10,unsymmetric,0.833333,real:12,real,1.0,exact_numerical_or_hermitian:2; partially_pattern_symmetric:1; pattern_symmetric_only:7; pattern_unsymmetric:2,pattern_symmetric_only,0.583333,no:10; yes:2,no,0.833333,computational fluid dynamics problem:2; computational fluid dynamics problem sequence:2; directed weighted random graph:1; structural problem:4; subsequent computational fluid dynamics problem:3,structural problem,0.333333,missing:1; true:11,true,0.916667,no:1; yes:11,yes,0.916667,no:10; yes:2,no,0.833333,10k_to_100k:8; 1k_to_10k:4,10k_to_100k,0.666667,Simon/raefsky1=3242; Simon/raefsky2=3242,Simon/venkat01=62424; Simon/venkat25=62424; Simon/venkat50=62424,Simon/raefsky1=3242; Simon/raefsky2=3242,Simon/venkat01=62424; Simon/venkat25=62424; Simon/venkat50=62424,Simon/raefsky6=130371,Simon/appu=1853104,Simon/raefsky6=137845,Simon/appu=1853104 -Sinclair,2,small_group_lt5,0.0,0,1,0.367716,0.369536,0.370966,2,4747234,292008,486475.5,680943,292008,486475.5,680943,12935272,21613049.5,30290827,14322744,23986666.5,33650589,1.0,1,1.0,1.0,1,1.0,square:2,square,1.0,Hermitian:2,Hermitian,1.0,complex:2,complex,1.0,exact_numerical_or_hermitian:2,exact_numerical_or_hermitian,1.0,no:2,no,1.0,materials problem:2,materials problem,1.0,true:2,true,1.0,yes:2,yes,1.0,yes:2,yes,1.0,100k_to_1m:2,100k_to_1m,1.0,Sinclair/3Dspectralwave2=292008,Sinclair/3Dspectralwave=680943,Sinclair/3Dspectralwave2=292008,Sinclair/3Dspectralwave=680943,Sinclair/3Dspectralwave2=12935272,Sinclair/3Dspectralwave=30290827,Sinclair/3Dspectralwave2=14322744,Sinclair/3Dspectralwave=33650589 -Sorensen,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,324085,324085,324085,324085,324085,324085,1208908,1208908,1208908,1208908,1208908,1208908,0.0006324084497715383,0.0006324084497715383,0.0006324084497715383,0.0006125422157473015,0.0006125422157473015,0.0006125422157473015,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,integer:1,integer,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,directed weighted graph:1,directed weighted graph,1.0,missing:1,missing,1.0,no:1,no,1.0,no:1,no,1.0,100k_to_1m:1,100k_to_1m,1.0,Sorensen/Linux_call_graph=324085,Sorensen/Linux_call_graph=324085,Sorensen/Linux_call_graph=324085,Sorensen/Linux_call_graph=324085,Sorensen/Linux_call_graph=1208908,Sorensen/Linux_call_graph=1208908,Sorensen/Linux_call_graph=1208908,Sorensen/Linux_call_graph=1208908 -Springer,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,327062,327062,327062,37830,37830,37830,6019939,6019939,6019939,6019939,6019939,6019939,0.0,0,0.0,0.0,0,0.0,rectangular:1,rectangular,1.0,rectangular:1,rectangular,1.0,real:1,real,1.0,rectangular:1,rectangular,1.0,no:1,no,1.0,least squares problem:1,least squares problem,1.0,false:1,false,1.0,no:1,no,1.0,no:1,no,1.0,100k_to_1m:1,100k_to_1m,1.0,Springer/ESOC=327062,Springer/ESOC=327062,Springer/ESOC=37830,Springer/ESOC=37830,Springer/ESOC=6019939,Springer/ESOC=6019939,Springer/ESOC=6019939,Springer/ESOC=6019939 -Stevenson,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,2111154,2111154,2111154,801374,801374,801374,4944201,4944201,4944201,4944201,4944201,4944201,0.0,0,0.0,0.0,0,0.0,rectangular:1,rectangular,1.0,rectangular:1,rectangular,1.0,real:1,real,1.0,rectangular:1,rectangular,1.0,no:1,no,1.0,circuit simulation problem:1,circuit simulation problem,1.0,true:1,true,1.0,no:1,no,1.0,no:1,no,1.0,ge_1m:1,ge_1m,1.0,Stevenson/LargeRegFile=2111154,Stevenson/LargeRegFile=2111154,Stevenson/LargeRegFile=801374,Stevenson/LargeRegFile=801374,Stevenson/LargeRegFile=4944201,Stevenson/LargeRegFile=4944201,Stevenson/LargeRegFile=4944201,Stevenson/LargeRegFile=4944201 -Sumner,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,29493,29493,29493,11822,11822,11822,117954,117954,117954,117954,117954,117954,0.0,0,0.0,0.0,0,0.0,rectangular:1,rectangular,1.0,rectangular:1,rectangular,1.0,real:1,real,1.0,rectangular:1,rectangular,1.0,no:1,no,1.0,computer graphics/vision problem:1,computer graphics/vision problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Sumner/graphics=29493,Sumner/graphics=29493,Sumner/graphics=11822,Sumner/graphics=11822,Sumner/graphics=117954,Sumner/graphics=117954,Sumner/graphics=117954,Sumner/graphics=117954 -Sybrandt,2,small_group_lt5,0.166667,2,2,0.784156,0.239958,0.239435,1,8046672,30239687,107101882,183964077,30239687,107101882,183964077,6669254694,9128990329,11588725964,6677301366,9133013665,11588725964,1.0,1,1.0,1.0,1,1.0,square:2,square,1.0,symmetric:2,symmetric,1.0,binary:1; real:1,binary,0.5,exact_numerical_or_hermitian:2,exact_numerical_or_hermitian,1.0,no:2,no,1.0,undirected graph:1; undirected weighted graph:1,undirected graph,0.5,missing:2,missing,1.0,no:2,no,1.0,no:2,no,1.0,ge_1m:2,ge_1m,1.0,Sybrandt/MOLIERE_2016=30239687,Sybrandt/AGATHA_2015=183964077,Sybrandt/MOLIERE_2016=30239687,Sybrandt/AGATHA_2015=183964077,Sybrandt/MOLIERE_2016=6669254694,Sybrandt/AGATHA_2015=11588725964,Sybrandt/MOLIERE_2016=6677301366,Sybrandt/AGATHA_2015=11588725964 -Szczerba,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,20896,20896,20896,20896,20896,20896,191368,191368,191368,191368,191368,191368,0.9897436481194791,0.9897436481194791,0.9897436481194791,0.3299904380293234,0.3299904380293234,0.3299904380293234,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,computational fluid dynamics problem:1,computational fluid dynamics problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Szczerba/Ill_Stokes=20896,Szczerba/Ill_Stokes=20896,Szczerba/Ill_Stokes=20896,Szczerba/Ill_Stokes=20896,Szczerba/Ill_Stokes=191368,Szczerba/Ill_Stokes=191368,Szczerba/Ill_Stokes=191368,Szczerba/Ill_Stokes=191368 -TAMU_SmartGridCenter,3,small_group_lt5,0.111111,2,1,1.243032,0.922799,0.910319,2,2679,4000,20000,69999,4000,20000,69999,28505,135888,238627,29336,137736,238627,1.0,1,1.0,0.007345439706182412,0.02328109899213033,1.0,square:3,square,1.0,symmetric:1; unsymmetric:2,unsymmetric,0.666667,real:3,real,1.0,exact_numerical_or_hermitian:1; pattern_symmetric_only:2,pattern_symmetric_only,0.666667,no:3,no,1.0,power network problem:3,power network problem,1.0,true:3,true,1.0,no:3,no,1.0,no:3,no,1.0,10k_to_100k:2; 1k_to_10k:1,10k_to_100k,0.666667,TAMU_SmartGridCenter/ACTIVSg2000=4000,TAMU_SmartGridCenter/ACTIVSg70K=69999,TAMU_SmartGridCenter/ACTIVSg2000=4000,TAMU_SmartGridCenter/ACTIVSg70K=69999,TAMU_SmartGridCenter/ACTIVSg2000=28505,TAMU_SmartGridCenter/ACTIVSg70K=238627,TAMU_SmartGridCenter/ACTIVSg2000=29336,TAMU_SmartGridCenter/ACTIVSg70K=238627 -TKK,12,categorically_heterogeneous,0.222222,5,2,2.049179,2.186313,2.186313,1,3602,1282,13681,143571,1282,13681,143571,30644,695378,4706073,30644,695378,4706073,1.0,1,1.0,0.6937221589143272,1,1.0,square:12,square,1.0,symmetric:10; unsymmetric:2,symmetric,0.833333,binary:3; integer:4; real:5,real,0.416667,exact_numerical_or_hermitian:10; pattern_symmetric_only:2,exact_numerical_or_hermitian,0.833333,no:9; yes:3,no,0.75,structural problem:10; structural problem sequence:2,structural problem,0.833333,true:12,true,1.0,yes:12,yes,1.0,no:2; yes:10,yes,0.833333,100k_to_1m:1; 10k_to_100k:6; 1k_to_10k:5,10k_to_100k,0.5,TKK/plbuckle=1282,TKK/engine=143571,TKK/plbuckle=1282,TKK/engine=143571,TKK/plbuckle=30644,TKK/engine=4706073,TKK/plbuckle=30644,TKK/engine=4706073 -TOKAMAK,4,small_group_lt5,0.0,0,1,1.296665,1.424462,1.424462,0,0,300,2380,5940,300,2380,5940,3155,31860,83842,3155,31860,83842,0.4651488616462347,0.5239919806861203,0.5290493183743677,0.0,0,0.0,square:4,square,1.0,unsymmetric:4,unsymmetric,1.0,real:4,real,1.0,partially_pattern_symmetric:4,partially_pattern_symmetric,1.0,no:4,no,1.0,electromagnetics problem:4,electromagnetics problem,1.0,true:4,true,1.0,yes:4,yes,1.0,no:4,no,1.0,1k_to_10k:3; lt_1k:1,1k_to_10k,0.75,TOKAMAK/utm300=300,TOKAMAK/utm5940=5940,TOKAMAK/utm300=300,TOKAMAK/utm5940=5940,TOKAMAK/utm300=3155,TOKAMAK/utm5940=83842,TOKAMAK/utm300=3155,TOKAMAK/utm5940=83842 -TSOPF,27,mixed_some_attributes,0.148148,2,1,1.690088,2.810246,2.810246,0,0,2454,29214,120216,2454,29214,120216,25032,1977600,16171169,25032,1977600,16171169,0.002485718029041526,0.05962284731399325,1.0,8.236061201778597e-05,0.0008133934303355415,1.0,square:27,square,1.0,symmetric:12; unsymmetric:15,unsymmetric,0.555556,real:27,real,1.0,exact_numerical_or_hermitian:12; partially_pattern_symmetric:15,partially_pattern_symmetric,0.555556,no:27,no,1.0,power network problem:27,power network problem,1.0,true:27,true,1.0,no:27,no,1.0,no:27,no,1.0,100k_to_1m:1; 10k_to_100k:23; 1k_to_10k:3,10k_to_100k,0.851852,TSOPF/TSOPF_FS_b9_c1=2454,TSOPF/TSOPF_FS_b39_c30=120216,TSOPF/TSOPF_FS_b9_c1=2454,TSOPF/TSOPF_FS_b39_c30=120216,TSOPF/TSOPF_FS_b9_c1=25032,TSOPF/TSOPF_RS_b2383=16171169; TSOPF/TSOPF_RS_b2383_c1=16171169,TSOPF/TSOPF_FS_b9_c1=25032,TSOPF/TSOPF_RS_b2383=16171169; TSOPF/TSOPF_RS_b2383_c1=16171169 -Toledo,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,68600,68600,68600,21961,21961,21961,247424,247424,247424,247424,247424,247424,0.0,0,0.0,0.0,0,0.0,rectangular:1,rectangular,1.0,rectangular:1,rectangular,1.0,real:1,real,1.0,rectangular:1,rectangular,1.0,no:1,no,1.0,counter-example problem:1,counter-example problem,1.0,true:1,true,1.0,no:1,no,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Toledo/deltaX=68600,Toledo/deltaX=68600,Toledo/deltaX=21961,Toledo/deltaX=21961,Toledo/deltaX=247424,Toledo/deltaX=247424,Toledo/deltaX=247424,Toledo/deltaX=247424 -Tromble,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,399130,399130,399130,399130,399130,399130,1216334,1216334,1216334,1216334,1216334,1216334,0.05790231080611451,0.05790231080611451,0.05790231080611451,0.0,0,0.0,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,directed weighted graph:1,directed weighted graph,1.0,missing:1,missing,1.0,no:1,no,1.0,no:1,no,1.0,100k_to_1m:1,100k_to_1m,1.0,Tromble/language=399130,Tromble/language=399130,Tromble/language=399130,Tromble/language=399130,Tromble/language=1216334,Tromble/language=1216334,Tromble/language=1216334,Tromble/language=1216334 -UTEP,3,small_group_lt5,0.0,0,1,0.95879,1.157565,1.157565,1,6,16129,65025,146689,16129,65025,146689,253009,1030225,3636643,253009,1030225,3636649,1.0,1,1.0,1.0,1,1.0,square:3,square,1.0,symmetric:3,symmetric,1.0,real:3,real,1.0,exact_numerical_or_hermitian:3,exact_numerical_or_hermitian,1.0,yes:3,yes,1.0,2D/3D problem:3,2D/3D problem,1.0,true:3,true,1.0,yes:3,yes,1.0,yes:3,yes,1.0,100k_to_1m:1; 10k_to_100k:2,10k_to_100k,0.666667,UTEP/Dubcova1=16129,UTEP/Dubcova3=146689,UTEP/Dubcova1=16129,UTEP/Dubcova3=146689,UTEP/Dubcova1=253009,UTEP/Dubcova3=3636643,UTEP/Dubcova1=253009,UTEP/Dubcova3=3636649 -Um,2,small_group_lt5,0.0,0,1,0.408189,0.410876,0.410876,0,0,101492,180640.5,259789,101492,180640.5,259789,1647264,2944968.5,4242673,1647264,2944968.5,4242673,1.0,1,1.0,1.0,1,1.0,square:2,square,1.0,symmetric:2,symmetric,1.0,real:2,real,1.0,exact_numerical_or_hermitian:2,exact_numerical_or_hermitian,1.0,yes:2,yes,1.0,electromagnetics problem:2,electromagnetics problem,1.0,true:2,true,1.0,yes:2,yes,1.0,yes:2,yes,1.0,100k_to_1m:2,100k_to_1m,1.0,Um/2cubes_sphere=101492,Um/offshore=259789,Um/2cubes_sphere=101492,Um/offshore=259789,Um/2cubes_sphere=1647264,Um/offshore=4242673,Um/2cubes_sphere=1647264,Um/offshore=4242673 -VDOL,91,homogeneous,0.0,0,1,2.270973,2.384904,2.384904,0,0,99,2513,18476,99,2513,18476,927,28757,224897,927,28757,224897,1.0,1,1.0,1.0,1,1.0,square:91,square,1.0,symmetric:91,symmetric,1.0,real:91,real,1.0,exact_numerical_or_hermitian:91,exact_numerical_or_hermitian,1.0,no:91,no,1.0,optimal control problem:91,optimal control problem,1.0,false:1; true:90,true,0.989011,no:91,no,1.0,no:91,no,1.0,10k_to_100k:13; 1k_to_10k:54; lt_1k:24,1k_to_10k,0.593407,VDOL/spaceStation_1=99,VDOL/lowThrust_13=18476,VDOL/spaceStation_1=99,VDOL/lowThrust_13=18476,VDOL/spaceStation_1=927,VDOL/lowThrust_13=224897,VDOL/spaceStation_1=927,VDOL/lowThrust_13=224897 -VLSI,14,mixed_some_attributes,0.142857,2,2,2.386745,2.616323,2.616323,8,34910073,46994,741786,11449533,46994,741786,11449533,845089,18026852,349321980,845089,23860888.5,349321980,0.420954312323566,1,1.0,0.04200954350296261,0.1936182841296341,0.3018551635520223,square:14,square,1.0,unsymmetric:14,unsymmetric,1.0,real:14,real,1.0,partially_pattern_symmetric:6; pattern_symmetric_only:8,pattern_symmetric_only,0.571429,no:14,no,1.0,semiconductor device problem:8; semiconductor process problem:6,semiconductor device problem,0.571429,true:14,true,1.0,yes:14,yes,1.0,no:14,no,1.0,100k_to_1m:5; 10k_to_100k:2; ge_1m:7,ge_1m,0.5,VLSI/mosfet2=46994,VLSI/stokes=11449533,VLSI/mosfet2=46994,VLSI/stokes=11449533,VLSI/ss1=845089,VLSI/stokes=349321980,VLSI/ss1=845089,VLSI/stokes=349321980 -VanVelzen,10,homogeneous,0.0,0,1,0.016534,0.584432,0.584529,5,2270,21982,22835,22835,21982,22835,22835,498771,981060,1915726,498771,981319,1916152,1.37377639453759e-05,0.15207874478358313,0.3593020610225873,0.0,1.447027227264308e-06,8.389455293641422e-06,square:10,square,1.0,unsymmetric:10,unsymmetric,1.0,real:10,real,1.0,partially_pattern_symmetric:10,partially_pattern_symmetric,1.0,no:10,no,1.0,chemical process simulation problem:10,chemical process simulation problem,1.0,true:10,true,1.0,no:10,no,1.0,no:10,no,1.0,10k_to_100k:10,10k_to_100k,1.0,VanVelzen/std1_Jac2_db=21982; VanVelzen/std1_Jac2=21982; VanVelzen/std1_Jac3_db=21982; VanVelzen/std1_Jac3=21982,VanVelzen/Zd_Jac2_db=22835; VanVelzen/Zd_Jac2=22835; VanVelzen/Zd_Jac3_db=22835; VanVelzen/Zd_Jac3=22835; VanVelzen/Zd_Jac6_db=22835; VanVelzen/Zd_Jac6=22835,VanVelzen/std1_Jac2_db=21982; VanVelzen/std1_Jac2=21982; VanVelzen/std1_Jac3_db=21982; VanVelzen/std1_Jac3=21982,VanVelzen/Zd_Jac2_db=22835; VanVelzen/Zd_Jac2=22835; VanVelzen/Zd_Jac3_db=22835; VanVelzen/Zd_Jac3=22835; VanVelzen/Zd_Jac6_db=22835; VanVelzen/Zd_Jac6=22835,VanVelzen/std1_Jac2_db=498771,VanVelzen/Zd_Jac3=1915726,VanVelzen/std1_Jac2_db=498771,VanVelzen/Zd_Jac3=1916152 -Vavasis,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,41092,41092,41092,41092,41092,41092,1683902,1683902,1683902,1683902,1683902,1683902,0.0009359570219938022,0.0009359570219938022,0.0009359570219938022,0.0,0,0.0,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,2D/3D problem:1,2D/3D problem,1.0,true:1,true,1.0,yes:1,yes,1.0,no:1,no,1.0,10k_to_100k:1,10k_to_100k,1.0,Vavasis/av41092=41092,Vavasis/av41092=41092,Vavasis/av41092=41092,Vavasis/av41092=41092,Vavasis/av41092=1683902,Vavasis/av41092=1683902,Vavasis/av41092=1683902,Vavasis/av41092=1683902 -Wang,6,mixed_some_attributes,0.111111,1,3,0.953261,0.96758,0.96758,0,0,2903,3169,26068,2903,3169,26068,19093,20841,177196,19093,20841,177196,1.0,1,1.0,0.0,0.4273151697055724,0.9780019059720457,square:6,square,1.0,unsymmetric:6,unsymmetric,1.0,real:6,real,1.0,pattern_symmetric_only:6,pattern_symmetric_only,1.0,no:6,no,1.0,semiconductor device problem:2; semiconductor device problem sequence:2; subsequent semiconductor device problem:2,semiconductor device problem,0.333333,true:6,true,1.0,yes:6,yes,1.0,no:6,no,1.0,10k_to_100k:2; 1k_to_10k:4,1k_to_10k,0.666667,Wang/wang1=2903; Wang/wang2=2903,Wang/wang4=26068,Wang/wang1=2903; Wang/wang2=2903,Wang/wang4=26068,Wang/wang1=19093; Wang/wang2=19093,Wang/wang4=177196,Wang/wang1=19093; Wang/wang2=19093,Wang/wang4=177196 -Watson,2,small_group_lt5,0.0,0,1,0.443643,0.570464,0.577544,1,12300,40401,76306,112211,40401,76306,112211,201201,474766,748331,201201,480916,760631,1.0,1,1.0,0.0,0,0.0,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,real:2,real,1.0,pattern_symmetric_only:2,pattern_symmetric_only,1.0,no:2,no,1.0,2D/3D problem:2,2D/3D problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,100k_to_1m:1; 10k_to_100k:1,100k_to_1m,0.5,Watson/chem_master1=40401,Watson/Baumann=112211,Watson/chem_master1=40401,Watson/Baumann=112211,Watson/chem_master1=201201,Watson/Baumann=748331,Watson/chem_master1=201201,Watson/Baumann=760631 -Williams,7,categorically_heterogeneous,0.309524,5,4,1.438698,0.673948,0.673948,0,0,36417,121192,1000005,36417,121192,1000005,1273389,3105536,6010480,1273389,3105536,6010480,0.0,1,1.0,0.0,1,1.0,square:7,square,1.0,symmetric:4; unsymmetric:3,symmetric,0.571429,integer:1; real:6,real,0.857143,exact_numerical_or_hermitian:4; partially_pattern_symmetric:2; pattern_unsymmetric:1,exact_numerical_or_hermitian,0.571429,no:4; yes:3,no,0.571429,2D/3D problem:4; economic problem:1; weighted directed graph:1; weighted undirected graph:1,2D/3D problem,0.571429,false:1; missing:2; true:4,true,0.571429,no:3; yes:4,yes,0.571429,no:4; yes:3,no,0.571429,100k_to_1m:3; 10k_to_100k:3; ge_1m:1,100k_to_1m,0.428571,Williams/pdb1HYS=36417,Williams/webbase-1M=1000005,Williams/pdb1HYS=36417,Williams/webbase-1M=1000005,Williams/mac_econ_fwd500=1273389,Williams/consph=6010480,Williams/mac_econ_fwd500=1273389,Williams/consph=6010480 -Wissgott,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,525825,525825,525825,525825,525825,525825,3674625,3674625,3674625,3674625,3674625,3674625,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,yes:1,yes,1.0,computational fluid dynamics problem:1,computational fluid dynamics problem,1.0,true:1,true,1.0,yes:1,yes,1.0,yes:1,yes,1.0,100k_to_1m:1,100k_to_1m,1.0,Wissgott/parabolic_fem=525825,Wissgott/parabolic_fem=525825,Wissgott/parabolic_fem=525825,Wissgott/parabolic_fem=525825,Wissgott/parabolic_fem=3674625,Wissgott/parabolic_fem=3674625,Wissgott/parabolic_fem=3674625,Wissgott/parabolic_fem=3674625 -YCheng,3,small_group_lt5,0.0,0,1,0.301,0.302956,0.302956,0,0,14318,26722,28634,11028,11028,11028,57376,102432,115262,57376,102432,115262,0.0,0,0.0,0.0,0,0.0,rectangular:3,rectangular,1.0,rectangular:3,rectangular,1.0,real:3,real,1.0,rectangular:3,rectangular,1.0,no:3,no,1.0,power network problem:3,power network problem,1.0,true:3,true,1.0,no:3,no,1.0,no:3,no,1.0,10k_to_100k:3,10k_to_100k,1.0,YCheng/psse1=14318,YCheng/psse2=28634,YCheng/psse0=11028; YCheng/psse1=11028; YCheng/psse2=11028,YCheng/psse0=11028; YCheng/psse1=11028; YCheng/psse2=11028,YCheng/psse1=57376,YCheng/psse2=115262,YCheng/psse1=57376,YCheng/psse2=115262 -YZhou,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,1020,1020,1020,1020,1020,1020,5883,5883,5883,5883,5883,5883,0.4379791625712601,0.4379791625712601,0.4379791625712601,0.3727147631216827,0.3727147631216827,0.3727147631216827,square:1,square,1.0,unsymmetric:1,unsymmetric,1.0,real:1,real,1.0,partially_pattern_symmetric:1,partially_pattern_symmetric,1.0,no:1,no,1.0,circuit simulation problem:1,circuit simulation problem,1.0,true:1,true,1.0,no:1,no,1.0,no:1,no,1.0,1k_to_10k:1,1k_to_10k,1.0,YZhou/circuit204=1020,YZhou/circuit204=1020,YZhou/circuit204=1020,YZhou/circuit204=1020,YZhou/circuit204=5883,YZhou/circuit204=5883,YZhou/circuit204=5883,YZhou/circuit204=5883 -Yoshiyasu,2,small_group_lt5,0.0,0,1,0.010953,0.079084,0.079084,0,0,234023,237011.5,240000,9393,64696.5,120000,711683,782756,853829,711683,782756,853829,0.0,0,0.0,0.0,0,0.0,rectangular:2,rectangular,1.0,rectangular:2,rectangular,1.0,real:2,real,1.0,rectangular:2,rectangular,1.0,no:2,no,1.0,computer graphics/vision problem:2,computer graphics/vision problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,100k_to_1m:2,100k_to_1m,1.0,Yoshiyasu/mesh_deform=234023,Yoshiyasu/image_interp=240000,Yoshiyasu/mesh_deform=9393,Yoshiyasu/image_interp=120000,Yoshiyasu/image_interp=711683,Yoshiyasu/mesh_deform=853829,Yoshiyasu/image_interp=711683,Yoshiyasu/mesh_deform=853829 -Zaoui,1,small_group_lt5,0.0,0,1,0.0,0.0,0.0,1,1841302,2063494,2063494,2063494,2063494,2063494,2063494,12771361,12771361,12771361,14612663,14612663,14612663,1.0,1,1.0,1.0,1,1.0,square:1,square,1.0,symmetric:1,symmetric,1.0,real:1,real,1.0,exact_numerical_or_hermitian:1,exact_numerical_or_hermitian,1.0,no:1,no,1.0,optimization problem:1,optimization problem,1.0,true:1,true,1.0,no:1,no,1.0,no:1,no,1.0,ge_1m:1,ge_1m,1.0,Zaoui/kkt_power=2063494,Zaoui/kkt_power=2063494,Zaoui/kkt_power=2063494,Zaoui/kkt_power=2063494,Zaoui/kkt_power=12771361,Zaoui/kkt_power=12771361,Zaoui/kkt_power=14612663,Zaoui/kkt_power=14612663 -Zhao,2,small_group_lt5,0.0,0,1,0.0,0.0,0.0,0,0,33861,33861,33861,33861,33861,33861,166453,166453,166453,166453,166453,166453,0.9224990949680222,0.9224990949680222,0.9224990949680222,0.0,0,0.0,square:2,square,1.0,unsymmetric:2,unsymmetric,1.0,real:2,real,1.0,partially_pattern_symmetric:2,partially_pattern_symmetric,1.0,no:2,no,1.0,electromagnetics problem:2,electromagnetics problem,1.0,true:2,true,1.0,yes:2,yes,1.0,no:2,no,1.0,10k_to_100k:2,10k_to_100k,1.0,Zhao/Zhao1=33861; Zhao/Zhao2=33861,Zhao/Zhao1=33861; Zhao/Zhao2=33861,Zhao/Zhao1=33861; Zhao/Zhao2=33861,Zhao/Zhao1=33861; Zhao/Zhao2=33861,Zhao/Zhao1=166453; Zhao/Zhao2=166453,Zhao/Zhao1=166453; Zhao/Zhao2=166453,Zhao/Zhao1=166453; Zhao/Zhao2=166453,Zhao/Zhao1=166453; Zhao/Zhao2=166453 -Zitney,8,mixed_some_attributes,0.041667,1,2,0.70457,0.935,0.917952,5,3211,1048,3017.5,5308,1048,3017.5,5308,10965,22636,94408,11404,23752,94408,0.003859310586790632,0.024917542604827172,0.1404154186743065,0.0,0,0.0006197432492253209,square:8,square,1.0,unsymmetric:8,unsymmetric,1.0,real:8,real,1.0,partially_pattern_symmetric:8,partially_pattern_symmetric,1.0,no:8,no,1.0,chemical process simulation problem:6; chemical process simulation problem sequence:2,chemical process simulation problem,0.75,true:8,true,1.0,no:8,no,1.0,no:8,no,1.0,1k_to_10k:8,1k_to_10k,1.0,Zitney/radfr1=1048,Zitney/hydr1=5308; Zitney/hydr1c=5308,Zitney/radfr1=1048,Zitney/hydr1=5308; Zitney/hydr1c=5308,Zitney/extr1b=10965,Zitney/rdist1=94408,Zitney/extr1b=11404,Zitney/rdist1=94408 -vanHeukelum,13,type_homogeneous_size_diverse,0.0,0,1,6.013247,6.717756,6.717756,0,0,5,3534,5154859,5,3534,5154859,19,41594,99199551,19,41594,99199551,1.0,1,1.0,0.0,0.1535470310036784,0.2131193964673732,square:13,square,1.0,unsymmetric:13,unsymmetric,1.0,real:13,real,1.0,pattern_symmetric_only:13,pattern_symmetric_only,1.0,no:13,no,1.0,directed weighted graph:13,directed weighted graph,1.0,missing:12; true:1,missing,0.923077,no:13,no,1.0,no:13,no,1.0,100k_to_1m:2; 10k_to_100k:2; 1k_to_10k:2; ge_1m:2; lt_1k:5,lt_1k,0.384615,vanHeukelum/cage3=5,vanHeukelum/cage15=5154859,vanHeukelum/cage3=5,vanHeukelum/cage15=5154859,vanHeukelum/cage3=19,vanHeukelum/cage15=99199551,vanHeukelum/cage3=19,vanHeukelum/cage15=99199551 diff --git a/reports/suitesparse-groups/output/kind_source_mismatches.csv b/reports/suitesparse-groups/output/kind_source_mismatches.csv deleted file mode 100644 index 64f7f68..0000000 --- a/reports/suitesparse-groups/output/kind_source_mismatches.csv +++ /dev/null @@ -1,5 +0,0 @@ -id,matrix,ssstats_kind,website_repo_kind -2555,Chevron/Chevron1,other problem,2D/3D problem -2556,Chevron/Chevron2,other problem,2D/3D problem -2557,Chevron/Chevron3,other problem,2D/3D problem -2558,Chevron/Chevron4,other problem,2D/3D problem diff --git a/reports/suitesparse-groups/output/largest_matrices.csv b/reports/suitesparse-groups/output/largest_matrices.csv deleted file mode 100644 index 30db0b2..0000000 --- a/reports/suitesparse-groups/output/largest_matrices.csv +++ /dev/null @@ -1,81 +0,0 @@ -metric,rank,id,matrix,value,nrows,ncols,nnz,nentries,structure,entry_type,kind -nrows,1,2804,MAWI/mawi_201512020330,226196185,226196185,226196185,480047894,480047894,symmetric,integer,undirected weighted graph -nrows,2,2808,GenBank/kmer_V1r,214005017,214005017,214005017,465410904,465410904,symmetric,binary,undirected graph -nrows,3,2893,Sybrandt/AGATHA_2015,183964077,183964077,183964077,11588725964,11588725964,symmetric,binary,undirected graph -nrows,4,2805,GenBank/kmer_A2a,170728175,170728175,170728175,360585172,360585172,symmetric,binary,undirected graph -nrows,5,2806,GenBank/kmer_P1a,139353211,139353211,139353211,297829984,297829984,symmetric,binary,undirected graph -nrows,6,2856,GAP/GAP-urand,134217728,134217728,134217728,4294966740,4294966740,symmetric,integer,random undirected weighted graph -nrows,7,2855,GAP/GAP-kron,134217726,134217726,134217726,4223264644,4223264644,symmetric,integer,random undirected weighted graph -nrows,8,2803,MAWI/mawi_201512020130,128568730,128568730,128568730,270234840,270234840,symmetric,integer,undirected weighted graph -nrows,9,2449,LAW/webbase-2001,118142155,118142155,118142155,1019903190,1019903190,unsymmetric,binary,directed graph -nrows,10,2899,FlowIPM22/Spielman_k600,72180402,72180402,72180402,216902404,216902404,symmetric,real,undirected weighted graph -nrows,11,2802,MAWI/mawi_201512020030,68863315,68863315,68863315,143414960,143414960,symmetric,integer,undirected weighted graph 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-55,HB,bcsstk33,8738,8738,591904,1,1,1,0,1.0,1.0,structural problem,591904,binary,symmetric,no,yes,true,structural problem -56,HB,bcsstm01,48,48,24,1,0,1,0,1.0,1.0,structural problem,48,integer,symmetric,no,no,false,structural problem -57,HB,bcsstm02,66,66,66,1,0,1,1,1.0,1.0,structural problem,66,real,symmetric,yes,yes,true,structural problem -58,HB,bcsstm03,112,112,72,1,0,1,0,1.0,1.0,structural problem,112,real,symmetric,no,no,false,structural problem -59,HB,bcsstm04,132,132,66,1,0,1,0,1.0,1.0,structural problem,132,real,symmetric,no,no,false,structural problem -60,HB,bcsstm05,153,153,153,1,0,1,1,1.0,1.0,structural problem,153,real,symmetric,yes,yes,true,structural problem -61,HB,bcsstm06,420,420,420,1,0,1,1,1.0,1.0,structural problem,420,real,symmetric,yes,yes,true,structural problem -62,HB,bcsstm07,420,420,7252,1,0,1,1,1.0,1.0,structural problem,7252,real,symmetric,yes,yes,true,structural problem -63,HB,bcsstm08,1074,1074,1074,1,0,1,1,1.0,1.0,structural problem,1074,real,symmetric,yes,yes,true,structural problem -64,HB,bcsstm09,1083,1083,1083,1,0,1,1,1.0,1.0,structural problem,1083,real,symmetric,yes,yes,true,structural problem -65,HB,bcsstm10,1086,1086,22092,1,0,1,0,1.0,1.0,structural problem,22092,real,symmetric,no,yes,true,structural problem -66,HB,bcsstm11,1473,1473,1473,1,0,1,1,1.0,1.0,structural problem,1473,real,symmetric,yes,yes,true,structural problem -67,HB,bcsstm12,1473,1473,19659,1,0,1,1,1.0,1.0,structural problem,19659,real,symmetric,yes,yes,true,structural problem -68,HB,bcsstm13,2003,2003,21181,1,0,1,0,1.0,1.0,computational fluid dynamics problem,21943,real,symmetric,no,no,false,computational fluid dynamics problem -69,HB,bcsstm19,817,817,817,1,0,1,1,1.0,1.0,structural problem,817,real,symmetric,yes,yes,true,structural problem -70,HB,bcsstm20,485,485,485,1,0,1,1,1.0,1.0,structural problem,485,real,symmetric,yes,yes,true,structural problem -71,HB,bcsstm21,3600,3600,3600,1,0,1,1,1.0,1.0,structural problem,3600,real,symmetric,yes,yes,true,structural problem -72,HB,bcsstm22,138,138,138,1,0,1,1,1.0,1.0,structural problem,138,real,symmetric,yes,yes,true,structural problem -73,HB,bcsstm23,3134,3134,3134,1,0,1,1,1.0,1.0,structural problem,3134,real,symmetric,yes,yes,true,structural problem -74,HB,bcsstm24,3562,3562,3562,1,0,1,1,1.0,1.0,structural problem,3562,real,symmetric,yes,yes,true,structural problem -75,HB,bcsstm25,15439,15439,15439,1,0,1,1,1.0,1.0,structural problem,15439,real,symmetric,yes,yes,true,structural problem -76,HB,bcsstm26,1922,1922,1922,1,0,1,1,1.0,1.0,structural problem,1922,real,symmetric,yes,yes,true,structural problem -77,HB,bcsstm27,1224,1224,56126,1,0,1,0,1.0,1.0,structural problem,56126,real,symmetric,no,yes,true,structural problem -78,HB,beacxc,497,506,50409,1,0,0,0,0.0,0.0,economic problem,50409,real,rectangular,no,no,false,economic problem -79,HB,beaflw,497,507,53403,1,0,0,0,0.0,0.0,economic problem,53403,real,rectangular,no,no,false,economic problem -80,HB,beause,497,507,44551,1,0,0,0,0.0,0.0,economic problem,44551,real,rectangular,no,no,false,economic problem -81,HB,blckhole,2132,2132,14872,1,1,1,0,1.0,1.0,structural problem,14872,binary,symmetric,no,yes,true,structural problem -82,HB,bp_0,822,822,3276,1,0,0,0,0.00916030534351145,0.001221374045801527,optimization problem sequence,3276,real,unsymmetric,no,no,true,optimization problem sequence -83,HB,bp_1000,822,822,4661,1,0,0,0,0.009448142581060769,0.0004294610264118531,subsequent optimization problem,4661,real,unsymmetric,no,no,true,subsequent optimization problem -84,HB,bp_1200,822,822,4726,1,0,0,0,0.009322033898305085,0.000423728813559322,subsequent optimization problem,4726,real,unsymmetric,no,no,true,subsequent optimization problem -85,HB,bp_1400,822,822,4790,1,0,0,0,0.01044932079414838,0.0004179728317659352,subsequent optimization problem,4790,real,unsymmetric,no,no,true,subsequent optimization problem -86,HB,bp_1600,822,822,4841,1,0,0,0,0.01075491209927611,0.0004136504653567735,subsequent optimization problem,4841,real,unsymmetric,no,no,true,subsequent optimization problem -87,HB,bp_200,822,822,3802,1,0,0,0,0.00631578947368421,0.0,subsequent optimization problem,3802,real,unsymmetric,no,no,true,subsequent optimization problem -88,HB,bp_400,822,822,4028,1,0,0,0,0.00546448087431694,0.0004967709885742673,subsequent optimization problem,4028,real,unsymmetric,no,no,true,subsequent optimization problem -89,HB,bp_600,822,822,4172,1,0,0,0,0.006235011990407674,0.0004796163069544365,subsequent optimization problem,4172,real,unsymmetric,no,no,true,subsequent optimization problem -90,HB,bp_800,822,822,4534,1,0,0,0,0.005298013245033113,0.0004415011037527594,subsequent optimization problem,4534,real,unsymmetric,no,no,true,subsequent optimization problem -91,HB,can_1054,1054,1054,12196,1,1,1,0,1.0,1.0,structural problem,12196,binary,symmetric,no,yes,true,structural problem -92,HB,can_1072,1072,1072,12444,1,1,1,0,1.0,1.0,structural problem,12444,binary,symmetric,no,yes,true,structural problem -93,HB,can_144,144,144,1296,1,1,1,0,1.0,1.0,structural problem,1296,binary,symmetric,no,yes,true,structural problem -94,HB,can_161,161,161,1377,1,1,1,0,1.0,1.0,structural problem,1377,binary,symmetric,no,yes,true,structural problem -95,HB,can_187,187,187,1491,1,1,1,0,1.0,1.0,structural problem,1491,binary,symmetric,no,yes,true,structural problem -96,HB,can_229,229,229,1777,1,1,1,0,1.0,1.0,structural problem,1777,binary,symmetric,no,yes,true,structural problem -97,HB,can_24,24,24,160,1,1,1,0,1.0,1.0,structural problem,160,binary,symmetric,no,yes,true,structural problem -98,HB,can_256,256,256,2916,1,1,1,0,1.0,1.0,structural problem,2916,binary,symmetric,no,yes,true,structural problem -99,HB,can_268,268,268,3082,1,1,1,0,1.0,1.0,structural problem,3082,binary,symmetric,no,yes,true,structural problem -100,HB,can_292,292,292,2540,1,1,1,0,1.0,1.0,structural problem,2540,binary,symmetric,no,yes,true,structural problem -101,HB,can_445,445,445,3809,1,1,1,0,1.0,1.0,structural problem,3809,binary,symmetric,no,yes,true,structural problem -102,HB,can_61,61,61,557,1,1,1,0,1.0,1.0,structural problem,557,binary,symmetric,no,yes,true,structural problem -103,HB,can_62,62,62,218,1,1,1,0,1.0,1.0,structural problem,218,binary,symmetric,no,yes,true,structural problem -104,HB,can_634,634,634,7228,1,1,1,0,1.0,1.0,structural problem,7228,binary,symmetric,no,yes,true,structural problem -105,HB,can_715,715,715,6665,1,1,1,0,1.0,1.0,structural problem,6665,binary,symmetric,no,yes,true,structural problem -106,HB,can_73,73,73,377,1,1,1,0,1.0,1.0,structural problem,377,binary,symmetric,no,yes,true,structural problem -107,HB,can_838,838,838,10010,1,1,1,0,1.0,1.0,structural problem,10010,binary,symmetric,no,yes,true,structural problem -108,HB,can_96,96,96,768,1,1,1,0,1.0,1.0,structural problem,768,binary,symmetric,no,yes,true,structural problem -109,HB,curtis54,54,54,291,1,1,1,0,0.9535864978902954,0.9535864978902954,2D/3D problem,291,binary,unsymmetric,no,no,true,2D/3D problem -110,HB,dwt_1005,1005,1005,8621,1,1,1,0,1.0,1.0,structural problem,8621,binary,symmetric,no,yes,true,structural problem -111,HB,dwt_1007,1007,1007,8575,1,1,1,0,1.0,1.0,structural problem,8575,binary,symmetric,no,yes,true,structural problem -112,HB,dwt_1242,1242,1242,10426,1,1,1,0,1.0,1.0,structural problem,10426,binary,symmetric,no,yes,true,structural problem -113,HB,dwt_162,162,162,1182,1,1,1,0,1.0,1.0,structural problem,1182,binary,symmetric,no,yes,true,structural problem -114,HB,dwt_193,193,193,3493,1,1,1,0,1.0,1.0,structural problem,3493,binary,symmetric,no,yes,true,structural problem -115,HB,dwt_198,198,198,1392,1,1,1,0,1.0,1.0,structural problem,1392,binary,symmetric,no,yes,true,structural problem -116,HB,dwt_209,209,209,1743,1,1,1,0,1.0,1.0,structural problem,1743,binary,symmetric,no,yes,true,structural problem -117,HB,dwt_221,221,221,1629,1,1,1,0,1.0,1.0,structural problem,1629,binary,symmetric,no,yes,true,structural problem -118,HB,dwt_234,234,234,834,1,1,1,0,1.0,1.0,structural problem,834,binary,symmetric,no,yes,true,structural problem -119,HB,dwt_245,245,245,1461,1,1,1,0,1.0,1.0,structural problem,1461,binary,symmetric,no,yes,true,structural problem -120,HB,dwt_2680,2680,2680,25026,1,1,1,0,1.0,1.0,structural problem,25026,binary,symmetric,no,yes,true,structural problem -121,HB,dwt_307,307,307,2523,1,1,1,0,1.0,1.0,structural problem,2523,binary,symmetric,no,yes,true,structural problem -122,HB,dwt_310,310,310,2448,1,1,1,0,1.0,1.0,structural problem,2448,binary,symmetric,no,yes,true,structural problem -123,HB,dwt_346,346,346,3226,1,1,1,0,1.0,1.0,structural problem,3226,binary,symmetric,no,yes,true,structural problem -124,HB,dwt_361,361,361,2953,1,1,1,0,1.0,1.0,structural problem,2953,binary,symmetric,no,yes,true,structural problem -125,HB,dwt_419,419,419,3563,1,1,1,0,1.0,1.0,structural problem,3563,binary,symmetric,no,yes,true,structural problem -126,HB,dwt_492,492,492,3156,1,1,1,0,1.0,1.0,structural problem,3156,binary,symmetric,no,yes,true,structural problem -127,HB,dwt_503,503,503,6027,1,1,1,0,1.0,1.0,structural problem,6027,binary,symmetric,no,yes,true,structural problem -128,HB,dwt_512,512,512,3502,1,1,1,0,1.0,1.0,structural problem,3502,binary,symmetric,no,yes,true,structural problem -129,HB,dwt_59,59,59,267,1,1,1,0,1.0,1.0,structural problem,267,binary,symmetric,no,yes,true,structural problem -130,HB,dwt_592,592,592,5104,1,1,1,0,1.0,1.0,structural problem,5104,binary,symmetric,no,yes,true,structural problem -131,HB,dwt_607,607,607,5131,1,1,1,0,1.0,1.0,structural problem,5131,binary,symmetric,no,yes,true,structural problem -132,HB,dwt_66,66,66,320,1,1,1,0,1.0,1.0,structural problem,320,binary,symmetric,no,yes,true,structural problem -133,HB,dwt_72,72,72,222,1,1,1,0,1.0,1.0,structural problem,222,binary,symmetric,no,yes,true,structural problem -134,HB,dwt_758,758,758,5994,1,1,1,0,1.0,1.0,structural problem,5994,binary,symmetric,no,yes,true,structural problem -135,HB,dwt_869,869,869,7285,1,1,1,0,1.0,1.0,structural problem,7285,binary,symmetric,no,yes,true,structural problem -136,HB,dwt_87,87,87,541,1,1,1,0,1.0,1.0,structural problem,541,binary,symmetric,no,yes,true,structural problem -137,HB,dwt_878,878,878,7448,1,1,1,0,1.0,1.0,structural problem,7448,binary,symmetric,no,yes,true,structural problem -138,HB,dwt_918,918,918,7384,1,1,1,0,1.0,1.0,structural problem,7384,binary,symmetric,no,yes,true,structural problem -139,HB,dwt_992,992,992,16744,1,1,1,0,1.0,1.0,structural problem,16744,binary,symmetric,no,yes,true,structural problem -140,HB,eris1176,1176,1176,18552,1,1,0,0,1.0,1.0,power network problem,18552,binary,symmetric,no,yes,true,power network problem -141,HB,fs_183_1,183,183,998,1,0,1,0,0.417607223476298,0.0,2D/3D problem sequence,1069,real,unsymmetric,no,no,true,2D/3D problem sequence -142,HB,fs_183_3,183,183,1069,1,0,1,0,0.417607223476298,0.0,subsequent 2D/3D problem,1069,real,unsymmetric,no,no,true,subsequent 2D/3D problem -143,HB,fs_183_4,183,183,1069,1,0,1,0,0.417607223476298,0.0,subsequent 2D/3D problem,1069,real,unsymmetric,no,no,true,subsequent 2D/3D problem -144,HB,fs_183_6,183,183,1000,1,0,1,0,0.417607223476298,0.0,subsequent 2D/3D problem,1069,real,unsymmetric,no,no,true,subsequent 2D/3D problem -145,HB,fs_541_1,541,541,4282,1,0,1,0,0.6826923076923077,0.0,2D/3D problem sequence,4285,real,unsymmetric,no,no,true,2D/3D problem sequence -146,HB,fs_541_2,541,541,4282,1,0,1,0,0.6826923076923077,0.0,subsequent 2D/3D problem,4285,real,unsymmetric,no,no,true,subsequent 2D/3D problem -147,HB,fs_541_3,541,541,4282,1,0,1,0,0.6826923076923077,0.0,subsequent 2D/3D problem,4285,real,unsymmetric,no,no,true,subsequent 2D/3D problem -148,HB,fs_541_4,541,541,4273,1,0,1,0,0.6826923076923077,0.0,subsequent 2D/3D problem,4285,real,unsymmetric,no,no,true,subsequent 2D/3D problem -149,HB,fs_680_1,680,680,2184,1,0,1,0,0.5106815869786369,0.0,2D/3D problem sequence,2646,real,unsymmetric,no,no,true,2D/3D problem sequence -150,HB,fs_680_2,680,680,2424,1,0,1,0,0.5106815869786369,0.0,subsequent 2D/3D problem,2646,real,unsymmetric,no,no,true,subsequent 2D/3D problem -151,HB,fs_680_3,680,680,2471,1,0,1,0,0.5106815869786369,0.0,subsequent 2D/3D problem,2646,real,unsymmetric,no,no,true,subsequent 2D/3D problem -152,HB,fs_760_1,760,760,5739,1,0,1,0,0.6510736196319018,0.0,2D/3D problem sequence,5976,real,unsymmetric,no,no,true,2D/3D problem sequence -153,HB,fs_760_2,760,760,5739,1,0,1,0,0.6510736196319018,0.0,subsequent 2D/3D problem,5976,real,unsymmetric,no,no,true,subsequent 2D/3D problem -154,HB,fs_760_3,760,760,5816,1,0,1,0,0.6510736196319018,0.0,subsequent 2D/3D problem,5976,real,unsymmetric,no,no,true,subsequent 2D/3D problem -155,HB,gemat1,4929,10595,46591,1,0,0,0,0.0,0.0,power network problem,47369,real,rectangular,no,no,true,power network problem -156,HB,gemat11,4929,4929,33108,1,0,0,0,0.001326419872181358,0.0,power network problem sequence,33185,real,unsymmetric,no,no,true,power network problem sequence -157,HB,gemat12,4929,4929,33044,1,0,0,0,0.001450063440275512,0.0,subsequent power network problem,33111,real,unsymmetric,no,no,true,subsequent power network problem -158,HB,gent113,113,113,655,1,1,0,0,0.05663716814159292,0.05663716814159292,statistical/mathematical problem,655,binary,unsymmetric,no,no,true,statistical/mathematical problem -159,HB,gr_30_30,900,900,7744,1,0,1,1,1.0,1.0,2D/3D problem,7744,integer,symmetric,yes,yes,true,2D/3D problem -160,HB,gre_1107,1107,1107,5664,1,0,0,0,0.0,0.0,directed weighted graph,5664,real,unsymmetric,no,no,missing,directed weighted graph -161,HB,gre_115,115,115,421,1,0,0,0,0.2549019607843137,0.0,directed weighted graph,421,real,unsymmetric,no,no,missing,directed weighted graph -162,HB,gre_185,185,185,975,1,0,0,0,0.4146341463414634,0.0,directed weighted graph,1005,real,unsymmetric,no,no,missing,directed weighted graph -163,HB,gre_216a,216,216,812,1,0,0,0,0.0,0.0,directed weighted graph,876,real,unsymmetric,no,no,missing,directed weighted graph -164,HB,gre_216b,216,216,812,1,0,0,0,0.0,0.0,directed weighted graph,876,real,unsymmetric,no,no,missing,directed weighted graph -165,HB,gre_343,343,343,1310,1,0,0,0,0.0,0.0,directed weighted graph,1435,real,unsymmetric,no,no,missing,directed weighted graph -166,HB,gre_512,512,512,1976,1,0,0,0,0.0,0.0,directed weighted graph,2192,real,unsymmetric,no,no,missing,directed weighted graph -167,HB,hor_131,434,434,4182,1,0,0,0,1.0,0.3740661686232657,directed weighted graph,4710,real,unsymmetric,no,no,missing,directed weighted graph -168,HB,ibm32,32,32,126,1,1,0,0,0.0851063829787234,0.0851063829787234,directed graph,126,binary,unsymmetric,no,no,missing,directed graph -169,HB,illc1033,1033,320,4719,1,0,0,0,0.0,0.0,least squares problem,4732,real,rectangular,no,no,true,least squares problem -170,HB,illc1850,1850,712,8636,1,0,0,0,0.0,0.0,least squares problem,8758,real,rectangular,no,no,true,least squares problem -171,HB,impcol_a,207,207,572,1,0,0,0,0.02482269503546099,0.01063829787234043,chemical process simulation problem,572,real,unsymmetric,no,no,true,chemical process simulation problem -172,HB,impcol_b,59,59,271,1,0,0,0,0.09491525423728814,0.0,chemical process simulation problem,312,real,unsymmetric,no,no,true,chemical process simulation problem -173,HB,impcol_c,137,137,400,1,0,0,0,0.04444444444444445,0.01146131805157593,chemical process simulation problem,411,real,unsymmetric,no,no,true,chemical process simulation problem -174,HB,impcol_d,425,425,1255,1,0,0,0,0.05674846625766871,0.0228198859005705,chemical process simulation problem,1339,real,unsymmetric,no,no,true,chemical process simulation problem -175,HB,impcol_e,225,225,1303,1,0,0,0,0.1038338658146965,0.001603849238171612,chemical process simulation problem,1308,real,unsymmetric,no,no,true,chemical process simulation problem -176,HB,jagmesh1,936,936,6264,1,1,1,0,1.0,1.0,2D/3D problem,6264,binary,symmetric,no,yes,true,2D/3D problem -177,HB,jagmesh2,1009,1009,6865,1,1,1,0,1.0,1.0,2D/3D problem,6865,binary,symmetric,no,yes,true,2D/3D problem -178,HB,jagmesh3,1089,1089,7361,1,1,1,0,1.0,1.0,2D/3D problem,7361,binary,symmetric,no,yes,true,2D/3D problem -179,HB,jagmesh4,1440,1440,9504,1,1,1,0,1.0,1.0,2D/3D problem,9504,binary,symmetric,no,yes,true,2D/3D problem -180,HB,jagmesh5,1180,1180,7750,1,1,1,0,1.0,1.0,2D/3D problem,7750,binary,symmetric,no,yes,true,2D/3D problem -181,HB,jagmesh6,1377,1377,8993,1,1,1,0,1.0,1.0,2D/3D problem,8993,binary,symmetric,no,yes,true,2D/3D problem -182,HB,jagmesh7,1138,1138,7450,1,1,1,0,1.0,1.0,2D/3D problem,7450,binary,symmetric,no,yes,true,2D/3D problem -183,HB,jagmesh8,1141,1141,7465,1,1,1,0,1.0,1.0,2D/3D problem,7465,binary,symmetric,no,yes,true,2D/3D problem -184,HB,jagmesh9,1349,1349,9101,1,1,1,0,1.0,1.0,2D/3D problem,9101,binary,symmetric,no,yes,true,2D/3D problem -185,HB,jgl009,9,9,50,1,1,0,0,0.4761904761904762,0.4761904761904762,counter-example problem,50,binary,unsymmetric,no,no,true,counter-example problem -186,HB,jgl011,11,11,76,1,1,0,0,0.5151515151515151,0.5151515151515151,counter-example problem,76,binary,unsymmetric,no,no,true,counter-example problem -187,HB,jpwh_991,991,991,6027,1,0,1,0,0.9364575059571089,0.9364575059571089,semiconductor device problem,6027,integer,unsymmetric,no,no,true,semiconductor device problem -188,HB,lns_131,131,131,536,1,0,1,0,0.7028301886792453,0.01886792452830189,computational fluid dynamics problem,536,real,unsymmetric,no,no,true,computational fluid dynamics problem -189,HB,lns_3937,3937,3937,25407,1,0,1,0,0.849666726715907,0.001351107908484958,computational fluid dynamics problem,25407,real,unsymmetric,no,no,true,computational fluid dynamics problem -190,HB,lns_511,511,511,2796,1,0,1,0,0.7994945240101096,0.02021903959561921,computational fluid dynamics problem,2796,real,unsymmetric,no,no,true,computational fluid dynamics problem -191,HB,lnsp3937,3937,3937,25407,1,0,1,0,0.849666726715907,0.001351107908484958,computational fluid dynamics problem,25407,real,unsymmetric,no,no,true,computational fluid dynamics problem -192,HB,lnsp_131,131,131,536,1,0,1,0,0.7028301886792453,0.01886792452830189,computational fluid dynamics problem,536,real,unsymmetric,no,no,true,computational fluid dynamics problem -193,HB,lnsp_511,511,511,2796,1,0,1,0,0.7994945240101096,0.02021903959561921,computational fluid dynamics problem,2796,real,unsymmetric,no,no,true,computational fluid dynamics problem -194,HB,lshp1009,1009,1009,6865,1,1,1,0,1.0,1.0,duplicate thermal problem,6865,binary,symmetric,no,yes,true,duplicate thermal problem -195,HB,lshp1270,1270,1270,8668,1,1,1,0,1.0,1.0,thermal problem,8668,binary,symmetric,no,yes,true,thermal problem -196,HB,lshp1561,1561,1561,10681,1,1,1,0,1.0,1.0,thermal problem,10681,binary,symmetric,no,yes,true,thermal problem -197,HB,lshp1882,1882,1882,12904,1,1,1,0,1.0,1.0,thermal problem,12904,binary,symmetric,no,yes,true,thermal problem -198,HB,lshp2233,2233,2233,15337,1,1,1,0,1.0,1.0,thermal problem,15337,binary,symmetric,no,yes,true,thermal problem -199,HB,lshp2614,2614,2614,17980,1,1,1,0,1.0,1.0,thermal problem,17980,binary,symmetric,no,yes,true,thermal problem -200,HB,lshp3025,3025,3025,20833,1,1,1,0,1.0,1.0,thermal problem,20833,binary,symmetric,no,yes,true,thermal problem -201,HB,lshp3466,3466,3466,23896,1,1,1,0,1.0,1.0,thermal problem,23896,binary,symmetric,no,yes,true,thermal problem -202,HB,lshp_265,265,265,1753,1,1,1,0,1.0,1.0,thermal problem,1753,binary,symmetric,no,yes,true,thermal problem -203,HB,lshp_406,406,406,2716,1,1,1,0,1.0,1.0,thermal problem,2716,binary,symmetric,no,yes,true,thermal problem -204,HB,lshp_577,577,577,3889,1,1,1,0,1.0,1.0,thermal problem,3889,binary,symmetric,no,yes,true,thermal problem -205,HB,lshp_778,778,778,5272,1,1,1,0,1.0,1.0,thermal problem,5272,binary,symmetric,no,yes,true,thermal problem -206,HB,lund_a,147,147,2449,1,0,1,1,1.0,1.0,structural problem,2449,real,symmetric,yes,yes,true,structural problem -207,HB,lund_b,147,147,2441,1,0,1,1,1.0,1.0,structural problem,2441,real,symmetric,yes,yes,true,structural problem -208,HB,mahindas,1258,1258,7682,1,0,0,0,0.01663146779303062,0.0,economic problem,7682,real,unsymmetric,no,no,true,economic problem -209,HB,mbeacxc,496,496,49920,1,0,0,0,0.3162614104531868,0.0,economic problem,49920,real,unsymmetric,no,no,false,economic problem -210,HB,mbeaflw,496,496,49920,1,0,0,0,0.3162614104531868,0.0,economic problem,49920,real,unsymmetric,no,no,false,economic problem -211,HB,mbeause,496,496,41063,1,0,0,0,0.2190689324447286,0.007771192484568281,economic problem,41063,real,unsymmetric,no,no,false,economic problem -212,HB,mcca,180,180,2659,1,0,1,0,0.6446147640177491,0.0,2D/3D problem,2659,real,unsymmetric,no,no,true,2D/3D problem -213,HB,mcfe,765,765,24382,1,0,1,0,0.699411440911208,0.0,2D/3D problem,24382,real,unsymmetric,no,no,true,2D/3D problem -214,HB,nnc1374,1374,1374,8588,1,0,1,0,0.8169596690796277,0.5918631769888573,2D/3D problem,8606,real,unsymmetric,no,no,true,2D/3D problem -215,HB,nnc261,261,261,1500,1,0,1,0,0.8237037037037037,0.6133333333333333,2D/3D problem,1500,real,unsymmetric,no,no,true,2D/3D problem -216,HB,nnc666,666,666,4032,1,0,1,0,0.817831590533847,0.5919381557150746,2D/3D problem,4044,real,unsymmetric,no,no,true,2D/3D problem -217,HB,nos1,237,237,1017,1,0,1,1,1.0,1.0,structural problem,1017,real,symmetric,yes,yes,true,structural problem -218,HB,nos2,957,957,4137,1,0,1,1,1.0,1.0,structural problem,4137,real,symmetric,yes,yes,true,structural problem -219,HB,nos3,960,960,15844,1,0,1,1,1.0,1.0,structural problem,15844,real,symmetric,yes,yes,true,structural problem -220,HB,nos4,100,100,594,1,0,1,1,1.0,1.0,structural problem,594,real,symmetric,yes,yes,true,structural problem -221,HB,nos5,468,468,5172,1,0,1,1,1.0,1.0,structural problem,5172,real,symmetric,yes,yes,true,structural problem -222,HB,nos6,675,675,3255,1,0,1,1,1.0,1.0,2D/3D problem,3255,real,symmetric,yes,yes,true,2D/3D problem -223,HB,nos7,729,729,4617,1,0,1,1,1.0,1.0,2D/3D problem,4617,real,symmetric,yes,yes,true,2D/3D problem -224,HB,orani678,2529,2529,90158,1,0,0,0,0.07068607068607069,2.223531635296341e-05,economic problem,90158,real,unsymmetric,no,no,true,economic problem -225,HB,orsirr_1,1030,1030,6858,1,0,1,0,1.0,0.4094028826355525,computational fluid dynamics problem,6858,real,unsymmetric,no,no,true,computational fluid dynamics problem -226,HB,orsirr_2,886,886,5970,1,0,1,0,1.0,0.4118804091266719,computational fluid dynamics problem,5970,real,unsymmetric,no,no,true,computational fluid dynamics problem -227,HB,orsreg_1,2205,2205,14133,1,0,1,0,1.0,0.4119718309859155,computational fluid dynamics problem,14133,real,unsymmetric,no,no,true,computational fluid dynamics problem -228,HB,plat1919,1919,1919,32399,1,0,1,1,1.0,1.0,2D/3D problem,32399,real,symmetric,yes,yes,true,2D/3D problem -229,HB,plat362,362,362,5786,1,0,1,1,1.0,1.0,2D/3D problem,5786,real,symmetric,yes,yes,true,2D/3D problem -230,HB,plsk1919,1919,1919,9662,1,0,1,0,1.0,0.0,2D/3D problem,9662,real,skew-symmetric,no,no,true,2D/3D problem -231,HB,plskz362,362,362,1760,1,0,1,0,1.0,0.0,2D/3D problem,1760,real,skew-symmetric,no,no,true,2D/3D problem -232,HB,pores_1,30,30,180,1,0,1,0,0.6266666666666667,0.2933333333333333,computational fluid dynamics problem,180,real,unsymmetric,no,no,true,computational fluid dynamics problem -233,HB,pores_2,1224,1224,9613,1,0,1,0,0.6119918941470974,0.3914650137084277,computational fluid dynamics problem,9613,real,unsymmetric,no,no,true,computational fluid dynamics problem -234,HB,pores_3,532,532,3474,1,0,1,0,0.7423521414004078,0.3052345343303875,computational fluid dynamics problem,3474,real,unsymmetric,no,no,true,computational fluid dynamics problem -235,HB,psmigr_1,3140,3140,543160,1,0,0,0,0.4786508697793794,0.01044772250019444,economic problem,543162,integer,unsymmetric,no,no,true,economic problem -236,HB,psmigr_2,3140,3140,540022,1,0,0,0,0.4786508697793794,0.0,economic problem,540022,real,unsymmetric,no,no,true,economic problem -237,HB,psmigr_3,3140,3140,543160,1,0,0,0,0.4786508697793794,0.0,economic problem,543162,real,unsymmetric,no,no,true,economic problem -238,HB,rgg010,10,10,76,1,1,0,0,0.6363636363636364,0.6363636363636364,counter-example problem,76,binary,unsymmetric,no,no,true,counter-example problem -239,HB,saylr1,238,238,1128,1,0,1,0,1.0,0.647191011235955,computational fluid dynamics problem,1128,real,unsymmetric,no,no,true,computational fluid dynamics problem -240,HB,saylr3,1000,1000,3750,1,0,1,0,1.0,1.0,computational fluid dynamics problem,3750,real,symmetric,no,no,true,computational fluid dynamics problem -241,HB,saylr4,3564,3564,22316,1,0,1,0,1.0,1.0,computational fluid dynamics problem,22316,real,symmetric,no,no,true,computational fluid dynamics problem -242,HB,sherman1,1000,1000,3750,1,0,1,0,1.0,1.0,computational fluid dynamics problem,3750,real,symmetric,no,no,true,computational fluid dynamics problem -243,HB,sherman2,1080,1080,23094,1,0,1,0,0.6708458253838466,0.004905968928863451,computational fluid dynamics problem,23094,real,unsymmetric,no,no,true,computational fluid dynamics problem -244,HB,sherman3,5005,5005,20033,1,0,1,0,1.0,0.4532871972318339,computational fluid dynamics problem,20033,real,unsymmetric,no,no,true,computational fluid dynamics problem -245,HB,sherman4,1104,1104,3786,1,0,1,0,1.0,0.0,computational fluid dynamics problem,3786,real,unsymmetric,no,no,true,computational fluid dynamics problem -246,HB,sherman5,3312,3312,20793,1,0,1,0,0.7386305131285396,0.1534237171786511,computational fluid dynamics problem,20793,real,unsymmetric,no,no,true,computational fluid dynamics problem -247,HB,shl_0,663,663,1687,1,0,0,0,0.0,0.0,optimization problem sequence,1687,integer,unsymmetric,no,no,true,optimization problem sequence -248,HB,shl_200,663,663,1726,1,0,0,0,0.0,0.0,subsequent optimization problem,1726,integer,unsymmetric,no,no,true,subsequent optimization problem -249,HB,shl_400,663,663,1712,1,0,0,0,0.0,0.0,subsequent optimization problem,1712,integer,unsymmetric,no,no,true,subsequent optimization problem -250,HB,sstmodel,3345,3345,22749,1,1,1,0,1.0,1.0,structural problem,22749,binary,symmetric,no,yes,true,structural problem -251,HB,steam1,240,240,2248,1,0,1,0,1.0,0.2729083665338645,computational fluid dynamics problem,3762,real,unsymmetric,no,no,true,computational fluid dynamics problem -252,HB,steam2,600,600,5660,1,0,1,0,1.0,0.1877470355731225,computational fluid dynamics problem,13760,real,unsymmetric,no,no,true,computational fluid dynamics problem -253,HB,steam3,80,80,314,1,0,1,0,1.0,0.3247863247863248,computational fluid dynamics problem,928,real,unsymmetric,no,no,true,computational fluid dynamics problem -254,HB,str_0,363,363,2454,1,0,0,0,0.004893964110929853,0.0,optimization problem sequence,2454,real,unsymmetric,no,no,true,optimization problem sequence -255,HB,str_200,363,363,3068,1,0,0,0,0.01108936725375081,0.0,subsequent optimization problem,3068,real,unsymmetric,no,no,true,subsequent optimization problem -256,HB,str_400,363,363,3157,1,0,0,0,0.0196513470681458,0.0,subsequent optimization problem,3157,real,unsymmetric,no,no,true,subsequent optimization problem -257,HB,str_600,363,363,3279,1,0,0,0,0.01893129770992366,0.0,subsequent optimization problem,3279,real,unsymmetric,no,no,true,subsequent optimization problem -258,HB,watt_1,1856,1856,11360,1,0,1,0,0.9865319865319865,0.9865319865319865,computational fluid dynamics problem,11360,real,unsymmetric,no,no,true,computational fluid dynamics problem -259,HB,watt_2,1856,1856,11550,1,0,1,0,0.9804002475758201,0.9671962038374252,computational fluid dynamics problem,11550,real,unsymmetric,no,no,true,computational fluid dynamics problem -260,HB,well1033,1033,320,4732,1,0,0,0,0.0,0.0,least squares problem,4732,real,rectangular,no,no,true,least squares problem -261,HB,well1850,1850,712,8755,1,0,0,0,0.0,0.0,least squares problem,8758,real,rectangular,no,no,true,least squares problem -262,HB,west0067,67,67,294,1,0,0,0,0.03424657534246575,0.0,chemical process simulation problem,294,real,unsymmetric,no,no,true,chemical process simulation problem -263,HB,west0132,132,132,413,1,0,0,0,0.0196078431372549,0.004914004914004914,chemical process simulation problem,414,real,unsymmetric,no,no,true,chemical process simulation problem -264,HB,west0156,156,156,362,1,0,0,0,0.0,0.0,chemical process simulation problem,371,real,unsymmetric,no,no,true,chemical process simulation problem -265,HB,west0167,167,167,506,1,0,0,0,0.01622718052738337,0.0,chemical process simulation problem,507,real,unsymmetric,no,no,true,chemical process simulation problem -266,HB,west0381,381,381,2134,1,0,0,0,0.005565862708719851,0.0009376465072667605,chemical process simulation problem,2157,real,unsymmetric,no,no,true,chemical process simulation problem -267,HB,west0479,479,479,1888,1,0,0,0,0.01366982124079916,0.0,chemical process simulation problem,1910,real,unsymmetric,no,no,true,chemical process simulation problem -268,HB,west0497,497,497,1721,1,0,0,0,0.006972690296339338,0.0,chemical process simulation problem,1727,real,unsymmetric,no,no,true,chemical process simulation problem -269,HB,west0655,655,655,2808,1,0,0,0,0.005616005616005616,0.0,chemical process simulation problem,2854,real,unsymmetric,no,no,true,chemical process simulation problem -270,HB,west0989,989,989,3518,1,0,0,0,0.01812004530011325,0.003985197836606889,chemical process simulation problem,3537,real,unsymmetric,no,no,true,chemical process simulation problem -271,HB,west1505,1505,1505,5414,1,0,0,0,0.001102941176470588,0.0,chemical process simulation problem,5445,real,unsymmetric,no,no,true,chemical process simulation problem -272,HB,west2021,2021,2021,7310,1,0,0,0,0.00326619488296135,0.0,chemical process simulation problem,7353,real,unsymmetric,no,no,true,chemical process simulation problem -273,HB,will199,199,199,701,1,1,1,0,0.05596465390279824,0.05596465390279824,structural problem,701,binary,unsymmetric,no,no,true,structural problem -274,HB,will57,57,57,281,1,1,1,0,0.8660714285714286,0.8660714285714286,semiconductor device problem,281,binary,unsymmetric,no,no,true,semiconductor device problem -275,HB,wm1,207,277,2909,1,0,0,0,0.0,0.0,economic problem,2909,real,rectangular,no,no,true,economic problem -276,HB,wm2,207,260,2942,1,0,0,0,0.0,0.0,economic problem,2942,real,rectangular,no,no,true,economic problem -277,HB,wm3,207,260,2948,1,0,0,0,0.0,0.0,economic problem,2948,real,rectangular,no,no,true,economic problem -278,HB,young1c,841,841,4089,0,0,1,0,1.0,0.8479064039408867,acoustics problem,4089,complex,unsymmetric,no,no,true,acoustics problem -279,HB,young2c,841,841,4089,0,0,1,0,1.0,0.8479064039408867,duplicate acoustics problem,4089,complex,unsymmetric,no,no,true,duplicate acoustics problem -280,HB,young3c,841,841,3988,1,0,1,0,0.9380362249761678,0.8071178900540197,acoustics problem,3988,real,unsymmetric,no,no,true,acoustics problem -281,HB,young4c,841,841,4089,0,0,1,0,1.0,0.8004926108374384,acoustics problem,4089,complex,unsymmetric,no,no,true,acoustics problem -282,HB,zenios,2873,2873,1314,1,0,0,0,1.0,1.0,optimization problem,27191,real,symmetric,no,no,false,optimization problem -283,ATandT,onetone1,36057,36057,335552,1,0,0,0,0.0742956282869174,0.04331285111284008,frequency-domain circuit simulation problem,341088,real,unsymmetric,no,no,true,frequency-domain circuit simulation problem -284,ATandT,onetone2,36057,36057,222596,1,0,0,0,0.1128682425939654,0.06622764699821082,frequency-domain circuit simulation problem,227628,real,unsymmetric,no,no,true,frequency-domain circuit simulation problem -285,ATandT,pre2,659033,659033,5834044,1,0,0,0,0.3317760910564062,0.06536187196755584,frequency-domain circuit simulation problem,5959282,real,unsymmetric,no,no,true,frequency-domain circuit simulation problem -286,ATandT,twotone,120750,120750,1206265,1,0,0,0,0.2445668624008717,0.1058542372000797,frequency-domain circuit simulation problem,1224224,real,unsymmetric,no,no,true,frequency-domain circuit simulation problem -287,Averous,epb0,1794,1794,7764,1,0,1,0,0.5008375209380235,0.0,thermal problem,7764,real,unsymmetric,no,no,true,thermal problem -288,Averous,epb1,14734,14734,95053,1,0,1,0,0.7293915511896314,0.0,thermal problem,95053,real,unsymmetric,no,no,true,thermal problem -289,Averous,epb2,25228,25228,175027,1,0,1,0,0.6705385216189694,0.0,thermal problem,175027,real,unsymmetric,no,no,true,thermal problem -290,Averous,epb3,84617,84617,463625,1,0,1,0,0.6674107142857143,0.03155606214116852,thermal problem,463625,real,unsymmetric,no,no,true,thermal problem -291,Bai,af23560,23560,23560,460598,1,0,1,0,0.9973084203031934,0.0,computational fluid dynamics problem,484256,real,unsymmetric,no,no,true,computational fluid dynamics problem -292,Bai,bfwa398,398,398,3678,1,0,1,0,0.9890243902439024,0.9432926829268292,electromagnetics problem,3678,real,unsymmetric,no,no,true,electromagnetics problem -293,Bai,bfwa62,62,62,450,1,0,1,0,0.9690721649484536,0.8144329896907216,electromagnetics problem,450,real,unsymmetric,no,no,true,electromagnetics problem -294,Bai,bfwa782,782,782,7514,1,0,1,0,0.9916815210932858,0.9604872251931076,electromagnetics problem,7514,real,unsymmetric,no,no,true,electromagnetics problem -295,Bai,bfwb398,398,398,2910,1,0,1,0,1.0,1.0,electromagnetics problem,2910,real,symmetric,no,no,true,electromagnetics problem -296,Bai,bfwb62,62,62,342,1,0,1,0,1.0,1.0,electromagnetics problem,342,real,symmetric,no,no,true,electromagnetics problem -297,Bai,bfwb782,782,782,5982,1,0,1,0,1.0,1.0,electromagnetics problem,5982,real,symmetric,no,no,true,electromagnetics problem -298,Bai,bwm200,200,200,796,1,0,0,0,1.0,0.6644295302013423,chemical process simulation problem,796,real,unsymmetric,no,no,true,chemical process simulation problem -299,Bai,bwm2000,2000,2000,7996,1,0,0,0,1.0,0.6664442961974649,chemical process simulation problem,7996,real,unsymmetric,no,no,true,chemical process simulation problem -300,Bai,cdde1,961,961,4681,1,0,1,0,1.0,0.0,computational fluid dynamics problem sequence,4681,real,unsymmetric,no,no,true,computational fluid dynamics problem sequence -301,Bai,cdde2,961,961,4681,1,0,1,0,1.0,0.0,subsequent computational fluid dynamics problem,4681,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -302,Bai,cdde3,961,961,4681,1,0,1,0,1.0,0.0,subsequent computational fluid dynamics problem,4681,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -303,Bai,cdde4,961,961,4681,1,0,1,0,1.0,0.0,subsequent computational fluid dynamics problem,4681,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -304,Bai,cdde5,961,961,4681,1,0,1,0,1.0,0.0,subsequent computational fluid dynamics problem,4681,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -305,Bai,cdde6,961,961,4681,1,0,1,0,1.0,0.0,subsequent computational fluid dynamics problem,4681,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -306,Bai,ck104,104,104,992,1,0,1,0,1.0,0.0,2D/3D problem,992,real,unsymmetric,no,no,true,2D/3D problem -307,Bai,ck400,400,400,2860,1,0,1,0,0.9886178861788618,0.001626016260162602,2D/3D problem,2860,real,unsymmetric,no,no,true,2D/3D problem -308,Bai,ck656,656,656,3884,1,0,1,0,0.9913258983890955,0.0,2D/3D problem,3884,real,unsymmetric,no,no,true,2D/3D problem -309,Bai,dw1024,2048,2048,10114,1,0,1,0,0.9848747830399207,0.9481775353334987,electromagnetics problem,10114,real,unsymmetric,no,no,true,electromagnetics problem -310,Bai,dw256A,512,512,2480,1,0,1,0,0.9796747967479674,0.9146341463414634,electromagnetics problem,2480,real,unsymmetric,no,no,true,electromagnetics problem -311,Bai,dw256B,512,512,2500,1,0,1,0,0.9698189134808853,0.8832997987927566,electromagnetics problem,2500,real,unsymmetric,no,no,true,electromagnetics problem -312,Bai,dw4096,8192,8192,41746,1,0,1,0,0.9626274065685164,0.9154199201287477,electromagnetics problem,41746,real,unsymmetric,no,no,true,electromagnetics problem -313,Bai,lop163,163,163,935,1,0,0,0,0.4507772020725389,0.0,statistical/mathematical problem,935,real,unsymmetric,no,no,true,statistical/mathematical problem -314,Bai,mhda416,416,416,8562,1,0,1,0,0.7675276752767528,0.2675871920009523,electromagnetics problem,8562,real,unsymmetric,no,no,true,electromagnetics problem -315,Bai,mhdb416,416,416,2312,1,0,1,1,1.0,1.0,electromagnetics problem,2312,real,symmetric,yes,yes,true,electromagnetics problem -316,Bai,odepa400,400,400,1201,1,0,1,0,0.9962546816479401,0.9937578027465668,2D/3D problem,1201,real,unsymmetric,no,no,true,2D/3D problem -317,Bai,odepb400,400,400,399,1,0,1,0,1.0,1.0,2D/3D problem,399,real,symmetric,no,no,false,2D/3D problem -318,Bai,olm100,100,100,396,1,0,1,0,0.668918918918919,0.3310810810810811,computational fluid dynamics problem,396,real,unsymmetric,no,no,true,computational fluid dynamics problem -319,Bai,olm1000,1000,1000,3996,1,0,1,0,0.6668891855807744,0.3331108144192256,computational fluid dynamics problem,3996,real,unsymmetric,no,no,true,computational fluid dynamics problem -320,Bai,olm2000,2000,2000,7996,1,0,1,0,0.6667778519012675,0.3332221480987325,computational fluid dynamics problem,7996,real,unsymmetric,no,no,true,computational fluid dynamics problem -321,Bai,olm500,500,500,1996,1,0,1,0,0.6671122994652406,0.3328877005347594,computational fluid dynamics problem,1996,real,unsymmetric,no,no,true,computational fluid dynamics problem -322,Bai,olm5000,5000,5000,19996,1,0,1,0,0.6667111229661243,0.3332888770338757,computational fluid dynamics problem,19996,real,unsymmetric,no,no,true,computational fluid dynamics problem -323,Bai,pde225,225,225,1065,1,0,1,0,1.0,0.5,2D/3D problem,1065,real,unsymmetric,no,no,true,2D/3D problem -324,Bai,pde2961,2961,2961,14585,1,0,1,0,1.0,0.5013764624913971,2D/3D problem,14585,real,unsymmetric,no,no,true,2D/3D problem -325,Bai,pde900,900,900,4380,1,0,1,0,1.0,0.5,2D/3D problem,4380,real,unsymmetric,no,no,true,2D/3D problem -326,Bai,qc2534,2534,2534,463360,0,0,1,0,1.0,0.0,electromagnetics problem,463360,complex,symmetric,no,no,true,electromagnetics problem -327,Bai,qc324,324,324,26730,0,0,1,0,1.0,0.0,electromagnetics problem,26730,complex,symmetric,no,no,true,electromagnetics problem -328,Bai,qh882,882,882,3354,1,0,0,0,0.9264705882352942,0.0,power network problem,3354,real,unsymmetric,no,no,true,power network problem -329,Bai,rbsa480,480,480,17088,1,0,1,0,0.07485627126598615,0.0007039774727208729,robotics problem,17088,integer,unsymmetric,no,no,true,robotics problem -330,Bai,rbsb480,480,480,17088,1,0,1,0,0.07568201818715166,0.0008213552361396304,robotics problem,17088,integer,unsymmetric,no,no,true,robotics problem -331,Bai,rdb2048,2048,2048,12032,1,0,1,0,1.0,0.7948717948717948,computational fluid dynamics problem,12032,real,unsymmetric,no,no,true,computational fluid dynamics problem -332,Bai,rdb5000,5000,5000,29600,1,0,1,0,1.0,0.7967479674796748,computational fluid dynamics problem,29600,real,unsymmetric,no,no,true,computational fluid dynamics problem -333,Bai,rdb968,968,968,5632,1,0,1,0,1.0,0.7924528301886793,computational fluid dynamics problem,5632,real,unsymmetric,no,no,true,computational fluid dynamics problem -334,Bai,rw136,136,136,479,1,0,0,0,0.44258872651357,0.02922755741127349,statistical/mathematical problem,479,real,unsymmetric,no,no,true,statistical/mathematical problem -335,Bai,rw496,496,496,1859,1,0,0,0,0.4690693921463152,0.0,statistical/mathematical problem,1859,real,unsymmetric,no,no,true,statistical/mathematical problem -336,Bai,rw5151,5151,5151,20199,1,0,0,0,0.4902222882320907,0.0,statistical/mathematical problem,20199,real,unsymmetric,no,no,true,statistical/mathematical problem -337,Bai,tub100,100,100,396,1,0,1,0,1.0,0.0,computational fluid dynamics problem,396,real,unsymmetric,no,no,true,computational fluid dynamics problem -338,Bai,tub1000,1000,1000,3996,1,0,1,0,1.0,0.0,computational fluid dynamics problem,3996,real,unsymmetric,no,no,true,computational fluid dynamics problem -339,Boeing,bcsstk34,588,588,21418,1,0,1,1,1.0,1.0,structural problem,21418,real,symmetric,yes,yes,true,structural problem -340,Boeing,bcsstk35,30237,30237,1450163,1,0,1,0,1.0,1.0,structural problem,1450163,real,symmetric,no,yes,true,structural problem -341,Boeing,bcsstk36,23052,23052,1143140,1,0,1,1,1.0,1.0,structural problem,1143140,real,symmetric,yes,yes,true,structural problem -342,Boeing,bcsstk37,25503,25503,1140977,1,0,1,0,1.0,1.0,structural problem,1140977,real,symmetric,no,yes,true,structural problem -343,Boeing,bcsstk38,8032,8032,355460,1,0,1,1,1.0,1.0,structural problem,355460,real,symmetric,yes,yes,true,structural problem -344,Boeing,bcsstm34,588,588,24270,1,0,1,0,1.0,1.0,structural problem,24270,real,symmetric,no,no,true,structural problem -345,Boeing,bcsstm35,30237,30237,20619,1,0,1,0,1.0,1.0,structural problem,20619,real,symmetric,no,no,false,structural problem -346,Boeing,bcsstm36,23052,23052,320606,1,0,1,0,1.0,1.0,structural problem,320606,real,symmetric,no,no,false,structural problem -347,Boeing,bcsstm37,25503,25503,15525,1,0,1,0,1.0,1.0,structural problem,15525,real,symmetric,no,no,false,structural problem -348,Boeing,bcsstm38,8032,8032,10485,1,0,1,0,1.0,1.0,structural problem,10485,real,symmetric,no,no,false,structural problem -349,Boeing,bcsstm39,46772,46772,46772,1,0,1,1,1.0,1.0,structural problem,46772,real,symmetric,yes,yes,true,structural problem -350,Boeing,crystk01,4875,4875,315891,1,0,1,0,1.0,1.0,materials problem,315891,real,symmetric,no,yes,true,materials problem -351,Boeing,crystk02,13965,13965,968583,1,0,1,0,1.0,1.0,materials problem,968583,real,symmetric,no,yes,true,materials problem -352,Boeing,crystk03,24696,24696,1751178,1,0,1,0,1.0,1.0,materials problem,1751178,real,symmetric,no,yes,true,materials problem -353,Boeing,crystm01,4875,4875,105339,1,0,1,1,1.0,1.0,materials problem,105339,real,symmetric,yes,yes,true,materials problem -354,Boeing,crystm02,13965,13965,322905,1,0,1,1,1.0,1.0,materials problem,322905,real,symmetric,yes,yes,true,materials problem -355,Boeing,crystm03,24696,24696,583770,1,0,1,1,1.0,1.0,materials problem,583770,real,symmetric,yes,yes,true,materials problem -356,Boeing,ct20stif,52329,52329,2600295,1,0,1,1,1.0,1.0,structural problem,2698463,real,symmetric,yes,yes,true,structural problem -357,Boeing,msc00726,726,726,34518,1,0,1,1,1.0,1.0,structural problem,34518,real,symmetric,yes,yes,true,structural problem -358,Boeing,msc01050,1050,1050,26198,1,0,1,1,1.0,1.0,structural problem,29156,real,symmetric,yes,yes,true,structural problem -359,Boeing,msc01440,1440,1440,44998,1,0,1,1,1.0,1.0,structural problem,46270,real,symmetric,yes,yes,true,structural problem -360,Boeing,msc04515,4515,4515,97707,1,0,1,1,1.0,1.0,structural problem,97707,real,symmetric,yes,yes,true,structural problem -361,Boeing,msc10848,10848,10848,1229776,1,0,1,1,1.0,1.0,structural problem,1229778,real,symmetric,yes,yes,true,structural problem -362,Boeing,msc23052,23052,23052,1142686,1,0,1,1,1.0,1.0,structural problem,1154814,real,symmetric,yes,yes,true,structural problem -363,Boeing,nasa1824,1824,1824,39208,1,0,1,0,1.0,1.0,duplicate structural problem,39208,real,symmetric,no,yes,true,duplicate structural problem -364,Boeing,nasa2910,2910,2910,174296,1,1,1,0,1.0,1.0,duplicate structural problem,174296,binary,symmetric,no,yes,true,duplicate structural problem -365,Boeing,nasa4704,4704,4704,104756,1,1,1,0,1.0,1.0,duplicate structural problem,104756,binary,symmetric,no,yes,true,duplicate structural problem -366,Boeing,pcrystk02,13965,13965,968583,1,1,1,0,1.0,1.0,duplicate materials problem,968583,binary,symmetric,no,yes,true,duplicate materials problem -367,Boeing,pcrystk03,24696,24696,1751178,1,1,1,0,1.0,1.0,duplicate materials problem,1751178,binary,symmetric,no,yes,true,duplicate materials problem -368,Boeing,pct20stif,52329,52329,2698463,1,1,1,0,1.0,1.0,structural problem,2698463,binary,symmetric,no,yes,true,structural problem -369,Boeing,pwtk,217918,217918,11524432,1,0,1,1,1.0,1.0,structural problem,11634424,real,symmetric,yes,yes,true,structural problem -370,Bomhof,circuit_1,2624,2624,35823,1,0,0,0,0.999548641410646,0.2060602413263924,circuit simulation problem,35823,real,unsymmetric,no,no,true,circuit simulation problem -371,Bomhof,circuit_2,4510,4510,21199,1,0,0,0,0.806563660318601,0.4152593124925141,circuit simulation problem,21199,real,unsymmetric,no,no,true,circuit simulation problem -372,Bomhof,circuit_3,12127,12127,48137,1,0,0,0,0.7700717835450027,0.3001104362230811,circuit simulation problem,48137,real,unsymmetric,no,no,true,circuit simulation problem -373,Bomhof,circuit_4,80209,80209,307604,1,0,0,0,0.8292028705104302,0.3635452210926232,circuit simulation problem,307604,real,unsymmetric,no,no,true,circuit simulation problem -374,Bova,rma10,46835,46835,2329092,1,0,1,0,1.0,0.2355549300354827,computational fluid dynamics problem,2374001,real,unsymmetric,no,no,true,computational fluid dynamics problem -375,Brethour,coater1,1348,1348,19457,1,0,1,0,0.5922916666666667,0.395,computational fluid dynamics problem,19457,real,unsymmetric,no,no,false,computational fluid dynamics problem -376,Brethour,coater2,9540,9540,207308,1,0,1,0,0.5941884735804569,0.1280874338061923,computational fluid dynamics problem,207308,real,unsymmetric,no,no,false,computational fluid dynamics problem -377,Brunetiere,thermal,3456,3456,66528,1,0,1,0,1.0,0.0730593607305936,thermal problem,66528,real,unsymmetric,no,no,true,thermal problem -378,Cote,mplate,5962,5962,142190,0,0,1,0,1.0,0.05311683354376486,acoustics problem,142190,complex,symmetric,no,no,true,acoustics problem -379,Cote,vibrobox,12328,12328,301700,1,0,1,0,1.0,1.0,acoustics problem,342828,real,symmetric,no,yes,true,acoustics problem -380,DRIVCAV,cavity01,317,317,7280,1,0,1,0,0.8037831733483908,0.5198237885462555,computational fluid dynamics problem sequence,7327,real,unsymmetric,no,no,true,computational fluid dynamics problem sequence -381,DRIVCAV,cavity02,317,317,5923,1,0,1,0,0.8037831733483908,0.0,subsequent computational fluid dynamics problem,7327,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -382,DRIVCAV,cavity03,317,317,7311,1,0,1,0,0.8037831733483908,0.0,subsequent computational fluid dynamics problem,7327,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -383,DRIVCAV,cavity04,317,317,5923,1,0,1,0,0.8037831733483908,0.0,subsequent computational fluid dynamics problem,7327,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -384,DRIVCAV,cavity05,1182,1182,32632,1,0,1,0,0.9049083605322621,0.6103499322813317,computational fluid dynamics problem sequence,32747,real,unsymmetric,no,no,true,computational fluid dynamics problem sequence -385,DRIVCAV,cavity06,1182,1182,29675,1,0,1,0,0.9049083605322621,0.0,subsequent computational fluid dynamics problem,32747,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -386,DRIVCAV,cavity07,1182,1182,32702,1,0,1,0,0.9049083605322621,0.0,subsequent computational fluid dynamics problem,32747,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -387,DRIVCAV,cavity08,1182,1182,29675,1,0,1,0,0.9049083605322621,0.0,subsequent computational fluid dynamics problem,32747,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -388,DRIVCAV,cavity09,1182,1182,32702,1,0,1,0,0.9049083605322621,0.0,subsequent computational fluid dynamics problem,32747,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -389,DRIVCAV,cavity10,2597,2597,76171,1,0,1,0,0.9372682821986997,0.6400172395216033,computational fluid dynamics problem sequence,76367,real,unsymmetric,no,no,true,computational fluid dynamics problem sequence -390,DRIVCAV,cavity11,2597,2597,71601,1,0,1,0,0.9372682821986997,0.0,subsequent computational fluid dynamics problem,76367,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -391,DRIVCAV,cavity12,2597,2597,76258,1,0,1,0,0.9372682821986997,0.0,subsequent computational fluid dynamics problem,76367,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -392,DRIVCAV,cavity13,2597,2597,71601,1,0,1,0,0.9372682821986997,0.0,subsequent computational fluid dynamics problem,76367,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -393,DRIVCAV,cavity14,2597,2597,76258,1,0,1,0,0.9372682821986997,0.0,subsequent computational fluid dynamics problem,76367,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -394,DRIVCAV,cavity15,2597,2597,71601,1,0,1,0,0.9372682821986997,0.0,subsequent computational fluid dynamics problem,76367,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -395,DRIVCAV,cavity16,4562,4562,137887,1,0,1,0,0.9531982436361479,0.6542773036781541,computational fluid dynamics problem sequence,138187,real,unsymmetric,no,no,true,computational fluid dynamics problem sequence -396,DRIVCAV,cavity17,4562,4562,131735,1,0,1,0,0.9531982436361479,0.0,subsequent computational fluid dynamics problem,138187,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -397,DRIVCAV,cavity18,4562,4562,138040,1,0,1,0,0.9531982436361479,0.0,subsequent computational fluid dynamics problem,138187,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -398,DRIVCAV,cavity19,4562,4562,131735,1,0,1,0,0.9531982436361479,0.0,subsequent computational fluid dynamics problem,138187,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -399,DRIVCAV,cavity20,4562,4562,138040,1,0,1,0,0.9531982436361479,0.0,subsequent computational fluid dynamics problem,138187,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -400,DRIVCAV,cavity21,4562,4562,131735,1,0,1,0,0.9531982436361479,0.0,subsequent computational fluid dynamics problem,138187,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -401,DRIVCAV,cavity22,4562,4562,138040,1,0,1,0,0.9531982436361479,0.0,subsequent computational fluid dynamics problem,138187,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -402,DRIVCAV,cavity23,4562,4562,131735,1,0,1,0,0.9531982436361479,0.0,subsequent computational fluid dynamics problem,138187,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -403,DRIVCAV,cavity24,4562,4562,138040,1,0,1,0,0.9531982436361479,0.0,subsequent computational fluid dynamics problem,138187,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -404,DRIVCAV,cavity25,4562,4562,131735,1,0,1,0,0.9531982436361479,0.0,subsequent computational fluid dynamics problem,138187,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -405,DRIVCAV,cavity26,4562,4562,138040,1,0,1,0,0.9531982436361479,0.0,subsequent computational fluid dynamics problem,138187,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -406,FIDAP,ex1,216,216,4317,1,0,1,0,1.0,0.9980657640232108,computational fluid dynamics problem,4352,real,unsymmetric,no,no,true,computational fluid dynamics problem -407,FIDAP,ex10,2410,2410,54840,1,0,1,1,1.0,1.0,computational fluid dynamics problem,54840,real,symmetric,yes,yes,true,computational fluid dynamics problem -408,FIDAP,ex10hs,2548,2548,57308,1,0,1,1,1.0,1.0,computational fluid dynamics problem,57308,real,symmetric,yes,yes,true,computational fluid dynamics problem -409,FIDAP,ex11,16614,16614,1096948,1,0,1,0,1.0,0.9999981487206734,computational fluid dynamics problem,1096948,real,unsymmetric,no,no,true,computational fluid dynamics problem -410,FIDAP,ex12,3973,3973,79077,1,0,1,0,1.0,1.0,computational fluid dynamics problem,80211,real,symmetric,no,no,true,computational fluid dynamics problem -411,FIDAP,ex13,2568,2568,75628,1,0,1,1,1.0,1.0,computational fluid dynamics problem,75628,real,symmetric,yes,yes,true,computational fluid dynamics problem -412,FIDAP,ex14,3251,3251,65875,1,0,1,0,1.0,1.0,computational fluid dynamics problem,66775,real,symmetric,no,no,true,computational fluid dynamics problem -413,FIDAP,ex15,6867,6867,98671,1,0,1,1,1.0,1.0,computational fluid dynamics problem,98671,real,symmetric,yes,yes,true,computational fluid dynamics problem -414,FIDAP,ex18,5773,5773,71701,1,0,1,0,1.0,0.4973910933139182,computational fluid dynamics problem,71805,real,unsymmetric,no,no,true,computational fluid dynamics problem -415,FIDAP,ex19,12005,12005,259577,1,0,1,0,1.0,0.4284006268883396,computational fluid dynamics problem,259879,real,unsymmetric,no,no,true,computational fluid dynamics problem -416,FIDAP,ex2,441,441,26839,1,0,1,0,1.0,1.0,computational fluid dynamics problem,26839,real,symmetric,no,no,true,computational fluid dynamics problem -417,FIDAP,ex20,2203,2203,67830,1,0,1,0,1.0,0.9705407160221662,computational fluid dynamics problem,69981,real,unsymmetric,no,no,true,computational fluid dynamics problem -418,FIDAP,ex21,656,656,18964,1,0,1,0,1.0,0.9422327996538296,computational fluid dynamics problem,19144,real,unsymmetric,no,no,true,computational fluid dynamics problem -419,FIDAP,ex22,839,839,22460,1,0,1,0,1.0,0.88617551561714,computational fluid dynamics problem,22715,real,unsymmetric,no,no,true,computational fluid dynamics problem -420,FIDAP,ex23,1409,1409,42760,1,0,1,0,1.0,0.9386855719486492,computational fluid dynamics problem,43703,real,unsymmetric,no,no,true,computational fluid dynamics problem -421,FIDAP,ex24,2283,2283,47901,1,0,1,0,1.0,0.862361129122898,computational fluid dynamics problem,48737,real,unsymmetric,no,no,true,computational fluid dynamics problem -422,FIDAP,ex25,848,848,24369,1,0,1,0,1.0,0.9755481671646816,computational fluid dynamics problem,24612,real,unsymmetric,no,no,true,computational fluid dynamics problem -423,FIDAP,ex26,2163,2163,74464,1,0,1,0,1.0,0.7325380026938618,computational fluid dynamics problem,94033,real,unsymmetric,no,no,true,computational fluid dynamics problem -424,FIDAP,ex27,974,974,37652,1,0,1,0,1.0,0.5304041225928939,computational fluid dynamics problem,40782,real,unsymmetric,no,no,true,computational fluid dynamics problem -425,FIDAP,ex28,2603,2603,77031,1,0,1,0,1.0,0.9877623772912288,computational fluid dynamics problem,77781,real,unsymmetric,no,no,true,computational fluid dynamics problem -426,FIDAP,ex29,2870,2870,23754,1,0,1,0,1.0,0.9963608504117986,computational fluid dynamics problem,23754,real,unsymmetric,no,no,true,computational fluid dynamics problem -427,FIDAP,ex3,1821,1821,52685,1,0,1,1,1.0,1.0,computational fluid dynamics problem,52685,real,symmetric,yes,yes,true,computational fluid dynamics problem -428,FIDAP,ex31,3909,3909,91223,1,0,1,0,1.0,0.7266897116346767,computational fluid dynamics problem,115357,real,unsymmetric,no,no,true,computational fluid dynamics problem -429,FIDAP,ex32,1159,1159,11047,1,0,1,0,1.0,1.0,computational fluid dynamics problem,11343,real,symmetric,no,no,true,computational fluid dynamics problem -430,FIDAP,ex33,1733,1733,22189,1,0,1,1,1.0,1.0,computational fluid dynamics problem,22189,real,symmetric,yes,yes,true,computational fluid dynamics problem -431,FIDAP,ex35,19716,19716,227872,1,0,1,0,1.0,0.4608754972232364,computational fluid dynamics problem,228208,real,unsymmetric,no,no,true,computational fluid dynamics problem -432,FIDAP,ex36,3079,3079,53099,1,0,1,0,1.0,0.9952497822816879,computational fluid dynamics problem,53843,real,unsymmetric,no,no,true,computational fluid dynamics problem -433,FIDAP,ex37,3565,3565,67591,1,0,1,0,1.0,0.9536750695030144,computational fluid dynamics problem,67591,real,unsymmetric,no,no,true,computational fluid dynamics problem -434,FIDAP,ex4,1601,1601,31849,1,0,1,0,1.0,1.0,computational fluid dynamics problem,32299,real,symmetric,no,no,true,computational fluid dynamics problem -435,FIDAP,ex40,7740,7740,456188,1,0,1,0,1.0,0.7709917560940942,computational fluid dynamics problem,458012,real,unsymmetric,no,no,true,computational fluid dynamics problem -436,FIDAP,ex5,27,27,279,1,0,1,1,1.0,1.0,computational fluid dynamics problem,279,real,symmetric,yes,yes,true,computational fluid dynamics problem -437,FIDAP,ex6,1651,1651,49062,1,0,1,0,1.0,0.9792406332233408,computational fluid dynamics problem,49533,real,unsymmetric,no,no,true,computational fluid dynamics problem -438,FIDAP,ex7,1633,1633,46626,1,0,1,0,1.0,0.8240392950014447,computational fluid dynamics problem,54543,real,unsymmetric,no,no,true,computational fluid dynamics problem -439,FIDAP,ex8,3096,3096,90841,1,0,1,0,1.0,0.4310900906034532,computational fluid dynamics problem,106344,real,unsymmetric,no,no,true,computational fluid dynamics problem -440,FIDAP,ex9,3363,3363,99471,1,0,1,1,1.0,1.0,computational fluid dynamics problem,99471,real,symmetric,yes,yes,true,computational fluid dynamics problem -441,Gaertner,big,13209,13209,91465,1,0,0,0,1.0,0.999974442854222,directed weighted graph,91465,integer,unsymmetric,no,no,missing,directed weighted graph -442,Gaertner,nopoly,10774,10774,70842,1,0,0,0,1.0,1.0,undirected weighted graph,70842,integer,symmetric,no,yes,missing,undirected weighted graph -443,Gaertner,pesa,11738,11738,79566,1,0,0,0,1.0,0.999970513652179,directed weighted graph,79566,integer,unsymmetric,no,no,missing,directed weighted graph -444,Garon,garon1,3175,3175,84723,1,0,1,0,1.0,0.6720847366622736,computational fluid dynamics problem,88927,real,unsymmetric,no,no,true,computational fluid dynamics problem -445,Garon,garon2,13535,13535,373235,1,0,1,0,1.0,0.6726819817326322,computational fluid dynamics problem,390607,real,unsymmetric,no,no,true,computational fluid dynamics problem -446,Goodwin,goodwin,7320,7320,324772,1,0,1,0,0.6353114022282706,0.001199255331506196,computational fluid dynamics problem,324784,real,unsymmetric,no,no,true,computational fluid dynamics problem -447,Goodwin,rim,22560,22560,1014951,1,0,1,0,0.6390640220938991,0.0,computational fluid dynamics problem,1014951,real,unsymmetric,no,no,true,computational fluid dynamics problem -448,Graham,graham1,9035,9035,335472,1,0,1,0,0.718197839525901,0.0004322586733311619,computational fluid dynamics problem,335504,real,unsymmetric,no,no,true,computational fluid dynamics problem -449,Grund,b1_ss,7,7,15,1,0,0,0,0.0,0.0,chemical process simulation problem,15,real,unsymmetric,no,no,true,chemical process simulation problem -450,Grund,b2_ss,1089,1089,3895,1,0,0,0,0.0146710837671557,0.0005136106831022085,chemical process simulation problem,4228,real,unsymmetric,no,no,true,chemical process simulation problem -451,Grund,b_dyn,1089,1089,4144,1,0,0,0,0.01454716095729704,0.0004828585224529213,chemical process simulation problem,4264,real,unsymmetric,no,no,true,chemical process simulation problem -452,Grund,bayer01,57735,57735,275094,1,0,0,0,0.0002304047924196823,2.908118011428904e-05,chemical process simulation problem,277774,real,unsymmetric,no,no,true,chemical process simulation problem -453,Grund,bayer02,13935,13935,63307,1,0,0,0,0.001884570082449941,0.000284346713425904,chemical process simulation problem,63679,real,unsymmetric,no,no,true,chemical process simulation problem -454,Grund,bayer03,6747,6747,29195,1,0,0,0,0.003132397173723459,0.0002740852405097985,chemical process simulation problem,56196,real,unsymmetric,no,no,true,chemical process simulation problem -455,Grund,bayer04,20545,20545,85537,1,0,0,0,0.001584088708967702,0.0002104352502425851,chemical process simulation problem,159082,real,unsymmetric,no,no,true,chemical process simulation problem -456,Grund,bayer05,3268,3268,20712,1,0,0,0,0.006682474671265359,0.00135200386286818,chemical process simulation problem,27836,real,unsymmetric,no,no,true,chemical process simulation problem -457,Grund,bayer06,3008,3008,20715,1,0,0,0,0.006745484877058098,0.001351808043257857,chemical process simulation problem,27576,real,unsymmetric,no,no,true,chemical process simulation problem -458,Grund,bayer07,3268,3268,20963,1,0,0,0,0.006682474671265359,0.0013358141310052,chemical process simulation problem,27836,real,unsymmetric,no,no,true,chemical process simulation problem -459,Grund,bayer08,3008,3008,20698,1,0,0,0,0.006745484877058098,0.001352918438345574,chemical process simulation problem,27576,real,unsymmetric,no,no,true,chemical process simulation problem -460,Grund,bayer09,3083,3083,11767,1,0,0,0,0.02112012068640392,0.0001700246535747683,chemical process simulation problem,21216,real,unsymmetric,no,no,true,chemical process simulation problem -461,Grund,bayer10,13436,13436,71594,1,0,0,0,0.002528365095919851,0.0002234917796929782,chemical process simulation problem,94926,real,unsymmetric,no,no,true,chemical process simulation problem -462,Grund,d_dyn,87,87,230,1,0,0,0,0.07692307692307693,0.01769911504424779,chemical process simulation problem,238,real,unsymmetric,no,no,true,chemical process simulation problem -463,Grund,d_dyn1,87,87,232,1,0,0,0,0.07692307692307693,0.01754385964912281,chemical process simulation problem,238,real,unsymmetric,no,no,true,chemical process simulation problem -464,Grund,d_ss,53,53,144,1,0,0,0,0.04137931034482759,0.0,chemical process simulation problem,149,real,unsymmetric,no,no,true,chemical process simulation problem -465,Grund,meg1,2904,2904,58142,1,0,0,0,0.002374602082078637,0.001996042329863202,circuit simulation problem,58142,real,unsymmetric,no,no,true,circuit simulation problem -466,Grund,meg4,5860,5860,25258,1,0,0,0,1.0,1.0,circuit simulation problem,46842,real,symmetric,no,no,true,circuit simulation problem -467,Grund,poli,4008,4008,8188,1,0,0,0,0.0291866028708134,0.002392344497607655,economic problem,8188,real,unsymmetric,no,no,true,economic problem -468,Grund,poli_large,15575,15575,33033,1,0,0,0,0.003543059603405909,0.0004582426394776034,economic problem,33074,real,unsymmetric,no,no,true,economic problem -469,Gset,G1,800,800,38352,1,1,0,0,1.0,1.0,undirected random graph,38352,binary,symmetric,no,no,missing,undirected random graph -470,Gset,G10,800,800,38352,1,0,0,0,1.0,1.0,undirected weighted random graph,38352,integer,symmetric,no,no,missing,undirected weighted random graph -471,Gset,G11,800,800,3200,1,0,0,0,1.0,1.0,undirected weighted random graph,3200,integer,symmetric,no,no,missing,undirected weighted random graph -472,Gset,G12,800,800,3200,1,0,0,0,1.0,1.0,undirected weighted random graph,3200,integer,symmetric,no,no,missing,undirected weighted random graph -473,Gset,G13,800,800,3200,1,0,0,0,1.0,1.0,undirected weighted random graph,3200,integer,symmetric,no,no,missing,undirected weighted random graph -474,Gset,G14,800,800,9388,1,1,0,0,1.0,1.0,duplicate undirected random graph,9388,binary,symmetric,no,no,missing,duplicate undirected random graph -475,Gset,G15,800,800,9322,1,1,0,0,1.0,1.0,duplicate undirected random graph,9322,binary,symmetric,no,no,missing,duplicate undirected random graph -476,Gset,G16,800,800,9344,1,1,0,0,1.0,1.0,duplicate undirected random graph,9344,binary,symmetric,no,no,missing,duplicate undirected random graph -477,Gset,G17,800,800,9334,1,1,0,0,1.0,1.0,duplicate undirected random graph,9334,binary,symmetric,no,no,missing,duplicate undirected random graph -478,Gset,G18,800,800,9388,1,0,0,0,1.0,1.0,undirected weighted random graph,9388,integer,symmetric,no,no,missing,undirected weighted random graph -479,Gset,G19,800,800,9322,1,0,0,0,1.0,1.0,undirected weighted random graph,9322,integer,symmetric,no,no,missing,undirected weighted random graph -480,Gset,G2,800,800,38352,1,1,0,0,1.0,1.0,undirected random graph,38352,binary,symmetric,no,no,missing,undirected random graph -481,Gset,G20,800,800,9344,1,0,0,0,1.0,1.0,undirected weighted random graph,9344,integer,symmetric,no,no,missing,undirected weighted random graph -482,Gset,G21,800,800,9334,1,0,0,0,1.0,1.0,undirected weighted random graph,9334,integer,symmetric,no,no,missing,undirected weighted random graph -483,Gset,G22,2000,2000,39980,1,1,0,0,1.0,1.0,undirected random graph,39980,binary,symmetric,no,no,missing,undirected random graph -484,Gset,G23,2000,2000,39980,1,1,0,0,1.0,1.0,undirected random graph,39980,binary,symmetric,no,no,missing,undirected random graph -485,Gset,G24,2000,2000,39980,1,1,0,0,1.0,1.0,undirected random graph,39980,binary,symmetric,no,no,missing,undirected random graph -486,Gset,G25,2000,2000,39980,1,1,0,0,1.0,1.0,undirected random graph,39980,binary,symmetric,no,no,missing,undirected random graph -487,Gset,G26,2000,2000,39980,1,1,0,0,1.0,1.0,undirected random graph,39980,binary,symmetric,no,no,missing,undirected random graph -488,Gset,G27,2000,2000,39980,1,0,0,0,1.0,1.0,undirected weighted random graph,39980,integer,symmetric,no,no,missing,undirected weighted random graph -489,Gset,G28,2000,2000,39980,1,0,0,0,1.0,1.0,undirected weighted random graph,39980,integer,symmetric,no,no,missing,undirected weighted random graph -490,Gset,G29,2000,2000,39980,1,0,0,0,1.0,1.0,undirected weighted random graph,39980,integer,symmetric,no,no,missing,undirected weighted random graph -491,Gset,G3,800,800,38352,1,1,0,0,1.0,1.0,undirected random graph,38352,binary,symmetric,no,no,missing,undirected random graph -492,Gset,G30,2000,2000,39980,1,0,0,0,1.0,1.0,undirected weighted random graph,39980,integer,symmetric,no,no,missing,undirected weighted random graph -493,Gset,G31,2000,2000,39980,1,0,0,0,1.0,1.0,undirected weighted random graph,39980,integer,symmetric,no,no,missing,undirected weighted random graph -494,Gset,G32,2000,2000,8000,1,0,0,0,1.0,1.0,undirected weighted random graph,8000,integer,symmetric,no,no,missing,undirected weighted random graph -495,Gset,G33,2000,2000,8000,1,0,0,0,1.0,1.0,undirected weighted random graph,8000,integer,symmetric,no,no,missing,undirected weighted random graph -496,Gset,G34,2000,2000,8000,1,0,0,0,1.0,1.0,undirected weighted random graph,8000,integer,symmetric,no,no,missing,undirected weighted random graph -497,Gset,G35,2000,2000,23556,1,1,0,0,1.0,1.0,duplicate undirected random graph,23556,binary,symmetric,no,no,missing,duplicate undirected random graph -498,Gset,G36,2000,2000,23532,1,1,0,0,1.0,1.0,duplicate undirected random graph,23532,binary,symmetric,no,no,missing,duplicate undirected random graph -499,Gset,G37,2000,2000,23570,1,1,0,0,1.0,1.0,duplicate undirected random graph,23570,binary,symmetric,no,no,missing,duplicate undirected random graph -500,Gset,G38,2000,2000,23558,1,1,0,0,1.0,1.0,duplicate undirected random graph,23558,binary,symmetric,no,no,missing,duplicate undirected random graph -501,Gset,G39,2000,2000,23556,1,0,0,0,1.0,1.0,undirected weighted random graph,23556,integer,symmetric,no,no,missing,undirected weighted random graph -502,Gset,G4,800,800,38352,1,1,0,0,1.0,1.0,undirected random graph,38352,binary,symmetric,no,no,missing,undirected random graph -503,Gset,G40,2000,2000,23532,1,0,0,0,1.0,1.0,undirected weighted random graph,23532,integer,symmetric,no,no,missing,undirected weighted random graph -504,Gset,G41,2000,2000,23570,1,0,0,0,1.0,1.0,undirected weighted random graph,23570,integer,symmetric,no,no,missing,undirected weighted random graph -505,Gset,G42,2000,2000,23558,1,0,0,0,1.0,1.0,undirected weighted random graph,23558,integer,symmetric,no,no,missing,undirected weighted random graph -506,Gset,G43,1000,1000,19980,1,1,0,0,1.0,1.0,undirected random graph,19980,binary,symmetric,no,no,missing,undirected random graph -507,Gset,G44,1000,1000,19980,1,1,0,0,1.0,1.0,undirected random graph,19980,binary,symmetric,no,no,missing,undirected random graph -508,Gset,G45,1000,1000,19980,1,1,0,0,1.0,1.0,undirected random graph,19980,binary,symmetric,no,no,missing,undirected random graph -509,Gset,G46,1000,1000,19980,1,1,0,0,1.0,1.0,undirected random graph,19980,binary,symmetric,no,no,missing,undirected random graph -510,Gset,G47,1000,1000,19980,1,1,0,0,1.0,1.0,undirected random graph,19980,binary,symmetric,no,no,missing,undirected random graph -511,Gset,G48,3000,3000,12000,1,1,0,0,1.0,1.0,undirected random graph,12000,binary,symmetric,no,no,missing,undirected random graph -512,Gset,G49,3000,3000,12000,1,1,0,0,1.0,1.0,undirected random graph,12000,binary,symmetric,no,no,missing,undirected random graph -513,Gset,G5,800,800,38352,1,1,0,0,1.0,1.0,undirected random graph,38352,binary,symmetric,no,no,missing,undirected random graph -514,Gset,G50,3000,3000,12000,1,1,0,0,1.0,1.0,undirected random graph,12000,binary,symmetric,no,no,missing,undirected random graph -515,Gset,G51,1000,1000,11818,1,1,0,0,1.0,1.0,undirected random graph,11818,binary,symmetric,no,no,missing,undirected random graph -516,Gset,G52,1000,1000,11832,1,1,0,0,1.0,1.0,undirected random graph,11832,binary,symmetric,no,no,missing,undirected random graph -517,Gset,G53,1000,1000,11828,1,1,0,0,1.0,1.0,undirected random graph,11828,binary,symmetric,no,no,missing,undirected random graph -518,Gset,G54,1000,1000,11832,1,1,0,0,1.0,1.0,undirected random graph,11832,binary,symmetric,no,no,missing,undirected random graph -519,Gset,G55,5000,5000,24996,1,1,0,0,1.0,1.0,duplicate undirected random graph,24996,binary,symmetric,no,no,missing,duplicate undirected random graph -520,Gset,G56,5000,5000,24996,1,0,0,0,1.0,1.0,undirected weighted random graph,24996,integer,symmetric,no,no,missing,undirected weighted random graph -521,Gset,G57,5000,5000,20000,1,0,0,0,1.0,1.0,undirected weighted random graph,20000,integer,symmetric,no,no,missing,undirected weighted random graph -522,Gset,G58,5000,5000,59140,1,1,0,0,1.0,1.0,duplicate undirected random graph,59140,binary,symmetric,no,no,missing,duplicate undirected random graph -523,Gset,G59,5000,5000,59140,1,0,0,0,1.0,1.0,undirected weighted random graph,59140,integer,symmetric,no,no,missing,undirected weighted random graph -524,Gset,G6,800,800,38352,1,0,0,0,1.0,1.0,undirected weighted random graph,38352,integer,symmetric,no,no,missing,undirected weighted random graph -525,Gset,G60,7000,7000,34296,1,1,0,0,1.0,1.0,duplicate undirected random graph,34296,binary,symmetric,no,no,missing,duplicate undirected random graph -526,Gset,G61,7000,7000,34296,1,0,0,0,1.0,1.0,undirected weighted random graph,34296,integer,symmetric,no,no,missing,undirected weighted random graph -527,Gset,G62,7000,7000,28000,1,0,0,0,1.0,1.0,undirected weighted random graph,28000,integer,symmetric,no,no,missing,undirected weighted random graph -528,Gset,G63,7000,7000,82918,1,1,0,0,1.0,1.0,duplicate undirected random graph,82918,binary,symmetric,no,no,missing,duplicate undirected random graph -529,Gset,G64,7000,7000,82918,1,0,0,0,1.0,1.0,undirected weighted random graph,82918,integer,symmetric,no,no,missing,undirected weighted random graph -530,Gset,G65,8000,8000,32000,1,0,0,0,1.0,1.0,undirected weighted random graph,32000,integer,symmetric,no,no,missing,undirected weighted random graph -531,Gset,G66,9000,9000,36000,1,0,0,0,1.0,1.0,undirected weighted random graph,36000,integer,symmetric,no,no,missing,undirected weighted random graph -532,Gset,G67,10000,10000,40000,1,0,0,0,1.0,1.0,undirected weighted random graph,40000,integer,symmetric,no,no,missing,undirected weighted random graph -533,Gset,G7,800,800,38352,1,0,0,0,1.0,1.0,undirected weighted random graph,38352,integer,symmetric,no,no,missing,undirected weighted random graph -534,Gset,G8,800,800,38352,1,0,0,0,1.0,1.0,undirected weighted random graph,38352,integer,symmetric,no,no,missing,undirected weighted random graph -535,Gset,G9,800,800,38352,1,0,0,0,1.0,1.0,undirected weighted random graph,38352,integer,symmetric,no,no,missing,undirected weighted random graph -536,Gupta,gupta1,31802,31802,2164210,1,1,0,0,1.0,1.0,optimization problem,2164210,binary,symmetric,no,yes,true,optimization problem -537,Gupta,gupta2,62064,62064,4248286,1,1,0,0,1.0,1.0,optimization problem,4248286,binary,symmetric,no,yes,true,optimization problem -538,Gupta,gupta3,16783,16783,9323427,1,1,0,0,1.0,1.0,optimization problem,9323427,binary,symmetric,no,yes,true,optimization problem -539,Hamm,add20,2395,2395,13151,1,0,0,0,1.0,0.5265898103384158,circuit simulation problem,17319,real,unsymmetric,no,no,true,circuit simulation problem -540,Hamm,add32,4960,4960,19848,1,0,0,0,1.0,0.3052122514777001,circuit simulation problem,23884,real,unsymmetric,no,no,true,circuit simulation problem -541,Hamm,bcircuit,68902,68902,375558,1,0,0,0,1.0,0.9084315976207868,circuit simulation problem,375558,real,unsymmetric,no,no,true,circuit simulation problem -542,Hamm,hcircuit,105676,105676,513072,1,0,0,0,0.9999410961997232,0.1951139297670355,circuit simulation problem,513072,real,unsymmetric,no,no,true,circuit simulation problem -543,Hamm,memplus,17758,17758,99147,1,0,0,0,1.0,0.4962095614886533,circuit simulation problem,126150,real,unsymmetric,no,no,true,circuit simulation problem -544,Hamm,scircuit,170998,170998,958936,1,0,0,0,0.9999467019956295,0.8008355096685118,circuit simulation problem,958936,real,unsymmetric,no,no,true,circuit simulation problem -545,Hollinger,g7jac010,2880,2880,18229,1,0,0,0,0.07102357505222322,0.0,economic problem,19635,real,unsymmetric,no,no,true,economic problem -546,Hollinger,g7jac010sc,2880,2880,18229,1,0,0,0,0.07102357505222322,0.0,economic problem,19635,real,unsymmetric,no,no,true,economic problem -547,Hollinger,g7jac020,5850,5850,42568,1,0,0,0,0.06184526063359838,0.0,economic problem,45465,real,unsymmetric,no,no,true,economic problem -548,Hollinger,g7jac020sc,5850,5850,42568,1,0,0,0,0.06184526063359838,0.0,economic problem,45465,real,unsymmetric,no,no,true,economic problem -549,Hollinger,g7jac040,11790,11790,107383,1,0,0,0,0.04830823961664447,0.0,economic problem,114671,real,unsymmetric,no,no,true,economic problem -550,Hollinger,g7jac040sc,11790,11790,107383,1,0,0,0,0.04830823961664447,0.0,economic problem,114671,real,unsymmetric,no,no,true,economic problem -551,Hollinger,g7jac050sc,14760,14760,145157,1,0,0,0,0.04349647420233192,0.0,economic problem,157990,real,unsymmetric,no,no,true,economic problem -552,Hollinger,g7jac060,17730,17730,183325,1,0,0,0,0.04035864774282543,0.0,economic problem,203316,real,unsymmetric,no,no,true,economic problem -553,Hollinger,g7jac060sc,17730,17730,183325,1,0,0,0,0.04035864774282543,0.0,economic problem,203316,real,unsymmetric,no,no,true,economic problem -554,Hollinger,g7jac080,23670,23670,259648,1,0,0,0,0.03703210435580416,0.0,economic problem,293976,real,unsymmetric,no,no,true,economic problem -555,Hollinger,g7jac080sc,23670,23670,259648,1,0,0,0,0.03703210435580416,0.0,economic problem,293976,real,unsymmetric,no,no,true,economic problem -556,Hollinger,g7jac100,29610,29610,335972,1,0,0,0,0.03529318979455026,0.0,economic problem,384636,real,unsymmetric,no,no,true,economic problem -557,Hollinger,g7jac100sc,29610,29610,335972,1,0,0,0,0.03529318979455026,0.0,economic problem,384636,real,unsymmetric,no,no,true,economic problem -558,Hollinger,g7jac120,35550,35550,412306,1,0,0,0,0.0342243022108217,0.0,economic problem,475296,real,unsymmetric,no,no,true,economic problem -559,Hollinger,g7jac120sc,35550,35550,412306,1,0,0,0,0.0342243022108217,0.0,economic problem,475296,real,unsymmetric,no,no,true,economic problem -560,Hollinger,g7jac140,41490,41490,488633,1,0,0,0,0.03350074170680273,0.0,economic problem,565956,real,unsymmetric,no,no,true,economic problem -561,Hollinger,g7jac140sc,41490,41490,488633,1,0,0,0,0.03350074170680273,0.0,economic problem,565956,real,unsymmetric,no,no,true,economic problem -562,Hollinger,g7jac160,47430,47430,564952,1,0,0,0,0.03297843351620031,0.0,economic problem,656616,real,unsymmetric,no,no,true,economic problem -563,Hollinger,g7jac160sc,47430,47430,564952,1,0,0,0,0.03297843351620031,0.0,economic problem,656616,real,unsymmetric,no,no,true,economic problem -564,Hollinger,g7jac180,53370,53370,641290,1,0,0,0,0.03258366406977314,0.0,economic problem,747276,real,unsymmetric,no,no,true,economic problem -565,Hollinger,g7jac180sc,53370,53370,641290,1,0,0,0,0.03258366406977314,0.0,economic problem,747276,real,unsymmetric,no,no,true,economic problem -566,Hollinger,g7jac200,59310,59310,717620,1,0,0,0,0.03227480202305086,0.0,economic problem,837936,real,unsymmetric,no,no,true,economic problem -567,Hollinger,g7jac200sc,59310,59310,717620,1,0,0,0,0.03227480202305086,0.0,economic problem,837936,real,unsymmetric,no,no,true,economic problem -568,Hollinger,jan99jac020,6774,6774,33744,1,0,0,0,0.003759634062284604,0.0,economic problem,38692,real,unsymmetric,no,no,true,economic problem -569,Hollinger,jan99jac020sc,6774,6774,33744,1,0,0,0,0.003759634062284604,0.0,economic problem,38692,real,unsymmetric,no,no,true,economic problem -570,Hollinger,jan99jac040,13694,13694,72734,1,0,0,0,0.003470816220281136,0.0,economic problem,82842,real,unsymmetric,no,no,true,economic problem -571,Hollinger,jan99jac040sc,13694,13694,72734,1,0,0,0,0.003470816220281136,0.0,economic problem,82842,real,unsymmetric,no,no,true,economic problem -572,Hollinger,jan99jac060,20614,20614,111903,1,0,0,0,0.003378124765408002,0.0,economic problem,127182,real,unsymmetric,no,no,true,economic problem -573,Hollinger,jan99jac060sc,20614,20614,111903,1,0,0,0,0.003378124765408002,0.0,economic problem,127182,real,unsymmetric,no,no,true,economic problem -574,Hollinger,jan99jac080,27534,27534,151063,1,0,0,0,0.003333611134261188,0.0,economic problem,171522,real,unsymmetric,no,no,true,economic problem -575,Hollinger,jan99jac080sc,27534,27534,151063,1,0,0,0,0.003333611134261188,0.0,economic problem,171522,real,unsymmetric,no,no,true,economic problem -576,Hollinger,jan99jac100,34454,34454,190224,1,0,0,0,0.003307461633445052,0.0,economic problem,215862,real,unsymmetric,no,no,true,economic problem -577,Hollinger,jan99jac100sc,34454,34454,190224,1,0,0,0,0.003307461633445052,0.0,economic problem,215862,real,unsymmetric,no,no,true,economic problem -578,Hollinger,jan99jac120,41374,41374,229385,1,0,0,0,0.003290255360374358,0.0,economic problem,260202,real,unsymmetric,no,no,true,economic problem -579,Hollinger,jan99jac120sc,41374,41374,229385,1,0,0,0,0.003290255360374358,0.0,economic problem,260202,real,unsymmetric,no,no,true,economic problem -580,Hollinger,mark3jac020,9129,9129,52883,1,0,0,0,0.07363006419249245,0.01351166293878669,economic problem,56175,real,unsymmetric,no,no,true,economic problem -581,Hollinger,mark3jac020sc,9129,9129,52883,1,0,0,0,0.07363006419249245,0.007349249098461679,economic problem,56175,real,unsymmetric,no,no,true,economic problem -582,Hollinger,mark3jac040,18289,18289,106803,1,0,0,0,0.07382338721543732,0.01389985784236298,economic problem,113435,real,unsymmetric,no,no,true,economic problem -583,Hollinger,mark3jac040sc,18289,18289,106803,1,0,0,0,0.07382338721543732,0.01173364623056615,economic problem,113435,real,unsymmetric,no,no,true,economic problem -584,Hollinger,mark3jac060,27449,27449,160723,1,0,0,0,0.07388687991287715,0.01402730528871371,economic problem,170695,real,unsymmetric,no,no,true,economic problem -585,Hollinger,mark3jac060sc,27449,27449,160723,1,0,0,0,0.07388687991287715,0.0121839735039789,economic problem,170695,real,unsymmetric,no,no,true,economic problem -586,Hollinger,mark3jac080,36609,36609,214643,1,0,0,0,0.07391845139171971,0.01409066941898426,economic problem,227955,real,unsymmetric,no,no,true,economic problem -587,Hollinger,mark3jac080sc,36609,36609,214643,1,0,0,0,0.07391845139171971,0.0125985842018466,economic problem,227955,real,unsymmetric,no,no,true,economic problem -588,Hollinger,mark3jac100,45769,45769,268563,1,0,0,0,0.07393733869014309,0.01412857360574466,economic problem,285215,real,unsymmetric,no,no,true,economic problem -589,Hollinger,mark3jac100sc,45769,45769,268563,1,0,0,0,0.07393733869014309,0.01261351717213372,economic problem,285215,real,unsymmetric,no,no,true,economic problem 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-610,LPnetlib,lp_cre_b,9648,77137,260785,1,0,0,0,0.0,0.0,linear programming problem,260785,real,rectangular,no,no,false,linear programming problem -611,LPnetlib,lp_cre_c,3068,6411,15977,1,0,0,0,0.0,0.0,linear programming problem,15977,real,rectangular,no,no,false,linear programming problem -612,LPnetlib,lp_cre_d,8926,73948,246614,1,0,0,0,0.0,0.0,linear programming problem,246614,real,rectangular,no,no,false,linear programming problem -613,LPnetlib,lp_cycle,1903,3371,21234,1,0,0,0,0.0,0.0,linear programming problem,21234,real,rectangular,no,no,false,linear programming problem -614,LPnetlib,lp_czprob,929,3562,10708,1,0,0,0,0.0,0.0,linear programming problem,10708,real,rectangular,no,no,true,linear programming problem -615,LPnetlib,lp_d2q06c,2171,5831,33081,1,0,0,0,0.0,0.0,linear programming problem,33081,real,rectangular,no,no,true,linear programming problem -616,LPnetlib,lp_d6cube,415,6184,37704,1,0,0,0,0.0,0.0,linear programming problem,37704,integer,rectangular,no,no,false,linear programming problem -617,LPnetlib,lp_degen2,444,757,4201,1,0,0,0,0.0,0.0,linear programming problem,4201,integer,rectangular,no,no,true,linear programming problem -618,LPnetlib,lp_degen3,1503,2604,25432,1,0,0,0,0.0,0.0,linear programming problem,25432,integer,rectangular,no,no,true,linear programming problem -619,LPnetlib,lp_dfl001,6071,12230,35632,1,0,0,0,0.0,0.0,linear programming problem,35632,real,rectangular,no,no,true,linear programming problem -620,LPnetlib,lp_e226,223,472,2768,1,0,0,0,0.0,0.0,linear programming problem,2768,real,rectangular,no,no,true,linear programming problem -621,LPnetlib,lp_etamacro,400,816,2537,1,0,0,0,0.0,0.0,linear programming problem,2537,real,rectangular,no,no,true,linear programming problem -622,LPnetlib,lp_fffff800,524,1028,6401,1,0,0,0,0.0,0.0,linear programming problem,6401,real,rectangular,no,no,true,linear programming problem -623,LPnetlib,lp_finnis,497,1064,2760,1,0,0,0,0.0,0.0,linear programming problem,2760,real,rectangular,no,no,true,linear programming problem -624,LPnetlib,lp_fit1d,24,1049,13427,1,0,0,0,0.0,0.0,linear programming problem,13427,real,rectangular,no,no,true,linear programming problem -625,LPnetlib,lp_fit1p,627,1677,9868,1,0,0,0,0.0,0.0,linear programming problem,9868,real,rectangular,no,no,true,linear programming problem -626,LPnetlib,lp_fit2d,25,10524,129042,1,0,0,0,0.0,0.0,linear programming problem,129042,real,rectangular,no,no,true,linear programming problem -627,LPnetlib,lp_fit2p,3000,13525,50284,1,0,0,0,0.0,0.0,linear programming problem,50284,real,rectangular,no,no,true,linear programming problem -628,LPnetlib,lp_ganges,1309,1706,6937,1,0,0,0,0.0,0.0,linear programming problem,6937,real,rectangular,no,no,true,linear programming problem -629,LPnetlib,lp_gfrd_pnc,616,1160,2445,1,0,0,0,0.0,0.0,linear programming problem,2445,real,rectangular,no,no,true,linear programming problem -630,LPnetlib,lp_greenbea,2392,5598,31070,1,0,0,0,0.0,0.0,linear programming problem,31070,real,rectangular,no,no,false,linear programming problem -631,LPnetlib,lp_greenbeb,2392,5598,31070,1,0,0,0,0.0,0.0,linear programming problem,31070,real,rectangular,no,no,false,linear programming problem -632,LPnetlib,lp_grow15,300,645,5620,1,0,0,0,0.0,0.0,linear programming problem,5620,real,rectangular,no,no,true,linear programming problem -633,LPnetlib,lp_grow22,440,946,8252,1,0,0,0,0.0,0.0,linear programming problem,8252,real,rectangular,no,no,true,linear programming problem -634,LPnetlib,lp_grow7,140,301,2612,1,0,0,0,0.0,0.0,linear programming problem,2612,real,rectangular,no,no,true,linear programming problem -635,LPnetlib,lp_israel,174,316,2443,1,0,0,0,0.0,0.0,linear programming problem,2443,real,rectangular,no,no,true,linear programming problem -636,LPnetlib,lp_kb2,43,68,313,1,0,0,0,0.0,0.0,linear programming problem,313,real,rectangular,no,no,true,linear programming problem -637,LPnetlib,lp_ken_07,2426,3602,8404,1,0,0,0,0.0,0.0,linear programming problem,8404,integer,rectangular,no,no,true,linear programming problem -638,LPnetlib,lp_ken_11,14694,21349,49058,1,0,0,0,0.0,0.0,linear programming problem,49058,integer,rectangular,no,no,true,linear programming problem -639,LPnetlib,lp_ken_13,28632,42659,97246,1,0,0,0,0.0,0.0,linear programming problem,97246,integer,rectangular,no,no,true,linear programming problem -640,LPnetlib,lp_ken_18,105127,154699,358171,1,0,0,0,0.0,0.0,linear programming problem,358171,integer,rectangular,no,no,true,linear programming problem -641,LPnetlib,lp_lotfi,153,366,1136,1,0,0,0,0.0,0.0,linear programming problem,1136,real,rectangular,no,no,true,linear programming problem -642,LPnetlib,lp_maros,846,1966,10137,1,0,0,0,0.0,0.0,linear programming problem,10137,real,rectangular,no,no,true,linear programming problem -643,LPnetlib,lp_maros_r7,3136,9408,144848,1,0,0,0,0.0,0.0,linear programming problem,144848,real,rectangular,no,no,true,linear programming problem -644,LPnetlib,lp_modszk1,687,1620,3168,1,0,0,0,0.0,0.0,linear programming problem,3168,real,rectangular,no,no,false,linear programming problem -645,LPnetlib,lp_osa_07,1118,25067,144812,1,0,0,0,0.0,0.0,linear programming problem,144812,real,rectangular,no,no,true,linear programming problem -646,LPnetlib,lp_osa_14,2337,54797,317097,1,0,0,0,0.0,0.0,linear programming problem,317097,real,rectangular,no,no,true,linear programming problem -647,LPnetlib,lp_osa_30,4350,104374,604488,1,0,0,0,0.0,0.0,linear programming problem,604488,real,rectangular,no,no,true,linear programming problem -648,LPnetlib,lp_osa_60,10280,243246,1408073,1,0,0,0,0.0,0.0,linear programming problem,1408073,real,rectangular,no,no,true,linear programming problem -649,LPnetlib,lp_pds_02,2953,7716,16571,1,0,0,0,0.0,0.0,linear programming problem,16571,integer,rectangular,no,no,true,linear programming problem -650,LPnetlib,lp_pds_06,9881,29351,63220,1,0,0,0,0.0,0.0,linear programming problem,63220,integer,rectangular,no,no,true,linear programming problem -651,LPnetlib,lp_pds_10,16558,49932,107605,1,0,0,0,0.0,0.0,linear programming problem,107605,integer,rectangular,no,no,true,linear programming problem -652,LPnetlib,lp_pds_20,33874,108175,232647,1,0,0,0,0.0,0.0,linear programming problem,232647,integer,rectangular,no,no,false,linear programming problem -653,LPnetlib,lp_perold,625,1506,6148,1,0,0,0,0.0,0.0,linear programming problem,6148,real,rectangular,no,no,true,linear programming problem -654,LPnetlib,lp_pilot,1441,4860,44375,1,0,0,0,0.0,0.0,linear programming problem,44375,real,rectangular,no,no,true,linear programming problem -655,LPnetlib,lp_pilot4,410,1123,5264,1,0,0,0,0.0,0.0,linear programming problem,5264,real,rectangular,no,no,true,linear programming problem -656,LPnetlib,lp_pilot87,2030,6680,74949,1,0,0,0,0.0,0.0,linear programming problem,74949,real,rectangular,no,no,true,linear programming problem -657,LPnetlib,lp_pilot_ja,940,2267,14977,1,0,0,0,0.0,0.0,linear programming problem,14977,real,rectangular,no,no,true,linear programming problem -658,LPnetlib,lp_pilot_we,722,2928,9265,1,0,0,0,0.0,0.0,linear programming problem,9265,real,rectangular,no,no,true,linear programming problem -659,LPnetlib,lp_pilotnov,975,2446,13331,1,0,0,0,0.0,0.0,linear programming problem,13331,real,rectangular,no,no,true,linear programming problem -660,LPnetlib,lp_qap12,3192,8856,38304,1,0,0,0,0.0,0.0,linear programming problem,38304,integer,rectangular,no,no,true,linear programming problem -661,LPnetlib,lp_qap15,6330,22275,94950,1,0,0,0,0.0,0.0,linear programming problem,94950,integer,rectangular,no,no,true,linear programming problem -662,LPnetlib,lp_qap8,912,1632,7296,1,0,0,0,0.0,0.0,linear programming problem,7296,integer,rectangular,no,no,true,linear programming problem -663,LPnetlib,lp_recipe,91,204,687,1,0,0,0,0.0,0.0,linear programming problem,687,real,rectangular,no,no,true,linear programming problem -664,LPnetlib,lp_sc105,105,163,340,1,0,0,0,0.0,0.0,linear programming problem,340,real,rectangular,no,no,true,linear programming problem -665,LPnetlib,lp_sc205,205,317,665,1,0,0,0,0.0,0.0,linear programming problem,665,real,rectangular,no,no,true,linear programming problem -666,LPnetlib,lp_sc50a,50,78,160,1,0,0,0,0.0,0.0,linear programming problem,160,real,rectangular,no,no,true,linear programming problem -667,LPnetlib,lp_sc50b,50,78,148,1,0,0,0,0.0,0.0,linear programming problem,148,real,rectangular,no,no,true,linear programming problem -668,LPnetlib,lp_scagr25,471,671,1725,1,0,0,0,0.0,0.0,linear programming problem,1725,real,rectangular,no,no,true,linear programming problem -669,LPnetlib,lp_scagr7,129,185,465,1,0,0,0,0.0,0.0,linear programming problem,465,real,rectangular,no,no,true,linear programming problem -670,LPnetlib,lp_scfxm1,330,600,2732,1,0,0,0,0.0,0.0,linear programming problem,2732,real,rectangular,no,no,true,linear programming problem -671,LPnetlib,lp_scfxm2,660,1200,5469,1,0,0,0,0.0,0.0,linear programming problem,5469,real,rectangular,no,no,true,linear programming problem -672,LPnetlib,lp_scfxm3,990,1800,8206,1,0,0,0,0.0,0.0,linear programming problem,8206,real,rectangular,no,no,true,linear programming problem -673,LPnetlib,lp_scorpion,388,466,1534,1,0,0,0,0.0,0.0,linear programming problem,1534,real,rectangular,no,no,true,linear programming problem -674,LPnetlib,lp_scrs8,490,1275,3288,1,0,0,0,0.0,0.0,linear programming problem,3288,real,rectangular,no,no,true,linear programming problem -675,LPnetlib,lp_scsd1,77,760,2388,1,0,0,0,0.0,0.0,linear programming problem,2388,real,rectangular,no,no,true,linear programming problem -676,LPnetlib,lp_scsd6,147,1350,4316,1,0,0,0,0.0,0.0,linear programming problem,4316,real,rectangular,no,no,true,linear programming problem -677,LPnetlib,lp_scsd8,397,2750,8584,1,0,0,0,0.0,0.0,linear programming problem,8584,real,rectangular,no,no,true,linear programming problem -678,LPnetlib,lp_sctap1,300,660,1872,1,0,0,0,0.0,0.0,linear programming problem,1872,integer,rectangular,no,no,true,linear programming problem -679,LPnetlib,lp_sctap2,1090,2500,7334,1,0,0,0,0.0,0.0,linear programming problem,7334,integer,rectangular,no,no,true,linear programming problem -680,LPnetlib,lp_sctap3,1480,3340,9734,1,0,0,0,0.0,0.0,linear programming problem,9734,integer,rectangular,no,no,true,linear programming problem -681,LPnetlib,lp_share1b,117,253,1179,1,0,0,0,0.0,0.0,linear programming problem,1179,real,rectangular,no,no,true,linear programming problem -682,LPnetlib,lp_share2b,96,162,777,1,0,0,0,0.0,0.0,linear programming problem,777,real,rectangular,no,no,true,linear programming problem -683,LPnetlib,lp_shell,536,1777,3558,1,0,0,0,0.0,0.0,linear programming problem,3558,integer,rectangular,no,no,true,linear programming problem -684,LPnetlib,lp_ship04l,402,2166,6380,1,0,0,0,0.0,0.0,linear programming problem,6380,real,rectangular,no,no,false,linear programming problem -685,LPnetlib,lp_ship04s,402,1506,4400,1,0,0,0,0.0,0.0,linear programming problem,4400,real,rectangular,no,no,false,linear programming problem -686,LPnetlib,lp_ship08l,778,4363,12882,1,0,0,0,0.0,0.0,linear programming problem,12882,real,rectangular,no,no,false,linear programming problem -687,LPnetlib,lp_ship08s,778,2467,7194,1,0,0,0,0.0,0.0,linear programming problem,7194,real,rectangular,no,no,false,linear programming problem -688,LPnetlib,lp_ship12l,1151,5533,16276,1,0,0,0,0.0,0.0,linear programming problem,16276,real,rectangular,no,no,false,linear programming problem -689,LPnetlib,lp_ship12s,1151,2869,8284,1,0,0,0,0.0,0.0,linear programming problem,8284,real,rectangular,no,no,false,linear programming problem -690,LPnetlib,lp_sierra,1227,2735,8001,1,0,0,0,0.0,0.0,linear programming problem,8001,integer,rectangular,no,no,true,linear programming problem -691,LPnetlib,lp_stair,356,614,4003,1,0,0,0,0.0,0.0,linear programming problem,4003,real,rectangular,no,no,true,linear programming problem -692,LPnetlib,lp_standata,359,1274,3230,1,0,0,0,0.0,0.0,linear programming problem,3230,real,rectangular,no,no,true,linear programming problem -693,LPnetlib,lp_standgub,361,1383,3338,1,0,0,0,0.0,0.0,linear programming problem,3339,real,rectangular,no,no,false,linear programming problem -694,LPnetlib,lp_standmps,467,1274,3878,1,0,0,0,0.0,0.0,linear programming problem,3878,real,rectangular,no,no,true,linear programming problem -695,LPnetlib,lp_stocfor1,117,165,501,1,0,0,0,0.0,0.0,linear programming problem,501,real,rectangular,no,no,true,linear programming problem -696,LPnetlib,lp_stocfor2,2157,3045,9357,1,0,0,0,0.0,0.0,linear programming problem,9357,real,rectangular,no,no,true,linear programming problem -697,LPnetlib,lp_stocfor3,16675,23541,72721,1,0,0,0,0.0,0.0,linear programming problem,76473,real,rectangular,no,no,true,linear programming problem -698,LPnetlib,lp_truss,1000,8806,27836,1,0,0,0,0.0,0.0,linear programming problem,27836,real,rectangular,no,no,true,linear programming problem -699,LPnetlib,lp_tuff,333,628,4561,1,0,0,0,0.0,0.0,linear programming problem,4561,real,rectangular,no,no,false,linear programming problem -700,LPnetlib,lp_vtp_base,198,346,1051,1,0,0,0,0.0,0.0,linear programming problem,1051,real,rectangular,no,no,true,linear programming problem -701,LPnetlib,lp_wood1p,244,2595,70216,1,0,0,0,0.0,0.0,linear programming problem,70216,real,rectangular,no,no,true,linear programming problem -702,LPnetlib,lp_woodw,1098,8418,37487,1,0,0,0,0.0,0.0,linear programming problem,37487,real,rectangular,no,no,true,linear programming problem -703,LPnetlib,lpi_bgdbg1,348,629,1662,1,0,0,0,0.0,0.0,linear programming problem,1662,real,rectangular,no,no,true,linear programming problem -704,LPnetlib,lpi_bgetam,400,816,2537,1,0,0,0,0.0,0.0,linear programming problem,2537,real,rectangular,no,no,true,linear programming problem -705,LPnetlib,lpi_bgindy,2671,10880,66266,1,0,0,0,0.0,0.0,linear programming problem,66266,real,rectangular,no,no,true,linear programming problem -706,LPnetlib,lpi_bgprtr,20,40,70,1,0,0,0,0.0,0.0,linear programming problem,70,integer,rectangular,no,no,true,linear programming problem -707,LPnetlib,lpi_box1,231,261,651,1,0,0,0,0.0,0.0,linear programming problem,651,integer,rectangular,no,no,true,linear programming problem -708,LPnetlib,lpi_ceria3d,3576,4400,21178,1,0,0,0,0.0,0.0,linear programming problem,21178,integer,rectangular,no,no,true,linear programming problem -709,LPnetlib,lpi_chemcom,288,744,1590,1,0,0,0,0.0,0.0,linear programming problem,1590,real,rectangular,no,no,true,linear programming problem -710,LPnetlib,lpi_cplex1,3005,5224,10947,1,0,0,0,0.0,0.0,linear programming problem,10947,real,rectangular,no,no,true,linear programming problem -711,LPnetlib,lpi_cplex2,224,378,1215,1,0,0,0,0.0,0.0,linear programming problem,1215,real,rectangular,no,no,true,linear programming problem -712,LPnetlib,lpi_ex72a,197,215,467,1,0,0,0,0.0,0.0,linear programming problem,467,integer,rectangular,no,no,true,linear programming problem -713,LPnetlib,lpi_ex73a,193,211,457,1,0,0,0,0.0,0.0,linear programming problem,457,integer,rectangular,no,no,true,linear programming problem -714,LPnetlib,lpi_forest6,66,131,246,1,0,0,0,0.0,0.0,linear programming problem,246,real,rectangular,no,no,true,linear programming problem -715,LPnetlib,lpi_galenet,8,14,22,1,0,0,0,0.0,0.0,linear programming problem,22,integer,rectangular,no,no,true,linear programming problem -716,LPnetlib,lpi_gosh,3792,13455,99953,1,0,0,0,0.0,0.0,linear programming problem,99953,real,rectangular,no,no,false,linear programming problem -717,LPnetlib,lpi_gran,2658,2525,20111,1,0,0,0,0.0,0.0,linear programming problem,20111,real,rectangular,no,no,false,linear programming problem -718,LPnetlib,lpi_greenbea,2393,5596,31074,1,0,0,0,0.0,0.0,linear programming problem,31074,real,rectangular,no,no,false,linear programming problem -719,LPnetlib,lpi_itest2,9,13,26,1,0,0,0,0.0,0.0,linear programming problem,26,real,rectangular,no,no,true,linear programming problem -720,LPnetlib,lpi_itest6,11,17,29,1,0,0,0,0.0,0.0,linear programming problem,29,real,rectangular,no,no,true,linear programming problem -721,LPnetlib,lpi_klein1,54,108,750,1,0,0,0,0.0,0.0,linear programming problem,750,integer,rectangular,no,no,true,linear programming problem -722,LPnetlib,lpi_klein2,477,531,5062,1,0,0,0,0.0,0.0,linear programming problem,5062,integer,rectangular,no,no,true,linear programming problem -723,LPnetlib,lpi_klein3,994,1082,13101,1,0,0,0,0.0,0.0,linear programming problem,13101,integer,rectangular,no,no,true,linear programming problem -724,LPnetlib,lpi_mondou2,312,604,1208,1,0,0,0,0.0,0.0,linear programming problem,1208,integer,rectangular,no,no,true,linear programming problem -725,LPnetlib,lpi_pang,361,741,2933,1,0,0,0,0.0,0.0,linear programming problem,2933,real,rectangular,no,no,true,linear programming problem -726,LPnetlib,lpi_pilot4i,410,1123,5264,1,0,0,0,0.0,0.0,linear programming problem,5264,real,rectangular,no,no,true,linear programming problem -727,LPnetlib,lpi_qual,323,464,1646,1,0,0,0,0.0,0.0,linear programming problem,1646,real,rectangular,no,no,true,linear programming problem -728,LPnetlib,lpi_reactor,318,808,2591,1,0,0,0,0.0,0.0,linear programming problem,2591,real,rectangular,no,no,true,linear programming problem -729,LPnetlib,lpi_refinery,323,464,1626,1,0,0,0,0.0,0.0,linear programming problem,1626,real,rectangular,no,no,true,linear programming problem -730,LPnetlib,lpi_vol1,323,464,1646,1,0,0,0,0.0,0.0,linear programming problem,1646,real,rectangular,no,no,true,linear programming problem -731,LPnetlib,lpi_woodinfe,35,89,140,1,0,0,0,0.0,0.0,linear programming problem,140,integer,rectangular,no,no,true,linear programming problem -732,Li,li,22695,22695,1215181,1,0,1,0,1.0,0.8685670104303111,electromagnetics problem,1350309,real,unsymmetric,no,no,true,electromagnetics problem -733,Li,pli,22695,22695,1350309,1,1,1,0,1.0,1.0,duplicate electromagnetics problem,1350309,binary,symmetric,no,yes,true,duplicate electromagnetics problem -734,Mallya,lhr01,1477,1477,18427,1,0,0,0,0.007324823611784348,0.0,chemical process simulation problem,18592,real,unsymmetric,no,no,true,chemical process simulation problem -735,Mallya,lhr02,2954,2954,36875,1,0,0,0,0.007320486597050275,0.0,chemical process simulation problem,37206,real,unsymmetric,no,no,true,chemical process simulation problem -736,Mallya,lhr04,4101,4101,81057,1,0,0,0,0.01515885315769082,0.0,chemical process simulation problem,82682,real,unsymmetric,no,no,true,chemical process simulation problem -737,Mallya,lhr04c,4101,4101,82682,1,0,0,0,0.01515885315769082,0.0,chemical process simulation problem,82682,real,unsymmetric,no,no,true,chemical process simulation problem -738,Mallya,lhr07,7337,7337,154660,1,0,0,0,0.0173680609288971,0.0,chemical process simulation problem,156508,real,unsymmetric,no,no,true,chemical process simulation problem -739,Mallya,lhr07c,7337,7337,156508,1,0,0,0,0.0173680609288971,0.0,chemical process simulation problem,156508,real,unsymmetric,no,no,true,chemical process simulation problem -740,Mallya,lhr10,10672,10672,228395,1,0,0,0,0.008791586915847653,1.752395305332977e-05,chemical process simulation problem,232633,real,unsymmetric,no,no,true,chemical process simulation problem -741,Mallya,lhr10c,10672,10672,232633,1,0,0,0,0.008791586915847653,1.720467106819501e-05,chemical process simulation problem,232633,real,unsymmetric,no,no,true,chemical process simulation problem -742,Mallya,lhr11,10964,10964,231806,1,0,0,0,0.00820058037510379,3.452591601570929e-05,chemical process simulation problem,233741,real,unsymmetric,no,no,true,chemical process simulation problem -743,Mallya,lhr11c,10964,10964,233741,1,0,0,0,0.00820058037510379,4.280052387841227e-05,chemical process simulation problem,233741,real,unsymmetric,no,no,true,chemical process simulation problem -744,Mallya,lhr14,14270,14270,305750,1,0,0,0,0.006622946983926842,0.0,chemical process simulation problem,307858,real,unsymmetric,no,no,true,chemical process simulation problem -745,Mallya,lhr14c,14270,14270,307858,1,0,0,0,0.006622946983926842,0.0,chemical process simulation problem,307858,real,unsymmetric,no,no,true,chemical process simulation problem -746,Mallya,lhr17,17576,17576,379761,1,0,0,0,0.001513390098553639,0.0001527473453564807,chemical process simulation problem,381975,real,unsymmetric,no,no,true,chemical process simulation problem -747,Mallya,lhr17c,17576,17576,381975,1,0,0,0,0.001513390098553639,5.236643939631969e-06,chemical process simulation problem,381975,real,unsymmetric,no,no,true,chemical process simulation problem -748,Mallya,lhr34,35152,35152,746972,1,0,0,0,0.00151326330781556,0.0001553140154832006,chemical process simulation problem,764014,real,unsymmetric,no,no,true,chemical process simulation problem -749,Mallya,lhr34c,35152,35152,764014,1,0,0,0,0.00151326330781556,5.236205217354879e-06,chemical process simulation problem,764014,real,unsymmetric,no,no,true,chemical process simulation problem -750,Mallya,lhr71,70304,70304,1494006,1,0,0,0,0.001513199920413014,0.0001553075692357127,chemical process simulation problem,1528092,real,unsymmetric,no,no,true,chemical process simulation problem -751,Mallya,lhr71c,70304,70304,1528092,1,0,0,0,0.001513199920413014,5.235985883782057e-06,chemical process simulation problem,1528092,real,unsymmetric,no,no,true,chemical process simulation problem -752,Mulvey,finan512,74752,74752,596992,1,0,0,1,1.0,1.0,economic problem,596992,real,symmetric,yes,yes,true,economic problem -753,Mulvey,pfinan512,74752,74752,596992,1,1,0,0,1.0,1.0,duplicate economic problem,596992,binary,symmetric,no,yes,true,duplicate economic problem -754,Nasa,barth,6691,6691,26439,1,1,1,0,0.0,0.0,duplicate structural problem,26439,binary,unsymmetric,no,no,true,duplicate structural problem -755,Nasa,barth4,6019,6019,23492,1,1,1,0,0.0,0.0,duplicate structural problem,23492,binary,unsymmetric,no,no,true,duplicate structural problem -756,Nasa,barth5,15606,15606,61484,1,1,1,0,0.0,0.0,duplicate structural problem,61484,binary,unsymmetric,no,no,true,duplicate structural problem -757,Nasa,nasa1824,1824,1824,39208,1,0,1,1,1.0,1.0,structural problem,39208,real,symmetric,yes,yes,true,structural problem -758,Nasa,nasa2146,2146,2146,72250,1,0,1,1,1.0,1.0,structural problem,72250,real,symmetric,yes,yes,true,structural problem -759,Nasa,nasa2910,2910,2910,174296,1,0,1,1,1.0,1.0,structural problem,174296,real,symmetric,yes,yes,true,structural problem -760,Nasa,nasa4704,4704,4704,104756,1,0,1,1,1.0,1.0,structural problem,104756,real,symmetric,yes,yes,true,structural problem -761,Nasa,nasasrb,54870,54870,2677324,1,0,1,1,1.0,1.0,structural problem,2677324,real,symmetric,yes,yes,true,structural problem -762,Nasa,pwt,36519,36519,326107,1,1,1,0,1.0,1.0,duplicate structural problem,326107,binary,symmetric,no,yes,true,duplicate structural problem -763,Nasa,shuttle_eddy,10429,10429,103599,1,1,1,0,1.0,1.0,duplicate structural problem,103599,binary,symmetric,no,yes,true,duplicate structural problem -764,Nasa,skirt,12598,12598,196520,1,1,1,0,1.0,1.0,duplicate structural problem,196520,binary,symmetric,no,yes,true,duplicate structural problem -765,Nemeth,nemeth01,9506,9506,725054,1,0,0,0,1.0,1.0,theoretical/quantum chemistry problem sequence,725054,real,symmetric,no,no,true,theoretical/quantum chemistry problem sequence -766,Nemeth,nemeth02,9506,9506,394808,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,394808,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -767,Nemeth,nemeth03,9506,9506,394808,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,394808,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -768,Nemeth,nemeth04,9506,9506,394808,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,394808,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -769,Nemeth,nemeth05,9506,9506,394808,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,394808,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -770,Nemeth,nemeth06,9506,9506,394808,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,394808,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -771,Nemeth,nemeth07,9506,9506,394812,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,394812,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -772,Nemeth,nemeth08,9506,9506,394816,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,394816,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -773,Nemeth,nemeth09,9506,9506,395506,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,395506,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -774,Nemeth,nemeth10,9506,9506,401448,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,401448,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -775,Nemeth,nemeth11,9506,9506,408264,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,408264,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -776,Nemeth,nemeth12,9506,9506,446818,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,446818,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -777,Nemeth,nemeth13,9506,9506,474472,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,474472,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -778,Nemeth,nemeth14,9506,9506,496144,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,496144,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -779,Nemeth,nemeth15,9506,9506,539802,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,539802,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -780,Nemeth,nemeth16,9506,9506,587012,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,587012,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -781,Nemeth,nemeth17,9506,9506,629620,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,629620,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -782,Nemeth,nemeth18,9506,9506,695234,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,695234,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -783,Nemeth,nemeth19,9506,9506,818302,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,818302,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -784,Nemeth,nemeth20,9506,9506,971870,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,971870,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -785,Nemeth,nemeth21,9506,9506,1173746,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,1173746,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -786,Nemeth,nemeth22,9506,9506,1358832,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,1358832,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -787,Nemeth,nemeth23,9506,9506,1506810,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,1506810,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -788,Nemeth,nemeth24,9506,9506,1506550,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,1506550,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -789,Nemeth,nemeth25,9506,9506,1511758,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,1511758,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -790,Nemeth,nemeth26,9506,9506,1511760,1,0,0,0,1.0,1.0,subsequent theoretical/quantum chemistry problem,1511760,real,symmetric,no,no,true,subsequent theoretical/quantum chemistry problem -791,Okunbor,aft01,8205,8205,125567,1,0,1,1,1.0,1.0,acoustics problem,125567,real,symmetric,yes,yes,true,acoustics problem -792,Okunbor,aft02,8184,8184,127762,0,0,1,0,1.0,0.9963538443526401,acoustics problem,127762,complex,unsymmetric,no,no,true,acoustics problem -793,Qaplib,lp_nug05,210,225,1050,1,0,0,0,0.0,0.0,linear programming problem,1050,integer,rectangular,no,no,true,linear programming problem -794,Qaplib,lp_nug06,372,486,2232,1,0,0,0,0.0,0.0,linear programming problem,2232,integer,rectangular,no,no,true,linear programming problem -795,Qaplib,lp_nug07,602,931,4214,1,0,0,0,0.0,0.0,linear programming problem,4214,integer,rectangular,no,no,true,linear programming problem -796,Qaplib,lp_nug08,912,1632,7296,1,0,0,0,0.0,0.0,linear programming problem,7296,integer,rectangular,no,no,true,linear programming problem -797,Qaplib,lp_nug12,3192,8856,38304,1,0,0,0,0.0,0.0,linear programming problem,38304,integer,rectangular,no,no,true,linear programming problem -798,Qaplib,lp_nug15,6330,22275,94950,1,0,0,0,0.0,0.0,linear programming problem,94950,integer,rectangular,no,no,true,linear programming problem -799,Qaplib,lp_nug20,15240,72600,304800,1,0,0,0,0.0,0.0,linear programming problem,304800,integer,rectangular,no,no,true,linear programming problem -800,Qaplib,lp_nug30,52260,379350,1567800,1,0,0,0,0.0,0.0,linear programming problem,1567800,integer,rectangular,no,no,true,linear programming problem -801,Ronis,xenon1,48600,48600,1181120,1,0,1,0,1.0,0.775813230671423,materials problem,1181120,real,unsymmetric,no,no,true,materials problem -802,Ronis,xenon2,157464,157464,3866688,1,0,1,0,1.0,0.7756792256277864,materials problem,3866688,real,unsymmetric,no,no,true,materials problem -803,Rothberg,3dtube,45330,45330,3213618,1,1,1,0,1.0,1.0,computational fluid dynamics problem,3213618,binary,symmetric,no,yes,true,computational fluid dynamics problem -804,Rothberg,cfd1,70656,70656,1825580,1,0,1,1,1.0,1.0,computational fluid dynamics problem,1828364,real,symmetric,yes,yes,true,computational fluid dynamics problem -805,Rothberg,cfd2,123440,123440,3085406,1,0,1,1,1.0,1.0,computational fluid dynamics problem,3087898,real,symmetric,yes,yes,true,computational fluid dynamics problem -806,Rothberg,gearbox,153746,153746,9080404,1,1,1,0,1.0,1.0,structural problem,9080404,binary,symmetric,no,yes,true,structural problem -807,Rothberg,struct3,53570,53570,1173694,1,1,1,0,1.0,1.0,structural problem,1173694,binary,symmetric,no,yes,true,structural problem -808,Rothberg,struct4,4350,4350,237798,1,1,1,0,1.0,1.0,structural problem,237798,binary,symmetric,no,yes,true,structural problem -809,Shyy,shyy161,76480,76480,329762,1,0,1,0,0.7263358696509635,0.1824427535660244,computational fluid dynamics problem,329762,real,unsymmetric,no,no,true,computational fluid dynamics problem -810,Shyy,shyy41,4720,4720,20042,1,0,1,0,0.7234369459874719,0.1843753693416854,computational fluid dynamics problem,20042,real,unsymmetric,no,no,true,computational fluid dynamics problem -811,Simon,appu,14000,14000,1853104,1,0,0,0,1.0,0.9429754924136972,directed weighted random graph,1853104,real,unsymmetric,no,no,missing,directed weighted random graph -812,Simon,bbmat,38744,38744,1771722,1,0,1,0,0.5295358625441292,0.0005851199495896658,computational fluid dynamics problem,1771722,real,unsymmetric,no,no,true,computational fluid dynamics problem -813,Simon,olafu,16146,16146,1015156,1,0,1,1,1.0,1.0,structural problem,1015156,real,symmetric,yes,yes,true,structural problem -814,Simon,raefsky1,3242,3242,293409,1,0,1,0,1.0,0.09040311269027836,computational fluid dynamics problem sequence,294276,real,unsymmetric,no,no,true,computational fluid dynamics problem sequence -815,Simon,raefsky2,3242,3242,293551,1,0,1,0,1.0,0.09753745147411895,subsequent computational fluid dynamics problem,294276,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -816,Simon,raefsky3,21200,21200,1488768,1,0,1,0,1.0,0.4763745189320018,computational fluid dynamics problem,1488768,real,unsymmetric,no,no,true,computational fluid dynamics problem -817,Simon,raefsky4,19779,19779,1316789,1,0,1,1,1.0,1.0,structural problem,1328611,real,symmetric,yes,yes,true,structural problem -818,Simon,raefsky5,6316,6316,167178,1,0,1,0,0.0,0.0,structural problem,168658,real,unsymmetric,no,no,true,structural problem -819,Simon,raefsky6,3402,3402,130371,1,0,1,0,0.0,0.0,structural problem,137845,real,unsymmetric,no,no,true,structural problem -820,Simon,venkat01,62424,62424,1717792,1,0,1,0,1.0,0.05542936676316082,computational fluid dynamics problem sequence,1717792,real,unsymmetric,no,no,true,computational fluid dynamics problem sequence -821,Simon,venkat25,62424,62424,1717763,1,0,1,0,1.0,0.05539650790563141,subsequent computational fluid dynamics problem,1717792,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -822,Simon,venkat50,62424,62424,1717777,1,0,1,0,1.0,0.05539603939461855,subsequent computational fluid dynamics problem,1717792,real,unsymmetric,no,no,true,subsequent computational fluid dynamics problem -823,TOKAMAK,utm1700b,1700,1700,21509,1,0,1,0,0.5232974910394266,0.0,electromagnetics problem,21509,real,unsymmetric,no,no,true,electromagnetics problem -824,TOKAMAK,utm300,300,300,3155,1,0,1,0,0.4651488616462347,0.0,electromagnetics problem,3155,real,unsymmetric,no,no,true,electromagnetics problem -825,TOKAMAK,utm3060,3060,3060,42211,1,0,1,0,0.524686470332814,0.0,electromagnetics problem,42211,real,unsymmetric,no,no,true,electromagnetics problem -826,TOKAMAK,utm5940,5940,5940,83842,1,0,1,0,0.5290493183743677,0.0,electromagnetics problem,83842,real,unsymmetric,no,no,true,electromagnetics problem -827,Vavasis,av41092,41092,41092,1683902,1,0,1,0,0.0009359570219938022,0.0,2D/3D problem,1683902,real,unsymmetric,no,no,true,2D/3D problem -828,Wang,swang1,3169,3169,20841,1,0,1,0,1.0,0.0,semiconductor device problem sequence,20841,real,unsymmetric,no,no,true,semiconductor device problem sequence -829,Wang,swang2,3169,3169,20841,1,0,1,0,1.0,0.0,subsequent semiconductor device problem,20841,real,unsymmetric,no,no,true,subsequent semiconductor device problem -830,Wang,wang1,2903,2903,19093,1,0,1,0,1.0,0.808153180975911,semiconductor device problem sequence,19093,real,unsymmetric,no,no,true,semiconductor device problem sequence -831,Wang,wang2,2903,2903,19093,1,0,1,0,1.0,0.808153180975911,subsequent semiconductor device problem,19093,real,unsymmetric,no,no,true,subsequent semiconductor device problem -832,Wang,wang3,26064,26064,177168,1,0,1,0,1.0,0.9780019059720457,semiconductor device problem,177168,real,unsymmetric,no,no,true,semiconductor device problem -833,Wang,wang4,26068,26068,177196,1,0,1,0,1.0,0.04647715843523371,semiconductor device problem,177196,real,unsymmetric,no,no,true,semiconductor device problem -834,Zhao,Zhao1,33861,33861,166453,1,0,1,0,0.9224990949680222,0.0,electromagnetics problem,166453,real,unsymmetric,no,no,true,electromagnetics problem -835,Zhao,Zhao2,33861,33861,166453,1,0,1,0,0.9224990949680222,0.0,electromagnetics problem,166453,real,unsymmetric,no,no,true,electromagnetics problem -836,Zitney,extr1,2837,2837,10967,1,0,0,0,0.004209049456331112,0.000364797081623347,chemical process simulation problem,11407,real,unsymmetric,no,no,true,chemical process simulation problem -837,Zitney,hydr1,5308,5308,22680,1,0,0,0,0.004126315789473685,0.0006173383896287151,chemical process simulation problem,23752,real,unsymmetric,no,no,true,chemical process simulation problem -838,Zitney,radfr1,1048,1048,13299,1,0,0,0,0.05374309287714783,0.0,chemical process simulation problem,13299,real,unsymmetric,no,no,true,chemical process simulation problem -839,Zitney,rdist1,4134,4134,94408,1,0,0,0,0.05878038669628716,0.0,chemical process simulation problem,94408,real,unsymmetric,no,no,true,chemical process simulation problem -840,Zitney,rdist2,3198,3198,56834,1,0,0,0,0.04562603575332323,0.0,chemical process simulation problem,56934,real,unsymmetric,no,no,true,chemical process simulation problem -841,Zitney,rdist3a,2398,2398,61896,1,0,0,0,0.1404154186743065,0.0,chemical process simulation problem,61896,real,unsymmetric,no,no,true,chemical process simulation problem -842,Cunningham,k3plates,11107,11107,378927,1,0,1,0,1.0,0.999260507857104,acoustics problem,378927,real,unsymmetric,no,no,true,acoustics problem -843,Cunningham,m3plates,11107,11107,6639,1,0,1,0,1.0,1.0,acoustics problem,6639,real,symmetric,no,no,false,acoustics problem -844,Cunningham,qa8fk,66127,66127,1660579,1,0,1,0,1.0,1.0,acoustics problem,1660579,real,symmetric,no,yes,true,acoustics problem -845,Cunningham,qa8fm,66127,66127,1660579,1,0,1,1,1.0,1.0,acoustics problem,1660579,real,symmetric,yes,yes,true,acoustics problem -846,Boeing,bcsstk39,46772,46772,2060662,1,0,1,0,1.0,1.0,structural problem,2089294,real,symmetric,no,yes,true,structural problem -847,Chen,pkustk01,22044,22044,979380,1,1,1,0,1.0,1.0,structural problem,979380,binary,symmetric,no,yes,true,structural problem -848,Chen,pkustk02,10800,10800,810000,1,1,1,0,1.0,1.0,structural problem,810000,binary,symmetric,no,yes,true,structural problem -849,Chen,pkustk03,63336,63336,3130416,1,1,1,0,1.0,1.0,structural problem,3130416,binary,symmetric,no,yes,true,structural problem -850,Chen,pkustk04,55590,55590,4218660,1,1,1,0,1.0,1.0,structural problem,4218660,binary,symmetric,no,yes,true,structural problem -851,Chen,pkustk05,37164,37164,2205144,1,1,1,0,1.0,1.0,structural problem,2205144,binary,symmetric,no,yes,true,structural problem -852,Chen,pkustk06,43164,43164,2571768,1,1,1,0,1.0,1.0,structural problem,2571768,binary,symmetric,no,yes,true,structural problem -853,Chen,pkustk07,16860,16860,2418804,1,1,1,0,1.0,1.0,structural problem,2418804,binary,symmetric,no,yes,true,structural problem -854,Chen,pkustk08,22209,22209,3226671,1,1,1,0,1.0,1.0,structural problem,3226671,binary,symmetric,no,yes,true,structural problem -855,Chen,pkustk09,33960,33960,1583640,1,1,1,0,1.0,1.0,structural problem,1583640,binary,symmetric,no,yes,true,structural problem -856,Chen,pkustk10,80676,80676,4308984,1,1,1,0,1.0,1.0,structural problem,4308984,binary,symmetric,no,yes,true,structural problem -857,Chen,pkustk11,87804,87804,5217912,1,1,1,0,1.0,1.0,structural problem,5217912,binary,symmetric,no,yes,true,structural problem -858,Chen,pkustk12,94653,94653,7512317,1,1,1,0,1.0,1.0,structural problem,7512317,binary,symmetric,no,yes,true,structural problem -859,Chen,pkustk13,94893,94893,6616827,1,1,1,0,1.0,1.0,structural problem,6616827,binary,symmetric,no,yes,true,structural problem -860,Chen,pkustk14,151926,151926,14836504,1,1,1,0,1.0,1.0,structural problem,14836504,binary,symmetric,no,yes,true,structural problem -861,MathWorks,pivtol,102,102,306,1,0,0,0,0.9901960784313726,0.0,statistical/mathematical problem,306,real,unsymmetric,no,no,true,statistical/mathematical problem -862,Kim,kim1,38415,38415,933195,0,0,1,0,0.9897360244976419,0.0,2D/3D problem,933195,complex,unsymmetric,no,no,true,2D/3D problem -863,Kim,kim2,456976,456976,11330020,0,0,1,0,0.9970363405132914,0.0,2D/3D problem,11330020,complex,unsymmetric,no,no,true,2D/3D problem -864,Langemyr,comsol,1500,1500,97645,1,0,1,0,0.9990327110094128,0.0008944822923708982,structural problem,97645,real,unsymmetric,no,no,true,structural problem -865,Pothen,barth,6691,6691,46187,1,1,1,0,1.0,1.0,structural problem,46187,binary,symmetric,no,yes,true,structural problem -866,Pothen,barth4,6019,6019,40965,1,1,1,0,1.0,1.0,structural problem,40965,binary,symmetric,no,yes,true,structural problem -867,Pothen,barth5,15606,15606,107362,1,1,1,0,1.0,1.0,structural problem,107362,binary,symmetric,no,yes,true,structural problem -868,Pothen,bodyy4,17546,17546,121550,1,0,1,1,1.0,1.0,structural problem,121938,real,symmetric,yes,yes,true,structural problem -869,Pothen,bodyy5,18589,18589,128853,1,0,1,1,1.0,1.0,structural problem,129281,real,symmetric,yes,yes,true,structural problem -870,Pothen,bodyy6,19366,19366,134208,1,0,1,1,1.0,1.0,structural problem,134748,real,symmetric,yes,yes,true,structural problem -871,Pothen,commanche_dual,7920,7920,31680,1,1,1,0,1.0,1.0,structural problem,31680,binary,symmetric,no,yes,true,structural problem -872,Pothen,mesh1e1,48,48,306,1,0,1,1,1.0,1.0,structural problem,306,real,symmetric,yes,yes,true,structural problem -873,Pothen,mesh1em1,48,48,306,1,0,1,1,1.0,1.0,structural problem,306,real,symmetric,yes,yes,true,structural problem -874,Pothen,mesh1em6,48,48,306,1,0,1,1,1.0,1.0,structural problem,306,real,symmetric,yes,yes,true,structural problem -875,Pothen,mesh2e1,306,306,2018,1,0,1,1,1.0,1.0,structural problem,2018,real,symmetric,yes,yes,true,structural problem -876,Pothen,mesh2em5,306,306,2018,1,0,1,1,1.0,1.0,structural problem,2018,real,symmetric,yes,yes,true,structural problem -877,Pothen,mesh3e1,289,289,1377,1,0,1,1,1.0,1.0,structural problem,1889,real,symmetric,yes,yes,true,structural problem -878,Pothen,mesh3em5,289,289,1377,1,0,1,1,1.0,1.0,structural problem,1889,real,symmetric,yes,yes,true,structural problem -879,Pothen,onera_dual,85567,85567,419201,1,1,1,0,1.0,1.0,structural problem,419201,binary,symmetric,no,yes,true,structural problem -880,Pothen,pwt,36519,36519,326107,1,1,1,0,1.0,1.0,structural problem,326107,binary,symmetric,no,yes,true,structural problem -881,Pothen,shuttle_eddy,10429,10429,103599,1,1,1,0,1.0,1.0,structural problem,103599,binary,symmetric,no,yes,true,structural problem -882,Pothen,skirt,12598,12598,196520,1,1,1,0,1.0,1.0,structural problem,196520,binary,symmetric,no,yes,true,structural problem -883,Pothen,sphere2,66,66,450,1,1,1,0,1.0,1.0,structural problem,450,binary,symmetric,no,yes,true,structural problem -884,Pothen,sphere3,258,258,1794,1,1,1,0,1.0,1.0,structural problem,1794,binary,symmetric,no,yes,true,structural problem -885,Pothen,tandem_dual,94069,94069,460493,1,1,1,0,1.0,1.0,structural problem,460493,binary,symmetric,no,yes,true,structural problem -886,Pothen,tandem_vtx,18454,18454,253350,1,1,1,0,1.0,1.0,structural problem,253350,binary,symmetric,no,yes,true,structural problem -887,Norris,fv1,9604,9604,85264,1,0,1,1,1.0,1.0,2D/3D problem,85264,real,symmetric,yes,yes,true,2D/3D problem -888,Norris,fv2,9801,9801,87025,1,0,1,1,1.0,1.0,2D/3D problem,87025,real,symmetric,yes,yes,true,2D/3D problem -889,Norris,fv3,9801,9801,87025,1,0,1,1,1.0,1.0,2D/3D problem,87025,real,symmetric,yes,yes,true,2D/3D problem -890,Norris,heart1,3557,3557,1385317,1,0,1,0,1.0,0.0,2D/3D problem,1387773,real,unsymmetric,no,no,true,2D/3D problem -891,Norris,heart2,2339,2339,680341,1,0,1,0,1.0,0.0,2D/3D problem,682797,real,unsymmetric,no,no,true,2D/3D problem -892,Norris,heart3,2339,2339,680341,1,0,1,0,1.0,0.0,2D/3D problem,682797,real,unsymmetric,no,no,true,2D/3D problem -893,Norris,lung1,1650,1650,7419,1,0,1,0,0.5713295198474606,0.0,computational fluid dynamics problem,7419,real,unsymmetric,no,no,true,computational fluid dynamics problem -894,Norris,lung2,109460,109460,492564,1,0,1,0,0.5714270798529903,0.0,computational fluid dynamics problem,492564,real,unsymmetric,no,no,true,computational fluid dynamics problem -895,Norris,stomach,213360,213360,3021648,1,0,1,0,0.8474914253808726,0.0,2D/3D problem,3021648,real,unsymmetric,no,no,true,2D/3D problem -896,Norris,torso1,116158,116158,8516500,1,0,1,0,0.4243874832715144,0.0,2D/3D problem,8516500,real,unsymmetric,no,no,true,2D/3D problem -897,Norris,torso2,115967,115967,1033473,1,0,1,0,0.9916360219987662,0.0,2D/3D problem,1033473,real,unsymmetric,no,no,true,2D/3D problem -898,Norris,torso3,259156,259156,4429042,1,0,1,0,0.9513722917125312,0.0,2D/3D problem,4429042,real,unsymmetric,no,no,true,2D/3D problem -899,Shen,e40r0100,17281,17281,553562,1,0,1,0,0.3079877899832364,0.001965387548691824,2D/3D problem,553562,real,unsymmetric,no,no,true,2D/3D problem -900,Shen,shermanACa,3432,3432,25220,1,0,1,0,0.163759867817147,0.02157150725169818,2D/3D problem,25220,real,unsymmetric,no,no,true,2D/3D problem -901,Shen,shermanACb,18510,18510,145149,1,0,1,0,0.1493062958488302,0.02665845434660729,2D/3D problem,145149,real,unsymmetric,no,no,true,2D/3D problem -902,Shen,shermanACd,6136,6136,53329,1,0,1,0,0.1520140698832454,0.01665501239590617,2D/3D problem,53329,real,unsymmetric,no,no,true,2D/3D problem -903,Pereyra,landmark,71952,2704,1146848,1,0,0,0,0.0,0.0,least squares problem,1151232,real,rectangular,no,no,false,least squares problem -904,vanHeukelum,cage3,5,5,19,1,0,0,0,1.0,0.0,directed weighted graph,19,real,unsymmetric,no,no,missing,directed weighted graph -905,vanHeukelum,cage4,9,9,49,1,0,0,0,1.0,0.0,directed weighted graph,49,real,unsymmetric,no,no,missing,directed weighted graph -906,vanHeukelum,cage5,37,37,233,1,0,0,0,1.0,0.04081632653061224,directed weighted graph,233,real,unsymmetric,no,no,missing,directed weighted graph -907,vanHeukelum,cage6,93,93,785,1,0,0,0,1.0,0.09248554913294797,directed weighted graph,785,real,unsymmetric,no,no,missing,directed weighted graph -908,vanHeukelum,cage7,340,340,3084,1,0,0,0,1.0,0.1020408163265306,directed weighted graph,3084,real,unsymmetric,no,no,missing,directed weighted graph -909,vanHeukelum,cage8,1015,1015,11003,1,0,0,0,1.0,0.144973968762515,directed weighted graph,11003,real,unsymmetric,no,no,missing,directed weighted graph -910,vanHeukelum,cage9,3534,3534,41594,1,0,0,0,1.0,0.1535470310036784,directed weighted graph,41594,real,unsymmetric,no,no,missing,directed weighted graph -911,vanHeukelum,cage10,11397,11397,150645,1,0,0,0,1.0,0.1743077099850626,directed weighted graph,150645,real,unsymmetric,no,no,missing,directed weighted graph -912,vanHeukelum,cage11,39082,39082,559722,1,0,0,0,1.0,0.1821988322065151,directed weighted graph,559722,real,unsymmetric,no,no,missing,directed weighted graph -913,vanHeukelum,cage12,130228,130228,2032536,1,0,0,0,1.0,0.1935123018985359,directed weighted graph,2032536,real,unsymmetric,no,no,missing,directed weighted graph -914,vanHeukelum,cage13,445315,445315,7479343,1,0,0,0,1.0,0.2004785878020389,directed weighted graph,7479343,real,unsymmetric,no,no,missing,directed weighted graph -915,vanHeukelum,cage14,1505785,1505785,27130349,1,0,0,0,1.0,0.2073316837703073,directed weighted graph,27130349,real,unsymmetric,no,no,missing,directed weighted graph -916,vanHeukelum,cage15,5154859,5154859,99199551,1,0,0,0,1.0,0.2131193964673732,directed weighted graph,99199551,real,unsymmetric,no,no,true,directed weighted graph -917,Hohn,fd12,7500,7500,28462,1,0,1,0,0.0002810765230834094,0.0,materials problem,28462,real,unsymmetric,no,no,true,materials problem -918,Hohn,fd15,11532,11532,44206,1,0,1,0,0.0,0.0,materials problem,44206,real,unsymmetric,no,no,true,materials problem -919,Hohn,fd18,16428,16428,63406,1,0,1,0,3.154275620603729e-05,0.0,materials problem,63406,real,unsymmetric,no,no,true,materials problem -920,Hohn,sinc12,7500,7500,283992,1,0,1,0,0.01755337541442645,0.0,materials problem,294986,real,unsymmetric,no,no,true,materials problem -921,Hohn,sinc15,11532,11532,551184,1,0,1,0,0.01378652867239141,0.0,materials problem,568526,real,unsymmetric,no,no,true,materials problem -922,Hohn,sinc18,16428,16428,948696,1,0,1,0,0.01157085557378834,0.0,materials problem,973826,real,unsymmetric,no,no,true,materials problem -923,Alemdar,Alemdar,6245,6245,42581,1,0,1,0,1.0,1.0,2D/3D problem,42581,integer,symmetric,no,yes,true,2D/3D problem -924,Andrews,Andrews,60000,60000,760154,1,0,1,1,1.0,1.0,computer graphics/vision problem,760154,integer,symmetric,yes,yes,true,computer graphics/vision problem -925,FEMLAB,ns3Da,20414,20414,1679599,1,0,1,0,0.9999825339398674,0.0,computational fluid dynamics problem,1679599,real,unsymmetric,no,no,true,computational fluid dynamics problem -926,FEMLAB,poisson2D,367,367,2417,1,0,1,0,1.0,0.0,computational fluid dynamics problem,2417,real,unsymmetric,no,no,true,computational fluid dynamics problem -927,FEMLAB,poisson3Da,13514,13514,352762,1,0,1,0,1.0,0.0,computational fluid dynamics problem,352762,real,unsymmetric,no,no,true,computational fluid dynamics problem -928,FEMLAB,poisson3Db,85623,85623,2374949,1,0,1,0,1.0,0.0,computational fluid dynamics problem,2374949,real,unsymmetric,no,no,true,computational fluid dynamics problem -929,FEMLAB,problem1,415,415,2779,1,0,1,0,1.0,0.6548223350253807,2D/3D problem,2779,real,unsymmetric,no,no,true,2D/3D problem -930,FEMLAB,sme3Da,12504,12504,874887,1,0,1,0,0.9999710105602732,0.4370888572710733,structural problem,874887,real,unsymmetric,no,no,true,structural problem -931,FEMLAB,sme3Db,29067,29067,2081063,1,0,1,0,0.999978557463075,0.4406373111838425,structural problem,2081063,real,unsymmetric,no,no,true,structural problem -932,FEMLAB,sme3Dc,42930,42930,3148656,1,0,1,0,0.9999793929020139,0.4391179389295772,structural problem,3148656,real,unsymmetric,no,no,true,structural problem -933,FEMLAB,waveguide3D,21036,21036,303468,0,0,1,0,1.0,0.0,electromagnetics problem,303468,complex,unsymmetric,no,no,true,electromagnetics problem -934,Lucifora,cell1,7055,7055,30082,1,0,0,0,0.9970503597122302,0.0,directed weighted graph,34855,real,unsymmetric,no,no,missing,directed weighted graph -935,Lucifora,cell2,7055,7055,30082,1,0,0,0,0.9970503597122302,0.004342728101793546,directed weighted graph,34855,real,unsymmetric,no,no,missing,directed weighted graph -936,ND,nd3k,9000,9000,3279690,1,0,1,1,1.0,1.0,2D/3D problem,3279690,real,symmetric,yes,yes,true,2D/3D problem -937,ND,nd6k,18000,18000,6897316,1,0,1,1,1.0,1.0,2D/3D problem,6897316,real,symmetric,yes,yes,true,2D/3D problem -938,ND,nd12k,36000,36000,14220946,1,0,1,1,1.0,1.0,2D/3D problem,14220946,real,symmetric,yes,yes,true,2D/3D problem -939,ND,nd24k,72000,72000,28715634,1,0,1,1,1.0,1.0,2D/3D problem,28715634,real,symmetric,yes,yes,true,2D/3D problem -940,Schenk_AFE,af_shell1,504855,504855,17562051,1,0,1,0,1.0,1.0,structural problem sequence,17588875,real,symmetric,no,yes,true,structural problem sequence -941,Schenk_AFE,af_shell2,504855,504855,17562051,1,0,1,0,1.0,1.0,subsequent structural problem,17588875,real,symmetric,no,yes,true,subsequent structural problem -942,Schenk_AFE,af_shell3,504855,504855,17562051,1,0,1,1,1.0,1.0,subsequent structural problem,17588875,real,symmetric,yes,yes,true,subsequent structural problem -943,Schenk_AFE,af_shell4,504855,504855,17562051,1,0,1,1,1.0,1.0,subsequent structural problem,17588875,real,symmetric,yes,yes,true,subsequent structural problem -944,Schenk_AFE,af_shell5,504855,504855,17579155,1,0,1,0,1.0,1.0,subsequent structural problem,17588875,real,symmetric,no,yes,true,subsequent structural problem -945,Schenk_AFE,af_shell6,504855,504855,17579155,1,0,1,0,1.0,1.0,subsequent structural problem,17588875,real,symmetric,no,yes,true,subsequent structural problem -946,Schenk_AFE,af_shell7,504855,504855,17579155,1,0,1,1,1.0,1.0,subsequent structural problem,17588875,real,symmetric,yes,yes,true,subsequent structural problem -947,Schenk_AFE,af_shell8,504855,504855,17579155,1,0,1,1,1.0,1.0,subsequent structural problem,17588875,real,symmetric,yes,yes,true,subsequent structural problem -948,Schenk_AFE,af_shell9,504855,504855,17588845,1,0,1,0,1.0,1.0,subsequent structural problem,17588875,real,symmetric,no,yes,true,subsequent structural problem -949,Schenk_IBMNA,c-62,41731,41731,559341,1,0,0,0,1.0,1.0,optimization problem sequence,559343,real,symmetric,no,no,true,optimization problem sequence -950,Schenk_IBMNA,c-64,51035,51035,707985,1,0,0,0,1.0,1.0,optimization problem sequence,717841,real,symmetric,no,no,true,optimization problem sequence -951,Schenk_IBMNA,c-66,49989,49989,444853,1,0,0,0,1.0,1.0,optimization problem sequence,499007,real,symmetric,no,no,true,optimization problem sequence -952,Schenk_IBMNA,c-67,57975,57975,530229,1,0,0,0,1.0,1.0,optimization problem sequence,531935,real,symmetric,no,no,true,optimization problem sequence -953,Schenk_IBMNA,c-73,169422,169422,1279274,1,0,0,0,1.0,1.0,optimization problem sequence,1279274,real,symmetric,no,no,true,optimization problem sequence -954,Schenk_IBMSDS,2D_27628_bjtcai,27628,27628,206670,1,0,1,0,0.9995761793531919,0.2185967538342959,semiconductor device problem,442898,real,unsymmetric,no,no,true,semiconductor device problem -955,Schenk_IBMSDS,2D_54019_highK,54019,54019,486129,1,0,1,0,0.998088911762053,0.189734095484946,semiconductor device problem,996414,real,unsymmetric,no,no,true,semiconductor device problem -956,Schenk_IBMSDS,3D_28984_Tetra,28984,28984,285092,1,0,1,0,0.9874041102377119,0.3613007012666531,semiconductor device problem,599170,real,unsymmetric,no,no,true,semiconductor device problem -957,Schenk_IBMSDS,3D_51448_3D,51448,51448,537038,1,0,1,0,0.9920908271502504,0.1870343293725159,semiconductor device problem,1056610,real,unsymmetric,no,no,true,semiconductor device problem -958,Schenk_IBMSDS,ibm_matrix_2,51448,51448,537038,1,0,1,0,0.9920908271502504,0.185666920653226,semiconductor device problem,1056610,real,unsymmetric,no,no,true,semiconductor device problem -959,Schenk_IBMSDS,matrix_9,103430,103430,1205518,1,0,1,0,0.9966503478484926,0.1744833443427385,semiconductor device problem,2121550,real,unsymmetric,no,no,true,semiconductor device problem -960,Schenk_IBMSDS,matrix-new_3,125329,125329,893984,1,0,1,0,0.9871118002084263,0.2826079320371298,semiconductor device problem,2678750,real,unsymmetric,no,no,true,semiconductor device problem -961,Schenk_ISEI,barrier2-10,115625,115625,2158759,1,0,1,0,1.0,0.1989334147759642,subsequent semiconductor device problem,3897557,real,unsymmetric,no,no,true,subsequent semiconductor device problem -962,Schenk_ISEI,barrier2-11,115625,115625,2158759,1,0,1,0,1.0,0.1988805553760946,subsequent semiconductor device problem,3897557,real,unsymmetric,no,no,true,subsequent semiconductor device problem -963,Schenk_ISEI,barrier2-12,115625,115625,2158759,1,0,1,0,1.0,0.1983617501551521,subsequent semiconductor device problem,3897557,real,unsymmetric,no,no,true,subsequent semiconductor device problem -964,Schenk_ISEI,barrier2-1,113076,113076,2129496,1,0,1,0,1.0,0.1972208170916773,semiconductor device problem sequence,3805068,real,unsymmetric,no,no,true,semiconductor device problem sequence -965,Schenk_ISEI,barrier2-2,113076,113076,2129496,1,0,1,0,1.0,0.1971642812509299,subsequent semiconductor device problem,3805068,real,unsymmetric,no,no,true,subsequent semiconductor device problem -966,Schenk_ISEI,barrier2-3,113076,113076,2129496,1,0,1,0,1.0,0.1980361234266671,subsequent semiconductor device problem,3805068,real,unsymmetric,no,no,true,subsequent semiconductor device problem -967,Schenk_ISEI,barrier2-4,113076,113076,2129496,1,0,1,0,1.0,0.189249263546285,subsequent semiconductor device problem,3805068,real,unsymmetric,no,no,true,subsequent semiconductor device problem -968,Schenk_ISEI,barrier2-9,115625,115625,2158759,1,0,1,0,1.0,0.2036036406422221,semiconductor device problem sequence,3897557,real,unsymmetric,no,no,true,semiconductor device problem sequence -969,Schenk_ISEI,igbt3,10938,10938,130500,1,0,1,0,1.0,0.1720279018417223,semiconductor device problem,234006,real,unsymmetric,no,no,true,semiconductor device problem -970,Schenk_ISEI,nmos3,18588,18588,237130,1,0,1,0,1.0,0.1670891636390259,semiconductor device problem,386594,real,unsymmetric,no,no,true,semiconductor device problem -971,Schenk_ISEI,ohne2,181343,181343,6869939,1,0,1,0,1.0,0.09493801090692276,semiconductor device problem,11063545,real,unsymmetric,no,no,true,semiconductor device problem -972,Schenk_ISEI,para-10,155924,155924,2094873,1,0,1,0,1.0,0.1980866954210761,subsequent semiconductor device problem,5416358,real,unsymmetric,no,no,true,subsequent semiconductor device problem -973,Schenk_ISEI,para-4,153226,153226,2930882,1,0,1,0,1.0,0.2900222345747638,semiconductor device problem,5326228,real,unsymmetric,no,no,true,semiconductor device problem -974,Schenk_ISEI,para-5,155924,155924,2094873,1,0,1,0,1.0,0.3692588097985042,semiconductor device problem sequence,5416358,real,unsymmetric,no,no,true,semiconductor device problem sequence -975,Schenk_ISEI,para-6,155924,155924,2094873,1,0,1,0,1.0,0.3692588097985042,subsequent semiconductor device problem,5416358,real,unsymmetric,no,no,true,subsequent semiconductor device problem -976,Schenk_ISEI,para-7,155924,155924,2094873,1,0,1,0,1.0,0.3692588097985042,subsequent semiconductor device problem,5416358,real,unsymmetric,no,no,true,subsequent semiconductor device problem -977,Schenk_ISEI,para-8,155924,155924,2094873,1,0,1,0,1.0,0.1779654854253516,subsequent semiconductor device problem,5416358,real,unsymmetric,no,no,true,subsequent semiconductor device problem -978,Schenk_ISEI,para-9,155924,155924,2094873,1,0,1,0,1.0,0.1810599453621524,subsequent semiconductor device problem,5416358,real,unsymmetric,no,no,true,subsequent semiconductor device problem -979,Kamvar,Stanford,281903,281903,2312497,1,1,0,0,0.2766368994208425,0.2766368994208425,directed graph,2312497,binary,unsymmetric,no,no,missing,directed graph -980,Kamvar,Stanford_Berkeley,683446,683446,7583376,1,1,0,0,0.2502051856587356,0.2502051856587356,directed graph,7583376,binary,unsymmetric,no,no,missing,directed graph -981,Sumner,graphics,29493,11822,117954,1,0,1,0,0.0,0.0,computer graphics/vision problem,117954,real,rectangular,no,no,true,computer graphics/vision problem -982,Tromble,language,399130,399130,1216334,1,0,0,0,0.05790231080611451,0.0,directed weighted graph,1216334,real,unsymmetric,no,no,missing,directed weighted graph -983,Sanghavi,ecl32,51993,51993,380415,1,0,1,0,0.9219175329301935,0.6030290297239527,semiconductor device problem,380415,real,unsymmetric,no,no,true,semiconductor device problem -984,Sandia,adder_dcop_01,1813,1813,11156,1,0,0,0,0.6544094067343666,0.01582041688936398,subsequent circuit simulation problem,11156,real,unsymmetric,no,no,true,subsequent circuit simulation problem -985,Sandia,adder_dcop_02,1813,1813,11246,1,0,0,0,0.6481736368448915,0.02604552673372154,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -986,Sandia,adder_dcop_03,1813,1813,11148,1,0,0,0,0.6429870546699475,0.03509147319995721,subsequent circuit simulation problem,11148,real,unsymmetric,no,no,true,subsequent circuit simulation problem -987,Sandia,adder_dcop_04,1813,1813,11107,1,0,0,0,0.6505480335267569,0.003868471953578337,subsequent circuit simulation problem,11107,real,unsymmetric,no,no,true,subsequent circuit simulation problem -988,Sandia,adder_dcop_05,1813,1813,11097,1,0,0,0,0.6473752151462995,0.01333907056798623,subsequent circuit simulation problem,11097,real,unsymmetric,no,no,true,subsequent circuit simulation problem -989,Sandia,adder_dcop_06,1813,1813,11224,1,0,0,0,0.6486257030669638,0.01464501751034702,subsequent circuit simulation problem,11224,real,unsymmetric,no,no,true,subsequent circuit simulation problem -990,Sandia,adder_dcop_07,1813,1813,11226,1,0,0,0,0.6484880636604774,0.005941644562334218,subsequent circuit simulation problem,11226,real,unsymmetric,no,no,true,subsequent circuit simulation problem -991,Sandia,adder_dcop_08,1813,1813,11242,1,0,0,0,0.6478127317021501,0.005296049147336087,subsequent circuit simulation problem,11242,real,unsymmetric,no,no,true,subsequent circuit simulation problem -992,Sandia,adder_dcop_09,1813,1813,11239,1,0,0,0,0.6484424666242848,0.006145369781733418,subsequent circuit simulation problem,11239,real,unsymmetric,no,no,true,subsequent circuit simulation problem -993,Sandia,adder_dcop_10,1813,1813,11232,1,0,0,0,0.6478634291167427,0.00572579790054077,subsequent circuit simulation problem,11232,real,unsymmetric,no,no,true,subsequent circuit simulation problem -994,Sandia,adder_dcop_11,1813,1813,11243,1,0,0,0,0.6481677610675705,0.00487184918449481,subsequent circuit simulation problem,11243,real,unsymmetric,no,no,true,subsequent circuit simulation problem -995,Sandia,adder_dcop_12,1813,1813,11246,1,0,0,0,0.6481736368448915,0.004446797247220751,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -996,Sandia,adder_dcop_13,1813,1813,11245,1,0,0,0,0.6482422702244811,0.002753070732740364,subsequent circuit simulation problem,11245,real,unsymmetric,no,no,true,subsequent circuit simulation problem -997,Sandia,adder_dcop_14,1813,1813,11246,1,0,0,0,0.6481736368448915,0.008258337744838539,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -998,Sandia,adder_dcop_15,1813,1813,11246,1,0,0,0,0.6481736368448915,0.005929062996294336,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -999,Sandia,adder_dcop_16,1813,1813,11246,1,0,0,0,0.6481736368448915,0.008258337744838539,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1000,Sandia,adder_dcop_17,1813,1813,11246,1,0,0,0,0.6481736368448915,0.009105346744309158,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1001,Sandia,adder_dcop_18,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1002,Sandia,adder_dcop_19,1813,1813,11245,1,0,0,0,0.6482422702244811,0.007623888182973317,subsequent circuit simulation problem,11245,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1003,Sandia,adder_dcop_20,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1004,Sandia,adder_dcop_21,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1005,Sandia,adder_dcop_22,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1006,Sandia,adder_dcop_23,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1007,Sandia,adder_dcop_24,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1008,Sandia,adder_dcop_25,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1009,Sandia,adder_dcop_26,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1010,Sandia,adder_dcop_27,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1011,Sandia,adder_dcop_28,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1012,Sandia,adder_dcop_29,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1013,Sandia,adder_dcop_30,1813,1813,11246,1,0,0,0,0.6481736368448915,0.008046585494970884,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1014,Sandia,adder_dcop_31,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1015,Sandia,adder_dcop_32,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1016,Sandia,adder_dcop_33,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1017,Sandia,adder_dcop_34,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1018,Sandia,adder_dcop_35,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1019,Sandia,adder_dcop_36,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1020,Sandia,adder_dcop_37,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1021,Sandia,adder_dcop_38,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1022,Sandia,adder_dcop_39,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1023,Sandia,adder_dcop_40,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1024,Sandia,adder_dcop_41,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1025,Sandia,adder_dcop_42,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1026,Sandia,adder_dcop_43,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1027,Sandia,adder_dcop_44,1813,1813,11245,1,0,0,0,0.6482422702244811,0.007623888182973317,subsequent circuit simulation problem,11245,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1028,Sandia,adder_dcop_45,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1029,Sandia,adder_dcop_46,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1030,Sandia,adder_dcop_47,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1031,Sandia,adder_dcop_48,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1032,Sandia,adder_dcop_49,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1033,Sandia,adder_dcop_50,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1034,Sandia,adder_dcop_51,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1035,Sandia,adder_dcop_52,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1036,Sandia,adder_dcop_53,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007411328745367919,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1037,Sandia,adder_dcop_54,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1038,Sandia,adder_dcop_55,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1039,Sandia,adder_dcop_56,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007623080995235574,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1040,Sandia,adder_dcop_57,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1041,Sandia,adder_dcop_58,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1042,Sandia,adder_dcop_59,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1043,Sandia,adder_dcop_60,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1044,Sandia,adder_dcop_61,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1045,Sandia,adder_dcop_62,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1046,Sandia,adder_dcop_63,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1047,Sandia,adder_dcop_64,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1048,Sandia,adder_dcop_65,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1049,Sandia,adder_dcop_66,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1050,Sandia,adder_dcop_67,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1051,Sandia,adder_dcop_68,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1052,Sandia,adder_dcop_69,1813,1813,11246,1,0,0,0,0.6481736368448915,0.007834833245103229,subsequent circuit simulation problem,11246,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1053,Sandia,adder_trans_01,1814,1814,14579,1,0,0,0,1.0,0.03115703773289494,circuit simulation problem sequence,14579,real,unsymmetric,no,no,true,circuit simulation problem sequence -1054,Sandia,adder_trans_02,1814,1814,14579,1,0,0,0,1.0,0.03115703773289494,subsequent circuit simulation problem,14579,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1055,Sandia,fpga_dcop_01,1220,1220,5892,1,0,0,0,0.8183347350714887,0.2733389402859546,circuit simulation problem sequence,5892,real,unsymmetric,no,no,true,circuit simulation problem sequence -1056,Sandia,fpga_dcop_02,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3271656854499579,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1057,Sandia,fpga_dcop_03,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3448275862068966,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1058,Sandia,fpga_dcop_04,1220,1220,5884,1,0,0,0,0.818871103622578,0.3546756529064869,subsequent circuit simulation problem,5884,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1059,Sandia,fpga_dcop_05,1220,1220,5852,1,0,0,0,0.8210347752332485,0.359626802374894,subsequent circuit simulation problem,5852,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1060,Sandia,fpga_dcop_06,1220,1220,5860,1,0,0,0,0.8204911092294666,0.3539373412362405,subsequent circuit simulation problem,5860,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1061,Sandia,fpga_dcop_07,1220,1220,5855,1,0,0,0,0.8205128205128205,0.3432930705657978,subsequent circuit simulation problem,5855,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1062,Sandia,fpga_dcop_08,1220,1220,5888,1,0,0,0,0.8186026936026936,0.3425925925925926,subsequent circuit simulation problem,5888,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1063,Sandia,fpga_dcop_09,1220,1220,5888,1,0,0,0,0.8186026936026936,0.3337542087542087,subsequent circuit simulation problem,5888,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1064,Sandia,fpga_dcop_10,1220,1220,5884,1,0,0,0,0.818871103622578,0.3323504633529907,subsequent circuit simulation problem,5884,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1065,Sandia,fpga_dcop_11,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3301093355761144,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1066,Sandia,fpga_dcop_12,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3216989066442388,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1067,Sandia,fpga_dcop_13,1220,1220,5892,1,0,0,0,0.8183347350714887,0.325063078216989,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1068,Sandia,fpga_dcop_14,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3187552565180824,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1069,Sandia,fpga_dcop_15,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3221194280908327,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1070,Sandia,fpga_dcop_16,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3221194280908327,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1071,Sandia,fpga_dcop_17,1220,1220,5892,1,0,0,0,0.8183347350714887,0.31959629941127,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1072,Sandia,fpga_dcop_18,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3191757779646762,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1073,Sandia,fpga_dcop_19,1220,1220,5892,1,0,0,0,0.8183347350714887,0.31959629941127,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1074,Sandia,fpga_dcop_20,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3212783851976451,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1075,Sandia,fpga_dcop_21,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3216989066442388,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1076,Sandia,fpga_dcop_22,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3263246425567704,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1077,Sandia,fpga_dcop_23,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3216989066442388,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1078,Sandia,fpga_dcop_24,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3254835996635829,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1079,Sandia,fpga_dcop_25,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3259041211101766,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1080,Sandia,fpga_dcop_26,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3254835996635829,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1081,Sandia,fpga_dcop_27,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3271656854499579,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1082,Sandia,fpga_dcop_28,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3284272497897393,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1083,Sandia,fpga_dcop_29,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3280067283431455,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1084,Sandia,fpga_dcop_30,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3292682926829268,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1085,Sandia,fpga_dcop_31,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3284272497897393,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1086,Sandia,fpga_dcop_32,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3280067283431455,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1087,Sandia,fpga_dcop_33,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3280067283431455,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1088,Sandia,fpga_dcop_34,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3275862068965517,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1089,Sandia,fpga_dcop_35,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3288477712363331,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1090,Sandia,fpga_dcop_36,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3288477712363331,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1091,Sandia,fpga_dcop_37,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3296888141295206,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1092,Sandia,fpga_dcop_38,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3296888141295206,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1093,Sandia,fpga_dcop_39,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3296888141295206,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1094,Sandia,fpga_dcop_40,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3313708999158957,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1095,Sandia,fpga_dcop_41,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3301093355761144,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1096,Sandia,fpga_dcop_42,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3313708999158957,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1097,Sandia,fpga_dcop_43,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3317914213624895,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1098,Sandia,fpga_dcop_44,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3322119428090833,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1099,Sandia,fpga_dcop_45,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3322119428090833,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1100,Sandia,fpga_dcop_46,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3317914213624895,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1101,Sandia,fpga_dcop_47,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3317914213624895,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1102,Sandia,fpga_dcop_48,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3317914213624895,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1103,Sandia,fpga_dcop_49,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3317914213624895,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1104,Sandia,fpga_dcop_50,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3317914213624895,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1105,Sandia,fpga_dcop_51,1220,1220,5892,1,0,0,0,0.8183347350714887,0.3322119428090833,subsequent circuit simulation problem,5892,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1106,Sandia,fpga_trans_01,1220,1220,7382,1,0,0,0,1.0,0.214193962748876,circuit simulation problem sequence,7382,real,unsymmetric,no,no,true,circuit simulation problem sequence -1107,Sandia,fpga_trans_02,1220,1220,7382,1,0,0,0,1.0,0.2138728323699422,subsequent circuit simulation problem,7382,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1108,Sandia,init_adder1,1813,1813,11156,1,0,0,0,0.6544094067343666,0.01582041688936398,circuit simulation problem sequence,11156,real,unsymmetric,no,no,true,circuit simulation problem sequence -1109,Sandia,mult_dcop_01,25187,25187,193276,1,0,0,0,0.6144759258929472,0.003383612629779353,circuit simulation problem sequence,193276,real,unsymmetric,no,no,true,circuit simulation problem sequence -1110,Sandia,mult_dcop_02,25187,25187,193276,1,0,0,0,0.6144759258929472,0.003383612629779353,subsequent circuit simulation problem,193276,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1111,Sandia,mult_dcop_03,25187,25187,193216,1,0,0,0,0.6142197994049846,0.01294544504421048,subsequent circuit simulation problem,193216,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1112,Sandia,oscil_dcop_01,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,circuit simulation problem sequence,1544,real,unsymmetric,no,no,true,circuit simulation problem sequence -1113,Sandia,oscil_dcop_02,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1114,Sandia,oscil_dcop_03,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1115,Sandia,oscil_dcop_04,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1116,Sandia,oscil_dcop_05,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1117,Sandia,oscil_dcop_06,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1118,Sandia,oscil_dcop_07,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1119,Sandia,oscil_dcop_08,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1120,Sandia,oscil_dcop_09,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1121,Sandia,oscil_dcop_10,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1122,Sandia,oscil_dcop_11,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1123,Sandia,oscil_dcop_12,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1124,Sandia,oscil_dcop_13,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1125,Sandia,oscil_dcop_14,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1126,Sandia,oscil_dcop_15,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1127,Sandia,oscil_dcop_16,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1128,Sandia,oscil_dcop_17,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1129,Sandia,oscil_dcop_18,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1130,Sandia,oscil_dcop_19,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1131,Sandia,oscil_dcop_20,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1132,Sandia,oscil_dcop_21,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1133,Sandia,oscil_dcop_22,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1134,Sandia,oscil_dcop_23,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1135,Sandia,oscil_dcop_24,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1136,Sandia,oscil_dcop_25,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1137,Sandia,oscil_dcop_26,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1138,Sandia,oscil_dcop_27,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1139,Sandia,oscil_dcop_28,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1140,Sandia,oscil_dcop_29,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1141,Sandia,oscil_dcop_30,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1142,Sandia,oscil_dcop_31,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1143,Sandia,oscil_dcop_32,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1144,Sandia,oscil_dcop_33,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1145,Sandia,oscil_dcop_34,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1146,Sandia,oscil_dcop_35,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1147,Sandia,oscil_dcop_36,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1148,Sandia,oscil_dcop_37,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1149,Sandia,oscil_dcop_38,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1150,Sandia,oscil_dcop_39,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1151,Sandia,oscil_dcop_40,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1152,Sandia,oscil_dcop_41,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1153,Sandia,oscil_dcop_42,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1154,Sandia,oscil_dcop_43,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1155,Sandia,oscil_dcop_44,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1156,Sandia,oscil_dcop_45,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1157,Sandia,oscil_dcop_46,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1158,Sandia,oscil_dcop_47,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1159,Sandia,oscil_dcop_48,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1160,Sandia,oscil_dcop_49,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1161,Sandia,oscil_dcop_50,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1162,Sandia,oscil_dcop_51,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1163,Sandia,oscil_dcop_52,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1164,Sandia,oscil_dcop_53,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1165,Sandia,oscil_dcop_54,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1166,Sandia,oscil_dcop_55,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1167,Sandia,oscil_dcop_56,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1168,Sandia,oscil_dcop_57,430,430,1544,1,0,0,0,0.9762308998302207,0.6977928692699491,subsequent circuit simulation problem,1544,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1169,Sandia,oscil_trans_01,430,430,1614,1,0,0,0,0.9737274220032841,0.7175697865353038,circuit simulation problem,1614,real,unsymmetric,no,no,true,circuit simulation problem -1170,MathWorks,Pd,8081,8081,13036,1,0,0,0,0.0,0.0,counter-example problem,13036,real,unsymmetric,no,no,true,counter-example problem -1171,MathWorks,Pd_rhs,8081,12406,6323,1,0,0,0,0.0,0.0,counter-example problem,6323,real,rectangular,no,no,false,counter-example problem -1172,MathWorks,Harvard500,500,500,2636,1,1,0,0,0.4057744830277019,0.4057744830277019,directed graph,2636,binary,unsymmetric,no,no,missing,directed graph -1173,HB,cegb2802,2802,2802,277362,1,1,1,0,1.0,1.0,structural problem,277362,binary,symmetric,no,no,false,structural problem -1174,HB,cegb2919,2919,2919,321543,1,1,1,0,1.0,1.0,structural problem,321543,binary,symmetric,no,no,false,structural problem -1175,HB,cegb3024,3024,3024,79848,1,1,1,0,1.0,1.0,structural problem,79848,binary,symmetric,no,no,false,structural problem -1176,HB,cegb3306,3306,3306,74916,1,1,1,0,1.0,1.0,structural problem,74916,binary,symmetric,no,no,false,structural problem -1177,HB,lap_25,25,25,169,1,1,1,0,1.0,1.0,structural problem,169,binary,symmetric,no,yes,true,structural problem -1178,HB,lock1074,1074,1074,51588,1,1,1,0,1.0,1.0,structural problem,51588,binary,symmetric,no,no,false,structural problem -1179,HB,lock2232,2232,2232,80352,1,1,1,0,1.0,1.0,structural problem,80352,binary,symmetric,no,no,false,structural problem -1180,HB,lock3491,3491,3491,160444,1,1,1,0,1.0,1.0,structural problem,160444,binary,symmetric,no,no,false,structural problem -1181,HB,lock_700,700,700,22175,1,1,1,0,1.0,1.0,structural problem,22175,binary,symmetric,no,no,false,structural problem -1182,HB,man_5976,5976,5976,225046,1,1,1,0,1.0,1.0,structural problem,225046,binary,symmetric,no,no,false,structural problem -1183,IBM_Austin,coupled,11341,11341,97193,1,0,0,0,1.0,0.7823373235828404,circuit simulation problem,98523,real,unsymmetric,no,no,true,circuit simulation problem -1184,ACUSIM,Pres_Poisson,14822,14822,715804,1,0,1,1,1.0,1.0,computational fluid dynamics problem,715804,real,symmetric,yes,yes,true,computational fluid dynamics problem -1185,Rajat,rajat01,6833,6833,43250,1,1,0,0,0.9957479284779764,0.9957479284779764,circuit simulation problem,43250,binary,unsymmetric,no,no,true,circuit simulation problem -1186,Rajat,rajat02,1960,1960,11187,1,1,0,0,1.0,1.0,circuit simulation problem,11187,binary,symmetric,no,no,true,circuit simulation problem -1187,Rajat,rajat03,7602,7602,32653,1,0,0,0,1.0,0.4024380527448012,circuit simulation problem,32653,real,unsymmetric,no,no,true,circuit simulation problem -1188,Rajat,rajat04,1041,1041,8725,1,0,0,0,1.0,0.03991188285603214,circuit simulation problem,9642,real,unsymmetric,no,no,true,circuit simulation problem -1189,Rajat,rajat05,301,301,1250,1,0,0,0,0.7697974217311234,0.7058823529411765,circuit simulation problem,1384,real,unsymmetric,no,no,true,circuit simulation problem -1190,Rajat,rajat06,10922,10922,46983,1,1,0,0,1.0,1.0,circuit simulation problem,46983,binary,symmetric,no,no,true,circuit simulation problem -1191,Rajat,rajat07,14842,14842,63913,1,1,0,0,1.0,1.0,circuit simulation problem,63913,binary,symmetric,no,no,true,circuit simulation problem -1192,Rajat,rajat08,19362,19362,83443,1,1,0,0,1.0,1.0,circuit simulation problem,83443,binary,symmetric,no,no,true,circuit simulation problem -1193,Rajat,rajat09,24482,24482,105573,1,1,0,0,1.0,1.0,circuit simulation problem,105573,binary,symmetric,no,no,true,circuit simulation problem -1194,Rajat,rajat10,30202,30202,130303,1,1,0,0,1.0,1.0,circuit simulation problem,130303,binary,symmetric,no,no,true,circuit simulation problem -1195,Rajat,rajat11,135,135,665,1,0,0,0,0.8911764705882353,0.6303939962476548,circuit simulation problem,812,real,unsymmetric,no,no,true,circuit simulation problem -1196,Rajat,rajat12,1879,1879,12818,1,0,0,0,1.0,0.4548776032151991,circuit simulation problem,12926,real,unsymmetric,no,no,true,circuit simulation problem -1197,Rajat,rajat13,7598,7598,48762,1,0,0,0,1.0,0.2965265970366772,circuit simulation problem,48922,real,unsymmetric,no,no,true,circuit simulation problem -1198,Rajat,rajat14,180,180,1475,1,0,0,0,1.0,0.0245398773006135,circuit simulation problem,1503,real,unsymmetric,no,no,true,circuit simulation problem -1199,Hamrle,Hamrle1,32,32,98,1,0,0,0,0.06451612903225806,0.0,circuit simulation problem,98,real,unsymmetric,no,no,true,circuit simulation problem -1200,Hamrle,Hamrle2,5952,5952,22162,1,0,0,0,0.0009930486593843098,0.0001805543017062382,circuit simulation problem,22162,real,unsymmetric,no,no,true,circuit simulation problem -1201,Hamrle,Hamrle3,1447360,1447360,5514242,1,0,0,0,2.538882993038383e-06,0.0,circuit simulation problem,5514242,real,unsymmetric,no,no,true,circuit simulation problem -1202,Oberwolfach,gyro_k,17361,17361,1021159,1,0,1,1,1.0,1.0,duplicate model reduction problem,1021159,real,symmetric,yes,yes,true,duplicate model reduction problem -1203,Oberwolfach,gyro_m,17361,17361,340431,1,0,1,1,1.0,1.0,duplicate model reduction problem,340431,real,symmetric,yes,yes,true,duplicate model reduction problem -1204,Oberwolfach,t2dah_a,11445,11445,176117,1,0,1,0,1.0,1.0,duplicate model reduction problem,176117,real,symmetric,no,no,true,duplicate model reduction problem -1205,Oberwolfach,t2dah_e,11445,11445,176117,1,0,1,1,1.0,1.0,duplicate model reduction problem,176117,real,symmetric,yes,yes,true,duplicate model reduction problem -1206,Oberwolfach,t2dal_a,4257,4257,37465,1,0,1,0,1.0,1.0,duplicate model reduction problem,37465,real,symmetric,no,no,true,duplicate model reduction problem -1207,Oberwolfach,t2dal_e,4257,4257,4257,1,0,1,1,1.0,1.0,duplicate model reduction problem,4257,real,symmetric,yes,yes,true,duplicate model reduction problem -1208,Oberwolfach,t3dh_a,79171,79171,4352105,1,0,1,0,1.0,1.0,duplicate model reduction problem,4352105,real,symmetric,no,no,true,duplicate model reduction problem -1209,Oberwolfach,t3dh_e,79171,79171,4352105,1,0,1,0,1.0,1.0,duplicate model reduction problem,4352105,real,symmetric,no,yes,true,duplicate model reduction problem -1210,Oberwolfach,t3dl_a,20360,20360,509866,1,0,1,0,1.0,1.0,duplicate model reduction problem,509866,real,symmetric,no,no,true,duplicate model reduction problem -1211,Oberwolfach,t3dl_e,20360,20360,20360,1,0,1,1,1.0,1.0,duplicate model reduction problem,20360,real,symmetric,yes,yes,true,duplicate model reduction problem -1212,LiuWenzhuo,powersim,15838,15838,64424,1,0,0,0,0.5913695769855386,0.5337339974478245,power network problem,67562,real,unsymmetric,no,no,true,power network problem -1213,Lin,Lin,256000,256000,1766400,1,0,1,0,1.0,1.0,structural problem,1766400,real,symmetric,no,yes,true,structural problem -1214,Cannizzo,sts4098,4098,4098,72356,1,0,1,1,1.0,1.0,structural problem,72356,real,symmetric,yes,yes,true,structural problem -1215,GHS_indef,aug2d,29008,29008,76832,1,0,1,0,1.0,1.0,2D/3D problem,76832,integer,symmetric,no,no,false,2D/3D problem -1216,GHS_indef,aug2dc,30200,30200,80000,1,0,1,0,1.0,1.0,2D/3D problem,80000,integer,symmetric,no,no,false,2D/3D problem -1217,GHS_indef,aug3d,24300,24300,69984,1,0,1,0,1.0,1.0,2D/3D problem,69984,integer,symmetric,no,no,false,2D/3D problem -1218,GHS_indef,aug3dcqp,35543,35543,128115,1,0,1,0,1.0,1.0,2D/3D problem,128115,real,symmetric,no,no,true,2D/3D problem -1219,GHS_indef,bmw3_2,227362,227362,11288630,1,0,1,0,1.0,1.0,structural problem,11288630,real,symmetric,no,yes,true,structural problem -1220,GHS_indef,c-55,32780,32780,403450,1,0,0,0,1.0,1.0,optimization problem,403450,real,symmetric,no,no,true,optimization problem -1221,GHS_indef,c-58,37595,37595,552551,1,0,0,0,1.0,1.0,optimization problem,552557,real,symmetric,no,no,true,optimization problem -1222,GHS_indef,c-59,41282,41282,480536,1,0,0,0,1.0,1.0,optimization problem,480536,real,symmetric,no,no,true,optimization problem -1223,GHS_indef,c-62ghs,41731,41731,559339,1,0,0,0,1.0,1.0,subsequent optimization problem,559343,real,symmetric,no,no,true,subsequent optimization problem -1224,GHS_indef,c-63,44234,44234,434704,1,0,0,0,1.0,1.0,optimization problem,434704,real,symmetric,no,no,true,optimization problem -1225,GHS_indef,c-68,64810,64810,565996,1,0,0,0,1.0,1.0,optimization problem,566006,real,symmetric,no,no,true,optimization problem -1226,GHS_indef,c-69,67458,67458,623914,1,0,0,0,1.0,1.0,optimization problem,623970,real,symmetric,no,no,true,optimization problem -1227,GHS_indef,c-70,68924,68924,658986,1,0,0,0,1.0,1.0,optimization problem,658986,real,symmetric,no,no,true,optimization problem -1228,GHS_indef,c-71,76638,76638,859520,1,0,0,0,1.0,1.0,optimization problem,859554,real,symmetric,no,no,true,optimization problem -1229,GHS_indef,c-72,84064,84064,707546,1,0,0,0,1.0,1.0,optimization problem,707558,real,symmetric,no,no,true,optimization problem -1230,GHS_indef,copter2,55476,55476,759952,1,0,1,0,1.0,1.0,computational fluid dynamics problem,759952,real,symmetric,no,no,true,computational fluid dynamics problem -1231,GHS_indef,d_pretok,182730,182730,1641672,1,0,1,0,1.0,1.0,2D/3D problem,1641672,real,symmetric,no,no,true,2D/3D problem -1232,GHS_indef,darcy003,389874,389874,2097566,1,0,1,0,1.0,1.0,2D/3D problem,2101242,real,symmetric,no,no,true,2D/3D problem -1233,GHS_indef,dawson5,51537,51537,1010777,1,0,1,0,1.0,1.0,structural problem,1010777,real,symmetric,no,no,true,structural problem -1234,GHS_indef,dtoc,24993,24993,69972,1,0,0,0,1.0,1.0,optimization problem,69972,real,symmetric,no,no,false,optimization problem -1235,GHS_indef,exdata_1,6001,6001,2269500,1,0,0,0,1.0,1.0,optimization problem,2269501,real,symmetric,no,no,true,optimization problem -1236,GHS_indef,helm2d03,392257,392257,2741935,1,0,1,0,1.0,1.0,2D/3D problem,2741935,real,symmetric,no,yes,true,2D/3D problem -1237,GHS_indef,helm3d01,32226,32226,428444,1,0,1,0,1.0,1.0,2D/3D problem,428444,real,symmetric,no,no,true,2D/3D problem -1238,GHS_indef,k1_san,67759,67759,559774,1,0,1,0,1.0,1.0,2D/3D problem,559774,real,symmetric,no,no,false,2D/3D problem -1239,GHS_indef,laser,3002,3002,9000,1,0,1,0,1.0,1.0,materials problem,9000,real,symmetric,no,no,false,materials problem -1240,GHS_indef,mario001,38434,38434,204912,1,0,1,0,1.0,1.0,2D/3D problem,206156,real,symmetric,no,no,true,2D/3D problem -1241,GHS_indef,mario002,389874,389874,2097566,1,0,1,0,1.0,1.0,duplicate 2D/3D problem,2101242,real,symmetric,no,no,true,duplicate 2D/3D problem -1242,GHS_indef,ncvxqp1,12111,12111,73963,1,0,0,0,1.0,1.0,optimization problem,73963,real,symmetric,no,no,true,optimization problem -1243,GHS_indef,ncvxqp9,16554,16554,54040,1,0,0,0,1.0,1.0,optimization problem,54040,real,symmetric,no,no,true,optimization problem -1244,GHS_indef,olesnik0,88263,88263,744216,1,0,1,0,1.0,1.0,2D/3D problem,744216,real,symmetric,no,no,true,2D/3D problem -1245,GHS_indef,sit100,10262,10262,61046,1,0,1,0,1.0,1.0,2D/3D problem,61046,real,symmetric,no,no,true,2D/3D problem -1246,GHS_indef,stokes128,49666,49666,558594,1,0,1,0,1.0,1.0,computational fluid dynamics problem,558594,real,symmetric,no,no,true,computational fluid dynamics problem -1247,GHS_indef,stokes64,12546,12546,140034,1,0,1,0,1.0,1.0,computational fluid dynamics problem,140034,real,symmetric,no,no,true,computational fluid dynamics problem -1248,GHS_indef,stokes64s,12546,12546,140034,1,0,1,0,1.0,1.0,computational fluid dynamics problem,140034,real,symmetric,no,no,true,computational fluid dynamics problem -1249,GHS_indef,tuma1,22967,22967,87760,1,0,1,0,1.0,1.0,2D/3D problem,87760,real,symmetric,no,no,true,2D/3D problem -1250,GHS_indef,tuma2,12992,12992,49365,1,0,1,0,1.0,1.0,2D/3D problem,49365,real,symmetric,no,no,true,2D/3D problem -1251,GHS_indef,turon_m,189924,189924,1690876,1,0,1,0,1.0,1.0,2D/3D problem,1690876,real,symmetric,no,no,true,2D/3D problem -1252,GHS_psdef,audikw_1,943695,943695,77651847,1,0,1,1,1.0,1.0,structural problem,77651847,real,symmetric,yes,yes,true,structural problem -1253,GHS_psdef,bmw7st_1,141347,141347,7318399,1,0,1,1,1.0,1.0,structural problem,7339667,real,symmetric,yes,yes,true,structural problem -1254,GHS_psdef,bmwcra_1,148770,148770,10641602,1,0,1,1,1.0,1.0,structural problem,10644002,real,symmetric,yes,yes,true,structural problem -1255,GHS_psdef,copter1,17222,17222,211064,1,1,1,0,1.0,1.0,computational fluid dynamics problem,211064,binary,symmetric,no,yes,true,computational fluid dynamics problem -1256,GHS_psdef,copter2,55476,55476,759952,1,1,1,0,1.0,1.0,duplicate computational fluid dynamics problem,759952,binary,symmetric,no,yes,true,duplicate computational fluid dynamics problem -1257,GHS_psdef,crankseg_1,52804,52804,10614210,1,0,1,1,1.0,1.0,structural problem,10614210,real,symmetric,yes,yes,true,structural problem -1258,GHS_psdef,crankseg_2,63838,63838,14148858,1,0,1,1,1.0,1.0,structural problem,14148858,real,symmetric,yes,yes,true,structural problem -1259,DNVS,crplat2,18010,18010,960946,1,1,1,0,1.0,1.0,structural problem,960946,binary,symmetric,no,yes,true,structural problem -1260,DNVS,fcondp2,201822,201822,11294316,1,1,1,0,1.0,1.0,structural problem,11294316,binary,symmetric,no,yes,true,structural problem -1261,GHS_psdef,finance256,37376,37376,298496,1,1,0,0,1.0,1.0,optimization problem,298496,binary,symmetric,no,yes,true,optimization problem -1262,GHS_psdef,ford1,18728,18728,101576,1,1,1,0,1.0,1.0,structural problem,101576,binary,symmetric,no,yes,true,structural problem -1263,GHS_psdef,ford2,100196,100196,544688,1,1,1,0,1.0,1.0,structural problem,544688,binary,symmetric,no,yes,true,structural problem -1264,DNVS,fullb,199187,199187,11708077,1,1,1,0,1.0,1.0,structural problem,11708077,binary,symmetric,no,yes,true,structural problem -1265,DNVS,halfb,224617,224617,12387821,1,1,1,0,1.0,1.0,structural problem,12387821,binary,symmetric,no,yes,true,structural problem -1266,GHS_psdef,hood,220542,220542,9895422,1,0,1,1,1.0,1.0,structural problem,10768436,real,symmetric,yes,yes,true,structural problem -1267,GHS_psdef,inline_1,503712,503712,36816170,1,0,1,1,1.0,1.0,structural problem,36816342,real,symmetric,yes,yes,true,structural problem -1268,GHS_psdef,ldoor,952203,952203,42493817,1,0,1,1,1.0,1.0,structural problem,46522475,real,symmetric,yes,yes,true,structural problem -1269,DNVS,m_t1,97578,97578,9753570,1,0,1,1,1.0,1.0,structural problem,9753570,real,symmetric,yes,yes,true,structural problem -1270,GHS_psdef,oilpan,73752,73752,2148558,1,0,1,1,1.0,1.0,structural problem,3597188,real,symmetric,yes,yes,true,structural problem -1271,GHS_psdef,opt1,15449,15449,1930655,1,1,1,0,1.0,1.0,structural problem,1930655,binary,symmetric,no,yes,true,structural problem -1272,GHS_psdef,pds10,16558,16558,149658,1,1,0,0,1.0,1.0,optimization problem,149658,binary,symmetric,no,yes,true,optimization problem -1273,GHS_psdef,pwt,36519,36519,326107,1,1,1,0,1.0,1.0,duplicate structural problem,326107,binary,symmetric,no,yes,true,duplicate structural problem -1274,GHS_psdef,ramage02,16830,16830,2866352,1,1,1,0,1.0,1.0,computational fluid dynamics problem,2866352,binary,symmetric,no,yes,true,computational fluid dynamics problem -1275,GHS_psdef,s3dkq4m2,90449,90449,4427725,1,0,1,1,1.0,1.0,structural problem,4820891,real,symmetric,yes,yes,true,structural problem -1276,GHS_psdef,s3dkt3m2,90449,90449,3686223,1,0,1,1,1.0,1.0,structural problem,3753461,real,symmetric,yes,yes,true,structural problem -1277,DNVS,ship_001,34920,34920,3896496,1,0,1,1,1.0,1.0,structural problem,4644230,real,symmetric,yes,yes,true,structural problem -1278,DNVS,ship_003,121728,121728,3777036,1,0,1,1,1.0,1.0,structural problem,8086034,real,symmetric,yes,yes,true,structural problem -1279,DNVS,shipsec1,140874,140874,3568176,1,0,1,1,1.0,1.0,structural problem,7813404,real,symmetric,yes,yes,true,structural problem -1280,DNVS,shipsec5,179860,179860,4598604,1,0,1,1,1.0,1.0,structural problem,10113096,real,symmetric,yes,yes,true,structural problem -1281,DNVS,shipsec8,114919,114919,3303553,1,0,1,1,1.0,1.0,structural problem,6653399,real,symmetric,yes,yes,true,structural problem -1282,GHS_psdef,srb1,54924,54924,2962152,1,1,1,0,1.0,1.0,structural problem,2962152,binary,symmetric,no,yes,true,structural problem -1283,DNVS,thread,29736,29736,4444880,1,0,1,1,1.0,1.0,structural problem,4470048,real,symmetric,yes,yes,true,structural problem -1284,DNVS,trdheim,22098,22098,1935324,1,1,1,0,1.0,1.0,structural problem,1935324,binary,symmetric,no,yes,true,structural problem -1285,DNVS,troll,213453,213453,11985111,1,1,1,0,1.0,1.0,structural problem,11985111,binary,symmetric,no,yes,true,structural problem -1286,DNVS,tsyl201,20685,20685,2454957,1,1,1,0,1.0,1.0,structural problem,2454957,binary,symmetric,no,yes,true,structural problem -1287,GHS_psdef,vanbody,47072,47072,2329056,1,0,1,1,1.0,1.0,structural problem,2336898,real,symmetric,yes,yes,true,structural problem -1288,GHS_psdef,wathen100,30401,30401,471601,1,0,1,1,1.0,1.0,random 2D/3D problem,471601,real,symmetric,yes,yes,true,random 2D/3D problem -1289,GHS_psdef,wathen120,36441,36441,565761,1,0,1,1,1.0,1.0,random 2D/3D problem,565761,real,symmetric,yes,yes,true,random 2D/3D problem -1290,DNVS,x104,108384,108384,8713602,1,0,1,1,1.0,1.0,structural problem,10167624,real,symmetric,yes,yes,true,structural problem -1291,GHS_indef,a0nsdsil,80016,80016,355034,1,0,0,0,1.0,1.0,optimization problem,355034,real,symmetric,no,no,true,optimization problem -1292,GHS_indef,a2nnsnsl,80016,80016,347222,1,0,0,0,1.0,1.0,optimization problem,347222,real,symmetric,no,no,true,optimization problem -1293,GHS_indef,a5esindl,60008,60008,255004,1,0,0,0,1.0,1.0,optimization problem,255004,real,symmetric,no,no,true,optimization problem -1294,GHS_indef,blockqp1,60012,60012,640033,1,0,0,0,1.0,1.0,optimization problem,640033,real,symmetric,no,no,true,optimization problem -1295,GHS_indef,bloweya,30004,30004,150009,1,0,1,0,1.0,1.0,materials problem,150009,real,symmetric,no,no,true,materials problem -1296,GHS_indef,boyd1,93279,93279,1211231,1,0,0,0,1.0,1.0,optimization problem,1211231,real,symmetric,no,no,true,optimization problem -1297,GHS_indef,boyd2,466316,466316,1500397,1,0,0,0,1.0,1.0,optimization problem,1500397,real,symmetric,no,no,true,optimization problem -1298,GHS_indef,brainpc2,27607,27607,179395,1,0,0,0,1.0,1.0,optimization problem,179395,real,symmetric,no,no,true,optimization problem -1299,GHS_indef,bratu3d,27792,27792,173796,1,0,1,0,1.0,1.0,2D/3D problem,173796,real,symmetric,no,no,true,2D/3D problem -1300,GHS_indef,cont-201,80595,80595,438795,1,0,0,0,1.0,1.0,optimization problem,438795,real,symmetric,no,no,true,optimization problem -1301,GHS_indef,cont-300,180895,180895,988195,1,0,0,0,1.0,1.0,optimization problem,988195,real,symmetric,no,no,true,optimization problem -1302,GHS_indef,dixmaanl,60000,60000,299998,1,0,0,0,1.0,1.0,optimization problem,299998,real,symmetric,no,yes,true,optimization problem -1303,GHS_indef,linverse,11999,11999,95977,1,0,0,0,1.0,1.0,statistical/mathematical problem,95977,real,symmetric,no,yes,true,statistical/mathematical problem -1304,GHS_indef,ncvxbqp1,50000,50000,349968,1,0,0,0,1.0,1.0,optimization problem,349968,real,symmetric,no,no,true,optimization problem -1305,GHS_indef,ncvxqp3,75000,75000,499964,1,0,0,0,1.0,1.0,optimization problem,499964,real,symmetric,no,no,true,optimization problem -1306,GHS_indef,ncvxqp5,62500,62500,424966,1,0,0,0,1.0,1.0,optimization problem,424966,real,symmetric,no,no,true,optimization problem -1307,GHS_indef,ncvxqp7,87500,87500,574962,1,0,0,0,1.0,1.0,optimization problem,574962,real,symmetric,no,no,true,optimization problem -1308,GHS_indef,sparsine,50000,50000,1548988,1,0,1,0,1.0,1.0,structural problem,1548988,real,symmetric,no,no,true,structural problem -1309,GHS_indef,spmsrtls,29995,29995,229947,1,0,0,0,1.0,1.0,statistical/mathematical problem,229947,real,symmetric,no,no,true,statistical/mathematical problem -1310,GHS_psdef,cvxbqp1,50000,50000,349968,1,0,0,1,1.0,1.0,optimization problem,349968,real,symmetric,yes,yes,true,optimization problem -1311,GHS_psdef,gridgena,48962,48962,512084,1,0,0,1,1.0,1.0,optimization problem,512084,real,symmetric,yes,yes,true,optimization problem -1312,GHS_psdef,jnlbrng1,40000,40000,199200,1,0,0,1,1.0,1.0,optimization problem,199200,real,symmetric,yes,yes,true,optimization problem -1313,GHS_psdef,minsurfo,40806,40806,203622,1,0,0,1,1.0,1.0,optimization problem,203622,real,symmetric,yes,yes,true,optimization problem -1314,GHS_psdef,obstclae,40000,40000,197608,1,0,0,1,1.0,1.0,optimization problem,197608,real,symmetric,yes,yes,true,optimization problem -1315,GHS_psdef,torsion1,40000,40000,197608,1,0,0,1,1.0,1.0,duplicate optimization problem,197608,real,symmetric,yes,yes,true,duplicate optimization problem -1316,Rajat,rajat15,37261,37261,443573,1,0,0,0,1.0,0.943060217954145,circuit simulation problem,443573,real,unsymmetric,no,no,true,circuit simulation problem -1317,Bydder,mri1,65536,147456,589824,1,0,1,0,0.0,0.0,computer graphics/vision problem,589824,real,rectangular,no,no,false,computer graphics/vision problem -1318,Bydder,mri2,63240,147456,569160,1,0,1,0,0.0,0.0,computer graphics/vision problem,569160,real,rectangular,no,no,false,computer graphics/vision problem -1319,Engwirda,airfoil_2d,14214,14214,259688,1,0,1,0,0.981668119637925,0.0,computational fluid dynamics problem,259688,real,unsymmetric,no,no,true,computational fluid dynamics problem -1320,IBM_EDA,dc1,116835,116835,766396,1,0,0,0,0.8542477149952045,0.2426284829292399,circuit simulation problem sequence,766396,real,unsymmetric,no,no,true,circuit simulation problem sequence -1321,IBM_EDA,dc2,116835,116835,766396,1,0,0,0,0.8542477149952045,0.2426284829292399,subsequent circuit simulation problem,766396,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1322,IBM_EDA,dc3,116835,116835,766396,1,0,0,0,0.8542477149952045,0.2426407989395915,subsequent circuit simulation problem,766396,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1323,IBM_EDA,trans4,116835,116835,749800,1,0,0,0,0.8542477149952045,0.3020988522272163,circuit simulation problem sequence,766396,real,unsymmetric,no,no,true,circuit simulation problem sequence -1324,IBM_EDA,trans5,116835,116835,749800,1,0,0,0,0.8542477149952045,0.3018997890878642,subsequent circuit simulation problem,766396,real,unsymmetric,no,no,true,subsequent circuit simulation problem -1325,Morandini,robot,120,120,870,1,0,1,0,0.2177215189873418,0.05316455696202532,robotics problem,870,real,unsymmetric,no,no,true,robotics problem -1326,Morandini,rotor1,100,100,708,1,0,1,0,0.514018691588785,0.07165109034267912,structural problem,708,real,unsymmetric,no,no,true,structural problem -1327,Morandini,rotor2,791,791,10685,1,0,1,0,0.2341063574570172,0.1155537784886046,structural problem,10685,real,unsymmetric,no,no,true,structural problem -1328,MathWorks,tomography,500,500,28726,1,0,1,0,1.0,0.9331113158081201,computer graphics/vision problem,28726,real,unsymmetric,no,no,true,computer graphics/vision problem -1329,Kemelmacher,Kemelmacher,28452,9693,100875,1,0,1,0,0.0,0.0,computer graphics/vision problem,100875,real,rectangular,no,no,true,computer graphics/vision problem -1330,MathWorks,Kuu,7102,7102,340200,1,0,1,1,1.0,1.0,structural problem,340200,real,symmetric,yes,yes,true,structural problem -1331,MathWorks,Muu,7102,7102,170134,1,0,1,1,1.0,1.0,structural problem,170134,real,symmetric,yes,yes,true,structural problem -1332,Toledo,deltaX,68600,21961,247424,1,0,0,0,0.0,0.0,counter-example problem,247424,real,rectangular,no,no,true,counter-example problem -1333,VanVelzen,std1_Jac2_db,21982,21982,498771,1,0,0,0,0.3261820218167785,8.389455293641422e-06,chemical process simulation problem,498771,real,unsymmetric,no,no,true,chemical process simulation problem -1334,VanVelzen,std1_Jac2,21982,21982,1248213,1,0,0,0,1.441471454060305e-05,0.0,chemical process simulation problem,1248731,real,unsymmetric,no,no,true,chemical process simulation problem -1335,VanVelzen,std1_Jac3_db,21982,21982,531826,1,0,0,0,0.3593020610225873,7.84553706623987e-06,chemical process simulation problem,531826,real,unsymmetric,no,no,true,chemical process simulation problem -1336,VanVelzen,std1_Jac3,21982,21982,1455374,1,0,0,0,1.37377639453759e-05,0.0,chemical process simulation problem,1455848,real,unsymmetric,no,no,true,chemical process simulation problem -1337,VanVelzen,Zd_Jac2_db,22835,22835,676439,1,0,0,0,0.3038965489807284,3.059956793410077e-06,chemical process simulation problem,676439,real,unsymmetric,no,no,true,chemical process simulation problem -1338,VanVelzen,Zd_Jac2,22835,22835,1642407,1,0,0,0,0.000249569948369456,0.0,chemical process simulation problem,1642833,real,unsymmetric,no,no,true,chemical process simulation problem -1339,VanVelzen,Zd_Jac3_db,22835,22835,713907,1,0,0,0,0.3299540424152621,2.894054454528616e-06,chemical process simulation problem,713907,real,unsymmetric,no,no,true,chemical process simulation problem -1340,VanVelzen,Zd_Jac3,22835,22835,1915726,1,0,0,0,0.0002609405864378739,0.0,chemical process simulation problem,1916152,real,unsymmetric,no,no,true,chemical process simulation problem -1341,VanVelzen,Zd_Jac6_db,22835,22835,663643,1,0,0,0,0.3245527521504101,3.121059662176502e-06,chemical process simulation problem,663643,real,unsymmetric,no,no,true,chemical process simulation problem -1342,VanVelzen,Zd_Jac6,22835,22835,1711557,1,0,0,0,0.0002161245251101651,0.0,chemical process simulation problem,1711983,real,unsymmetric,no,no,true,chemical process simulation problem -1343,Rajat,rajat16,94294,94294,476766,1,0,0,0,0.9900237892855388,0.6363336955309653,circuit simulation problem,641159,real,unsymmetric,no,no,true,circuit simulation problem -1344,Rajat,rajat17,94294,94294,479246,1,0,0,0,0.9900237892855388,0.2705152684049277,circuit simulation problem,641159,real,unsymmetric,no,no,true,circuit simulation problem -1345,Rajat,rajat18,94294,94294,479151,1,0,0,0,0.626033640830624,0.2849580148146621,circuit simulation problem,485143,real,unsymmetric,no,no,true,circuit simulation problem -1346,Rajat,rajat19,1157,1157,3699,1,0,0,0,0.9041281299345816,0.9193154034229829,circuit simulation problem,5399,real,unsymmetric,no,no,true,circuit simulation problem -1347,Lourakis,bundle1,10581,10581,770811,1,0,1,1,1.0,1.0,computer graphics/vision problem,770901,real,symmetric,yes,yes,true,computer graphics/vision problem -1348,PARSEC,benzene,8219,8219,242669,1,0,0,0,1.0,1.0,theoretical/quantum chemistry problem,242669,real,symmetric,no,yes,true,theoretical/quantum chemistry problem -1349,PARSEC,CO,221119,221119,7666057,1,0,0,0,1.0,1.0,theoretical/quantum chemistry problem,7666057,real,symmetric,no,yes,true,theoretical/quantum chemistry problem -1350,PARSEC,Ga10As10H30,113081,113081,6115633,1,0,0,0,1.0,1.0,theoretical/quantum chemistry problem,6115633,real,symmetric,no,yes,true,theoretical/quantum chemistry problem -1351,PARSEC,Ga19As19H42,133123,133123,8884839,1,0,0,0,1.0,1.0,theoretical/quantum chemistry problem,8884839,real,symmetric,no,yes,true,theoretical/quantum chemistry problem -1352,PARSEC,Ga3As3H12,61349,61349,5970947,1,0,0,0,1.0,1.0,theoretical/quantum chemistry problem,5970947,real,symmetric,no,yes,true,theoretical/quantum chemistry problem -1353,PARSEC,Ga41As41H72,268096,268096,18488476,1,0,0,0,1.0,1.0,theoretical/quantum chemistry problem,18488476,real,symmetric,no,yes,true,theoretical/quantum chemistry problem -1354,PARSEC,GaAsH6,61349,61349,3381809,1,0,0,0,1.0,1.0,theoretical/quantum chemistry problem,3381809,real,symmetric,no,yes,true,theoretical/quantum chemistry problem -1355,PARSEC,Ge87H76,112985,112985,7892195,1,0,0,0,1.0,1.0,theoretical/quantum chemistry problem,7892195,real,symmetric,no,yes,true,theoretical/quantum chemistry problem -1356,PARSEC,Ge99H100,112985,112985,8451395,1,0,0,0,1.0,1.0,theoretical/quantum chemistry problem,8451395,real,symmetric,no,yes,true,theoretical/quantum chemistry problem -1357,PARSEC,H2O,67024,67024,2216736,1,0,0,0,1.0,1.0,theoretical/quantum chemistry problem,2216736,real,symmetric,no,yes,true,theoretical/quantum chemistry problem -1358,PARSEC,Na5,5832,5832,305630,1,0,0,0,1.0,1.0,theoretical/quantum chemistry problem,305630,real,symmetric,no,yes,true,theoretical/quantum chemistry problem -1359,PARSEC,Si10H16,17077,17077,875923,1,0,0,0,1.0,1.0,theoretical/quantum chemistry problem,875923,real,symmetric,no,yes,true,theoretical/quantum chemistry problem -1360,PARSEC,Si2,769,769,17801,1,0,0,0,1.0,1.0,theoretical/quantum chemistry problem,17801,real,symmetric,no,yes,true,theoretical/quantum chemistry problem -1361,PARSEC,Si34H36,97569,97569,5156379,1,0,0,0,1.0,1.0,theoretical/quantum chemistry problem,5156379,real,symmetric,no,yes,true,theoretical/quantum chemistry problem -1362,PARSEC,Si41Ge41H72,185639,185639,15011265,1,0,0,0,1.0,1.0,theoretical/quantum chemistry problem,15011265,real,symmetric,no,yes,true,theoretical/quantum chemistry problem -1363,PARSEC,Si5H12,19896,19896,738598,1,0,0,0,1.0,1.0,theoretical/quantum chemistry problem,738598,real,symmetric,no,yes,true,theoretical/quantum chemistry problem -1364,PARSEC,Si87H76,240369,240369,10661631,1,0,0,0,1.0,1.0,theoretical/quantum chemistry problem,10661631,real,symmetric,no,yes,true,theoretical/quantum chemistry problem -1365,PARSEC,SiH4,5041,5041,171903,1,0,0,0,1.0,1.0,theoretical/quantum chemistry problem,171903,real,symmetric,no,yes,true,theoretical/quantum chemistry problem -1366,PARSEC,SiNa,5743,5743,198787,1,0,0,0,1.0,1.0,theoretical/quantum chemistry problem,198787,real,symmetric,no,yes,true,theoretical/quantum chemistry problem -1367,PARSEC,SiO2,155331,155331,11283503,1,0,0,0,1.0,1.0,theoretical/quantum chemistry problem,11283503,real,symmetric,no,yes,true,theoretical/quantum chemistry problem -1368,PARSEC,SiO,33401,33401,1317655,1,0,0,0,1.0,1.0,theoretical/quantum chemistry problem,1317655,real,symmetric,no,yes,true,theoretical/quantum chemistry problem -1369,Rajat,rajat20,86916,86916,604299,1,0,0,0,0.9906228863063395,0.1122042623922186,circuit simulation problem,605045,real,unsymmetric,no,no,true,circuit simulation problem -1370,Rajat,rajat21,411676,411676,1876011,1,0,0,0,0.7647530446640561,0.6145057622905241,circuit simulation problem,1893370,real,unsymmetric,no,no,true,circuit simulation problem -1371,Rajat,rajat22,39899,39899,195429,1,0,0,0,0.980070207933121,0.3342735182643515,circuit simulation problem,197264,real,unsymmetric,no,no,true,circuit simulation problem -1372,Rajat,rajat23,110355,110355,555441,1,0,0,0,0.9891581094231137,0.3324988529606978,circuit simulation problem,556938,real,unsymmetric,no,no,true,circuit simulation problem -1373,Rajat,rajat24,358172,358172,1946979,1,0,0,0,0.996935347128458,0.2716106951495262,circuit simulation problem,1948235,real,unsymmetric,no,no,true,circuit simulation problem -1374,Rajat,rajat25,87190,87190,606489,1,0,0,0,0.9906572831072957,0.1118151951820715,circuit simulation problem,607235,real,unsymmetric,no,no,true,circuit simulation problem -1375,Rajat,rajat26,51032,51032,247528,1,0,0,0,0.9853537517311467,0.3271800826884766,circuit simulation problem,249302,real,unsymmetric,no,no,true,circuit simulation problem -1376,Rajat,rajat27,20640,20640,97353,1,0,0,0,0.9645908149382882,0.3035747337767303,circuit simulation problem,99777,real,unsymmetric,no,no,true,circuit simulation problem -1377,Rajat,rajat28,87190,87190,606489,1,0,0,0,0.9906572831072957,0.1072533437604223,circuit simulation problem,607235,real,unsymmetric,no,no,true,circuit simulation problem -1378,MathWorks,Sieber,2290,2290,14873,1,0,0,0,0.7272727272727273,0.0,counter-example problem,14873,real,unsymmetric,no,no,true,counter-example problem -1379,Andrianov,ex3sta1,16782,16782,678998,1,1,0,0,1.0,1.0,optimization problem,678998,binary,symmetric,no,yes,true,optimization problem -1380,Andrianov,fxm3_6,5026,5026,94026,1,1,0,0,1.0,1.0,optimization problem,94026,binary,symmetric,no,yes,true,optimization problem -1381,Andrianov,fxm4_6,18892,18892,497844,1,1,0,0,1.0,1.0,optimization problem,497844,binary,symmetric,no,yes,true,optimization problem -1382,Andrianov,ins2,309412,309412,2751484,1,1,0,0,1.0,1.0,optimization problem,2751484,binary,symmetric,no,yes,true,optimization problem -1383,Andrianov,lp1,534388,534388,1643420,1,1,0,0,1.0,1.0,optimization problem,1643420,binary,symmetric,no,yes,true,optimization problem -1384,Andrianov,lpl1,32460,32460,328036,1,1,0,0,1.0,1.0,optimization problem,328036,binary,symmetric,no,yes,true,optimization problem -1385,Andrianov,mip1,66463,66463,10352819,1,1,0,0,1.0,1.0,optimization problem,10352819,binary,symmetric,no,yes,true,optimization problem -1386,Andrianov,net100,29920,29920,2033200,1,1,0,0,1.0,1.0,optimization problem,2033200,binary,symmetric,no,yes,true,optimization problem -1387,Andrianov,net125,36720,36720,2577200,1,1,0,0,1.0,1.0,optimization problem,2577200,binary,symmetric,no,yes,true,optimization problem -1388,Andrianov,net150,43520,43520,3121200,1,1,0,0,1.0,1.0,optimization problem,3121200,binary,symmetric,no,yes,true,optimization problem -1389,Andrianov,net25,9520,9520,401200,1,1,0,0,1.0,1.0,optimization problem,401200,binary,symmetric,no,yes,true,optimization problem -1390,Andrianov,net4-1,88343,88343,2441727,1,1,0,0,1.0,1.0,optimization problem,2441727,binary,symmetric,no,yes,true,optimization problem -1391,Andrianov,net50,16320,16320,945200,1,1,0,0,1.0,1.0,optimization problem,945200,binary,symmetric,no,yes,true,optimization problem -1392,Andrianov,net75,23120,23120,1489200,1,1,0,0,1.0,1.0,optimization problem,1489200,binary,symmetric,no,yes,true,optimization problem -1393,Andrianov,pattern1,19242,19242,9323432,1,1,0,0,1.0,1.0,optimization problem,9323432,binary,symmetric,no,yes,true,optimization problem -1394,Andrianov,pf2177,9728,9728,725144,1,1,0,0,1.0,1.0,optimization problem,725144,binary,symmetric,no,yes,true,optimization problem -1395,MKS,fp,7548,7548,834222,1,0,1,0,0.7575174939506899,0.002912877385765126,electromagnetics problem,848553,real,unsymmetric,no,no,true,electromagnetics problem -1396,Rajat,rajat29,643994,643994,3760246,1,0,0,0,0.6856406275660725,0.6910598008173955,circuit simulation problem,4866270,real,unsymmetric,no,no,true,circuit simulation problem -1397,Rajat,rajat30,643994,643994,6175244,1,0,0,0,0.9945912407336421,0.009194565053000587,circuit simulation problem,6175377,real,unsymmetric,no,no,true,circuit simulation problem -1398,Rajat,rajat31,4690002,4690002,20316253,1,0,0,0,1.0,0.4000960614178773,circuit simulation problem,20316253,real,unsymmetric,no,no,true,circuit simulation problem -1399,Raju,laminar_duct3D,67173,67173,3788857,1,0,1,0,0.9022908019592145,0.5518386226500883,computational fluid dynamics problem,3833077,real,unsymmetric,no,no,true,computational fluid dynamics problem -1400,Bates,Chem97Zt,2541,31022,62044,1,1,0,0,0.0,0.0,statistical/mathematical problem,62044,binary,rectangular,no,no,true,statistical/mathematical problem -1401,Bates,Chem97ZtZ,2541,2541,7361,1,0,0,1,1.0,1.0,statistical/mathematical problem,7361,real,symmetric,yes,yes,true,statistical/mathematical problem -1402,Schmid,thermal1,82654,82654,574458,1,0,1,1,1.0,1.0,thermal problem,574458,real,symmetric,yes,yes,true,thermal problem -1403,Schmid,thermal2,1228045,1228045,8580313,1,0,1,1,1.0,1.0,thermal problem,8580313,real,symmetric,yes,yes,true,thermal problem -1404,MathWorks,Kaufhold,8765,8765,42471,1,0,0,0,1.0,0.778437073518068,counter-example problem,42471,real,unsymmetric,no,no,true,counter-example problem -1405,Bindel,ted_A,10605,10605,424587,1,0,1,0,0.5657922767749357,0.1057079149564699,thermal problem,424587,real,unsymmetric,no,no,true,thermal problem -1406,Bindel,ted_B,10605,10605,144579,1,0,1,1,1.0,1.0,thermal problem,144579,real,symmetric,yes,yes,true,thermal problem -1407,Bindel,ted_AB,10605,10605,522387,0,0,1,0,0.6415700434950818,0.0,thermal problem,522387,complex,unsymmetric,no,no,true,thermal problem -1408,Bindel,ted_A_unscaled,10605,10605,424587,1,0,1,0,0.5657922767749357,0.1057079149564699,thermal problem,424587,real,unsymmetric,no,no,true,thermal problem -1409,Bindel,ted_B_unscaled,10605,10605,144579,1,0,1,1,1.0,1.0,thermal problem,144579,real,symmetric,yes,yes,true,thermal problem -1410,Bindel,ted_AB_unscaled,10605,10605,522387,0,0,1,0,0.6415700434950818,0.0,thermal problem,522387,complex,unsymmetric,no,no,true,thermal problem -1411,Koutsovasilis,F1,343791,343791,26837113,1,0,1,0,1.0,1.0,structural problem,26837113,real,symmetric,no,yes,true,structural problem -1412,AMD,G2_circuit,150102,150102,726674,1,0,0,1,1.0,1.0,circuit simulation problem,726674,real,symmetric,yes,yes,true,circuit simulation problem -1413,IBM_EDA,ckt11752_dc_1,49702,49702,333029,1,0,0,0,0.9838102263462359,0.7355387943965807,circuit simulation problem,333029,real,unsymmetric,no,no,true,circuit simulation problem -1414,IBM_EDA,ckt11752_tr_0,49702,49702,332807,1,0,0,0,0.9838102263462359,0.7360237367761078,circuit simulation problem,333029,real,unsymmetric,no,no,true,circuit simulation problem -1415,Sandia,ASIC_100k,99340,99340,940621,1,0,0,0,1.0,0.002631466347182405,circuit simulation problem,954163,real,unsymmetric,no,no,true,circuit simulation problem -1416,Sandia,ASIC_100ks,99190,99190,578890,1,0,0,0,1.0,0.0007546383156139254,circuit simulation problem,578890,real,unsymmetric,no,no,true,circuit simulation problem -1417,Sandia,ASIC_320k,321821,321821,1931828,1,0,0,0,1.0,0.3578041366837217,circuit simulation problem,2635364,real,unsymmetric,no,no,true,circuit simulation problem -1418,Sandia,ASIC_320ks,321671,321671,1316085,1,0,0,0,1.0,0.2891089626654492,circuit simulation problem,1827807,real,unsymmetric,no,no,true,circuit simulation problem -1419,Sandia,ASIC_680k,682862,682862,2638997,1,0,0,0,1.0,0.004444308126428144,circuit simulation problem,3871773,real,unsymmetric,no,no,true,circuit simulation problem -1420,Sandia,ASIC_680ks,682712,682712,1693767,1,0,0,0,1.0,0.005801860432914134,circuit simulation problem,2329176,real,unsymmetric,no,no,true,circuit simulation problem -1421,AMD,G3_circuit,1585478,1585478,7660826,1,0,0,1,1.0,1.0,circuit simulation problem,7660826,real,symmetric,yes,yes,true,circuit simulation problem -1422,GHS_psdef,apache1,80800,80800,542184,1,0,1,1,1.0,1.0,structural problem,542184,real,symmetric,yes,yes,true,structural problem -1423,GHS_psdef,apache2,715176,715176,4817870,1,0,1,1,1.0,1.0,structural problem,4817870,real,symmetric,yes,yes,true,structural problem -1424,GHS_indef,bloweybl,30003,30003,109999,1,0,1,0,1.0,1.0,materials problem,120000,real,symmetric,no,no,false,materials problem -1425,GHS_indef,bloweybq,10001,10001,49999,1,0,1,1,1.0,1.0,materials problem,69991,real,symmetric,yes,yes,true,materials problem -1426,GHS_indef,cvxqp3,17500,17500,114962,1,0,0,0,1.0,1.0,optimization problem,122462,real,symmetric,no,no,true,optimization problem -1427,GHS_indef,qpband,20000,20000,45000,1,0,0,0,1.0,1.0,optimization problem,45000,real,symmetric,no,no,true,optimization problem -1428,Oberwolfach,chipcool0,20082,20082,281150,1,0,1,0,1.0,0.8688617524936032,model reduction problem,281150,real,unsymmetric,no,no,true,model reduction problem -1429,Oberwolfach,chipcool1,20082,20082,281150,1,0,1,0,1.0,0.08556391438245974,model reduction problem,281150,real,unsymmetric,no,no,true,model reduction problem -1430,Oberwolfach,filter2D,1668,1668,10750,1,0,1,0,1.0,1.0,model reduction problem,10750,real,symmetric,no,no,true,model reduction problem -1431,Oberwolfach,filter3D,106437,106437,2707179,1,0,1,0,1.0,1.0,model reduction problem,2707179,real,symmetric,no,no,true,model reduction problem -1432,Oberwolfach,flowmeter0,9669,9669,67391,1,0,1,0,1.0,1.0,model reduction problem,67391,real,symmetric,no,no,true,model reduction problem -1433,Oberwolfach,flowmeter5,9669,9669,67391,1,0,1,0,1.0,0.06222930598385364,model reduction problem,67391,real,unsymmetric,no,no,true,model reduction problem -1434,Oberwolfach,gas_sensor,66917,66917,1703365,1,0,1,0,1.0,1.0,model reduction problem,1703365,real,symmetric,no,no,true,model reduction problem -1435,Oberwolfach,gyro,17361,17361,1021159,1,0,1,1,1.0,1.0,model reduction problem,1021159,real,symmetric,yes,yes,true,model reduction problem -1436,Oberwolfach,inlet,11730,11730,328323,1,0,1,0,0.6083330964361183,9.475888601453601e-05,model reduction problem,328323,real,unsymmetric,no,no,true,model reduction problem -1437,Oberwolfach,LF10000,19998,19998,99982,1,0,1,1,1.0,1.0,model reduction problem,99982,real,symmetric,yes,yes,true,model reduction problem -1438,Oberwolfach,LF10,18,18,82,1,0,1,1,1.0,1.0,model reduction problem,82,real,symmetric,yes,yes,true,model reduction problem -1439,Oberwolfach,LFAT5000,19994,19994,79966,1,0,1,1,1.0,1.0,model reduction problem,79966,real,symmetric,yes,yes,true,model reduction problem -1440,Oberwolfach,LFAT5,14,14,46,1,0,1,1,1.0,1.0,model reduction problem,46,real,symmetric,yes,yes,true,model reduction problem -1441,Oberwolfach,piston,2025,2025,100015,1,0,1,0,1.0,0.03275844473925911,model reduction problem,100015,real,unsymmetric,no,no,true,model reduction problem -1442,Oberwolfach,rail_1357,1357,1357,8985,1,0,1,0,1.0,1.0,model reduction problem,8985,real,symmetric,no,no,true,model reduction problem -1443,Oberwolfach,rail_20209,20209,20209,139233,1,0,1,0,1.0,1.0,model reduction problem,139233,real,symmetric,no,no,true,model reduction problem -1444,Oberwolfach,rail_5177,5177,5177,35185,1,0,1,0,1.0,1.0,model reduction problem,35185,real,symmetric,no,no,true,model reduction problem -1445,Oberwolfach,rail_79841,79841,79841,553921,1,0,1,0,1.0,1.0,model reduction problem,553921,real,symmetric,no,no,true,model reduction problem -1446,Oberwolfach,spiral,1434,1434,18228,1,0,1,0,1.0,1.0,model reduction problem,18228,real,symmetric,no,no,true,model reduction problem -1447,Oberwolfach,t2dah,11445,11445,176117,1,0,1,0,1.0,1.0,model reduction problem,176117,real,symmetric,no,no,true,model reduction problem -1448,Oberwolfach,t2dal_bci,4257,4257,37465,1,0,1,0,1.0,1.0,model reduction problem,37465,real,symmetric,no,no,true,model reduction problem -1449,Oberwolfach,t2dal,4257,4257,37465,1,0,1,0,1.0,1.0,model reduction problem,37465,real,symmetric,no,no,true,model reduction problem -1450,Oberwolfach,t3dh,79171,79171,4352105,1,0,1,0,1.0,1.0,model reduction problem,4352105,real,symmetric,no,no,true,model reduction problem -1451,Oberwolfach,t3dl,20360,20360,509866,1,0,1,0,1.0,1.0,model reduction problem,509866,real,symmetric,no,no,true,model reduction problem -1452,Oberwolfach,windscreen,22692,22692,1482390,0,0,1,0,1.0,0.0,model reduction problem,1482390,complex,symmetric,no,no,true,model reduction problem -1453,Oberwolfach,bone010,986703,986703,47851783,1,0,1,1,1.0,1.0,model reduction problem,71666325,real,symmetric,yes,yes,true,model reduction problem -1454,Oberwolfach,boneS01,127224,127224,5516602,1,0,1,1,1.0,1.0,model reduction problem,6715152,real,symmetric,yes,yes,true,model reduction problem -1455,Oberwolfach,boneS10,914898,914898,40878708,1,0,1,1,1.0,1.0,model reduction problem,55468422,real,symmetric,yes,yes,true,model reduction problem -1456,Pajek,California,9664,9664,16150,1,1,0,0,0.02241486068111455,0.02241486068111455,directed graph,16150,binary,unsymmetric,no,no,missing,directed graph -1457,Pajek,Cities,55,46,1342,1,0,0,0,0.0,0.0,weighted bipartite graph,1342,integer,rectangular,no,no,true,weighted bipartite graph -1458,Pajek,CSphd,1882,1882,1740,1,1,0,0,0.0,0.0,directed graph,1740,binary,unsymmetric,no,no,missing,directed graph -1459,Pajek,dictionary28,52652,52652,178076,1,1,0,0,1.0,1.0,undirected graph,178076,binary,symmetric,no,no,missing,undirected graph -1460,Pajek,divorce,50,9,225,1,1,0,0,0.0,0.0,bipartite graph,225,binary,rectangular,no,no,true,bipartite graph -1461,Pajek,EAT_RS,23219,23219,325592,1,0,0,0,0.1236262721980876,0.03102501938294547,directed weighted graph,325592,integer,unsymmetric,no,no,missing,directed weighted graph -1462,Pajek,EAT_SR,23219,23219,325589,1,0,0,0,0.1236274132759018,0.03102530574571187,directed weighted graph,325589,integer,unsymmetric,no,no,missing,directed weighted graph -1463,Pajek,EPA,4772,4772,8965,1,1,0,0,0.01249302844394869,0.01249302844394869,directed graph,8965,binary,unsymmetric,no,no,missing,directed graph -1464,Pajek,Erdos02,6927,6927,16944,1,1,0,0,1.0,1.0,undirected graph,16944,binary,symmetric,no,no,missing,undirected graph -1465,Pajek,Erdos971,472,472,2628,1,1,0,0,1.0,1.0,undirected graph,2628,binary,symmetric,no,no,missing,undirected graph -1466,Pajek,Erdos972,5488,5488,14170,1,1,0,0,1.0,1.0,undirected graph,14170,binary,symmetric,no,no,missing,undirected graph -1467,Pajek,Erdos981,485,485,2762,1,1,0,0,1.0,1.0,undirected graph,2762,binary,symmetric,no,no,missing,undirected graph -1468,Pajek,Erdos982,5822,5822,14750,1,1,0,0,1.0,1.0,undirected graph,14750,binary,symmetric,no,no,missing,undirected graph -1469,Pajek,Erdos991,492,492,2834,1,1,0,0,1.0,1.0,undirected graph,2834,binary,symmetric,no,no,missing,undirected graph -1470,Pajek,Erdos992,6100,6100,15030,1,1,0,0,1.0,1.0,undirected graph,15030,binary,symmetric,no,no,missing,undirected graph -1471,Pajek,EVA,8497,8497,6726,1,1,0,0,0.003866745984533016,0.003866745984533016,directed graph,6726,binary,unsymmetric,no,no,missing,directed graph -1472,Pajek,FA,10617,10617,72176,1,0,0,0,0.232333869090506,0.005847142936318794,directed weighted graph,72176,real,unsymmetric,no,no,missing,directed weighted graph -1473,Pajek,foldoc,13356,13356,120238,1,0,0,0,0.4785009730700777,0.459472047106572,directed weighted graph,120238,integer,unsymmetric,no,no,missing,directed weighted graph -1474,Pajek,football,35,35,118,1,0,0,0,0.0,0.0,directed weighted graph,118,integer,unsymmetric,no,no,missing,directed weighted graph -1475,Pajek,GD00_a,352,352,458,1,1,0,0,0.0,0.0,directed graph,458,binary,unsymmetric,no,no,missing,directed graph -1476,Pajek,GD00_c,638,638,1041,1,0,0,0,0.02133850630455868,0.01551891367604268,directed multigraph,1041,integer,unsymmetric,no,no,missing,directed multigraph -1477,Pajek,GD01_a,311,311,645,1,0,0,0,0.01550387596899225,0.01550387596899225,directed weighted graph,645,integer,unsymmetric,no,no,missing,directed weighted graph -1478,Pajek,GD01_Acap,953,953,645,1,0,0,0,0.01550387596899225,0.01550387596899225,directed multigraph,645,integer,unsymmetric,no,no,missing,directed multigraph -1479,Pajek,GD01_b,18,18,37,1,1,0,0,0.5142857142857142,0.5142857142857142,directed graph,37,binary,unsymmetric,no,no,missing,directed graph -1480,Pajek,GD01_c,33,33,135,1,0,0,0,0.0,0.0,directed multigraph,135,integer,unsymmetric,no,no,missing,directed multigraph -1481,Pajek,GD02_a,23,23,87,1,1,0,0,0.6436781609195402,0.6436781609195402,directed graph,87,binary,unsymmetric,no,no,missing,directed graph -1482,Pajek,GD02_b,80,80,232,1,1,0,0,0.0,0.0,directed graph,232,binary,unsymmetric,no,no,missing,directed graph -1483,Pajek,GD06_Java,1538,1538,8032,1,1,0,0,0.05353585657370518,0.05353585657370518,directed graph,8032,binary,unsymmetric,no,no,missing,directed graph -1484,Pajek,GD06_theory,101,101,380,1,1,0,0,1.0,1.0,undirected graph,380,binary,symmetric,no,no,missing,undirected graph -1485,Pajek,GD95_a,36,36,57,1,1,0,0,0.03508771929824561,0.03508771929824561,directed graph,57,binary,unsymmetric,no,no,missing,directed graph -1486,Pajek,GD95_b,73,73,96,1,1,0,0,0.0,0.0,directed graph,96,binary,unsymmetric,no,no,missing,directed graph -1487,Pajek,GD95_c,62,62,287,1,1,0,0,0.9965156794425087,0.9965156794425087,directed graph,287,binary,unsymmetric,no,no,missing,directed graph -1488,Pajek,GD96_a,1096,1096,1677,1,0,0,0,0.0,0.0,directed multigraph,1677,integer,unsymmetric,no,no,missing,directed multigraph -1489,Pajek,GD96_b,111,111,193,1,1,0,0,0.0,0.0,directed graph,193,binary,unsymmetric,no,no,missing,directed graph -1490,Pajek,GD96_c,65,65,250,1,1,0,0,1.0,1.0,undirected graph,250,binary,symmetric,no,no,missing,undirected graph -1491,Pajek,GD96_d,180,180,229,1,1,0,0,0.008733624454148471,0.008733624454148471,directed graph,229,binary,unsymmetric,no,no,missing,directed graph -1492,Pajek,GD97_a,84,84,332,1,1,0,0,1.0,1.0,directed graph,332,binary,symmetric,no,no,missing,directed graph -1493,Pajek,GD97_b,47,47,264,1,0,0,0,1.0,1.0,undirected weighted graph,264,real,symmetric,no,no,missing,undirected weighted graph -1494,Pajek,GD97_c,452,452,460,1,0,0,0,0.0,0.0,directed multigraph,460,integer,unsymmetric,no,no,missing,directed multigraph -1495,Pajek,GD98_a,38,38,50,1,1,0,0,0.16,0.16,directed graph,50,binary,unsymmetric,no,no,missing,directed graph -1496,Pajek,GD98_b,121,121,207,1,1,0,0,0.7246376811594203,0.7246376811594203,directed graph,207,binary,unsymmetric,no,no,missing,directed graph -1497,Pajek,GD98_c,112,112,336,1,1,0,0,1.0,1.0,undirected graph,336,binary,symmetric,no,no,missing,undirected graph -1498,Pajek,GD99_b,64,64,252,1,0,0,0,1.0,1.0,undirected multigraph,252,integer,symmetric,no,no,missing,undirected multigraph -1499,Pajek,GD99_c,105,105,149,1,1,0,0,0.3892617449664429,0.3892617449664429,directed graph,149,binary,unsymmetric,no,no,missing,directed graph -1500,Pajek,geom,7343,7343,23796,1,0,0,0,1.0,1.0,undirected weighted graph,23796,integer,symmetric,no,no,missing,undirected weighted graph -1501,Pajek,GlossGT,72,72,122,1,1,0,0,0.06557377049180328,0.06557377049180328,directed graph,122,binary,unsymmetric,no,no,missing,directed graph -1502,Pajek,HEP-th,27240,27240,342437,1,1,0,0,0.002774548916757691,0.002774548916757691,directed graph,342437,binary,unsymmetric,no,no,missing,directed graph -1503,Pajek,HEP-th-new,27770,27770,352807,1,1,0,0,0.002738343613933237,0.002738343613933237,directed graph,352807,binary,unsymmetric,no,no,missing,directed graph -1504,Pajek,IMDB,428440,896308,3782463,1,1,0,0,0.0,0.0,bipartite graph,3782463,binary,rectangular,no,no,false,bipartite graph -1505,Pajek,internet,124651,124651,207214,1,0,0,0,0.01444913320583093,0.002245819852432203,directed weighted graph,207214,integer,unsymmetric,no,no,missing,directed weighted graph -1506,Pajek,Journals,124,124,12068,1,0,0,1,1.0,1.0,undirected weighted graph,12068,integer,symmetric,yes,yes,missing,undirected weighted graph -1507,Pajek,Kohonen,4470,4470,12731,1,1,0,0,0.001728336868567837,0.001728336868567837,directed graph,12731,binary,unsymmetric,no,no,missing,directed graph -1508,Pajek,Lederberg,8843,8843,41601,1,0,0,0,0.003029210241615579,0.003029210241615579,directed multigraph,41601,integer,unsymmetric,no,no,missing,directed multigraph -1509,Barabasi,NotreDame_actors,392400,127823,1470404,1,1,0,0,0.0,0.0,bipartite graph,1470404,binary,rectangular,no,no,false,bipartite graph -1510,Barabasi,NotreDame_www,325729,325729,929849,1,1,0,0,0.3315875213545298,0.3315875213545298,directed graph,929849,binary,unsymmetric,no,no,missing,directed graph -1511,Barabasi,NotreDame_yeast,2114,2114,4480,1,1,0,0,1.0,1.0,undirected graph,4480,binary,symmetric,no,no,missing,undirected graph -1512,Pajek,ODLIS,2909,2909,18246,1,0,0,0,0.2043747601556932,0.1997697494654898,directed multigraph,18246,integer,unsymmetric,no,no,missing,directed multigraph -1513,Pajek,patents_main,240547,240547,560943,1,0,0,0,0.0,0.0,directed weighted graph,560943,real,unsymmetric,no,no,missing,directed weighted graph -1514,Pajek,patents,3774768,3774768,14970767,1,1,0,0,0.0,0.0,directed graph,14970767,binary,unsymmetric,no,no,missing,directed graph -1515,Pajek,Ragusa16,24,24,81,1,0,0,0,0.3661971830985916,0.1971830985915493,directed weighted graph,81,integer,unsymmetric,no,no,missing,directed weighted graph -1516,Pajek,Ragusa18,23,23,64,1,0,0,0,0.3278688524590164,0.1967213114754098,directed weighted graph,64,integer,unsymmetric,no,no,missing,directed weighted graph -1517,Pajek,Reuters911,13332,13332,296076,1,0,0,0,1.0,1.0,undirected weighted graph sequence,296076,integer,symmetric,no,no,missing,undirected weighted graph sequence -1518,Pajek,Roget,1022,1022,5075,1,1,0,0,0.5620811982656682,0.5620811982656682,directed graph,5075,binary,unsymmetric,no,no,missing,directed graph -1519,Pajek,Sandi_authors,86,86,248,1,0,0,0,1.0,1.0,undirected weighted graph,248,integer,symmetric,no,no,missing,undirected weighted graph -1520,Pajek,Sandi_sandi,314,360,613,1,1,0,0,0.0,0.0,bipartite graph,613,binary,rectangular,no,no,false,bipartite graph -1521,Pajek,SciMet,3084,3084,10413,1,0,0,0,0.002497118709181713,0.002497118709181713,directed multigraph,10413,integer,unsymmetric,no,no,missing,directed multigraph -1522,Pajek,SmaGri,1059,1059,4919,1,0,0,0,0.0008133387555917039,0.0008133387555917039,directed multigraph,4919,integer,unsymmetric,no,no,missing,directed multigraph -1523,Pajek,SmallW,396,396,994,1,0,0,0,0.0,0.0,directed multigraph,994,integer,unsymmetric,no,no,missing,directed multigraph -1524,Pajek,Stranke94,10,10,90,1,0,0,0,1.0,1.0,undirected weighted graph,90,integer,symmetric,no,no,missing,undirected weighted graph -1525,Pajek,Tina_AskCal,11,11,29,1,1,0,0,0.2758620689655172,0.2758620689655172,directed graph,29,binary,unsymmetric,no,no,missing,directed graph -1526,Pajek,Tina_AskCog,11,11,36,1,1,0,0,0.5,0.5,directed graph,36,binary,unsymmetric,no,no,missing,directed graph -1527,Pajek,Tina_DisCal,11,11,41,1,1,0,0,0.4390243902439024,0.4390243902439024,directed graph,41,binary,unsymmetric,no,no,missing,directed graph -1528,Pajek,Tina_DisCog,11,11,48,1,1,0,0,0.5,0.5,directed graph,48,binary,unsymmetric,no,no,missing,directed graph -1529,Pajek,USAir97,332,332,4252,1,0,0,0,1.0,1.0,undirected weighted graph,4252,real,symmetric,no,no,missing,undirected weighted graph -1530,Pajek,USpowerGrid,4941,4941,13188,1,1,0,0,1.0,1.0,undirected graph,13188,binary,symmetric,no,no,missing,undirected graph -1531,Pajek,Wordnet3,82670,82670,132964,1,0,0,0,0.1765463136851033,0.1741230992912098,directed weighted graph,132964,integer,unsymmetric,no,no,missing,directed weighted graph -1532,Pajek,WorldCities,315,100,7518,1,0,0,0,0.0,0.0,weighted bipartite graph,7518,integer,rectangular,no,no,true,weighted bipartite graph -1533,Pajek,yeast,2361,2361,13828,1,1,0,0,1.0,1.0,undirected graph,13828,binary,symmetric,no,no,missing,undirected graph -1534,Pajek,Zewail,6752,6752,54233,1,0,0,0,0.001843929783153858,0.001843929783153858,directed multigraph,54233,integer,unsymmetric,no,no,missing,directed multigraph -1535,Zitney,extr1b,2836,2836,10965,1,0,0,0,0.003859310586790632,0.0,chemical process simulation problem sequence,11404,real,unsymmetric,no,no,true,chemical process simulation problem sequence -1536,Zitney,hydr1c,5308,5308,22592,1,0,0,0,0.004126315789473685,0.0006197432492253209,chemical process simulation problem sequence,23752,real,unsymmetric,no,no,true,chemical process simulation problem sequence -1537,Schenk_IBMNA,c-18,2169,2169,15145,1,0,0,0,1.0,1.0,optimization problem,15145,real,symmetric,no,no,true,optimization problem -1538,Schenk_IBMNA,c-19,2327,2327,21817,1,0,0,0,1.0,1.0,optimization problem,21817,real,symmetric,no,no,true,optimization problem -1539,Schenk_IBMNA,c-20,2921,2921,20445,1,0,0,0,1.0,1.0,optimization problem,22685,real,symmetric,no,no,true,optimization problem -1540,Schenk_IBMNA,c-21,3509,3509,32145,1,0,0,0,1.0,1.0,optimization problem,32157,real,symmetric,no,no,true,optimization problem -1541,Schenk_IBMNA,c-22,3792,3792,28870,1,0,0,0,1.0,1.0,optimization problem,28872,real,symmetric,no,no,true,optimization problem -1542,Schenk_IBMNA,c-23,3969,3969,31079,1,0,0,0,1.0,1.0,optimization problem,31079,real,symmetric,no,no,true,optimization problem -1543,Schenk_IBMNA,c-24,4119,4119,35699,1,0,0,0,1.0,1.0,optimization problem,35699,real,symmetric,no,no,true,optimization problem -1544,Schenk_IBMNA,c-25,3797,3797,49635,1,0,0,0,1.0,1.0,optimization problem,49635,real,symmetric,no,no,true,optimization problem -1545,Schenk_IBMNA,c-26,4307,4307,34537,1,0,0,0,1.0,1.0,optimization problem,34537,real,symmetric,no,no,true,optimization problem -1546,Schenk_IBMNA,c-27,4563,4563,30927,1,0,0,0,1.0,1.0,optimization problem,31375,real,symmetric,no,no,true,optimization problem -1547,Schenk_IBMNA,c-28,4598,4598,30590,1,0,0,0,1.0,1.0,optimization problem,30590,real,symmetric,no,no,true,optimization problem -1548,Schenk_IBMNA,c-29,5033,5033,43731,1,0,0,0,1.0,1.0,optimization problem,43731,real,symmetric,no,no,true,optimization problem -1549,Schenk_IBMNA,c-30,5321,5321,65693,1,0,0,0,1.0,1.0,optimization problem,65693,real,symmetric,no,no,true,optimization problem -1550,Schenk_IBMNA,c-31,5339,5339,78571,1,0,0,0,1.0,1.0,optimization problem,78571,real,symmetric,no,no,true,optimization problem -1551,Schenk_IBMNA,c-32,5975,5975,54471,1,0,0,0,1.0,1.0,optimization problem,54471,real,symmetric,no,no,true,optimization problem -1552,Schenk_IBMNA,c-33,6317,6317,56123,1,0,0,0,1.0,1.0,optimization problem,56123,real,symmetric,no,no,true,optimization problem -1553,Schenk_IBMNA,c-34,6611,6611,64333,1,0,0,0,1.0,1.0,optimization problem,64333,real,symmetric,no,no,true,optimization problem -1554,Schenk_IBMNA,c-35,6537,6537,62891,1,0,0,0,1.0,1.0,optimization problem,62891,real,symmetric,no,no,true,optimization problem -1555,Schenk_IBMNA,c-36,7479,7479,65941,1,0,0,0,1.0,1.0,optimization problem,65941,real,symmetric,no,no,true,optimization problem -1556,Schenk_IBMNA,c-37,8204,8204,74676,1,0,0,0,1.0,1.0,optimization problem,74676,real,symmetric,no,no,true,optimization problem -1557,Schenk_IBMNA,c-38,8127,8127,77689,1,0,0,0,1.0,1.0,optimization problem,77689,real,symmetric,no,no,true,optimization problem -1558,Schenk_IBMNA,c-39,9271,9271,116587,1,0,0,0,1.0,1.0,optimization problem,136811,real,symmetric,no,no,true,optimization problem -1559,Schenk_IBMNA,c-40,9941,9941,81501,1,0,0,0,1.0,1.0,optimization problem,81501,real,symmetric,no,no,true,optimization problem -1560,Schenk_IBMNA,c-41,9769,9769,101635,1,0,0,0,1.0,1.0,optimization problem,101745,real,symmetric,no,no,true,optimization problem -1561,Schenk_IBMNA,c-42,10471,10471,110285,1,0,0,0,1.0,1.0,optimization problem,110285,real,symmetric,no,no,true,optimization problem -1562,Schenk_IBMNA,c-43,11125,11125,123659,1,0,0,0,1.0,1.0,optimization problem,123675,real,symmetric,no,no,true,optimization problem -1563,Schenk_IBMNA,c-44,10728,10728,85000,1,0,0,0,1.0,1.0,optimization problem,85000,real,symmetric,no,no,true,optimization problem -1564,Schenk_IBMNA,c-45,13206,13206,174452,1,0,0,0,1.0,1.0,optimization problem,174452,real,symmetric,no,no,true,optimization problem -1565,Schenk_IBMNA,c-46,14913,14913,130397,1,0,0,0,1.0,1.0,optimization problem,130397,real,symmetric,no,no,true,optimization problem -1566,Schenk_IBMNA,c-47,15343,15343,211401,1,0,0,0,1.0,1.0,optimization problem,211401,real,symmetric,no,no,true,optimization problem -1567,Schenk_IBMNA,c-48,18354,18354,166080,1,0,0,0,1.0,1.0,optimization problem,166080,real,symmetric,no,no,true,optimization problem -1568,Schenk_IBMNA,c-49,21132,21132,157040,1,0,0,0,1.0,1.0,optimization problem,157042,real,symmetric,no,no,true,optimization problem -1569,Schenk_IBMNA,c-50,22401,22401,180245,1,0,0,0,1.0,1.0,optimization problem,193625,real,symmetric,no,no,true,optimization problem -1570,Schenk_IBMNA,c-51,23196,23196,203048,1,0,0,0,1.0,1.0,optimization problem,203050,real,symmetric,no,no,true,optimization problem -1571,Schenk_IBMNA,c-52,23948,23948,202708,1,0,0,0,1.0,1.0,optimization problem,202716,real,symmetric,no,no,true,optimization problem -1572,Schenk_IBMNA,c-53,30235,30235,355139,1,0,0,0,1.0,1.0,optimization problem,372213,real,symmetric,no,no,true,optimization problem -1573,Schenk_IBMNA,c-54,31793,31793,385987,1,0,0,0,1.0,1.0,optimization problem,391693,real,symmetric,no,no,true,optimization problem -1574,Schenk_IBMNA,c-56,35910,35910,380240,1,0,0,0,1.0,1.0,optimization problem,380900,real,symmetric,no,no,true,optimization problem -1575,Schenk_IBMNA,c-57,37833,37833,403373,1,0,0,0,1.0,1.0,optimization problem,405197,real,symmetric,no,no,true,optimization problem -1576,Schenk_IBMNA,c-60,43640,43640,298570,1,0,0,0,1.0,1.0,optimization problem,298578,real,symmetric,no,no,true,optimization problem -1577,Schenk_IBMNA,c-61,43618,43618,310016,1,0,0,0,1.0,1.0,optimization problem,310016,real,symmetric,no,no,true,optimization problem -1578,Schenk_IBMNA,c-65,48066,48066,360428,1,0,0,0,1.0,1.0,optimization problem,360528,real,symmetric,no,no,true,optimization problem -1579,Schenk_IBMNA,c-big,345241,345241,2340859,1,0,0,0,1.0,1.0,optimization problem,2341011,real,symmetric,no,no,true,optimization problem -1580,Schenk_AFE,af_0_k101,503625,503625,17550675,1,0,1,1,1.0,1.0,structural problem,17550675,real,symmetric,yes,yes,true,structural problem -1581,Schenk_AFE,af_1_k101,503625,503625,17550675,1,0,1,1,1.0,1.0,structural problem,17550675,real,symmetric,yes,yes,true,structural problem -1582,Schenk_AFE,af_2_k101,503625,503625,17550675,1,0,1,1,1.0,1.0,structural problem,17550675,real,symmetric,yes,yes,true,structural problem -1583,Schenk_AFE,af_3_k101,503625,503625,17550675,1,0,1,1,1.0,1.0,structural problem,17550675,real,symmetric,yes,yes,true,structural problem -1584,Schenk_AFE,af_4_k101,503625,503625,17550675,1,0,1,1,1.0,1.0,structural problem,17550675,real,symmetric,yes,yes,true,structural problem -1585,Schenk_AFE,af_5_k101,503625,503625,17550675,1,0,1,1,1.0,1.0,structural problem,17550675,real,symmetric,yes,yes,true,structural problem -1586,Schenk_AFE,af_shell10,1508065,1508065,52259885,1,0,1,0,1.0,1.0,structural problem,52672325,real,symmetric,no,yes,true,structural problem -1587,Schenk_IBMNA,c-64b,51035,51035,707601,1,0,0,0,1.0,1.0,subsequent optimization problem,717841,real,symmetric,no,no,true,subsequent optimization problem -1588,Schenk_IBMNA,c-66b,49989,49989,444851,1,0,0,0,1.0,1.0,subsequent optimization problem,499007,real,symmetric,no,no,true,subsequent optimization problem -1589,Schenk_IBMNA,c-67b,57975,57975,530583,1,0,0,0,1.0,1.0,subsequent optimization problem,531935,real,symmetric,no,no,true,subsequent optimization problem -1590,Schenk_IBMNA,c-73b,169422,169422,1279274,1,0,0,0,1.0,1.0,subsequent optimization problem,1279274,real,symmetric,no,no,true,subsequent optimization problem -1591,QCD,conf5_0-4x4-10,3072,3072,119808,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,119808,complex,unsymmetric,no,no,true,theoretical/quantum chemistry problem -1592,QCD,conf5_0-4x4-14,3072,3072,119808,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,119808,complex,unsymmetric,no,no,true,theoretical/quantum chemistry problem -1593,QCD,conf5_0-4x4-18,3072,3072,119808,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,119808,complex,unsymmetric,no,no,true,theoretical/quantum chemistry problem -1594,QCD,conf5_0-4x4-22,3072,3072,119808,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,119808,complex,unsymmetric,no,no,true,theoretical/quantum chemistry problem -1595,QCD,conf5_0-4x4-26,3072,3072,119808,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,119808,complex,unsymmetric,no,no,true,theoretical/quantum chemistry problem -1596,QCD,conf6_0-4x4-20,3072,3072,119808,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,119808,complex,unsymmetric,no,no,true,theoretical/quantum chemistry problem -1597,QCD,conf6_0-4x4-30,3072,3072,119808,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,119808,complex,unsymmetric,no,no,true,theoretical/quantum chemistry problem -1598,QCD,conf5_4-8x8-05,49152,49152,1916928,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,1916928,complex,unsymmetric,no,no,true,theoretical/quantum chemistry problem -1599,QCD,conf5_4-8x8-10,49152,49152,1916928,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,1916928,complex,unsymmetric,no,no,true,theoretical/quantum chemistry problem -1600,QCD,conf5_4-8x8-15,49152,49152,1916928,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,1916928,complex,unsymmetric,no,no,true,theoretical/quantum chemistry problem -1601,QCD,conf5_4-8x8-20,49152,49152,1916928,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,1916928,complex,unsymmetric,no,no,true,theoretical/quantum chemistry problem -1602,QCD,conf6_0-8x8-20,49152,49152,1916928,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,1916928,complex,unsymmetric,no,no,true,theoretical/quantum chemistry problem -1603,QCD,conf6_0-8x8-30,49152,49152,1916928,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,1916928,complex,unsymmetric,no,no,true,theoretical/quantum chemistry problem -1604,QCD,conf6_0-8x8-80,49152,49152,1916928,0,0,0,0,0.9230769230769231,0.4615384615384616,theoretical/quantum chemistry problem,1916928,complex,unsymmetric,no,no,true,theoretical/quantum chemistry problem -1605,Cylshell,s1rmq4m1,5489,5489,262411,1,0,1,1,1.0,1.0,structural problem,281111,real,symmetric,yes,yes,true,structural problem -1606,Cylshell,s2rmq4m1,5489,5489,263351,1,0,1,1,1.0,1.0,structural problem,281111,real,symmetric,yes,yes,true,structural problem -1607,Cylshell,s3rmq4m1,5489,5489,262943,1,0,1,1,1.0,1.0,structural problem,281111,real,symmetric,yes,yes,true,structural problem -1608,Cylshell,s1rmt3m1,5489,5489,217651,1,0,1,1,1.0,1.0,structural problem,219521,real,symmetric,yes,yes,true,structural problem -1609,Cylshell,s2rmt3m1,5489,5489,217681,1,0,1,1,1.0,1.0,structural problem,219521,real,symmetric,yes,yes,true,structural problem -1610,Cylshell,s3rmt3m1,5489,5489,217669,1,0,1,1,1.0,1.0,structural problem,219521,real,symmetric,yes,yes,true,structural problem -1611,Cylshell,s3rmt3m3,5357,5357,207123,1,0,1,1,1.0,1.0,structural problem,207695,real,symmetric,yes,yes,true,structural problem -1612,Bai,cryg10000,10000,10000,49699,1,0,1,0,0.9974558553112169,0.0,materials problem,49699,real,unsymmetric,no,no,true,materials problem -1613,Bai,cryg2500,2500,2500,12349,1,0,1,0,0.9948218093207433,0.0,materials problem,12349,real,unsymmetric,no,no,true,materials problem -1614,Bai,dw2048,2048,2048,10114,1,0,1,0,0.9848747830399207,0.9481775353334987,electromagnetics problem,10114,real,unsymmetric,no,no,true,electromagnetics problem -1615,Bai,dw8192,8192,8192,41746,1,0,1,0,0.9626274065685164,0.9154199201287477,electromagnetics problem,41746,real,unsymmetric,no,no,true,electromagnetics problem -1616,Bai,dwa512,512,512,2480,1,0,1,0,0.9796747967479674,0.9146341463414634,electromagnetics problem,2480,real,unsymmetric,no,no,true,electromagnetics problem -1617,Bai,dwb512,512,512,2500,1,0,1,0,0.9698189134808853,0.8832997987927566,electromagnetics problem,2500,real,unsymmetric,no,no,true,electromagnetics problem -1618,Bai,dwg961a,961,961,3405,0,0,1,0,1.0,0.36,electromagnetics problem,3405,complex,symmetric,no,no,false,electromagnetics problem -1619,Bai,dwg961b,961,961,10591,0,0,1,0,1.0,0.3476635514018692,electromagnetics problem,10591,complex,symmetric,no,no,true,electromagnetics problem -1620,Bai,mhd1280a,1280,1280,47906,0,0,1,0,0.8978403914290535,0.3060992070187278,electromagnetics problem,47906,complex,unsymmetric,no,no,true,electromagnetics problem -1621,Bai,mhd1280b,1280,1280,22778,0,0,1,1,1.0,1.0,electromagnetics problem,22778,complex,Hermitian,yes,yes,true,electromagnetics problem -1622,Bai,mhd3200a,3200,3200,68026,1,0,1,0,0.7676029990571827,0.2674009667619461,electromagnetics problem,68026,real,unsymmetric,no,no,true,electromagnetics problem -1623,Bai,mhd3200b,3200,3200,18316,1,0,1,1,1.0,1.0,electromagnetics problem,18316,real,symmetric,yes,yes,true,electromagnetics problem -1624,Bai,mhd4800a,4800,4800,102252,1,0,1,0,0.7677879876950033,0.2677033659541848,electromagnetics problem,102252,real,unsymmetric,no,no,true,electromagnetics problem -1625,Bai,mhd4800b,4800,4800,27520,1,0,1,1,1.0,1.0,electromagnetics problem,27520,real,symmetric,yes,yes,true,electromagnetics problem -1626,Bai,qh1484,1484,1484,6110,1,0,0,0,1.0,0.05377207062600321,power network problem,6110,real,unsymmetric,no,no,true,power network problem -1627,Bai,qh768,768,768,2934,1,0,0,0,0.934730056406124,0.0,power network problem,2934,real,unsymmetric,no,no,true,power network problem -1628,Bai,rdb1250,1250,1250,7300,1,0,1,0,1.0,0.7933884297520661,computational fluid dynamics problem,7300,real,unsymmetric,no,no,true,computational fluid dynamics problem -1629,Bai,rdb1250l,1250,1250,7300,1,0,1,0,1.0,0.7933884297520661,computational fluid dynamics problem,7300,real,unsymmetric,no,no,true,computational fluid dynamics problem -1630,Bai,rdb200,200,200,1120,1,0,1,0,1.0,0.782608695652174,computational fluid dynamics problem,1120,real,unsymmetric,no,no,true,computational fluid dynamics problem -1631,Bai,rdb200l,200,200,1120,1,0,1,0,1.0,0.782608695652174,computational fluid dynamics problem,1120,real,unsymmetric,no,no,true,computational fluid dynamics problem -1632,Bai,rdb2048_noL,2048,2048,12032,1,0,1,0,1.0,0.7948717948717948,computational fluid dynamics problem,12032,real,unsymmetric,no,no,true,computational fluid dynamics problem -1633,Bai,rdb3200l,3200,3200,18880,1,0,1,0,1.0,0.7959183673469388,computational fluid dynamics problem,18880,real,unsymmetric,no,no,true,computational fluid dynamics problem -1634,Bai,rdb450,450,450,2580,1,0,1,0,1.0,0.7887323943661971,computational fluid dynamics problem,2580,real,unsymmetric,no,no,true,computational fluid dynamics problem -1635,Bai,rdb450l,450,450,2580,1,0,1,0,1.0,0.7887323943661971,computational fluid dynamics problem,2580,real,unsymmetric,no,no,true,computational fluid dynamics problem -1636,Bai,rdb800l,800,800,4640,1,0,1,0,1.0,0.7916666666666666,computational fluid dynamics problem,4640,real,unsymmetric,no,no,true,computational fluid dynamics problem -1637,Bai,tols1090,1090,1090,3546,1,0,1,0,0.3178758414360509,0.0,computational fluid dynamics problem,3546,real,unsymmetric,no,no,true,computational fluid dynamics problem -1638,Bai,tols2000,2000,2000,5184,1,0,1,0,0.3387276785714285,0.0,computational fluid dynamics problem,5184,real,unsymmetric,no,no,true,computational fluid dynamics problem -1639,Bai,tols340,340,340,2196,1,0,1,0,0.2858627858627859,0.0,computational fluid dynamics problem,2196,real,unsymmetric,no,no,true,computational fluid dynamics problem -1640,Bai,tols4000,4000,4000,8784,1,0,1,0,0.3606733524355301,0.0,computational fluid dynamics problem,8784,real,unsymmetric,no,no,true,computational fluid dynamics problem -1641,Bai,tols90,90,90,1746,1,0,1,0,0.2688172043010753,0.0,computational fluid dynamics problem,1746,real,unsymmetric,no,no,true,computational fluid dynamics problem -1642,POLYFLOW,invextr1_new,30412,30412,1793881,1,0,1,0,0.971442083756505,0.7213486599424203,computational fluid dynamics problem,1793881,real,unsymmetric,no,no,true,computational fluid dynamics problem -1643,POLYFLOW,mixtank_new,29957,29957,1990919,1,0,1,0,1.0,0.9890905426943581,computational fluid dynamics problem,1995041,real,unsymmetric,no,no,true,computational fluid dynamics problem -1644,INPRO,msdoor,415863,415863,19173163,1,0,1,1,1.0,1.0,structural problem,20240935,real,symmetric,yes,yes,true,structural problem -1645,Mittelmann,nug08-3rd,19728,29856,148416,1,0,0,0,0.0,0.0,linear programming problem,148416,integer,rectangular,no,no,true,linear programming problem -1646,Mittelmann,pds-30,49944,158489,340635,1,0,0,0,0.0,0.0,linear programming problem,340635,integer,rectangular,no,no,false,linear programming problem -1647,Mittelmann,pds-40,66844,217531,466800,1,0,0,0,0.0,0.0,linear programming problem,466800,integer,rectangular,no,no,false,linear programming problem -1648,Mittelmann,pds-50,83060,275814,590833,1,0,0,0,0.0,0.0,linear programming problem,590833,integer,rectangular,no,no,false,linear programming problem -1649,Mittelmann,pds-60,99431,336421,719557,1,0,0,0,0.0,0.0,linear programming problem,719557,integer,rectangular,no,no,false,linear programming problem -1650,Mittelmann,pds-70,114944,390005,833465,1,0,0,0,0.0,0.0,linear programming problem,833465,integer,rectangular,no,no,false,linear programming problem -1651,Mittelmann,pds-80,129181,434580,927826,1,0,0,0,0.0,0.0,linear programming problem,927826,integer,rectangular,no,no,false,linear programming problem -1652,Mittelmann,pds-90,142823,475448,1014136,1,0,0,0,0.0,0.0,linear programming problem,1014136,integer,rectangular,no,no,false,linear programming problem -1653,Mittelmann,pds-100,156243,514577,1096002,1,0,0,0,0.0,0.0,linear programming problem,1096002,integer,rectangular,no,no,false,linear programming problem -1654,Mittelmann,rail507,507,63516,409856,1,0,0,0,0.0,0.0,linear programming problem,409856,integer,rectangular,no,no,true,linear programming problem -1655,Mittelmann,rail516,516,47827,315412,1,0,0,0,0.0,0.0,linear programming problem,315412,integer,rectangular,no,no,true,linear programming problem -1656,Mittelmann,rail582,582,56097,402290,1,0,0,0,0.0,0.0,linear programming problem,402290,integer,rectangular,no,no,true,linear programming problem -1657,Mittelmann,rail2586,2586,923269,8011362,1,0,0,0,0.0,0.0,linear programming problem,8011362,integer,rectangular,no,no,true,linear programming problem -1658,Mittelmann,rail4284,4284,1096894,11284032,1,0,0,0,0.0,0.0,linear programming problem,11284032,integer,rectangular,no,no,true,linear programming problem -1659,Mittelmann,sgpf5y6,246077,312540,831976,1,0,0,0,0.0,0.0,linear programming problem,831976,integer,rectangular,no,no,true,linear programming problem -1660,Mittelmann,stormG2_1000,528185,1377306,3459881,1,0,0,0,0.0,0.0,linear programming problem,3459881,real,rectangular,no,no,false,linear programming problem -1661,Mittelmann,watson_1,201155,386992,1055093,1,0,0,0,0.0,0.0,linear programming problem,1055093,real,rectangular,no,no,true,linear programming problem -1662,Mittelmann,watson_2,352013,677224,1846391,1,0,0,0,0.0,0.0,linear programming problem,1846391,real,rectangular,no,no,true,linear programming problem -1663,Mittelmann,cont11_l,1468599,1961394,5382999,1,0,0,0,0.0,0.0,linear programming problem,5382999,real,rectangular,no,no,true,linear programming problem -1664,Mittelmann,cont1_l,1918399,1921596,7031999,1,0,0,0,0.0,0.0,linear programming problem,7031999,real,rectangular,no,no,true,linear programming problem -1665,Mittelmann,fome11,12142,24460,71264,1,0,0,0,0.0,0.0,linear programming problem,71264,real,rectangular,no,no,true,linear programming problem -1666,Mittelmann,fome12,24284,48920,142528,1,0,0,0,0.0,0.0,linear programming problem,142528,real,rectangular,no,no,true,linear programming problem -1667,Mittelmann,fome13,48568,97840,285056,1,0,0,0,0.0,0.0,linear programming problem,285056,real,rectangular,no,no,true,linear programming problem -1668,Mittelmann,fome20,33874,108175,232647,1,0,0,0,0.0,0.0,linear programming problem,232647,integer,rectangular,no,no,false,linear programming problem -1669,Mittelmann,fome21,67748,216350,465294,1,0,0,0,0.0,0.0,linear programming problem,465294,integer,rectangular,no,no,false,linear programming problem -1670,Mittelmann,neos,479119,515905,1526794,1,0,0,0,0.0,0.0,linear programming problem,1526794,real,rectangular,no,no,true,linear programming problem -1671,Mittelmann,neos1,131581,133473,599590,1,0,0,0,0.0,0.0,linear programming problem,599590,integer,rectangular,no,no,true,linear programming problem -1672,Mittelmann,neos2,132568,134128,685087,1,0,0,0,0.0,0.0,linear programming problem,685087,integer,rectangular,no,no,true,linear programming problem -1673,Mittelmann,neos3,512209,518832,2055024,1,0,0,0,0.0,0.0,linear programming problem,2055024,integer,rectangular,no,no,true,linear programming problem -1674,Mittelmann,spal_004,10203,321696,46168124,1,0,0,0,0.0,0.0,linear programming problem,46168124,real,rectangular,no,no,true,linear programming problem -1675,Meszaros,gams10am,114,171,407,1,0,0,0,0.0,0.0,linear programming problem,407,real,rectangular,no,no,true,linear programming problem -1676,Meszaros,gams30am,354,531,1287,1,0,0,0,0.0,0.0,linear programming problem,1287,real,rectangular,no,no,true,linear programming problem -1677,Meszaros,gams60am,714,1071,2607,1,0,0,0,0.0,0.0,linear programming problem,2607,real,rectangular,no,no,true,linear programming problem -1678,Meszaros,gas11,459,862,2166,1,0,0,0,0.0,0.0,linear programming problem,2166,real,rectangular,no,no,true,linear programming problem -1679,Meszaros,aa01,823,8904,72965,1,1,0,0,0.0,0.0,linear programming problem,72965,binary,rectangular,no,no,true,linear programming problem -1680,Meszaros,aa03,825,8627,70806,1,1,0,0,0.0,0.0,linear programming problem,70806,binary,rectangular,no,no,false,linear programming problem -1681,Meszaros,aa3,825,8627,70806,1,1,0,0,0.0,0.0,linear programming problem,70806,binary,rectangular,no,no,false,linear programming problem -1682,Meszaros,aa4,426,7195,52121,1,1,0,0,0.0,0.0,linear programming problem,52121,binary,rectangular,no,no,true,linear programming problem -1683,Meszaros,aa5,801,8308,65953,1,1,0,0,0.0,0.0,linear programming problem,65953,binary,rectangular,no,no,false,linear programming problem -1684,Meszaros,aa6,646,7292,51728,1,1,0,0,0.0,0.0,linear programming problem,51728,binary,rectangular,no,no,true,linear programming problem -1685,Meszaros,air02,50,6774,61555,1,1,0,0,0.0,0.0,linear programming problem,61555,binary,rectangular,no,no,true,linear programming problem -1686,Meszaros,air03,124,10757,91028,1,1,0,0,0.0,0.0,linear programming problem,91028,binary,rectangular,no,no,true,linear programming problem -1687,Meszaros,air04,823,8904,72965,1,1,0,0,0.0,0.0,linear programming problem,72965,binary,rectangular,no,no,true,linear programming problem -1688,Meszaros,air05,426,7195,52121,1,1,0,0,0.0,0.0,linear programming problem,52121,binary,rectangular,no,no,true,linear programming problem -1689,Meszaros,air06,825,8627,70806,1,1,0,0,0.0,0.0,linear programming problem,70806,binary,rectangular,no,no,false,linear programming problem -1690,Meszaros,aircraft,3754,7517,20267,1,0,0,0,0.0,0.0,linear programming problem,20267,integer,rectangular,no,no,true,linear programming problem -1691,Meszaros,bas1lp,5411,9825,587775,1,0,0,0,0.0,0.0,linear programming problem,587775,integer,rectangular,no,no,true,linear programming problem -1692,Meszaros,baxter,27441,30733,111576,1,0,0,0,0.0,0.0,linear programming problem,111576,real,rectangular,no,no,false,linear programming problem -1693,Meszaros,car4,16384,33052,63724,1,0,0,0,0.0,0.0,linear programming problem,63724,real,rectangular,no,no,true,linear programming problem -1694,Meszaros,cari,400,1200,152800,1,0,0,0,0.0,0.0,linear programming problem,152800,real,rectangular,no,no,true,linear programming problem -1695,Meszaros,ch,3700,8291,24102,1,0,0,0,0.0,0.0,linear programming problem,24102,real,rectangular,no,no,true,linear programming problem -1696,Meszaros,co5,5774,12325,57993,1,0,0,0,0.0,0.0,linear programming problem,57993,real,rectangular,no,no,true,linear programming problem -1697,Meszaros,co9,10789,22924,109651,1,0,0,0,0.0,0.0,linear programming problem,109651,real,rectangular,no,no,true,linear programming problem -1698,Meszaros,complex,1023,1408,46463,1,0,0,0,0.0,0.0,linear programming problem,46463,integer,rectangular,no,no,true,linear programming problem -1699,Meszaros,cq5,5048,11748,51571,1,0,0,0,0.0,0.0,linear programming problem,51571,real,rectangular,no,no,true,linear programming problem -1700,Meszaros,cq9,9278,21534,96653,1,0,0,0,0.0,0.0,linear programming problem,96653,real,rectangular,no,no,true,linear programming problem -1701,Meszaros,cr42,905,1513,6614,1,0,0,0,0.0,0.0,linear programming problem,6614,real,rectangular,no,no,true,linear programming problem -1702,Meszaros,crew1,135,6469,46950,1,1,0,0,0.0,0.0,linear programming problem,46950,binary,rectangular,no,no,true,linear programming problem -1703,Meszaros,dano3mip,3202,15851,81633,1,0,0,0,0.0,0.0,linear programming problem,81633,real,rectangular,no,no,true,linear programming problem -1704,Meszaros,dbic1,43200,226317,1081843,1,0,0,0,0.0,0.0,linear programming problem,1081843,integer,rectangular,no,no,true,linear programming problem -1705,Meszaros,dbir1,18804,45775,1077025,1,0,0,0,0.0,0.0,linear programming problem,1077025,integer,rectangular,no,no,false,linear programming problem -1706,Meszaros,dbir2,18906,45877,1158159,1,0,0,0,0.0,0.0,linear programming problem,1158159,integer,rectangular,no,no,false,linear programming problem -1707,Meszaros,df2177,630,10358,22336,1,1,0,0,0.0,0.0,linear programming problem,22336,binary,rectangular,no,no,true,linear programming problem -1708,Meszaros,e18,24617,38602,156466,1,0,0,0,0.0,0.0,linear programming problem,156466,integer,rectangular,no,no,true,linear programming problem -1709,Meszaros,ex3sta1,17443,17516,68779,1,0,0,0,0.0,0.0,linear programming problem,68779,real,rectangular,no,no,true,linear programming problem -1710,Meszaros,farm,7,17,41,1,0,0,0,0.0,0.0,linear programming problem,41,integer,rectangular,no,no,true,linear programming problem -1711,Meszaros,gams10a,114,171,407,1,0,0,0,0.0,0.0,linear programming problem,407,real,rectangular,no,no,true,linear programming problem -1712,Meszaros,gams30a,354,531,1287,1,0,0,0,0.0,0.0,linear programming problem,1287,real,rectangular,no,no,true,linear programming problem -1713,Meszaros,ge,10099,16369,44825,1,0,0,0,0.0,0.0,linear programming problem,44825,real,rectangular,no,no,true,linear programming problem -1714,Meszaros,iiasa,669,3639,7317,1,0,0,0,0.0,0.0,linear programming problem,7317,real,rectangular,no,no,true,linear programming problem -1715,Meszaros,jendrec1,2109,4228,89608,1,0,0,0,0.0,0.0,linear programming problem,89608,real,rectangular,no,no,true,linear programming problem -1716,Meszaros,kl02,71,36699,212536,1,1,0,0,0.0,0.0,linear programming problem,212536,binary,rectangular,no,no,true,linear programming problem -1717,Meszaros,l9,244,1483,4659,1,0,0,0,0.0,0.0,linear programming problem,4659,real,rectangular,no,no,true,linear programming problem -1718,Meszaros,lp22,2958,16392,68518,1,0,0,0,0.0,0.0,linear programming problem,68518,integer,rectangular,no,no,true,linear programming problem -1719,Meszaros,lpl2,3294,10881,32232,1,0,0,0,0.0,0.0,linear programming problem,32232,integer,rectangular,no,no,true,linear programming problem -1720,Meszaros,lpl3,10828,33686,100525,1,0,0,0,0.0,0.0,linear programming problem,100525,integer,rectangular,no,no,true,linear programming problem -1721,Meszaros,mod2,34774,66409,199810,1,0,0,0,0.0,0.0,linear programming problem,199810,real,rectangular,no,no,true,linear programming problem -1722,Meszaros,model1,362,798,3028,1,0,0,0,0.0,0.0,linear programming problem,3028,real,rectangular,no,no,true,linear programming problem -1723,Meszaros,model2,379,1321,7607,1,0,0,0,0.0,0.0,linear programming problem,7607,real,rectangular,no,no,true,linear programming problem -1724,Meszaros,model3,1609,4578,23974,1,0,0,0,0.0,0.0,linear programming problem,23974,real,rectangular,no,no,true,linear programming problem -1725,Meszaros,model4,1337,4962,45753,1,0,0,0,0.0,0.0,linear programming problem,45753,real,rectangular,no,no,true,linear programming problem -1726,Meszaros,model5,1888,11802,89925,1,0,0,0,0.0,0.0,linear programming problem,89925,real,rectangular,no,no,false,linear programming problem -1727,Meszaros,model6,2096,5289,27628,1,0,0,0,0.0,0.0,linear programming problem,27628,real,rectangular,no,no,false,linear programming problem -1728,Meszaros,model7,3358,9582,51027,1,0,0,0,0.0,0.0,linear programming problem,51027,real,rectangular,no,no,true,linear programming problem -1729,Meszaros,model8,2896,6464,25277,1,0,0,0,0.0,0.0,linear programming problem,25277,real,rectangular,no,no,true,linear programming problem -1730,Meszaros,model9,2879,10939,55956,1,0,0,0,0.0,0.0,linear programming problem,55956,real,rectangular,no,no,false,linear programming problem -1731,Meszaros,model10,4400,16819,150372,1,0,0,0,0.0,0.0,linear programming problem,150372,real,rectangular,no,no,true,linear programming problem -1732,Meszaros,nemsafm,334,2348,2826,1,0,0,0,0.0,0.0,linear programming problem,2826,real,rectangular,no,no,true,linear programming problem -1733,Meszaros,nemscem,651,1712,3840,1,0,0,0,0.0,0.0,linear programming problem,3840,real,rectangular,no,no,true,linear programming problem -1734,Meszaros,nemsemm1,3945,75352,1053986,1,0,0,0,0.0,0.0,linear programming problem,1053986,real,rectangular,no,no,true,linear programming problem -1735,Meszaros,nemsemm2,6943,48878,182012,1,0,0,0,0.0,0.0,linear programming problem,182012,real,rectangular,no,no,true,linear programming problem -1736,Meszaros,nemspmm1,2372,8903,55867,1,0,0,0,0.0,0.0,linear programming problem,55867,real,rectangular,no,no,false,linear programming problem -1737,Meszaros,nemspmm2,2301,8734,68225,1,0,0,0,0.0,0.0,linear programming problem,68225,real,rectangular,no,no,true,linear programming problem -1738,Meszaros,nemswrld,7138,28550,192283,1,0,0,0,0.0,0.0,linear programming problem,192283,real,rectangular,no,no,false,linear programming problem -1739,Meszaros,nl,7039,15325,47035,1,0,0,0,0.0,0.0,linear programming problem,47035,real,rectangular,no,no,true,linear programming problem -1740,Meszaros,nw14,73,123409,904910,1,1,0,0,0.0,0.0,linear programming problem,904910,binary,rectangular,no,no,true,linear programming problem -1741,Meszaros,p0033,15,48,113,1,0,0,0,0.0,0.0,linear programming problem,113,integer,rectangular,no,no,true,linear programming problem -1742,Meszaros,p0040,23,63,133,1,0,0,0,0.0,0.0,linear programming problem,133,integer,rectangular,no,no,true,linear programming problem -1743,Meszaros,p010,10090,19090,118000,1,0,0,0,0.0,0.0,linear programming problem,118000,real,rectangular,no,no,true,linear programming problem -1744,Meszaros,p0201,133,334,2056,1,0,0,0,0.0,0.0,linear programming problem,2056,integer,rectangular,no,no,true,linear programming problem -1745,Meszaros,p0282,241,523,2207,1,0,0,0,0.0,0.0,linear programming problem,2207,integer,rectangular,no,no,true,linear programming problem -1746,Meszaros,p0291,252,543,2283,1,0,0,0,0.0,0.0,linear programming problem,2283,real,rectangular,no,no,true,linear programming problem -1747,Meszaros,p0548,176,724,1887,1,0,0,0,0.0,0.0,linear programming problem,1887,integer,rectangular,no,no,true,linear programming problem -1748,Meszaros,p05,5090,9590,59045,1,0,0,0,0.0,0.0,linear programming problem,59045,real,rectangular,no,no,true,linear programming problem -1749,Meszaros,p2756,755,3511,9692,1,0,0,0,0.0,0.0,linear programming problem,9692,integer,rectangular,no,no,true,linear programming problem -1750,Meszaros,p6000,2095,7967,19826,1,0,0,0,0.0,0.0,linear programming problem,19826,integer,rectangular,no,no,true,linear programming problem -1751,Meszaros,pcb1000,1565,2820,20463,1,0,0,0,0.0,0.0,linear programming problem,20463,integer,rectangular,no,no,true,linear programming problem -1752,Meszaros,pcb3000,3960,7732,57479,1,0,0,0,0.0,0.0,linear programming problem,57479,integer,rectangular,no,no,true,linear programming problem -1753,Meszaros,pf2177,9728,10178,30984,1,1,0,0,0.0,0.0,linear programming problem,30984,binary,rectangular,no,no,true,linear programming problem -1754,Meszaros,primagaz,1554,10836,21665,1,0,0,0,0.0,0.0,linear programming problem,21665,integer,rectangular,no,no,true,linear programming problem -1755,Meszaros,problem,12,46,86,1,0,0,0,0.0,0.0,linear programming problem,86,integer,rectangular,no,no,true,linear programming problem -1756,Meszaros,progas,1650,1900,8897,1,0,0,0,0.0,0.0,linear programming problem,8897,real,rectangular,no,no,true,linear programming problem -1757,Meszaros,qiulp,1192,1900,4492,1,0,0,0,0.0,0.0,linear programming problem,4492,real,rectangular,no,no,true,linear programming problem -1758,Meszaros,r05,5190,9690,104145,1,0,0,0,0.0,0.0,linear programming problem,104145,real,rectangular,no,no,true,linear programming problem -1759,Meszaros,refine,29,62,153,1,0,0,0,0.0,0.0,linear programming problem,153,real,rectangular,no,no,true,linear programming problem -1760,Meszaros,rlfddd,4050,61521,264627,1,0,0,0,0.0,0.0,linear programming problem,264627,integer,rectangular,no,no,true,linear programming problem -1761,Meszaros,rlfdual,8052,74970,282031,1,0,0,0,0.0,0.0,linear programming problem,282031,integer,rectangular,no,no,true,linear programming problem -1762,Meszaros,rlfprim,58866,62716,320591,1,0,0,0,0.0,0.0,linear programming problem,320591,integer,rectangular,no,no,true,linear programming problem -1763,Meszaros,rosen1,520,1544,23794,1,0,0,0,0.0,0.0,linear programming problem,23794,integer,rectangular,no,no,true,linear programming problem -1764,Meszaros,rosen2,1032,3080,47536,1,0,0,0,0.0,0.0,linear programming problem,47536,integer,rectangular,no,no,true,linear programming problem -1765,Meszaros,rosen7,264,776,8034,1,0,0,0,0.0,0.0,linear programming problem,8034,integer,rectangular,no,no,true,linear programming problem -1766,Meszaros,rosen8,520,1544,16058,1,0,0,0,0.0,0.0,linear programming problem,16058,integer,rectangular,no,no,true,linear programming problem -1767,Meszaros,rosen10,2056,6152,64192,1,0,0,0,0.0,0.0,linear programming problem,64192,integer,rectangular,no,no,true,linear programming problem -1768,Meszaros,route,20894,43019,206782,1,0,0,0,0.0,0.0,linear programming problem,206782,real,rectangular,no,no,true,linear programming problem -1769,Meszaros,seymourl,4944,6316,38493,1,0,0,0,0.0,0.0,linear programming problem,38493,integer,rectangular,no,no,true,linear programming problem -1770,Meszaros,slptsk,2861,3347,72465,1,0,0,0,0.0,0.0,linear programming problem,72465,real,rectangular,no,no,true,linear programming problem -1771,Meszaros,south31,18425,36321,112398,1,0,0,0,0.0,0.0,linear programming problem,112398,real,rectangular,no,no,true,linear programming problem -1772,Meszaros,stat96v1,5995,197472,588798,1,0,0,0,0.0,0.0,linear programming problem,588798,real,rectangular,no,no,true,linear programming problem -1773,Meszaros,stat96v2,29089,957432,2852184,1,0,0,0,0.0,0.0,linear programming problem,2852184,real,rectangular,no,no,true,linear programming problem -1774,Meszaros,stat96v3,33841,1113780,3317736,1,0,0,0,0.0,0.0,linear programming problem,3317736,real,rectangular,no,no,true,linear programming problem -1775,Meszaros,stat96v4,3173,63076,491336,1,0,0,0,0.0,0.0,linear programming problem,491336,real,rectangular,no,no,true,linear programming problem -1776,Meszaros,stat96v5,2307,75779,233921,1,0,0,0,0.0,0.0,linear programming problem,233921,real,rectangular,no,no,false,linear programming problem -1777,Meszaros,t0331-4l,664,46915,430982,1,1,0,0,0.0,0.0,linear programming problem,430982,binary,rectangular,no,no,true,linear programming problem -1778,Meszaros,testbig,17613,31223,61639,1,0,0,0,0.0,0.0,linear programming problem,61639,real,rectangular,no,no,true,linear programming problem -1779,Meszaros,ulevimin,6590,46937,164538,1,0,0,0,0.0,0.0,linear programming problem,164538,real,rectangular,no,no,true,linear programming problem -1780,Meszaros,us04,163,28016,297538,1,1,0,0,0.0,0.0,linear programming problem,297538,binary,rectangular,no,no,false,linear programming problem -1781,Meszaros,world,34506,67147,198883,1,0,0,0,0.0,0.0,linear programming problem,198883,real,rectangular,no,no,true,linear programming problem -1782,Meszaros,zed,116,142,666,1,0,0,0,0.0,0.0,linear programming problem,666,real,rectangular,no,no,true,linear programming problem -1783,Meszaros,de063155,852,1848,4913,1,0,0,0,0.0,0.0,linear programming problem,4913,real,rectangular,no,no,true,linear programming problem -1784,Meszaros,de063157,936,1908,5119,1,0,0,0,0.0,0.0,linear programming problem,5119,real,rectangular,no,no,true,linear programming problem -1785,Meszaros,de080285,936,1908,5082,1,0,0,0,0.0,0.0,linear programming problem,5082,real,rectangular,no,no,true,linear programming problem -1786,Meszaros,gen1,769,2561,63086,1,0,0,0,0.0,0.0,linear programming problem,63086,real,rectangular,no,no,true,linear programming problem -1787,Meszaros,gen2,1121,3264,81855,1,0,0,0,0.0,0.0,linear programming problem,81855,real,rectangular,no,no,true,linear programming problem -1788,Meszaros,gen4,1537,4298,107103,1,0,0,0,0.0,0.0,linear programming problem,107103,real,rectangular,no,no,true,linear programming problem -1789,Meszaros,gen,769,2561,63086,1,0,0,0,0.0,0.0,linear programming problem,63086,real,rectangular,no,no,true,linear programming problem -1790,Meszaros,iprob,3001,3001,9000,1,0,0,0,1.0,0.5,linear programming problem,9000,real,unsymmetric,no,no,true,linear programming problem -1791,Meszaros,l30,2701,16281,52070,1,0,0,0,0.0,0.0,linear programming problem,52070,real,rectangular,no,no,true,linear programming problem -1792,Meszaros,stoch_aircraft,3754,7517,20267,1,0,0,0,0.0,0.0,linear programming problem,20267,integer,rectangular,no,no,true,linear programming problem -1793,Meszaros,cep1,1521,4769,8233,1,0,0,0,0.0,0.0,linear programming problem,8233,real,rectangular,no,no,true,linear programming problem -1794,Meszaros,deter0,1923,5468,11173,1,0,0,0,0.0,0.0,linear programming problem,11173,real,rectangular,no,no,true,linear programming problem -1795,Meszaros,deter1,5527,15737,32187,1,0,0,0,0.0,0.0,linear programming problem,32187,real,rectangular,no,no,true,linear programming problem -1796,Meszaros,deter2,6095,17313,35731,1,0,0,0,0.0,0.0,linear programming problem,35731,real,rectangular,no,no,true,linear programming problem -1797,Meszaros,deter3,7647,21777,44547,1,0,0,0,0.0,0.0,linear programming problem,44547,real,rectangular,no,no,true,linear programming problem -1798,Meszaros,deter4,3235,9133,19231,1,0,0,0,0.0,0.0,linear programming problem,19231,real,rectangular,no,no,true,linear programming problem -1799,Meszaros,deter5,5103,14529,29715,1,0,0,0,0.0,0.0,linear programming problem,29715,real,rectangular,no,no,true,linear programming problem -1800,Meszaros,deter6,4255,12113,24771,1,0,0,0,0.0,0.0,linear programming problem,24771,real,rectangular,no,no,true,linear programming problem -1801,Meszaros,deter7,6375,18153,37131,1,0,0,0,0.0,0.0,linear programming problem,37131,real,rectangular,no,no,true,linear programming problem -1802,Meszaros,deter8,3831,10905,22299,1,0,0,0,0.0,0.0,linear programming problem,22299,real,rectangular,no,no,true,linear programming problem -1803,Meszaros,fxm2-6,1520,2845,12812,1,0,0,0,0.0,0.0,linear programming problem,12812,real,rectangular,no,no,true,linear programming problem -1804,Meszaros,fxm2-16,3900,7335,32972,1,0,0,0,0.0,0.0,linear programming problem,32972,real,rectangular,no,no,true,linear programming problem -1805,Meszaros,fxm3_6,6200,12625,57722,1,0,0,0,0.0,0.0,linear programming problem,57722,real,rectangular,no,no,true,linear programming problem -1806,Meszaros,fxm3_16,41340,85575,392252,1,0,0,0,0.0,0.0,linear programming problem,392252,real,rectangular,no,no,true,linear programming problem -1807,Meszaros,fxm4_6,22400,47185,265442,1,0,0,0,0.0,0.0,linear programming problem,265442,real,rectangular,no,no,true,linear programming problem -1808,Meszaros,pgp2,4034,13254,22474,1,0,0,0,0.0,0.0,linear programming problem,22474,integer,rectangular,no,no,true,linear programming problem -1809,Meszaros,stormg2-125,66185,172431,433256,1,0,0,0,0.0,0.0,linear programming problem,433256,real,rectangular,no,no,false,linear programming problem -1810,Meszaros,stormg2-27,14441,37485,94274,1,0,0,0,0.0,0.0,linear programming problem,94274,real,rectangular,no,no,false,linear programming problem -1811,Meszaros,stormg2-8,4409,11322,28553,1,0,0,0,0.0,0.0,linear programming problem,28553,real,rectangular,no,no,false,linear programming problem -1812,Meszaros,degme,185501,659415,8127528,1,0,0,0,0.0,0.0,linear programming problem,8127528,real,rectangular,no,no,true,linear programming problem -1813,Meszaros,karted,46502,133115,1770349,1,0,0,0,0.0,0.0,linear programming problem,1770349,real,rectangular,no,no,true,linear programming problem -1814,Meszaros,tp-6,142752,1014301,11537419,1,0,0,0,0.0,0.0,linear programming problem,11537419,real,rectangular,no,no,true,linear programming problem -1815,Meszaros,ts-palko,22002,47235,1076903,1,0,0,0,0.0,0.0,linear programming problem,1076903,real,rectangular,no,no,true,linear programming problem -1816,Meszaros,delf,3170,6654,15397,1,0,0,0,0.0,0.0,linear programming problem sequence,15397,real,rectangular,no,no,true,linear programming problem sequence -1817,Meszaros,kleemin,8,16,44,1,0,0,0,0.0,0.0,linear programming problem sequence,44,integer,rectangular,no,no,true,linear programming problem sequence -1818,Meszaros,large,4282,8617,20635,1,0,0,0,0.0,0.0,linear programming problem sequence,20635,real,rectangular,no,no,true,linear programming problem sequence -1819,Meszaros,nsct,23003,37563,697738,1,0,0,0,0.0,0.0,linear programming problem sequence,697738,integer,rectangular,no,no,false,linear programming problem sequence -1820,Meszaros,nsic,465,897,3449,1,0,0,0,0.0,0.0,linear programming problem sequence,3449,integer,rectangular,no,no,true,linear programming problem sequence -1821,Meszaros,nsir,4453,10057,154939,1,0,0,0,0.0,0.0,linear programming problem sequence,154939,integer,rectangular,no,no,true,linear programming problem sequence -1822,Meszaros,plddb,3069,5049,10839,1,0,0,0,0.0,0.0,linear programming problem sequence,10839,real,rectangular,no,no,true,linear programming problem sequence -1823,Meszaros,rat,3136,9408,268908,1,0,0,0,0.0,0.0,linear programming problem sequence,268908,real,rectangular,no,no,true,linear programming problem sequence -1824,Meszaros,small,677,1400,3207,1,0,0,0,0.0,0.0,linear programming problem sequence,3207,real,rectangular,no,no,true,linear programming problem sequence -1825,Meszaros,pltexpa,26894,70364,143059,1,0,0,0,0.0,0.0,linear programming problem sequence,143059,integer,rectangular,no,no,true,linear programming problem sequence -1826,Meszaros,sc205-2r,35213,62423,123239,1,0,0,0,0.0,0.0,linear programming problem sequence,123239,real,rectangular,no,no,true,linear programming problem sequence -1827,Meszaros,scagr7-2b,9743,13847,35885,1,0,0,0,0.0,0.0,linear programming problem sequence,35885,real,rectangular,no,no,true,linear programming problem sequence -1828,Meszaros,scagr7-2c,2447,3479,9005,1,0,0,0,0.0,0.0,linear programming problem sequence,9005,real,rectangular,no,no,true,linear programming problem sequence -1829,Meszaros,scagr7-2r,32847,46679,120141,1,0,0,0,0.0,0.0,linear programming problem sequence,120141,real,rectangular,no,no,true,linear programming problem sequence -1830,Meszaros,scfxm1-2b,19036,33047,111052,1,0,0,0,0.0,0.0,linear programming problem sequence,111052,real,rectangular,no,no,true,linear programming problem sequence -1831,Meszaros,scfxm1-2r,37980,65943,221388,1,0,0,0,0.0,0.0,linear programming problem sequence,221388,real,rectangular,no,no,true,linear programming problem sequence -1832,Meszaros,scrs8-2b,1820,3499,7367,1,0,0,0,0.0,0.0,linear programming problem sequence,7367,real,rectangular,no,no,true,linear programming problem sequence -1833,Meszaros,scrs8-2c,1820,3499,7367,1,0,0,0,0.0,0.0,linear programming problem sequence,7367,real,rectangular,no,no,true,linear programming problem sequence -1834,Meszaros,scrs8-2r,14364,27691,58439,1,0,0,0,0.0,0.0,linear programming problem sequence,58439,real,rectangular,no,no,true,linear programming problem sequence -1835,Meszaros,scsd8-2b,5130,35910,112770,1,0,0,0,0.0,0.0,linear programming problem sequence,112770,real,rectangular,no,no,true,linear programming problem sequence -1836,Meszaros,scsd8-2c,5130,35910,112770,1,0,0,0,0.0,0.0,linear programming problem sequence,112770,real,rectangular,no,no,true,linear programming problem sequence -1837,Meszaros,scsd8-2r,8650,60550,190210,1,0,0,0,0.0,0.0,linear programming problem sequence,190210,real,rectangular,no,no,true,linear programming problem sequence -1838,Meszaros,sctap1-2b,15390,33858,99454,1,0,0,0,0.0,0.0,linear programming problem sequence,99454,integer,rectangular,no,no,true,linear programming problem sequence -1839,Meszaros,sctap1-2c,3390,7458,21854,1,0,0,0,0.0,0.0,linear programming problem sequence,21854,integer,rectangular,no,no,true,linear programming problem sequence -1840,Meszaros,sctap1-2r,28830,63426,186366,1,0,0,0,0.0,0.0,linear programming problem sequence,186366,integer,rectangular,no,no,true,linear programming problem sequence -1841,Gleich,wb-cs-stanford,9914,9914,36854,1,1,0,0,0.4572071438616228,0.4572071438616228,directed graph,36854,binary,unsymmetric,no,no,missing,directed graph -1842,Gleich,wb-edu,9845725,9845725,57156537,1,1,0,0,0.3281596169311674,0.3281596169311674,directed graph,57156537,binary,unsymmetric,no,no,missing,directed graph -1843,Gleich,wikipedia-20051105,1634989,1634989,19753078,1,1,0,0,0.1211282592272251,0.1211282592272251,directed graph,19753078,binary,unsymmetric,no,no,missing,directed graph -1844,Gleich,wikipedia-20060925,2983494,2983494,37269096,1,1,0,0,0.1182841447575249,0.1182841447575249,directed graph,37269096,binary,unsymmetric,no,no,missing,directed graph -1845,Gleich,wikipedia-20061104,3148440,3148440,39383235,1,1,0,0,0.1179357318284389,0.1179357318284389,directed graph,39383235,binary,unsymmetric,no,no,missing,directed graph -1846,Gleich,wikipedia-20070206,3566907,3566907,45030389,1,1,0,0,0.1171832378930545,0.1171832378930545,directed graph,45030389,binary,unsymmetric,no,no,missing,directed graph -1847,UTEP,Dubcova1,16129,16129,253009,1,0,1,1,1.0,1.0,2D/3D problem,253009,real,symmetric,yes,yes,true,2D/3D problem -1848,UTEP,Dubcova2,65025,65025,1030225,1,0,1,1,1.0,1.0,2D/3D problem,1030225,real,symmetric,yes,yes,true,2D/3D problem -1849,UTEP,Dubcova3,146689,146689,3636643,1,0,1,1,1.0,1.0,2D/3D problem,3636649,real,symmetric,yes,yes,true,2D/3D problem -1850,BenElechi,BenElechi1,245874,245874,13150496,1,0,1,1,1.0,1.0,2D/3D problem,13150496,real,symmetric,yes,yes,true,2D/3D problem -1851,Botonakis,FEM_3D_thermal1,17880,17880,430740,1,0,1,0,1.0,0.950733904955675,thermal problem,430740,real,unsymmetric,no,no,true,thermal problem -1852,Botonakis,FEM_3D_thermal2,147900,147900,3489300,1,0,1,0,1.0,0.9502082959238642,thermal problem,3489300,real,unsymmetric,no,no,true,thermal problem -1853,Wissgott,parabolic_fem,525825,525825,3674625,1,0,1,1,1.0,1.0,computational fluid dynamics problem,3674625,real,symmetric,yes,yes,true,computational fluid dynamics problem -1854,Watson,chem_master1,40401,40401,201201,1,0,1,0,1.0,0.0,2D/3D problem,201201,real,unsymmetric,no,no,true,2D/3D problem -1855,Watson,Baumann,112211,112211,748331,1,0,1,0,1.0,0.0,2D/3D problem,760631,real,unsymmetric,no,no,true,2D/3D problem -1856,Sinclair,3Dspectralwave,680943,680943,30290827,0,0,1,0,1.0,1.0,materials problem,33650589,complex,Hermitian,no,yes,true,materials problem -1857,Sinclair,3Dspectralwave2,292008,292008,12935272,0,0,1,0,1.0,1.0,materials problem,14322744,complex,Hermitian,no,yes,true,materials problem -1858,QLi,crashbasis,160000,160000,1750416,1,0,0,0,0.5503013048158469,0.0002439613283568576,optimization problem,1750416,real,unsymmetric,no,no,true,optimization problem -1859,QLi,majorbasis,160000,160000,1750416,1,0,0,0,0.5503013048158469,0.0002376736652548767,optimization problem,1750416,real,unsymmetric,no,no,true,optimization problem -1860,Springer,ESOC,327062,37830,6019939,1,0,0,0,0.0,0.0,least squares problem,6019939,real,rectangular,no,no,false,least squares problem -1861,Koutsovasilis,F2,71505,71505,5294285,1,0,1,0,1.0,1.0,structural problem,5294285,real,symmetric,no,yes,true,structural problem -1862,Szczerba,Ill_Stokes,20896,20896,191368,1,0,1,0,0.9897436481194791,0.3299904380293234,computational fluid dynamics problem,191368,real,unsymmetric,no,no,true,computational fluid dynamics problem -1863,Rajat,Raj1,263743,263743,1300261,1,0,0,0,0.998585875116119,0.5762090211971218,circuit simulation problem,1302464,real,unsymmetric,no,no,true,circuit simulation problem -1864,Muite,Chebyshev1,261,261,2319,1,0,1,0,0.5014577259475219,0.0,structural problem,2319,real,unsymmetric,no,no,true,structural problem -1865,Muite,Chebyshev2,2053,2053,18447,1,0,1,0,0.5001829937782115,0.0,structural problem,18447,real,unsymmetric,no,no,true,structural problem -1866,Muite,Chebyshev3,4101,4101,36879,1,0,1,0,0.5000915248032217,0.0,structural problem,36879,real,unsymmetric,no,no,true,structural problem -1867,Muite,Chebyshev4,68121,68121,5377761,1,0,1,0,0.3020408163265306,0.0002369275506437348,structural problem,5377761,real,unsymmetric,no,no,true,structural problem -1868,Quaglino,viscoplastic1,4326,4326,61166,1,0,1,0,0.7408163265306122,0.0,materials problem,61166,real,unsymmetric,no,no,true,materials problem -1869,Quaglino,viscoplastic2,32769,32769,381326,1,0,1,0,0.5699899872904575,0.0,materials problem,381326,real,unsymmetric,no,no,true,materials problem -1870,YCheng,psse0,26722,11028,102432,1,0,0,0,0.0,0.0,power network problem,102432,real,rectangular,no,no,true,power 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graphics/vision problem,37056,real,rectangular,no,no,true,computer graphics/vision problem -1919,Um,2cubes_sphere,101492,101492,1647264,1,0,1,1,1.0,1.0,electromagnetics problem,1647264,real,symmetric,yes,yes,true,electromagnetics problem -1920,JGD_BIBD,bibd_9_3,36,84,252,1,1,0,0,0.0,0.0,combinatorial problem,252,binary,rectangular,no,no,true,combinatorial problem -1921,JGD_BIBD,bibd_9_5,36,126,1260,1,1,0,0,0.0,0.0,combinatorial problem,1260,binary,rectangular,no,no,true,combinatorial problem -1922,JGD_BIBD,bibd_11_5,55,462,4620,1,1,0,0,0.0,0.0,combinatorial problem,4620,binary,rectangular,no,no,true,combinatorial problem -1923,JGD_BIBD,bibd_12_4,66,495,2970,1,1,0,0,0.0,0.0,combinatorial problem,2970,binary,rectangular,no,no,true,combinatorial problem -1924,JGD_BIBD,bibd_12_5,66,792,7920,1,1,0,0,0.0,0.0,combinatorial problem,7920,binary,rectangular,no,no,true,combinatorial problem -1925,JGD_BIBD,bibd_13_6,78,1716,25740,1,1,0,0,0.0,0.0,combinatorial problem,25740,binary,rectangular,no,no,true,combinatorial problem -1926,JGD_BIBD,bibd_14_7,91,3432,72072,1,1,0,0,0.0,0.0,combinatorial problem,72072,binary,rectangular,no,no,true,combinatorial problem -1927,JGD_BIBD,bibd_15_3,105,455,1365,1,1,0,0,0.0,0.0,combinatorial problem,1365,binary,rectangular,no,no,true,combinatorial problem -1928,JGD_BIBD,bibd_15_7,105,6435,135135,1,1,0,0,0.0,0.0,combinatorial problem,135135,binary,rectangular,no,no,true,combinatorial problem -1929,JGD_BIBD,bibd_16_8,120,12870,360360,1,1,0,0,0.0,0.0,combinatorial problem,360360,binary,rectangular,no,no,true,combinatorial problem -1930,JGD_BIBD,bibd_17_3,136,680,2040,1,1,0,0,0.0,0.0,combinatorial problem,2040,binary,rectangular,no,no,true,combinatorial problem -1931,JGD_BIBD,bibd_17_4b,136,2380,14280,1,1,0,0,0.0,0.0,combinatorial problem,14280,binary,rectangular,no,no,true,combinatorial problem -1932,JGD_BIBD,bibd_17_4,136,2380,14280,1,1,0,0,0.0,0.0,combinatorial problem,14280,binary,rectangular,no,no,true,combinatorial problem -1933,JGD_BIBD,bibd_17_8,136,24310,680680,1,1,0,0,0.0,0.0,combinatorial problem,680680,binary,rectangular,no,no,true,combinatorial problem -1934,JGD_BIBD,bibd_18_9,153,48620,1750320,1,1,0,0,0.0,0.0,combinatorial problem,1750320,binary,rectangular,no,no,true,combinatorial problem -1935,JGD_BIBD,bibd_19_9,171,92378,3325608,1,1,0,0,0.0,0.0,combinatorial problem,3325608,binary,rectangular,no,no,true,combinatorial problem -1936,JGD_BIBD,bibd_20_10,190,184756,8314020,1,1,0,0,0.0,0.0,combinatorial problem,8314020,binary,rectangular,no,no,true,combinatorial problem -1937,JGD_BIBD,bibd_22_8,231,319770,8953560,1,1,0,0,0.0,0.0,combinatorial problem,8953560,binary,rectangular,no,no,true,combinatorial problem -1938,JGD_BIBD,bibd_49_3,1176,18424,55272,1,1,0,0,0.0,0.0,combinatorial problem,55272,binary,rectangular,no,no,true,combinatorial problem -1939,JGD_BIBD,bibd_81_2,3240,3240,3240,1,1,0,1,1.0,1.0,combinatorial problem,3240,binary,symmetric,yes,yes,true,combinatorial problem -1940,JGD_BIBD,bibd_81_3,3240,85320,255960,1,1,0,0,0.0,0.0,combinatorial problem,255960,binary,rectangular,no,no,true,combinatorial problem -1941,JGD_CAG,CAG_mat1916,1916,1916,195985,1,0,0,0,0.3002540333592691,0.2119864584245809,combinatorial problem,195985,integer,unsymmetric,no,no,true,combinatorial problem -1942,JGD_CAG,CAG_mat364,364,364,13585,1,0,0,0,0.4155510173209288,0.2804628999319265,combinatorial problem,13585,integer,unsymmetric,no,no,true,combinatorial problem -1943,JGD_CAG,CAG_mat72,72,72,1012,1,0,0,0,0.5574468085106383,0.3340425531914893,combinatorial problem,1012,integer,unsymmetric,no,no,true,combinatorial problem -1944,JGD_Forest,TF10,99,107,622,1,0,0,0,0.0,0.0,combinatorial problem,622,integer,rectangular,no,no,true,combinatorial problem -1945,JGD_Forest,TF11,216,236,1607,1,0,0,0,0.0,0.0,combinatorial problem,1607,integer,rectangular,no,no,true,combinatorial problem 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problem,323509,integer,rectangular,no,no,false,combinatorial problem -1975,JGD_G5,IG5-16,18846,18485,588326,1,0,0,0,0.0,0.0,combinatorial problem,588326,integer,rectangular,no,no,false,combinatorial problem -1976,JGD_G5,IG5-17,30162,27944,1035008,1,0,0,0,0.0,0.0,combinatorial problem,1035008,integer,rectangular,no,no,false,combinatorial problem -1977,JGD_G5,IG5-18,47894,41550,1790490,1,0,0,0,0.0,0.0,combinatorial problem,1790490,integer,rectangular,no,no,false,combinatorial problem -1978,JGD_GL6,GL6_D_6,469,201,2526,1,0,0,0,0.0,0.0,combinatorial problem,2526,integer,rectangular,no,no,false,combinatorial problem -1979,JGD_GL6,GL6_D_7,636,470,5378,1,0,0,0,0.0,0.0,combinatorial problem,5378,integer,rectangular,no,no,false,combinatorial problem -1980,JGD_GL6,GL6_D_8,544,637,6153,1,0,0,0,0.0,0.0,combinatorial problem,6153,integer,rectangular,no,no,false,combinatorial problem -1981,JGD_GL6,GL6_D_9,340,545,4349,1,0,0,0,0.0,0.0,combinatorial problem,4349,integer,rectangular,no,no,false,combinatorial problem -1982,JGD_GL6,GL6_D_10,163,341,2053,1,0,0,0,0.0,0.0,combinatorial problem,2053,integer,rectangular,no,no,false,combinatorial problem -1983,JGD_GL7d,GL7d10,1,60,8,1,0,0,0,0.0,0.0,combinatorial problem,8,integer,rectangular,no,no,true,combinatorial problem -1984,JGD_GL7d,GL7d11,1019,60,1513,1,0,0,0,0.0,0.0,combinatorial problem,1513,integer,rectangular,no,no,true,combinatorial problem -1985,JGD_GL7d,GL7d12,8899,1019,37519,1,0,0,0,0.0,0.0,combinatorial problem,37519,integer,rectangular,no,no,true,combinatorial problem -1986,JGD_GL7d,GL7d13,47271,8899,356232,1,0,0,0,0.0,0.0,combinatorial problem,356232,integer,rectangular,no,no,false,combinatorial problem -1987,JGD_GL7d,GL7d14,171375,47271,1831183,1,0,0,0,0.0,0.0,combinatorial problem,1831183,integer,rectangular,no,no,false,combinatorial problem -1988,JGD_GL7d,GL7d15,460261,171375,6080381,1,0,0,0,0.0,0.0,combinatorial problem,6080381,integer,rectangular,no,no,false,combinatorial problem -1989,JGD_GL7d,GL7d16,955128,460261,14488881,1,0,0,0,0.0,0.0,combinatorial problem,14488881,integer,rectangular,no,no,false,combinatorial problem -1990,JGD_GL7d,GL7d17,1548650,955128,25978098,1,0,0,0,0.0,0.0,combinatorial problem,25978098,integer,rectangular,no,no,false,combinatorial problem -1991,JGD_GL7d,GL7d18,1955309,1548650,35590540,1,0,0,0,0.0,0.0,combinatorial problem,35590540,integer,rectangular,no,no,false,combinatorial problem -1992,JGD_GL7d,GL7d19,1911130,1955309,37322725,1,0,0,0,0.0,0.0,combinatorial problem,37322725,integer,rectangular,no,no,true,combinatorial problem -1993,JGD_GL7d,GL7d20,1437547,1911130,29893084,1,0,0,0,0.0,0.0,combinatorial problem,29893084,integer,rectangular,no,no,false,combinatorial problem -1994,JGD_GL7d,GL7d21,822922,1437547,18174775,1,0,0,0,0.0,0.0,combinatorial problem,18174775,integer,rectangular,no,no,false,combinatorial problem 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problem,71760,binary,symmetric,no,no,true,combinatorial problem -2198,JGD_SPG,EX4,2600,2600,71760,1,1,0,0,1.0,1.0,combinatorial problem,71760,binary,symmetric,no,no,true,combinatorial problem -2199,JGD_SPG,EX5,6545,6545,295680,1,1,0,0,1.0,1.0,combinatorial problem,295680,binary,symmetric,no,no,true,combinatorial problem -2200,JGD_SPG,EX6,6545,6545,295680,1,1,0,0,1.0,1.0,combinatorial problem,295680,binary,symmetric,no,no,true,combinatorial problem -2201,JGD_Taha,abtaha2,37932,331,137228,1,0,0,0,0.0,0.0,combinatorial problem,137228,integer,rectangular,no,no,true,combinatorial problem -2202,JGD_Taha,abtaha1,14596,209,51307,1,0,0,0,0.0,0.0,combinatorial problem,51307,integer,rectangular,no,no,true,combinatorial problem -2203,JGD_Trefethen,Trefethen_20b,19,19,147,1,0,0,1,1.0,1.0,combinatorial problem,147,integer,symmetric,yes,yes,true,combinatorial problem -2204,JGD_Trefethen,Trefethen_20,20,20,158,1,0,0,1,1.0,1.0,combinatorial problem,158,integer,symmetric,yes,yes,true,combinatorial problem -2205,JGD_Trefethen,Trefethen_150,150,150,2040,1,0,0,1,1.0,1.0,combinatorial problem,2040,integer,symmetric,yes,yes,true,combinatorial problem -2206,JGD_Trefethen,Trefethen_200b,199,199,2873,1,0,0,1,1.0,1.0,combinatorial problem,2873,integer,symmetric,yes,yes,true,combinatorial problem -2207,JGD_Trefethen,Trefethen_200,200,200,2890,1,0,0,1,1.0,1.0,combinatorial problem,2890,integer,symmetric,yes,yes,true,combinatorial problem -2208,JGD_Trefethen,Trefethen_300,300,300,4678,1,0,0,1,1.0,1.0,combinatorial problem,4678,integer,symmetric,yes,yes,true,combinatorial problem -2209,JGD_Trefethen,Trefethen_500,500,500,8478,1,0,0,1,1.0,1.0,combinatorial problem,8478,integer,symmetric,yes,yes,true,combinatorial problem -2210,JGD_Trefethen,Trefethen_700,700,700,12654,1,0,0,1,1.0,1.0,combinatorial problem,12654,integer,symmetric,yes,yes,true,combinatorial problem -2211,JGD_Trefethen,Trefethen_2000,2000,2000,41906,1,0,0,1,1.0,1.0,combinatorial problem,41906,integer,symmetric,yes,yes,true,combinatorial problem -2212,JGD_Trefethen,Trefethen_20000b,19999,19999,554435,1,0,0,1,1.0,1.0,combinatorial problem,554435,integer,symmetric,yes,yes,true,combinatorial problem -2213,JGD_Trefethen,Trefethen_20000,20000,20000,554466,1,0,0,1,1.0,1.0,combinatorial problem,554466,integer,symmetric,yes,yes,true,combinatorial problem -2214,QY,case9,14454,14454,147972,1,0,0,0,1.0,1.0,power network problem sequence,147972,real,symmetric,no,no,true,power network problem sequence -2215,FreeFieldTechnologies,mono_500Hz,169410,169410,5033796,0,0,1,0,1.0,0.820796293715178,acoustics problem,5036288,complex,unsymmetric,no,no,true,acoustics problem -2216,Freescale,Freescale1,3428755,3428755,17052626,1,0,0,0,0.9061379876258038,0.3945743467477048,circuit simulation problem,18920347,real,unsymmetric,no,no,true,circuit simulation problem -2217,NYPA,Maragal_8,33212,75077,1308415,1,0,0,0,0.0,0.0,least squares problem,1308415,real,rectangular,no,no,false,least squares problem -2218,Bates,sls,1748122,62729,6804304,1,0,0,0,0.0,0.0,least squares problem,6804304,real,rectangular,no,no,true,least squares problem -2219,TSOPF,TSOPF_RS_b2383,38120,38120,16171169,1,0,0,0,0.002485718029041526,0.0003350401831305099,power network problem,16171169,real,unsymmetric,no,no,true,power network problem -2220,TSOPF,TSOPF_FS_b300,29214,29214,4400122,1,0,0,0,1.0,1.0,power network problem,4400122,real,symmetric,no,no,true,power network problem -2221,TSOPF,TSOPF_FS_b162_c1,10798,10798,608540,1,0,0,0,1.0,1.0,power network problem,608540,real,symmetric,no,no,true,power network problem -2222,TSOPF,TSOPF_FS_b162_c3,30798,30798,1801300,1,0,0,0,1.0,1.0,power network problem,1801300,real,symmetric,no,no,true,power network problem -2223,TSOPF,TSOPF_FS_b162_c4,40798,40798,2398220,1,0,0,0,1.0,1.0,power network problem,2398220,real,symmetric,no,no,true,power network problem -2224,TSOPF,TSOPF_FS_b300_c1,29214,29214,4400122,1,0,0,0,1.0,1.0,power network problem,4400122,real,symmetric,no,no,true,power network problem -2225,TSOPF,TSOPF_FS_b300_c2,56814,56814,8767466,1,0,0,0,1.0,1.0,power network problem,8767466,real,symmetric,no,no,true,power network problem -2226,TSOPF,TSOPF_FS_b300_c3,84414,84414,13135930,1,0,0,0,1.0,1.0,power network problem,13135930,real,symmetric,no,no,true,power network problem -2227,TSOPF,TSOPF_FS_b39_c19,76216,76216,1977600,1,0,0,0,1.0,1.0,power network problem,1977600,real,symmetric,no,no,true,power network problem -2228,TSOPF,TSOPF_FS_b39_c30,120216,120216,3121160,1,0,0,0,1.0,1.0,power network problem,3121160,real,symmetric,no,no,true,power network problem -2229,TSOPF,TSOPF_FS_b39_c7,28216,28216,730080,1,0,0,0,1.0,1.0,power network problem,730080,real,symmetric,no,no,true,power network problem -2230,TSOPF,TSOPF_FS_b9_c1,2454,2454,25032,1,0,0,0,1.0,1.0,power network problem,25032,real,symmetric,no,no,true,power network problem -2231,TSOPF,TSOPF_FS_b9_c6,14454,14454,147972,1,0,0,0,1.0,1.0,power network problem,147972,real,symmetric,no,no,true,power network problem -2232,TSOPF,TSOPF_RS_b162_c1,5374,5374,205399,1,0,0,0,0.02981942459702805,0.002418331351367556,power network problem,205399,real,unsymmetric,no,no,true,power network problem -2233,TSOPF,TSOPF_RS_b162_c3,15374,15374,610299,1,0,0,0,0.02683526094131803,0.0008133934303355415,power network problem,610299,real,unsymmetric,no,no,true,power network problem -2234,TSOPF,TSOPF_RS_b162_c4,20374,20374,812749,1,0,0,0,0.02645844479664045,0.0006107347997698388,power network problem,812749,real,unsymmetric,no,no,true,power network problem -2235,TSOPF,TSOPF_RS_b2052_c1,25626,25626,6761100,1,0,0,0,0.003886561090908282,0.0002050869579089441,power network problem,6761100,real,unsymmetric,no,no,true,power network problem -2236,TSOPF,TSOPF_RS_b2383_c1,38120,38120,16171169,1,0,0,0,0.002485718029041526,0.0003350401831305099,power network problem,16171169,real,unsymmetric,no,no,true,power network problem -2237,TSOPF,TSOPF_RS_b300_c1,14538,14538,1474325,1,0,0,0,0.01034334678703541,0.0003506683588241378,power network problem,1474325,real,unsymmetric,no,no,true,power network problem -2238,TSOPF,TSOPF_RS_b300_c2,28338,28338,2943887,1,0,0,0,0.00991209710027776,0.0001755931670937033,power network problem,2943887,real,unsymmetric,no,no,true,power network problem -2239,TSOPF,TSOPF_RS_b300_c3,42138,42138,4413449,1,0,0,0,0.009768063952888569,0.0001171197776188222,power network problem,4413449,real,unsymmetric,no,no,true,power network problem -2240,TSOPF,TSOPF_RS_b39_c19,38098,38098,684206,1,0,0,0,0.05914025923776359,0.0001300058038305281,power network problem,684206,real,unsymmetric,no,no,true,power network problem -2241,TSOPF,TSOPF_RS_b39_c30,60098,60098,1079986,1,0,0,0,0.05903687108113011,8.236061201778597e-05,power network problem,1079986,real,unsymmetric,no,no,true,power network problem -2242,TSOPF,TSOPF_RS_b39_c7,14098,14098,252446,1,0,0,0,0.05962284731399325,0.0003524007299729407,power network problem,252446,real,unsymmetric,no,no,true,power network problem -2243,TSOPF,TSOPF_RS_b678_c1,18696,18696,4396289,1,0,0,0,0.004448301852317109,0.0003256971884064591,power network problem,4396289,real,unsymmetric,no,no,true,power network problem -2244,TSOPF,TSOPF_RS_b678_c2,35696,35696,8781949,1,0,0,0,0.004170134538170976,0.0001630280691806068,power network problem,8781949,real,unsymmetric,no,no,true,power network problem -2245,TSOPF,TSOPF_RS_b9_c6,7224,7224,54082,1,0,0,0,0.1544436146377894,0.0002560546249866638,power network problem,54082,real,unsymmetric,no,no,true,power network problem -2246,Lee,fem_hifreq_circuit,491100,491100,20239237,0,0,1,0,0.999999037883928,0.4990933575151925,electromagnetics problem,20239237,complex,unsymmetric,no,no,true,electromagnetics problem -2247,Buss,connectus,512,394792,1127525,1,1,0,0,0.0,0.0,undirected bipartite graph,1127525,binary,rectangular,no,no,false,undirected bipartite graph -2248,Yoshiyasu,image_interp,240000,120000,711683,1,0,1,0,0.0,0.0,computer graphics/vision problem,711683,real,rectangular,no,no,true,computer graphics/vision problem -2249,Yoshiyasu,mesh_deform,234023,9393,853829,1,0,1,0,0.0,0.0,computer graphics/vision problem,853829,real,rectangular,no,no,true,computer graphics/vision problem -2250,Priebel,130bit,584,575,6120,1,1,0,0,0.0,0.0,combinatorial problem,6120,binary,rectangular,no,no,false,combinatorial problem -2251,Priebel,145bit,1002,993,11315,1,1,0,0,0.0,0.0,combinatorial problem,11315,binary,rectangular,no,no,false,combinatorial problem -2252,Priebel,162bit,3606,3597,37118,1,1,0,0,0.0,0.0,combinatorial problem,37118,binary,rectangular,no,no,false,combinatorial problem -2253,Priebel,176bit,7441,7431,82270,1,1,0,0,0.0,0.0,combinatorial problem,82270,binary,rectangular,no,no,false,combinatorial problem -2254,Priebel,192bit,13691,13682,154303,1,1,0,0,0.0,0.0,combinatorial problem,154303,binary,rectangular,no,no,false,combinatorial problem -2255,Priebel,208bit,24430,24421,299756,1,1,0,0,0.0,0.0,combinatorial problem,299756,binary,rectangular,no,no,false,combinatorial problem -2256,MathWorks,TS,2142,2142,45262,1,0,0,0,0.9525732031943213,0.04130434782608695,counter-example problem,45262,real,unsymmetric,no,no,false,counter-example problem -2257,Botonakis,thermomech_TC,102158,102158,711558,1,0,1,1,1.0,1.0,thermal problem,711558,real,symmetric,yes,yes,true,thermal problem -2258,Botonakis,thermomech_TK,102158,102158,711558,1,0,1,1,1.0,1.0,thermal problem,711558,real,symmetric,yes,yes,true,thermal problem -2259,Botonakis,thermomech_dM,204316,204316,1423116,1,0,1,1,1.0,1.0,thermal problem,1423116,real,symmetric,yes,yes,true,thermal problem -2260,Botonakis,thermomech_dK,204316,204316,2846228,1,0,1,0,1.0,0.6652727267221619,thermal problem,2846228,real,unsymmetric,no,no,true,thermal problem -2261,MaxPlanck,shallow_water1,81920,81920,327680,1,0,1,1,1.0,1.0,computational fluid dynamics problem,327680,real,symmetric,yes,yes,true,computational fluid dynamics problem -2262,MaxPlanck,shallow_water2,81920,81920,327680,1,0,1,1,1.0,1.0,computational fluid dynamics problem,327680,real,symmetric,yes,yes,true,computational fluid dynamics problem -2263,Buss,12month1,12471,872622,22624727,1,1,0,0,0.0,0.0,undirected bipartite graph,22624727,binary,rectangular,no,no,false,undirected bipartite graph -2264,Clark,tomographic1,73159,59498,647495,1,0,1,0,0.0,0.0,computer graphics/vision problem,647495,real,rectangular,no,no,false,computer graphics/vision problem -2265,Bourchtein,atmosmodd,1270432,1270432,8814880,1,0,1,0,1.0,0.6690207156308852,computational fluid dynamics problem,8814880,real,unsymmetric,no,no,true,computational fluid dynamics problem -2266,Bourchtein,atmosmodj,1270432,1270432,8814880,1,0,1,0,1.0,0.6690207156308852,computational fluid dynamics problem,8814880,real,unsymmetric,no,no,true,computational fluid dynamics problem -2267,Bourchtein,atmosmodl,1489752,1489752,10319760,1,0,1,0,1.0,0.6714503542918647,computational fluid dynamics problem,10319760,real,unsymmetric,no,no,true,computational fluid dynamics problem -2268,Bourchtein,atmosmodm,1489752,1489752,10319760,1,0,1,0,1.0,0.6714503542918647,computational fluid dynamics problem,10319760,integer,unsymmetric,no,no,true,computational fluid dynamics problem -2269,Puri,ABACUS_shell_ud,23412,23412,218484,1,0,1,0,1.0,0.9753731955380578,model reduction problem,218484,real,unsymmetric,no,no,true,model reduction problem -2270,Puri,ABACUS_shell_ld,23412,23412,218484,0,0,1,0,1.0,0.8850885826771654,model reduction problem,218484,complex,unsymmetric,no,no,true,model reduction problem -2271,Puri,ABACUS_shell_md,23412,23412,218484,0,0,1,0,1.0,0.8850885826771654,model reduction problem,218484,complex,unsymmetric,no,no,true,model reduction problem -2272,Puri,ABACUS_shell_hd,23412,23412,218484,0,0,1,0,1.0,0.8850885826771654,model reduction problem,218484,complex,unsymmetric,no,no,true,model reduction problem -2273,Stevenson,LargeRegFile,2111154,801374,4944201,1,0,0,0,0.0,0.0,circuit simulation problem,4944201,real,rectangular,no,no,true,circuit simulation problem -2274,Freescale,memchip,2707524,2707524,13343948,1,0,0,0,0.9058013004223939,0.4015860962358702,circuit simulation problem,14810202,real,unsymmetric,no,no,true,circuit simulation problem -2275,Freescale,transient,178866,178866,961368,1,0,0,0,0.9996742983044353,0.243597788123496,circuit simulation problem,961790,real,unsymmetric,no,no,true,circuit simulation problem -2276,Freescale,circuit5M,5558326,5558326,59524291,1,0,0,0,0.9999998517635799,0.4198865531852634,circuit simulation problem,59524291,real,unsymmetric,no,no,true,circuit simulation problem -2277,Freescale,circuit5M_dc,3523317,3523317,14865409,1,0,0,0,0.9072118240231114,0.7150571517141635,circuit simulation problem,19194193,real,unsymmetric,no,no,true,circuit simulation problem -2278,Grund,poli3,16955,16955,37849,1,0,0,0,0.00507322676366421,0.0005743275581506652,economic problem,37849,real,unsymmetric,no,no,true,economic problem -2279,Grund,poli4,33833,33833,73249,1,0,0,0,0.003551857113862391,0.0002029632636492795,economic problem,73249,real,unsymmetric,no,no,true,economic problem -2280,Belcastro,human_gene1,22283,22283,24669643,1,0,0,0,1.0,1.0,undirected weighted graph,24669643,real,symmetric,no,yes,missing,undirected weighted graph -2281,Belcastro,human_gene2,14340,14340,18068388,1,0,0,0,1.0,1.0,undirected weighted graph,18068388,real,symmetric,no,yes,missing,undirected weighted graph -2282,Belcastro,mouse_gene,45101,45101,28967291,1,0,0,0,1.0,1.0,undirected weighted graph,28967291,real,symmetric,no,yes,missing,undirected weighted graph -2283,Um,offshore,259789,259789,4242673,1,0,1,1,1.0,1.0,electromagnetics problem,4242673,real,symmetric,yes,yes,true,electromagnetics problem -2284,SNAP,soc-Epinions1,75888,75888,508837,1,1,0,0,0.405226035056413,0.405226035056413,directed graph,508837,binary,unsymmetric,no,no,missing,directed graph -2285,SNAP,soc-LiveJournal1,4847571,4847571,68993773,1,1,0,0,0.7484193555024753,0.7484193555024753,directed graph,68993773,binary,unsymmetric,no,no,missing,directed graph -2286,SNAP,soc-Slashdot0811,77360,77360,905468,1,1,0,0,0.8669352939826918,0.8669352939826918,directed graph,905468,binary,unsymmetric,no,no,missing,directed graph -2287,SNAP,soc-Slashdot0902,82168,82168,948464,1,1,0,0,0.8410650442849082,0.8410650442849082,directed graph,948464,binary,unsymmetric,no,no,missing,directed graph -2288,SNAP,wiki-Vote,8297,8297,103689,1,1,0,0,0.0564572905515532,0.0564572905515532,directed graph,103689,binary,unsymmetric,no,no,missing,directed graph -2289,SNAP,email-EuAll,265214,265214,420045,1,1,0,0,0.2600511748250413,0.2600511748250413,directed graph,420045,binary,unsymmetric,no,no,missing,directed graph -2290,SNAP,email-Enron,36692,36692,367662,1,1,0,0,1.0,1.0,directed graph,367662,binary,symmetric,no,no,missing,directed graph -2291,SNAP,wiki-Talk,2394385,2394385,5021410,1,1,0,0,0.1441208744157517,0.1441208744157517,directed graph,5021410,binary,unsymmetric,no,no,missing,directed graph -2292,SNAP,cit-HepPh,34546,34546,421578,1,1,0,0,0.003117186276789061,0.003117186276789061,directed graph,421578,binary,unsymmetric,no,no,missing,directed graph -2293,SNAP,cit-HepTh,27770,27770,352807,1,1,0,0,0.002738343613933237,0.002738343613933237,directed graph,352807,binary,unsymmetric,no,no,missing,directed graph -2294,SNAP,cit-Patents,3774768,3774768,16518948,1,1,0,0,0.0,0.0,directed graph,16518948,binary,unsymmetric,no,no,missing,directed graph -2295,SNAP,ca-AstroPh,18772,18772,396160,1,1,0,0,1.0,1.0,undirected graph,396160,binary,symmetric,no,no,missing,undirected graph -2296,SNAP,ca-CondMat,23133,23133,186936,1,1,0,0,1.0,1.0,undirected graph,186936,binary,symmetric,no,no,missing,undirected graph -2297,SNAP,ca-GrQc,5242,5242,28980,1,1,0,0,1.0,1.0,undirected graph,28980,binary,symmetric,no,no,missing,undirected graph -2298,SNAP,ca-HepPh,12008,12008,237010,1,1,0,0,1.0,1.0,undirected graph,237010,binary,symmetric,no,no,missing,undirected graph -2299,SNAP,ca-HepTh,9877,9877,51971,1,1,0,0,1.0,1.0,undirected graph,51971,binary,symmetric,no,no,missing,undirected graph -2300,SNAP,web-BerkStan,685230,685230,7600595,1,1,0,0,0.2502764586193581,0.2502764586193581,directed graph,7600595,binary,unsymmetric,no,no,missing,directed graph -2301,SNAP,web-Google,916428,916428,5105039,1,1,0,0,0.3067510355944391,0.3067510355944391,directed graph,5105039,binary,unsymmetric,no,no,missing,directed graph -2302,SNAP,web-NotreDame,325729,325729,1497134,1,1,0,0,0.5165359238309862,0.5165359238309862,directed graph,1497134,binary,unsymmetric,no,no,missing,directed graph -2303,SNAP,web-Stanford,281903,281903,2312497,1,1,0,0,0.2766368994208425,0.2766368994208425,directed graph,2312497,binary,unsymmetric,no,no,missing,directed graph -2304,SNAP,amazon0302,262111,262111,1234877,1,1,0,0,0.5427018237443891,0.5427018237443891,directed graph,1234877,binary,unsymmetric,no,no,missing,directed graph -2305,SNAP,amazon0312,400727,400727,3200440,1,1,0,0,0.5315337891039982,0.5315337891039982,directed graph,3200440,binary,unsymmetric,no,no,missing,directed graph -2306,SNAP,amazon0505,410236,410236,3356824,1,1,0,0,0.5465803390347542,0.5465803390347542,directed graph,3356824,binary,unsymmetric,no,no,missing,directed graph -2307,SNAP,amazon0601,403394,403394,3387388,1,1,0,0,0.5573497928197183,0.5573497928197183,directed graph,3387388,binary,unsymmetric,no,no,missing,directed graph -2308,SNAP,p2p-Gnutella04,10879,10879,39994,1,1,0,0,0.0,0.0,directed graph,39994,binary,unsymmetric,no,no,missing,directed graph -2309,SNAP,p2p-Gnutella05,8846,8846,31839,1,1,0,0,0.0,0.0,directed graph,31839,binary,unsymmetric,no,no,missing,directed graph -2310,SNAP,p2p-Gnutella06,8717,8717,31525,1,1,0,0,0.0,0.0,directed graph,31525,binary,unsymmetric,no,no,missing,directed graph -2311,SNAP,p2p-Gnutella08,6301,6301,20777,1,1,0,0,0.0,0.0,directed graph,20777,binary,unsymmetric,no,no,missing,directed graph -2312,SNAP,p2p-Gnutella09,8114,8114,26013,1,1,0,0,0.0,0.0,directed graph,26013,binary,unsymmetric,no,no,missing,directed graph -2313,SNAP,p2p-Gnutella24,26518,26518,65369,1,1,0,0,0.0,0.0,directed graph,65369,binary,unsymmetric,no,no,missing,directed graph -2314,SNAP,p2p-Gnutella25,22687,22687,54705,1,1,0,0,0.0,0.0,directed graph,54705,binary,unsymmetric,no,no,missing,directed graph -2315,SNAP,p2p-Gnutella30,36682,36682,88328,1,1,0,0,0.0,0.0,directed graph,88328,binary,unsymmetric,no,no,missing,directed graph -2316,SNAP,p2p-Gnutella31,62586,62586,147892,1,1,0,0,0.0,0.0,directed graph,147892,binary,unsymmetric,no,no,missing,directed graph -2317,SNAP,roadNet-CA,1971281,1971281,5533214,1,1,0,0,1.0,1.0,undirected graph,5533214,binary,symmetric,no,no,missing,undirected graph -2318,SNAP,roadNet-PA,1090920,1090920,3083796,1,1,0,0,1.0,1.0,undirected graph,3083796,binary,symmetric,no,no,missing,undirected graph -2319,SNAP,roadNet-TX,1393383,1393383,3843320,1,1,0,0,1.0,1.0,undirected graph,3843320,binary,symmetric,no,no,missing,undirected graph -2320,SNAP,as-735,7716,7716,26467,1,1,0,0,1.0,1.0,undirected graph sequence,26467,binary,symmetric,no,no,missing,undirected graph sequence -2321,SNAP,as-Skitter,1696415,1696415,22190596,1,1,0,0,1.0,1.0,undirected graph,22190596,binary,symmetric,no,no,missing,undirected graph -2322,SNAP,as-caida,31379,31379,106762,1,0,0,0,1.0,0.07869841329311927,directed weighted graph sequence,106762,integer,unsymmetric,no,no,missing,directed weighted graph sequence -2323,SNAP,Oregon-1,11492,11492,46818,1,1,0,0,1.0,1.0,undirected graph sequence,46818,binary,symmetric,no,no,missing,undirected graph sequence -2324,SNAP,Oregon-2,11806,11806,65460,1,1,0,0,1.0,1.0,undirected graph sequence,65460,binary,symmetric,no,no,missing,undirected graph sequence -2325,SNAP,soc-sign-epinions,131828,131828,841372,1,0,0,0,0.3082520316984202,0.3018224331855771,directed weighted graph,841372,integer,unsymmetric,no,no,missing,directed weighted graph -2326,SNAP,soc-sign-Slashdot081106,77357,77357,516575,1,0,0,0,0.1859207278710739,0.1786110438948846,directed weighted graph,516575,integer,unsymmetric,no,no,missing,directed weighted graph -2327,SNAP,soc-sign-Slashdot090216,81871,81871,545671,1,0,0,0,0.175926519826049,0.168574104176326,directed weighted graph,545671,integer,unsymmetric,no,no,missing,directed weighted graph -2328,SNAP,soc-sign-Slashdot090221,82144,82144,549202,1,0,0,0,0.1774246998372184,0.1703271291801559,directed weighted graph,549202,integer,unsymmetric,no,no,missing,directed weighted graph -2329,Dziekonski,gsm_106857,589446,589446,21758924,1,0,1,0,1.0,1.0,electromagnetics problem,21758924,real,symmetric,no,no,true,electromagnetics problem -2330,Gleich,flickr,820878,820878,9837214,1,1,0,0,0.653017002578169,0.653017002578169,directed graph,9837214,binary,unsymmetric,no,no,missing,directed graph -2331,Gleich,minnesota,2642,2642,6606,1,1,0,0,1.0,1.0,undirected graph,6606,binary,symmetric,no,no,missing,undirected graph -2332,Gleich,usroads-48,126146,126146,323900,1,1,0,0,1.0,1.0,undirected graph,323900,binary,symmetric,no,no,missing,undirected graph -2333,Gleich,usroads,129164,129164,330870,1,1,0,0,1.0,1.0,undirected graph,330870,binary,symmetric,no,no,missing,undirected graph -2334,Fluorem,DK01R,903,903,11766,1,0,1,0,0.9614287029365737,0.0,computational fluid dynamics problem,11766,real,unsymmetric,no,no,true,computational fluid dynamics problem -2335,Fluorem,GT01R,7980,7980,430909,1,0,1,0,0.8811455350661695,9.457852263618717e-06,computational fluid dynamics problem,430909,real,unsymmetric,no,no,true,computational fluid dynamics problem -2336,Fluorem,PR02R,161070,161070,8185136,1,0,1,0,0.9476492341912441,0.0001697393815055858,computational fluid dynamics problem,8185136,real,unsymmetric,no,no,true,computational fluid dynamics problem -2337,Fluorem,RM07R,381689,381689,37464962,1,0,1,0,0.9261667922354103,4.260681089287885e-06,computational fluid dynamics problem,37464962,real,unsymmetric,no,no,true,computational fluid dynamics problem -2338,Rommes,ww_36_pmec_36,66,66,1194,1,0,1,0,0.3339191564147627,0.0,eigenvalue/model reduction problem,1194,real,unsymmetric,no,no,true,eigenvalue/model reduction problem -2339,Rommes,ww_vref_6405,13251,13251,48737,1,0,1,0,0.707540090603374,0.5856980072816208,eigenvalue/model reduction problem,48737,real,unsymmetric,no,no,true,eigenvalue/model reduction problem -2340,Rommes,xingo_afonso_itaipu,13250,13250,48735,1,0,1,0,0.7075597554196776,0.5857142857142857,eigenvalue/model reduction problem,48735,real,unsymmetric,no,no,true,eigenvalue/model reduction problem -2341,Rommes,mimo8x8_system,13309,13309,48872,1,0,1,0,0.7060291752177048,0.584447279383216,eigenvalue/model reduction problem,48872,real,unsymmetric,no,no,true,eigenvalue/model reduction problem -2342,Rommes,mimo28x28_system,13251,13251,48737,1,0,1,0,0.707540090603374,0.5856980072816208,eigenvalue/model reduction problem,48737,real,unsymmetric,no,no,true,eigenvalue/model reduction problem -2343,Rommes,mimo46x46_system,13250,13250,48735,1,0,1,0,0.7075597554196776,0.5857142857142857,eigenvalue/model reduction problem,48735,real,unsymmetric,no,no,true,eigenvalue/model reduction problem -2344,Rommes,juba40k,40337,40337,144945,1,0,1,0,0.6934462083788981,0.5830536596402579,eigenvalue/model reduction problem,144945,real,unsymmetric,no,no,false,eigenvalue/model reduction problem -2345,Rommes,bauru5727,40366,40366,145019,1,0,1,0,0.6931521565005984,0.5828064190876452,eigenvalue/model reduction problem,145019,real,unsymmetric,no,no,false,eigenvalue/model reduction problem -2346,Rommes,zeros_nopss_13k,13296,13296,48827,1,0,1,0,0.7083056110002218,0.5842204479929031,eigenvalue/model reduction problem,48827,real,unsymmetric,no,no,true,eigenvalue/model reduction problem -2347,Rommes,descriptor_xingo6u,20738,20738,73916,1,0,1,0,0.6848202865775669,0.5745268504272713,eigenvalue/model reduction problem,73916,real,unsymmetric,no,no,false,eigenvalue/model reduction problem -2348,Rommes,nopss_11k,11685,11685,44941,1,0,1,0,0.7502077151335311,0.6233827893175075,eigenvalue/model reduction problem,44941,real,unsymmetric,no,no,true,eigenvalue/model reduction problem -2349,Rommes,xingo3012,20944,20944,74386,1,0,1,0,0.681002526603102,0.5712336093539642,eigenvalue/model reduction problem,74386,real,unsymmetric,no,no,false,eigenvalue/model reduction problem -2350,Rommes,bips98_606,7135,7135,34738,1,0,1,0,0.9086859688195991,0.7583878152166104,eigenvalue/model reduction problem,34738,real,unsymmetric,no,no,true,eigenvalue/model reduction problem -2351,Rommes,bips98_1142,9735,9735,40983,1,0,1,0,0.8020552507215579,0.6694154587839767,eigenvalue/model reduction problem,40983,real,unsymmetric,no,no,true,eigenvalue/model reduction problem -2352,Rommes,bips98_1450,11305,11305,44678,1,0,1,0,0.7593684272891549,0.6277809047012471,eigenvalue/model reduction problem,44678,real,unsymmetric,no,no,true,eigenvalue/model reduction problem -2353,Rommes,bips07_1693,13275,13275,49044,1,0,1,0,0.706761938919083,0.5845449278560985,eigenvalue/model reduction problem,49044,real,unsymmetric,no,no,true,eigenvalue/model reduction problem -2354,Rommes,bips07_1998,15066,15066,62198,1,0,1,0,0.7978660841804587,0.6687809795849786,eigenvalue/model reduction problem,62198,real,unsymmetric,no,no,true,eigenvalue/model reduction problem -2355,Rommes,bips07_2476,16861,16861,66498,1,0,1,0,0.7574573297176583,0.6349098739830914,eigenvalue/model reduction problem,66498,real,unsymmetric,no,no,false,eigenvalue/model reduction problem -2356,Rommes,bips07_3078,21128,21128,75729,1,0,1,0,0.6861363964744979,0.5750975292587777,eigenvalue/model reduction problem,75729,real,unsymmetric,no,no,false,eigenvalue/model reduction problem -2357,Rommes,M10PI_n1,528,528,1317,1,0,1,0,1.0,0.0,eigenvalue/model reduction problem,1317,real,unsymmetric,no,no,false,eigenvalue/model reduction problem -2358,Rommes,M20PI_n1,1028,1028,2547,1,0,1,0,1.0,0.0,eigenvalue/model reduction problem,2547,real,unsymmetric,no,no,false,eigenvalue/model reduction problem -2359,Rommes,M40PI_n1,2028,2028,5007,1,0,1,0,1.0,0.0,eigenvalue/model reduction problem,5007,real,unsymmetric,no,no,false,eigenvalue/model reduction problem -2360,Rommes,M80PI_n1,4028,4028,9927,1,0,1,0,1.0,0.0,eigenvalue/model reduction problem,9927,real,unsymmetric,no,no,false,eigenvalue/model reduction problem -2361,Rommes,M10PI_n,682,682,1633,1,0,1,0,1.0,0.1132075471698113,eigenvalue/model reduction problem,1633,real,unsymmetric,no,no,false,eigenvalue/model reduction problem -2362,Rommes,M20PI_n,1182,1182,2881,1,0,1,0,1.0,0.04173622704507512,eigenvalue/model reduction problem,2881,real,unsymmetric,no,no,false,eigenvalue/model reduction problem -2363,Rommes,M40PI_n,2182,2182,5341,1,0,1,0,1.0,0.02274795268425842,eigenvalue/model reduction problem,5341,real,unsymmetric,no,no,false,eigenvalue/model reduction problem -2364,Rommes,M80PI_n,4182,4182,10261,1,0,1,0,1.0,0.01191043353978085,eigenvalue/model reduction problem,10261,real,unsymmetric,no,no,false,eigenvalue/model reduction problem -2365,Rommes,S10PI_n1,528,528,1317,1,0,1,0,1.0,0.0,eigenvalue/model reduction problem,1317,real,unsymmetric,no,no,false,eigenvalue/model reduction problem -2366,Rommes,S20PI_n1,1028,1028,2547,1,0,1,0,1.0,0.0,eigenvalue/model reduction problem,2547,real,unsymmetric,no,no,false,eigenvalue/model reduction problem -2367,Rommes,S40PI_n1,2028,2028,5007,1,0,1,0,1.0,0.0,eigenvalue/model reduction problem,5007,real,unsymmetric,no,no,false,eigenvalue/model reduction problem -2368,Rommes,S80PI_n1,4028,4028,9927,1,0,1,0,1.0,0.0,eigenvalue/model reduction problem,9927,real,unsymmetric,no,no,false,eigenvalue/model reduction problem -2369,Rommes,S10PI_n,682,682,1633,1,0,1,0,1.0,0.1132075471698113,eigenvalue/model reduction problem,1633,real,unsymmetric,no,no,false,eigenvalue/model reduction problem -2370,Rommes,S20PI_n,1182,1182,2881,1,0,1,0,1.0,0.04173622704507512,eigenvalue/model reduction problem,2881,real,unsymmetric,no,no,false,eigenvalue/model reduction problem -2371,Rommes,S40PI_n,2182,2182,5341,1,0,1,0,1.0,0.02274795268425842,eigenvalue/model reduction problem,5341,real,unsymmetric,no,no,false,eigenvalue/model reduction problem -2372,Rommes,S80PI_n,4182,4182,10261,1,0,1,0,1.0,0.01191043353978085,eigenvalue/model reduction problem,10261,real,unsymmetric,no,no,false,eigenvalue/model reduction problem -2373,Williams,pdb1HYS,36417,36417,4344765,1,0,0,1,1.0,1.0,weighted undirected graph,4344765,real,symmetric,yes,yes,missing,weighted undirected graph -2374,Williams,consph,83334,83334,6010480,1,0,1,1,1.0,1.0,2D/3D problem,6010480,real,symmetric,yes,yes,true,2D/3D problem -2375,Williams,cant,62451,62451,4007383,1,0,1,1,1.0,1.0,2D/3D problem,4007383,real,symmetric,yes,yes,true,2D/3D problem -2376,Williams,mac_econ_fwd500,206500,206500,1273389,1,0,0,0,0.05995735832675801,0.005599217836274841,economic problem,1273389,real,unsymmetric,no,no,true,economic problem -2377,Williams,mc2depi,525825,525825,2100225,1,0,1,0,0.0,0.0,2D/3D problem,2100225,integer,unsymmetric,no,no,true,2D/3D problem -2378,Williams,cop20k_A,121192,121192,2624331,1,0,1,0,1.0,1.0,2D/3D problem,2624331,real,symmetric,no,no,false,2D/3D problem -2379,Williams,webbase-1M,1000005,1000005,3105536,1,0,0,0,0.09552554676231316,0.01588957844838191,weighted directed graph,3105536,real,unsymmetric,no,no,missing,weighted directed graph -2380,Freescale,FullChip,2987012,2987012,26621983,1,0,0,0,0.9999996192108144,0.005614865134161801,circuit simulation problem,26621990,real,unsymmetric,no,no,true,circuit simulation problem -2381,Dattorro,EternityII_E,11077,262144,1503732,1,0,0,0,0.0,0.0,optimization problem,1503732,integer,rectangular,no,no,true,optimization problem -2382,Dattorro,EternityII_Etilde,10054,204304,1170516,1,0,0,0,0.0,0.0,optimization problem,1170516,integer,rectangular,no,no,true,optimization problem -2383,Dattorro,EternityII_A,7362,150638,782087,1,0,0,0,0.0,0.0,optimization problem,782087,integer,rectangular,no,no,true,optimization problem -2384,Fluorem,HV15R,2017169,2017169,283073458,1,0,1,0,0.8446615474952066,0.0007082780488857875,computational fluid dynamics problem,283073458,real,unsymmetric,no,no,true,computational fluid dynamics problem -2385,Dziekonski,dielFilterV2clx,607232,607232,25309272,0,0,1,0,1.0,0.9712899015627859,electromagnetics problem,25309272,complex,symmetric,no,no,true,electromagnetics problem -2386,Dziekonski,dielFilterV2real,1157456,1157456,48538952,1,0,1,0,1.0,1.0,electromagnetics problem,48538952,real,symmetric,no,no,true,electromagnetics problem -2387,Dziekonski,dielFilterV3clx,420408,420408,32886208,0,0,1,0,1.0,0.8873627016737613,electromagnetics problem,32886208,complex,symmetric,no,no,true,electromagnetics problem -2388,Dziekonski,dielFilterV3real,1102824,1102824,89306020,1,0,1,0,1.0,1.0,electromagnetics problem,89306020,real,symmetric,no,no,true,electromagnetics problem -2389,Newman,adjnoun,112,112,850,1,1,0,0,1.0,1.0,undirected graph,850,binary,symmetric,no,no,missing,undirected graph -2390,Newman,as-22july06,22963,22963,96872,1,1,0,0,1.0,1.0,undirected graph,96872,binary,symmetric,no,no,missing,undirected graph -2391,Newman,astro-ph,16706,16706,242502,1,0,0,0,1.0,1.0,undirected weighted graph,242502,real,symmetric,no,no,missing,undirected weighted graph -2392,Newman,celegansneural,297,297,2345,1,0,0,0,0.1680170575692964,0.04946695095948828,directed weighted graph,2345,integer,unsymmetric,no,no,missing,directed weighted graph -2393,Newman,cond-mat,16726,16726,95188,1,0,0,0,1.0,1.0,undirected weighted graph,95188,real,symmetric,no,no,missing,undirected weighted graph -2394,Newman,cond-mat-2003,31163,31163,240058,1,0,0,0,1.0,1.0,undirected weighted graph,240058,real,symmetric,no,no,missing,undirected weighted graph -2395,Newman,cond-mat-2005,40421,40421,351382,1,0,0,0,1.0,1.0,undirected weighted graph,351386,real,symmetric,no,no,missing,undirected weighted graph -2396,Newman,dolphins,62,62,318,1,1,0,0,1.0,1.0,undirected graph,318,binary,symmetric,no,no,missing,undirected graph -2397,Newman,football,115,115,1226,1,0,0,0,1.0,1.0,undirected multigraph,1226,integer,symmetric,no,no,missing,undirected multigraph -2398,Newman,hep-th,8361,8361,31502,1,0,0,0,1.0,1.0,undirected weighted graph,31502,real,symmetric,no,no,missing,undirected weighted graph -2399,Newman,karate,34,34,156,1,1,0,0,1.0,1.0,undirected graph,156,binary,symmetric,no,no,missing,undirected graph -2400,Newman,lesmis,77,77,508,1,0,0,0,1.0,1.0,undirected weighted graph,508,integer,symmetric,no,no,missing,undirected weighted graph -2401,Newman,netscience,1589,1589,5484,1,0,0,0,1.0,1.0,undirected weighted graph,5484,real,symmetric,no,no,missing,undirected weighted graph -2402,Newman,polblogs,1490,1490,19025,1,0,0,0,0.242561244874356,0.2414046893071181,directed multigraph,19025,integer,unsymmetric,no,no,missing,directed multigraph -2403,Newman,polbooks,105,105,882,1,1,0,0,1.0,1.0,undirected graph,882,binary,symmetric,no,no,missing,undirected graph -2404,Newman,power,4941,4941,13188,1,1,0,0,1.0,1.0,undirected graph,13188,binary,symmetric,no,no,missing,undirected graph -2405,Arenas,PGPgiantcompo,10680,10680,48632,1,0,0,0,1.0,1.0,undirected multigraph,48632,integer,symmetric,no,no,missing,undirected multigraph -2406,Arenas,jazz,198,198,5484,1,0,0,0,1.0,1.0,undirected multigraph,5484,integer,symmetric,no,no,missing,undirected multigraph 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graph,427586,integer,symmetric,no,no,missing,undirected weighted graph -2632,DIMACS10,vsp_barth5_1Ksep_50in_5Kout,32212,32212,203610,1,1,0,0,1.0,1.0,random unweighted graph,203610,binary,symmetric,no,no,missing,random unweighted graph -2633,DIMACS10,vsp_bcsstk30_500sep_10in_1Kout,58348,58348,4033156,1,1,0,0,1.0,1.0,random unweighted graph,4033156,binary,symmetric,no,no,missing,random unweighted graph -2634,DIMACS10,vsp_befref_fxm_2_4_air02,14109,14109,196448,1,1,0,0,1.0,1.0,random unweighted graph,196448,binary,symmetric,no,no,missing,random unweighted graph -2635,DIMACS10,vsp_bump2_e18_aa01_model1_crew1,56438,56438,601602,1,1,0,0,1.0,1.0,random unweighted graph,601602,binary,symmetric,no,no,missing,random unweighted graph -2636,DIMACS10,vsp_c-30_data_data,11023,11023,124368,1,1,0,0,1.0,1.0,random unweighted graph,124368,binary,symmetric,no,no,missing,random unweighted graph -2637,DIMACS10,vsp_c-60_data_cti_cs4,85830,85830,482160,1,1,0,0,1.0,1.0,random unweighted graph,482160,binary,symmetric,no,no,missing,random unweighted graph -2638,DIMACS10,vsp_data_and_seymourl,9167,9167,111732,1,1,0,0,1.0,1.0,random unweighted graph,111732,binary,symmetric,no,no,missing,random unweighted graph -2639,DIMACS10,vsp_finan512_scagr7-2c_rlfddd,139752,139752,1104040,1,1,0,0,1.0,1.0,random unweighted graph,1104040,binary,symmetric,no,no,missing,random unweighted graph -2640,DIMACS10,vsp_mod2_pgp2_slptsk,101364,101364,778736,1,1,0,0,1.0,1.0,random unweighted graph,778736,binary,symmetric,no,no,missing,random unweighted graph -2641,DIMACS10,vsp_model1_crew1_cr42_south31,45101,45101,379952,1,1,0,0,1.0,1.0,random unweighted graph,379952,binary,symmetric,no,no,missing,random unweighted graph -2642,DIMACS10,vsp_msc10848_300sep_100in_1Kout,21996,21996,2442056,1,1,0,0,1.0,1.0,random unweighted graph,2442056,binary,symmetric,no,no,missing,random unweighted graph -2643,DIMACS10,vsp_p0291_seymourl_iiasa,10498,10498,107736,1,1,0,0,1.0,1.0,random unweighted graph,107736,binary,symmetric,no,no,missing,random unweighted graph -2644,DIMACS10,vsp_sctap1-2b_and_seymourl,40174,40174,281662,1,1,0,0,1.0,1.0,random unweighted graph,281662,binary,symmetric,no,no,missing,random unweighted graph -2645,DIMACS10,vsp_south31_slptsk,39668,39668,379828,1,1,0,0,1.0,1.0,random unweighted graph,379828,binary,symmetric,no,no,missing,random unweighted graph -2646,DIMACS10,vsp_vibrobox_scagr7-2c_rlfddd,77328,77328,871172,1,1,0,0,1.0,1.0,random unweighted graph,871172,binary,symmetric,no,no,missing,random unweighted graph -2647,Janna,CoupCons3D,416800,416800,17277420,1,0,1,0,1.0,0.008881642549324996,structural problem,22322336,real,unsymmetric,no,no,true,structural problem -2648,Janna,ML_Laplace,377002,377002,27582698,1,0,1,0,1.0,7.351401706466176e-08,structural problem,27689972,real,unsymmetric,no,no,true,structural problem -2649,Janna,Transport,1602111,1602111,23487281,1,0,1,0,1.0,0.0,structural problem,23500731,real,unsymmetric,no,no,true,structural problem -2650,Janna,ML_Geer,1504002,1504002,110686677,1,0,1,0,1.0,2.381330188145693e-07,structural problem,110879972,real,unsymmetric,no,no,true,structural problem -2651,ANSYS,Delor64K,64719,1785345,652140,1,0,0,0,0.0,0.0,least squares problem,652140,real,rectangular,no,no,true,least squares problem -2652,ANSYS,Delor295K,295734,1823928,2401323,1,0,0,0,0.0,0.0,least squares problem,2401323,real,rectangular,no,no,true,least squares problem -2653,ANSYS,Delor338K,343236,887058,4211599,1,0,0,0,0.0,0.0,least squares problem,4211599,real,rectangular,no,no,true,least squares problem -2654,Sorensen,Linux_call_graph,324085,324085,1208908,1,0,0,0,0.0006324084497715383,0.0006125422157473015,directed weighted graph,1208908,integer,unsymmetric,no,no,missing,directed weighted graph -2655,LeGresley,LeGresley_2508,2508,2508,16727,1,0,0,0,0.837611646388635,0.2557141852450946,power network problem,16727,real,unsymmetric,no,no,true,power network problem -2656,LeGresley,LeGresley_4908,4908,4908,30482,1,0,0,0,0.9773607038123168,0.02995112414467253,power network problem,30482,real,unsymmetric,no,no,true,power network problem -2657,LeGresley,LeGresley_87936,87936,87936,593276,1,0,0,0,0.9792020608292309,0.03427471928313486,power network problem,593276,real,unsymmetric,no,no,true,power network problem -2658,Brogan,specular,477976,1600,7647040,1,0,1,0,0.0,0.0,computer vision problem,7647616,real,rectangular,no,no,false,computer vision problem -2659,Janna,Bump_2911,2911419,2911419,127729899,1,0,1,1,1.0,1.0,2D/3D problem,127729899,real,symmetric,yes,yes,true,2D/3D problem -2660,Janna,Queen_4147,4147110,4147110,316548962,1,0,1,1,1.0,1.0,2D/3D problem,329499284,real,symmetric,yes,yes,true,2D/3D problem -2661,Janna,PFlow_742,742793,742793,37138461,1,0,1,1,1.0,1.0,2D/3D problem,37138461,real,symmetric,yes,yes,true,2D/3D problem -2662,Freescale,Freescale2,2999349,2999349,14313235,1,0,0,0,0.9853083953765489,0.5600838032920651,circuit simulation matrix,23042677,real,unsymmetric,no,no,true,circuit simulation matrix -2663,Moqri,MISKnowledgeMap,2427,2427,57022,1,0,0,0,1.0,1.0,undirected weighted graph,57022,real,symmetric,no,no,missing,undirected weighted graph -2664,Mazaheri,bundle_adj,513351,513351,20207907,1,0,1,1,1.0,1.0,computer vision problem,20208051,real,symmetric,yes,yes,true,computer vision problem -2665,VDOL,dynamicSoaringProblem_1,647,647,5367,1,0,0,0,1.0,1.0,optimal control problem,5367,real,symmetric,no,no,true,optimal control problem -2666,VDOL,dynamicSoaringProblem_2,1591,1591,15588,1,0,0,0,1.0,1.0,optimal control problem,15588,real,symmetric,no,no,true,optimal control problem -2667,VDOL,dynamicSoaringProblem_3,2871,2871,32022,1,0,0,0,1.0,1.0,optimal control problem,32022,real,symmetric,no,no,true,optimal control problem -2668,VDOL,dynamicSoaringProblem_4,3191,3191,36516,1,0,0,0,1.0,1.0,optimal control problem,36516,real,symmetric,no,no,true,optimal control problem -2669,VDOL,dynamicSoaringProblem_5,3271,3271,36789,1,0,0,0,1.0,1.0,optimal control problem,36789,real,symmetric,no,no,true,optimal control problem -2670,VDOL,dynamicSoaringProblem_6,3431,3431,36741,1,0,0,0,1.0,1.0,optimal control problem,36741,real,symmetric,no,no,true,optimal control problem -2671,VDOL,dynamicSoaringProblem_7,3511,3511,37680,1,0,0,0,1.0,1.0,optimal control problem,37680,real,symmetric,no,no,true,optimal control problem -2672,VDOL,dynamicSoaringProblem_8,3543,3543,38136,1,0,0,0,1.0,1.0,optimal control problem,38136,real,symmetric,no,no,true,optimal control problem -2673,VDOL,freeFlyingRobot_1,798,798,5246,1,0,0,0,1.0,1.0,optimal control problem,5246,real,symmetric,no,no,true,optimal control problem -2674,VDOL,freeFlyingRobot_2,1338,1338,11600,1,0,0,0,1.0,1.0,optimal control problem,11600,real,symmetric,no,no,true,optimal control problem -2675,VDOL,freeFlyingRobot_3,1718,1718,12922,1,0,0,0,1.0,1.0,optimal control problem,12922,real,symmetric,no,no,true,optimal control problem -2676,VDOL,freeFlyingRobot_4,2358,2358,18218,1,0,0,0,1.0,1.0,optimal control problem,18218,real,symmetric,no,no,true,optimal control problem -2677,VDOL,freeFlyingRobot_5,2878,2878,24582,1,0,0,0,1.0,1.0,optimal control problem,24582,real,symmetric,no,no,true,optimal control problem -2678,VDOL,freeFlyingRobot_6,3358,3358,27030,1,0,0,0,1.0,1.0,optimal control problem,27030,real,symmetric,no,no,true,optimal control problem -2679,VDOL,freeFlyingRobot_7,3918,3918,31046,1,0,0,0,1.0,1.0,optimal control problem,31046,real,symmetric,no,no,true,optimal control problem -2680,VDOL,freeFlyingRobot_8,4398,4398,34958,1,0,0,0,1.0,1.0,optimal control problem,34958,real,symmetric,no,no,true,optimal control problem -2681,VDOL,freeFlyingRobot_9,4778,4778,39964,1,0,0,0,1.0,1.0,optimal control problem,39964,real,symmetric,no,no,true,optimal control problem -2682,VDOL,freeFlyingRobot_10,5218,5218,40080,1,0,0,0,1.0,1.0,optimal control problem,40080,real,symmetric,no,no,true,optimal control problem -2683,VDOL,freeFlyingRobot_11,5438,5438,40054,1,0,0,0,1.0,1.0,optimal control problem,40054,real,symmetric,no,no,true,optimal control problem -2684,VDOL,freeFlyingRobot_12,5578,5578,41940,1,0,0,0,1.0,1.0,optimal control problem,41940,real,symmetric,no,no,true,optimal control problem -2685,VDOL,freeFlyingRobot_13,5718,5718,43994,1,0,0,0,1.0,1.0,optimal control problem,43994,real,symmetric,no,no,true,optimal control problem -2686,VDOL,freeFlyingRobot_14,5958,5958,43298,1,0,0,0,1.0,1.0,optimal control problem,43298,real,symmetric,no,no,true,optimal control problem -2687,VDOL,freeFlyingRobot_15,6038,6038,43378,1,0,0,0,1.0,1.0,optimal control problem,43378,real,symmetric,no,no,true,optimal control problem -2688,VDOL,freeFlyingRobot_16,6078,6078,44006,1,0,0,0,1.0,1.0,optimal control problem,44006,real,symmetric,no,no,true,optimal control problem -2689,VDOL,goddardRocketProblem_1,831,831,8498,1,0,0,0,1.0,1.0,optimal control problem,8498,real,symmetric,no,no,true,optimal control problem -2690,VDOL,goddardRocketProblem_2,867,867,9058,1,0,0,0,1.0,1.0,optimal control problem,9058,real,symmetric,no,no,true,optimal control problem -2691,VDOL,hangGlider_1,360,360,3477,1,0,0,0,1.0,1.0,optimal control problem,3477,real,symmetric,no,no,true,optimal control problem -2692,VDOL,hangGlider_2,1647,1647,14754,1,0,0,0,1.0,1.0,optimal control problem,14754,real,symmetric,no,no,true,optimal control problem -2693,VDOL,hangGlider_3,10260,10260,92703,1,0,0,0,1.0,1.0,optimal control problem,92703,real,symmetric,no,no,true,optimal control problem -2694,VDOL,hangGlider_4,15561,15561,149532,1,0,0,0,1.0,1.0,optimal control problem,149532,real,symmetric,no,no,true,optimal control problem -2695,VDOL,hangGlider_5,16011,16011,155246,1,0,0,0,1.0,1.0,optimal control problem,155246,real,symmetric,no,no,true,optimal control problem -2696,VDOL,kineticBatchReactor_1,2052,2052,20600,1,0,0,0,1.0,1.0,optimal control problem,20600,real,symmetric,no,no,true,optimal control problem -2697,VDOL,kineticBatchReactor_2,4361,4361,44840,1,0,0,0,1.0,1.0,optimal control problem,44840,real,symmetric,no,no,true,optimal control problem -2698,VDOL,kineticBatchReactor_3,5082,5082,53219,1,0,0,0,1.0,1.0,optimal control problem,53219,real,symmetric,no,no,true,optimal control problem -2699,VDOL,kineticBatchReactor_4,7105,7105,74869,1,0,0,0,1.0,1.0,optimal control problem,74869,real,symmetric,no,no,true,optimal control problem -2700,VDOL,kineticBatchReactor_5,7641,7641,80767,1,0,0,0,1.0,1.0,optimal control problem,80767,real,symmetric,no,no,true,optimal control problem -2701,VDOL,kineticBatchReactor_6,7773,7773,82500,1,0,0,0,1.0,1.0,optimal control problem,82500,real,symmetric,no,no,true,optimal control problem -2702,VDOL,kineticBatchReactor_7,7968,7968,84508,1,0,0,0,1.0,1.0,optimal control problem,84508,real,symmetric,no,no,true,optimal control problem -2703,VDOL,kineticBatchReactor_8,8052,8052,85194,1,0,0,0,1.0,1.0,optimal control problem,85194,real,symmetric,no,no,true,optimal control problem -2704,VDOL,kineticBatchReactor_9,8115,8115,86183,1,0,0,0,1.0,1.0,optimal control problem,86183,real,symmetric,no,no,true,optimal control problem -2705,VDOL,lowThrust_1,584,584,6133,1,0,0,0,1.0,1.0,optimal control problem,6133,real,symmetric,no,no,false,optimal control problem -2706,VDOL,lowThrust_2,2312,2312,26261,1,0,0,0,1.0,1.0,optimal control problem,26261,real,symmetric,no,no,true,optimal control problem -2707,VDOL,lowThrust_3,7064,7064,80645,1,0,0,0,1.0,1.0,optimal control problem,80645,real,symmetric,no,no,true,optimal control problem -2708,VDOL,lowThrust_4,13562,13562,160947,1,0,0,0,1.0,1.0,optimal control problem,160947,real,symmetric,no,no,true,optimal control problem -2709,VDOL,lowThrust_5,16262,16262,198369,1,0,0,0,1.0,1.0,optimal control problem,198369,real,symmetric,no,no,true,optimal control problem -2710,VDOL,lowThrust_6,16928,16928,207349,1,0,0,0,1.0,1.0,optimal control problem,207349,real,symmetric,no,no,true,optimal control problem -2711,VDOL,lowThrust_7,17378,17378,211561,1,0,0,0,1.0,1.0,optimal control problem,211561,real,symmetric,no,no,true,optimal control problem -2712,VDOL,lowThrust_8,17702,17702,216445,1,0,0,0,1.0,1.0,optimal control problem,216445,real,symmetric,no,no,true,optimal control problem -2713,VDOL,lowThrust_9,18044,18044,219589,1,0,0,0,1.0,1.0,optimal control problem,219589,real,symmetric,no,no,true,optimal control problem -2714,VDOL,lowThrust_10,18260,18260,222005,1,0,0,0,1.0,1.0,optimal control problem,222005,real,symmetric,no,no,true,optimal control problem -2715,VDOL,lowThrust_11,18368,18368,223801,1,0,0,0,1.0,1.0,optimal control problem,223801,real,symmetric,no,no,true,optimal control problem -2716,VDOL,lowThrust_12,18458,18458,224593,1,0,0,0,1.0,1.0,optimal control problem,224593,real,symmetric,no,no,true,optimal control problem -2717,VDOL,lowThrust_13,18476,18476,224897,1,0,0,0,1.0,1.0,optimal control problem,224897,real,symmetric,no,no,true,optimal control problem -2718,VDOL,orbitRaising_1,442,442,2906,1,0,0,0,1.0,1.0,optimal control problem,2906,real,symmetric,no,no,true,optimal control problem -2719,VDOL,orbitRaising_2,739,739,6526,1,0,0,0,1.0,1.0,optimal control problem,6526,real,symmetric,no,no,true,optimal control problem -2720,VDOL,orbitRaising_3,761,761,6098,1,0,0,0,1.0,1.0,optimal control problem,6098,real,symmetric,no,no,true,optimal control problem -2721,VDOL,orbitRaising_4,915,915,7790,1,0,0,0,1.0,1.0,optimal control problem,7790,real,symmetric,no,no,true,optimal control problem -2722,VDOL,reorientation_1,677,677,7326,1,0,0,0,1.0,1.0,optimal control problem,7326,real,symmetric,no,no,true,optimal control problem -2723,VDOL,reorientation_2,1544,1544,17910,1,0,0,0,1.0,1.0,optimal control problem,17910,real,symmetric,no,no,true,optimal control problem -2724,VDOL,reorientation_3,2513,2513,32166,1,0,0,0,1.0,1.0,optimal control problem,32166,real,symmetric,no,no,true,optimal control problem -2725,VDOL,reorientation_4,2717,2717,33630,1,0,0,0,1.0,1.0,optimal control problem,33630,real,symmetric,no,no,true,optimal control problem -2726,VDOL,reorientation_5,2904,2904,35590,1,0,0,0,1.0,1.0,optimal control problem,35590,real,symmetric,no,no,true,optimal control problem -2727,VDOL,reorientation_6,3006,3006,37342,1,0,0,0,1.0,1.0,optimal control problem,37342,real,symmetric,no,no,true,optimal control problem -2728,VDOL,reorientation_7,3057,3057,37150,1,0,0,0,1.0,1.0,optimal control problem,37150,real,symmetric,no,no,true,optimal control problem -2729,VDOL,reorientation_8,3108,3108,37894,1,0,0,0,1.0,1.0,optimal control problem,37894,real,symmetric,no,no,true,optimal control problem -2730,VDOL,spaceShuttleEntry_1,560,560,6891,1,0,0,0,1.0,1.0,optimal control problem,6891,real,symmetric,no,no,true,optimal control problem -2731,VDOL,spaceShuttleEntry_2,1428,1428,24073,1,0,0,0,1.0,1.0,optimal control problem,24073,real,symmetric,no,no,true,optimal control problem -2732,VDOL,spaceShuttleEntry_3,1834,1834,28757,1,0,0,0,1.0,1.0,optimal control problem,28757,real,symmetric,no,no,true,optimal control problem -2733,VDOL,spaceShuttleEntry_4,2450,2450,39669,1,0,0,0,1.0,1.0,optimal control problem,39669,real,symmetric,no,no,true,optimal control problem -2734,VDOL,spaceStation_1,99,99,927,1,0,0,0,1.0,1.0,optimal control problem,927,real,symmetric,no,no,true,optimal control problem -2735,VDOL,spaceStation_2,329,329,5817,1,0,0,0,1.0,1.0,optimal control problem,5817,real,symmetric,no,no,true,optimal control problem -2736,VDOL,spaceStation_3,467,467,5103,1,0,0,0,1.0,1.0,optimal control problem,5103,real,symmetric,no,no,true,optimal control problem -2737,VDOL,spaceStation_4,950,950,13626,1,0,0,0,1.0,1.0,optimal control problem,13626,real,symmetric,no,no,true,optimal control problem -2738,VDOL,spaceStation_5,1019,1019,15219,1,0,0,0,1.0,1.0,optimal control problem,15219,real,symmetric,no,no,true,optimal control problem -2739,VDOL,spaceStation_6,1111,1111,17595,1,0,0,0,1.0,1.0,optimal control problem,17595,real,symmetric,no,no,true,optimal control problem -2740,VDOL,spaceStation_7,1134,1134,18252,1,0,0,0,1.0,1.0,optimal control problem,18252,real,symmetric,no,no,true,optimal control problem -2741,VDOL,spaceStation_8,1157,1157,18945,1,0,0,0,1.0,1.0,optimal control problem,18945,real,symmetric,no,no,true,optimal control problem -2742,VDOL,spaceStation_9,1180,1180,19674,1,0,0,0,1.0,1.0,optimal control problem,19674,real,symmetric,no,no,true,optimal control problem -2743,VDOL,spaceStation_10,1272,1272,17478,1,0,0,0,1.0,1.0,optimal control problem,17478,real,symmetric,no,no,true,optimal control problem -2744,VDOL,spaceStation_11,1364,1364,18954,1,0,0,0,1.0,1.0,optimal control problem,18954,real,symmetric,no,no,true,optimal control problem -2745,VDOL,spaceStation_12,1410,1410,19728,1,0,0,0,1.0,1.0,optimal control problem,19728,real,symmetric,no,no,true,optimal control problem -2746,VDOL,spaceStation_13,1617,1617,23649,1,0,0,0,1.0,1.0,optimal control problem,23649,real,symmetric,no,no,true,optimal control problem -2747,VDOL,spaceStation_14,1640,1640,24324,1,0,0,0,1.0,1.0,optimal control problem,24324,real,symmetric,no,no,true,optimal control problem -2748,VDOL,tumorAntiAngiogenesis_1,205,205,1783,1,0,0,0,1.0,1.0,optimal control problem,1783,real,symmetric,no,no,true,optimal control problem -2749,VDOL,tumorAntiAngiogenesis_2,305,305,2699,1,0,0,0,1.0,1.0,optimal control problem,2699,real,symmetric,no,no,true,optimal control problem -2750,VDOL,tumorAntiAngiogenesis_3,410,410,3952,1,0,0,0,1.0,1.0,optimal control problem,3952,real,symmetric,no,no,true,optimal control problem -2751,VDOL,tumorAntiAngiogenesis_4,455,455,4593,1,0,0,0,1.0,1.0,optimal control problem,4593,real,symmetric,no,no,true,optimal control problem -2752,VDOL,tumorAntiAngiogenesis_5,460,460,4690,1,0,0,0,1.0,1.0,optimal control problem,4690,real,symmetric,no,no,true,optimal control problem -2753,VDOL,tumorAntiAngiogenesis_6,465,465,4795,1,0,0,0,1.0,1.0,optimal control problem,4795,real,symmetric,no,no,true,optimal control problem -2754,VDOL,tumorAntiAngiogenesis_7,475,475,4597,1,0,0,0,1.0,1.0,optimal control problem,4597,real,symmetric,no,no,true,optimal control problem -2755,VDOL,tumorAntiAngiogenesis_8,490,490,4776,1,0,0,0,1.0,1.0,optimal control problem,4776,real,symmetric,no,no,true,optimal control problem -2756,Meng,iChem_Jacobian,274087,274087,4137369,0,0,0,0,0.9801722990969854,0.6457048695901567,computational chemistry problem,4137369,complex,unsymmetric,no,no,true,computational chemistry problem -2757,Harvard_Seismology,JP,87616,67320,13734559,1,0,0,0,0.0,0.0,tomography problem,13734559,real,rectangular,no,no,false,tomography problem -2758,Mycielski,mycielskian2,2,2,2,1,1,0,0,1.0,1.0,undirected graph,2,binary,symmetric,no,no,missing,undirected graph -2759,Mycielski,mycielskian3,5,5,10,1,1,0,0,1.0,1.0,undirected graph,10,binary,symmetric,no,no,missing,undirected graph -2760,Mycielski,mycielskian4,11,11,40,1,1,0,0,1.0,1.0,undirected graph,40,binary,symmetric,no,no,missing,undirected graph -2761,Mycielski,mycielskian5,23,23,142,1,1,0,0,1.0,1.0,undirected graph,142,binary,symmetric,no,no,missing,undirected graph -2762,Mycielski,mycielskian6,47,47,472,1,1,0,0,1.0,1.0,undirected graph,472,binary,symmetric,no,no,missing,undirected graph -2763,Mycielski,mycielskian7,95,95,1510,1,1,0,0,1.0,1.0,undirected graph,1510,binary,symmetric,no,no,missing,undirected graph -2764,Mycielski,mycielskian8,191,191,4720,1,1,0,0,1.0,1.0,undirected graph,4720,binary,symmetric,no,no,missing,undirected graph -2765,Mycielski,mycielskian9,383,383,14542,1,1,0,0,1.0,1.0,undirected graph,14542,binary,symmetric,no,no,missing,undirected graph -2766,Mycielski,mycielskian10,767,767,44392,1,1,0,0,1.0,1.0,undirected graph,44392,binary,symmetric,no,no,missing,undirected graph -2767,Mycielski,mycielskian11,1535,1535,134710,1,1,0,0,1.0,1.0,undirected graph,134710,binary,symmetric,no,no,missing,undirected graph -2768,Mycielski,mycielskian12,3071,3071,407200,1,1,0,0,1.0,1.0,undirected graph,407200,binary,symmetric,no,no,missing,undirected graph -2769,Mycielski,mycielskian13,6143,6143,1227742,1,1,0,0,1.0,1.0,undirected graph,1227742,binary,symmetric,no,no,missing,undirected graph -2770,Mycielski,mycielskian14,12287,12287,3695512,1,1,0,0,1.0,1.0,undirected graph,3695512,binary,symmetric,no,no,missing,undirected graph -2771,Mycielski,mycielskian15,24575,24575,11111110,1,1,0,0,1.0,1.0,undirected graph,11111110,binary,symmetric,no,no,missing,undirected graph -2772,Mycielski,mycielskian16,49151,49151,33382480,1,1,0,0,1.0,1.0,undirected graph,33382480,binary,symmetric,no,no,missing,undirected graph -2773,Mycielski,mycielskian17,98303,98303,100245742,1,1,0,0,1.0,1.0,undirected graph,100245742,binary,symmetric,no,no,missing,undirected graph -2774,Mycielski,mycielskian18,196607,196607,300933832,1,1,0,0,1.0,1.0,undirected graph,300933832,binary,symmetric,no,no,missing,undirected graph -2775,Mycielski,mycielskian19,393215,393215,903194710,1,1,0,0,1.0,1.0,undirected graph,903194710,binary,symmetric,no,no,missing,undirected graph -2776,Mycielski,mycielskian20,786431,786431,2710370560,1,1,0,0,1.0,1.0,undirected graph,2710370560,binary,symmetric,no,no,missing,undirected graph -2777,SNAP,CollegeMsg,1899,1899,20296,1,0,0,0,0.6363815530153725,0.263204572329523,directed temporal multigraph,20296,integer,unsymmetric,no,no,missing,directed temporal multigraph -2778,SNAP,com-Amazon,334863,334863,1851744,1,1,0,0,1.0,1.0,undirected graph with communities,1851744,binary,symmetric,no,no,missing,undirected graph with communities -2779,SNAP,com-DBLP,317080,317080,2099732,1,1,0,0,1.0,1.0,undirected graph with communities,2099732,binary,symmetric,no,no,missing,undirected graph with communities -2780,SNAP,com-Friendster,65608366,65608366,3612134270,1,1,0,0,1.0,1.0,undirected graph with communities,3612134270,binary,symmetric,no,no,missing,undirected graph with communities -2781,SNAP,com-LiveJournal,3997962,3997962,69362378,1,1,0,0,1.0,1.0,undirected graph with communities,69362378,binary,symmetric,no,no,missing,undirected graph with communities -2782,SNAP,com-Orkut,3072441,3072441,234370166,1,1,0,0,1.0,1.0,undirected graph with communities,234370166,binary,symmetric,no,no,missing,undirected graph with communities -2783,SNAP,com-Youtube,1134890,1134890,5975248,1,1,0,0,1.0,1.0,undirected graph with communities,5975248,binary,symmetric,no,no,missing,undirected graph with communities -2784,SNAP,email-Eu-core,1005,1005,25571,1,1,0,0,0.7112198644149385,0.7112198644149385,directed graph with communities,25571,binary,unsymmetric,no,no,missing,directed graph with communities -2785,SNAP,email-Eu-core-temporal,1005,1005,24929,1,0,0,0,0.7112198644149385,0.1435276184363592,directed temporal multigraph,24929,integer,unsymmetric,no,no,missing,directed temporal multigraph -2786,SNAP,higgs-twitter,456626,456626,14855842,1,1,0,0,0.3160251211999823,0.3160251211999823,directed temporal multigraph,14855842,binary,unsymmetric,no,no,missing,directed temporal multigraph -2787,SNAP,loc-Brightkite,58228,58228,428156,1,1,0,0,1.0,1.0,undirected graph,428156,binary,symmetric,no,no,missing,undirected graph -2788,SNAP,loc-Gowalla,196591,196591,1900654,1,1,0,0,1.0,1.0,undirected graph,1900654,binary,symmetric,no,no,missing,undirected graph -2789,SNAP,soc-Pokec,1632803,1632803,30622564,1,1,0,0,0.5434293483720044,0.5434293483720044,directed graph,30622564,binary,unsymmetric,no,no,missing,directed graph -2790,SNAP,soc-sign-bitcoin-alpha,3783,3783,24186,1,0,0,0,0.832051600099231,0.5957165302240965,directed weighted temporal graph,24186,integer,unsymmetric,no,no,missing,directed weighted temporal graph -2791,SNAP,soc-sign-bitcoin-otc,5881,5881,35592,1,0,0,0,0.7923128792987189,0.5689480782198246,directed weighted temporal graph,35592,integer,unsymmetric,no,no,missing,directed weighted temporal graph -2792,SNAP,sx-askubuntu,159316,159316,596933,1,0,0,0,0.3265757288095759,0.1849102403322678,directed temporal multigraph,596933,integer,unsymmetric,no,no,missing,directed temporal multigraph -2793,SNAP,sx-mathoverflow,24818,24818,239978,1,0,0,0,0.3509349053252102,0.1609975832379348,directed temporal multigraph,239978,integer,unsymmetric,no,no,missing,directed temporal multigraph -2794,SNAP,sx-stackoverflow,2601977,2601977,36233450,1,0,0,0,0.3837726021373516,0.2153393177894375,directed temporal multigraph,36233450,integer,unsymmetric,no,no,missing,directed temporal multigraph -2795,SNAP,sx-superuser,194085,194085,924886,1,0,0,0,0.3272723867800749,0.197851768013418,directed temporal multigraph,924886,integer,unsymmetric,no,no,missing,directed temporal multigraph -2796,SNAP,twitter7,41652230,41652230,1468365182,1,1,0,0,0.3621059600332965,0.3621059600332965,directed graph,1468365182,binary,unsymmetric,no,no,missing,directed graph -2797,SNAP,wiki-RfA,11380,11380,188077,1,0,0,0,0.07451698016852498,0.06452676823559451,directed weighted temporal multigraph,188077,integer,unsymmetric,no,no,missing,directed weighted temporal multigraph -2798,SNAP,wiki-talk-temporal,1140149,1140149,3309592,1,0,0,0,0.2189736490582744,0.07817954976807415,directed temporal multigraph,3309592,integer,unsymmetric,no,no,missing,directed temporal multigraph -2799,SNAP,wiki-topcats,1791489,1791489,28511807,1,1,0,0,0.2149515115699757,0.2149515115699757,directed graph with communities,28511807,binary,unsymmetric,no,no,missing,directed graph with communities -2800,MAWI,mawi_201512012345,18571154,18571154,38040320,1,0,0,0,1.0,1.0,undirected weighted graph,38040320,integer,symmetric,no,no,missing,undirected weighted graph -2801,MAWI,mawi_201512020000,35991342,35991342,74485420,1,0,0,0,1.0,1.0,undirected weighted graph,74485420,integer,symmetric,no,no,missing,undirected weighted graph -2802,MAWI,mawi_201512020030,68863315,68863315,143414960,1,0,0,0,1.0,1.0,undirected weighted graph,143414960,integer,symmetric,no,no,missing,undirected weighted graph -2803,MAWI,mawi_201512020130,128568730,128568730,270234840,1,0,0,0,1.0,1.0,undirected weighted graph,270234840,integer,symmetric,no,no,missing,undirected weighted graph -2804,MAWI,mawi_201512020330,226196185,226196185,480047894,1,0,0,0,1.0,1.0,undirected weighted graph,480047894,integer,symmetric,no,no,missing,undirected weighted graph -2805,GenBank,kmer_A2a,170728175,170728175,360585172,1,1,0,0,1.0,1.0,undirected graph,360585172,binary,symmetric,no,no,missing,undirected graph -2806,GenBank,kmer_P1a,139353211,139353211,297829984,1,1,0,0,1.0,1.0,undirected graph,297829984,binary,symmetric,no,no,missing,undirected graph -2807,GenBank,kmer_U1a,67716231,67716231,138778562,1,1,0,0,1.0,1.0,undirected graph,138778562,binary,symmetric,no,no,missing,undirected graph -2808,GenBank,kmer_V1r,214005017,214005017,465410904,1,1,0,0,1.0,1.0,undirected graph,465410904,binary,symmetric,no,no,missing,undirected graph -2809,GenBank,kmer_V2a,55042369,55042369,117217600,1,1,0,0,1.0,1.0,undirected graph,117217600,binary,symmetric,no,no,missing,undirected graph -2810,Sybrandt,MOLIERE_2016,30239687,30239687,6669254694,1,0,0,0,1.0,1.0,undirected weighted graph,6677301366,real,symmetric,no,no,missing,undirected weighted graph -2811,Guettel,TEM27623,27623,27623,439469,1,0,1,0,1.0,1.0,electromagnetics problem,439469,real,symmetric,no,yes,true,electromagnetics problem -2812,Guettel,TEM152078,152078,152078,6459326,1,0,1,0,1.0,1.0,electromagnetics problem,6459326,real,symmetric,no,yes,true,electromagnetics problem -2813,Guettel,TEM181302,181302,181302,7839010,1,0,1,0,1.0,1.0,electromagnetics problem,7839010,real,symmetric,no,yes,true,electromagnetics problem -2814,Grueninger,windtunnel_evap2d,8256,8256,109368,1,0,1,0,1.0,0.0,computational fluid dynamics problem,233468,real,unsymmetric,no,no,true,computational fluid dynamics problem -2815,Grueninger,windtunnel_evap3d,40816,40816,803978,1,0,1,0,1.0,0.0,computational fluid dynamics problem,2730600,real,unsymmetric,no,no,true,computational fluid dynamics problem -2816,PowerSystem,power197k,197156,197156,805839,1,0,0,0,0.2139400937122758,0.02415229830828888,power network problem,3698357,real,unsymmetric,no,no,true,power network problem -2817,Goodwin,Goodwin_010,1182,1182,32282,1,0,1,0,0.9018121596229179,0.05496990350011147,computational fluid dynamics,32282,real,unsymmetric,no,no,true,computational fluid dynamics -2818,Goodwin,Goodwin_013,1965,1965,56069,1,0,1,0,0.925032045412928,0.05779161325764512,computational fluid dynamics,56069,real,unsymmetric,no,no,true,computational fluid dynamics -2819,Goodwin,Goodwin_017,3317,3317,97773,1,0,1,0,0.9419021841757411,0.06036381947504249,computational fluid dynamics,97773,real,unsymmetric,no,no,true,computational fluid dynamics -2820,Goodwin,Goodwin_023,6005,6005,182168,1,0,1,0,0.9572036973485083,0.06214380952916754,computational fluid dynamics,182168,real,unsymmetric,no,no,true,computational fluid dynamics -2821,Goodwin,Goodwin_030,10142,10142,312814,1,0,1,0,0.966280360610536,0.06332624905442888,computational fluid dynamics,312814,real,unsymmetric,no,no,true,computational fluid dynamics -2822,Goodwin,Goodwin_040,17922,17922,561677,1,0,1,0,0.9746314856878265,0.06442027585251406,computational fluid dynamics,561677,real,unsymmetric,no,no,true,computational fluid dynamics -2823,Goodwin,Goodwin_054,32510,32510,1030878,1,0,1,0,0.9804679703906704,0.06537882962720246,computational fluid dynamics,1030878,real,unsymmetric,no,no,true,computational fluid dynamics -2824,Goodwin,Goodwin_071,56021,56021,1797934,1,0,1,0,0.9849308636424431,0.06556841497181332,computational fluid dynamics,1797934,real,unsymmetric,no,no,true,computational fluid dynamics -2825,Goodwin,Goodwin_095,100037,100037,3226066,1,0,1,0,0.9880933163299772,0.06443620776105316,computational fluid dynamics,3226066,real,unsymmetric,no,no,true,computational fluid dynamics -2826,Goodwin,Goodwin_127,178437,178437,5778545,1,0,1,0,0.9902002197399873,0.06589012929818107,computational fluid dynamics,5778545,real,unsymmetric,no,no,true,computational fluid dynamics -2827,TAMU_SmartGridCenter,ACTIVSg2000,4000,4000,28505,1,0,0,0,1.0,0.007345439706182412,power network problem,29336,real,unsymmetric,no,no,true,power network problem -2828,TAMU_SmartGridCenter,ACTIVSg10K,20000,20000,135888,1,0,0,0,1.0,0.02328109899213033,power network problem,137736,real,unsymmetric,no,no,true,power network problem -2829,TAMU_SmartGridCenter,ACTIVSg70K,69999,69999,238627,1,0,0,0,1.0,1.0,power network problem,238627,real,symmetric,no,no,true,power network problem -2830,Embree,ifiss_mat,96307,96307,3599932,1,0,1,0,1.0,0.2845118352876235,computational fluid dynamics,3599932,real,unsymmetric,no,no,true,computational fluid dynamics -2831,Hardesty,Hardesty1,938905,938905,12143314,1,0,1,0,1.0,1.0,computer graphics/vision problem,12143314,integer,symmetric,no,no,true,computer graphics/vision problem -2832,Hardesty,Hardesty2,929901,303645,4020731,1,0,1,0,0.0,0.0,computer graphics/vision problem,4020731,real,rectangular,no,no,true,computer graphics/vision problem -2833,Hardesty,Hardesty3,8217820,7591564,40451632,1,0,1,0,0.0,0.0,computer graphics/vision problem,40451632,real,rectangular,no,no,true,computer graphics/vision problem -2834,VLSI,nv1,75468,75468,1635003,1,0,1,0,1.0,0.2136393219773843,semiconductor device problem,2449194,real,unsymmetric,no,no,true,semiconductor device problem -2835,VLSI,nv2,1453908,1453908,37475646,1,0,1,0,1.0,0.1982255825635065,semiconductor device problem,52728362,real,unsymmetric,no,no,true,semiconductor device problem -2836,VLSI,imagesensor,118758,118758,1446396,1,0,1,0,1.0,0.2826177015120085,semiconductor device problem,1893170,real,unsymmetric,no,no,true,semiconductor device problem -2837,VLSI,mosfet2,46994,46994,1013062,1,0,1,0,1.0,0.1797368301196189,semiconductor device problem,1499460,real,unsymmetric,no,no,true,semiconductor device problem -2838,VLSI,power9,155376,155376,1887730,1,0,1,0,1.0,0.3018551635520223,semiconductor device problem,2512300,real,unsymmetric,no,no,true,semiconductor device problem -2839,VLSI,radiation,223104,223104,5526637,1,0,1,0,1.0,0.1890109856957617,semiconductor device problem,7637688,real,unsymmetric,no,no,true,semiconductor device problem -2840,VLSI,test1,392908,392908,9447535,1,0,1,0,1.0,0.2651539373184561,semiconductor device problem,12968200,real,unsymmetric,no,no,true,semiconductor device problem -2841,VLSI,dgreen,1200611,1200611,26606169,1,0,1,0,1.0,0.1732208361650628,semiconductor device problem,38259877,real,unsymmetric,no,no,true,semiconductor device problem -2842,VLSI,vas_stokes_1M,1090664,1090664,34767207,1,0,1,0,0.420954312323566,0.1713153870930279,semiconductor process problem,34767207,real,unsymmetric,no,no,true,semiconductor process problem -2843,VLSI,vas_stokes_2M,2146677,2146677,65129037,1,0,1,0,0.464592974921867,0.2072952172640085,semiconductor process problem,65129037,real,unsymmetric,no,no,true,semiconductor process problem -2844,VLSI,vas_stokes_4M,4382246,4382246,131577616,1,0,1,0,0.4636465777016884,0.1138555436412505,semiconductor process problem,131577616,real,unsymmetric,no,no,true,semiconductor process problem -2845,VLSI,stokes,11449533,11449533,349321980,1,0,1,0,0.4729070672045655,0.1293167418294988,semiconductor process problem,349321980,real,unsymmetric,no,no,true,semiconductor process problem -2846,VLSI,ss1,205282,205282,845089,1,0,1,0,0.9936840328411537,0.04200954350296261,semiconductor process problem,845089,real,unsymmetric,no,no,true,semiconductor process problem -2847,VLSI,ss,1652680,1652680,34753577,1,0,1,0,0.6780372145201986,0.2889301761218133,semiconductor process problem,34753577,real,unsymmetric,no,no,true,semiconductor process problem -2848,Freescale,nxp1,414604,414604,2655880,1,0,0,0,0.9998398784682034,0.8220579996645768,circuit simulation problem,2656177,real,unsymmetric,no,no,true,circuit simulation problem -2849,Martin,marine1,400320,400320,6226538,1,0,0,0,0.922267240944297,0.0,chemical oceanography problem,6226538,real,unsymmetric,no,no,missing,chemical oceanography problem -2850,Negre,dendrimer,730,730,63024,1,0,0,0,1.0,1.0,computational chemistry problem,63024,real,symmetric,no,yes,true,computational chemistry problem -2851,Precima,analytics,303813,303813,2006126,1,0,0,0,1.0,1.0,data analytics problem,2006126,real,symmetric,no,no,true,data analytics problem -2852,GAP,GAP-twitter,61578415,61578415,1468364884,1,0,0,0,0.3621059600332965,0.001422518355458029,directed weighted graph,1468364884,integer,unsymmetric,no,no,missing,directed weighted graph -2853,GAP,GAP-web,50636151,50636151,1930292948,1,0,0,0,0.1245713694644861,0.0004892386935249789,directed weighted graph,1930292948,integer,unsymmetric,no,no,missing,directed weighted graph -2854,GAP,GAP-road,23947347,23947347,57708624,1,0,0,0,1.0,1.0,directed weighted graph,57708624,integer,symmetric,no,no,missing,directed weighted graph -2855,GAP,GAP-kron,134217726,134217726,4223264644,1,0,0,0,1.0,1.0,random undirected weighted graph,4223264644,integer,symmetric,no,no,missing,random undirected weighted graph -2856,GAP,GAP-urand,134217728,134217728,4294966740,1,0,0,0,1.0,1.0,random undirected weighted graph,4294966740,integer,symmetric,no,no,missing,random undirected weighted graph -2857,ML_Graph,Binaryalphadigs_10NN,1404,1404,19392,1,0,0,0,1.0,1.0,undirected weighted graph,19392,real,symmetric,no,no,missing,undirected weighted graph -2858,ML_Graph,breasttissue_10NN,106,106,1412,1,0,0,0,1.0,1.0,undirected weighted graph,1412,real,symmetric,no,no,missing,undirected weighted graph -2859,ML_Graph,cnae9_10NN,1080,1080,18278,1,0,0,0,1.0,1.0,undirected weighted graph,18278,real,symmetric,no,no,missing,undirected weighted graph -2860,ML_Graph,collins_15NN,1000,1000,16492,1,0,0,0,1.0,1.0,undirected weighted graph,16492,real,symmetric,no,no,missing,undirected weighted graph -2861,ML_Graph,dataset12mfeatfactors_10NN,2000,2000,27442,1,0,0,0,1.0,1.0,undirected weighted graph,27442,real,symmetric,no,no,missing,undirected weighted graph -2862,ML_Graph,dataset16mfeatkarhunen_10NN,2000,2000,27668,1,0,0,0,1.0,1.0,undirected weighted graph,27668,real,symmetric,no,no,missing,undirected weighted graph -2863,ML_Graph,dataset18mfeatmorphological_10NN,2000,2000,22832,1,0,0,0,1.0,1.0,undirected weighted graph,22832,real,symmetric,no,no,missing,undirected weighted graph -2864,ML_Graph,dataset20mfeatpixel_10NN,2000,2000,27932,1,0,0,0,1.0,1.0,undirected weighted graph,27932,real,symmetric,no,no,missing,undirected weighted graph -2865,ML_Graph,dataset22mfeatzernike_10NN,2000,2000,27414,1,0,0,0,1.0,1.0,undirected weighted graph,27414,real,symmetric,no,no,missing,undirected weighted graph -2866,ML_Graph,dermatology_5NN,366,366,2440,1,0,0,0,1.0,1.0,undirected weighted graph,2440,real,symmetric,no,no,missing,undirected weighted graph -2867,ML_Graph,Ecoli_10NN,336,336,4560,1,0,0,0,1.0,1.0,undirected weighted graph,4560,real,symmetric,no,no,missing,undirected weighted graph -2868,ML_Graph,Fashion_MNIST_norm_10NN,10000,10000,158304,1,0,0,0,1.0,1.0,undirected weighted graph,158304,real,symmetric,no,no,missing,undirected weighted graph -2869,ML_Graph,Glass_10NN,214,214,2986,1,0,0,0,1.0,1.0,undirected weighted graph,2986,real,symmetric,no,no,missing,undirected weighted graph -2870,ML_Graph,har_10NN,10299,10299,151736,1,0,0,0,1.0,1.0,undirected weighted graph,151736,real,symmetric,no,no,missing,undirected weighted graph -2871,ML_Graph,indianpines_10NN,9144,9144,124656,1,0,0,0,1.0,1.0,undirected weighted graph,124656,real,symmetric,no,no,missing,undirected weighted graph -2872,ML_Graph,iris_dataset_30NN,150,150,5518,1,0,0,0,1.0,1.0,undirected weighted graph,5518,real,symmetric,no,no,missing,undirected weighted graph -2873,ML_Graph,JapaneseVowelsSmall_10NN,9961,9961,131144,1,0,0,0,1.0,1.0,undirected weighted graph,131144,real,symmetric,no,no,missing,undirected weighted graph -2874,ML_Graph,k49_norm_10NN,38547,38547,618158,1,0,0,0,1.0,1.0,undirected weighted graph,618158,real,symmetric,no,no,missing,undirected weighted graph -2875,ML_Graph,kmnist_norm_10NN,10000,10000,156932,1,0,0,0,1.0,1.0,undirected weighted graph,156932,real,symmetric,no,no,missing,undirected weighted graph -2876,ML_Graph,mice_10NN,1077,1077,13484,1,0,0,0,1.0,1.0,undirected weighted graph,13484,real,symmetric,no,no,missing,undirected weighted graph -2877,ML_Graph,micromass_10NN,571,571,9668,1,0,0,0,1.0,1.0,undirected weighted graph,9668,real,symmetric,no,no,missing,undirected weighted graph -2878,ML_Graph,mnist_test_norm_10NN,10000,10000,145600,1,0,0,0,1.0,1.0,undirected weighted graph,145600,real,symmetric,no,no,missing,undirected weighted graph -2879,ML_Graph,Olivetti_norm_10NN,400,400,5656,1,0,0,0,1.0,1.0,undirected weighted graph,5656,real,symmetric,no,no,missing,undirected weighted graph -2880,ML_Graph,optdigits_10NN,5620,5620,79650,1,0,0,0,1.0,1.0,undirected weighted graph,79650,real,symmetric,no,no,missing,undirected weighted graph -2881,ML_Graph,Plants_10NN,1600,1600,21930,1,0,0,0,1.0,1.0,undirected weighted graph,21930,real,symmetric,no,no,missing,undirected weighted graph -2882,ML_Graph,plantsmargin_12NN,1600,1600,25482,1,0,0,0,1.0,1.0,undirected weighted graph,25482,real,symmetric,no,no,missing,undirected weighted graph -2883,ML_Graph,plantstexture_10NN,1599,1599,21204,1,0,0,0,1.0,1.0,undirected weighted graph,21204,real,symmetric,no,no,missing,undirected weighted graph -2884,ML_Graph,semeion_10NN,1593,1593,22226,1,0,0,0,1.0,1.0,undirected weighted graph,22226,real,symmetric,no,no,missing,undirected weighted graph -2885,ML_Graph,Spectro_10NN,531,531,7422,1,0,0,0,1.0,1.0,undirected weighted graph,7422,real,symmetric,no,no,missing,undirected weighted graph -2886,ML_Graph,umistfacesnorm_10NN,575,575,6990,1,0,0,0,1.0,1.0,undirected weighted graph,6990,real,symmetric,no,no,missing,undirected weighted graph -2887,ML_Graph,usps_norm_5NN,11000,11000,81112,1,0,0,0,1.0,1.0,undirected weighted graph,81112,real,symmetric,no,no,missing,undirected weighted graph -2888,ML_Graph,Vehicle_10NN,846,846,10894,1,0,0,0,1.0,1.0,undirected weighted graph,10894,real,symmetric,no,no,missing,undirected weighted graph -2889,ML_Graph,worms20_10NN,20055,20055,240826,1,0,0,0,1.0,1.0,undirected weighted graph,240826,real,symmetric,no,no,missing,undirected weighted graph -2890,ML_Graph,YaleA_10NN,165,165,2268,1,0,0,0,1.0,1.0,undirected weighted graph,2268,real,symmetric,no,no,missing,undirected weighted graph -2891,ML_Graph,YaleB_10NN,2414,2414,34808,1,0,0,0,1.0,1.0,undirected weighted graph,34808,real,symmetric,no,no,missing,undirected weighted graph -2892,ML_Graph,yeast_30NN,1484,1484,62350,1,0,0,0,1.0,1.0,undirected weighted graph,62350,real,symmetric,no,no,missing,undirected weighted graph -2893,Sybrandt,AGATHA_2015,183964077,183964077,11588725964,1,1,0,0,1.0,1.0,undirected graph,11588725964,binary,symmetric,no,no,missing,undirected graph -2894,FlowIPM22,Spielman_k100,338402,338402,1025404,1,0,0,1,1.0,1.0,undirected weighted graph,1025404,real,symmetric,yes,yes,missing,undirected weighted graph -2895,FlowIPM22,Spielman_k200,2686802,2686802,8100804,1,0,0,0,1.0,1.0,undirected weighted graph,8100804,real,symmetric,no,yes,missing,undirected weighted graph -2896,FlowIPM22,Spielman_k300,9045202,9045202,27226204,1,0,0,0,1.0,1.0,undirected weighted graph,27226204,real,symmetric,no,yes,missing,undirected weighted graph -2897,FlowIPM22,Spielman_k400,21413602,21413602,64401604,1,0,0,0,1.0,1.0,undirected weighted graph,64401604,real,symmetric,no,yes,missing,undirected weighted graph -2898,FlowIPM22,Spielman_k500,41792002,41792002,125627004,1,0,0,0,1.0,1.0,undirected weighted graph,125627004,real,symmetric,no,yes,missing,undirected weighted graph -2899,FlowIPM22,Spielman_k600,72180402,72180402,216902404,1,0,0,0,1.0,1.0,undirected weighted graph,216902404,real,symmetric,no,yes,missing,undirected weighted graph -2900,FlowIPM22,uni_chimera_i1,100000,100000,1100592,1,0,0,1,1.0,1.0,undirected weighted graph,1100592,real,symmetric,yes,yes,missing,undirected weighted graph -2901,FlowIPM22,uni_chimera_i2,100000,100000,797974,1,0,0,1,1.0,1.0,undirected weighted graph,797974,real,symmetric,yes,yes,missing,undirected weighted graph -2902,FlowIPM22,uni_chimera_i3,100000,100000,499696,1,0,0,0,1.0,1.0,undirected weighted graph,499696,real,symmetric,no,yes,missing,undirected weighted graph -2903,FlowIPM22,uni_chimera_i4,100000,100000,814436,1,0,0,0,1.0,1.0,undirected weighted graph,814436,real,symmetric,no,yes,missing,undirected weighted graph -2904,FlowIPM22,uni_chimera_i5,100000,100000,499982,1,0,0,1,1.0,1.0,undirected weighted graph,499982,real,symmetric,yes,yes,missing,undirected weighted graph diff --git a/setup.py b/setup.py deleted file mode 100644 index d50bb30..0000000 --- a/setup.py +++ /dev/null @@ -1,57 +0,0 @@ -"""Include public runtime assets without maintaining a second source copy.""" - -from __future__ import annotations - -import pathlib -import shutil - -from setuptools import setup -from setuptools.command.build_py import build_py - -ROOT = pathlib.Path(__file__).resolve().parent -RESOURCE_PATTERNS = ( - "benchmark.toml", - "data/*.json", - "data/*.md", - "data/releases/**/*.json", - "data/releases/**/*.md", - "native/CMakeLists.txt", - "native/include/*.h", - "native/include/NOTICE-*", - "native/include/LICENSE-*", - "native/src/*.cpp", -) - - -def resource_files() -> list[pathlib.Path]: - """Allow only release metadata and native sources into installed resources.""" - return sorted( - { - path - for pattern in RESOURCE_PATTERNS - for path in ROOT.glob(pattern) - if path.is_file() and not path.is_symlink() - } - ) - - -class BuildWithResources(build_py): - def run(self) -> None: - super().run() - destination = pathlib.Path(self.build_lib) / "linear_solver_bench/_resources" - # A reused build directory must not retain removed release manifests. - if destination.exists(): - shutil.rmtree(destination) - for source in resource_files(): - target = destination / source.relative_to(ROOT) - target.parent.mkdir(parents=True, exist_ok=True) - shutil.copy2(source, target) - - def get_outputs(self, include_bytecode: bool = True) -> list[str]: - destination = pathlib.Path(self.build_lib) / "linear_solver_bench/_resources" - return super().get_outputs(include_bytecode) + [ - str(destination / source.relative_to(ROOT)) for source in resource_files() - ] - - -setup(cmdclass={"build_py": BuildWithResources}) diff --git a/src/linear_solver_bench/__init__.py b/src/linear_solver_bench/__init__.py index a4c38a2..d243b0f 100644 --- a/src/linear_solver_bench/__init__.py +++ b/src/linear_solver_bench/__init__.py @@ -1,14 +1,6 @@ -"""Trusted infrastructure for LinearSolverBench.""" +"""Public API for LinearSolverBench.""" -from .accuracy import ACCURACY_CONTRACT_ID, AccuracyThresholds -from .models import CsrMatrix, EvaluationSystem, MatrixInput - -__all__ = [ - "ACCURACY_CONTRACT_ID", - "AccuracyThresholds", - "CsrMatrix", - "EvaluationSystem", - "MatrixInput", -] +from .benchmark import evaluate +__all__ = ["evaluate"] __version__ = "0.1.0" diff --git a/src/linear_solver_bench/accuracy.py b/src/linear_solver_bench/accuracy.py deleted file mode 100644 index 806e6f1..0000000 --- a/src/linear_solver_bench/accuracy.py +++ /dev/null @@ -1,56 +0,0 @@ -"""Versioned acceptance limits, independent of a solver's stopping request.""" - -from __future__ import annotations - -import math -from dataclasses import dataclass - -import numpy as np - -ACCURACY_CONTRACT_ID = "three-gate-v1" -NS_MESH_ACCURACY_CONTRACT_ID = "ns-mesh-accuracy-v1" -FLASH_REPLAY_ACCURACY_CONTRACT_ID = "flash-replay-accuracy-v1" -FLOAT64_EPSILON = float(np.finfo(np.float64).eps) -FORWARD_FACTOR = 5.0 -FORWARD_FLOOR = 2.0e-12 -FORWARD_CEILING = 2.0e-1 - - -@dataclass(frozen=True) -class AccuracyThresholds: - normwise_backward_error: float - componentwise_backward_error: float | None - forward_error: float | None - relative_residual: float | None = None - - @classmethod - def for_contract(cls, contract_id: str, tolerance: object) -> AccuracyThresholds: - legacy = cls.from_tolerance(tolerance) - if contract_id == ACCURACY_CONTRACT_ID: - return legacy - if contract_id == NS_MESH_ACCURACY_CONTRACT_ID: - return cls(1.0e-10, 1.0e-8, 1.0e-5) - if contract_id == FLASH_REPLAY_ACCURACY_CONTRACT_ID: - return cls( - normwise_backward_error=legacy.normwise_backward_error, - componentwise_backward_error=None, - forward_error=None, - relative_residual=float(tolerance) * (1.0 + 1.0e-6), - ) - raise ValueError(f"unknown accuracy contract: {contract_id}") - - @classmethod - def from_tolerance(cls, value: object) -> AccuracyThresholds: - if isinstance(value, bool): - raise TypeError("tolerance must be a number") - tolerance = float(value) - if not math.isfinite(tolerance) or tolerance <= 0.0: - raise ValueError("tolerance must be finite and positive") - return cls( - normwise_backward_error=max(tolerance, 20.0 * FLOAT64_EPSILON), - componentwise_backward_error=max(5.0 * tolerance, 100.0 * FLOAT64_EPSILON), - forward_error=min( - FORWARD_CEILING, - max(FORWARD_FACTOR * tolerance, FORWARD_FLOOR), - ), - ) diff --git a/src/linear_solver_bench/archive.py b/src/linear_solver_bench/archive.py deleted file mode 100644 index 0c4f26f..0000000 --- a/src/linear_solver_bench/archive.py +++ /dev/null @@ -1,130 +0,0 @@ -"""Stable trusted archive codec; archives never enter candidate workspaces.""" - -from __future__ import annotations - -import io - -import numpy as np - -from .models import CsrMatrix, EvaluationSystem, MatrixInput - -ARCHIVE_KEYS = frozenset( - { - "b", - "case_id", - "column_indices", - "row_offsets", - "tolerance", - "values", - "x0", - "x_star", - } -) -ARCHIVE_SCHEMA_VERSION = 2 -V2_REQUIRED_KEYS = (ARCHIVE_KEYS - {"x_star"}) | { - "schema_version", - "reference_kind", -} - - -def encode_system( - system: EvaluationSystem, *, schema_version: int | None = None -) -> bytes: - """Encode a trusted system; existing manufactured callers retain v1 bytes. - - Pilot preparation must explicitly request schema_version=2, including for - manufactured targets. Systems with optional numerical references use v2 by - default because v1 cannot express their reference semantics. - """ - if schema_version is None: - schema_version = 1 if system.reference_kind == "manufactured" else 2 - if type(schema_version) is not int or schema_version not in (1, 2): - raise ValueError("unsupported system archive schema version") - if schema_version == 1 and system.reference_kind != "manufactured": - raise ValueError("legacy archives require a manufactured reference") - buffer = io.BytesIO() - public = system.public - fields = dict( - case_id=np.asarray(system.case_id), - row_offsets=public.matrix.row_offsets, - column_indices=public.matrix.column_indices, - values=public.matrix.values, - b=public.b, - x0=public.x0, - tolerance=np.asarray(public.tolerance, dtype=np.float64), - ) - if schema_version == 2: - fields["schema_version"] = np.asarray(2, dtype=np.uint32) - fields["reference_kind"] = np.asarray(system.reference_kind) - if system.x_star is not None: - fields["x_star"] = system.x_star - np.savez_compressed(buffer, **fields) - return buffer.getvalue() - - -def _string_scalar(archive: object, key: str) -> str: - value = np.asarray(archive[key]) - if value.shape != () or value.dtype.kind not in "SU": - raise ValueError(f"archive {key} must be a string scalar") - scalar = value.item() - if isinstance(scalar, bytes): - try: - return scalar.decode("ascii") - except UnicodeDecodeError as exc: - raise ValueError(f"archive {key} must be ASCII") from exc - return scalar - - -def decode_system(payload: bytes) -> EvaluationSystem: - try: - with np.load(io.BytesIO(payload), allow_pickle=False) as archive: - keys = set(archive.files) - if len(keys) != len(archive.files): - raise ValueError("system archive contains duplicate fields") - reference_kind = "manufactured" - if "schema_version" in keys: - version = np.asarray(archive["schema_version"]) - if ( - version.shape != () - or version.dtype != np.uint32 - or int(version) != ARCHIVE_SCHEMA_VERSION - ): - raise ValueError("unsupported system archive schema version") - if not V2_REQUIRED_KEYS <= keys: - raise ValueError("system archive schema mismatch") - reference_kind = _string_scalar(archive, "reference_kind") - expected = V2_REQUIRED_KEYS | ( - {"x_star"} if reference_kind != "none" else set() - ) - else: - expected = ARCHIVE_KEYS - if keys != expected: - raise ValueError("system archive schema mismatch") - case = _string_scalar(archive, "case_id") - tolerance = np.asarray(archive["tolerance"]) - if tolerance.shape != () or tolerance.dtype != np.float64: - raise ValueError("archive tolerance must be a float64 scalar") - rows = np.asarray(archive["row_offsets"]) - matrix = CsrMatrix( - n=int(rows.size - 1), - row_offsets=rows, - column_indices=np.asarray(archive["column_indices"]), - values=np.asarray(archive["values"]), - ) - return EvaluationSystem( - case_id=case, - public=MatrixInput( - matrix=matrix, - b=np.asarray(archive["b"]), - x0=np.asarray(archive["x0"]), - tolerance=float(tolerance), - ), - x_star=( - np.asarray(archive["x_star"]) if reference_kind != "none" else None - ), - reference_kind=reference_kind, - ) - except (OSError, ValueError) as exc: - if isinstance(exc, ValueError): - raise - raise ValueError("cannot read system archive") from exc diff --git a/src/linear_solver_bench/assets.py b/src/linear_solver_bench/assets.py new file mode 100644 index 0000000..9783210 --- /dev/null +++ b/src/linear_solver_bench/assets.py @@ -0,0 +1,24 @@ +"""Locate the benchmark assets in a checkout or an installed wheel.""" + +from __future__ import annotations + +import os +import sysconfig +from pathlib import Path + + +def asset_root(*required: str) -> Path: + candidates = [] + if configured := os.environ.get("LINEAR_SOLVER_BENCH_ROOT"): + candidates.append(Path(configured)) + candidates.extend( + ( + Path(__file__).resolve().parents[2], + Path(sysconfig.get_path("data")) / "share" / "linear-solver-bench", + ) + ) + required = required or ("families", "native", "reference", "examples") + for candidate in candidates: + if all((candidate / name).is_dir() for name in required): + return candidate + raise RuntimeError("LinearSolverBench assets are missing; reinstall the package") diff --git a/src/linear_solver_bench/benchmark.py b/src/linear_solver_bench/benchmark.py new file mode 100644 index 0000000..177d302 --- /dev/null +++ b/src/linear_solver_bench/benchmark.py @@ -0,0 +1,221 @@ +"""The complete benchmark workflow: load, build, run, verify, report.""" + +from __future__ import annotations + +import json +import math +import pathlib +import shutil +import tempfile +from collections.abc import Callable, Iterable, Iterator, Mapping + +from .assets import asset_root +from .data import iter_cases, load_manifest +from .models import BenchmarkCase, RawExecution +from .verify import verify + +Progress = Callable[[str], None] + + +def repository_root() -> pathlib.Path: + return asset_root("reference", "examples") + + +def default_cache() -> pathlib.Path: + return pathlib.Path.home() / ".cache" / "linear-solver-bench" + + +def _geometric_mean(values: list[float]) -> float: + return math.exp(sum(math.log(value) for value in values) / len(values)) + + +def _case_rows( + family: str, + cases: Iterator[BenchmarkCase], + run_reference: Callable[[object], RawExecution], + run_candidate: Callable[[object], RawExecution], + progress: Progress, +) -> list[dict[str, object]]: + rows = [] + for index, case in enumerate(cases, 1): + progress(f"Running {case.case_id} ({index})") + reference = verify(run_reference(case.input), case, family) + if not reference.passed: + raise RuntimeError( + f"fixed reference failed on {case.case_id}: {reference.failure}" + ) + candidate = verify(run_candidate(case.input), case, family) + speedup = None + if ( + candidate.passed + and reference.elapsed_seconds is not None + and candidate.elapsed_seconds is not None + and candidate.elapsed_seconds > 0 + ): + speedup = reference.elapsed_seconds / candidate.elapsed_seconds + rows.append( + { + "case_id": case.case_id, + "role": case.role, + "input_sha256": case.input_sha256, + "passed": candidate.passed, + "reference_seconds": reference.elapsed_seconds, + "candidate_seconds": candidate.elapsed_seconds, + "speedup": speedup, + "reference": reference.to_dict(), + "candidate": candidate.to_dict(), + } + ) + return rows + + +def _report( + manifest: Mapping[str, object], + identities: Mapping[str, object], + rows: list[dict[str, object]], +) -> dict[str, object]: + complete = [row["case_id"] for row in rows] == [ + case["case_id"] for case in manifest["cases"] + ] + all_passed = all(row["passed"] for row in rows) + scored = [ + row["speedup"] + for row in rows + if row["role"] == "scored" and row["speedup"] is not None + ] + aggregate = _geometric_mean(scored) if complete and all_passed and scored else None + return { + "schema_version": 1, + "benchmark": "linear-solver-bench", + "official": complete + and identities.get("venue", {}).get("id") == "modal-cpu-v1", + "release_id": manifest["release_id"], + "family": manifest["family"], + "split": manifest["split"], + "manifest_sha256": manifest["manifest_sha256"], + "complete": complete, + "all_passed": all_passed, + "geometric_mean_speedup": aggregate, + **dict(identities), + "cases": rows, + } + + +def _local_evaluation( + candidate: pathlib.Path, + reference: pathlib.Path, + manifest: Mapping[str, object], + cases: Iterator[BenchmarkCase], + cache: pathlib.Path, + progress: Progress, +) -> dict[str, object]: + from .build import ( + REFERENCE_ID, + build_reference, + build_runtime, + build_solver, + load_runtime, + run_solver, + ) + + progress("Preparing the pinned HYPRE runtime") + runtime_path = cache / "runtime" + runtime = ( + load_runtime(runtime_path) + if runtime_path.exists() + else build_runtime(runtime_path) + ) + with tempfile.TemporaryDirectory(prefix="lsb-build-") as temporary: + workspace = pathlib.Path(temporary) + progress("Compiling the fixed reference and candidate") + reference_build = build_reference(reference, workspace / "reference", runtime) + candidate_build = build_solver(candidate, workspace / "candidate", runtime) + rows = _case_rows( + manifest["family"], + cases, + lambda value: run_solver(reference_build.executable, value), + lambda value: run_solver(candidate_build.executable, value), + progress, + ) + identities = { + "venue": {"id": "local-uncontrolled"}, + "runtime_sha256": runtime.manifest_sha256, + "reference": { + "id": REFERENCE_ID, + "source_sha256": reference_build.source_sha256, + "executable_sha256": reference_build.executable_sha256, + }, + "candidate": { + "source_sha256": candidate_build.source_sha256, + "executable_sha256": candidate_build.executable_sha256, + }, + } + return _report(manifest, identities, rows) + + +def _modal_evaluation( + candidate: pathlib.Path, + reference: pathlib.Path, + manifest: Mapping[str, object], + cases: Iterator[BenchmarkCase], + progress: Progress, +) -> dict[str, object]: + from .modal import open_session + + progress("Starting the Modal CPU sandbox") + with open_session(candidate, reference, manifest["execution"]) as session: + progress("Compiling the fixed reference and candidate") + identities = session.build() + rows = _case_rows( + manifest["family"], + cases, + session.run_reference, + session.run_candidate, + progress, + ) + return _report(manifest, identities, rows) + + +def evaluate( + source: str | pathlib.Path, + *, + family: str, + venue: str = "modal", + case_ids: Iterable[str] | None = None, + output: str | pathlib.Path | None = None, + cache: str | pathlib.Path | None = None, + progress: Progress | None = None, +) -> dict[str, object]: + """Evaluate one C source file against a public development benchmark.""" + if venue not in {"local", "modal"}: + raise ValueError("venue must be 'local' or 'modal'") + emit = progress or (lambda _message: None) + source = pathlib.Path(source).expanduser() + if not source.is_file(): + raise ValueError(f"candidate source does not exist: {source}") + cache_path = pathlib.Path(cache).expanduser() if cache else default_cache() + cache_path.mkdir(parents=True, exist_ok=True) + manifest = load_manifest(family) + selected = tuple(case_ids) if case_ids is not None else None + cases = iter_cases(manifest, cache_path / "downloads", selected) + reference = repository_root() / "reference" / "solver.c" + if not reference.is_file(): + raise RuntimeError("fixed reference solver is missing") + + # Snapshot source so edits during a long run cannot change the candidate. + with tempfile.TemporaryDirectory(prefix="lsb-source-") as temporary: + snapshot = pathlib.Path(temporary) / "solver.c" + shutil.copyfile(source, snapshot) + report = ( + _local_evaluation(snapshot, reference, manifest, cases, cache_path, emit) + if venue == "local" + else _modal_evaluation(snapshot, reference, manifest, cases, emit) + ) + if output is not None: + destination = pathlib.Path(output).expanduser() + destination.parent.mkdir(parents=True, exist_ok=True) + destination.write_text( + json.dumps(report, indent=2, sort_keys=True, allow_nan=False) + "\n", + encoding="utf-8", + ) + return report diff --git a/src/linear_solver_bench/build.py b/src/linear_solver_bench/build.py new file mode 100644 index 0000000..ee9fc5d --- /dev/null +++ b/src/linear_solver_bench/build.py @@ -0,0 +1,746 @@ +"""Build the pinned HYPRE runtime and run one solver process. + +This module is deliberately the whole native boundary. A runtime is built +once, a solver is compiled once, and every case is executed in a fresh process. +Correctness checking lives in :mod:`linear_solver_bench.verify`. +""" + +from __future__ import annotations + +import hashlib +import json +import os +import re +import shutil +import signal +import stat +import struct +import subprocess +import tempfile +import threading +import time +import uuid +from dataclasses import dataclass +from pathlib import Path + +import numpy as np + +from .assets import asset_root +from .models import CaseInput, RawExecution + +HYPRE_REPOSITORY = "https://github.com/hypre-space/hypre.git" +HYPRE_COMMIT = "e0ce7986941ce27111587ee7ea635ef7ca2eca94" +HYPRE_TREE = "c985eacc03d590011ab7652fe6fc4e9b25f1064d" +BUILD_KIND = "sequential-cpu-float64-int32-static" +CANDIDATE_ABI = "hypre-native-krylov-solver-v5" + +MAX_SOURCE_BYTES = 65_536 +DIAGNOSTIC_LIMIT = 8_000 +FACTORY_EXPORT = "solver_create" +RUN_TIMEOUT_SECONDS = 600.0 + +INPUT_MAGIC = b"LSBIN001" +OUTPUT_MAGIC = b"LSBOUT01" +PROTOCOL_VERSION = 1 +INPUT_HEADER = struct.Struct("<8sIIQQd") +OUTPUT_HEADER = struct.Struct("<8sIiQQQQQB7x") + +REPOSITORY_ROOT = asset_root("native") +NATIVE_DIR = REPOSITORY_ROOT / "native" +EXAMPLE_SOURCE = REPOSITORY_ROOT / "examples" / "solver.c" +REFERENCE_SOURCE = REPOSITORY_ROOT / "reference" / "solver.c" +REFERENCE_ID = "hypre-gmres-ilut-v1" +REFERENCE_SOURCE_SHA256 = ( + "44e651128abe0102a0925c6fa0c800484ec7c9aa2369ee5c3599adb19c2ad241" +) + +_REFERENCE_SYMBOLS = { + "HYPRE_ParCSRGMRESCreate", + "HYPRE_ParCSRGMRESDestroy", + "HYPRE_ParCSRGMRESSetup", + "HYPRE_ParCSRGMRESSolve", + "HYPRE_ParCSRGMRESSetKDim", + "HYPRE_ParCSRGMRESSetMaxIter", + "HYPRE_ParCSRGMRESSetTol", + "HYPRE_ParCSRGMRESSetAbsoluteTol", + "HYPRE_ParCSRGMRESSetPrecond", +} + +_INCLUDE = re.compile( + r'^[ \t]*#[ \t]*include[ \t]*([<"])([^>"]+)[>"][ \t]*(?://.*)?$', + re.MULTILINE, +) +_INCLUDE_DIRECTIVE = re.compile(r"^[ \t]*#[ \t]*include\b.*$", re.MULTILINE) + + +class BuildError(RuntimeError): + """A candidate failed a named build or audit stage.""" + + def __init__(self, stage: str, message: str): + self.stage = stage + super().__init__(f"{stage}: {message}") + + +@dataclass(frozen=True) +class Runtime: + root: Path + manifest_sha256: str + + +@dataclass(frozen=True) +class SolverBuild: + executable: Path + source_sha256: str + executable_sha256: str + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as source: + for chunk in iter(lambda: source.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def _canonical_sha256(value: object) -> str: + encoded = json.dumps( + value, sort_keys=True, separators=(",", ":"), allow_nan=False + ).encode("ascii") + return hashlib.sha256(encoded).hexdigest() + + +def _regular_file(path: Path, label: str) -> Path: + try: + mode = path.lstat().st_mode + except OSError as exc: + raise ValueError(f"{label} is unavailable: {path}") from exc + if not stat.S_ISREG(mode) or path.is_symlink(): + raise ValueError(f"{label} must be a regular, non-symlink file") + return path + + +def validate_source(source: Path) -> str: + """Return the candidate text after enforcing the public source contract.""" + + source = _regular_file(Path(source), "solver source") + if source.stat().st_size > MAX_SOURCE_BYTES: + raise BuildError("source audit", "source exceeds 65,536 bytes") + try: + text = source.read_text(encoding="utf-8") + except (OSError, UnicodeDecodeError) as exc: + raise BuildError("source audit", "source must be UTF-8") from exc + if "\0" in text or "\r" in text: + raise BuildError("source audit", "source must use LF text without NUL bytes") + + includes = tuple(match.group(2) for match in _INCLUDE.finditer(text)) + directives = tuple(_INCLUDE_DIRECTIVE.finditer(text)) + if includes != ("HYPRE_parcsr_ls.h",) or len(directives) != 1: + raise BuildError( + "source audit", "source must include exactly HYPRE_parcsr_ls.h" + ) + return text + + +def _tool(name: str) -> str: + path = shutil.which(name) + if path is None: + raise RuntimeError(f"required build tool is unavailable: {name}") + return path + + +def _run( + command: tuple[str, ...], + *, + timeout: float = 900.0, + env: dict[str, str] | None = None, + error_type: type[Exception] = RuntimeError, + stage: str = "command", +) -> str: + """Run a tool while retaining only the tail of its combined diagnostics.""" + + with tempfile.TemporaryFile() as output: + try: + completed = subprocess.run( + command, + stdin=subprocess.DEVNULL, + stdout=output, + stderr=subprocess.STDOUT, + check=False, + timeout=timeout, + env=env, + ) + except subprocess.TimeoutExpired as exc: + message = f"timed out after {timeout:g} seconds" + if error_type is BuildError: + raise BuildError(stage, message) from exc + raise error_type(f"{stage} {message}") from exc + output.seek(0, os.SEEK_END) + size = output.tell() + output.seek(max(0, size - DIAGNOSTIC_LIMIT)) + detail = output.read().decode("utf-8", errors="replace").strip() + if completed.returncode != 0: + message = f"exited {completed.returncode}: {detail or 'no diagnostics'}" + if error_type is BuildError: + raise BuildError(stage, message) + raise error_type(f"{stage} {message}") + return detail + + +def _version(tool: str) -> str: + return _run((tool, "--version"), timeout=30).splitlines()[0] + + +def _tree_sha256(root: Path) -> str: + digest = hashlib.sha256(b"linear-solve-bench-tree-v1\0") + files = sorted(path for path in root.rglob("*") if path.is_file()) + if not files: + raise ValueError(f"file tree is empty: {root}") + for path in files: + _regular_file(path, "runtime file") + relative = path.relative_to(root).as_posix().encode() + digest.update(len(relative).to_bytes(4, "little")) + digest.update(relative) + digest.update(bytes.fromhex(_sha256(path))) + return digest.hexdigest() + + +def _manifest(root: Path, tools: dict[str, str]) -> dict[str, object]: + files = { + "policy_header": "include/nsl_hypre_solver.h", + "hypre_library": "lib/libHYPRE.a", + "driver_object": "trusted/driver.o", + "allowed_symbols": "trusted/allowed-symbols.txt", + } + return { + "schema_version": 1, + "build_kind": BUILD_KIND, + "candidate_abi": CANDIDATE_ABI, + "factory_export": FACTORY_EXPORT, + "hypre": { + "repository": HYPRE_REPOSITORY, + "commit": HYPRE_COMMIT, + "tree": HYPRE_TREE, + }, + "include_tree_sha256": _tree_sha256(root / "include"), + "files": { + name: {"path": relative, "sha256": _sha256(root / relative)} + for name, relative in files.items() + }, + "tool_versions": {name: _version(path) for name, path in tools.items()}, + } + + +def build_runtime(root: Path, jobs: int = 2) -> Runtime: + """Build the one immutable, sequential HYPRE runtime used by the benchmark.""" + + root = Path(root).expanduser().resolve() + if root.exists(): + raise ValueError(f"runtime output already exists: {root}") + if type(jobs) is not int or jobs <= 0: + raise ValueError("jobs must be a positive integer") + + tools = { + name: _tool(executable) + for name, executable in { + "cc": "cc", + "cxx": "c++", + "cmake": "cmake", + "git": "git", + "ninja": "ninja", + "nm": "nm", + }.items() + } + root.parent.mkdir(parents=True, exist_ok=True) + staging = root.parent / f".{root.name}.{uuid.uuid4().hex}.tmp" + + try: + with tempfile.TemporaryDirectory(prefix="lsb-runtime-") as temporary: + work = Path(temporary) + source = work / "hypre" + build = work / "hypre-build" + install = work / "hypre-install" + + _run( + ( + tools["git"], + "clone", + "--no-checkout", + HYPRE_REPOSITORY, + str(source), + ), + stage="cloning HYPRE", + ) + _run( + (tools["git"], "-C", str(source), "checkout", "--detach", HYPRE_COMMIT), + stage="checking out HYPRE", + ) + commit = _run( + (tools["git"], "-C", str(source), "rev-parse", "HEAD"), + stage="reading HYPRE commit", + ) + tree = _run( + (tools["git"], "-C", str(source), "rev-parse", "HEAD^{tree}"), + stage="reading HYPRE tree", + ) + if commit != HYPRE_COMMIT or tree != HYPRE_TREE: + raise RuntimeError( + "downloaded HYPRE source identity does not match the pin" + ) + + source_date = _run( + (tools["git"], "-C", str(source), "show", "-s", "--format=%ct", "HEAD"), + stage="reading HYPRE source date", + ) + build_env = { + **os.environ, + "SOURCE_DATE_EPOCH": source_date, + "ZERO_AR_DATE": "1", + } + prefix_map = f"-ffile-prefix-map={work}=/linear-solve-bench/runtime" + _run( + ( + tools["cmake"], + "-S", + str(source / "src"), + "-B", + str(build), + "-G", + "Ninja", + f"-DCMAKE_INSTALL_PREFIX={install}", + "-DCMAKE_BUILD_TYPE=Release", + f"-DCMAKE_C_COMPILER={tools['cc']}", + f"-DCMAKE_CXX_COMPILER={tools['cxx']}", + f"-DCMAKE_C_FLAGS={prefix_map}", + f"-DCMAKE_CXX_FLAGS={prefix_map}", + "-DBUILD_SHARED_LIBS=OFF", + "-DHYPRE_ENABLE_MPI=OFF", + "-DHYPRE_ENABLE_OPENMP=OFF", + "-DHYPRE_ENABLE_CUDA=OFF", + "-DHYPRE_ENABLE_HIP=OFF", + "-DHYPRE_ENABLE_SYCL=OFF", + "-DHYPRE_ENABLE_SINGLE=OFF", + "-DHYPRE_ENABLE_LONG_DOUBLE=OFF", + "-DHYPRE_ENABLE_BIGINT=OFF", + "-DHYPRE_ENABLE_MIXEDINT=OFF", + "-DHYPRE_BUILD_EXAMPLES=OFF", + "-DHYPRE_BUILD_TESTS=OFF", + ), + env=build_env, + stage="configuring HYPRE", + ) + _run( + ( + tools["cmake"], + "--build", + str(build), + "--target", + "install", + "--parallel", + str(jobs), + ), + env=build_env, + stage="building HYPRE", + ) + libraries = tuple(install.rglob("libHYPRE.a")) + if len(libraries) != 1: + raise RuntimeError( + "HYPRE build did not produce exactly one static library" + ) + + (staging / "include").mkdir(parents=True) + (staging / "lib").mkdir() + (staging / "trusted").mkdir() + for header in (install / "include").iterdir(): + if header.is_file(): + shutil.copy2(header, staging / "include" / header.name) + shutil.copy2( + NATIVE_DIR / "include" / "nsl_hypre_solver.h", + staging / "include" / "nsl_hypre_solver.h", + ) + shutil.copy2(libraries[0], staging / "lib" / "libHYPRE.a") + shutil.copy2( + NATIVE_DIR / "allowed-symbols.txt", + staging / "trusted" / "allowed-symbols.txt", + ) + + driver_build = work / "driver-build" + _run( + ( + tools["cmake"], + "-S", + str(NATIVE_DIR), + "-B", + str(driver_build), + "-G", + "Ninja", + f"-DCMAKE_C_COMPILER={tools['cc']}", + f"-DCMAKE_CXX_COMPILER={tools['cxx']}", + f"-DCMAKE_INSTALL_PREFIX={staging}", + f"-DHYPRE_INCLUDE_DIR={staging / 'include'}", + f"-DLSB_RUNTIME_ROOT={staging}", + ), + env=build_env, + stage="configuring the native driver", + ) + _run( + (tools["cmake"], "--build", str(driver_build), "--target", "install"), + env=build_env, + stage="building the native driver", + ) + + manifest = _manifest(staging, tools) + manifest["manifest_sha256"] = _canonical_sha256(manifest) + (staging / "manifest.json").write_text( + json.dumps(manifest, indent=2, sort_keys=True) + "\n", encoding="utf-8" + ) + os.replace(staging, root) + finally: + if staging.exists(): + shutil.rmtree(staging) + return load_runtime(root) + + +def load_runtime(root: Path) -> Runtime: + """Load a runtime after checking its pinned identity and trusted files.""" + + root = Path(root).expanduser().resolve() + try: + manifest = json.loads((root / "manifest.json").read_text(encoding="utf-8")) + except (OSError, UnicodeDecodeError, json.JSONDecodeError) as exc: + raise ValueError(f"runtime manifest is unreadable: {root}") from exc + if not isinstance(manifest, dict) or manifest.get("schema_version") != 1: + raise ValueError("runtime manifest schema mismatch") + + digest = manifest.get("manifest_sha256") + body = {key: value for key, value in manifest.items() if key != "manifest_sha256"} + if not isinstance(digest, str) or digest != _canonical_sha256(body): + raise ValueError("runtime manifest digest mismatch") + if ( + manifest.get("build_kind") != BUILD_KIND + or manifest.get("candidate_abi") != CANDIDATE_ABI + or manifest.get("factory_export") != FACTORY_EXPORT + or manifest.get("hypre") + != { + "repository": HYPRE_REPOSITORY, + "commit": HYPRE_COMMIT, + "tree": HYPRE_TREE, + } + or manifest.get("include_tree_sha256") != _tree_sha256(root / "include") + ): + raise ValueError("runtime scientific identity mismatch") + + expected = { + "policy_header": "include/nsl_hypre_solver.h", + "hypre_library": "lib/libHYPRE.a", + "driver_object": "trusted/driver.o", + "allowed_symbols": "trusted/allowed-symbols.txt", + } + records = manifest.get("files") + if not isinstance(records, dict) or set(records) != set(expected): + raise ValueError("runtime manifest file list mismatch") + for name, relative in expected.items(): + record = records[name] + path = root / relative + if ( + not isinstance(record, dict) + or record.get("path") != relative + or not path.is_file() + or path.is_symlink() + or record.get("sha256") != _sha256(path) + ): + raise ValueError(f"runtime file identity mismatch: {name}") + return Runtime(root, digest) + + +def _symbols(output: str, *, undefined: bool) -> tuple[str, ...]: + symbols: set[str] = set() + for line in output.splitlines(): + fields = line.split() + if not fields: + continue + if undefined: + if len(fields) >= 2 and fields[-2].upper() == "U": + symbols.add(fields[-1]) + elif len(fields) == 1: + symbols.add(fields[0]) + elif len(fields) >= 2 and fields[-2].upper() not in {"U", "?"}: + symbols.add(fields[-1]) + return tuple(sorted(symbols)) + + +def _normalize(symbol: str, allowed: set[str]) -> str: + if symbol.startswith("_") and symbol[1:] in allowed: + return symbol[1:] + return symbol + + +def _build_solver( + source: Path, + output: Path, + runtime: Runtime, + additional_symbols: set[str] | None = None, +) -> SolverBuild: + + text = validate_source(source) + if not isinstance(runtime, Runtime): + raise TypeError("runtime must be a Runtime") + loaded = load_runtime(runtime.root) + if loaded.manifest_sha256 != runtime.manifest_sha256: + raise ValueError("runtime identity changed after it was loaded") + + output = Path(output).expanduser().resolve() + if output.exists(): + raise ValueError(f"solver output already exists: {output}") + output.parent.mkdir(parents=True, exist_ok=True) + cc, cxx, nm = _tool("cc"), _tool("c++"), _tool("nm") + root = loaded.root + + with tempfile.TemporaryDirectory( + prefix="lsb-solver-", dir=output.parent + ) as temporary: + work = Path(temporary) + staged = work / "solver.c" + staged.write_text(text, encoding="utf-8") + candidate_object = work / "solver.o" + executable = work / "solver" + + _run( + ( + cc, + "-std=c17", + "-O3", + "-DNDEBUG", + "-fno-omit-frame-pointer", + "-fvisibility=hidden", + f"-ffile-prefix-map={work}=/linear-solve-bench/candidate", + "-include", + str(root / "include" / "nsl_hypre_solver.h"), + "-I", + str(root / "include"), + "-c", + str(staged), + "-o", + str(candidate_object), + ), + timeout=60, + error_type=BuildError, + stage="candidate compilation", + ) + + allowed = set( + (root / "trusted" / "allowed-symbols.txt") + .read_text(encoding="utf-8") + .splitlines() + ) + allowed.update( + { + "_GLOBAL_OFFSET_TABLE_", + "__stack_chk_fail", + "memcpy", + "memmove", + "memset", + "sqrt", + } + ) + allowed.update(additional_symbols or ()) + undefined = { + _normalize(symbol, allowed) + for symbol in _symbols( + _run( + (nm, "-u", str(candidate_object)), + timeout=30, + error_type=BuildError, + stage="candidate symbol audit", + ), + undefined=True, + ) + } + forbidden = sorted(undefined - allowed) + if forbidden: + raise BuildError( + "candidate build", "forbidden symbols: " + ", ".join(forbidden) + ) + + exports = { + _normalize(symbol, {FACTORY_EXPORT}) + for symbol in _symbols( + _run( + (nm, "-g", str(candidate_object)), + timeout=30, + error_type=BuildError, + stage="candidate export audit", + ), + undefined=False, + ) + } + if exports != {FACTORY_EXPORT}: + raise BuildError( + "candidate build", + f"global exports must be exactly {FACTORY_EXPORT}; " + f"observed {sorted(exports)}", + ) + + _run( + ( + cxx, + str(root / "trusted" / "driver.o"), + str(candidate_object), + str(root / "lib" / "libHYPRE.a"), + "-lm", + "-o", + str(executable), + ), + timeout=60, + error_type=BuildError, + stage="candidate link", + ) + executable.chmod(0o755) + executable_sha256 = _sha256(executable) + os.replace(executable, output) + + return SolverBuild(output, _sha256(Path(source)), executable_sha256) + + +def build_solver(source: Path, output: Path, runtime: Runtime) -> SolverBuild: + """Compile, audit, and link one participant translation unit.""" + + return _build_solver(source, output, runtime) + + +def build_reference(source: Path, output: Path, runtime: Runtime) -> SolverBuild: + """Build the immutable reference, with its small stock-GMRES allowance.""" + + source = Path(source) + if _sha256(source) != REFERENCE_SOURCE_SHA256: + raise BuildError("reference audit", "source does not match the fixed reference") + return _build_solver(source, output, runtime, _REFERENCE_SYMBOLS) + + +def encode_input(value: CaseInput) -> bytes: + """Encode one validated case for the trusted native driver.""" + + matrix = value.matrix + return b"".join( + ( + INPUT_HEADER.pack( + INPUT_MAGIC, + PROTOCOL_VERSION, + 0, + matrix.n, + len(matrix.values), + value.tolerance, + ), + np.asarray(matrix.row_offsets, dtype=" int: + return OUTPUT_HEADER.size + n * 8 + + +def run_solver( + executable: Path, + value: CaseInput, + timeout_seconds: float = RUN_TIMEOUT_SECONDS, +) -> RawExecution: + """Run one case in a fresh process and retain bounded raw evidence.""" + + if not isinstance(value, CaseInput): + raise TypeError("value must be a CaseInput") + timeout_seconds = float(timeout_seconds) + if not np.isfinite(timeout_seconds) or timeout_seconds <= 0: + raise ValueError("timeout_seconds must be positive and finite") + executable = _regular_file(Path(executable), "solver executable") + with tempfile.TemporaryDirectory(prefix="lsb-case-") as temporary: + root = Path(temporary) + input_path = root / "input.bin" + output_path = root / "output.bin" + input_path.write_bytes(encode_input(value)) + + started = time.monotonic() + process = subprocess.Popen( + (str(executable), str(input_path), str(output_path)), + stdin=subprocess.DEVNULL, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + start_new_session=True, + env={ + **os.environ, + "OMP_NUM_THREADS": "1", + "OPENBLAS_NUM_THREADS": "1", + "MKL_NUM_THREADS": "1", + }, + ) + diagnostics = b"" + + def read_diagnostics() -> None: + nonlocal diagnostics + assert process.stdout is not None + with process.stdout: + while chunk := process.stdout.read(64 * 1024): + diagnostics = (diagnostics + chunk)[-DIAGNOSTIC_LIMIT:] + + reader = threading.Thread(target=read_diagnostics, daemon=True) + reader.start() + timed_out = False + try: + try: + process.wait(timeout=timeout_seconds) + except subprocess.TimeoutExpired: + timed_out = True + finally: + try: + os.killpg(process.pid, signal.SIGKILL) + except ProcessLookupError: + pass + returncode = process.wait() + reader.join() + wall_seconds = time.monotonic() - started + + if timed_out: + returncode = 124 + timeout_message = f"\nsolver timed out after {timeout_seconds:g} seconds" + diagnostics = (diagnostics + timeout_message.encode())[-DIAGNOSTIC_LIMIT:] + + payload = None + if returncode == 0: + try: + with output_path.open("rb") as result: + # The extra byte lets the verifier detect oversized output. + payload = result.read(output_size(value.matrix.n) + 1) + except FileNotFoundError: + pass + return RawExecution( + returncode=returncode, + wall_seconds=wall_seconds, + diagnostics=diagnostics.decode("utf-8", errors="replace"), + output=payload, + ) + + +__all__ = [ + "BuildError", + "CANDIDATE_ABI", + "EXAMPLE_SOURCE", + "HYPRE_COMMIT", + "HYPRE_TREE", + "REFERENCE_SOURCE", + "REFERENCE_ID", + "REFERENCE_SOURCE_SHA256", + "RUN_TIMEOUT_SECONDS", + "Runtime", + "SolverBuild", + "build_runtime", + "build_reference", + "build_solver", + "encode_input", + "load_runtime", + "output_size", + "run_solver", + "validate_source", +] diff --git a/src/linear_solver_bench/capabilities.py b/src/linear_solver_bench/capabilities.py deleted file mode 100644 index ee84ba3..0000000 --- a/src/linear_solver_bench/capabilities.py +++ /dev/null @@ -1,323 +0,0 @@ -"""Canonical audit identity of the candidate-visible native HYPRE surface.""" - -from __future__ import annotations - -import hashlib - -KRYLOV_PRIMITIVE_ABI = "hypre-native-krylov-solver-v4" - -_STANDARD_SETUP_SOLVE = ( - "HYPRE_Int(HYPRE_Solver,HYPRE_ParCSRMatrix,HYPRE_ParVector,HYPRE_ParVector)" -) -_SOLVER_INT_SETTER = "HYPRE_Int(HYPRE_Solver,HYPRE_Int)" -_SOLVER_REAL_SETTER = "HYPRE_Int(HYPRE_Solver,HYPRE_Real)" - -# This is trusted audit and prompt metadata, not an operation table passed to a -# candidate. Candidate source calls every name below directly. -NATIVE_HYPRE_CAPABILITY_GROUPS = ( - ( - "krylov", - ( - ("hypre_ParKrylovCAlloc", "void *(size_t,size_t,HYPRE_MemoryLocation)"), - ("hypre_ParKrylovFree", "HYPRE_Int(void *)"), - ("hypre_ParKrylovCommInfo", "HYPRE_Int(void *,HYPRE_Int *,HYPRE_Int *)"), - ("hypre_ParKrylovCreateVector", "void *(void *)"), - ("hypre_ParKrylovCreateVectorArray", "void *(HYPRE_Int,void *)"), - ("hypre_ParKrylovDestroyVector", "HYPRE_Int(void *)"), - ("hypre_ParKrylovCopyVector", "HYPRE_Int(void *,void *)"), - ("hypre_ParKrylovClearVector", "HYPRE_Int(void *)"), - ("hypre_ParKrylovScaleVector", "HYPRE_Int(HYPRE_Complex,void *)"), - ("hypre_ParKrylovAxpy", "HYPRE_Int(HYPRE_Complex,void *,void *)"), - ("hypre_ParKrylovInnerProd", "HYPRE_Real(void *,void *)"), - ( - "hypre_ParKrylovInnerProdTagged", - "HYPRE_Int(void *,void *,HYPRE_Int *,HYPRE_Complex **)", - ), - ( - "hypre_ParKrylovMassInnerProd", - "HYPRE_Int(void *,void **,HYPRE_Int,HYPRE_Int,void *)", - ), - ( - "hypre_ParKrylovMassDotpTwo", - "HYPRE_Int(void *,void *,void **,HYPRE_Int,HYPRE_Int,void *,void *)", - ), - ( - "hypre_ParKrylovMassAxpy", - "HYPRE_Int(HYPRE_Complex *,void **,void *,HYPRE_Int,HYPRE_Int)", - ), - ("hypre_ParKrylovMatvecCreate", "void *(void *,void *)"), - ( - "hypre_ParKrylovMatvec", - "HYPRE_Int(void *,HYPRE_Complex,void *,void *,HYPRE_Complex,void *)", - ), - ( - "hypre_ParKrylovMatvecT", - "HYPRE_Int(void *,HYPRE_Complex,void *,void *,HYPRE_Complex,void *)", - ), - ("hypre_ParKrylovMatvecDestroy", "HYPRE_Int(void *)"), - ("hypre_ParKrylovIdentitySetup", "HYPRE_Int(void *,void *,void *,void *)"), - ("hypre_ParKrylovIdentity", "HYPRE_Int(void *,void *,void *,void *)"), - ), - ), - ( - "matrix", - ( - ( - "HYPRE_ParCSRMatrixGetComm", - "HYPRE_Int(HYPRE_ParCSRMatrix,MPI_Comm *)", - ), - ( - "HYPRE_ParCSRMatrixGetDims", - "HYPRE_Int(HYPRE_ParCSRMatrix,HYPRE_BigInt *,HYPRE_BigInt *)", - ), - ( - "HYPRE_ParCSRMatrixGetLocalRange", - ( - "HYPRE_Int(HYPRE_ParCSRMatrix,HYPRE_BigInt *,HYPRE_BigInt *," - "HYPRE_BigInt *,HYPRE_BigInt *)" - ), - ), - ), - ), - ( - "diagonal_scaling", - ( - ("HYPRE_ParCSRDiagScaleSetup", _STANDARD_SETUP_SOLVE), - ("HYPRE_ParCSRDiagScale", _STANDARD_SETUP_SOLVE), - ), - ), - ( - "boomeramg", - ( - ("HYPRE_BoomerAMGCreate", "HYPRE_Int(HYPRE_Solver *)"), - ("HYPRE_BoomerAMGDestroy", "HYPRE_Int(HYPRE_Solver)"), - ("HYPRE_BoomerAMGSetup", _STANDARD_SETUP_SOLVE), - ("HYPRE_BoomerAMGSolve", _STANDARD_SETUP_SOLVE), - ("HYPRE_BoomerAMGSolveT", _STANDARD_SETUP_SOLVE), - ("HYPRE_BoomerAMGSetTol", _SOLVER_REAL_SETTER), - ("HYPRE_BoomerAMGSetMaxIter", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetMinIter", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetMaxCoarseSize", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetMinCoarseSize", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetMaxLevels", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetStrongThreshold", _SOLVER_REAL_SETTER), - ("HYPRE_BoomerAMGSetMaxRowSum", _SOLVER_REAL_SETTER), - ("HYPRE_BoomerAMGSetNonGalerkinTol", _SOLVER_REAL_SETTER), - ("HYPRE_BoomerAMGSetCoarsenType", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetMeasureType", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetAggNumLevels", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetNumPaths", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetInterpType", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetInterpRefine", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetTruncFactor", _SOLVER_REAL_SETTER), - ("HYPRE_BoomerAMGSetPMaxElmts", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetAggInterpType", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetAggTruncFactor", _SOLVER_REAL_SETTER), - ("HYPRE_BoomerAMGSetAggPMaxElmts", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetCycleType", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetNumSweeps", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetRelaxType", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetRelaxOrder", _SOLVER_INT_SETTER), - ( - "HYPRE_BoomerAMGSetCycleNumSweeps", - "HYPRE_Int(HYPRE_Solver,HYPRE_Int,HYPRE_Int)", - ), - ( - "HYPRE_BoomerAMGSetCycleRelaxType", - "HYPRE_Int(HYPRE_Solver,HYPRE_Int,HYPRE_Int)", - ), - ("HYPRE_BoomerAMGSetRelaxWt", _SOLVER_REAL_SETTER), - ("HYPRE_BoomerAMGSetOuterWt", _SOLVER_REAL_SETTER), - ("HYPRE_BoomerAMGSetChebyOrder", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetChebyScale", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetChebyVariant", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetChebyEigEst", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetChebyFraction", _SOLVER_REAL_SETTER), - ("HYPRE_BoomerAMGSetSmoothType", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetSmoothNumLevels", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGSetSmoothNumSweeps", _SOLVER_INT_SETTER), - ), - ), - ( - "boomeramg_dd", - ( - ("HYPRE_BoomerAMGDDCreate", "HYPRE_Int(HYPRE_Solver *)"), - ("HYPRE_BoomerAMGDDDestroy", "HYPRE_Int(HYPRE_Solver)"), - ("HYPRE_BoomerAMGDDSetup", _STANDARD_SETUP_SOLVE), - ("HYPRE_BoomerAMGDDSolve", _STANDARD_SETUP_SOLVE), - ("HYPRE_BoomerAMGDDSetFACNumRelax", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGDDSetFACNumCycles", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGDDSetFACCycleType", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGDDSetFACRelaxType", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGDDSetFACRelaxWeight", _SOLVER_REAL_SETTER), - ("HYPRE_BoomerAMGDDSetStartLevel", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGDDSetPadding", _SOLVER_INT_SETTER), - ("HYPRE_BoomerAMGDDSetNumGhostLayers", _SOLVER_INT_SETTER), - ), - ), - ( - "fsai", - ( - ("HYPRE_FSAICreate", "HYPRE_Int(HYPRE_Solver *)"), - ("HYPRE_FSAIDestroy", "HYPRE_Int(HYPRE_Solver)"), - ("HYPRE_FSAISetup", _STANDARD_SETUP_SOLVE), - ("HYPRE_FSAISolve", _STANDARD_SETUP_SOLVE), - ("HYPRE_FSAISetAlgoType", _SOLVER_INT_SETTER), - ("HYPRE_FSAISetMaxSteps", _SOLVER_INT_SETTER), - ("HYPRE_FSAISetMaxStepSize", _SOLVER_INT_SETTER), - ("HYPRE_FSAISetMaxNnzRow", _SOLVER_INT_SETTER), - ("HYPRE_FSAISetNumLevels", _SOLVER_INT_SETTER), - ("HYPRE_FSAISetMaxIterations", _SOLVER_INT_SETTER), - ("HYPRE_FSAISetEigMaxIters", _SOLVER_INT_SETTER), - ("HYPRE_FSAISetZeroGuess", _SOLVER_INT_SETTER), - ("HYPRE_FSAISetThreshold", _SOLVER_REAL_SETTER), - ("HYPRE_FSAISetKapTolerance", _SOLVER_REAL_SETTER), - ("HYPRE_FSAISetOmega", _SOLVER_REAL_SETTER), - ("HYPRE_FSAISetTolerance", _SOLVER_REAL_SETTER), - ), - ), - ( - "parcheby", - ( - ("HYPRE_ParChebyCreate", "HYPRE_Int(HYPRE_Solver *)"), - ("HYPRE_ParChebyDestroy", "HYPRE_Int(HYPRE_Solver)"), - ("HYPRE_ParChebySetup", _STANDARD_SETUP_SOLVE), - ("HYPRE_ParChebySolve", _STANDARD_SETUP_SOLVE), - ("HYPRE_ParChebySetMaxIterations", _SOLVER_INT_SETTER), - ("HYPRE_ParChebySetOrder", _SOLVER_INT_SETTER), - ("HYPRE_ParChebySetVariant", _SOLVER_INT_SETTER), - ("HYPRE_ParChebySetScale", _SOLVER_INT_SETTER), - ("HYPRE_ParChebySetEigEst", _SOLVER_INT_SETTER), - ("HYPRE_ParChebySetZeroGuess", _SOLVER_INT_SETTER), - ("HYPRE_ParChebySetTolerance", _SOLVER_REAL_SETTER), - ("HYPRE_ParChebySetEigRatio", _SOLVER_REAL_SETTER), - ( - "HYPRE_ParChebySetMinMaxEigEst", - "HYPRE_Int(HYPRE_Solver,HYPRE_Real,HYPRE_Real)", - ), - ), - ), - ( - "parasails", - ( - ("HYPRE_ParaSailsCreate", "HYPRE_Int(MPI_Comm,HYPRE_Solver *)"), - ("HYPRE_ParaSailsDestroy", "HYPRE_Int(HYPRE_Solver)"), - ("HYPRE_ParaSailsSetup", _STANDARD_SETUP_SOLVE), - ("HYPRE_ParaSailsSolve", _STANDARD_SETUP_SOLVE), - ( - "HYPRE_ParaSailsSetParams", - "HYPRE_Int(HYPRE_Solver,HYPRE_Real,HYPRE_Int)", - ), - ("HYPRE_ParaSailsSetFilter", _SOLVER_REAL_SETTER), - ("HYPRE_ParaSailsSetSym", _SOLVER_INT_SETTER), - ("HYPRE_ParaSailsSetLoadbal", _SOLVER_REAL_SETTER), - ("HYPRE_ParaSailsSetReuse", _SOLVER_INT_SETTER), - ), - ), - ( - "euclid", - ( - ("HYPRE_EuclidCreate", "HYPRE_Int(MPI_Comm,HYPRE_Solver *)"), - ("HYPRE_EuclidDestroy", "HYPRE_Int(HYPRE_Solver)"), - ("HYPRE_EuclidSetup", _STANDARD_SETUP_SOLVE), - ("HYPRE_EuclidSolve", _STANDARD_SETUP_SOLVE), - ("HYPRE_EuclidSetLevel", _SOLVER_INT_SETTER), - ("HYPRE_EuclidSetBJ", _SOLVER_INT_SETTER), - ("HYPRE_EuclidSetRowScale", _SOLVER_INT_SETTER), - ("HYPRE_EuclidSetSparseA", _SOLVER_REAL_SETTER), - ("HYPRE_EuclidSetILUT", _SOLVER_REAL_SETTER), - ), - ), - ( - "pilut", - ( - ("HYPRE_ParCSRPilutCreate", "HYPRE_Int(MPI_Comm,HYPRE_Solver *)"), - ("HYPRE_ParCSRPilutDestroy", "HYPRE_Int(HYPRE_Solver)"), - ("HYPRE_ParCSRPilutSetup", _STANDARD_SETUP_SOLVE), - ("HYPRE_ParCSRPilutSolve", _STANDARD_SETUP_SOLVE), - ("HYPRE_ParCSRPilutSetMaxIter", _SOLVER_INT_SETTER), - ("HYPRE_ParCSRPilutSetFactorRowSize", _SOLVER_INT_SETTER), - ("HYPRE_ParCSRPilutSetDropTolerance", _SOLVER_REAL_SETTER), - ), - ), - ( - "ilu", - ( - ("HYPRE_ILUCreate", "HYPRE_Int(HYPRE_Solver *)"), - ("HYPRE_ILUDestroy", "HYPRE_Int(HYPRE_Solver)"), - ("HYPRE_ILUSetup", _STANDARD_SETUP_SOLVE), - ("HYPRE_ILUSolve", _STANDARD_SETUP_SOLVE), - ("HYPRE_ILUSetMaxIter", _SOLVER_INT_SETTER), - ("HYPRE_ILUSetTriSolve", _SOLVER_INT_SETTER), - ("HYPRE_ILUSetLowerJacobiIters", _SOLVER_INT_SETTER), - ("HYPRE_ILUSetUpperJacobiIters", _SOLVER_INT_SETTER), - ("HYPRE_ILUSetLevelOfFill", _SOLVER_INT_SETTER), - ("HYPRE_ILUSetMaxNnzPerRow", _SOLVER_INT_SETTER), - ("HYPRE_ILUSetSchurMaxIter", _SOLVER_INT_SETTER), - ("HYPRE_ILUSetType", _SOLVER_INT_SETTER), - ("HYPRE_ILUSetLocalReordering", _SOLVER_INT_SETTER), - ("HYPRE_ILUSetTol", _SOLVER_REAL_SETTER), - ("HYPRE_ILUSetDropThreshold", _SOLVER_REAL_SETTER), - ("HYPRE_ILUSetNSHDropThreshold", _SOLVER_REAL_SETTER), - ), - ), - ( - "schwarz", - ( - ("HYPRE_SchwarzCreate", "HYPRE_Int(HYPRE_Solver *)"), - ("HYPRE_SchwarzDestroy", "HYPRE_Int(HYPRE_Solver)"), - ("HYPRE_SchwarzSetup", _STANDARD_SETUP_SOLVE), - ("HYPRE_SchwarzSolve", _STANDARD_SETUP_SOLVE), - ("HYPRE_SchwarzSetVariant", _SOLVER_INT_SETTER), - ("HYPRE_SchwarzSetOverlap", _SOLVER_INT_SETTER), - ("HYPRE_SchwarzSetDomainType", _SOLVER_INT_SETTER), - ("HYPRE_SchwarzSetNonSymm", _SOLVER_INT_SETTER), - ("HYPRE_SchwarzSetLocalSolverType", _SOLVER_INT_SETTER), - ("HYPRE_SchwarzSetILUKLevelOfFill", _SOLVER_INT_SETTER), - ("HYPRE_SchwarzSetILUTMaxNnzPerRow", _SOLVER_INT_SETTER), - ("HYPRE_SchwarzSetMaxIter", _SOLVER_INT_SETTER), - ("HYPRE_SchwarzSetRelaxWeight", _SOLVER_REAL_SETTER), - ("HYPRE_SchwarzSetILUTDroptol", _SOLVER_REAL_SETTER), - ("HYPRE_SchwarzSetTol", _SOLVER_REAL_SETTER), - ), - ), -) - -KRYLOV_PRIMITIVE_SIGNATURES = tuple( - item for _group, signatures in NATIVE_HYPRE_CAPABILITY_GROUPS for item in signatures -) -KRYLOV_PRIMITIVE_SYMBOLS = tuple( - symbol for symbol, _signature in KRYLOV_PRIMITIVE_SIGNATURES -) -STOCK_PRECONDITIONER_FAMILIES = tuple( - group - for group, _signatures in NATIVE_HYPRE_CAPABILITY_GROUPS - if group not in {"krylov", "matrix"} -) - -if len(KRYLOV_PRIMITIVE_SYMBOLS) != len(set(KRYLOV_PRIMITIVE_SYMBOLS)): - raise RuntimeError("native HYPRE capability catalog contains duplicate symbols") - -KRYLOV_PRIMITIVE_SURFACE_CANONICAL = ( - KRYLOV_PRIMITIVE_ABI - + "\n" - + "\n".join( - f"{group}/{symbol}:{signature}" - for group, signatures in NATIVE_HYPRE_CAPABILITY_GROUPS - for symbol, signature in signatures - ) - + "\n" -).encode("ascii") -KRYLOV_PRIMITIVE_SURFACE_SHA256 = hashlib.sha256( - KRYLOV_PRIMITIVE_SURFACE_CANONICAL -).hexdigest() - -__all__ = [ - "KRYLOV_PRIMITIVE_ABI", - "KRYLOV_PRIMITIVE_SIGNATURES", - "KRYLOV_PRIMITIVE_SURFACE_CANONICAL", - "KRYLOV_PRIMITIVE_SURFACE_SHA256", - "KRYLOV_PRIMITIVE_SYMBOLS", - "NATIVE_HYPRE_CAPABILITY_GROUPS", - "STOCK_PRECONDITIONER_FAMILIES", -] diff --git a/src/linear_solver_bench/cli.py b/src/linear_solver_bench/cli.py index 23b5022..6e1c78e 100644 --- a/src/linear_solver_bench/cli.py +++ b/src/linear_solver_bench/cli.py @@ -1,470 +1,88 @@ -"""Small operator/developer command line for the benchmark.""" +"""Participant commands for LinearSolverBench.""" from __future__ import annotations import argparse -import json -import math import pathlib +import shutil import sys -import tempfile from collections.abc import Sequence -from .compiler import build_candidate, validate_source -from .dataset import ( - dataset_summary, - load_prepared, - load_prepared_manifest, - load_split, - prepare_split, -) -from .families import family_listing, resolve_track -from .runner import evaluate -from .runtime import HYPRE_REPOSITORY, build_runtime, load_runtime -from .scoring import calibration_from_report, score_report, validate_calibration - - -def _release_path(value: str) -> pathlib.Path: - from .paths import data_dir - - path = pathlib.Path(value).expanduser() - if path.is_file(): - return path - if path.name != value or value in {".", ".."}: - raise ValueError("release must name an installed manifest or an existing file") - installed = data_dir() / f"{value}.json" - if not installed.is_file(): - raise ValueError( - f"release {value!r} is unavailable; supply a release manifest path" - ) - return installed - - -def _check_selection(args: argparse.Namespace, release: dict) -> None: - family = getattr(args, "family", None) or release["family"] - track = getattr(args, "track", None) or release["track"] - selected = resolve_track(family, track) - if (selected.family, selected.track) != (release["family"], release["track"]): - raise ValueError("requested family/track differs from the prepared release") - split = getattr(args, "split", None) - if split is not None and split != release["split"]: - raise ValueError("requested split differs from the release manifest") - - -def _pilot_dataset_command(args: argparse.Namespace) -> int: - from .manifests import load_release - from .pilot_dataset import prepare_release - - path = _release_path(args.release) - release = load_release(path) - _check_selection(args, release) - if args.dataset_action in {"summary", "list"}: - value = { - key: release[key] - for key in ( - "release_id", - "family", - "track", - "split", - "contracts", - "execution", - "manifest_sha256", - ) - } - value["case_count"] = len(release["cases"]) - if args.dataset_action == "list": - value["cases"] = release["cases"] - _emit(value) - else: - _emit( - prepare_release( - path, - args.output, - rhs_key=args.rhs_key_file.read_bytes() if args.rhs_key_file else None, - cache=args.cache, - offline=args.offline, - case_ids=args.case, - ) - ) - return 0 - - -def _json_file(path: pathlib.Path) -> dict[str, object]: - try: - value = json.loads(path.read_text(encoding="utf-8")) - except (OSError, UnicodeDecodeError, json.JSONDecodeError) as exc: - raise ValueError(f"cannot read JSON file: {path}") from exc - if not isinstance(value, dict): - raise ValueError(f"JSON file must contain an object: {path}") - return value - - -def _emit(value: object, output: pathlib.Path | None = None) -> None: - text = json.dumps(value, indent=2, sort_keys=True, allow_nan=False) + "\n" - if output is None: - print(text, end="") - else: - output.parent.mkdir(parents=True, exist_ok=True) - output.write_text(text, encoding="utf-8") - print(output) - - -def _calibration( - calibration_path: pathlib.Path | None, -) -> dict[str, object] | None: - if calibration_path is None: - return None - calibration = _json_file(calibration_path) - validate_calibration(calibration) - return calibration - - -def _dataset_command(args: argparse.Namespace) -> int: - if args.dataset_action == "families": - _emit({"tracks": family_listing()}) - return 0 - if args.release: - return _pilot_dataset_command(args) - if args.family or args.track: - raise ValueError( - "pilot families require --release with a published or draft manifest" - ) - if getattr(args, "offline", False): - raise ValueError("--offline is supported by pilot release preparation") - if getattr(args, "split", None) is None: - args.split = "dev" - if args.dataset_action == "summary": - _emit(dataset_summary()) - elif args.dataset_action == "list": - manifest = load_split(args.split) - _emit( - { - "split": args.split, - "case_count": manifest["case_count"], - "cases": [ - { - key: case[key] - for key in ("index", "case_id", "group", "name", "n", "nnz") - } - for case in manifest["cases"] - ], - } - ) - elif args.dataset_action == "prepare": - key = args.rhs_key_file.read_bytes() if args.rhs_key_file else None - _emit( - prepare_split( - args.split, - args.output, - rhs_key=key, - cache=args.cache, - case_ids=args.case, - ) - ) - return 0 - - -def _runtime_command(args: argparse.Namespace) -> int: - runtime = build_runtime(args.output, hypre_source=args.hypre_source, jobs=args.jobs) - _emit( - { - "runtime": str(runtime.root), - "manifest_sha256": runtime.manifest_sha256, - } - ) - return 0 - - -def _candidate_command(args: argparse.Namespace) -> int: - if args.candidate_action == "source": - validate_source(args.source) - _emit({"ok": True, "source": str(args.source.resolve())}) - return 0 - runtime = load_runtime(args.runtime) - if args.candidate_action == "build": - build = build_candidate(args.source, args.output, runtime=runtime) - _emit( - { - "ok": True, - "executable": str(build.executable), - "source_sha256": build.source_sha256, - "object_sha256": build.object_sha256, - "executable_sha256": build.executable_sha256, - "compile_wall_s": build.compile_wall_s, - "undefined_symbols": build.undefined_symbols, - "exported_symbols": build.exported_symbols, - } - ) - return 0 - with tempfile.TemporaryDirectory(prefix="lsb-validate-") as temporary: - build = build_candidate( - args.source, pathlib.Path(temporary) / "candidate", runtime=runtime - ) - _emit( - { - "ok": True, - "source_sha256": build.source_sha256, - "object_sha256": build.object_sha256, - "executable_sha256": build.executable_sha256, - "compile_wall_s": build.compile_wall_s, - "undefined_symbols": build.undefined_symbols, - "exported_symbols": build.exported_symbols, - } - ) - return 0 - - -def _run_command(args: argparse.Namespace) -> int: - if _json_file(args.cases / "manifest.json").get("schema_version") == 2: - return _run_pilot_command(args) - if args.family or args.track or args.release: - raise ValueError("pilot selectors cannot reinterpret v1 prepared cases") - args.deadline = 120.0 if args.deadline is None else args.deadline - if not math.isfinite(args.deadline) or not 0.0 < args.deadline <= 120.0: - raise ValueError("case deadline must lie in (0, 120]") - runtime = load_runtime(args.runtime) - prepared_manifest = load_prepared_manifest(args.cases) - systems = load_prepared(args.cases) - calibration = _calibration(args.calibration) - deadlines = None - if calibration is not None: - observed_cases = [str(case["case_id"]) for case in calibration["cases"]] - expected_cases = [system.case_id for system in systems] - if observed_cases != expected_cases: - raise ValueError("calibration case order differs from prepared cases") - expected_identity = { - "venue_id": "local-uncontrolled", - "runtime_manifest_sha256": runtime.manifest_sha256, - "dataset_manifest_sha256": prepared_manifest["source_manifest_sha256"], - "prepared_manifest_sha256": prepared_manifest["manifest_sha256"], - } - mismatches = sorted( - key - for key, expected in expected_identity.items() - if calibration.get(key) != expected - ) - if mismatches: - raise ValueError("calibration identity mismatch: " + ", ".join(mismatches)) - deadlines = { - str(case["case_id"]): float(case["deadline_s"]) - for case in calibration["cases"] - } - with tempfile.TemporaryDirectory(prefix="lsb-run-") as temporary: - build = build_candidate( - args.source, pathlib.Path(temporary) / "candidate", runtime=runtime - ) - report = evaluate( - build.executable, - systems, - deadline_s=args.deadline, - deadline_by_case=deadlines, - source_sha256=build.source_sha256, - executable_sha256=build.executable_sha256, - runtime_manifest_sha256=runtime.manifest_sha256, - dataset_manifest_sha256=str(prepared_manifest["source_manifest_sha256"]), - prepared_manifest_sha256=str(prepared_manifest["manifest_sha256"]), - ) - _emit(report, args.output) - return 0 if report["solved_count"] == report["case_count"] else 1 - - -def _run_pilot_command(args: argparse.Namespace) -> int: - from .manifests import load_release - from .pilot_dataset import iter_pilot_prepared, load_pilot_prepared_manifest - from .pilot_runner import evaluate_pilot - from .pilot_scoring import score_pilot_report, validate_replay_reference - - prepared = load_pilot_prepared_manifest(args.cases) - release = prepared["source_release"] - _check_selection(args, release) - if ( - args.release - and load_release(_release_path(args.release))["manifest_sha256"] - != release["manifest_sha256"] - ): - raise ValueError("selected release differs from prepared cases") - if ( - args.deadline is not None - and args.deadline != release["execution"]["case_timeout_seconds"] - ): - raise ValueError( - "pilot deadlines are frozen in the release and cannot be overridden" - ) - reference = _json_file(args.calibration) if args.calibration else None - if reference is not None: - if release["track"] != "replay": - raise ValueError("NS coverage does not use a timing reference") - validate_replay_reference(reference) - anchor = reference["reference_report"] - if ( - anchor["release_manifest_sha256"] != release["manifest_sha256"] - or anchor["prepared_manifest_sha256"] != prepared["manifest_sha256"] - ): - raise ValueError("reference numerical release differs from prepared cases") - if anchor["venue"]["id"] != "local-uncontrolled": - raise ValueError("local execution requires a local reference") - runtime = load_runtime(args.runtime) - if ( - reference - and reference["reference_report"]["runtime_manifest_sha256"] - != runtime.manifest_sha256 - ): - raise ValueError("reference runtime differs from selected runtime") - with tempfile.TemporaryDirectory(prefix="lsb-pilot-") as temporary: - build = build_candidate( - args.source, pathlib.Path(temporary) / "candidate", runtime=runtime - ) - report = evaluate_pilot( - build.executable, - iter_pilot_prepared(args.cases), - prepared, - source_sha256=build.source_sha256, - executable_sha256=build.executable_sha256, - runtime_manifest_sha256=runtime.manifest_sha256, - ) - if reference is not None: - score_pilot_report(report, reference) - _emit(report, args.output) - # Partial coverage is a valid result. Scoring reports candidate eligibility. - return 0 - - -def _calibrate_command(args: argparse.Namespace) -> int: - report = _json_file(args.report) - if report.get("schema_version") == 2: - from .pilot_scoring import replay_reference_from_report - - if (args.multiplier, args.minimum_deadline, args.maximum_deadline) != ( - 4.0, - 1.0, - 120.0, - ): - raise ValueError( - "pilot reference qualification does not alter frozen deadlines" - ) - _emit(replay_reference_from_report(report), args.output) - return 0 - if args.maximum_deadline > 120.0: - raise ValueError("v1 calibration deadlines cannot exceed 120 seconds") - calibration = calibration_from_report( - _json_file(args.report), - deadline_multiplier=args.multiplier, - minimum_deadline_s=args.minimum_deadline, - maximum_deadline_s=args.maximum_deadline, - ) - _emit(calibration, args.output) - return 0 - - -def _score_command(args: argparse.Namespace) -> int: - report = _json_file(args.report) - calibration = _json_file(args.calibration) if args.calibration else None - if report.get("schema_version") == 2: - from .pilot_scoring import score_pilot_report - - score = score_pilot_report(report, calibration) - else: - if calibration is None: - raise ValueError("v1 scoring requires a calibration artifact") - score = score_report(report, calibration) - _emit(score, args.output) - return 0 +from .benchmark import evaluate, repository_root +from .submission import package def parser() -> argparse.ArgumentParser: root = argparse.ArgumentParser(prog="linear-solver-bench") commands = root.add_subparsers(dest="command", required=True) - dataset = commands.add_parser("dataset", help="inspect or prepare corpus data") - dataset_commands = dataset.add_subparsers(dest="dataset_action", required=True) - summary = dataset_commands.add_parser("summary") - dataset_commands.add_parser("families") - listing = dataset_commands.add_parser("list") - listing.add_argument("--split", choices=("dev", "ranked")) - prepare = dataset_commands.add_parser("prepare") - prepare.add_argument("--split", choices=("dev", "ranked")) - prepare.add_argument("--output", type=pathlib.Path, required=True) - prepare.add_argument("--cache", type=pathlib.Path) - prepare.add_argument("--rhs-key-file", type=pathlib.Path) - prepare.add_argument("--case", action="append") - prepare.add_argument("--offline", action="store_true") - for action in (summary, listing, prepare): - action.add_argument( - "--release", help="installed release ID or explicit draft manifest path" - ) - action.add_argument("--family") - action.add_argument("--track") - - runtime = commands.add_parser("runtime", help="build the pinned HYPRE runtime") - runtime_commands = runtime.add_subparsers(dest="runtime_action", required=True) - runtime_build = runtime_commands.add_parser("build") - runtime_build.add_argument("--output", type=pathlib.Path, required=True) - runtime_build.add_argument("--hypre-source", default=HYPRE_REPOSITORY) - runtime_build.add_argument("--jobs", type=int, default=2) - - candidate = commands.add_parser("candidate", help="validate candidate source") - candidate_commands = candidate.add_subparsers( - dest="candidate_action", required=True - ) - source = candidate_commands.add_parser("source", help="run static validation only") - source.add_argument("source", type=pathlib.Path) - validate = candidate_commands.add_parser( - "validate", help="compile, audit, and link" - ) - validate.add_argument("source", type=pathlib.Path) - validate.add_argument("--runtime", type=pathlib.Path, required=True) - build = candidate_commands.add_parser( - "build", help="build into a persistent workspace" - ) - build.add_argument("source", type=pathlib.Path) - build.add_argument("--runtime", type=pathlib.Path, required=True) - build.add_argument("--output", type=pathlib.Path, required=True) + init = commands.add_parser("init", help="copy the starter solver") + init.add_argument("output", type=pathlib.Path) - run = commands.add_parser("run", help="evaluate a candidate locally") + run = commands.add_parser("run", help="evaluate a solver") run.add_argument("source", type=pathlib.Path) - run.add_argument("--runtime", type=pathlib.Path, required=True) - run.add_argument("--cases", type=pathlib.Path, required=True) - run.add_argument("--output", type=pathlib.Path, required=True) - run.add_argument("--deadline", type=float) - run.add_argument("--calibration", type=pathlib.Path) - run.add_argument("--release") - run.add_argument("--family") - run.add_argument("--track") - - calibrate = commands.add_parser("calibrate", help="freeze reference timings") - calibrate.add_argument("report", type=pathlib.Path) - calibrate.add_argument("--output", type=pathlib.Path, required=True) - calibrate.add_argument("--multiplier", type=float, default=4.0) - calibrate.add_argument("--minimum-deadline", type=float, default=1.0) - calibrate.add_argument("--maximum-deadline", type=float, default=120.0) - - score = commands.add_parser("score", help="score an evaluation report") - score.add_argument("report", type=pathlib.Path) - score.add_argument("calibration", type=pathlib.Path, nargs="?") - score.add_argument("--output", type=pathlib.Path) + run.add_argument("--family", choices=("ns_mesh_pde", "flash"), required=True) + run.add_argument("--venue", choices=("local", "modal"), default="modal") + run.add_argument("--case", action="append", dest="case_ids") + run.add_argument("--output", type=pathlib.Path, default=pathlib.Path("result.json")) + run.add_argument("--cache", type=pathlib.Path) + + submit = commands.add_parser("submit", help="create a pull-request bundle") + submit.add_argument("source", type=pathlib.Path) + submit.add_argument("--name", required=True) + submit.add_argument( + "--family", choices=("ns_mesh_pde", "flash"), action="append", required=True + ) + submit.add_argument("--license", dest="license_name", required=True) + submit.add_argument("--submitter", required=True) + submit.add_argument("--output", type=pathlib.Path, required=True) return root -def _main(argv: Sequence[str] | None = None) -> int: - args = parser().parse_args(argv) - if args.command == "dataset": - return _dataset_command(args) - if args.command == "runtime": - return _runtime_command(args) - if args.command == "candidate": - return _candidate_command(args) - if args.command == "run": - return _run_command(args) - if args.command == "calibrate": - return _calibrate_command(args) - if args.command == "score": - return _score_command(args) - raise AssertionError("unreachable command") +def _run(args: argparse.Namespace) -> int: + if args.command == "init": + if args.output.exists(): + raise ValueError(f"output already exists: {args.output}") + args.output.parent.mkdir(parents=True, exist_ok=True) + shutil.copyfile(repository_root() / "examples" / "solver.c", args.output) + print(args.output) + return 0 + if args.command == "submit": + package( + args.source, + args.output, + name=args.name, + families=args.family, + license_name=args.license_name, + submitter=args.submitter, + ) + print(args.output) + return 0 + report = evaluate( + args.source, + family=args.family, + venue=args.venue, + case_ids=args.case_ids, + output=args.output, + cache=args.cache, + progress=lambda message: print(message, file=sys.stderr), + ) + for case in report["cases"]: + speedup = "-" if case["speedup"] is None else f"{case['speedup']:.3f}x" + print(f"{case['case_id']}: {'PASS' if case['passed'] else 'FAIL'} {speedup}") + aggregate = report["geometric_mean_speedup"] + if aggregate is not None: + print(f"Geometric mean speedup: {aggregate:.3f}x") + print(f"Report: {args.output}") + return 0 if report["all_passed"] else 1 def main(argv: Sequence[str] | None = None) -> int: try: - return _main(argv) - except (ValueError, OSError) as exc: - print(f"linear-solver-bench: {exc}", file=sys.stderr) + return _run(parser().parse_args(argv)) + except KeyboardInterrupt: + print("linear-solver-bench: cancelled", file=sys.stderr) + return 130 + except (OSError, RuntimeError, ValueError) as error: + print(f"linear-solver-bench: {error}", file=sys.stderr) return 2 diff --git a/src/linear_solver_bench/compiler.py b/src/linear_solver_bench/compiler.py deleted file mode 100644 index b05f41d..0000000 --- a/src/linear_solver_bench/compiler.py +++ /dev/null @@ -1,213 +0,0 @@ -"""Compile and link one audited C translation unit.""" - -from __future__ import annotations - -import hashlib -import pathlib -import re -import shutil -import stat -from dataclasses import dataclass - -from .capabilities import KRYLOV_PRIMITIVE_SYMBOLS -from .process import ProcessTimeout, bounded_process - -MAX_SOURCE_BYTES = 65_536 -FACTORY_EXPORT = "solver_create" -_INCLUDE_RE = re.compile( - r"^[ \t]*#[ \t]*include[ \t]*([<\"])([^>\"]+)[>\"][ \t]*(?://.*)?$", - re.MULTILINE, -) -_INCLUDE_DIRECTIVE_RE = re.compile(r"^[ \t]*#[ \t]*include\b.*$", re.MULTILINE) - - -class CandidateBuildError(RuntimeError): - def __init__(self, stage: str, message: str): - self.stage = stage - super().__init__(f"CANDIDATE_{stage.upper()}_FAILED: {message}") - - -@dataclass(frozen=True) -class CandidateBuild: - executable: pathlib.Path - source_sha256: str - object_sha256: str - executable_sha256: str - undefined_symbols: tuple[str, ...] - exported_symbols: tuple[str, ...] - compile_wall_s: float - - -def _regular(path: pathlib.Path, label: str) -> pathlib.Path: - try: - mode = path.lstat().st_mode - except OSError as exc: - raise ValueError(f"{label} is unavailable: {path}") from exc - if not stat.S_ISREG(mode) or path.is_symlink(): - raise ValueError(f"{label} must be a regular, non-symlink file") - return path - - -def sha256_file(path: pathlib.Path) -> str: - digest = hashlib.sha256() - with path.open("rb") as handle: - for chunk in iter(lambda: handle.read(1024 * 1024), b""): - digest.update(chunk) - return digest.hexdigest() - - -def validate_source(path: pathlib.Path) -> str: - source = _regular(pathlib.Path(path), "candidate source") - if source.stat().st_size > MAX_SOURCE_BYTES: - raise CandidateBuildError("static", "candidate source exceeds 65,536 bytes") - try: - text = source.read_text(encoding="utf-8") - except (OSError, UnicodeDecodeError) as exc: - raise CandidateBuildError("static", "candidate source is not UTF-8") from exc - if "\x00" in text or "\r" in text: - raise CandidateBuildError("static", "candidate source must use LF text") - includes = tuple(match.group(2) for match in _INCLUDE_RE.finditer(text)) - directives = tuple(_INCLUDE_DIRECTIVE_RE.finditer(text)) - if includes != ("HYPRE_parcsr_ls.h",) or len(directives) != 1: - raise CandidateBuildError( - "static", - "candidate must include exactly HYPRE_parcsr_ls.h", - ) - return text - - -def _symbols(output: bytes, *, undefined: bool) -> tuple[str, ...]: - symbols = set() - for line in output.decode("utf-8", errors="replace").splitlines(): - fields = line.split() - if not fields: - continue - if undefined: - if len(fields) >= 2 and fields[-2].upper() == "U": - symbols.add(fields[-1]) - elif len(fields) == 1: - symbols.add(fields[0]) - elif len(fields) >= 2 and fields[-2].upper() not in {"U", "?"}: - symbols.add(fields[-1]) - return tuple(sorted(symbols)) - - -def _normalize(symbol: str, allowed: set[str]) -> str: - return symbol[1:] if symbol.startswith("_") and symbol[1:] in allowed else symbol - - -def _checked(command: tuple[str, ...], timeout_s: float, stage: str): - try: - result = bounded_process(command, timeout_s=timeout_s) - except ProcessTimeout as exc: - raise CandidateBuildError(stage, f"{command[0]} timed out") from exc - if result.returncode != 0: - detail = result.output.decode("utf-8", errors="replace").strip() - raise CandidateBuildError( - stage, - f"command exited {result.returncode}: {detail[-8000:] or 'no diagnostics'}", - ) - return result - - -def build_candidate( - source: pathlib.Path, - workspace: pathlib.Path, - *, - runtime: object, - timeout_s: float = 60.0, -) -> CandidateBuild: - validate_source(source) - workspace = pathlib.Path(workspace) - if workspace.exists(): - raise ValueError("candidate build workspace already exists") - source_dir = workspace / "source" - build_dir = workspace / "build" - source_dir.mkdir(parents=True) - build_dir.mkdir() - staged = source_dir / "policy.c" - shutil.copyfile(source, staged) - candidate_object = build_dir / "policy.o" - executable = build_dir / "lsb_driver" - compile_command = ( - runtime.cc, - "-std=c17", - "-O3", - "-DNDEBUG", - "-fno-omit-frame-pointer", - "-fvisibility=hidden", - "-include", - str(runtime.policy_header), - "-I", - str(runtime.include_dir), - "-c", - str(staged), - "-o", - str(candidate_object), - ) - compiled = _checked(compile_command, timeout_s, "compile") - - undefined_result = _checked( - (runtime.nm, "-u", str(candidate_object)), timeout_s, "abi" - ) - allowed = { - "__stack_chk_fail", - "memcpy", - "memmove", - "memset", - "sqrt", - *KRYLOV_PRIMITIVE_SYMBOLS, - } - undefined = tuple( - sorted( - _normalize(symbol, allowed) - for symbol in _symbols(undefined_result.output, undefined=True) - ) - ) - forbidden = sorted(set(undefined) - allowed) - if forbidden: - raise CandidateBuildError("abi", "forbidden symbols: " + ", ".join(forbidden)) - - export_result = _checked( - (runtime.nm, "-g", str(candidate_object)), timeout_s, "abi" - ) - exports = tuple( - sorted( - _normalize(symbol, {FACTORY_EXPORT}) - for symbol in _symbols(export_result.output, undefined=False) - ) - ) - if exports != (FACTORY_EXPORT,): - raise CandidateBuildError( - "abi", - f"global exports must be exactly {FACTORY_EXPORT}; observed {exports!r}", - ) - - linked = _checked( - ( - runtime.cxx, - str(runtime.driver_object), - str(candidate_object), - str(runtime.hypre_library), - "-lm", - "-o", - str(executable), - ), - timeout_s, - "link", - ) - executable.chmod(0o755) - return CandidateBuild( - executable=executable, - source_sha256=sha256_file(staged), - object_sha256=sha256_file(candidate_object), - executable_sha256=sha256_file(executable), - undefined_symbols=undefined, - exported_symbols=exports, - compile_wall_s=( - compiled.wall_s - + undefined_result.wall_s - + export_result.wall_s - + linked.wall_s - ), - ) diff --git a/src/linear_solver_bench/data.py b/src/linear_solver_bench/data.py new file mode 100644 index 0000000..13be430 --- /dev/null +++ b/src/linear_solver_bench/data.py @@ -0,0 +1,555 @@ +"""Load the two public datasets directly from their source archives.""" + +from __future__ import annotations + +import hashlib +import hmac +import io +import json +import os +import tarfile +import tempfile +import urllib.error +import urllib.request +import warnings +import zipfile +from collections.abc import Iterable, Iterator, Mapping +from pathlib import Path, PurePosixPath +from urllib.parse import unquote, urlsplit + +import numpy as np +from scipy import sparse +from scipy.io import mmread + +from .assets import asset_root +from .families import FLASH, NS_MESH_PDE, Family, resolve_family +from .models import BenchmarkCase, CaseInput, CsrMatrix + +MAX_ARCHIVE_BYTES = 8 * 1024**3 +_MANIFEST_DIGESTS = { + NS_MESH_PDE.id: "4f40ea3871e0e7c8cc9aa221793a21c87ecdd2909c2e48155ede06d3648844c0", + FLASH.id: "0746f3d4c419ce91469252407588eb585de08766b5425c5796cd1f76f1f54168", +} +_FLASH_FILES = {"matrix.npz", "b.npy", "x0.npy", "case.json"} + + +def _json_bytes(value: object) -> bytes: + return json.dumps( + value, + sort_keys=True, + separators=(",", ":"), + ensure_ascii=True, + allow_nan=False, + ).encode("ascii") + + +def _sha256_file(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for block in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(block) + return digest.hexdigest() + + +def _digest(value: object, name: str) -> str: + if ( + not isinstance(value, str) + or len(value) != 64 + or any(char not in "0123456789abcdef" for char in value) + ): + raise ValueError(f"{name} must be a lowercase SHA-256 digest") + return value + + +def _positive_int(value: object, name: str) -> int: + if type(value) is not int or value <= 0: + raise ValueError(f"{name} must be a positive integer") + return value + + +def _strict_keys(value: object, keys: set[str], name: str) -> dict: + if not isinstance(value, dict) or set(value) != keys: + raise ValueError(f"{name} has unexpected fields") + return value + + +def _validate_manifest(manifest: object, family: Family | None = None) -> dict: + if not isinstance(manifest, dict) or not isinstance(manifest.get("cases"), list): + raise ValueError("benchmark manifest must contain a case list") + ids = {NS_MESH_PDE.id, FLASH.id} + if manifest.get("family") not in ids: + raise ValueError("manifest has an unsupported family") + if family is not None and manifest["family"] != family.id: + raise ValueError("manifest family does not match the requested family") + if not manifest["cases"]: + raise ValueError("benchmark manifest contains no cases") + seen = set() + for index, case in enumerate(manifest["cases"]): + if not isinstance(case, dict) or case.get("index") != index: + raise ValueError("manifest case order is invalid") + case_id = case.get("case_id") + if not isinstance(case_id, str) or not case_id or case_id in seen: + raise ValueError("manifest case IDs must be unique, nonempty strings") + seen.add(case_id) + _positive_int(case.get("n"), "n") + _positive_int(case.get("nnz"), "nnz") + tolerance = case.get("tolerance") + if not isinstance(tolerance, (int, float)) or not 0 < tolerance < 1: + raise ValueError("case tolerance must be between zero and one") + source = case.get("source") + if not isinstance(source, dict): + raise ValueError("case source must be an object") + _digest(source.get("archive_sha256"), "archive_sha256") + _digest(source.get("matrix_sha256"), "matrix_sha256") + archive_bytes = _positive_int(source.get("archive_bytes"), "archive_bytes") + if archive_bytes > MAX_ARCHIVE_BYTES: + raise ValueError("source archive exceeds the download limit") + return manifest + + +def load_manifest(family: str | Family) -> dict: + """Load and authenticate a bundled public development manifest.""" + family = resolve_family(family) if isinstance(family, str) else family + if family not in {NS_MESH_PDE, FLASH}: + raise ValueError("unsupported benchmark family") + try: + path = asset_root("families") / "families" / family.manifest + manifest = json.loads(path.read_text("utf-8")) + except (OSError, UnicodeError, json.JSONDecodeError) as exc: + raise ValueError("cannot read the bundled benchmark manifest") from exc + manifest = _validate_manifest(manifest, family) + claimed = _digest(manifest.get("manifest_sha256"), "manifest_sha256") + body = {key: value for key, value in manifest.items() if key != "manifest_sha256"} + actual = hashlib.sha256(_json_bytes(body)).hexdigest() + if claimed != actual or claimed != _MANIFEST_DIGESTS[family.id]: + raise ValueError("bundled benchmark manifest has the wrong digest") + return manifest + + +def _fetch(source: Mapping[str, object], cache: Path) -> Path: + """Download one archive and return it only after size and hash verification.""" + expected_hash = _digest(source.get("archive_sha256"), "archive_sha256") + expected_size = _positive_int(source.get("archive_bytes"), "archive_bytes") + if expected_size > MAX_ARCHIVE_BYTES: + raise ValueError("source archive exceeds the download limit") + cache.mkdir(parents=True, exist_ok=True) + target = cache / expected_hash + if target.is_file() and not target.is_symlink(): + if ( + target.stat().st_size == expected_size + and _sha256_file(target) == expected_hash + ): + return target + target.unlink() + + url = source.get("url") + if not isinstance(url, str): + raise ValueError("source URL must be text") + parsed = urlsplit(url) + if parsed.scheme not in {"https", "file"}: + raise ValueError("source URL must use HTTPS") + temporary = None + try: + with tempfile.NamedTemporaryFile(dir=cache, delete=False) as output: + temporary = Path(output.name) + digest = hashlib.sha256() + total = 0 + if parsed.scheme == "file": + response = Path(unquote(parsed.path)).open("rb") + else: + request = urllib.request.Request( + url, + headers={ + "User-Agent": "linear-solver-bench/1", + "Accept-Encoding": "identity", + }, + ) + response = urllib.request.urlopen(request, timeout=120) + with response: + while block := response.read(1024 * 1024): + total += len(block) + if total > expected_size: + raise ValueError("download exceeds its declared size") + digest.update(block) + output.write(block) + if total != expected_size: + raise ValueError("download size disagrees with the manifest") + if digest.hexdigest() != expected_hash: + raise ValueError("download digest disagrees with the manifest") + os.replace(temporary, target) + return target + except (OSError, urllib.error.URLError) as exc: + raise ValueError("archive download failed") from exc + finally: + if temporary is not None: + temporary.unlink(missing_ok=True) + + +def _matrix_sha256(matrix: CsrMatrix) -> str: + digest = hashlib.sha256(b"linear-solver-bench-csr-float64-v2\0") + digest.update(matrix.n.to_bytes(8, "little")) + digest.update(np.asarray(matrix.row_offsets, dtype=" str: + digest = hashlib.sha256(b"linear-solver-bench-vector-float64-v2\0") + digest.update(len(vector).to_bytes(8, "little")) + digest.update(np.asarray(vector, dtype=" str: + body = { + "case_id": case_id, + "matrix_sha256": _matrix_sha256(value.matrix), + "b_sha256": _vector_sha256(value.b), + "x0_sha256": _vector_sha256(value.x0), + "tolerance": value.tolerance, + "reference_kind": "none" if target is None else "manufactured", + "reference_sha256": None if target is None else _vector_sha256(target), + } + return hashlib.sha256(_json_bytes(body)).hexdigest() + + +def _load_suitesparse(path: Path, case: Mapping[str, object]) -> CsrMatrix: + source = case["source"] + expected = f"{source['name']}/{source['name']}.mtx" + matrix = None + expanded = 0 + seen = set() + try: + with tarfile.open(path, "r:gz") as archive: + for count, member in enumerate(archive, 1): + name = PurePosixPath(member.name) + expanded += member.size + if ( + count > 128 + or member.size < 0 + or expanded > MAX_ARCHIVE_BYTES + or name.is_absolute() + or ".." in name.parts + or member.name in seen + or not (member.isfile() or member.isdir()) + ): + raise ValueError("unsafe SuiteSparse archive member") + seen.add(member.name) + if member.name != expected: + continue + handle = archive.extractfile(member) + if not member.isfile() or handle is None: + raise ValueError("SuiteSparse matrix member is unreadable") + with handle: + header = handle.readline(1024).decode("ascii").lower().split() + if ( + len(header) != 5 + or header[:3] + != [ + "%%matrixmarket", + "matrix", + "coordinate", + ] + or header[3] not in {"real", "integer", "pattern"} + ): + raise ValueError( + "SuiteSparse matrix must be real coordinate data" + ) + dimensions = handle.readline(4096) + while dimensions.startswith(b"%"): + dimensions = handle.readline(4096) + size = [int(item) for item in dimensions.split()] + if len(size) != 3 or size[:2] != [case["n"], case["n"]]: + raise ValueError( + "Matrix Market dimensions disagree with the case" + ) + handle.seek(0) + with warnings.catch_warnings(): + warnings.filterwarnings( + "ignore", + message="The default value for `spmatrix` is changing", + category=DeprecationWarning, + ) + loaded = mmread(handle) + if not sparse.issparse(loaded) or np.iscomplexobj(loaded.data): + raise ValueError("SuiteSparse matrix must be real and sparse") + matrix = CsrMatrix.from_scipy(loaded) + except (OSError, tarfile.TarError, UnicodeError) as exc: + raise ValueError("SuiteSparse archive is unreadable") from exc + if matrix is None: + raise ValueError("SuiteSparse matrix is missing from its archive") + if ( + matrix.n != case["n"] + or matrix.nnz != case["nnz"] + or _matrix_sha256(matrix) != source["matrix_sha256"] + ): + raise ValueError("SuiteSparse matrix disagrees with the manifest") + scale = float(np.max(np.abs(matrix.values), initial=0)) + difference = matrix.to_scipy() - matrix.to_scipy().T + asymmetry = float(np.max(np.abs(difference.data), initial=0)) + if scale == 0 or asymmetry <= 1e-12 * scale: + raise ValueError("NS mesh matrices must be materially nonsymmetric") + return matrix + + +def _rademacher_target(manifest: Mapping[str, object], matrix: CsrMatrix) -> np.ndarray: + rhs = manifest.get("rhs") + if not isinstance(rhs, dict) or rhs.get("scheme") != "rademacher-hmac-sha256-v2": + raise ValueError("NS manifest has an unsupported RHS recipe") + try: + key = bytes.fromhex(rhs["public_key_hex"]) + except (KeyError, TypeError, ValueError) as exc: + raise ValueError("NS development manifest has no valid public RHS key") from exc + if len(key) < 16 or hashlib.sha256(key).hexdigest() != rhs.get("key_id"): + raise ValueError("NS public RHS key does not match its commitment") + release_id = manifest.get("release_id") + domain = _json_bytes( + { + "scheme": rhs["scheme"], + "release_id": ( + "ns-mesh-cohort-v2" if release_id == "ns-mesh" else release_id + ), + "family": manifest["family"], + "matrix_sha256": _matrix_sha256(matrix), + "rhs_kind": "rademacher", + "draw_index": rhs["draw_index"], + } + ) + raw = bytearray() + for block in range((matrix.n + 255) // 256): + message = domain + b"\0" + block.to_bytes(8, "little") + raw.extend(hmac.digest(key, message, "sha256")) + bits = np.unpackbits(np.frombuffer(raw, dtype=np.uint8), bitorder="little")[ + : matrix.n + ] + return bits.astype(np.float64) * 2 - 1 + + +def _read_npy(payload: bytes, shape: tuple[int, ...], kinds: set[str]) -> np.ndarray: + handle = io.BytesIO(payload) + version = np.lib.format.read_magic(handle) + if version == (1, 0): + actual, _, dtype = np.lib.format.read_array_header_1_0( + handle, max_header_size=4096 + ) + elif version == (2, 0): + actual, _, dtype = np.lib.format.read_array_header_2_0( + handle, max_header_size=4096 + ) + else: + raise ValueError("unsupported NPY format") + if actual != shape or dtype.hasobject or dtype.kind not in kinds: + raise ValueError("array shape or dtype disagrees with the case") + count = int(np.prod(actual, dtype=np.int64)) if actual else 1 + if len(payload) - handle.tell() != count * dtype.itemsize: + raise ValueError("array byte count disagrees with its header") + return np.load(io.BytesIO(payload), allow_pickle=False, max_header_size=4096) + + +def _flash_files(path: Path, case: Mapping[str, object]) -> dict[str, bytes]: + limits = { + "matrix.npz": 32 * case["nnz"] + 16 * (case["n"] + 1) + 65536, + "b.npy": 8 * case["n"] + 4096, + "x0.npy": 8 * case["n"] + 4096, + "case.json": 65536, + } + result = {} + try: + if zipfile.is_zipfile(path): + with zipfile.ZipFile(path) as archive: + for member in archive.infolist(): + if ( + member.filename not in _FLASH_FILES + or member.filename in result + or member.is_dir() + or member.file_size > limits[member.filename] + ): + raise ValueError("unsafe FLASH archive member") + result[member.filename] = archive.read(member) + else: + with tarfile.open(path, "r:gz") as archive: + for member in archive: + if ( + member.name not in _FLASH_FILES + or member.name in result + or not member.isfile() + or member.size < 0 + or member.size > limits[member.name] + ): + raise ValueError("unsafe FLASH archive member") + handle = archive.extractfile(member) + if handle is None: + raise ValueError("FLASH archive member is unreadable") + result[member.name] = handle.read(limits[member.name] + 1) + except (OSError, tarfile.TarError, zipfile.BadZipFile) as exc: + raise ValueError("FLASH archive is unreadable") from exc + if set(result) != _FLASH_FILES: + raise ValueError("FLASH archive must contain exactly four files") + return result + + +def _flash_matrix(payload: bytes, n: int, nnz: int) -> CsrMatrix: + limits = { + "data.npy": nnz * 8 + 4096, + "indices.npy": nnz * 8 + 4096, + "indptr.npy": (n + 1) * 8 + 4096, + "format.npy": 4096, + "shape.npy": 4096, + "_is_array.npy": 4096, + } + arrays = {} + try: + with zipfile.ZipFile(io.BytesIO(payload)) as archive: + for member in archive.infolist(): + if ( + member.filename not in limits + or member.filename in arrays + or member.file_size > limits[member.filename] + ): + raise ValueError("unsafe compressed matrix member") + arrays[member.filename] = archive.read(member) + except (OSError, zipfile.BadZipFile) as exc: + raise ValueError("FLASH matrix is unreadable") from exc + required = {"data.npy", "indices.npy", "indptr.npy", "format.npy", "shape.npy"} + if not required.issubset(arrays): + raise ValueError("FLASH matrix archive is incomplete") + values = _read_npy(arrays["data.npy"], (nnz,), {"f"}) + columns = _read_npy(arrays["indices.npy"], (nnz,), {"i", "u"}) + rows = _read_npy(arrays["indptr.npy"], (n + 1,), {"i", "u"}) + shape = _read_npy(arrays["shape.npy"], (2,), {"i", "u"}) + form = _read_npy(arrays["format.npy"], (), {"S"}) + if "_is_array.npy" in arrays: + _read_npy(arrays["_is_array.npy"], (), {"b"}) + if tuple(shape) != (n, n) or form.item() != b"csr" or values.dtype != np.float64: + raise ValueError("FLASH matrix must be float64 CSR") + if ( + np.any(columns < 0) + or np.any(columns >= n) + or np.any(rows < 0) + or np.any(rows > nnz) + ): + raise ValueError("FLASH CSR indices are outside the matrix") + if np.any(values == 0): + raise ValueError("FLASH matrix contains explicit zeros") + return CsrMatrix( + n, + np.asarray(rows, dtype=np.uint64), + np.asarray(columns, dtype=np.uint32), + values, + ) + + +def _load_flash(path: Path, case: Mapping[str, object]) -> CaseInput: + files = _flash_files(path, case) + try: + metadata = json.loads(files["case.json"].decode("utf-8")) + except (UnicodeError, json.JSONDecodeError) as exc: + raise ValueError("FLASH metadata is unreadable") from exc + expected_keys = { + "schema_version", + "case_id", + "family", + "capture_kind", + "n", + "nnz", + "relative_tolerance", + "absolute_tolerance", + "provenance_group", + "source_run", + "step", + "solve_index", + "matrix_sha256", + "b_sha256", + "x0_sha256", + "files", + } + _strict_keys(metadata, expected_keys, "FLASH metadata") + source = case["source"] + admission = case["admission"] + expected = { + "schema_version": 1, + "case_id": case["case_id"], + "family": FLASH.id, + "capture_kind": "scalar-magnetic-diffusion", + "n": case["n"], + "nnz": case["nnz"], + "relative_tolerance": case["tolerance"], + "absolute_tolerance": 0, + "provenance_group": case["provenance_group"], + "source_run": case["source_run"], + "step": admission["step"], + "solve_index": admission["solve_index"], + "matrix_sha256": source["matrix_sha256"], + "b_sha256": source["b_sha256"], + "x0_sha256": source["x0_sha256"], + } + if any(metadata.get(key) != value for key, value in expected.items()): + raise ValueError("FLASH metadata disagrees with the manifest") + digests = _strict_keys( + metadata["files"], {"matrix.npz", "b.npy", "x0.npy"}, "FLASH files" + ) + if any( + hashlib.sha256(files[name]).hexdigest() != digest + for name, digest in digests.items() + ): + raise ValueError("FLASH file digest mismatch") + matrix = _flash_matrix(files["matrix.npz"], case["n"], case["nnz"]) + b = _read_npy(files["b.npy"], (case["n"],), {"f"}) + x0 = _read_npy(files["x0.npy"], (case["n"],), {"f"}) + value = CaseInput(matrix, b, x0, case["tolerance"]) + if ( + _matrix_sha256(matrix) != source["matrix_sha256"] + or _vector_sha256(value.b) != source["b_sha256"] + or _vector_sha256(value.x0) != source["x0_sha256"] + ): + raise ValueError("FLASH numerical inputs disagree with the manifest") + return value + + +def iter_cases( + manifest: dict, + cache: Path, + case_ids: Iterable[str] | None = None, +) -> Iterator[BenchmarkCase]: + """Download, authenticate, decode, and yield one case at a time.""" + manifest = _validate_manifest(manifest) + requested = None if case_ids is None else list(case_ids) + known = {case["case_id"] for case in manifest["cases"]} + if requested is not None: + if not requested or len(requested) != len(set(requested)): + raise ValueError("case selection must contain distinct case IDs") + missing = set(requested) - known + if missing: + raise ValueError(f"unknown case ID: {sorted(missing)[0]}") + selected = set(requested) + else: + selected = known + + for case in manifest["cases"]: + if case["case_id"] not in selected: + continue + archive = _fetch(case["source"], Path(cache)) + if manifest["family"] == NS_MESH_PDE.id: + matrix = _load_suitesparse(archive, case) + target = _rademacher_target(manifest, matrix) + value = CaseInput( + matrix, + matrix.matvec(target), + np.zeros(matrix.n, dtype=np.float64), + case["tolerance"], + ) + else: + target = None + value = _load_flash(archive, case) + identity = _input_sha256(case["case_id"], value, target) + qualification = case.get("qualification") + if qualification is not None and qualification.get("system_sha256") != identity: + raise ValueError("decoded inputs differ from the qualified system") + yield BenchmarkCase( + case_id=case["case_id"], + input=value, + target=target, + input_sha256=identity, + role=case.get("role", "scored"), + ) diff --git a/src/linear_solver_bench/dataset.py b/src/linear_solver_bench/dataset.py deleted file mode 100644 index 95560b4..0000000 --- a/src/linear_solver_bench/dataset.py +++ /dev/null @@ -1,420 +0,0 @@ -"""Frozen corpus loading and trusted SuiteSparse system preparation.""" - -from __future__ import annotations - -import hashlib -import hmac -import json -import os -import pathlib -import shutil -import tarfile -import tempfile -import urllib.request -import uuid -from collections import Counter -from collections.abc import Iterable, Mapping - -import numpy as np -from scipy import sparse -from scipy.io import mmread - -from .archive import decode_system, encode_system -from .models import CsrMatrix, EvaluationSystem, MatrixInput -from .paths import cache_dir, data_dir - -CATALOGUE_SHA256 = "8226ebe538dcd0590d1415a2057da8df5e332d5b51886654a3d9a0c0181b92cd" -QUALIFICATION_SHA256 = ( - "30c7b765ed3f8c9712897c0be1d84890921cec90f5c6da122e9108100c8e3044" -) -RANKED_TOLERANCE_TIERS = (1.0e-4, 1.0e-3, 1.0e-2, 4.0e-2) -RANKED_MANIFEST_SHA256 = ( - "85ad37de2d7fc4b787e7eb0d5248eaebccf7ccaa04fd6f24e58637d37d6ef69f" -) -DEV_MANIFEST_SHA256 = "b743e8cf87f7c286b84d3ddac567d90460228b194df43e49c9e7fa8398cea454" -RHS_SCHEME = "rademacher-hmac-pcg64-v1" -MAX_DOWNLOAD_BYTES = 8 * 1024**3 -MAX_MEMBER_BYTES = 4 * 1024**3 -MAX_ARCHIVE_MEMBERS = 128 - - -def canonical_json(value: object) -> str: - return json.dumps( - value, - sort_keys=True, - separators=(",", ":"), - ensure_ascii=True, - allow_nan=False, - ) - - -def identity_sha256(value: object) -> str: - return hashlib.sha256(canonical_json(value).encode("ascii")).hexdigest() - - -def sha256_file(path: pathlib.Path) -> str: - digest = hashlib.sha256() - with pathlib.Path(path).open("rb") as handle: - for block in iter(lambda: handle.read(1024 * 1024), b""): - digest.update(block) - return digest.hexdigest() - - -def matrix_sha256(matrix: CsrMatrix) -> str: - digest = hashlib.sha256(b"nsl-suitesparse-csr-float64-v1\0") - digest.update(matrix.n.to_bytes(8, "little", signed=False)) - digest.update(np.asarray(matrix.row_offsets, dtype=" dict[str, object]: - try: - value = json.loads(path.read_text(encoding="utf-8")) - except (OSError, UnicodeDecodeError, json.JSONDecodeError) as exc: - raise ValueError(f"cannot read JSON artifact: {path}") from exc - if not isinstance(value, dict): - raise ValueError(f"JSON artifact must contain an object: {path}") - return value - - -def load_catalogue(root: pathlib.Path | None = None) -> dict[str, object]: - value = _load_json((root or data_dir()) / "catalogue.json") - claimed = value.get("catalogue_sha256") - body = {key: item for key, item in value.items() if key != "catalogue_sha256"} - if claimed != CATALOGUE_SHA256 or identity_sha256(body) != claimed: - raise ValueError("catalogue identity mismatch") - records = value.get("records") - if value.get("eligible_count") != 350 or not isinstance(records, list): - raise ValueError("catalogue count mismatch") - return value - - -def load_qualification(root: pathlib.Path | None = None) -> dict[str, object]: - value = _load_json((root or data_dir()) / "qualification.json") - claimed = value.get("condition_results_sha256") - body = { - key: item for key, item in value.items() if key != "condition_results_sha256" - } - if claimed != QUALIFICATION_SHA256 or identity_sha256(body) != claimed: - raise ValueError("qualification identity mismatch") - if ( - value.get("schema_version") != 2 - or value.get("kind") != "linear-solver-bench-condition-evidence-v2" - or value.get("method_counts") != {"spqr-guarded-onenormest-v1": 350} - or value.get("policy", {}).get("condition_norm") != "1" - ): - raise ValueError("qualification policy mismatch") - if value.get("complete") is not True or value.get("received_case_count") != 350: - raise ValueError("qualification artifact is incomplete") - results = value.get("results") - if not isinstance(results, list) or len(results) != 350: - raise ValueError("qualification result count mismatch") - observed = Counter( - result.get("condition", {}).get("status") - for result in results - if isinstance(result, Mapping) - ) - if observed != Counter({"estimated": 213, "rank_deficient": 137}): - raise ValueError("qualification status counts mismatch") - return value - - -def _validate_split(value: dict[str, object], expected_split: str) -> dict[str, object]: - if value.get("schema_version") != 1 or value.get("split") != expected_split: - raise ValueError("dataset split identity mismatch") - digest = value.get("manifest_sha256") - if not isinstance(digest, str): - raise ValueError("dataset split digest is missing") - body = {key: item for key, item in value.items() if key != "manifest_sha256"} - if identity_sha256(body) != digest: - raise ValueError("dataset split digest mismatch") - cases = value.get("cases") - if not isinstance(cases, list) or value.get("case_count") != len(cases): - raise ValueError("dataset split count mismatch") - for index, case in enumerate(cases): - if not isinstance(case, Mapping) or case.get("index") != index: - raise ValueError("dataset split case order is invalid") - return value - - -def load_split(split: str, root: pathlib.Path | None = None) -> dict[str, object]: - if split not in {"dev", "ranked"}: - raise ValueError("split must be 'dev' or 'ranked'") - filename = "dev-v1.json" if split == "dev" else "ranked-v1.json" - value = _load_json((root or data_dir()) / filename) - expected_count = 8 if split == "dev" else 203 - validated = _validate_split(value, split) - if validated.get("case_count") != expected_count: - raise ValueError(f"{split} split must contain {expected_count} cases") - expected_digest = DEV_MANIFEST_SHA256 if split == "dev" else RANKED_MANIFEST_SHA256 - if validated.get("manifest_sha256") != expected_digest: - raise ValueError(f"{split} split is not the frozen v1 manifest") - return validated - - -def dataset_summary(root: pathlib.Path | None = None) -> dict[str, object]: - catalogue = load_catalogue(root) - qualification = load_qualification(root) - ranked = load_split("ranked", root) - dev = load_split("dev", root) - return { - "screened": catalogue["eligible_count"], - "full_rank": qualification["status_counts"]["estimated"], - "rank_deficient": qualification["status_counts"]["rank_deficient"], - "condition_unsupported": sum( - result["condition"]["status"] == "estimated" - and result["condition"]["tightest_supported_tolerance"] is None - for result in qualification["results"] - ), - "ranked": ranked["case_count"], - "development": dev["case_count"], - "tolerance_tiers": list(RANKED_TOLERANCE_TIERS), - } - - -def rademacher_x_star(case_id: str, n: int, key: bytes) -> np.ndarray: - if not case_id or type(n) is not int or n <= 0: - raise ValueError("invalid case identity") - if not isinstance(key, bytes) or len(key) < 16: - raise ValueError("RHS key must contain at least 16 bytes") - message = f"{RHS_SCHEME}\0{case_id}".encode("ascii") - seed = int.from_bytes(hmac.new(key, message, hashlib.sha256).digest()[:16], "big") - generator = np.random.Generator(np.random.PCG64(seed)) - bits = generator.integers(0, 2, size=n, dtype=np.int8) - return np.asarray(2 * bits - 1, dtype=np.float64) - - -def materialize_system( - case: Mapping[str, object], matrix: sparse.spmatrix, key: bytes -) -> EvaluationSystem: - canonical = CsrMatrix.from_scipy(matrix) - if canonical.n != case.get("n") or canonical.nnz != case.get("nnz"): - raise ValueError("canonical matrix disagrees with frozen metadata") - expected_matrix_sha256 = case.get("matrix_sha256") - if matrix_sha256(canonical) != expected_matrix_sha256: - raise ValueError("canonical matrix digest mismatch") - case_id = str(case["case_id"]) - x_star = rademacher_x_star(case_id, canonical.n, key) - b = canonical.matvec(x_star) - if not np.all(np.isfinite(b)): - raise ValueError("manufactured right-hand side is nonfinite") - return EvaluationSystem( - case_id=case_id, - public=MatrixInput( - matrix=canonical, - b=b, - x0=np.zeros(canonical.n, dtype=np.float64), - tolerance=float(case["tolerance"]), - ), - x_star=x_star, - ) - - -def _download(url: str, target: pathlib.Path, expected_sha256: str) -> None: - target.parent.mkdir(parents=True, exist_ok=True) - if target.is_file() and sha256_file(target) == expected_sha256: - return - request = urllib.request.Request( - url, headers={"User-Agent": "linear-solver-bench/0.1"} - ) - temporary_path: pathlib.Path | None = None - try: - with urllib.request.urlopen(request, timeout=120) as response: - declared = response.headers.get("Content-Length") - if declared is not None and int(declared) > MAX_DOWNLOAD_BYTES: - raise ValueError("SuiteSparse archive exceeds the download limit") - with tempfile.NamedTemporaryFile( - dir=target.parent, prefix=f".{target.name}.", delete=False - ) as temporary: - temporary_path = pathlib.Path(temporary.name) - digest = hashlib.sha256() - total = 0 - while block := response.read(1024 * 1024): - total += len(block) - if total > MAX_DOWNLOAD_BYTES: - raise ValueError( - "SuiteSparse archive exceeds the download limit" - ) - digest.update(block) - temporary.write(block) - if digest.hexdigest() != expected_sha256: - raise ValueError("downloaded SuiteSparse archive digest mismatch") - os.replace(temporary_path, target) - temporary_path = None - finally: - if temporary_path is not None: - temporary_path.unlink(missing_ok=True) - - -def load_matrix_market_archive( - path: pathlib.Path, case: Mapping[str, object] -) -> sparse.csr_matrix: - expected_member = f"{case['name']}/{case['name']}.mtx" - try: - with tarfile.open(path, mode="r:gz") as archive: - members = tuple(archive.getmembers()) - if not members or len(members) > MAX_ARCHIVE_MEMBERS: - raise ValueError("unsafe SuiteSparse archive member count") - for member in members: - pure = pathlib.PurePosixPath(member.name) - if ( - pure.is_absolute() - or not pure.parts - or any(part in {"", ".", ".."} for part in pure.parts) - or member.issym() - or member.islnk() - or member.isdev() - or not (member.isfile() or member.isdir()) - ): - raise ValueError("unsafe SuiteSparse archive member") - matches = [ - member - for member in members - if member.isfile() and member.name == expected_member - ] - if len(matches) != 1 or matches[0].size > MAX_MEMBER_BYTES: - raise ValueError("expected Matrix Market member is absent or too large") - source = archive.extractfile(matches[0]) - if source is None: - raise ValueError("Matrix Market member is unreadable") - with source: - loaded = mmread(source) - except (OSError, tarfile.TarError) as exc: - raise ValueError("SuiteSparse archive is unreadable") from exc - if not sparse.issparse(loaded) or np.iscomplexobj(loaded.data): - raise ValueError("SuiteSparse matrix must be real and sparse") - matrix = sparse.csr_matrix(loaded, dtype=np.float64, copy=True) - matrix.sum_duplicates() - matrix.eliminate_zeros() - matrix.sort_indices() - if matrix.shape != (case["n"], case["n"]) or matrix.nnz != case["nnz"]: - raise ValueError("Matrix Market payload disagrees with frozen metadata") - return matrix - - -def prepare_split( - split: str, - output: pathlib.Path, - *, - rhs_key: bytes | None = None, - cache: pathlib.Path | None = None, - case_ids: Iterable[str] | None = None, -) -> dict[str, object]: - manifest = load_split(split) - if rhs_key is None: - public_key = manifest.get("public_rhs_key_hex") - if not isinstance(public_key, str): - raise ValueError("ranked preparation requires an operator-held RHS key") - rhs_key = bytes.fromhex(public_key) - selected_ids = set(case_ids or ()) - cases = [ - case - for case in manifest["cases"] - if not selected_ids or case["case_id"] in selected_ids - ] - if selected_ids != {case["case_id"] for case in cases} and selected_ids: - raise ValueError("one or more requested case IDs are not in the split") - output = pathlib.Path(output).resolve() - if output.exists() and (not output.is_dir() or any(output.iterdir())): - raise ValueError("prepared output directory must be absent or empty") - cache_root = pathlib.Path(cache or cache_dir() / "downloads").resolve() - if cache_root == output or output in cache_root.parents: - raise ValueError("download cache must be outside the prepared output") - output.parent.mkdir(parents=True, exist_ok=True) - staging = output.parent / f".{output.name}.{uuid.uuid4().hex}.tmp" - staging.mkdir() - try: - prepared_cases = [] - for output_index, case in enumerate(cases): - archive = ( - cache_root / f"{int(case['matrix_id']):04d}-{case['case_id']}.tar.gz" - ) - _download(str(case["source_url"]), archive, str(case["archive_sha256"])) - matrix = load_matrix_market_archive(archive, case) - system = materialize_system(case, matrix, rhs_key) - payload = encode_system(system) - filename = f"{output_index:04d}-{case['case_id']}.npz" - (staging / filename).write_bytes(payload) - prepared_cases.append( - { - "index": output_index, - "case_id": case["case_id"], - "source_index": case["index"], - "relpath": filename, - "sha256": hashlib.sha256(payload).hexdigest(), - "n": case["n"], - "nnz": case["nnz"], - "tolerance": case["tolerance"], - } - ) - body = { - "schema_version": 1, - "kind": "linear-solver-bench-prepared-systems", - "source_manifest_sha256": manifest["manifest_sha256"], - "rhs_scheme": RHS_SCHEME, - "case_count": len(prepared_cases), - "cases": prepared_cases, - } - prepared_manifest = {**body, "manifest_sha256": identity_sha256(body)} - (staging / "manifest.json").write_text( - json.dumps(prepared_manifest, indent=2, sort_keys=True) + "\n", - encoding="utf-8", - ) - if output.exists(): - output.rmdir() - os.replace(staging, output) - finally: - if staging.exists(): - shutil.rmtree(staging) - return prepared_manifest - - -def load_prepared_manifest(root: pathlib.Path) -> dict[str, object]: - root = pathlib.Path(root).resolve() - manifest = _load_json(root / "manifest.json") - if ( - manifest.get("schema_version") != 1 - or manifest.get("kind") != "linear-solver-bench-prepared-systems" - or manifest.get("rhs_scheme") != RHS_SCHEME - ): - raise ValueError("prepared manifest identity mismatch") - digest = manifest.get("manifest_sha256") - body = {key: value for key, value in manifest.items() if key != "manifest_sha256"} - if digest != identity_sha256(body): - raise ValueError("prepared manifest digest mismatch") - cases = manifest.get("cases") - if ( - not isinstance(cases, list) - or not cases - or manifest.get("case_count") != len(cases) - ): - raise ValueError("prepared case list is invalid") - return manifest - - -def load_prepared(root: pathlib.Path) -> tuple[EvaluationSystem, ...]: - root = pathlib.Path(root).resolve() - manifest = load_prepared_manifest(root) - cases = manifest["cases"] - systems = [] - for index, case in enumerate(cases): - if not isinstance(case, Mapping) or case.get("index") != index: - raise ValueError("prepared case order mismatch") - relpath = pathlib.PurePosixPath(str(case.get("relpath", ""))) - if relpath.is_absolute() or len(relpath.parts) != 1 or relpath.suffix != ".npz": - raise ValueError("prepared case path is invalid") - path = root / relpath.name - if not path.is_file() or sha256_file(path) != case["sha256"]: - raise ValueError("prepared case digest mismatch") - system = decode_system(path.read_bytes()) - if ( - system.case_id != case["case_id"] - or system.public.matrix.n != case["n"] - or system.public.matrix.nnz != case["nnz"] - or system.public.tolerance != case["tolerance"] - ): - raise ValueError("prepared case identity mismatch") - systems.append(system) - return tuple(systems) diff --git a/src/linear_solver_bench/families.py b/src/linear_solver_bench/families.py index 6451f75..e1275f7 100644 --- a/src/linear_solver_bench/families.py +++ b/src/linear_solver_bench/families.py @@ -1,49 +1,40 @@ -"""The two supported pilot tracks and their fixed scientific contracts.""" +"""The benchmark's two explicit numerical families.""" from __future__ import annotations -from dataclasses import asdict, dataclass +from dataclasses import dataclass @dataclass(frozen=True) -class TrackContract: - family: str - track: str - accuracy_contract_id: str - scoring_contract_id: str - execution_contract_id: str = "independent-case-once-v1" - repetitions: int = 1 - - @property - def contracts(self) -> dict[str, str]: - return { - "accuracy": self.accuracy_contract_id, - "execution": self.execution_contract_id, - "scoring": self.scoring_contract_id, - } - - -TRACKS = ( - TrackContract( - "ns-mesh-pde", "coverage", "ns-mesh-accuracy-v1", "ns-coverage-count-v1" - ), - TrackContract( - "magnetic_diffusion_flash", - "replay", - "flash-replay-accuracy-v1", - "flash-replay-speedup-v1", - ), +class Family: + selector: str + id: str + manifest: str + accuracy_contract: str + + +NS_MESH_PDE = Family( + selector="ns_mesh_pde", + id="ns-mesh-pde", + manifest="ns_mesh_pde/dev.json", + accuracy_contract="ns-mesh-accuracy-v1", ) +FLASH = Family( + selector="flash", + id="magnetic_diffusion_flash", + manifest="flash/dev.json", + accuracy_contract="flash-replay-accuracy-v1", +) -def resolve_track(family: str, track: str) -> TrackContract: - canonical = "ns-mesh-pde" if family == "ns_mesh_pde" else family - for contract in TRACKS: - if (contract.family, contract.track) == (canonical, track): - return contract - supported = ", ".join(f"{item.family}/{item.track}" for item in TRACKS) - raise ValueError(f"unsupported family/track {family}/{track}; choose {supported}") +_FAMILIES = {family.selector: family for family in (NS_MESH_PDE, FLASH)} -def family_listing() -> list[dict[str, object]]: - return [asdict(contract) for contract in TRACKS] +def resolve_family(selector: str) -> Family: + """Return one of the two benchmark families by its public selector.""" + try: + return _FAMILIES[selector] + except KeyError as exc: + raise ValueError( + f"unsupported family {selector!r}; choose ns_mesh_pde or flash" + ) from exc diff --git a/src/linear_solver_bench/manifests.py b/src/linear_solver_bench/manifests.py deleted file mode 100644 index e1e959a..0000000 --- a/src/linear_solver_bench/manifests.py +++ /dev/null @@ -1,690 +0,0 @@ -"""Strict, content-addressed public release manifests for the two pilot tracks. - -A digest makes a manifest immutable, not authoritative. Official evaluation also -requires its digest in the packaged release registry. Explicit file paths permit -unpublished, clearly identified draft releases for preparation and local work. -""" - -from __future__ import annotations - -import copy -import hashlib -import json -import math -import pathlib -import re -from collections.abc import Mapping -from urllib.parse import urlsplit - -from .dataset import identity_sha256 -from .families import resolve_track - -RELEASE_KIND = "linear-solver-bench-pilot-release" -RHS_SCHEME = "rademacher-hmac-sha256-v2" -# A release is added here only after its data and qualification are published. -PUBLISHED_RELEASES: dict[tuple[str, str, str, str], str] = { - ( - "ns-mesh-cohort-v2", - "ns-mesh-pde", - "coverage", - "dev", - ): "9927ea72bac431d871b0547b966f9ce78b550ca0d1285c7060cdcb2e3e688ecc", - ( - "ns-mesh-cohort-v2", - "ns-mesh-pde", - "coverage", - "ranked", - ): "a86a2d42765364bcadd48fdef22dc2f0900d04e98fe4fe49fe551b84d591ad52", - ( - "ns-mesh-dev-pilot", - "ns-mesh-pde", - "coverage", - "dev", - ): "93d48d6ef9298752f634e1411d8b21f7e8912e6193b15ece838fe80918c71b5e", - ( - "ns-mesh-pilot", - "ns-mesh-pde", - "coverage", - "dev", - ): "25e79afb724d2f33c2438513f3ea13aeaebfced24fb8c4bba6d5c78e5edfa296", - ( - "ns-mesh-pilot", - "ns-mesh-pde", - "coverage", - "ranked", - ): "61fb1ec691400d0e1bc83d8524f5e90f1e6479941e31e4f8e0ccd840163da241", - ( - "flash-replay-pilot", - "magnetic_diffusion_flash", - "replay", - "dev", - ): "02a8e24e667b96029f0c8db627f87c237549778a63f3342a73010e7278876669", - ( - "flash-replay-pilot", - "magnetic_diffusion_flash", - "replay", - "ranked", - ): "1ce0ec20c66123773215543e2d43375104f43113a9416c59fb0e9625a9975b79", -} -HEX256 = re.compile(r"[0-9a-f]{64}\Z") -PUBLIC_ID = re.compile(r"[A-Za-z0-9][A-Za-z0-9._:-]{0,159}\Z") -QUALIFICATION_LIMITS = { - "normwise_backward_error": 1e-11, - "componentwise_backward_error": 1e-9, - "relative_l2_forward_error": 1e-6, - "relative_linf_forward_error": 1e-6, -} -ITERATIVE_QUALIFICATION_METHOD = "independent-equilibrated-gmres-ilu-refined-v1" -# Offline reference configuration, not a candidate stopping rule or timing baseline. -ITERATIVE_QUALIFICATION_CONFIG = { - "equilibration": "max-absolute-row-then-column-v1", - "initial_guess": "zero", - "preconditioner": "scipy-superlu-ilu", - "drop_tol": 1e-4, - "fill_factor": 10.0, - "drop_rule": "basic,area", - "permc_spec": "COLAMD", - "diag_pivot_thresh": 1.0, - "superlu_equilibration": False, - "krylov": "scipy-gmres-left-preconditioned", - "restart": 50, - "max_restart_cycles": 250, - "rtol": 1e-13, - "atol": 0.0, - "refinement_steps": 2, - "refinement_residual": "extended-precision-residual-v1", -} - - -PYAMG_QUALIFICATION_METHOD = "independent-pyamg-sa-gmres-refined-v1" -PYAMG_QUALIFICATION_CONFIG = { - "pyamg_version": "5.3.0", - "operator": "original-float64-csr", - "symmetry": "nonsymmetric", - "right_near_nullspace": "ones", - "left_near_nullspace": "ones", - "strength": ["symmetric", {"theta": 0.0}], - "aggregate": "standard", - "smooth": ["jacobi", {"omega": 4.0 / 3.0, "degree": 1, "weighting": "local"}], - "presmoother": ["gauss_seidel", {"sweep": "symmetric", "iterations": 2}], - "postsmoother": ["gauss_seidel", {"sweep": "symmetric", "iterations": 2}], - "improve_candidates": None, - "diagonal_dominance": False, - "max_levels": 25, - "max_coarse": 500, - "coarse_solver": "splu", - "keep": False, - "preconditioner_cycle": "V", - "initial_guess": "zero", - "restart": 50, - "max_restart_cycles": 250, - "rtol": 1e-13, - "atol": 0.0, - "refinement_steps": 2, - "refinement_residual": "extended-precision-residual-v1", -} - - -# Keep v1 evidence reproducible; v2 changes only the two inner correction stops. -PYAMG_INEXACT_QUALIFICATION_METHOD = "independent-pyamg-sa-gmres-refined-v2" -PYAMG_INEXACT_QUALIFICATION_CONFIG = { - **copy.deepcopy(PYAMG_QUALIFICATION_CONFIG), - "refinement_rtol": 1e-2, -} - - -def strict_object(value: object, keys: set[str], label: str) -> dict: - if not isinstance(value, dict) or set(value) != keys: - raise ValueError(f"{label} fields do not match the public schema") - return value - - -def digest(value: object, label: str) -> str: - if not isinstance(value, str) or not HEX256.fullmatch(value): - raise ValueError(f"{label} must be a lowercase SHA-256 digest") - return value - - -def positive_int(value: object, label: str) -> int: - if type(value) is not int or value <= 0: - raise ValueError(f"{label} must be a positive integer") - return value - - -def finite_number(value: object, label: str, *, positive: bool = True) -> float: - if type(value) not in {int, float} or not math.isfinite(value): - raise ValueError(f"{label} must be a finite number") - if value < 0 or (positive and value == 0): - raise ValueError(f"{label} has an invalid sign") - return float(value) - - -def public_id(value: object, label: str) -> str: - if not isinstance(value, str) or not PUBLIC_ID.fullmatch(value): - raise ValueError(f"{label} must be a short public identifier") - return value - - -def https_url(value: object) -> str: - if not isinstance(value, str): - raise ValueError("public URL must be a string") - parts = urlsplit(value) - if ( - parts.scheme != "https" - or not parts.hostname - or parts.username - or parts.password - or parts.port not in {None, 443} - or parts.query - or parts.fragment - ): - raise ValueError("public source URLs must be plain HTTPS URLs") - return value - - -def _source(value: object, family: str) -> None: - common = {"kind", "url", "archive_sha256", "archive_bytes", "matrix_sha256"} - expected = common | ( - {"matrix_id", "group", "name"} - if family == "ns-mesh-pde" - else {"b_sha256", "x0_sha256"} - ) - source = strict_object(value, expected, "source") - url = urlsplit(https_url(source["url"])) - digest(source["archive_sha256"], "archive_sha256") - digest(source["matrix_sha256"], "matrix_sha256") - if family == "magnetic_diffusion_flash": - digest(source["b_sha256"], "b_sha256") - digest(source["x0_sha256"], "x0_sha256") - size = positive_int(source["archive_bytes"], "archive_bytes") - if size > 8 * 1024**3: - raise ValueError("source archive exceeds the download limit") - if family == "ns-mesh-pde": - if source["kind"] != "suitesparse": - raise ValueError("NS mesh requires a SuiteSparse source") - positive_int(source["matrix_id"], "matrix_id") - for key in ("group", "name"): - public_id(source[key], key) - if url.hostname not in { - "sparse.tamu.edu", - "suitesparse-collection-website.herokuapp.com", - }: - raise ValueError("SuiteSparse source host is not supported") - expected_path = f"/MM/{source['group']}/{source['name']}.tar.gz" - if url.path != expected_path: - raise ValueError( - "SuiteSparse URL must identify its original matrix archive" - ) - elif ( - source["kind"] != "huggingface" - or url.hostname != "huggingface.co" - or not re.fullmatch( - r"/datasets/[^/]+/[^/]+/resolve/[0-9a-f]{40}/[^?#]+\.(?:zip|tar\.gz)", - url.path, - ) - ): - raise ValueError("FLASH requires a commit-pinned Hugging Face case archive") - - -def validate_qualification(value: object, system_sha256: str | None = None) -> None: - dominance = ( - isinstance(value, dict) - and value.get("method") == "strict-row-diagonal-dominance-v1" - ) - iterative = isinstance(value, dict) and value.get("method") in { - ITERATIVE_QUALIFICATION_METHOD, - PYAMG_QUALIFICATION_METHOD, - PYAMG_INEXACT_QUALIFICATION_METHOD, - } - keys = { - "method", - "qualified", - "system_sha256", - "metrics", - "formation", - "uncertainty", - } - if dominance: - keys.add("certificate") - if iterative: - keys.add("reference_solver") - evidence = strict_object( - value, - keys, - "NS qualification", - ) - if ( - evidence["method"] - not in { - "independent-sparse-lu-v1", - "independent-sparse-lu-refined-v1", - "strict-row-diagonal-dominance-v1", - ITERATIVE_QUALIFICATION_METHOD, - PYAMG_QUALIFICATION_METHOD, - PYAMG_INEXACT_QUALIFICATION_METHOD, - } - or evidence["qualified"] is not True - ): - raise ValueError( - "NS qualification requires passing independent reference evidence" - ) - digest(evidence["system_sha256"], "qualified system digest") - if system_sha256 is not None and evidence["system_sha256"] != system_sha256: - raise ValueError("qualification is bound to different numerical inputs") - metrics = strict_object( - evidence["metrics"], set(QUALIFICATION_LIMITS), "qualification metrics" - ) - for name, limit in QUALIFICATION_LIMITS.items(): - if finite_number(metrics[name], name, positive=False) > limit: - raise ValueError(f"NS qualification lacks the tenfold margin for {name}") - formation = strict_object( - evidence["formation"], - {"method", "relative_linf_residual", "verified_forward_bound"}, - "RHS formation", - ) - expected_method = ( - "exact-binary64-residual-v1" if dominance else "extended-precision-residual-v1" - ) - if formation["method"] != expected_method: - raise ValueError("unknown RHS formation evidence") - finite_number( - formation["relative_linf_residual"], "formation residual", positive=False - ) - if dominance: - _validate_dominance_certificate(evidence) - elif formation["verified_forward_bound"] is not None: - raise ValueError( - "empirical reference evidence cannot claim a verified forward bound" - ) - if not dominance and evidence["uncertainty"] != "empirical-feasibility-only": - raise ValueError("qualification must state its empirical uncertainty") - if iterative: - _validate_iterative_reference(evidence["reference_solver"], evidence["method"]) - - -def _same_typed_value(value: object, expected: object) -> bool: - if type(value) is not type(expected): - return False - if isinstance(expected, dict): - return set(value) == set(expected) and all( - _same_typed_value(value[key], item) for key, item in expected.items() - ) - if isinstance(expected, list): - return len(value) == len(expected) and all( - _same_typed_value(actual, wanted) - for actual, wanted in zip(value, expected, strict=True) - ) - return value == expected - - -def _validate_iterative_reference(value: object, method: str) -> None: - inexact = method == PYAMG_INEXACT_QUALIFICATION_METHOD - multigrid = method in { - PYAMG_QUALIFICATION_METHOD, - PYAMG_INEXACT_QUALIFICATION_METHOD, - } - expected = { - PYAMG_QUALIFICATION_METHOD: PYAMG_QUALIFICATION_CONFIG, - PYAMG_INEXACT_QUALIFICATION_METHOD: PYAMG_INEXACT_QUALIFICATION_CONFIG, - ITERATIVE_QUALIFICATION_METHOD: ITERATIVE_QUALIFICATION_CONFIG, - }[method] - keys = {"configuration", "implementation", "solves"} - if multigrid: - keys.add("hierarchy") - reference = strict_object(value, keys, "iterative reference") - if not _same_typed_value(reference["configuration"], expected): - raise ValueError("iterative reference configuration does not match its version") - version_keys = {"numpy", "scipy"} | ({"pyamg"} if multigrid else set()) - versions = strict_object( - reference["implementation"], version_keys, "reference implementation" - ) - for version in versions.values(): - if not isinstance(version, str) or not re.fullmatch( - r"[0-9][A-Za-z0-9.+_-]{0,79}", version - ): - raise ValueError("reference library version must be a short version string") - if multigrid and versions["pyamg"] != expected["pyamg_version"]: - raise ValueError("PyAMG implementation does not match its frozen version") - solves = reference["solves"] - if not isinstance(solves, list) or len(solves) != 3: - raise ValueError("iterative reference requires one solve and two corrections") - limit = expected["restart"] * expected["max_restart_cycles"] - for index, solve in enumerate(solves): - solve_keys = {"info", "inner_iterations"} - if inexact: - solve_keys |= {"rtol", "residual_method", "relative_l2_residual"} - strict_object(solve, solve_keys, "reference solve") - if inexact: - rtol = expected["rtol"] if index == 0 else expected["refinement_rtol"] - if not _same_typed_value(solve["rtol"], rtol): - raise ValueError("reference solve tolerance differs from its version") - if solve["residual_method"] != "extended-precision-residual-v1": - raise ValueError("unknown reference solve residual diagnostic") - finite_number( - solve["relative_l2_residual"], "solve residual", positive=False - ) - if type(solve["info"]) is not int or solve["info"] != 0: - raise ValueError("each iterative reference solve must converge") - iterations = solve["inner_iterations"] - if type(iterations) is not int or not 0 <= iterations <= limit: - raise ValueError("reference iteration count exceeds the fixed budget") - if multigrid: - levels = reference["hierarchy"] - if ( - not isinstance(levels, list) - or not 1 <= len(levels) <= expected["max_levels"] - ): - raise ValueError("invalid multigrid hierarchy length") - previous = None - for level in levels: - strict_object(level, {"n", "nnz"}, "multigrid level") - size = positive_int(level["n"], "level n") - nnz = positive_int(level["nnz"], "level nnz") - if nnz > size * size or (previous is not None and size >= previous): - raise ValueError( - "multigrid hierarchy must have valid, decreasing dimensions" - ) - previous = size - if len(levels) < expected["max_levels"] and previous > expected["max_coarse"]: - raise ValueError("multigrid hierarchy stopped above its coarse-size limit") - - -def _validate_dominance_certificate(evidence: dict) -> None: - from fractions import Fraction - - certificate = strict_object( - evidence["certificate"], - { - "arithmetic", - "witness", - "metric_evaluation", - "minimum_row_margin", - "maximum_formation_error", - }, - "row dominance certificate", - ) - if ( - certificate["arithmetic"] != "exact-binary64-integer-v1" - or certificate["witness"] != "manufactured-target" - or certificate["metric_evaluation"] != "binary64" - or evidence["uncertainty"] != "verified-stored-system-forward-bound" - ): - raise ValueError("unknown dominance certificate or witness semantics") - margin = finite_number(certificate["minimum_row_margin"], "row dominance margin") - error = finite_number( - certificate["maximum_formation_error"], "formation error bound", positive=False - ) - forward = finite_number( - evidence["formation"]["verified_forward_bound"], - "verified forward bound", - positive=False, - ) - if Fraction(forward) * Fraction(margin) < Fraction(error): - raise ValueError("verified forward bound understates the dominance certificate") - if forward > min( - QUALIFICATION_LIMITS["relative_l2_forward_error"], - QUALIFICATION_LIMITS["relative_linf_forward_error"], - ): - raise ValueError("verified forward bound lacks the tenfold margin") - for name in ("relative_l2_forward_error", "relative_linf_forward_error"): - if evidence["metrics"][name] != 0.0: - raise ValueError( - "manufactured-target witness must have zero measured forward error" - ) - - -def validate_release(value: object, *, official: bool = False) -> dict: - release = strict_object( - value, - { - "schema_version", - "kind", - "release_id", - "family", - "track", - "split", - "contracts", - "execution", - "rhs", - "cases", - "manifest_sha256", - }, - "pilot release", - ) - if release["schema_version"] != 2 or release["kind"] != RELEASE_KIND: - raise ValueError("unsupported pilot release schema") - public_id(release["release_id"], "release_id") - track = resolve_track(release["family"], release["track"]) - if release["family"] != track.family: - raise ValueError("release manifests must use the canonical family ID") - if release["split"] not in ("dev", "ranked"): - raise ValueError("split must be dev or ranked") - expected_contracts = { - "accuracy": track.accuracy_contract_id, - "execution": track.execution_contract_id, - "scoring": track.scoring_contract_id, - } - if release["contracts"] != expected_contracts: - raise ValueError("release contracts do not match its family and track") - execution = strict_object( - release["execution"], - { - "repetitions", - "max_iterations", - "case_timeout_seconds", - "memory_bytes", - "cpus", - }, - "execution", - ) - if type(execution["repetitions"]) is not int or execution["repetitions"] != 1: - raise ValueError("pilot execution requires exactly one process per case") - for name in ("max_iterations", "memory_bytes", "cpus"): - positive_int(execution[name], name) - if execution["max_iterations"] > 2**31 - 1: - raise ValueError("iteration cap exceeds the native integer range") - finite_number(execution["case_timeout_seconds"], "case_timeout_seconds") - if track.family == "ns-mesh-pde": - rhs = strict_object( - release["rhs"], - {"scheme", "key_id", "public_key_hex", "draw_index"}, - "RHS recipe", - ) - if ( - rhs["scheme"] != RHS_SCHEME - or type(rhs["draw_index"]) is not int - or rhs["draw_index"] < 0 - ): - raise ValueError("unsupported manufactured RHS recipe") - digest(rhs["key_id"], "RHS key_id") - if rhs["public_key_hex"] is not None: - try: - key = bytes.fromhex(rhs["public_key_hex"]) - except (TypeError, ValueError) as exc: - raise ValueError("invalid public RHS key") from exc - if len(key) < 16 or hashlib.sha256(key).hexdigest() != rhs["key_id"]: - raise ValueError("public RHS key does not match its commitment") - if release["split"] == "dev" and rhs["public_key_hex"] is None: - raise ValueError("development RHS keys must be public") - if release["split"] == "ranked" and rhs["public_key_hex"] is not None: - raise ValueError("ranked RHS keys must remain operator-held") - elif release["rhs"] is not None: - raise ValueError( - "FLASH replay preserves captured RHS and has no manufactured recipe" - ) - cases = release["cases"] - if not isinstance(cases, list) or not cases: - raise ValueError("a pilot release must contain cases") - observed = set() - run_groups = {} - for index, case in enumerate(cases): - case = strict_object( - case, - { - "index", - "case_id", - "n", - "nnz", - "tolerance", - "role", - "provenance_group", - "source_run", - "weight", - "source", - "admission", - "qualification", - }, - "release case", - ) - if type(case["index"]) is not int or case["index"] != index: - raise ValueError("release case order mismatch") - for key in ("case_id", "provenance_group", "source_run"): - public_id(case[key], key) - if case["case_id"] in observed: - raise ValueError("duplicate release case ID") - observed.add(case["case_id"]) - group = run_groups.setdefault(case["source_run"], case["provenance_group"]) - if group != case["provenance_group"]: - raise ValueError("a source run cannot belong to multiple provenance groups") - positive_int(case["n"], "n") - positive_int(case["nnz"], "nnz") - if case["n"] > 2**31 - 1 or case["nnz"] > 2**31 - 1: - raise ValueError("case dimensions exceed the sequential native runtime") - if finite_number(case["tolerance"], "tolerance") >= 1: - raise ValueError("tolerance must be less than one") - if case["role"] not in ("scored", "control"): - raise ValueError("case role must be scored or control") - weight = finite_number(case["weight"], "weight", positive=False) - if (case["role"] == "control") != (weight == 0): - raise ValueError("only correctness controls have zero weight") - if track.track == "coverage" and (case["role"] != "scored" or weight != 1): - raise ValueError("coverage counts every case once with unit weight") - _source(case["source"], track.family) - admission_keys = {"statement", "citations"} - if track.family == "magnetic_diffusion_flash": - admission_keys |= { - "capture_kind", - "relative_tolerance", - "absolute_tolerance", - "step", - "solve_index", - } - admission = strict_object( - case["admission"], admission_keys, "scientific admission" - ) - if ( - not isinstance(admission["statement"], str) - or not admission["statement"].strip() - ): - raise ValueError("scientific admission must explain source provenance") - citations = admission["citations"] - if not isinstance(citations, list) or not citations: - raise ValueError("scientific admission needs public source citations") - for citation in citations: - https_url(citation) - if track.family == "magnetic_diffusion_flash": - finite_number( - admission["absolute_tolerance"], "absolute tolerance", positive=False - ) - finite_number(admission["relative_tolerance"], "relative tolerance") - if ( - admission["capture_kind"] != "scalar-magnetic-diffusion" - or admission["absolute_tolerance"] != 0 - or admission["relative_tolerance"] != case["tolerance"] - ): - raise ValueError( - "capture tolerance or scientific family is incompatible with replay" - ) - for name in ("step", "solve_index"): - if type(admission[name]) is not int or admission[name] < 0: - raise ValueError("capture ordering must use nonnegative integers") - if case["qualification"] is not None: - raise ValueError( - "FLASH reference qualification is a separate replay artifact" - ) - elif case["qualification"] is not None: - validate_qualification(case["qualification"]) - elif official: - raise ValueError("published NS cases need numerical qualification evidence") - if track.track == "replay": - scored = [case for case in cases if case["role"] == "scored"] - groups = {case["provenance_group"] for case in scored} - if not groups: - raise ValueError("replay requires at least one scored case") - for case in scored: - group = case["provenance_group"] - runs = { - item["source_run"] - for item in scored - if item["provenance_group"] == group - } - count = sum( - item["provenance_group"] == group - and item["source_run"] == case["source_run"] - for item in scored - ) - expected = 1 / (len(groups) * len(runs) * count) - if not math.isclose(case["weight"], expected, rel_tol=1e-14, abs_tol=0): - raise ValueError( - "replay weights must give equal mass to groups, runs, and cases" - ) - claimed = digest(release["manifest_sha256"], "manifest_sha256") - body = {key: item for key, item in release.items() if key != "manifest_sha256"} - if claimed != identity_sha256(body): - raise ValueError("release manifest digest mismatch") - identity = tuple(release[key] for key in ("release_id", "family", "track", "split")) - if official and PUBLISHED_RELEASES.get(identity) != claimed: - raise ValueError( - "release is not a trusted published pilot release; " - "use draft/local execution" - ) - return release - - -def load_release(path: pathlib.Path, *, official: bool = False) -> dict: - try: - value = json.loads(pathlib.Path(path).read_text(encoding="utf-8")) - except (OSError, UnicodeError, json.JSONDecodeError) as exc: - raise ValueError("cannot read pilot release manifest") from exc - return validate_release(value, official=official) - - -def seal_manifest(body: Mapping[str, object]) -> dict: - """Add a deterministic content identity; this does not publish a release.""" - if "manifest_sha256" in body: - raise ValueError("manifest body already contains its digest") - return {**body, "manifest_sha256": identity_sha256(body)} - - -def validate_release_splits(releases: list[dict]) -> None: - """Check publication exposure boundaries across a release's supplied splits. - - Scientific near-duplicate relationships must use a shared provenance group; - exact operators and source runs are checked independently of that declaration. - """ - splits = set() - identities = set() - exposures = {} - for value in releases: - release = validate_release(value) - identities.add(tuple(release[key] for key in ("release_id", "family", "track"))) - split = release["split"] - if split in splits: - raise ValueError("release split was supplied more than once") - splits.add(split) - for case in release["cases"]: - for kind, identity in ( - ("case", case["case_id"]), - ("provenance", case["provenance_group"]), - ("run", case["source_run"]), - ("operator", case["source"]["matrix_sha256"]), - ): - previous = exposures.setdefault((kind, identity), split) - if previous != split: - raise ValueError( - f"{kind} exposure crosses development and ranked splits" - ) - if len(identities) != 1: - raise ValueError( - "split checks require exactly one release/family/track identity" - ) diff --git a/src/linear_solver_bench/modal.py b/src/linear_solver_bench/modal.py new file mode 100644 index 0000000..5d3fff7 --- /dev/null +++ b/src/linear_solver_bench/modal.py @@ -0,0 +1,361 @@ +"""Run a candidate and the fixed reference in one Modal CPU sandbox. + +This module is only a transport. It builds programs and returns raw process +output; correctness checks and speedup calculations belong to ``benchmark``. +Importing it does not import Modal or require Modal credentials. +""" + +from __future__ import annotations + +import importlib +import json +import pathlib +import time +import zlib +from collections.abc import Mapping +from concurrent.futures import ThreadPoolExecutor +from contextlib import contextmanager + +from .assets import asset_root +from .build import OUTPUT_HEADER, REFERENCE_ID, RUN_TIMEOUT_SECONDS, encode_input +from .models import CaseInput, RawExecution + +APP_NAME = "linear-solver-bench" +VENUE_ID = "modal-cpu-v1" +BASE_IMAGE = ( + "debian:trixie-slim@sha256:" + "abc9cb88a5587630d7f915f47b23b0668fe250fbfc6457aa4d52b534c1bbf73f" +) +REMOTE_PROJECT = "/opt/linear-solver-bench" +REMOTE_RUNTIME = "/opt/lsb-runtime" +SANDBOX_LIFETIME_SECONDS = 24 * 60 * 60 +DIAGNOSTIC_LIMIT = 8_000 +BUILD_TIMEOUT_SECONDS = 300 + + +def require_modal(): + """Load the optional SDK only when a Modal run is requested.""" + try: + return importlib.import_module("modal") + except ModuleNotFoundError as exc: + if exc.name != "modal": + raise + raise RuntimeError( + "Modal evaluation requires: pip install 'linear-solver-bench[modal]'" + ) from exc + + +def create_image(): + """Describe the immutable image shared by reference and candidate runs.""" + modal = require_modal() + project = asset_root("native") + package = pathlib.Path(__file__).resolve().parent + return ( + modal.Image.from_registry(BASE_IMAGE, add_python="3.12") + .apt_install("build-essential", "cmake", "git", "ninja-build") + .pip_install("numpy==2.5.1", "scipy==1.18.0") + .add_local_dir( + package, + f"{REMOTE_PROJECT}/src/linear_solver_bench", + copy=True, + ignore=["**/__pycache__/**", "**/*.pyc"], + ) + .add_local_dir(project / "native", f"{REMOTE_PROJECT}/native", copy=True) + .env( + { + "LINEAR_SOLVER_BENCH_ROOT": REMOTE_PROJECT, + "PYTHONPATH": f"{REMOTE_PROJECT}/src", + } + ) + .run_commands( + 'python -c "from pathlib import Path; ' + "from linear_solver_bench.build import build_runtime; " + f"build_runtime(Path('{REMOTE_RUNTIME}'), jobs=2)\"" + ) + ) + + +_BUILD_EXEC = r""" +import json +import os +import pathlib +import sys + +from linear_solver_bench.build import build_reference, build_solver, load_runtime + +runtime = load_runtime(pathlib.Path(sys.argv[1])) +candidate = build_solver(pathlib.Path(sys.argv[2]), pathlib.Path(sys.argv[4]), runtime) +reference = build_reference( + pathlib.Path(sys.argv[3]), pathlib.Path(sys.argv[5]), runtime +) +result = { + "candidate_source_sha256": candidate.source_sha256, + "candidate_executable_sha256": candidate.executable_sha256, + "reference_source_sha256": reference.source_sha256, + "reference_executable_sha256": reference.executable_sha256, + "runtime_manifest_sha256": runtime.manifest_sha256, + "image_id": os.environ.get("MODAL_IMAGE_ID"), +} +pathlib.Path(sys.argv[6]).write_text(json.dumps(result), encoding="utf-8") +""" + +_INPUT_ERROR = "linear-solver-bench input preparation failed:" +_INPUT_EXEC = r""" +import os +import sys +import zlib + +try: + remaining = int(sys.argv[1]) + decoder = zlib.decompressobj() + with open(sys.argv[2], "rb") as source, open(sys.argv[4], "wb") as target: + while chunk := source.read(1024 * 1024): + while chunk: + decoded = decoder.decompress(chunk, min(1024 * 1024, remaining + 1)) + if len(decoded) > remaining or decoder.unused_data: + raise ValueError("compressed input exceeds its declared size") + target.write(decoded) + remaining -= len(decoded) + chunk = decoder.unconsumed_tail + if remaining or not decoder.eof: + raise ValueError("compressed input is incomplete") + os.unlink(sys.argv[2]) + os.execv(sys.argv[3], sys.argv[3:]) +except (OSError, ValueError, zlib.error) as error: + print("linear-solver-bench input preparation failed:", error, file=sys.stderr) + sys.exit(125) +""" + + +def _stream_tail(stream, limit: int = DIAGNOSTIC_LIMIT) -> str: + tail = "" + for chunk in stream: + if isinstance(chunk, bytes): + chunk = chunk.decode("utf-8", errors="replace") + tail = (tail + chunk)[-limit:] + return tail + + +def _diagnostics(process, *, sandbox) -> tuple[str, str]: + """Drain stdout and stderr together so neither pipe can block the other.""" + with ThreadPoolExecutor(max_workers=2) as pool: + stdout = pool.submit(_stream_tail, process.stdout) + stderr = pool.submit(_stream_tail, process.stderr) + try: + return stdout.result(), stderr.result() + except BaseException: + sandbox.terminate() + raise + + +def _read_output(sandbox, path: str, maximum_bytes: int) -> bytes | None: + process = sandbox.exec("head", "-c", str(maximum_bytes), path, text=False) + + def collect() -> bytes: + chunks: list[bytes] = [] + size = 0 + for chunk in process.stdout: + size += len(chunk) + if size > maximum_bytes: + raise RuntimeError("output transport exceeded its byte limit") + chunks.append(chunk) + return b"".join(chunks) + + with ThreadPoolExecutor(max_workers=2) as pool: + stdout = pool.submit(collect) + stderr = pool.submit(_stream_tail, process.stderr) + try: + payload = stdout.result() + stderr.result() + except BaseException: + sandbox.terminate() + raise + returncode = process.wait() + if returncode in {124, -1}: + raise RuntimeError( + "Modal interrupted output transport; evaluation is incomplete" + ) + return payload if returncode == 0 else None + + +class ModalSession: + """One paired evaluation session inside one isolated sandbox.""" + + def __init__( + self, + sandbox, + candidate_source: pathlib.Path, + reference_source: pathlib.Path, + execution: Mapping, + ): + self.sandbox = sandbox + self.candidate_source = pathlib.Path(candidate_source) + self.reference_source = pathlib.Path(reference_source) + self.execution = dict(execution) + self._build: dict | None = None + self._executables: dict[str, str] = {} + self._case_number = 0 + + def build(self) -> dict: + """Compile each solver once and return the identities used by reports.""" + if self._build is not None: + return dict(self._build) + if self.sandbox.exec("mkdir", "-p", "/work").wait() != 0: + raise RuntimeError("cannot initialize sandbox working directory") + self.sandbox.filesystem.copy_from_local( + self.candidate_source, "/work/candidate.c" + ) + self.sandbox.filesystem.copy_from_local( + self.reference_source, "/work/reference.c" + ) + process = self.sandbox.exec( + "python", + "-c", + _BUILD_EXEC, + REMOTE_RUNTIME, + "/work/candidate.c", + "/work/reference.c", + "/work/candidate", + "/work/reference", + "/work/build.json", + text=False, + timeout=BUILD_TIMEOUT_SECONDS, + ) + stdout, stderr = _diagnostics(process, sandbox=self.sandbox) + if process.wait() != 0: + detail = (stdout + stderr)[-DIAGNOSTIC_LIMIT:] or "no diagnostics" + raise RuntimeError(f"sandbox solver build failed: {detail}") + metadata = json.loads(self.sandbox.filesystem.read_text("/work/build.json")) + self._executables = { + "candidate": "/work/candidate", + "reference": "/work/reference", + } + self._build = { + "venue": { + "id": VENUE_ID, + "image_id": metadata["image_id"], + "base_image": BASE_IMAGE, + "cpus": self.execution["cpus"], + "memory_bytes": self.execution["memory_bytes"], + "limits_enforced": True, + }, + "runtime_sha256": metadata["runtime_manifest_sha256"], + "reference": { + "id": REFERENCE_ID, + "source_sha256": metadata["reference_source_sha256"], + "executable_sha256": metadata["reference_executable_sha256"], + }, + "candidate": { + "source_sha256": metadata["candidate_source_sha256"], + "executable_sha256": metadata["candidate_executable_sha256"], + }, + } + return dict(self._build) + + def run_reference(self, case: CaseInput) -> RawExecution: + """Measure a fresh reference process for one public input.""" + return self._run("reference", case) + + def run_candidate(self, case: CaseInput) -> RawExecution: + """Measure a fresh candidate process for one public input.""" + return self._run("candidate", case) + + def _run(self, solver: str, case: CaseInput) -> RawExecution: + if not isinstance(case, CaseInput): + raise TypeError("Modal transport accepts CaseInput only") + if solver not in self._executables: + raise RuntimeError("build the session before running cases") + case_root = f"/work/cases/{self._case_number:06d}" + self._case_number += 1 + compressed_path = f"{case_root}/input.zlib" + input_path = f"{case_root}/input.bin" + output_path = f"{case_root}/output.bin" + payload = encode_input(case) + self.sandbox.filesystem.write_bytes( + zlib.compress(payload, level=1), compressed_path + ) + try: + started = time.monotonic() + try: + process = self.sandbox.exec( + "python", + "-c", + _INPUT_EXEC, + str(len(payload)), + compressed_path, + self._executables[solver], + input_path, + output_path, + text=False, + env={ + "OMP_NUM_THREADS": "1", + "OPENBLAS_NUM_THREADS": "1", + "MKL_NUM_THREADS": "1", + }, + timeout=int(RUN_TIMEOUT_SECONDS), + ) + except Exception as exc: + exceptions = getattr(require_modal(), "exception", None) + timeout = getattr(exceptions, "ExecTimeoutError", ()) + if timeout and isinstance(exc, timeout): + raise RuntimeError( + "Modal interrupted the run; evaluation is incomplete" + ) from exc + raise + stdout, stderr = _diagnostics(process, sandbox=self.sandbox) + returncode = process.wait() + wall_seconds = time.monotonic() - started + diagnostics = (stdout + stderr)[-DIAGNOSTIC_LIMIT:] + if returncode == 125 and _INPUT_ERROR in diagnostics: + raise RuntimeError(diagnostics) + if returncode in {124, -1}: + raise RuntimeError( + "Modal interrupted the run; evaluation is incomplete" + ) + output = ( + _read_output( + self.sandbox, + output_path, + OUTPUT_HEADER.size + case.matrix.n * 8 + 1, + ) + if returncode == 0 + else None + ) + return RawExecution(returncode, wall_seconds, diagnostics, output) + finally: + self.sandbox.filesystem.remove(case_root, recursive=True) + + +@contextmanager +def open_session( + candidate_source: pathlib.Path, + reference_source: pathlib.Path, + execution: Mapping, +): + """Create one network-disabled sandbox for a paired evaluation.""" + cpus = execution.get("cpus") + memory_bytes = execution.get("memory_bytes") + if isinstance(cpus, bool) or not isinstance(cpus, (int, float)) or cpus <= 0: + raise ValueError("cpus must be a positive number") + if isinstance(memory_bytes, bool) or not isinstance(memory_bytes, int): + raise ValueError("memory_bytes must be a positive whole number of MiB") + memory_mib, remainder = divmod(memory_bytes, 1024**2) + if remainder or memory_mib < 1: + raise ValueError("memory_bytes must be a positive whole number of MiB") + + modal = require_modal() + try: + sandbox = modal.Sandbox.create( + app=modal.App.lookup(APP_NAME, create_if_missing=True), + image=create_image(), + cpu=(float(cpus), float(cpus)), + memory=(memory_mib, memory_mib), + timeout=SANDBOX_LIFETIME_SECONDS, + block_network=True, + ) + except Exception as exc: + raise RuntimeError(f"cannot start Modal sandbox: {exc}") from exc + try: + yield ModalSession(sandbox, candidate_source, reference_source, execution) + finally: + sandbox.terminate() diff --git a/src/linear_solver_bench/models.py b/src/linear_solver_bench/models.py index 48d7152..f8f383c 100644 --- a/src/linear_solver_bench/models.py +++ b/src/linear_solver_bench/models.py @@ -1,4 +1,4 @@ -"""Small, strict value types shared by preparation and verification.""" +"""The small set of values shared by the benchmark.""" from __future__ import annotations @@ -8,11 +8,9 @@ import numpy as np from scipy import sparse -NATIVE_INDEX_MAX = int(np.iinfo(np.int32).max) -REFERENCE_KINDS = frozenset({"manufactured", "numerical", "none"}) - -def _readonly_1d(value: object, dtype: object, name: str) -> np.ndarray: +def _array(value: object, dtype: object, name: str) -> np.ndarray: + """Copy a one-dimensional array and make it immutable.""" raw = np.asarray(value) expected = np.dtype(dtype) if raw.ndim != 1 or raw.dtype != expected: @@ -24,24 +22,26 @@ def _readonly_1d(value: object, dtype: object, name: str) -> np.ndarray: @dataclass(frozen=True, eq=False) class CsrMatrix: + """A canonical square float64 CSR matrix accepted by the native driver.""" + n: int row_offsets: np.ndarray column_indices: np.ndarray values: np.ndarray def __post_init__(self) -> None: - if type(self.n) is not int or not 0 < self.n <= NATIVE_INDEX_MAX: - raise ValueError("n must be a positive integer within native int32 bounds") - rows = _readonly_1d(self.row_offsets, np.uint64, "row_offsets") - columns = _readonly_1d(self.column_indices, np.uint32, "column_indices") - values = _readonly_1d(self.values, np.float64, "values") + if type(self.n) is not int or not 0 < self.n <= np.iinfo(np.int32).max: + raise ValueError("n must be a positive native int32") + rows = _array(self.row_offsets, np.uint64, "row_offsets") + columns = _array(self.column_indices, np.uint32, "column_indices") + values = _array(self.values, np.float64, "values") if rows.size != self.n + 1 or rows[0] != 0: - raise ValueError("row_offsets must contain n + 1 entries and start at zero") + raise ValueError("row_offsets must have n + 1 entries and start at zero") if np.any(rows[1:] < rows[:-1]): raise ValueError("row_offsets must be monotone") if int(rows[-1]) != values.size or columns.size != values.size: raise ValueError("CSR arrays disagree on nnz") - if values.size > NATIVE_INDEX_MAX: + if values.size > np.iinfo(np.int32).max: raise ValueError("nnz exceeds native int32 bounds") if columns.size and int(columns.max()) >= self.n: raise ValueError("column index lies outside the matrix") @@ -52,7 +52,7 @@ def __post_init__(self) -> None: if stop - start > 1 and np.any( columns[start + 1 : stop] <= columns[start : stop - 1] ): - raise ValueError("column indices must be strictly sorted in each row") + raise ValueError("columns must be strictly sorted within each row") object.__setattr__(self, "row_offsets", rows) object.__setattr__(self, "column_indices", columns) object.__setattr__(self, "values", values) @@ -63,37 +63,26 @@ def nnz(self) -> int: @classmethod def from_scipy(cls, matrix: sparse.spmatrix) -> CsrMatrix: + """Canonicalize a real sparse matrix without silently losing precision.""" if not sparse.issparse(matrix) or matrix.ndim != 2: raise ValueError("matrix must be sparse and two-dimensional") if matrix.shape[0] != matrix.shape[1]: raise ValueError("matrix must be square") - if not 0 < matrix.shape[0] <= NATIVE_INDEX_MAX: - raise ValueError("matrix dimension exceeds native int32 bounds") - if matrix.nnz > NATIVE_INDEX_MAX: - raise ValueError("nnz exceeds native int32 bounds") - # Validate before converting sparse formats or narrowing native indices. source = matrix.copy() if hasattr(source, "check_format"): source.check_format(full_check=True) source = source.tocoo() - values = source.data - if values.dtype.kind not in "fiu" or not np.all(np.isfinite(values)): + if source.data.dtype.kind not in "fiu" or not np.all(np.isfinite(source.data)): raise ValueError("matrix values must be finite real numbers") - with np.errstate(over="ignore", invalid="ignore"): - converted = values.astype(np.float64) + converted = source.data.astype(np.float64) if not np.all(np.isfinite(converted)): - raise ValueError("matrix values cannot be represented in float64") - if values.dtype.kind in "iu": - # Integer-to-float comparisons would coerce the integer and hide loss. - large = values > 2**53 - if values.dtype.kind == "i": - large |= values < -(2**53) - exact = all(int(value) == int(float(value)) for value in values[large]) + raise ValueError("matrix values cannot be represented as float64") + if source.data.dtype.kind in "iu": + exact = all(int(value) == int(float(value)) for value in source.data) else: - exact = bool(np.all(converted.astype(values.dtype) == values)) + exact = bool(np.all(converted.astype(source.data.dtype) == source.data)) if not exact: raise ValueError("matrix conversion to float64 would lose precision") - # Sum duplicate coordinates only after conversion, avoiding integer wrap. csr = sparse.coo_matrix( (converted, (source.row, source.col)), shape=source.shape ).tocsr() @@ -101,10 +90,10 @@ def from_scipy(cls, matrix: sparse.spmatrix) -> CsrMatrix: csr.eliminate_zeros() csr.sort_indices() return cls( - n=int(csr.shape[0]), - row_offsets=np.asarray(csr.indptr, dtype=np.uint64), - column_indices=np.asarray(csr.indices, dtype=np.uint32), - values=np.asarray(csr.data, dtype=np.float64), + int(csr.shape[0]), + np.asarray(csr.indptr, dtype=np.uint64), + np.asarray(csr.indices, dtype=np.uint32), + np.asarray(csr.data, dtype=np.float64), ) def to_scipy(self) -> sparse.csr_matrix: @@ -122,7 +111,9 @@ def matvec(self, vector: object) -> np.ndarray: @dataclass(frozen=True, eq=False) -class MatrixInput: +class CaseInput: + """Everything made visible to a candidate solver.""" + matrix: CsrMatrix b: np.ndarray x0: np.ndarray @@ -131,41 +122,65 @@ class MatrixInput: def __post_init__(self) -> None: if not isinstance(self.matrix, CsrMatrix): raise TypeError("matrix must be CsrMatrix") - b = _readonly_1d(self.b, np.float64, "b") - x0 = _readonly_1d(self.x0, np.float64, "x0") + b = _array(self.b, np.float64, "b") + x0 = _array(self.x0, np.float64, "x0") if b.shape != (self.matrix.n,) or x0.shape != (self.matrix.n,): - raise ValueError("b and x0 must match the matrix dimension") + raise ValueError("b and x0 must match the matrix") if not np.all(np.isfinite(b)) or not np.all(np.isfinite(x0)): raise ValueError("input vectors must be finite") tolerance = float(self.tolerance) - if not math.isfinite(tolerance) or not 0.0 < tolerance < 1.0: - raise ValueError("tolerance must lie strictly between zero and one") + if not math.isfinite(tolerance) or not 0 < tolerance < 1: + raise ValueError("tolerance must be between zero and one") object.__setattr__(self, "b", b) object.__setattr__(self, "x0", x0) object.__setattr__(self, "tolerance", tolerance) @dataclass(frozen=True, eq=False) -class EvaluationSystem: +class BenchmarkCase: + """A candidate input plus trusted verification data kept outside that input.""" + case_id: str - public: MatrixInput - x_star: np.ndarray | None = None - reference_kind: str = "manufactured" + input: CaseInput + target: np.ndarray | None = None + input_sha256: str = "" + role: str = "scored" def __post_init__(self) -> None: if not self.case_id or not self.case_id.isascii(): raise ValueError("case_id must be nonempty ASCII") - if not isinstance(self.public, MatrixInput): - raise TypeError("public must be MatrixInput") - if self.reference_kind not in REFERENCE_KINDS: - raise ValueError("unknown reference_kind") - if self.reference_kind == "none": - if self.x_star is not None: - raise ValueError("reference_kind 'none' must not contain x_star") - return - if self.x_star is None: - raise ValueError(f"{self.reference_kind} reference requires x_star") - truth = _readonly_1d(self.x_star, np.float64, "x_star") - if truth.shape != (self.public.matrix.n,) or not np.all(np.isfinite(truth)): - raise ValueError("x_star must be finite and match the matrix") - object.__setattr__(self, "x_star", truth) + if not isinstance(self.input, CaseInput): + raise TypeError("input must be CaseInput") + if self.target is not None: + target = _array(self.target, np.float64, "target") + if target.shape != (self.input.matrix.n,) or not np.all( + np.isfinite(target) + ): + raise ValueError("target must be finite and match the matrix") + object.__setattr__(self, "target", target) + if self.input_sha256 and ( + len(self.input_sha256) != 64 + or any(char not in "0123456789abcdef" for char in self.input_sha256) + ): + raise ValueError("input_sha256 must be a lowercase SHA-256 digest") + if self.role not in {"scored", "control"}: + raise ValueError("role must be scored or control") + + +@dataclass(frozen=True) +class RawExecution: + """Untrusted output returned by a local process or Modal sandbox.""" + + returncode: int + wall_seconds: float + diagnostics: str + output: bytes | None + + def __post_init__(self) -> None: + if type(self.returncode) is not int: + raise TypeError("returncode must be an integer") + if not math.isfinite(self.wall_seconds) or self.wall_seconds < 0: + raise ValueError("wall_seconds must be finite and nonnegative") + if not isinstance(self.diagnostics, str): + raise TypeError("diagnostics must be text") + object.__setattr__(self, "diagnostics", self.diagnostics[-8000:]) diff --git a/src/linear_solver_bench/paths.py b/src/linear_solver_bench/paths.py deleted file mode 100644 index 5b401b8..0000000 --- a/src/linear_solver_bench/paths.py +++ /dev/null @@ -1,62 +0,0 @@ -"""Public resources and writable caches, in a checkout or installed package.""" - -from __future__ import annotations - -import atexit -import os -import pathlib -from contextlib import ExitStack -from functools import cache -from importlib import resources - -_RESOURCE_CONTEXTS = ExitStack() -atexit.register(_RESOURCE_CONTEXTS.close) - - -@cache -def _installed_root() -> pathlib.Path: - # Keep an extracted directory alive for the process if imported from a zip. - resource = resources.files("linear_solver_bench").joinpath("_resources") - if not resource.is_dir(): - raise RuntimeError("installed package is missing public benchmark resources") - return _RESOURCE_CONTEXTS.enter_context(resources.as_file(resource)) - - -def repository_root() -> pathlib.Path: - """Return the root of the public benchmark assets, which may be installed.""" - override = os.environ.get("LINEAR_SOLVER_BENCH_ROOT") - if override: - root = pathlib.Path(override).expanduser().resolve() - else: - checkout = pathlib.Path(__file__).resolve().parents[2] - root = ( - checkout if (checkout / "benchmark.toml").is_file() else _installed_root() - ) - if not (root / "benchmark.toml").is_file(): - raise RuntimeError( - "cannot locate benchmark.toml; set LINEAR_SOLVER_BENCH_ROOT " - "to a directory containing public benchmark assets" - ) - return root - - -def data_dir() -> pathlib.Path: - override = os.environ.get("LINEAR_SOLVER_BENCH_DATA") - return ( - pathlib.Path(override).expanduser().resolve() - if override - else repository_root() / "data" - ) - - -def native_dir() -> pathlib.Path: - return repository_root() / "native" - - -def cache_dir() -> pathlib.Path: - """Return writable storage separately from read-only installed resources.""" - override = os.environ.get("LINEAR_SOLVER_BENCH_CACHE") - if override: - return pathlib.Path(override).expanduser().resolve() - base = pathlib.Path(os.environ.get("XDG_CACHE_HOME") or "~/.cache").expanduser() - return (base / "linear-solver-bench").resolve() diff --git a/src/linear_solver_bench/pilot_dataset.py b/src/linear_solver_bench/pilot_dataset.py deleted file mode 100644 index a35621b..0000000 --- a/src/linear_solver_bench/pilot_dataset.py +++ /dev/null @@ -1,309 +0,0 @@ -"""Prepare public pilot cases and stream one trusted numerical system at a time.""" - -from __future__ import annotations - -import hashlib -import json -import os -import pathlib -import shutil -import uuid -from collections.abc import Iterable, Iterator - -from .archive import decode_system, encode_system -from .dataset import sha256_file -from .manifests import ( - digest, - load_release, - seal_manifest, - strict_object, - validate_qualification, - validate_release, -) -from .models import EvaluationSystem -from .paths import cache_dir -from .sources.download import fetch_archive -from .sources.flash import load_flash -from .sources.suitesparse import load_suitesparse -from .workloads import ( - expected_flash_system_digest, - manufacture_ns, - matrix_digest, - qualify_ns, - system_digest, - vector_digest, -) - -PREPARED_KIND = "linear-solver-bench-pilot-prepared" -IDENTITY_FIELDS = ("release_id", "family", "track", "split", "contracts", "execution") - - -def validate_pilot_prepared_manifest(value: object, *, official: bool = False) -> dict: - prepared = strict_object( - value, - { - "schema_version", - "kind", - "source_release", - "source_manifest_sha256", - "case_count", - "cases", - "complete_split", - "manifest_sha256", - *IDENTITY_FIELDS, - }, - "prepared pilot manifest", - ) - if prepared["schema_version"] != 2 or prepared["kind"] != PREPARED_KIND: - raise ValueError("unsupported prepared pilot schema") - release = validate_release(prepared["source_release"], official=official) - if prepared["source_manifest_sha256"] != release["manifest_sha256"]: - raise ValueError("prepared release identity mismatch") - for name in IDENTITY_FIELDS: - if prepared[name] != release[name]: - raise ValueError("prepared contract or release metadata mismatch") - cases = prepared["cases"] - if ( - not isinstance(cases, list) - or not cases - or type(prepared["case_count"]) is not int - or prepared["case_count"] != len(cases) - ): - raise ValueError("prepared pilot case list is invalid") - previous = -1 - paths = set() - for index, case in enumerate(cases): - strict_object( - case, - { - "index", - "case_id", - "source_index", - "relpath", - "sha256", - "n", - "nnz", - "tolerance", - "system_sha256", - "qualification", - }, - "prepared case", - ) - source_index = case["source_index"] - if ( - type(case["index"]) is not int - or case["index"] != index - or type(source_index) is not int - or not previous < source_index < len(release["cases"]) - ): - raise ValueError("prepared case order or source index mismatch") - previous = source_index - source = release["cases"][source_index] - if any( - case[key] != source[key] for key in ("case_id", "n", "nnz", "tolerance") - ): - raise ValueError("prepared case disagrees with its release") - name = case["relpath"] - if ( - not isinstance(name, str) - or pathlib.PurePosixPath(name).name != name - or not name.endswith(".npz") - or "\\" in name - or name in paths - ): - raise ValueError("prepared case path is invalid") - paths.add(name) - digest(case["sha256"], "prepared archive digest") - digest(case["system_sha256"], "prepared numerical digest") - if release["family"] == "ns-mesh-pde": - validate_qualification(case["qualification"], case["system_sha256"]) - if ( - source["qualification"] is not None - and source["qualification"]["system_sha256"] != case["system_sha256"] - ): - raise ValueError( - "prepared NS system differs from the qualified release" - ) - else: - if case["qualification"] is not None: - raise ValueError( - "replay timing qualification belongs in its reference artifact" - ) - if case["system_sha256"] != expected_flash_system_digest(source): - raise ValueError( - "prepared FLASH inputs differ from the captured release" - ) - complete = len(cases) == len(release["cases"]) - if ( - type(prepared["complete_split"]) is not bool - or prepared["complete_split"] != complete - ): - raise ValueError("prepared split completeness mismatch") - if official and not complete: - raise ValueError( - "official evaluation requires every case in the declared split" - ) - body = {key: item for key, item in prepared.items() if key != "manifest_sha256"} - if seal_manifest(body)["manifest_sha256"] != prepared["manifest_sha256"]: - raise ValueError("prepared manifest digest mismatch") - return prepared - - -def load_pilot_prepared_manifest(root: pathlib.Path, *, official: bool = False) -> dict: - try: - value = json.loads( - (pathlib.Path(root) / "manifest.json").read_text(encoding="utf-8") - ) - except (OSError, UnicodeError, json.JSONDecodeError) as exc: - raise ValueError("cannot read prepared pilot manifest") from exc - return validate_pilot_prepared_manifest(value, official=official) - - -def prepare_release( - manifest_path: pathlib.Path, - output: pathlib.Path, - *, - rhs_key: bytes | None = None, - cache: pathlib.Path | None = None, - offline: bool = False, - case_ids: Iterable[str] | None = None, -) -> dict: - release = load_release(manifest_path) - if release["family"] == "ns-mesh-pde": - if rhs_key is None: - public_key = release["rhs"]["public_key_hex"] - if public_key is None: - raise ValueError( - "ranked preparation requires the operator-held RHS key" - ) - rhs_key = bytes.fromhex(public_key) - if ( - not isinstance(rhs_key, bytes) - or hashlib.sha256(rhs_key).hexdigest() != release["rhs"]["key_id"] - ): - raise ValueError("RHS key does not match the declared release") - elif rhs_key is not None: - raise ValueError("FLASH replay preserves captured RHS and accepts no RHS key") - requested = set(case_ids or ()) - cases = [ - case - for case in release["cases"] - if not requested or case["case_id"] in requested - ] - if requested and requested != {case["case_id"] for case in cases}: - raise ValueError("requested case ID is absent from the release") - output = pathlib.Path(output).resolve() - download_cache = pathlib.Path(cache or cache_dir() / "downloads").resolve() - if output.exists() and (not output.is_dir() or any(output.iterdir())): - raise ValueError("prepared output directory must be absent or empty") - if download_cache == output or output in download_cache.parents: - raise ValueError("download cache must be outside prepared output") - output.parent.mkdir(parents=True, exist_ok=True) - staging = output.parent / f".{output.name}.{uuid.uuid4().hex}.tmp" - staging.mkdir() - try: - prepared_cases = [] - for index, case in enumerate(cases): - archive = fetch_archive(case["source"], download_cache, offline=offline) - if release["family"] == "ns-mesh-pde": - matrix = load_suitesparse(archive, case) - system = manufacture_ns(case, release, rhs_key, matrix) - qualified = case["qualification"] - if qualified is None: - qualified = qualify_ns(system) - else: - # Qualification belongs to release preparation. Reuse its - # frozen evidence after binding it to these exact inputs; - # ordinary downloads must not repeat large factorizations or - # change prepared identity with platform-dependent metrics. - validate_qualification(qualified, system_digest(system)) - else: - system = load_flash(archive, case) - qualified = None - payload = encode_system(system, schema_version=2) - filename = f"{index:04d}.npz" - (staging / filename).write_bytes(payload) - prepared_cases.append( - { - "index": index, - "case_id": case["case_id"], - "source_index": case["index"], - "relpath": filename, - "sha256": hashlib.sha256(payload).hexdigest(), - "n": case["n"], - "nnz": case["nnz"], - "tolerance": case["tolerance"], - "system_sha256": system_digest(system), - "qualification": qualified, - } - ) - # Keep neither decoded systems nor matrix arrays across iterations. - del payload, system - if release["family"] == "ns-mesh-pde": - del matrix - body = { - "schema_version": 2, - "kind": PREPARED_KIND, - **{key: release[key] for key in IDENTITY_FIELDS}, - "source_release": release, - "source_manifest_sha256": release["manifest_sha256"], - "case_count": len(prepared_cases), - "cases": prepared_cases, - "complete_split": len(prepared_cases) == len(release["cases"]), - } - manifest = validate_pilot_prepared_manifest(seal_manifest(body)) - (staging / "manifest.json").write_text( - json.dumps(manifest, indent=2, sort_keys=True, allow_nan=False) + "\n", - encoding="utf-8", - ) - if output.exists(): - output.rmdir() - os.replace(staging, output) - return manifest - finally: - if staging.exists(): - shutil.rmtree(staging) - - -def iter_pilot_prepared( - root: pathlib.Path, *, official: bool = False -) -> Iterator[EvaluationSystem]: - """Validate and yield each trusted archive without retaining the prior case.""" - root = pathlib.Path(root).resolve() - manifest = load_pilot_prepared_manifest(root, official=official) - for case in manifest["cases"]: - source = manifest["source_release"]["cases"][case["source_index"]]["source"] - path = root / case["relpath"] - if ( - path.is_symlink() - or not path.is_file() - or sha256_file(path) != case["sha256"] - ): - raise ValueError("prepared archive is absent or has the wrong digest") - system = decode_system(path.read_bytes()) - if ( - system.case_id != case["case_id"] - or system.public.matrix.n != case["n"] - or system.public.matrix.nnz != case["nnz"] - or system.public.tolerance != case["tolerance"] - or system_digest(system) != case["system_sha256"] - or matrix_digest(system.public.matrix) != source["matrix_sha256"] - ): - raise ValueError("prepared numerical inputs disagree with the manifest") - if ( - manifest["family"] == "ns-mesh-pde" - and system.reference_kind != "manufactured" - ): - raise ValueError("NS coverage requires the declared manufactured target") - if manifest["family"] == "magnetic_diffusion_flash": - if system.reference_kind != "none": - raise ValueError("pilot FLASH captures cannot add a reference array") - if ( - vector_digest(system.public.b) != source["b_sha256"] - or vector_digest(system.public.x0) != source["x0_sha256"] - ): - raise ValueError( - "prepared FLASH vectors differ from the captured release" - ) - yield system - del system diff --git a/src/linear_solver_bench/pilot_runner.py b/src/linear_solver_bench/pilot_runner.py deleted file mode 100644 index bb0384d..0000000 --- a/src/linear_solver_bench/pilot_runner.py +++ /dev/null @@ -1,133 +0,0 @@ -"""One execution per case, with the same reporting for local and remote runs.""" - -from __future__ import annotations - -import pathlib -from collections.abc import Callable, Iterable, Mapping - -from .dataset import identity_sha256 -from .families import resolve_track -from .models import EvaluationSystem -from .runner import RepeatResult, run_repeat - -BENCHMARK_ID = "linear-solver-bench-pilot-cpu-v2" - - -def local_venue(execution: Mapping) -> dict: - return { - "id": "local-uncontrolled", - "cpus": execution["cpus"], - "memory_bytes": execution["memory_bytes"], - "limits_enforced": False, - } - - -def evaluate_pilot( - executable: pathlib.Path, - systems: Iterable[EvaluationSystem], - prepared: Mapping, - *, - source_sha256: str, - executable_sha256: str, - runtime_manifest_sha256: str, - venue: Mapping | None = None, - execute: Callable[..., RepeatResult] = run_repeat, -) -> dict: - """Consume each system once and retain only scalar reports between cases. - - A transport supplies ``execute`` to run the very same case contract remotely. - Input or infrastructure errors propagate and invalidate the whole evaluation; - candidate failures are recorded and do not stop subsequent cases. - """ - from .manifests import validate_release - from .pilot_dataset import validate_pilot_prepared_manifest - from .pilot_scoring import validate_pilot_report - from .workloads import system_digest - - validate_pilot_prepared_manifest(prepared) - release = prepared["source_release"] - validate_release(release) - contract = resolve_track(release["family"], release["track"]) - execution = release["execution"] - rows = [] - iterator = iter(systems) - for entry in prepared["cases"]: - try: - system = next(iterator) - except StopIteration as exc: - raise ValueError("prepared case sequence is incomplete") from exc - case = release["cases"][entry["source_index"]] - if ( - system.case_id != entry["case_id"] - or case["case_id"] != system.case_id - or system.public.matrix.n != case["n"] - or system.public.matrix.nnz != case["nnz"] - or system.public.tolerance != case["tolerance"] - or system_digest(system) != entry["system_sha256"] - ): - raise ValueError("loaded case differs from the selected release") - run = execute( - executable, - system, - index=0, - deadline_s=execution["case_timeout_seconds"], - maximum_iterations=execution["max_iterations"], - accuracy_contract_id=contract.accuracy_contract_id, - ) - # Only scalar diagnostics leave this iteration; no retained solution arrays. - row = { - "index": len(rows), - "source_index": entry["source_index"], - "case_id": system.case_id, - "n": case["n"], - "nnz": case["nnz"], - "tolerance": case["tolerance"], - "role": case["role"], - "provenance_group": case["provenance_group"], - "source_run": case["source_run"], - "weight": case["weight"], - "system_sha256": entry["system_sha256"], - "archive_sha256": entry["sha256"], - "deadline_s": execution["case_timeout_seconds"], - "max_iterations": execution["max_iterations"], - "passed": run.passed - and run.driver is not None - and run.driver.elapsed_s <= execution["case_timeout_seconds"], - "elapsed_s": run.driver.elapsed_s if run.driver else None, - "execution": run.to_dict(), - } - rows.append(row) - del run, system - try: - next(iterator) - except StopIteration: - pass - else: - raise ValueError("prepared case sequence has unexpected extra cases") - body = { - "schema_version": 2, - "kind": "linear-solver-bench-pilot-report", - "benchmark_id": BENCHMARK_ID, - "family": contract.family, - "track": contract.track, - "release_id": release["release_id"], - "split": release["split"], - "contracts": contract.contracts, - "repetitions": 1, - "release": release, - "release_manifest_sha256": release["manifest_sha256"], - "prepared_manifest_sha256": prepared["manifest_sha256"], - "prepared_manifest": dict(prepared), - "source_sha256": source_sha256, - "executable_sha256": executable_sha256, - "runtime_manifest_sha256": runtime_manifest_sha256, - "venue": dict(venue) if venue is not None else local_venue(execution), - "complete_split": [row["case_id"] for row in rows] - == [case["case_id"] for case in release["cases"]], - "case_count": len(rows), - "solved_count": sum(row["passed"] for row in rows), - "cases": rows, - } - result = {**body, "report_sha256": identity_sha256(body)} - validate_pilot_report(result) - return result diff --git a/src/linear_solver_bench/pilot_scoring.py b/src/linear_solver_bench/pilot_scoring.py deleted file mode 100644 index f328b78..0000000 --- a/src/linear_solver_bench/pilot_scoring.py +++ /dev/null @@ -1,349 +0,0 @@ -"""Coverage counts and replay speedups bound to a complete frozen case set.""" - -from __future__ import annotations - -import math -import re -from collections.abc import Mapping - -from .dataset import identity_sha256 -from .families import resolve_track - -# Add an entry only after the complete reference artifact has been published. -# One frozen calibration is trusted for each release, runtime, and venue. -# This registry expresses operator policy; it does not authenticate reports. -PUBLISHED_REPLAY_REFERENCES: dict[tuple[str, str, str], str] = { - ( - "02a8e24e667b96029f0c8db627f87c237549778a63f3342a73010e7278876669", - "4b09e68ce2fc17261c1d776bb01d63ec40f8f92388b9aff95c046f7c1f8bb076", - "modal-sandbox-pilot-cpu-v2", - ): "4553a052889aa53cca57aa213329952dc27c33cbb90fc25beaae81946cb02c25", - ( - "1ce0ec20c66123773215543e2d43375104f43113a9416c59fb0e9625a9975b79", - "4b09e68ce2fc17261c1d776bb01d63ec40f8f92388b9aff95c046f7c1f8bb076", - "modal-sandbox-pilot-cpu-v2", - ): "54d05b8d04970ffc885c82ffee41113382740256f2fe491025b8f47b72a6ff72", -} - - -def _hash(value: object) -> bool: - return isinstance(value, str) and re.fullmatch(r"[0-9a-f]{64}", value) is not None - - -def _positive(value: object) -> bool: - return type(value) in (int, float) and math.isfinite(value) and value > 0 - - -def _signed(value: Mapping, field: str) -> None: - if value.get(field) != identity_sha256( - {key: item for key, item in value.items() if key != field} - ): - raise ValueError(f"{field} mismatch") - - -def _metric_pass(verification: Mapping, contract, tolerance: float) -> bool: - metrics = verification.get("metrics") - if not isinstance(metrics, dict): - return False - if contract.track == "coverage": - gates = { - "normwise_backward_error": 1e-10, - "componentwise_backward_error": 1e-8, - "relative_l2_forward_error": 1e-5, - "relative_linf_forward_error": 1e-5, - } - else: - gates = { - "normwise_backward_error": max(tolerance, 20 * math.ulp(1.0)), - "relative_residual": tolerance * (1 + 1e-6), - } - return all( - type(metrics.get(name)) in (int, float) - and math.isfinite(metrics[name]) - and 0 <= metrics[name] <= limit - for name, limit in gates.items() - ) - - -def validate_pilot_report(report: Mapping) -> None: - from .manifests import validate_release - from .pilot_dataset import validate_pilot_prepared_manifest - - _signed(report, "report_sha256") - release = report.get("release") - if not isinstance(release, dict): - raise ValueError("report release is missing") - validate_release(release) - prepared = validate_pilot_prepared_manifest(report.get("prepared_manifest")) - if prepared["source_release"] != release or prepared[ - "manifest_sha256" - ] != report.get("prepared_manifest_sha256"): - raise ValueError("report prepared identity differs from its embedded manifest") - contract = resolve_track(release["family"], release["track"]) - if ( - report.get("schema_version") != 2 - or report.get("kind") != "linear-solver-bench-pilot-report" - or report.get("benchmark_id") != "linear-solver-bench-pilot-cpu-v2" - or report.get("contracts") != contract.contracts - or type(report.get("repetitions")) is not int - or report["repetitions"] != 1 - or any( - report.get(k) != release[k] - for k in ("family", "track", "release_id", "split") - ) - or report.get("release_manifest_sha256") != release["manifest_sha256"] - ): - raise ValueError("report contract differs from release") - for name in ( - "source_sha256", - "executable_sha256", - "runtime_manifest_sha256", - "prepared_manifest_sha256", - "release_manifest_sha256", - ): - if not _hash(report.get(name)): - raise ValueError(f"report {name} is invalid") - execution = release["execution"] - venue = report.get("venue") - if ( - not isinstance(venue, dict) - or set(venue) != {"id", "cpus", "memory_bytes", "limits_enforced"} - or venue["id"] not in {"local-uncontrolled", "modal-sandbox-pilot-cpu-v2"} - or venue["cpus"] != execution["cpus"] - or venue["memory_bytes"] != execution["memory_bytes"] - or venue["limits_enforced"] is not (venue["id"] != "local-uncontrolled") - ): - raise ValueError("report venue or resource contract is invalid") - cases = report.get("cases") - if ( - not isinstance(cases, list) - or not cases - or report.get("case_count") != len(cases) - or len(cases) != len(prepared["cases"]) - ): - raise ValueError("report case set is empty or incomplete") - indices = [] - for index, row in enumerate(cases): - if not isinstance(row, dict) or row.get("index") != index: - raise ValueError("report case order is invalid") - source_index = row.get("source_index") - if type(source_index) is not int or not 0 <= source_index < len( - release["cases"] - ): - raise ValueError("report case is outside release") - indices.append(source_index) - source = release["cases"][source_index] - entry = prepared["cases"][index] - if ( - any( - row.get(key) != entry[key] - for key in ("case_id", "source_index", "system_sha256") - ) - or row.get("archive_sha256") != entry["sha256"] - ): - raise ValueError("report case differs from prepared numerical identity") - for key in ( - "case_id", - "n", - "nnz", - "tolerance", - "role", - "provenance_group", - "source_run", - "weight", - ): - if row.get(key) != source[key]: - raise ValueError(f"report case {key} differs from release") - if ( - row.get("deadline_s") != execution["case_timeout_seconds"] - or row.get("max_iterations") != execution["max_iterations"] - or not _hash(row.get("system_sha256")) - or not _hash(row.get("archive_sha256")) - ): - raise ValueError("report numerical or execution identity is invalid") - run = row.get("execution") - if ( - not isinstance(run, dict) - or type(run.get("index")) is not int - or run["index"] != 0 - ): - raise ValueError("each pilot case must have one execution at index zero") - outcome = run.get("outcome") - if outcome not in {"completed", "timeout", "crash", "invalid_output"}: - raise ValueError("invalid candidate outcome") - if not _positive(run.get("process_wall_s")): - raise ValueError("invalid candidate process time") - passed = False - if outcome == "completed": - driver, verification = run.get("driver"), run.get("verification") - if not isinstance(driver, dict) or not isinstance(verification, dict): - raise ValueError("completed case lacks driver or verification evidence") - elapsed = driver.get("elapsed_s") - phases = [driver.get(k) for k in ("create_s", "setup_s", "solve_s")] - if ( - run.get("process_returncode") != 0 - or not _positive(elapsed) - or row.get("elapsed_s") != elapsed - or any( - type(t) not in (int, float) or not math.isfinite(t) or t < 0 - for t in phases - ) - or not math.isclose(sum(phases), elapsed, rel_tol=1e-12, abs_tol=1e-9) - or type(driver.get("status")) is not int - or type(driver.get("input_mutated")) is not bool - ): - raise ValueError("completed case timing or status is invalid") - passed = ( - driver["status"] == 0 - and not driver["input_mutated"] - and elapsed <= row["deadline_s"] - and verification.get("ok") is True - and _metric_pass(verification, contract, row["tolerance"]) - ) - if verification.get("ok") is True and not _metric_pass( - verification, contract, row["tolerance"] - ): - raise ValueError("verification verdict contradicts required metrics") - elif ( - any(run.get(k) is not None for k in ("driver", "verification")) - or row.get("elapsed_s") is not None - ): - raise ValueError( - "failed process cannot contain successful numerical output" - ) - if type(row.get("passed")) is not bool or row["passed"] != passed: - raise ValueError("case pass flag contradicts execution evidence") - if indices != sorted(set(indices)): - raise ValueError("report contains reordered or duplicate cases") - complete = indices == list(range(len(release["cases"]))) - if report.get("complete_split") is not complete: - raise ValueError("report completeness flag contradicts selected cases") - if type(report.get("solved_count")) is not int or report["solved_count"] != sum( - r["passed"] for r in cases - ): - raise ValueError("report solved count mismatch") - - -def replay_reference_from_report(report: Mapping) -> dict: - """Freeze a single declared reference only after all cases and controls pass.""" - validate_pilot_report(report) - if report["track"] != "replay": - raise ValueError("NS coverage does not require a timing reference") - if not report["complete_split"] or report["solved_count"] != report["case_count"]: - raise ValueError( - "replay reference must pass the complete split and its controls" - ) - body = { - "schema_version": 2, - "kind": "linear-solver-bench-replay-reference", - "reference_report": dict(report), - } - return {**body, "calibration_sha256": identity_sha256(body)} - - -def validate_replay_reference(reference: Mapping) -> None: - _signed(reference, "calibration_sha256") - if ( - reference.get("schema_version") != 2 - or reference.get("kind") != "linear-solver-bench-replay-reference" - ): - raise ValueError("invalid replay reference schema") - expected = replay_reference_from_report(reference["reference_report"]) - if dict(reference) != expected: - raise ValueError("replay reference differs from qualification report") - - -def score_pilot_report(report: Mapping, reference: Mapping | None = None) -> dict: - from .manifests import validate_release - - validate_pilot_report(report) - if not report["complete_split"]: - raise ValueError("scoring requires the complete frozen split") - published = True - try: - validate_release(report["release"], official=True) - except ValueError: - published = False - official = published and report["venue"]["limits_enforced"] - base = { - "schema_version": 2, - "family": report["family"], - "track": report["track"], - "release_id": report["release_id"], - "split": report["split"], - "report_sha256": report["report_sha256"], - "official": official, - "solved_count": report["solved_count"], - "case_count": report["case_count"], - "coverage_fraction": report["solved_count"] / report["case_count"], - } - if report["track"] == "coverage": - if reference is not None: - raise ValueError("coverage scoring does not use a timing reference") - return {**base, "eligible": True, "ranking_key": -report["solved_count"]} - base.update(official=False, reference_status="missing") - if reference is None: - return { - **base, - "eligible": False, - "speedup": None, - "reason": "awaiting_reference", - } - validate_replay_reference(reference) - anchor = reference["reference_report"] - for name in ( - "family", - "track", - "release_manifest_sha256", - "prepared_manifest_sha256", - "contracts", - "repetitions", - "runtime_manifest_sha256", - "venue", - "split", - ): - if report[name] != anchor[name]: - raise ValueError(f"candidate and reference {name} differ") - reference_key = ( - report["release_manifest_sha256"], - report["runtime_manifest_sha256"], - report["venue"]["id"], - ) - registered = ( - PUBLISHED_REPLAY_REFERENCES.get(reference_key) - == reference["calibration_sha256"] - ) - base.update( - official=official and registered, - reference_status="registered" if registered else "unregistered", - ) - per_case, weighted_logs = [], [] - for case, baseline in zip(report["cases"], anchor["cases"], strict=True): - for key in ( - "case_id", - "system_sha256", - "archive_sha256", - "role", - "weight", - "deadline_s", - "max_iterations", - ): - if case[key] != baseline[key]: - raise ValueError(f"candidate and reference case {key} differ") - speedup = baseline["elapsed_s"] / case["elapsed_s"] if case["passed"] else None - if speedup is not None and not _positive(speedup): - raise ValueError("per-case speedup is not finite and positive") - per_case.append( - {"case_id": case["case_id"], "role": case["role"], "speedup": speedup} - ) - if case["role"] == "scored" and speedup is not None: - weighted_logs.append(case["weight"] * math.log(speedup)) - eligible = report["solved_count"] == report["case_count"] - return { - **base, - "calibration_sha256": reference["calibration_sha256"], - "eligible": eligible, - "speedup": math.exp(math.fsum(weighted_logs)) if eligible else None, - "reason": None if eligible else "candidate_failed_required_cases", - "cases": per_case, - } diff --git a/src/linear_solver_bench/process.py b/src/linear_solver_bench/process.py deleted file mode 100644 index 86151a1..0000000 --- a/src/linear_solver_bench/process.py +++ /dev/null @@ -1,49 +0,0 @@ -"""Bounded child-process execution with combined diagnostics.""" - -from __future__ import annotations - -import subprocess -import time -from collections.abc import Mapping, Sequence -from dataclasses import dataclass - - -@dataclass(frozen=True) -class ProcessResult: - returncode: int - output: bytes - wall_s: float - - -class ProcessTimeout(RuntimeError): - def __init__(self, timeout_s: float, output: bytes): - self.timeout_s = timeout_s - self.output = output - super().__init__(f"process exceeded {timeout_s:g} seconds") - - -def bounded_process( - command: Sequence[str], - *, - timeout_s: float, - env: Mapping[str, str] | None = None, -) -> ProcessResult: - if not command or timeout_s <= 0.0: - raise ValueError("command and positive timeout are required") - start = time.monotonic() - try: - completed = subprocess.run( - tuple(command), - stdin=subprocess.DEVNULL, - stdout=subprocess.PIPE, - stderr=subprocess.STDOUT, - check=False, - timeout=timeout_s, - env=dict(env) if env is not None else None, - ) - except subprocess.TimeoutExpired as exc: - output = (exc.stdout or b"") + (exc.stderr or b"") - raise ProcessTimeout(timeout_s, output) from exc - return ProcessResult( - completed.returncode, completed.stdout, time.monotonic() - start - ) diff --git a/src/linear_solver_bench/protocol.py b/src/linear_solver_bench/protocol.py deleted file mode 100644 index fcd625c..0000000 --- a/src/linear_solver_bench/protocol.py +++ /dev/null @@ -1,75 +0,0 @@ -"""Binary protocol between the trusted orchestrator and native driver.""" - -from __future__ import annotations - -import struct -from dataclasses import dataclass - -import numpy as np - -from .models import MatrixInput - -INPUT_MAGIC = b"LSBIN001" -OUTPUT_MAGIC = b"LSBOUT01" -PROTOCOL_VERSION = 1 -INPUT_HEADER = struct.Struct("<8sIIQQd") -OUTPUT_HEADER = struct.Struct("<8sIiQQQQQB7x") - - -@dataclass(frozen=True) -class DriverOutput: - status: int - solution: np.ndarray - elapsed_s: float - create_s: float - setup_s: float - solve_s: float - input_mutated: bool - - -def encode_input(value: MatrixInput) -> bytes: - matrix = value.matrix - return b"".join( - ( - INPUT_HEADER.pack( - INPUT_MAGIC, - PROTOCOL_VERSION, - 0, - matrix.n, - matrix.nnz, - value.tolerance, - ), - np.asarray(matrix.row_offsets, dtype=" DriverOutput: - if len(payload) < OUTPUT_HEADER.size: - raise ValueError("driver output is truncated") - magic, version, status, n, elapsed, create, setup, solve, mutated = ( - OUTPUT_HEADER.unpack_from(payload) - ) - if magic != OUTPUT_MAGIC or version != PROTOCOL_VERSION or n != expected_n: - raise ValueError("driver output header is invalid") - if mutated not in (0, 1): - raise ValueError("driver mutation flag is invalid") - expected_size = OUTPUT_HEADER.size + expected_n * 8 - if len(payload) != expected_size: - raise ValueError("driver output size is invalid") - if elapsed <= 0 or create + setup + solve != elapsed: - raise ValueError("driver timing fields are invalid") - solution = np.frombuffer(payload, dtype=" dict[str, object]: - return { - "estimates": list(self.estimates), - "seeds": list(self.seeds), - "maximum_solve_residual": self.maximum_solve_residual, - } - - -def condition_ceiling(tolerance: float) -> float: - value = float(tolerance) - if not math.isfinite(value) or value <= 0.0: - raise ValueError("tolerance must be finite and positive") - return value * FORWARD_ERROR_FACTOR / ( - CONDITION_SAFETY_FACTOR * FLOAT64_EPSILON - ) - - -def minimum_safe_tolerance(condition_estimate: float) -> float: - value = float(condition_estimate) - if not math.isfinite(value) or value < 1.0: - raise ValueError("condition estimate must be finite and at least one") - return ( - CONDITION_SAFETY_FACTOR * value * FLOAT64_EPSILON - / FORWARD_ERROR_FACTOR - ) - - -def _matrix_one_norm(matrix: sparse.spmatrix) -> float: - columns = np.asarray(abs(matrix).sum(axis=0)).reshape(-1) - result = float(np.max(columns, initial=0.0)) - if not math.isfinite(result) or result <= 0.0: - raise ValueError("matrix one-norm must be finite and positive") - return result - - -def maximum_column_two_norm(matrix: sparse.spmatrix) -> float: - """Return the largest column 2-norm without unsafe unscaled squaring.""" - if not sparse.issparse(matrix) or matrix.ndim != 2: - raise ValueError("column norm requires a sparse matrix") - csc = sparse.csc_matrix(matrix, dtype=np.float64, copy=False) - if not np.all(np.isfinite(csc.data)): - raise ValueError("column norm requires finite matrix values") - scale = float(np.max(np.abs(csc.data), initial=0.0)) - if scale == 0.0: - return 0.0 - squared = np.square(csc.data / scale) - starts = csc.indptr[:-1] - stops = csc.indptr[1:] - nonempty = stops > starts - column_sums = np.zeros(csc.shape[1], dtype=np.float64) - column_sums[nonempty] = np.add.reduceat(squared, starts[nonempty]) - result = scale * math.sqrt(float(np.max(column_sums, initial=0.0))) - if not math.isfinite(result): - raise ValueError("column norm is not finite") - return result - - -def spqr_rank_tolerance(matrix: sparse.spmatrix) -> float: - """Return the explicit SuiteSparseQR default-style numerical-rank cutoff.""" - if not sparse.issparse(matrix) or matrix.shape[0] != matrix.shape[1]: - raise ValueError("SPQR rank tolerance requires a square sparse matrix") - n = int(matrix.shape[0]) - tolerance = ( - 20.0 * (2 * n) * FLOAT64_EPSILON * maximum_column_two_norm(matrix) - ) - if not math.isfinite(tolerance): - raise ValueError("SPQR rank tolerance is not finite") - return tolerance - - -def _seed(case_id: str, repeat: int) -> int: - payload = ( - f"linear-solver-bench-condition-onenormest-v1\0{case_id}\0{repeat}" - ).encode("ascii") - return int.from_bytes(hashlib.sha256(payload).digest()[:4], "big") - - -def _checked_solve( - solve: Callable[[np.ndarray], object], value: np.ndarray -) -> np.ndarray: - result = np.asarray(solve(value), dtype=np.float64) - if result.shape != value.shape or not np.all(np.isfinite(result)): - raise ValueError("factor solve returned an invalid value") - return result - - -def inverse_one_norm_evidence( - matrix: sparse.spmatrix, - solve: Callable[[np.ndarray], object], - solve_transpose: Callable[[np.ndarray], object], - *, - case_id: str, - repeats: int = ESTIMATOR_REPEATS, - t: int = ONENORMEST_T, - itmax: int = ONENORMEST_ITMAX, -) -> InverseNormEvidence: - """Estimate ``||A^-1||_1`` reproducibly from ordinary/transpose solves.""" - if not sparse.issparse(matrix) or matrix.shape[0] != matrix.shape[1]: - raise ValueError("inverse-norm estimation requires a square sparse matrix") - if not case_id or not case_id.isascii(): - raise ValueError("case_id must be nonempty ASCII") - if ( - type(repeats) is not int - or repeats < 1 - or type(t) is not int - or t < 1 - or type(itmax) is not int - or itmax < 2 - ): - raise ValueError("inverse-norm estimator controls are invalid") - n = int(matrix.shape[0]) - operator = LinearOperator( - shape=(n, n), - dtype=np.dtype(np.float64), - matvec=lambda value: _checked_solve(solve, value), - rmatvec=lambda value: _checked_solve(solve_transpose, value), - ) - probe = np.where(np.arange(n) % 2, -1.0, 1.0) - matrix_one_norm = _matrix_one_norm(matrix) - transpose_one_norm = _matrix_one_norm(matrix.T) - residuals = [] - for operator_matrix, solver, operator_norm in ( - (matrix, solve, matrix_one_norm), - (matrix.T, solve_transpose, transpose_one_norm), - ): - solution = _checked_solve(solver, probe) - residual = np.asarray(operator_matrix @ solution - probe) - denominator = operator_norm * float(np.linalg.norm(solution, 1)) + n - residuals.append(float(np.linalg.norm(residual, 1)) / denominator) - maximum_residual = max(residuals) - if ( - not math.isfinite(maximum_residual) - or maximum_residual > SOLVE_RESIDUAL_LIMIT - ): - raise ValueError("factor solve residual is too large") - - seeds = tuple(_seed(case_id, repeat) for repeat in range(repeats)) - estimates = [] - random_state = np.random.get_state() - try: - for seed in seeds: - np.random.seed(seed) - estimate = float(onenormest(operator, t=t, itmax=itmax)) - if not math.isfinite(estimate) or estimate <= 0.0: - raise ValueError("inverse-norm estimate must be finite and positive") - estimates.append(estimate) - finally: - np.random.set_state(random_state) - return InverseNormEvidence( - estimates=tuple(estimates), - seeds=seeds, - maximum_solve_residual=maximum_residual, - ) - - -def _empty_condition( - *, - status: str, - rank_estimate: int | None, - rank_tolerance: float | None, - reason: str, -) -> dict[str, object]: - return { - "status": status, - "method": CONDITION_METHOD, - "rank_estimate": rank_estimate, - "rank_tolerance": rank_tolerance, - "condition_norm": None, - "condition_estimate_raw": None, - "estimator_guard_factor": None, - "condition_estimate": None, - "roundoff_budget": None, - "minimum_safe_tolerance": None, - "supported_tolerances": [], - "tightest_supported_tolerance": None, - "reason": reason, - } - - -def indeterminate_condition(reason: str) -> dict[str, object]: - if not reason: - raise ValueError("indeterminate condition evidence requires a reason") - return _empty_condition( - status="indeterminate", - rank_estimate=None, - rank_tolerance=None, - reason=reason, - ) - - -def condition_from_factor( - matrix: sparse.spmatrix, - *, - case_id: str, - rank_estimate: int, - rank_tolerance: float, - solve: Callable[[np.ndarray], object], - solve_transpose: Callable[[np.ndarray], object], -) -> tuple[dict[str, object], InverseNormEvidence | None]: - """Build one uniform rank/condition record from a reusable SPQR factor.""" - n = int(matrix.shape[0]) - if type(rank_estimate) is not int or not 0 <= rank_estimate <= n: - raise ValueError("SPQR rank estimate is invalid") - if rank_estimate < n: - return ( - _empty_condition( - status="rank_deficient", - rank_estimate=rank_estimate, - rank_tolerance=rank_tolerance, - reason="SuiteSparseQR reports numerical rank below n", - ), - None, - ) - evidence = inverse_one_norm_evidence( - matrix, solve, solve_transpose, case_id=case_id - ) - raw = max(1.0, _matrix_one_norm(matrix) * max(evidence.estimates)) - guarded = raw * ESTIMATOR_GUARD_FACTOR - floor = minimum_safe_tolerance(guarded) - supported = tuple(tolerance for tolerance in TOLERANCE_TIERS if floor <= tolerance) - return ( - { - "status": "estimated", - "method": CONDITION_METHOD, - "rank_estimate": rank_estimate, - "rank_tolerance": rank_tolerance, - "condition_norm": CONDITION_NORM, - "condition_estimate_raw": raw, - "estimator_guard_factor": ESTIMATOR_GUARD_FACTOR, - "condition_estimate": guarded, - "roundoff_budget": CONDITION_SAFETY_FACTOR - * guarded - * FLOAT64_EPSILON, - "minimum_safe_tolerance": floor, - "supported_tolerances": list(supported), - "tightest_supported_tolerance": min(supported) if supported else None, - "reason": None, - }, - evidence, - ) - - -def qualify_matrix( - matrix: sparse.spmatrix, *, case_id: str -) -> tuple[dict[str, object], InverseNormEvidence | None]: - """Factor a matrix with SPQR and return uniform guarded 1-norm evidence.""" - try: - from sksparse.spqr import spqr_factor - except ImportError as exc: - raise RuntimeError( - "condition qualification requires scikit-sparse with SPQR" - ) from exc - csc = sparse.csc_array(matrix, dtype=np.float64, copy=True) - csc.sum_duplicates() - csc.eliminate_zeros() - csc.sort_indices() - rank_tolerance = spqr_rank_tolerance(csc) - factor = spqr_factor( - csc, use_singletons=True, order="colamd", tol=rank_tolerance - ) - return condition_from_factor( - csc, - case_id=case_id, - rank_estimate=int(factor.rank), - rank_tolerance=rank_tolerance, - solve=lambda value: factor.solve(value, transpose=False), - solve_transpose=lambda value: factor.solve(value, transpose=True), - ) - - -def qualification_document( - *, - catalogue_sha256: str, - input_manifest_sha256: str, - expected_case_count: int, - results: Sequence[Mapping[str, object]], - toolchain: Mapping[str, object], -) -> dict[str, object]: - """Build the content-identified aggregate v2 qualification artifact.""" - ordered = sorted( - (dict(result) for result in results), key=lambda item: item["index"] - ) - indices = tuple(result.get("index") for result in ordered) - if ( - type(expected_case_count) is not int - or expected_case_count <= 0 - or any(type(index) is not int for index in indices) - or any(not 0 <= index < expected_case_count for index in indices) - or len(set(indices)) != len(indices) - ): - raise ValueError("qualification result indices are invalid") - statuses = Counter(str(result["condition"]["status"]) for result in ordered) - methods = Counter(str(result["condition"]["method"]) for result in ordered) - body = { - "schema_version": QUALIFICATION_SCHEMA_VERSION, - "kind": QUALIFICATION_KIND, - "catalogue_sha256": catalogue_sha256, - "input_manifest_sha256": input_manifest_sha256, - "expected_case_count": expected_case_count, - "received_case_count": len(ordered), - "complete": len(ordered) == expected_case_count, - "policy": { - "condition_norm": CONDITION_NORM, - "method": CONDITION_METHOD, - "rank_method": "SuiteSparseQR", - "condition_safety_factor": CONDITION_SAFETY_FACTOR, - "forward_error_factor": FORWARD_ERROR_FACTOR, - "float64_epsilon": FLOAT64_EPSILON, - "minimum_safe_tolerance_formula": ( - "100*guarded_condition*float64_epsilon/5" - ), - "tolerance_tiers": list(TOLERANCE_TIERS), - "condition_ceilings": { - format(value, ".17g"): condition_ceiling(value) - for value in TOLERANCE_TIERS - }, - "estimator": { - "guard_factor": ESTIMATOR_GUARD_FACTOR, - "repeats": ESTIMATOR_REPEATS, - "onenormest_t": ONENORMEST_T, - "onenormest_itmax": ONENORMEST_ITMAX, - "solve_residual_limit": SOLVE_RESIDUAL_LIMIT, - }, - }, - "toolchain": dict(toolchain), - "status_counts": dict(sorted(statuses.items())), - "method_counts": dict(sorted(methods.items())), - "results": ordered, - } - return {**body, "condition_results_sha256": identity_sha256(body)} diff --git a/src/linear_solver_bench/reference_pyamg.py b/src/linear_solver_bench/reference_pyamg.py deleted file mode 100644 index c9c6fcb..0000000 --- a/src/linear_solver_bench/reference_pyamg.py +++ /dev/null @@ -1,185 +0,0 @@ -"""Optional fixed PyAMG reference for offline NS qualification. - -Every solve starts from zero and uses the original operator. Two fixed -extended-residual corrections strengthen the empirical feasibility witness; -no inverse-norm certificate or candidate timing baseline is claimed. -Operators must enforce memory and wall-time limits in an isolated process. -""" - -from __future__ import annotations - -import copy -import math - -import numpy as np -import scipy -from scipy.sparse.linalg import gmres - -from .manifests import ( - PYAMG_INEXACT_QUALIFICATION_CONFIG, - PYAMG_INEXACT_QUALIFICATION_METHOD, - PYAMG_QUALIFICATION_CONFIG, - PYAMG_QUALIFICATION_METHOD, - QUALIFICATION_LIMITS, - validate_qualification, -) -from .verify import accuracy_metrics -from .workloads import _extended_residual, system_digest - - -def qualify_ns_pyamg(system): - """Retain the original v1 method and its exact evidence format.""" - return _qualify(system, PYAMG_QUALIFICATION_METHOD, PYAMG_QUALIFICATION_CONFIG) - - -def qualify_ns_pyamg_inexact(system): - """Use fixed inexact inner corrections; only the final gates admit a case.""" - return _qualify( - system, PYAMG_INEXACT_QUALIFICATION_METHOD, PYAMG_INEXACT_QUALIFICATION_CONFIG - ) - - -def _qualify(system, method, config): - try: - import pyamg - except ImportError as exc: - raise RuntimeError( - "install linear-solver-bench[qualification] for this offline method" - ) from exc - - if pyamg.__version__ != config["pyamg_version"]: - raise ValueError("the reference requires exactly PyAMG 5.3.0") - if system.reference_kind != "manufactured" or system.x_star is None: - raise ValueError("NS qualification requires its manufactured target") - matrix = system.public.matrix - operator = matrix.to_scipy().copy() - # These constant coarse-space candidates depend on dimension only, never on - # the manufactured target. Local Jacobi weighting avoids randomized spectral - # estimates. 'symmetric' strength is a graph rule, not a symmetry assumption: - # symmetry='nonsymmetric' builds distinct restriction and prolongation. - hierarchy = pyamg.smoothed_aggregation_solver( - operator, - B=np.ones((matrix.n, 1)), - BH=np.ones((matrix.n, 1)), - symmetry=config["symmetry"], - strength=tuple(config["strength"]), - aggregate=config["aggregate"], - smooth=tuple(config["smooth"]), - presmoother=tuple(config["presmoother"]), - postsmoother=tuple(config["postsmoother"]), - improve_candidates=config["improve_candidates"], - diagonal_dominance=config["diagonal_dominance"], - max_levels=config["max_levels"], - max_coarse=config["max_coarse"], - coarse_solver=config["coarse_solver"], - keep=config["keep"], - ) - # PyAMG 5.3.0 aspreconditioner applies precisely one cycle with a zero guess. - # The hierarchy and linear cycle are reused unchanged for every GMRES call. - preconditioner = hierarchy.aspreconditioner(cycle=config["preconditioner_cycle"]) - solves = [] - - def solve(rhs, rtol): - if not np.all(np.isfinite(rhs)): - raise ValueError("reference correction is not finite") - iterations = 0 - - def count(_value): - nonlocal iterations - iterations += 1 - - answer, info = gmres( - operator, - rhs, - x0=np.zeros(matrix.n), - M=preconditioner, - restart=config["restart"], - maxiter=config["max_restart_cycles"], - rtol=rtol, - atol=config["atol"], - callback=count, - callback_type="pr_norm", - ) - if info != 0 or not np.all(np.isfinite(answer)): - raise ValueError("independent PyAMG-GMRES reference did not converge") - record = {"info": int(info), "inner_iterations": iterations} - if method == PYAMG_INEXACT_QUALIFICATION_METHOD: - # Diagnostic recomputation on the original operator, not the - # preconditioned callback norm or a convergence guarantee. - residual = _extended_residual(matrix, rhs, answer) - numerator = np.sqrt(np.sum(residual * residual, dtype=np.longdouble)) - extended_rhs = rhs.astype(np.longdouble) - denominator = np.sqrt( - np.sum(extended_rhs * extended_rhs, dtype=np.longdouble) - ) - relative = ( - float(numerator / denominator) - if denominator - else (0.0 if numerator == 0 else math.inf) - ) - if not math.isfinite(relative): - raise ValueError("reference solve residual diagnostic is not finite") - record.update( - rtol=rtol, - residual_method="extended-precision-residual-v1", - relative_l2_residual=relative, - ) - solves.append(record) - return answer - - reference = solve(system.public.b, config["rtol"]) - for _ in range(config["refinement_steps"]): - with np.errstate(over="ignore", invalid="ignore"): - residual = np.asarray( - _extended_residual(matrix, system.public.b, reference), dtype=np.float64 - ) - reference = reference + solve( - residual, config.get("refinement_rtol", config["rtol"]) - ) - if not np.all(np.isfinite(reference)): - raise ValueError("refined independent reference is not finite") - observed = accuracy_metrics(system, reference) - metrics = {} - for name, limit in QUALIFICATION_LIMITS.items(): - value = getattr(observed, name) - if value is None or not math.isfinite(value) or value > limit: - raise ValueError( - f"independent reference lacks the tenfold margin for {name}" - ) - metrics[name] = float(value) - formation = _extended_residual(matrix, system.public.b, system.x_star) - maximum = np.max(np.abs(formation), initial=np.longdouble(0)) - norm = np.max( - np.abs(system.public.b.astype(np.longdouble)), initial=np.longdouble(0) - ) - relative = float(maximum / norm) if norm else (0.0 if maximum == 0 else math.inf) - if not math.isfinite(relative): - raise ValueError("RHS formation diagnostic is not finite") - evidence = { - "method": method, - "qualified": True, - "system_sha256": system_digest(system), - "metrics": metrics, - "formation": { - "method": "extended-precision-residual-v1", - "relative_linf_residual": relative, - "verified_forward_bound": None, - }, - "uncertainty": "empirical-feasibility-only", - "reference_solver": { - "configuration": copy.deepcopy(config), - "implementation": { - "numpy": np.__version__, - "scipy": scipy.__version__, - "pyamg": pyamg.__version__, - }, - "solves": solves, - "hierarchy": [ - {"n": int(level.A.shape[0]), "nnz": int(level.A.nnz)} - for level in hierarchy.levels - ], - }, - } - - validate_qualification(evidence, system_digest(system)) - return evidence diff --git a/src/linear_solver_bench/runner.py b/src/linear_solver_bench/runner.py deleted file mode 100644 index 7d94a0b..0000000 --- a/src/linear_solver_bench/runner.py +++ /dev/null @@ -1,292 +0,0 @@ -"""Fresh-process local execution and report construction.""" - -from __future__ import annotations - -import hashlib -import json -import os -import pathlib -import statistics -import tempfile -from dataclasses import dataclass - -from .accuracy import ACCURACY_CONTRACT_ID -from .models import EvaluationSystem -from .process import ProcessTimeout, bounded_process -from .protocol import DriverOutput, decode_output, encode_input -from .verify import VerificationResult, verify_solution - -REPETITIONS = 3 -MAXIMUM_ITERATIONS = 10_000 -PROCESS_OVERHEAD_ALLOWANCE_S = 30.0 - - -@dataclass(frozen=True) -class RepeatResult: - index: int - outcome: str - process_returncode: int | None - process_wall_s: float | None - diagnostics: str - driver: DriverOutput | None - verification: VerificationResult | None - - @property - def passed(self) -> bool: - return ( - self.outcome == "completed" - and self.verification is not None - and self.verification.ok - ) - - def to_dict(self) -> dict[str, object]: - driver = None - if self.driver is not None: - driver = { - "status": self.driver.status, - "elapsed_s": self.driver.elapsed_s, - "create_s": self.driver.create_s, - "setup_s": self.driver.setup_s, - "solve_s": self.driver.solve_s, - "input_mutated": self.driver.input_mutated, - } - return { - "index": self.index, - "outcome": self.outcome, - "process_returncode": self.process_returncode, - "process_wall_s": self.process_wall_s, - "diagnostics": self.diagnostics, - "driver": driver, - "verification": self.verification.to_dict() if self.verification else None, - } - - -@dataclass(frozen=True) -class CaseResult: - case_id: str - n: int - nnz: int - tolerance: float - deadline_s: float - repetitions: tuple[RepeatResult, ...] - - @property - def passed(self) -> bool: - return len(self.repetitions) == REPETITIONS and all( - repeat.passed for repeat in self.repetitions - ) - - @property - def median_elapsed_s(self) -> float | None: - if not self.passed: - return None - return statistics.median( - repeat.driver.elapsed_s - for repeat in self.repetitions - if repeat.driver is not None - ) - - def to_dict(self) -> dict[str, object]: - return { - "case_id": self.case_id, - "n": self.n, - "nnz": self.nnz, - "tolerance": self.tolerance, - "deadline_s": self.deadline_s, - "passed": self.passed, - "median_elapsed_s": self.median_elapsed_s, - "repetitions": [repeat.to_dict() for repeat in self.repetitions], - } - - -def _diagnostics(payload: bytes) -> str: - return payload.decode("utf-8", errors="replace")[-8000:] - - -def run_repeat( - executable: pathlib.Path, - system: EvaluationSystem, - *, - index: int, - deadline_s: float, - maximum_iterations: int = MAXIMUM_ITERATIONS, - accuracy_contract_id: str = ACCURACY_CONTRACT_ID, -) -> RepeatResult: - with tempfile.TemporaryDirectory(prefix="lsb-case-") as temporary: - root = pathlib.Path(temporary) - input_path = root / "input.bin" - output_path = root / "output.bin" - input_path.write_bytes(encode_input(system.public)) - env = os.environ.copy() - env.update( - { - "OMP_NUM_THREADS": "1", - "OPENBLAS_NUM_THREADS": "1", - "MKL_NUM_THREADS": "1", - } - ) - command = ( - str(executable), - str(input_path), - str(output_path), - str(maximum_iterations), - format(deadline_s, ".17g"), - ) - try: - process = bounded_process( - command, - timeout_s=deadline_s + PROCESS_OVERHEAD_ALLOWANCE_S, - env=env, - ) - except ProcessTimeout as exc: - return RepeatResult( - index, - "timeout", - None, - deadline_s + PROCESS_OVERHEAD_ALLOWANCE_S, - _diagnostics(exc.output), - None, - None, - ) - if process.returncode == 124: - return RepeatResult( - index, - "timeout", - 124, - process.wall_s, - _diagnostics(process.output), - None, - None, - ) - if process.returncode != 0 or not output_path.is_file(): - return RepeatResult( - index, - "crash", - process.returncode, - process.wall_s, - _diagnostics(process.output), - None, - None, - ) - try: - driver = decode_output( - output_path.read_bytes(), expected_n=system.public.matrix.n - ) - except ValueError as exc: - return RepeatResult( - index, - "invalid_output", - process.returncode, - process.wall_s, - f"{_diagnostics(process.output)}\n{exc}".strip(), - None, - None, - ) - verification = verify_solution( - system, - driver.solution, - status=driver.status, - input_mutated=driver.input_mutated, - contract_id=accuracy_contract_id, - ) - return RepeatResult( - index, - "completed", - process.returncode, - process.wall_s, - _diagnostics(process.output), - driver, - verification, - ) - - -def evaluate_case( - executable: pathlib.Path, - system: EvaluationSystem, - *, - deadline_s: float, - repetitions: int = REPETITIONS, -) -> CaseResult: - if repetitions != REPETITIONS: - raise ValueError("v1 requires exactly three repetitions") - results = [] - for index in range(repetitions): - repeat = run_repeat(executable, system, index=index, deadline_s=deadline_s) - results.append(repeat) - if repeat.outcome in {"timeout", "crash", "invalid_output"}: - break - return CaseResult( - case_id=system.case_id, - n=system.public.matrix.n, - nnz=system.public.matrix.nnz, - tolerance=system.public.tolerance, - deadline_s=deadline_s, - repetitions=tuple(results), - ) - - -def build_report( - cases: tuple[CaseResult, ...], - *, - source_sha256: str, - executable_sha256: str, - runtime_manifest_sha256: str, - dataset_manifest_sha256: str, - prepared_manifest_sha256: str, - venue_id: str, -) -> dict[str, object]: - case_values = [case.to_dict() for case in cases] - body = { - "schema_version": 1, - "benchmark_id": "linear-solver-bench-cpu-v1", - "accuracy_contract_id": ACCURACY_CONTRACT_ID, - "source_sha256": source_sha256, - "executable_sha256": executable_sha256, - "runtime_manifest_sha256": runtime_manifest_sha256, - "dataset_manifest_sha256": dataset_manifest_sha256, - "prepared_manifest_sha256": prepared_manifest_sha256, - "venue_id": venue_id, - "case_count": len(cases), - "solved_count": sum(case.passed for case in cases), - "cases": case_values, - } - encoded = json.dumps(body, sort_keys=True, separators=(",", ":"), allow_nan=False) - return { - **body, - "report_sha256": hashlib.sha256(encoded.encode("ascii")).hexdigest(), - } - - -def evaluate( - executable: pathlib.Path, - systems: tuple[EvaluationSystem, ...], - *, - deadline_s: float = 120.0, - deadline_by_case: dict[str, float] | None = None, - source_sha256: str, - executable_sha256: str, - runtime_manifest_sha256: str, - dataset_manifest_sha256: str, - prepared_manifest_sha256: str, -) -> dict[str, object]: - cases = tuple( - evaluate_case( - executable, - system, - deadline_s=( - deadline_by_case[system.case_id] - if deadline_by_case is not None - else deadline_s - ), - ) - for system in systems - ) - return build_report( - cases, - source_sha256=source_sha256, - executable_sha256=executable_sha256, - runtime_manifest_sha256=runtime_manifest_sha256, - dataset_manifest_sha256=dataset_manifest_sha256, - prepared_manifest_sha256=prepared_manifest_sha256, - venue_id="local-uncontrolled", - ) diff --git a/src/linear_solver_bench/runtime.py b/src/linear_solver_bench/runtime.py deleted file mode 100644 index 1c1a7b2..0000000 --- a/src/linear_solver_bench/runtime.py +++ /dev/null @@ -1,316 +0,0 @@ -"""Build and validate the pinned, candidate-independent HYPRE runtime.""" - -from __future__ import annotations - -import hashlib -import json -import os -import pathlib -import shlex -import shutil -import tempfile -import uuid -from dataclasses import dataclass - -from .capabilities import KRYLOV_PRIMITIVE_ABI, KRYLOV_PRIMITIVE_SURFACE_SHA256 -from .compiler import FACTORY_EXPORT, sha256_file -from .paths import native_dir -from .process import bounded_process - -HYPRE_REPOSITORY = "https://github.com/hypre-space/hypre.git" -HYPRE_COMMIT = "e0ce7986941ce27111587ee7ea635ef7ca2eca94" -HYPRE_TREE = "c985eacc03d590011ab7652fe6fc4e9b25f1064d" -RUNTIME_SCHEMA = 1 -BUILD_KIND = "sequential-cpu-float64-int32-static" - - -@dataclass(frozen=True) -class Runtime: - root: pathlib.Path - manifest_sha256: str - include_dir: pathlib.Path - policy_header: pathlib.Path - hypre_library: pathlib.Path - driver_object: pathlib.Path - cc: str - cxx: str - nm: str - - -def _run( - command: tuple[str, ...], - *, - timeout_s: float = 900.0, - env: dict[str, str] | None = None, -) -> str: - result = bounded_process(command, timeout_s=timeout_s, env=env) - if result.returncode != 0: - output = result.output.decode("utf-8", errors="replace") - raise RuntimeError( - f"runtime command failed ({result.returncode}): {' '.join(command)}\n" - f"{output[-8000:]}" - ) - return result.output.decode("utf-8", errors="replace").strip() - - -def _tool(name: str) -> str: - path = shutil.which(name) - if path is None: - raise RuntimeError(f"required build tool is unavailable: {name}") - return path - - -def _tree_sha256(root: pathlib.Path) -> str: - digest = hashlib.sha256(b"linear-solver-bench-file-tree-v1\0") - files = sorted(path for path in root.rglob("*") if path.is_file()) - if not files: - raise ValueError(f"runtime file tree is empty: {root}") - for path in files: - if path.is_symlink(): - raise ValueError(f"runtime file tree contains a symlink: {path}") - relative = path.relative_to(root).as_posix().encode("utf-8") - digest.update(len(relative).to_bytes(4, "little")) - digest.update(relative) - digest.update(bytes.fromhex(sha256_file(path))) - return digest.hexdigest() - - -def _manifest_body(root: pathlib.Path, tools: dict[str, str]) -> dict[str, object]: - files = { - "policy_header": "include/nsl_hypre_solver.h", - "hypre_library": "lib/libHYPRE.a", - "driver_object": "trusted/driver.o", - } - return { - "schema_version": RUNTIME_SCHEMA, - "build_kind": BUILD_KIND, - "candidate_factory_export": FACTORY_EXPORT, - "hypre_repository": HYPRE_REPOSITORY, - "hypre_commit": HYPRE_COMMIT, - "hypre_tree": HYPRE_TREE, - "krylov_primitive_abi": KRYLOV_PRIMITIVE_ABI, - "krylov_primitive_surface_sha256": KRYLOV_PRIMITIVE_SURFACE_SHA256, - "include_tree_sha256": _tree_sha256(root / "include"), - "files": { - name: {"path": relpath, "sha256": sha256_file(root / relpath)} - for name, relpath in files.items() - }, - "tools": tools, - "tool_versions": { - name: _run((path, "--version"), timeout_s=30).splitlines()[0] - for name, path in tools.items() - }, - } - - -def build_runtime( - output: pathlib.Path, - *, - hypre_source: str = HYPRE_REPOSITORY, - jobs: int = 2, -) -> Runtime: - output = pathlib.Path(output).expanduser().resolve() - if output.exists(): - raise ValueError("runtime output already exists") - if type(jobs) is not int or jobs <= 0: - raise ValueError("jobs must be a positive integer") - tools = { - "cc": _tool("cc"), - "cxx": _tool("c++"), - "cmake": _tool("cmake"), - "git": _tool("git"), - "ninja": _tool("ninja"), - "nm": _tool("nm"), - } - output.parent.mkdir(parents=True, exist_ok=True) - staging = output.parent / f".{output.name}.{uuid.uuid4().hex}.tmp" - try: - with tempfile.TemporaryDirectory(prefix="lsb-runtime-") as temporary: - work = pathlib.Path(temporary) - source = work / "hypre" - build = work / "build" - install = work / "install" - _run((tools["git"], "clone", "--no-checkout", hypre_source, str(source))) - _run( - (tools["git"], "-C", str(source), "checkout", "--detach", HYPRE_COMMIT) - ) - commit = _run((tools["git"], "-C", str(source), "rev-parse", "HEAD")) - tree = _run((tools["git"], "-C", str(source), "rev-parse", "HEAD^{tree}")) - if commit != HYPRE_COMMIT or tree != HYPRE_TREE: - raise RuntimeError("HYPRE source identity mismatch") - # HYPRE error paths use __FILE__. Without normalization, random - # temporary directories alter the static library and invalidate - # an otherwise identical runtime's frozen reference calibration. - prefix_map = shlex.quote( - f"-ffile-prefix-map={work}=/linear-solver-bench/runtime" - ) - build_env = { - **os.environ, - "SOURCE_DATE_EPOCH": _run( - ( - tools["git"], - "-C", - str(source), - "show", - "-s", - "--format=%ct", - "HEAD", - ) - ), - "ZERO_AR_DATE": "1", - } - _run( - ( - tools["cmake"], - "-S", - str(source / "src"), - "-B", - str(build), - "-G", - "Ninja", - f"-DCMAKE_INSTALL_PREFIX={install}", - "-DCMAKE_BUILD_TYPE=Release", - f"-DCMAKE_C_FLAGS={prefix_map}", - f"-DCMAKE_CXX_FLAGS={prefix_map}", - "-DBUILD_SHARED_LIBS=OFF", - "-DHYPRE_ENABLE_MPI=OFF", - "-DHYPRE_ENABLE_OPENMP=OFF", - "-DHYPRE_ENABLE_CUDA=OFF", - "-DHYPRE_ENABLE_HIP=OFF", - "-DHYPRE_ENABLE_SYCL=OFF", - "-DHYPRE_ENABLE_SINGLE=OFF", - "-DHYPRE_ENABLE_LONG_DOUBLE=OFF", - "-DHYPRE_ENABLE_BIGINT=OFF", - "-DHYPRE_ENABLE_MIXEDINT=OFF", - "-DHYPRE_BUILD_EXAMPLES=OFF", - "-DHYPRE_BUILD_TESTS=OFF", - ), - env=build_env, - ) - _run( - ( - tools["cmake"], - "--build", - str(build), - "--target", - "install", - "--parallel", - str(jobs), - ), - env=build_env, - ) - libraries = tuple(install.rglob("libHYPRE.a")) - if len(libraries) != 1: - raise RuntimeError( - "pinned HYPRE build did not produce one static library" - ) - - (staging / "include").mkdir(parents=True) - (staging / "lib").mkdir() - (staging / "trusted").mkdir() - for header in (install / "include").iterdir(): - if header.is_file(): - shutil.copy2(header, staging / "include" / header.name) - shutil.copy2( - native_dir() / "include" / "nsl_hypre_solver.h", - staging / "include" / "nsl_hypre_solver.h", - ) - shutil.copy2(libraries[0], staging / "lib" / "libHYPRE.a") - _run( - ( - tools["cxx"], - "-std=c++17", - "-O3", - "-DNDEBUG", - "-fno-omit-frame-pointer", - f"-ffile-prefix-map={native_dir()}=/linear-solver-bench/native", - f"-ffile-prefix-map={staging}=/linear-solver-bench/runtime", - "-I", - str(staging / "include"), - "-c", - str(native_dir() / "src" / "driver.cpp"), - "-o", - str(staging / "trusted" / "driver.o"), - ), - env=build_env, - ) - body = _manifest_body(staging, tools) - body_json = json.dumps( - body, sort_keys=True, separators=(",", ":"), allow_nan=False - ) - manifest = { - **body, - "manifest_sha256": hashlib.sha256(body_json.encode("ascii")).hexdigest(), - } - (staging / "manifest.json").write_text( - json.dumps(manifest, indent=2, sort_keys=True) + "\n", - encoding="utf-8", - ) - os.replace(staging, output) - finally: - if staging.exists(): - shutil.rmtree(staging) - return load_runtime(output) - - -def load_runtime(root: pathlib.Path) -> Runtime: - root = pathlib.Path(root).expanduser().resolve() - try: - manifest = json.loads((root / "manifest.json").read_text(encoding="utf-8")) - except (OSError, UnicodeDecodeError, json.JSONDecodeError) as exc: - raise ValueError("runtime manifest is unreadable") from exc - if not isinstance(manifest, dict) or manifest.get("schema_version") != 1: - raise ValueError("runtime manifest schema mismatch") - digest = manifest.get("manifest_sha256") - body = {key: value for key, value in manifest.items() if key != "manifest_sha256"} - encoded = json.dumps(body, sort_keys=True, separators=(",", ":"), allow_nan=False) - observed = hashlib.sha256(encoded.encode("ascii")).hexdigest() - if digest != observed: - raise ValueError("runtime manifest digest mismatch") - if manifest.get("candidate_factory_export") != FACTORY_EXPORT: - raise ValueError( - f"runtime must be rebuilt for the {FACTORY_EXPORT} candidate entry point" - ) - if ( - manifest.get("hypre_commit") != HYPRE_COMMIT - or manifest.get("hypre_tree") != HYPRE_TREE - or manifest.get("build_kind") != BUILD_KIND - or manifest.get("krylov_primitive_abi") != KRYLOV_PRIMITIVE_ABI - or manifest.get("krylov_primitive_surface_sha256") - != KRYLOV_PRIMITIVE_SURFACE_SHA256 - or manifest.get("include_tree_sha256") != _tree_sha256(root / "include") - ): - raise ValueError("runtime scientific identity mismatch") - expected_files = { - "policy_header": "include/nsl_hypre_solver.h", - "hypre_library": "lib/libHYPRE.a", - "driver_object": "trusted/driver.o", - } - paths = {} - for name, record in manifest.get("files", {}).items(): - if name not in expected_files or record.get("path") != expected_files[name]: - raise ValueError(f"runtime file path mismatch: {name}") - path = root / expected_files[name] - if ( - not path.is_file() - or path.is_symlink() - or sha256_file(path) != record["sha256"] - ): - raise ValueError(f"runtime file identity mismatch: {name}") - paths[name] = path - tools = manifest.get("tools", {}) - if set(paths) != {"policy_header", "hypre_library", "driver_object"} or not all( - isinstance(tools.get(name), str) for name in ("cc", "cxx", "nm") - ): - raise ValueError("runtime manifest is incomplete") - return Runtime( - root=root, - manifest_sha256=str(digest), - include_dir=root / "include", - policy_header=paths["policy_header"], - hypre_library=paths["hypre_library"], - driver_object=paths["driver_object"], - cc=tools["cc"], - cxx=tools["cxx"], - nm=tools["nm"], - ) diff --git a/src/linear_solver_bench/scoring.py b/src/linear_solver_bench/scoring.py deleted file mode 100644 index e9ba601..0000000 --- a/src/linear_solver_bench/scoring.py +++ /dev/null @@ -1,236 +0,0 @@ -"""Calibration artifacts and the lexicographic v1 leaderboard score.""" - -from __future__ import annotations - -import hashlib -import json -import math -from collections.abc import Mapping - -from .accuracy import ACCURACY_CONTRACT_ID - - -def _digest(value: object) -> str: - encoded = json.dumps(value, sort_keys=True, separators=(",", ":"), allow_nan=False) - return hashlib.sha256(encoded.encode("ascii")).hexdigest() - - -def validate_report(report: Mapping[str, object]) -> None: - digest = report.get("report_sha256") - body = {key: value for key, value in report.items() if key != "report_sha256"} - if digest != _digest(body): - raise ValueError("evaluation report digest mismatch") - cases = report.get("cases") - if ( - report.get("schema_version") != 1 - or report.get("benchmark_id") != "linear-solver-bench-cpu-v1" - or report.get("accuracy_contract_id") != ACCURACY_CONTRACT_ID - or report.get("venue_id") - not in { - "local-uncontrolled", - "modal-sandbox-cpu-v1", - } - or not isinstance(cases, list) - or not cases - or report.get("case_count") != len(cases) - ): - raise ValueError("evaluation report case count mismatch") - case_ids = [] - for case in cases: - if not isinstance(case, Mapping) or not isinstance(case.get("case_id"), str): - raise ValueError("evaluation report contains an invalid case") - case_ids.append(case["case_id"]) - if case.get("passed") is True: - elapsed = float(case.get("median_elapsed_s", 0.0)) - if not math.isfinite(elapsed) or elapsed <= 0.0: - raise ValueError("evaluation report contains an invalid timing") - if len(set(case_ids)) != len(case_ids): - raise ValueError("evaluation report contains duplicate cases") - identity_fields = ( - "source_sha256", - "executable_sha256", - "runtime_manifest_sha256", - "dataset_manifest_sha256", - "prepared_manifest_sha256", - ) - if not all( - isinstance(report.get(key), str) and report[key] for key in identity_fields - ): - raise ValueError("evaluation report identity is incomplete") - solved = sum(case.get("passed") is True for case in cases) - if report.get("solved_count") != solved: - raise ValueError("evaluation report solved count mismatch") - - -def calibration_from_report( - report: Mapping[str, object], - *, - deadline_multiplier: float = 4.0, - minimum_deadline_s: float = 1.0, - maximum_deadline_s: float = 120.0, -) -> dict[str, object]: - validate_report(report) - if not 1.0 < deadline_multiplier <= 10.0: - raise ValueError("deadline multiplier must lie in (1, 10]") - if ( - not math.isfinite(minimum_deadline_s) - or not math.isfinite(maximum_deadline_s) - or minimum_deadline_s <= 0.0 - or maximum_deadline_s < minimum_deadline_s - or maximum_deadline_s > 120.0 - ): - raise ValueError("calibration deadline bounds are invalid") - cases = [] - for index, case in enumerate(report["cases"]): - if case.get("passed") is not True: - raise ValueError("calibration reference must solve every case") - reference = float(case["median_elapsed_s"]) - if not math.isfinite(reference) or reference <= 0.0: - raise ValueError("calibration reference time is invalid") - deadline = min( - maximum_deadline_s, - max(minimum_deadline_s, deadline_multiplier * reference), - ) - cases.append( - { - "index": index, - "case_id": case["case_id"], - "reference_median_s": reference, - "deadline_s": deadline, - } - ) - body = { - "schema_version": 1, - "kind": "linear-solver-bench-calibration-v1", - "reference_report_sha256": report["report_sha256"], - "venue_id": report["venue_id"], - "runtime_manifest_sha256": report["runtime_manifest_sha256"], - "dataset_manifest_sha256": report["dataset_manifest_sha256"], - "prepared_manifest_sha256": report["prepared_manifest_sha256"], - "deadline_multiplier": deadline_multiplier, - "minimum_deadline_s": minimum_deadline_s, - "maximum_deadline_s": maximum_deadline_s, - "case_count": len(cases), - "cases": cases, - } - return {**body, "calibration_sha256": _digest(body)} - - -def validate_calibration(value: Mapping[str, object]) -> None: - digest = value.get("calibration_sha256") - body = {key: item for key, item in value.items() if key != "calibration_sha256"} - if digest != _digest(body): - raise ValueError("calibration digest mismatch") - cases = value.get("cases") - if ( - value.get("schema_version") != 1 - or value.get("kind") != "linear-solver-bench-calibration-v1" - or value.get("venue_id") not in {"local-uncontrolled", "modal-sandbox-cpu-v1"} - or not isinstance(cases, list) - or not cases - or value.get("case_count") != len(cases) - ): - raise ValueError("calibration case count mismatch") - minimum = float(value.get("minimum_deadline_s", 0.0)) - maximum = float(value.get("maximum_deadline_s", 0.0)) - multiplier = float(value.get("deadline_multiplier", 0.0)) - if ( - not math.isfinite(minimum) - or not math.isfinite(maximum) - or not math.isfinite(multiplier) - or minimum <= 0.0 - or maximum < minimum - or maximum > 120.0 - or not 1.0 < multiplier <= 10.0 - ): - raise ValueError("calibration bounds are invalid") - case_ids = [] - for index, case in enumerate(cases): - if ( - not isinstance(case, Mapping) - or case.get("index") != index - or not isinstance(case.get("case_id"), str) - ): - raise ValueError("calibration contains an invalid case") - reference = float(case.get("reference_median_s", 0.0)) - deadline = float(case.get("deadline_s", 0.0)) - if ( - not math.isfinite(reference) - or not math.isfinite(deadline) - or reference <= 0.0 - or deadline <= 0.0 - or not minimum <= deadline <= maximum - ): - raise ValueError("calibration contains an invalid timing") - case_ids.append(case["case_id"]) - if len(set(case_ids)) != len(case_ids): - raise ValueError("calibration contains duplicate cases") - for key in ( - "reference_report_sha256", - "runtime_manifest_sha256", - "dataset_manifest_sha256", - "prepared_manifest_sha256", - ): - if not isinstance(value.get(key), str) or not value[key]: - raise ValueError("calibration identity is incomplete") - - -def score_report( - report: Mapping[str, object], calibration: Mapping[str, object] -) -> dict[str, object]: - validate_report(report) - validate_calibration(calibration) - report_cases = report["cases"] - calibration_cases = calibration["cases"] - for key in ( - "venue_id", - "runtime_manifest_sha256", - "dataset_manifest_sha256", - "prepared_manifest_sha256", - ): - if report.get(key) != calibration.get(key): - raise ValueError(f"report and calibration {key} differ") - if len(report_cases) != len(calibration_cases): - raise ValueError("report and calibration case counts differ") - ratios = [] - solved = 0 - per_case = [] - for index, (case, reference) in enumerate( - zip(report_cases, calibration_cases, strict=True) - ): - if ( - case.get("case_id") != reference.get("case_id") - or reference.get("index") != index - ): - raise ValueError("report and calibration case order differs") - reference_s = float(reference["reference_median_s"]) - deadline_s = float(reference["deadline_s"]) - if case.get("passed") is True: - cost_s = float(case["median_elapsed_s"]) - if not math.isfinite(cost_s) or cost_s <= 0.0: - raise ValueError("report contains an invalid passing time") - solved += 1 - else: - cost_s = 2.0 * deadline_s - ratio = cost_s / reference_s - ratios.append(ratio) - per_case.append( - { - "index": index, - "case_id": case["case_id"], - "passed": case.get("passed") is True, - "normalized_cost": ratio, - } - ) - penalized_geomean = math.exp(sum(math.log(value) for value in ratios) / len(ratios)) - return { - "schema_version": 1, - "benchmark_id": report["benchmark_id"], - "report_sha256": report["report_sha256"], - "calibration_sha256": calibration["calibration_sha256"], - "solved_count": solved, - "case_count": len(ratios), - "penalized_geomean": penalized_geomean, - "ranking_key": [-solved, penalized_geomean], - "cases": per_case, - } diff --git a/src/linear_solver_bench/sources/__init__.py b/src/linear_solver_bench/sources/__init__.py deleted file mode 100644 index 0ee3633..0000000 --- a/src/linear_solver_bench/sources/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Public, pinned numerical-data sources; never imported by candidate code.""" diff --git a/src/linear_solver_bench/sources/download.py b/src/linear_solver_bench/sources/download.py deleted file mode 100644 index f893fbf..0000000 --- a/src/linear_solver_bench/sources/download.py +++ /dev/null @@ -1,123 +0,0 @@ -"""Bounded, resumable downloads authenticated by frozen content hashes.""" - -from __future__ import annotations - -import os -import pathlib -import urllib.error -import urllib.request -from collections.abc import Mapping -from urllib.parse import urlsplit - -from ..dataset import sha256_file -from ..manifests import digest, https_url, positive_int - -MAX_DOWNLOAD_BYTES = 8 * 1024**3 - - -class _SourceRedirect(urllib.request.HTTPRedirectHandler): - def __init__(self, *, suitesparse: bool = False): - super().__init__() - self.suitesparse = suitesparse - - def redirect_request(self, req, fp, code, msg, headers, newurl): - # SuiteSparse's public archive service uses HTTP redirects. Only that - # known service may downgrade; the frozen SHA-256 authenticates its bytes. - # Hugging Face and its signed storage redirects must remain HTTPS. - redirect = urlsplit(newurl) - allowed_http = ( - self.suitesparse - and redirect.scheme == "http" - and redirect.hostname - in { - "sparse.tamu.edu", - "suitesparse-collection-website.herokuapp.com", - "sparse-files.engr.tamu.edu", - } - ) - if ( - redirect.username - or redirect.password - or redirect.port not in {None, 80 if allowed_http else 443} - ): - raise ValueError("invalid data redirect authority") - if redirect.scheme != "https" and not allowed_http: - raise ValueError("data downloads must not redirect outside HTTPS") - return super().redirect_request(req, fp, code, msg, headers, newurl) - - -def fetch_archive( - source: Mapping[str, object], cache: pathlib.Path, *, offline: bool = False -) -> pathlib.Path: - """Return verified bytes; interrupted downloads retain a resumable .part file.""" - url = https_url(source["url"]) - expected_hash = digest(source["archive_sha256"], "archive_sha256") - expected_size = positive_int(source["archive_bytes"], "archive_bytes") - if expected_size > MAX_DOWNLOAD_BYTES: - raise ValueError("archive exceeds the download limit") - cache = pathlib.Path(cache) - cache.mkdir(parents=True, exist_ok=True) - target = cache / expected_hash - if target.is_file(): - if ( - target.stat().st_size == expected_size - and sha256_file(target) == expected_hash - ): - return target - if offline: - raise ValueError("cached archive has invalid size or digest") - target.unlink() - if offline: - raise ValueError(f"archive {expected_hash} is unavailable in the offline cache") - partial = cache / f"{expected_hash}.part" - offset = partial.stat().st_size if partial.is_file() else 0 - if offset > expected_size: - partial.unlink() - offset = 0 - if offset == expected_size: - if sha256_file(partial) == expected_hash: - os.replace(partial, target) - return target - partial.unlink() - offset = 0 - headers = {"User-Agent": "linear-solver-bench/0.2", "Accept-Encoding": "identity"} - if offset: - headers["Range"] = f"bytes={offset}-" - request = urllib.request.Request(url, headers=headers) - opener = urllib.request.build_opener( - _SourceRedirect(suitesparse=source.get("kind") == "suitesparse") - ) - try: - with opener.open(request, timeout=120) as response: - status = response.status - if status == 206: - expected_range = f"bytes {offset}-{expected_size - 1}/{expected_size}" - if response.headers.get("Content-Range") != expected_range: - raise ValueError("download response has an inconsistent byte range") - elif status == 200: - offset = 0 # Servers may ignore Range; restart the local file. - else: - raise ValueError("unexpected archive download status") - declared = response.headers.get("Content-Length") - if declared is not None and int(declared) != expected_size - offset: - raise ValueError("download size disagrees with the release manifest") - with partial.open("ab" if offset else "wb") as handle: - total = offset - while block := response.read( - min(1024 * 1024, expected_size - total + 1) - ): - total += len(block) - if total > expected_size: - raise ValueError("download exceeds its declared size") - handle.write(block) - if total != expected_size: - raise ValueError("archive download is incomplete; partial data retained") - if sha256_file(partial) != expected_hash: - partial.unlink(missing_ok=True) - raise ValueError("downloaded archive digest mismatch") - os.replace(partial, target) - return target - except (OSError, urllib.error.URLError) as exc: - raise ValueError( - "archive download failed; partial data retained for resumption" - ) from exc diff --git a/src/linear_solver_bench/sources/flash.py b/src/linear_solver_bench/sources/flash.py deleted file mode 100644 index ff974c8..0000000 --- a/src/linear_solver_bench/sources/flash.py +++ /dev/null @@ -1,226 +0,0 @@ -"""Read the four-file public FLASH replay format and preserve captured arrays.""" - -from __future__ import annotations - -import hashlib -import io -import json -import pathlib -import stat -import tarfile -import zipfile -from collections.abc import Mapping - -import numpy as np - -from ..manifests import strict_object -from ..models import CsrMatrix, EvaluationSystem, MatrixInput -from ..workloads import matrix_digest, vector_digest - -CASE_FILES = {"matrix.npz", "b.npy", "x0.npy", "case.json"} - - -def _load_npy(payload: bytes, shape: tuple[int, ...], kinds: set[str]) -> np.ndarray: - """Reject deceptive NPY shapes before NumPy can allocate their arrays.""" - handle = io.BytesIO(payload) - version = np.lib.format.read_magic(handle) - if version == (1, 0): - actual, _, dtype = np.lib.format.read_array_header_1_0( - handle, max_header_size=4096 - ) - elif version == (2, 0): - actual, _, dtype = np.lib.format.read_array_header_2_0( - handle, max_header_size=4096 - ) - else: - raise ValueError("unsupported public NPY format version") - if actual != shape or dtype.hasobject or dtype.kind not in kinds: - raise ValueError("public array shape or dtype disagrees with its case") - count = 1 - for dimension in actual: - count *= dimension - if len(payload) - handle.tell() != count * dtype.itemsize: - raise ValueError("public array byte count disagrees with its header") - return np.load(io.BytesIO(payload), allow_pickle=False, max_header_size=4096) - - -def _archive_files(path: pathlib.Path, case: Mapping[str, object]) -> dict[str, bytes]: - limits = { - "matrix.npz": 32 * case["nnz"] + 16 * (case["n"] + 1) + 65536, - "b.npy": 8 * case["n"] + 4096, - "x0.npy": 8 * case["n"] + 4096, - "case.json": 65536, - } - result = {} - try: - if zipfile.is_zipfile(path): - with zipfile.ZipFile(path) as archive: - members = archive.infolist() - if len(members) != len(CASE_FILES): - raise ValueError("FLASH archive must contain exactly four files") - for member in members: - mode = member.external_attr >> 16 - if ( - member.filename not in CASE_FILES - or member.filename in result - or member.is_dir() - or stat.S_ISLNK(mode) - or member.file_size > limits[member.filename] - ): - raise ValueError("unsafe FLASH archive member") - result[member.filename] = archive.read(member) - else: - with tarfile.open(path, "r:gz") as archive: - for member in archive: - if ( - member.name not in CASE_FILES - or member.name in result - or not member.isfile() - or member.size > limits[member.name] - or member.size < 0 - ): - raise ValueError("unsafe FLASH archive member") - handle = archive.extractfile(member) - if handle is None: - raise ValueError("FLASH archive member is unreadable") - with handle: - result[member.name] = handle.read(limits[member.name] + 1) - except (OSError, tarfile.TarError, zipfile.BadZipFile) as exc: - raise ValueError("FLASH case archive is unreadable") from exc - if set(result) != CASE_FILES: - raise ValueError( - "FLASH archive must contain exactly the four public case files" - ) - return result - - -def _load_matrix(payload: bytes, n: int, nnz: int) -> CsrMatrix: - # NPZ itself is compressed: validate its inner members before NumPy allocates - # arrays. The outer archive limit alone does not bound decompression. - limits = { - "data.npy": nnz * 8 + 4096, - "indices.npy": nnz * 8 + 4096, - "indptr.npy": (n + 1) * 8 + 4096, - "format.npy": 4096, - "shape.npy": 4096, - "_is_array.npy": 4096, - } - try: - with zipfile.ZipFile(io.BytesIO(payload)) as inner: - names = set() - arrays = {} - for info in inner.infolist(): - if ( - info.filename not in limits - or info.filename in names - or info.file_size > limits[info.filename] - ): - raise ValueError("unsafe compressed matrix member") - names.add(info.filename) - arrays[info.filename] = inner.read(info) - if not { - "data.npy", - "indices.npy", - "indptr.npy", - "format.npy", - "shape.npy", - }.issubset(names): - raise ValueError("FLASH matrix archive is incomplete") - except (OSError, zipfile.BadZipFile) as exc: - raise ValueError("FLASH matrix archive is unreadable") from exc - values = _load_npy(arrays["data.npy"], (nnz,), {"f"}) - columns = _load_npy(arrays["indices.npy"], (nnz,), {"i", "u"}) - rows = _load_npy(arrays["indptr.npy"], (n + 1,), {"i", "u"}) - shape = _load_npy(arrays["shape.npy"], (2,), {"i", "u"}) - form = _load_npy(arrays["format.npy"], (), {"S"}) - if "_is_array.npy" in arrays: - _load_npy(arrays["_is_array.npy"], (), {"b"}) - if tuple(shape) != (n, n) or form.item() != b"csr" or values.dtype != np.float64: - raise ValueError( - "FLASH matrix must be float64 CSR with the declared dimensions" - ) - if ( - np.any(columns < 0) - or np.any(columns >= n) - or np.any(rows < 0) - or np.any(rows > nnz) - ): - raise ValueError("FLASH CSR index lies outside the declared dimensions") - if np.any(values == 0): - raise ValueError( - "FLASH matrix must already be canonical with no explicit zeros" - ) - return CsrMatrix( - n=n, - row_offsets=np.asarray(rows, dtype=np.uint64), - column_indices=np.asarray(columns, dtype=np.uint32), - values=values, - ) - - -def load_flash(path: pathlib.Path, case: Mapping[str, object]) -> EvaluationSystem: - files = _archive_files(path, case) - try: - metadata = json.loads(files["case.json"].decode("utf-8")) - except (UnicodeError, json.JSONDecodeError) as exc: - raise ValueError("FLASH case metadata is unreadable") from exc - strict_object( - metadata, - { - "schema_version", - "case_id", - "family", - "capture_kind", - "n", - "nnz", - "relative_tolerance", - "absolute_tolerance", - "provenance_group", - "source_run", - "step", - "solve_index", - "matrix_sha256", - "b_sha256", - "x0_sha256", - "files", - }, - "FLASH case metadata", - ) - expected = { - "schema_version": 1, - "case_id": case["case_id"], - "family": "magnetic_diffusion_flash", - "capture_kind": "scalar-magnetic-diffusion", - "n": case["n"], - "nnz": case["nnz"], - "relative_tolerance": case["tolerance"], - "absolute_tolerance": 0, - "provenance_group": case["provenance_group"], - "source_run": case["source_run"], - "step": case["admission"]["step"], - "solve_index": case["admission"]["solve_index"], - "matrix_sha256": case["source"]["matrix_sha256"], - "b_sha256": case["source"]["b_sha256"], - "x0_sha256": case["source"]["x0_sha256"], - } - if any(metadata[key] != value for key, value in expected.items()): - raise ValueError("FLASH case metadata disagrees with the frozen release") - strict_object( - metadata["files"], {"matrix.npz", "b.npy", "x0.npy"}, "FLASH member digests" - ) - for name, claimed in metadata["files"].items(): - if hashlib.sha256(files[name]).hexdigest() != claimed: - raise ValueError("FLASH file digest mismatch") - matrix = _load_matrix(files["matrix.npz"], case["n"], case["nnz"]) - b = _load_npy(files["b.npy"], (case["n"],), {"f"}) - x0 = _load_npy(files["x0.npy"], (case["n"],), {"f"}) - public = MatrixInput(matrix=matrix, b=b, x0=x0, tolerance=case["tolerance"]) - if ( - matrix_digest(matrix) != metadata["matrix_sha256"] - or vector_digest(public.b) != metadata["b_sha256"] - or vector_digest(public.x0) != metadata["x0_sha256"] - ): - raise ValueError("FLASH canonical numerical digest mismatch") - return EvaluationSystem( - case_id=case["case_id"], public=public, x_star=None, reference_kind="none" - ) diff --git a/src/linear_solver_bench/sources/suitesparse.py b/src/linear_solver_bench/sources/suitesparse.py deleted file mode 100644 index 0fe474f..0000000 --- a/src/linear_solver_bench/sources/suitesparse.py +++ /dev/null @@ -1,99 +0,0 @@ -"""Read original SuiteSparse Matrix Market operators without changing the problem.""" - -from __future__ import annotations - -import pathlib -import tarfile -from collections.abc import Mapping - -import numpy as np -from scipy import sparse -from scipy.io import mmread - -from ..models import CsrMatrix -from ..workloads import matrix_digest - -MAX_MEMBERS = 128 -MAX_EXPANDED_BYTES = 8 * 1024**3 - - -def load_suitesparse(path: pathlib.Path, case: Mapping[str, object]) -> CsrMatrix: - """Validate member paths, declared dimensions, and canonical matrix identity.""" - source = case["source"] - expected = f"{source['name']}/{source['name']}.mtx" - matrix = None - seen = set() - expanded = 0 - try: - with tarfile.open(path, "r:gz") as archive: - for count, member in enumerate(archive, start=1): - name = pathlib.PurePosixPath(member.name) - expanded += member.size - if ( - count > MAX_MEMBERS - or expanded > MAX_EXPANDED_BYTES - or member.size > 4 * 1024**3 - or member.size < 0 - or name.is_absolute() - or not name.parts - or ".." in name.parts - or member.name in seen - or not (member.isfile() or member.isdir()) - ): - raise ValueError("unsafe SuiteSparse archive member") - seen.add(member.name) - if member.name != expected: - continue - if not member.isfile(): - raise ValueError("expected Matrix Market member is not a file") - handle = archive.extractfile(member) - if handle is None: - raise ValueError("Matrix Market member is unreadable") - with handle: - header = handle.readline(1024).decode("ascii").lower().split() - if ( - len(header) != 5 - or header[:3] != ["%%matrixmarket", "matrix", "coordinate"] - or header[3] not in {"real", "integer", "pattern"} - ): - raise ValueError( - "SuiteSparse input must be a real sparse coordinate matrix" - ) - line = handle.readline(4096) - while line.startswith(b"%"): - line = handle.readline(4096) - dimensions = [int(item) for item in line.split()] - if ( - len(dimensions) != 3 - or dimensions[:2] != [case["n"], case["n"]] - or not 0 < dimensions[2] <= member.size // 3 - ): - raise ValueError( - "Matrix Market dimensions disagree with the declared case" - ) - handle.seek(0) - loaded = mmread(handle) - if not sparse.issparse(loaded) or np.iscomplexobj(loaded.data): - raise ValueError("SuiteSparse input must be real and sparse") - matrix = CsrMatrix.from_scipy(loaded) - except (OSError, tarfile.TarError, UnicodeError) as exc: - raise ValueError("SuiteSparse archive is unreadable") from exc - if matrix is None: - raise ValueError("original Matrix Market operator is missing from archive") - if ( - matrix.n != case["n"] - or matrix.nnz != case["nnz"] - or matrix_digest(matrix) != source["matrix_sha256"] - ): - raise ValueError( - "canonical matrix disagrees with the release digest or dimensions" - ) - # Scientific provenance is admitted in the public manifest. This independent - # numerical check prevents symmetric operators being relabelled NS mesh. - operator = matrix.to_scipy() - scale = float(np.max(np.abs(operator.data), initial=0)) - difference = operator - operator.T - asymmetry = float(np.max(np.abs(difference.data), initial=0)) - if scale == 0 or asymmetry <= 1e-12 * scale: - raise ValueError("NS mesh requires a materially nonsymmetric operator") - return matrix diff --git a/src/linear_solver_bench/submission.py b/src/linear_solver_bench/submission.py new file mode 100644 index 0000000..2b2605e --- /dev/null +++ b/src/linear_solver_bench/submission.py @@ -0,0 +1,116 @@ +"""Create and check the small pull-request submission bundle.""" + +from __future__ import annotations + +import hashlib +import json +import pathlib +import re + + +def _safe_name(value: object) -> str: + if not isinstance(value, str) or not re.fullmatch( + r"[a-z0-9][a-z0-9_-]{1,63}", value + ): + raise ValueError("name must use 2-64 lowercase letters, digits, '-' or '_'") + return value + + +def package( + source: str | pathlib.Path, + output: str | pathlib.Path, + *, + name: str, + families: list[str], + license_name: str, + submitter: str, +) -> dict[str, object]: + from .build import CANDIDATE_ABI, validate_source + + name = _safe_name(name) + if ( + not families + or not all(isinstance(family, str) for family in families) + or set(families) - {"ns_mesh_pde", "flash"} + ): + raise ValueError("select ns_mesh_pde, flash, or both") + if not isinstance(submitter, str) or not submitter.strip(): + raise ValueError("submitter must be nonempty text") + if not isinstance(license_name, str) or not license_name.strip(): + raise ValueError("license must be nonempty text") + validate_source(pathlib.Path(source)) + payload = pathlib.Path(source).read_bytes() + destination = pathlib.Path(output) + if destination.exists(): + raise ValueError(f"submission directory already exists: {destination}") + destination.mkdir(parents=True) + metadata = { + "schema_version": 1, + "name": name, + "submitter": submitter.strip(), + "license": license_name.strip(), + "families": sorted(set(families)), + "source": "solver.c", + "source_sha256": hashlib.sha256(payload).hexdigest(), + "candidate_abi": CANDIDATE_ABI, + } + (destination / "solver.c").write_bytes(payload) + (destination / "submission.json").write_text( + json.dumps(metadata, indent=2, sort_keys=True) + "\n", encoding="utf-8" + ) + (destination / "README.md").write_text( + f"# {name}\n\nDescribe the solver and how it was developed.\n", + encoding="utf-8", + ) + check(destination) + return metadata + + +def check(root: str | pathlib.Path) -> dict[str, object]: + from .build import CANDIDATE_ABI, validate_source + + root = pathlib.Path(root) + if not root.is_dir() or {path.name for path in root.iterdir()} != { + "README.md", + "solver.c", + "submission.json", + }: + raise ValueError( + "a submission must contain README.md, solver.c, submission.json" + ) + metadata = json.loads((root / "submission.json").read_text(encoding="utf-8")) + expected = { + "schema_version", + "name", + "submitter", + "license", + "families", + "source", + "source_sha256", + "candidate_abi", + } + if not isinstance(metadata, dict) or set(metadata) != expected: + raise ValueError("submission metadata fields are invalid") + _safe_name(metadata["name"]) + families = metadata["families"] + if ( + metadata["schema_version"] != 1 + or metadata["source"] != "solver.c" + or metadata["candidate_abi"] != CANDIDATE_ABI + or not isinstance(metadata["submitter"], str) + or not metadata["submitter"].strip() + or not isinstance(metadata["license"], str) + or not metadata["license"].strip() + or not isinstance(families, list) + or not families + or not all(isinstance(family, str) for family in families) + or set(families) - {"ns_mesh_pde", "flash"} + ): + raise ValueError("submission metadata is invalid") + source = root / "solver.c" + validate_source(source) + if hashlib.sha256(source.read_bytes()).hexdigest() != metadata["source_sha256"]: + raise ValueError("solver.c differs from its recorded hash") + if not (root / "README.md").read_text(encoding="utf-8").strip(): + raise ValueError("submission README is empty") + return metadata diff --git a/src/linear_solver_bench/verify.py b/src/linear_solver_bench/verify.py index 7f40b84..0a782d4 100644 --- a/src/linear_solver_bench/verify.py +++ b/src/linear_solver_bench/verify.py @@ -1,222 +1,208 @@ -"""Independent terminal metrics and explicit, fail-closed acceptance gates.""" +"""Decode native output and independently check a returned solution.""" from __future__ import annotations import math -from dataclasses import asdict, dataclass, field +import struct +from dataclasses import asdict, dataclass import numpy as np -from .accuracy import ACCURACY_CONTRACT_ID, AccuracyThresholds -from .models import EvaluationSystem +from .models import BenchmarkCase, RawExecution + +OUTPUT_MAGIC = b"LSBOUT01" +OUTPUT_HEADER = struct.Struct("<8sIiQQQQQB7x") +FLOAT64_EPSILON = float(np.finfo(np.float64).eps) @dataclass(frozen=True) -class AccuracyMetrics: - normwise_backward_error: float | None - componentwise_backward_error: float | None +class Metrics: + normwise_backward_error: float + componentwise_backward_error: float + relative_residual: float relative_l2_forward_error: float | None relative_linf_forward_error: float | None - relative_residual: float | None = None - diagnostic_reasons: dict[str, str] = field(default_factory=dict) - - @property - def forward_error(self) -> float | None: - if ( - self.relative_l2_forward_error is None - or self.relative_linf_forward_error is None - ): - return None - return max(self.relative_l2_forward_error, self.relative_linf_forward_error) @dataclass(frozen=True) -class VerificationResult: - ok: bool +class RunResult: + passed: bool + elapsed_seconds: float | None failure: str | None - metrics: AccuracyMetrics | None - thresholds: AccuracyThresholds - contract_id: str = ACCURACY_CONTRACT_ID + process_returncode: int + process_wall_seconds: float + status: int | None = None + create_seconds: float | None = None + setup_seconds: float | None = None + solve_seconds: float | None = None + input_mutated: bool | None = None + metrics: Metrics | None = None + diagnostics: str = "" def to_dict(self) -> dict[str, object]: - metrics = asdict(self.metrics) if self.metrics else None - thresholds = asdict(self.thresholds) - if self.contract_id == ACCURACY_CONTRACT_ID: - # Keep the established v1 report shape for existing callers. - thresholds.pop("relative_residual") - if metrics is not None: - metrics.pop("relative_residual") - metrics.pop("diagnostic_reasons") - result = { - "ok": self.ok, + return { + "passed": self.passed, + "elapsed_seconds": self.elapsed_seconds, "failure": self.failure, - "metrics": metrics, - "thresholds": thresholds, + "process_returncode": self.process_returncode, + "process_wall_seconds": self.process_wall_seconds, + "status": self.status, + "create_seconds": self.create_seconds, + "setup_seconds": self.setup_seconds, + "solve_seconds": self.solve_seconds, + "input_mutated": self.input_mutated, + "metrics": asdict(self.metrics) if self.metrics else None, + "diagnostics": self.diagnostics, } - if self.contract_id != ACCURACY_CONTRACT_ID: - result["accuracy_contract_id"] = self.contract_id - return result -def _safe_ratio(numerator: float, denominator: float) -> float: +def _max_abs(vector: np.ndarray) -> float: + return float(np.max(np.abs(vector), initial=0.0)) + + +def _ratio(numerator: float, denominator: float) -> float: if not math.isfinite(numerator) or not math.isfinite(denominator): return math.inf - if denominator > 0.0: + if denominator: return numerator / denominator return 0.0 if numerator == 0.0 else math.inf -def _max_abs(vector: np.ndarray) -> float: - return float(np.max(np.abs(vector), initial=0.0)) - - def _relative_l2(numerator: np.ndarray, denominator: np.ndarray) -> float: - """Evaluate a norm ratio without squaring large values or tiny values. - - Splitting scales into mantissas and exponents also avoids overflowing the - individual norms when their ratio is representable. - """ - nscale, dscale = _max_abs(numerator), _max_abs(denominator) - if not math.isfinite(nscale) or not math.isfinite(dscale): + """Compute a norm ratio without overflowing the individual norms.""" + numerator_scale = _max_abs(numerator) + denominator_scale = _max_abs(denominator) + if not numerator_scale or not denominator_scale: + return _ratio(numerator_scale, denominator_scale) + if not math.isfinite(numerator_scale) or not math.isfinite(denominator_scale): return math.inf - if not nscale or not dscale: - return _safe_ratio(nscale, dscale) - nunit = float(np.linalg.norm(numerator / nscale)) - dunit = float(np.linalg.norm(denominator / dscale)) - nmantissa, nexponent = math.frexp(nscale) - dmantissa, dexponent = math.frexp(dscale) + scaled = float(np.linalg.norm(numerator / numerator_scale)) / float( + np.linalg.norm(denominator / denominator_scale) + ) + numerator_mantissa, numerator_exponent = math.frexp(numerator_scale) + denominator_mantissa, denominator_exponent = math.frexp(denominator_scale) try: return math.ldexp( - (nmantissa / dmantissa) * (nunit / dunit), nexponent - dexponent + scaled * numerator_mantissa / denominator_mantissa, + numerator_exponent - denominator_exponent, ) except OverflowError: return math.inf -def accuracy_metrics(system: EvaluationSystem, solution: np.ndarray) -> AccuracyMetrics: - public = system.public - matrix = public.matrix.to_scipy() - reasons: dict[str, str] = {} - - def record(name: str, value: float, reason: str) -> float | None: - if not math.isfinite(value) or value < 0.0: - reasons[name] = reason - return None - return value - +def _metrics(case: BenchmarkCase, solution: np.ndarray) -> Metrics: + value = case.input + matrix = value.matrix.to_scipy() with np.errstate(over="ignore", invalid="ignore", divide="ignore"): - residual = np.asarray(public.b - matrix @ solution, dtype=np.float64) - abs_residual = np.abs(residual) - abs_matrix = abs(matrix) + residual = np.asarray(value.b - matrix @ solution, dtype=np.float64) + absolute_matrix = abs(matrix) matrix_norm = float( - np.max(np.asarray(abs_matrix.sum(axis=1)).reshape(-1), initial=0.0) - ) - normwise_denominator = matrix_norm * _max_abs(solution) + _max_abs(public.b) - normwise = record( - "normwise_backward_error", - _safe_ratio(_max_abs(residual), normwise_denominator), - "nonfinite_residual_or_normwise_denominator", - ) - denominator = np.asarray(abs_matrix @ np.abs(solution), dtype=np.float64) - denominator += np.abs(public.b) - if np.all(np.isfinite(denominator)) and np.all(np.isfinite(abs_residual)): - ratios = np.zeros_like(abs_residual) - positive = denominator > 0.0 - ratios[positive] = abs_residual[positive] / denominator[positive] - ratios[~positive & (abs_residual > 0.0)] = math.inf - componentwise_value = float(np.max(ratios, initial=0.0)) - else: - componentwise_value = math.inf - componentwise = record( - "componentwise_backward_error", - componentwise_value, - "nonfinite_residual_or_componentwise_ratio", + np.max(np.asarray(absolute_matrix.sum(axis=1)).reshape(-1), initial=0.0) ) - relative_residual = record( - "relative_residual", - _relative_l2(residual, public.b), - "zero_rhs_with_nonzero_residual" - if not np.any(public.b) and np.any(residual) - else "nonfinite_residual_ratio", + normwise = _ratio( + _max_abs(residual), matrix_norm * _max_abs(solution) + _max_abs(value.b) ) - if system.x_star is None: - l2 = linf = None - reasons["relative_l2_forward_error"] = "reference_unavailable" - reasons["relative_linf_forward_error"] = "reference_unavailable" + denominator = np.asarray(absolute_matrix @ np.abs(solution), dtype=np.float64) + denominator += np.abs(value.b) + ratios = np.zeros_like(residual) + positive = denominator > 0 + ratios[positive] = np.abs(residual[positive]) / denominator[positive] + ratios[~positive & (np.abs(residual) > 0)] = math.inf + componentwise = float(np.max(ratios, initial=0.0)) + relative_residual = _relative_l2(residual, value.b) + if case.target is None: + forward_l2 = forward_linf = None else: - difference = solution - system.x_star - l2 = record( - "relative_l2_forward_error", - _relative_l2(difference, system.x_star), - "nonfinite_forward_error_or_zero_reference", - ) - linf = record( - "relative_linf_forward_error", - _safe_ratio(_max_abs(difference), _max_abs(system.x_star)), - "nonfinite_forward_error_or_zero_reference", - ) - return AccuracyMetrics( - normwise, componentwise, l2, linf, relative_residual, reasons + error = solution - case.target + forward_l2 = _relative_l2(error, case.target) + forward_linf = _ratio(_max_abs(error), _max_abs(case.target)) + return Metrics( + normwise, + componentwise, + relative_residual, + forward_l2, + forward_linf, ) -def verify_solution( - system: EvaluationSystem, - solution: object, - *, - status: int = 0, - input_mutated: bool = False, - contract_id: str = ACCURACY_CONTRACT_ID, -) -> VerificationResult: - thresholds = AccuracyThresholds.for_contract(contract_id, system.public.tolerance) - - def result( - failure: str | None, metrics: AccuracyMetrics | None = None - ) -> VerificationResult: - return VerificationResult( - failure is None, failure, metrics, thresholds, contract_id +def _failure(family: str, tolerance: float, metrics: Metrics) -> str | None: + values = asdict(metrics) + required: dict[str, float] + if family == "ns-mesh-pde": + required = { + "normwise_backward_error": 1.0e-10, + "componentwise_backward_error": 1.0e-8, + "relative_l2_forward_error": 1.0e-5, + "relative_linf_forward_error": 1.0e-5, + } + elif family == "magnetic_diffusion_flash": + required = { + "normwise_backward_error": max(tolerance, 20 * FLOAT64_EPSILON), + "relative_residual": tolerance * (1 + 1.0e-6), + } + else: + raise ValueError(f"unknown benchmark family: {family}") + for name, limit in required.items(): + value = values[name] + if value is None or not math.isfinite(value): + return f"invalid {name}" + if value > limit: + return f"{name} exceeds {limit:g}" + return None + + +def _decode(payload: bytes, expected_size: int): + expected_bytes = OUTPUT_HEADER.size + expected_size * 8 + if len(payload) != expected_bytes: + raise ValueError("driver output has the wrong size") + magic, version, status, size, elapsed, create, setup, solve, mutated = ( + OUTPUT_HEADER.unpack_from(payload) + ) + if magic != OUTPUT_MAGIC or version != 1 or size != expected_size: + raise ValueError("driver output header is invalid") + if mutated not in (0, 1) or elapsed <= 0 or create + setup + solve != elapsed: + raise ValueError("driver timing or mutation fields are invalid") + solution = np.frombuffer(payload, " RunResult: + """Return a fail-closed result; candidate failures do not raise.""" + common = { + "process_returncode": raw.returncode, + "process_wall_seconds": raw.wall_seconds, + "diagnostics": raw.diagnostics, + } + if raw.returncode != 0: + return RunResult(False, None, "process failed", **common) + if raw.output is None: + return RunResult(False, None, "missing driver output", **common) + try: + status, elapsed, create, setup, solve, mutated, solution = _decode( + raw.output, case.input.matrix.n ) - - if thresholds.forward_error is not None and system.reference_kind != "manufactured": - return result("missing_manufactured_reference") - if thresholds.forward_error is None and system.reference_kind == "manufactured": - return result("invalid_reference_kind") + except ValueError as error: + return RunResult(False, None, str(error), **common) + seconds = elapsed * 1.0e-9 + details = { + "elapsed_seconds": seconds, + "status": status, + "create_seconds": create * 1.0e-9, + "setup_seconds": setup * 1.0e-9, + "solve_seconds": solve * 1.0e-9, + "input_mutated": mutated, + } if status != 0: - return result("nonzero_status") - if input_mutated: - return result("input_mutation") - raw = np.asarray(solution) - if raw.shape != (system.public.matrix.n,) or raw.dtype.kind not in "fiu": - return result("invalid_shape_or_dtype") - with np.errstate(over="ignore", invalid="ignore"): - candidate = np.asarray(raw, dtype=np.float64) - if not np.all(np.isfinite(candidate)): - return result("nonfinite_solution") + return RunResult(False, failure="solver returned an error", **common, **details) + if mutated: + return RunResult(False, failure="solver mutated its input", **common, **details) + if not np.all(np.isfinite(solution)): + return RunResult(False, failure="solution is not finite", **common, **details) try: - metrics = accuracy_metrics(system, candidate) + metrics = _metrics(case, solution) + failure = _failure(family, case.input.tolerance, metrics) except (FloatingPointError, OverflowError, ValueError): - return result("verification_error") - gates = { - "normwise_backward_error": ( - metrics.normwise_backward_error, - thresholds.normwise_backward_error, - ), - "componentwise_backward_error": ( - metrics.componentwise_backward_error, - thresholds.componentwise_backward_error, - ), - "forward_error": (metrics.forward_error, thresholds.forward_error), - "relative_residual": (metrics.relative_residual, thresholds.relative_residual), - } - required = [ - (name, value, limit) - for name, (value, limit) in gates.items() - if limit is not None - ] - if any( - value is None or not math.isfinite(value) or value < 0 - for _, value, _ in required - ): - return result("nonfinite_metric", metrics) - failures = [name for name, value, limit in required if value > limit] - return result(",".join(failures) if failures else None, metrics) + return RunResult(False, failure="verification failed", **common, **details) + return RunResult( + failure is None, failure=failure, metrics=metrics, **common, **details + ) diff --git a/src/linear_solver_bench/workloads.py b/src/linear_solver_bench/workloads.py deleted file mode 100644 index 1bbbfb6..0000000 --- a/src/linear_solver_bench/workloads.py +++ /dev/null @@ -1,526 +0,0 @@ -"""Versioned manufactured inputs and independent offline NS qualification.""" - -from __future__ import annotations - -import hashlib -import hmac -import math -from collections.abc import Mapping -from decimal import Decimal, localcontext - -import numpy as np -import scipy -from scipy.sparse.linalg import LinearOperator, gmres, spilu, splu - -from .dataset import canonical_json -from .manifests import ( - ITERATIVE_QUALIFICATION_CONFIG, - ITERATIVE_QUALIFICATION_METHOD, - QUALIFICATION_LIMITS, - RHS_SCHEME, - validate_qualification, -) -from .models import CsrMatrix, EvaluationSystem, MatrixInput -from .verify import accuracy_metrics - - -def matrix_digest(matrix: CsrMatrix) -> str: - """Hash canonical numerical arrays, independent of NPZ compression metadata.""" - value = hashlib.sha256(b"linear-solver-bench-csr-float64-v2\0") - value.update(matrix.n.to_bytes(8, "little")) - value.update(np.asarray(matrix.row_offsets, dtype=" str: - value = hashlib.sha256(b"linear-solver-bench-vector-float64-v2\0") - value.update(len(vector).to_bytes(8, "little")) - value.update(np.asarray(vector, dtype=" str: - body = { - "case_id": system.case_id, - "matrix_sha256": matrix_digest(system.public.matrix), - "b_sha256": vector_digest(system.public.b), - "x0_sha256": vector_digest(system.public.x0), - "tolerance": system.public.tolerance, - "reference_kind": system.reference_kind, - "reference_sha256": None - if system.x_star is None - else vector_digest(system.x_star), - } - return hashlib.sha256(canonical_json(body).encode("ascii")).hexdigest() - - -def expected_flash_system_digest(case: Mapping[str, object]) -> str: - """Bind prepared replay inputs to the arrays frozen in the public release. - - The pilot's four-file capture format contains no numerical reference array. - Its complete system identity can therefore be reconstructed from the release - without trusting a prepared archive's self-declared hash. - """ - source = case["source"] - body = { - "case_id": case["case_id"], - "matrix_sha256": source["matrix_sha256"], - "b_sha256": source["b_sha256"], - "x0_sha256": source["x0_sha256"], - "tolerance": float(case["tolerance"]), - "reference_kind": "none", - "reference_sha256": None, - } - return hashlib.sha256(canonical_json(body).encode("ascii")).hexdigest() - - -def rademacher_target( - *, - release_id: str, - family: str, - matrix_sha256: str, - n: int, - draw_index: int, - key: bytes, -) -> np.ndarray: - """Generate +/-1 directly from HMAC bits; no library RNG or version dependence. - - Each SHA-256 block supplies 256 bits, least significant bit first per byte. - A one bit maps to +1 and a zero bit to -1. Every target has exactly unit RMS. - """ - if not isinstance(key, bytes) or len(key) < 16: - raise ValueError("RHS key must contain at least 16 bytes") - if type(n) is not int or n <= 0 or type(draw_index) is not int or draw_index < 0: - raise ValueError("invalid manufactured target dimensions or draw index") - domain = canonical_json( - { - "scheme": RHS_SCHEME, - "release_id": release_id, - "family": family, - "matrix_sha256": matrix_sha256, - "rhs_kind": "rademacher", - "draw_index": draw_index, - } - ).encode("ascii") - raw = bytearray() - for block in range((n + 255) // 256): - raw.extend( - hmac.digest(key, domain + b"\0" + block.to_bytes(8, "little"), "sha256") - ) - bits = np.unpackbits(np.frombuffer(raw, dtype=np.uint8), bitorder="little")[:n] - return bits.astype(np.float64) * 2 - 1 - - -def manufacture_ns( - case: Mapping[str, object], - release: Mapping[str, object], - key: bytes, - matrix: CsrMatrix, -) -> EvaluationSystem: - if hashlib.sha256(key).hexdigest() != release["rhs"]["key_id"]: - raise ValueError("RHS key does not match the release commitment") - truth = rademacher_target( - release_id=release["release_id"], - family=release["family"], - matrix_sha256=matrix_digest(matrix), - n=matrix.n, - draw_index=release["rhs"]["draw_index"], - key=key, - ) - return EvaluationSystem( - case_id=case["case_id"], - public=MatrixInput( - matrix=matrix, - b=matrix.matvec(truth), - x0=np.zeros(matrix.n), - tolerance=case["tolerance"], - ), - x_star=truth, - reference_kind="manufactured", - ) - - -def _extended_residual( - matrix: CsrMatrix, rhs: np.ndarray, vector: np.ndarray -) -> np.ndarray: - """Evaluate b-Ax with extra precision; this is not interval arithmetic.""" - residual = np.empty(matrix.n, dtype=np.longdouble) - if np.finfo(np.longdouble).eps < np.finfo(np.float64).eps: - extended_rhs = rhs.astype(np.longdouble) - extended_vector = vector.astype(np.longdouble) - for row in range(matrix.n): - start, stop = int(matrix.row_offsets[row]), int(matrix.row_offsets[row + 1]) - dot = np.sum( - matrix.values[start:stop].astype(np.longdouble) - * extended_vector[matrix.column_indices[start:stop]], - dtype=np.longdouble, - ) - residual[row] = extended_rhs[row] - dot - else: - # Decimal.from_float preserves the binary64 operands exactly; products - # and sums then use 80 decimal digits. The result remains empirical - # higher-precision evidence, not an outward-rounded certificate. - decimal_vector = [Decimal.from_float(float(value)) for value in vector] - with localcontext() as context: - context.prec = 80 - for row in range(matrix.n): - start, stop = ( - int(matrix.row_offsets[row]), - int(matrix.row_offsets[row + 1]), - ) - dot = sum( - ( - Decimal.from_float(float(value)) * decimal_vector[column] - for value, column in zip( - matrix.values[start:stop], - matrix.column_indices[start:stop], - strict=True, - ) - ), - Decimal(0), - ) - residual[row] = np.longdouble( - str(Decimal.from_float(float(rhs[row])) - dot) - ) - return residual - - -def qualify_ns(system: EvaluationSystem, *, refine: bool = False) -> dict: - """Establish empirical feasibility against every gate with a tenfold margin. - - Sparse LU is an offline reference, not the timing reference or a condition - certificate. refine=True always performs exactly one correction using an - extended-precision residual and the same LU factors, then checks the result. - It never changes the target, matrix, RHS, or tolerances. The distinct method - ID records that fixed procedure. RHS formation is independently audited with - the same higher-precision evaluator; this does not certify forward error. - """ - if type(refine) is not bool: - raise ValueError("refine must be a boolean") - if system.x_star is None or system.reference_kind != "manufactured": - raise ValueError("NS qualification requires the manufactured target") - matrix = system.public.matrix - try: - factor = splu(matrix.to_scipy().tocsc()) - reference = factor.solve(system.public.b) - if not np.all(np.isfinite(reference)): - raise ValueError("independent reference solution is not finite") - if refine: - residual = _extended_residual(matrix, system.public.b, reference) - with np.errstate(over="ignore", invalid="ignore"): - correction_rhs = np.asarray(residual, dtype=np.float64) - if not np.all(np.isfinite(correction_rhs)): - raise ValueError("refinement residual is not finite float64") - with np.errstate(over="ignore", invalid="ignore"): - reference = reference + factor.solve(correction_rhs) - if not np.all(np.isfinite(reference)): - raise ValueError("refined reference solution is not finite") - result = accuracy_metrics(system, reference) - except (RuntimeError, ValueError, FloatingPointError) as exc: - raise ValueError( - "independent NS reference solution could not be computed" - ) from exc - metrics = {} - for name in QUALIFICATION_LIMITS: - value = getattr(result, name) - if value is None: - raise ValueError(f"independent NS qualification produced invalid {name}") - metrics[name] = float(value) - formation = _extended_residual(matrix, system.public.b, system.x_star) - maximum = np.max(np.abs(formation), initial=np.longdouble(0)) - norm_b = np.max( - np.abs(system.public.b.astype(np.longdouble)), initial=np.longdouble(0) - ) - relative = ( - float(maximum / norm_b) if norm_b else (0.0 if maximum == 0 else float("inf")) - ) - evidence = { - "method": "independent-sparse-lu-refined-v1" - if refine - else "independent-sparse-lu-v1", - "qualified": True, - "system_sha256": system_digest(system), - "metrics": metrics, - "formation": { - "method": "extended-precision-residual-v1", - "relative_linf_residual": relative, - "verified_forward_bound": None, - }, - "uncertainty": "empirical-feasibility-only", - } - validate_qualification(evidence, system_digest(system)) - return evidence - - -def qualify_ns_iterative(system: EvaluationSystem) -> dict: - """Qualify a fixed target with an independent, bounded-fill offline reference. - - The reference solves a row/column-equilibrated system from zero using ILU - and GMRES, followed by exactly two extended-residual correction solves. - Only the original stored A,b and manufactured target determine acceptance. - The target is never a solver input. This is empirical feasibility evidence, - without an inverse-norm bound or a candidate performance claim. - - ILU fill controls do not limit every temporary allocation. Operators must - run this expensive offline procedure in a process with explicit memory and - wall-time limits. Library versions and the complete fixed configuration - accompany successful evidence; unsuccessful solves do not admit a case. - """ - if system.x_star is None or system.reference_kind != "manufactured": - raise ValueError("NS qualification requires the manufactured target") - config = ITERATIVE_QUALIFICATION_CONFIG - matrix = system.public.matrix - scaled = matrix.to_scipy().copy() - row_maximum = np.asarray(abs(scaled).max(axis=1).toarray()).ravel() - if np.any(row_maximum == 0): - raise ValueError("independent reference cannot equilibrate an empty row") - with np.errstate(over="ignore", invalid="ignore", divide="ignore"): - row_scale = 1.0 / row_maximum - scaled.data *= np.repeat(row_scale, np.diff(scaled.indptr)) - column_maximum = np.asarray(abs(scaled).max(axis=0).toarray()).ravel() - column_scale = 1.0 / column_maximum - scaled.data *= column_scale[scaled.indices] - if ( - not np.all(np.isfinite(row_scale)) - or not np.all(np.isfinite(column_scale)) - or not np.all(np.isfinite(scaled.data)) - or np.any(scaled.data == 0) - ): - raise ValueError( - "independent reference equilibration is not finite and nonzero" - ) - try: - factor = spilu( - scaled.tocsc(), - drop_tol=config["drop_tol"], - fill_factor=config["fill_factor"], - drop_rule=config["drop_rule"], - permc_spec=config["permc_spec"], - diag_pivot_thresh=config["diag_pivot_thresh"], - options={"Equil": config["superlu_equilibration"]}, - ) - preconditioner = LinearOperator(scaled.shape, factor.solve, dtype=np.float64) - solves = [] - - def solve(rhs: np.ndarray) -> np.ndarray: - iterations = 0 - - def count_iteration(_residual: float) -> None: - nonlocal iterations - iterations += 1 - - with np.errstate(over="ignore", invalid="ignore"): - scaled_rhs = row_scale * rhs - if not np.all(np.isfinite(scaled_rhs)): - raise ValueError("scaled reference RHS is not finite") - vector, info = gmres( - scaled, - scaled_rhs, - x0=np.zeros(matrix.n), - rtol=config["rtol"], - atol=config["atol"], - restart=config["restart"], - maxiter=config["max_restart_cycles"], - M=preconditioner, - callback=count_iteration, - callback_type="pr_norm", - ) - solves.append({"info": int(info), "inner_iterations": iterations}) - with np.errstate(over="ignore", invalid="ignore"): - vector = column_scale * vector - if info != 0 or not np.all(np.isfinite(vector)): - raise ValueError("independent GMRES reference did not converge") - return vector - - reference = solve(system.public.b) - for _ in range(config["refinement_steps"]): - with np.errstate(over="ignore", invalid="ignore"): - residual = np.asarray( - _extended_residual(matrix, system.public.b, reference), - dtype=np.float64, - ) - reference = reference + solve(residual) - if not np.all(np.isfinite(reference)): - raise ValueError("refined reference solution is not finite") - except (RuntimeError, ValueError, FloatingPointError) as exc: - raise ValueError( - "independent iterative NS reference could not be computed" - ) from exc - - result = accuracy_metrics(system, reference) - metrics = {} - for name in QUALIFICATION_LIMITS: - value = getattr(result, name) - if value is None: - raise ValueError(f"independent NS qualification produced invalid {name}") - metrics[name] = float(value) - formation = _extended_residual(matrix, system.public.b, system.x_star) - maximum = np.max(np.abs(formation), initial=np.longdouble(0)) - norm_b = np.max( - np.abs(system.public.b.astype(np.longdouble)), initial=np.longdouble(0) - ) - relative = ( - float(maximum / norm_b) if norm_b else (0.0 if maximum == 0 else float("inf")) - ) - evidence = { - "method": ITERATIVE_QUALIFICATION_METHOD, - "qualified": True, - "system_sha256": system_digest(system), - "metrics": metrics, - "formation": { - "method": "extended-precision-residual-v1", - "relative_linf_residual": relative, - "verified_forward_bound": None, - }, - "uncertainty": "empirical-feasibility-only", - "reference_solver": { - "configuration": dict(config), - "implementation": {"numpy": np.__version__, "scipy": scipy.__version__}, - "solves": solves, - }, - } - validate_qualification(evidence, system_digest(system)) - return evidence - - -def qualify_ns_pyamg(system: EvaluationSystem) -> dict: - """Run the optional fixed multigrid offline reference without eager imports.""" - from .reference_pyamg import qualify_ns_pyamg as qualify - - return qualify(system) - - -def qualify_ns_pyamg_inexact(system: EvaluationSystem) -> dict: - """Run the optional v2 reference with fixed inexact inner corrections.""" - from .reference_pyamg import qualify_ns_pyamg_inexact as qualify - - return qualify(system) - - -def _binary64_units(value: float) -> int: - """Represent a finite binary64 number exactly in units of 2**-1074.""" - numerator, denominator = float(value).as_integer_ratio() - return numerator << (1074 - (denominator.bit_length() - 1)) - - -def _rounded_ratio(numerator: int, denominator: int, *, upward: bool) -> float: - """Round an exact nonnegative rational outwards, checking the direction.""" - if numerator < 0 or denominator <= 0: - raise ValueError("certificate ratio requires a nonnegative finite value") - try: - value = numerator / denominator - except OverflowError as exc: - raise ValueError("certificate bound cannot be represented in float64") from exc - if not math.isfinite(value): - raise ValueError("certificate bound cannot be represented in float64") - rounded_numerator, rounded_denominator = value.as_integer_ratio() - comparison = rounded_numerator * denominator - numerator * rounded_denominator - if (upward and comparison < 0) or (not upward and comparison > 0): - value = math.nextafter(value, math.inf if upward else -math.inf) - if not math.isfinite(value): - raise ValueError("certificate bound cannot be represented in float64") - bounded_numerator, bounded_denominator = value.as_integer_ratio() - check = bounded_numerator * denominator - numerator * bounded_denominator - if (upward and check < 0) or (not upward and check > 0): - raise ValueError("could not round certificate bound in the required direction") - return value - - -def qualify_ns_dominance(system: EvaluationSystem) -> dict: - """Certify stored-system consistency for a strictly row-dominant NS case. - - Every coefficient is an exact integer multiple of 2**-1074. With a +/-1 - manufactured target, row dot products and formation residuals are therefore - accumulated exactly using integers, including subnormal coefficients. - - Let d = min_i (|a_ii| - sum_{j != i}|a_ij|) > 0 and e = b - A x_target. - Taking the largest component of any vector proves ||A y||inf >= d||y||inf. - Thus A is invertible and ||A^-1||inf <= 1/d (the row-dominance bound): - https://doi.org/10.1016/0024-3795(75)90112-3 - The exact stored-system solution differs from x_target by at most - ||e||inf/d in Linf. Since ||x_target||inf=1 and ||x_target||2=sqrt(n), the - same bound covers both relative forward discrepancies. - - Required metric values below are *measured* by the public binary64 verifier - at x_target, an explicit feasible witness. They are not verified rounding - bounds for the verifier's floating-point operations or an independent LU - solve. The separate exact-arithmetic certificate proves stored-system - forward consistency. Both witness metrics and that bound need a tenfold - margin. This offline path does not make x_target available to candidates. - """ - target = system.x_star - if ( - system.reference_kind != "manufactured" - or target is None - or not np.all((target == 1.0) | (target == -1.0)) - ): - raise ValueError("dominance qualification requires a Rademacher target") - matrix = system.public.matrix - minimum_margin = None - maximum_error = maximum_rhs = 0 - for row in range(matrix.n): - start, stop = int(matrix.row_offsets[row]), int(matrix.row_offsets[row + 1]) - diagonal = row_sum = dot = 0 - for column, value in zip( - matrix.column_indices[start:stop], matrix.values[start:stop], strict=True - ): - integer = _binary64_units(value) - row_sum += abs(integer) - dot += integer if target[column] > 0 else -integer - if column == row: - diagonal = abs(integer) - margin = 2 * diagonal - row_sum - if margin <= 0: - raise ValueError( - f"matrix is not strictly row diagonally dominant at row {row}" - ) - minimum_margin = ( - margin if minimum_margin is None else min(minimum_margin, margin) - ) - rhs = _binary64_units(system.public.b[row]) - maximum_error = max(maximum_error, abs(rhs - dot)) - maximum_rhs = max(maximum_rhs, abs(rhs)) - margin_lower = _rounded_ratio(minimum_margin, 1 << 1074, upward=False) - error_upper = _rounded_ratio(maximum_error, 1 << 1074, upward=True) - # Build the reported bound from the outward-rounded summaries as well, so - # their exact rational relation can be checked without loading the matrix. - forward_bound = _rounded_ratio( - _binary64_units(error_upper), _binary64_units(margin_lower), upward=True - ) - relative_formation = ( - _rounded_ratio(maximum_error, maximum_rhs, upward=True) - if maximum_rhs - else 0.0 - if maximum_error == 0 - else math.inf - ) - observed = accuracy_metrics(system, target) - metrics = {} - for name in QUALIFICATION_LIMITS: - value = getattr(observed, name) - if value is None: - raise ValueError(f"dominance witness produced invalid {name}") - metrics[name] = float(value) - evidence = { - "method": "strict-row-diagonal-dominance-v1", - "qualified": True, - "system_sha256": system_digest(system), - "metrics": metrics, - "formation": { - "method": "exact-binary64-residual-v1", - "relative_linf_residual": relative_formation, - "verified_forward_bound": forward_bound, - }, - "certificate": { - "arithmetic": "exact-binary64-integer-v1", - "witness": "manufactured-target", - "metric_evaluation": "binary64", - "minimum_row_margin": margin_lower, - "maximum_formation_error": error_upper, - }, - "uncertainty": "verified-stored-system-forward-bound", - } - validate_qualification(evidence, system_digest(system)) - return evidence diff --git a/submissions/README.md b/submissions/README.md new file mode 100644 index 0000000..908b472 --- /dev/null +++ b/submissions/README.md @@ -0,0 +1,9 @@ +# Submissions + +Run `linear-solver-bench submit` to create a directory containing `solver.c`, +`submission.json`, and `README.md`. Describe the algorithm and development process +in that README, commit only those three files, and open a pull request. + +Maintainers evaluate the exact reviewed source on private cases using the same +paired Modal evaluator. Private inputs and publication credentials never enter +this repository. diff --git a/submissions/gmres_amg.c b/submissions/gmres_amg.c deleted file mode 100644 index 0d8957c..0000000 --- a/submissions/gmres_amg.c +++ /dev/null @@ -1,609 +0,0 @@ -#include "HYPRE_parcsr_ls.h" - -/* Flexible right-preconditioned GMRES(50) with one fixed BoomerAMG V-cycle. - Every cycle starts its correction at zero. The Arnoldi update retains the - actual preconditioned vectors and verifies the original residual after each - restart. Factory creation, AMG setup, and the solve are all timed. - - Fixed AMG configuration: HMIS coarsening, extended+i interpolation (at most - four entries per row), strength threshold 0.25, symmetric hybrid - Gauss-Seidel smoothing, one sweep, and at most 25 levels. This source uses - only the public candidate API and receives no source or case identities. */ - -enum -{ - REFERENCE_GMRES_RESTART = 50 -}; - -typedef struct -{ - hypre_Solver base; - - HYPRE_Real relative_tolerance; - HYPRE_Real absolute_tolerance; - HYPRE_Int maximum_iterations; - - HYPRE_Matrix matrix; - void **basis; - void **preconditioned; - HYPRE_Solver amg; - HYPRE_Real *hessenberg; - HYPRE_Real *cosines; - HYPRE_Real *sines; - HYPRE_Real *rhs; - HYPRE_Real *coefficients; -} reference_GMRESData; - -static HYPRE_Int reference_GMRESSetup(HYPRE_Solver solver, HYPRE_Matrix A, - HYPRE_Vector b, HYPRE_Vector x); -static HYPRE_Int reference_GMRESSolve(HYPRE_Solver solver, HYPRE_Matrix A, - HYPRE_Vector b, HYPRE_Vector x); -static HYPRE_Int reference_GMRESDestroy(HYPRE_Solver solver); - -static HYPRE_Int -reference_is_finite(HYPRE_Real value) -{ - return isfinite((double) value) ? 1 : 0; -} - -static HYPRE_Real -reference_absolute(HYPRE_Real value) -{ - return value < 0.0 ? -value : value; -} - -static HYPRE_Real -reference_pair_norm(HYPRE_Real left, HYPRE_Real right) -{ - HYPRE_Real larger = reference_absolute(left); - HYPRE_Real smaller = reference_absolute(right); - HYPRE_Real ratio; - - if (smaller > larger) - { - HYPRE_Real temporary = larger; - larger = smaller; - smaller = temporary; - } - if (larger == 0.0) - { - return 0.0; - } - ratio = smaller / larger; - return larger * sqrt(1.0 + ratio * ratio); -} - -static HYPRE_Real -reference_hessenberg_get(const reference_GMRESData *data, HYPRE_Int row, - HYPRE_Int column) -{ - return data->hessenberg[ - (size_t) row * (size_t) REFERENCE_GMRES_RESTART + (size_t) column]; -} - -static void -reference_hessenberg_set(reference_GMRESData *data, HYPRE_Int row, HYPRE_Int column, - HYPRE_Real value) -{ - data->hessenberg[ - (size_t) row * (size_t) REFERENCE_GMRES_RESTART + (size_t) column] = value; -} - -static HYPRE_Int -reference_vector_norm(void *vector, HYPRE_Real *norm) -{ - HYPRE_Real squared_norm; - - if (vector == NULL || norm == NULL) - { - return HYPRE_ERROR_GENERIC; - } - squared_norm = hypre_ParKrylovInnerProd(vector, vector); - if (!reference_is_finite(squared_norm) || squared_norm < 0.0) - { - return HYPRE_ERROR_GENERIC; - } - - *norm = sqrt(squared_norm); - return reference_is_finite(*norm) ? 0 : HYPRE_ERROR_GENERIC; -} - -static HYPRE_Int -reference_destroy_vector_array(void ***array_pointer, HYPRE_Int count) -{ - HYPRE_Int error = 0; - HYPRE_Int i; - void **array; - - if (array_pointer == NULL || *array_pointer == NULL) - { - return 0; - } - array = *array_pointer; - - for (i = 0; i < count; i++) - { - if (array[i] != NULL) - { - HYPRE_Int callback_error = hypre_ParKrylovDestroyVector(array[i]); - if (error == 0 && callback_error != 0) - { - error = callback_error; - } - } - } - { - HYPRE_Int callback_error = hypre_ParKrylovFree(array); - if (error == 0 && callback_error != 0) - { - error = callback_error; - } - } - *array_pointer = NULL; - return error; -} - -static HYPRE_Int -reference_destroy_workspace(reference_GMRESData *data) -{ - HYPRE_Int error = 0; - - if (data == NULL) - { - return 0; - } - - if (data->amg != NULL) - { - error = HYPRE_BoomerAMGDestroy(data->amg); - data->amg = NULL; - } - { - HYPRE_Int callback_error = reference_destroy_vector_array( - &data->preconditioned, REFERENCE_GMRES_RESTART); - if (error == 0) { error = callback_error; } - callback_error = reference_destroy_vector_array( - &data->basis, REFERENCE_GMRES_RESTART + 1); - if (error == 0) { error = callback_error; } - } -#define REFERENCE_FREE_HOST(pointer) \ - do \ - { \ - if ((pointer) != NULL) \ - { \ - HYPRE_Int callback_error = hypre_ParKrylovFree(pointer); \ - if (error == 0 && callback_error != 0) \ - { \ - error = callback_error; \ - } \ - (pointer) = NULL; \ - } \ - } while (0) - - REFERENCE_FREE_HOST(data->hessenberg); - REFERENCE_FREE_HOST(data->cosines); - REFERENCE_FREE_HOST(data->sines); - REFERENCE_FREE_HOST(data->rhs); - REFERENCE_FREE_HOST(data->coefficients); - -#undef REFERENCE_FREE_HOST - - data->matrix = NULL; - data->base.is_setup = 0; - return error; -} - -static HYPRE_Int -reference_allocate_workspace(reference_GMRESData *data, HYPRE_Vector sample) -{ - data->basis = hypre_ParKrylovCreateVectorArray( - REFERENCE_GMRES_RESTART + 1, sample); - data->preconditioned = hypre_ParKrylovCreateVectorArray( - REFERENCE_GMRES_RESTART, sample); - data->hessenberg = (HYPRE_Real *) hypre_ParKrylovCAlloc( - (size_t) (REFERENCE_GMRES_RESTART + 1), - (size_t) REFERENCE_GMRES_RESTART * sizeof(HYPRE_Real), - HYPRE_MEMORY_HOST); - data->cosines = (HYPRE_Real *) hypre_ParKrylovCAlloc( - REFERENCE_GMRES_RESTART, sizeof(HYPRE_Real), HYPRE_MEMORY_HOST); - data->sines = (HYPRE_Real *) hypre_ParKrylovCAlloc( - REFERENCE_GMRES_RESTART, sizeof(HYPRE_Real), HYPRE_MEMORY_HOST); - data->rhs = (HYPRE_Real *) hypre_ParKrylovCAlloc( - REFERENCE_GMRES_RESTART + 1, sizeof(HYPRE_Real), HYPRE_MEMORY_HOST); - data->coefficients = (HYPRE_Real *) hypre_ParKrylovCAlloc( - REFERENCE_GMRES_RESTART, sizeof(HYPRE_Real), HYPRE_MEMORY_HOST); - - if (data->basis == NULL || data->preconditioned == NULL || - data->hessenberg == NULL || - data->cosines == NULL || data->sines == NULL || data->rhs == NULL || - data->coefficients == NULL) - { - reference_destroy_workspace(data); - return HYPRE_ERROR_GENERIC; - } - return 0; -} - -HYPRE_Int -solver_create(HYPRE_Solver *solver, - HYPRE_Real relative_tolerance, - HYPRE_Real absolute_tolerance, - HYPRE_Int maximum_iterations) -{ - reference_GMRESData *data; - - if (solver == NULL) - { - return HYPRE_ERROR_GENERIC; - } - *solver = NULL; - if (!reference_is_finite(relative_tolerance) || - !reference_is_finite(absolute_tolerance) || - relative_tolerance < 0.0 || absolute_tolerance < 0.0 || - maximum_iterations < 0) - { - return HYPRE_ERROR_GENERIC; - } - - data = (reference_GMRESData *) hypre_ParKrylovCAlloc( - 1, sizeof(*data), HYPRE_MEMORY_HOST); - if (data == NULL) - { - return HYPRE_ERROR_GENERIC; - } - - data->base.setup = reference_GMRESSetup; - data->base.solve = reference_GMRESSolve; - data->base.destroy = reference_GMRESDestroy; - data->base.is_setup = 0; - - /* Leave a fixed tenfold residual margin for independent verification. */ - data->relative_tolerance = 0.1 * relative_tolerance; - data->absolute_tolerance = absolute_tolerance; - data->maximum_iterations = maximum_iterations; - *solver = (HYPRE_Solver) data; - return 0; -} - -static HYPRE_Int -reference_GMRESSetup(HYPRE_Solver solver, HYPRE_Matrix A, - HYPRE_Vector b, HYPRE_Vector x) -{ - reference_GMRESData *data = (reference_GMRESData *) solver; - HYPRE_Int error; - - if (data == NULL || A == NULL || b == NULL || x == NULL) - { - return HYPRE_ERROR_GENERIC; - } - - error = reference_destroy_workspace(data); - if (error != 0) - { - return error; - } - error = reference_allocate_workspace(data, b); - if (error != 0) - { - return error; - } - - error = HYPRE_BoomerAMGCreate(&data->amg); - if (error == 0) - { - error |= HYPRE_BoomerAMGSetTol(data->amg, 0.0); - error |= HYPRE_BoomerAMGSetMinIter(data->amg, 1); - error |= HYPRE_BoomerAMGSetMaxIter(data->amg, 1); - error |= HYPRE_BoomerAMGSetCycleType(data->amg, 1); - error |= HYPRE_BoomerAMGSetCoarsenType(data->amg, 10); - error |= HYPRE_BoomerAMGSetInterpType(data->amg, 6); - error |= HYPRE_BoomerAMGSetPMaxElmts(data->amg, 4); - error |= HYPRE_BoomerAMGSetStrongThreshold(data->amg, 0.25); - error |= HYPRE_BoomerAMGSetRelaxType(data->amg, 6); - error |= HYPRE_BoomerAMGSetNumSweeps(data->amg, 1); - error |= HYPRE_BoomerAMGSetMaxLevels(data->amg, 25); - error |= HYPRE_BoomerAMGSetMaxCoarseSize(data->amg, 9); - } - if (error == 0) - { - error = HYPRE_BoomerAMGSetup( - data->amg, (HYPRE_ParCSRMatrix) A, - (HYPRE_ParVector) b, (HYPRE_ParVector) x); - } - if (error != 0) - { - reference_destroy_workspace(data); - return error; - } - data->matrix = A; - data->base.is_setup = 1; - return 0; -} - -static HYPRE_Int -reference_apply_update(reference_GMRESData *data, HYPRE_Int dimension, HYPRE_Vector x) -{ - HYPRE_Int row; - HYPRE_Int column; - HYPRE_Int callback_error; - - if (dimension <= 0 || dimension > REFERENCE_GMRES_RESTART) - { - return HYPRE_ERROR_GENERIC; - } - - for (row = dimension - 1; row >= 0; row--) - { - HYPRE_Real value = data->rhs[row]; - HYPRE_Real diagonal; - - for (column = row + 1; column < dimension; column++) - { - value -= reference_hessenberg_get(data, row, column) * - data->coefficients[column]; - } - diagonal = reference_hessenberg_get(data, row, row); - if (!reference_is_finite(value) || !reference_is_finite(diagonal) || - reference_absolute(diagonal) < HYPRE_REAL_MIN) - { - return HYPRE_ERROR_GENERIC; - } - data->coefficients[row] = value / diagonal; - if (!reference_is_finite(data->coefficients[row])) - { - return HYPRE_ERROR_GENERIC; - } - } - - for (column = 0; column < dimension; column++) - { - callback_error = hypre_ParKrylovAxpy( - data->coefficients[column], data->preconditioned[column], x); - if (callback_error != 0) - { - return callback_error; - } - } - return 0; -} - -static HYPRE_Int -reference_recompute_residual(reference_GMRESData *data, HYPRE_Matrix A, - HYPRE_Vector b, HYPRE_Vector x, - HYPRE_Real *residual_norm) -{ - HYPRE_Int callback_error; - - callback_error = hypre_ParKrylovCopyVector(b, data->basis[0]); - if (callback_error != 0) - { - return callback_error; - } - callback_error = hypre_ParKrylovMatvec( - NULL, -1.0, A, x, 1.0, data->basis[0]); - if (callback_error != 0) - { - return callback_error; - } - return reference_vector_norm(data->basis[0], residual_norm); -} - -static HYPRE_Int -reference_GMRESSolve(HYPRE_Solver solver, HYPRE_Matrix A, - HYPRE_Vector b, HYPRE_Vector x) -{ - reference_GMRESData *data = (reference_GMRESData *) solver; - HYPRE_Real b_norm; - HYPRE_Real residual_norm; - HYPRE_Real denominator; - HYPRE_Real tolerance; - HYPRE_Int iteration = 0; - HYPRE_Int callback_error; - - if (data == NULL || A == NULL || b == NULL || x == NULL || - data->basis == NULL || data->hessenberg == NULL || - data->matrix != A || !data->base.is_setup) - { - return HYPRE_ERROR_GENERIC; - } - - if (reference_vector_norm(b, &b_norm) != 0 || - reference_recompute_residual(data, A, b, x, &residual_norm) != 0) - { - return HYPRE_ERROR_GENERIC; - } - if (b_norm == 0.0) - { - return hypre_ParKrylovClearVector(x); - } - denominator = b_norm; - tolerance = data->relative_tolerance * denominator; - if (data->absolute_tolerance > tolerance) - { - tolerance = data->absolute_tolerance; - } - if (!reference_is_finite(tolerance)) - { - return HYPRE_ERROR_GENERIC; - } - if (residual_norm == 0.0 || residual_norm <= tolerance) - { - return 0; - } - - while (iteration < data->maximum_iterations) - { - HYPRE_Int cycle_dimension = 0; - HYPRE_Int column; - - callback_error = hypre_ParKrylovScaleVector( - 1.0 / residual_norm, data->basis[0]); - if (callback_error != 0) - { - return callback_error; - } - data->rhs[0] = residual_norm; - - for (column = 0; - column < REFERENCE_GMRES_RESTART && - iteration < data->maximum_iterations; - column++) - { - HYPRE_Real next_norm; - HYPRE_Real diagonal; - HYPRE_Real subdiagonal; - HYPRE_Real rotation_norm; - HYPRE_Int row; - - callback_error = hypre_ParKrylovClearVector(data->preconditioned[column]); - if (callback_error == 0) - { - callback_error = HYPRE_BoomerAMGSolve( - data->amg, (HYPRE_ParCSRMatrix) A, - (HYPRE_ParVector) data->basis[column], - (HYPRE_ParVector) data->preconditioned[column]); - } - if (callback_error != 0) - { - return callback_error; - } - callback_error = hypre_ParKrylovMatvec( - NULL, 1.0, A, data->preconditioned[column], 0.0, - data->basis[column + 1]); - if (callback_error != 0) - { - return callback_error; - } - - /* Two-pass modified Gram-Schmidt controls loss of orthogonality. */ - for (row = 0; row <= column; row++) - { - HYPRE_Real projection = hypre_ParKrylovInnerProd( - data->basis[row], data->basis[column + 1]); - if (!reference_is_finite(projection)) - { - return HYPRE_ERROR_GENERIC; - } - reference_hessenberg_set(data, row, column, projection); - callback_error = hypre_ParKrylovAxpy( - -projection, data->basis[row], data->basis[column + 1]); - if (callback_error != 0) - { - return callback_error; - } - } - for (row = 0; row <= column; row++) - { - HYPRE_Real correction = hypre_ParKrylovInnerProd( - data->basis[row], data->basis[column + 1]); - HYPRE_Real projection = - reference_hessenberg_get(data, row, column) + correction; - if (!reference_is_finite(correction) || !reference_is_finite(projection)) - { - return HYPRE_ERROR_GENERIC; - } - reference_hessenberg_set(data, row, column, projection); - callback_error = hypre_ParKrylovAxpy( - -correction, data->basis[row], data->basis[column + 1]); - if (callback_error != 0) - { - return callback_error; - } - } - if (reference_vector_norm(data->basis[column + 1], &next_norm) != 0) - { - return HYPRE_ERROR_GENERIC; - } - reference_hessenberg_set(data, column + 1, column, next_norm); - if (next_norm > HYPRE_REAL_MIN) - { - callback_error = hypre_ParKrylovScaleVector( - 1.0 / next_norm, data->basis[column + 1]); - if (callback_error != 0) - { - return callback_error; - } - } - - for (row = 0; row < column; row++) - { - HYPRE_Real upper = reference_hessenberg_get(data, row, column); - HYPRE_Real lower = reference_hessenberg_get(data, row + 1, column); - reference_hessenberg_set( - data, row, column, - data->cosines[row] * upper + data->sines[row] * lower); - reference_hessenberg_set( - data, row + 1, column, - -data->sines[row] * upper + data->cosines[row] * lower); - } - - diagonal = reference_hessenberg_get(data, column, column); - subdiagonal = reference_hessenberg_get(data, column + 1, column); - rotation_norm = reference_pair_norm(diagonal, subdiagonal); - if (!reference_is_finite(rotation_norm) || - rotation_norm < HYPRE_REAL_MIN) - { - return HYPRE_ERROR_GENERIC; - } - data->cosines[column] = diagonal / rotation_norm; - data->sines[column] = subdiagonal / rotation_norm; - reference_hessenberg_set(data, column, column, rotation_norm); - reference_hessenberg_set(data, column + 1, column, 0.0); - - data->rhs[column + 1] = - -data->sines[column] * data->rhs[column]; - data->rhs[column] = data->cosines[column] * data->rhs[column]; - if (!reference_is_finite(data->rhs[column]) || - !reference_is_finite(data->rhs[column + 1])) - { - return HYPRE_ERROR_GENERIC; - } - - iteration++; - cycle_dimension = column + 1; - residual_norm = reference_absolute(data->rhs[column + 1]); - if (residual_norm <= tolerance || next_norm <= HYPRE_REAL_MIN) - { - break; - } - } - - callback_error = reference_apply_update(data, cycle_dimension, x); - if (callback_error != 0) - { - return callback_error; - } - callback_error = reference_recompute_residual( - data, A, b, x, &residual_norm); - if (callback_error != 0) - { - return callback_error; - } - if (residual_norm == 0.0 || residual_norm <= tolerance) - { - return 0; - } - } - - return HYPRE_ERROR_CONV; -} - -static HYPRE_Int -reference_GMRESDestroy(HYPRE_Solver solver) -{ - reference_GMRESData *data = (reference_GMRESData *) solver; - HYPRE_Int error; - HYPRE_Int free_error; - - if (data == NULL) - { - return 0; - } - error = reference_destroy_workspace(data); - free_error = hypre_ParKrylovFree(data); - if (error != 0) - { - return error; - } - return free_error; -} diff --git a/submissions/starter.c b/submissions/starter.c deleted file mode 100755 index 3e8c26a..0000000 --- a/submissions/starter.c +++ /dev/null @@ -1,544 +0,0 @@ -#include "HYPRE_parcsr_ls.h" - -enum -{ - NSL_GMRES_RESTART = 200 -}; - -typedef struct -{ - hypre_Solver base; - - HYPRE_Real relative_tolerance; - HYPRE_Real absolute_tolerance; - HYPRE_Int maximum_iterations; - - HYPRE_Matrix matrix; - void **basis; - HYPRE_Real *hessenberg; - HYPRE_Real *cosines; - HYPRE_Real *sines; - HYPRE_Real *rhs; - HYPRE_Real *coefficients; -} nsl_GMRESData; - -static HYPRE_Int nsl_GMRESSetup(HYPRE_Solver solver, HYPRE_Matrix A, - HYPRE_Vector b, HYPRE_Vector x); -static HYPRE_Int nsl_GMRESSolve(HYPRE_Solver solver, HYPRE_Matrix A, - HYPRE_Vector b, HYPRE_Vector x); -static HYPRE_Int nsl_GMRESDestroy(HYPRE_Solver solver); - -static HYPRE_Int -nsl_is_finite(HYPRE_Real value) -{ - return isfinite((double) value) ? 1 : 0; -} - -static HYPRE_Real -nsl_absolute(HYPRE_Real value) -{ - return value < 0.0 ? -value : value; -} - -static HYPRE_Real -nsl_pair_norm(HYPRE_Real left, HYPRE_Real right) -{ - HYPRE_Real larger = nsl_absolute(left); - HYPRE_Real smaller = nsl_absolute(right); - HYPRE_Real ratio; - - if (smaller > larger) - { - HYPRE_Real temporary = larger; - larger = smaller; - smaller = temporary; - } - if (larger == 0.0) - { - return 0.0; - } - ratio = smaller / larger; - return larger * sqrt(1.0 + ratio * ratio); -} - -static HYPRE_Real -nsl_hessenberg_get(const nsl_GMRESData *data, HYPRE_Int row, - HYPRE_Int column) -{ - return data->hessenberg[ - (size_t) row * (size_t) NSL_GMRES_RESTART + (size_t) column]; -} - -static void -nsl_hessenberg_set(nsl_GMRESData *data, HYPRE_Int row, HYPRE_Int column, - HYPRE_Real value) -{ - data->hessenberg[ - (size_t) row * (size_t) NSL_GMRES_RESTART + (size_t) column] = value; -} - -static HYPRE_Int -nsl_vector_norm(void *vector, HYPRE_Real *norm) -{ - HYPRE_Real squared_norm; - - if (vector == NULL || norm == NULL) - { - return HYPRE_ERROR_GENERIC; - } - squared_norm = hypre_ParKrylovInnerProd(vector, vector); - if (!nsl_is_finite(squared_norm) || squared_norm < 0.0) - { - return HYPRE_ERROR_GENERIC; - } - - *norm = sqrt(squared_norm); - return nsl_is_finite(*norm) ? 0 : HYPRE_ERROR_GENERIC; -} - -static HYPRE_Int -nsl_destroy_vector_array(void ***array_pointer, HYPRE_Int count) -{ - HYPRE_Int error = 0; - HYPRE_Int i; - void **array; - - if (array_pointer == NULL || *array_pointer == NULL) - { - return 0; - } - array = *array_pointer; - - for (i = 0; i < count; i++) - { - if (array[i] != NULL) - { - HYPRE_Int callback_error = hypre_ParKrylovDestroyVector(array[i]); - if (error == 0 && callback_error != 0) - { - error = callback_error; - } - } - } - { - HYPRE_Int callback_error = hypre_ParKrylovFree(array); - if (error == 0 && callback_error != 0) - { - error = callback_error; - } - } - *array_pointer = NULL; - return error; -} - -static HYPRE_Int -nsl_destroy_workspace(nsl_GMRESData *data) -{ - HYPRE_Int error = 0; - - if (data == NULL) - { - return 0; - } - - error = nsl_destroy_vector_array( - &data->basis, NSL_GMRES_RESTART + 1); -#define NSL_FREE_HOST(pointer) \ - do \ - { \ - if ((pointer) != NULL) \ - { \ - HYPRE_Int callback_error = hypre_ParKrylovFree(pointer); \ - if (error == 0 && callback_error != 0) \ - { \ - error = callback_error; \ - } \ - (pointer) = NULL; \ - } \ - } while (0) - - NSL_FREE_HOST(data->hessenberg); - NSL_FREE_HOST(data->cosines); - NSL_FREE_HOST(data->sines); - NSL_FREE_HOST(data->rhs); - NSL_FREE_HOST(data->coefficients); - -#undef NSL_FREE_HOST - - data->matrix = NULL; - data->base.is_setup = 0; - return error; -} - -static HYPRE_Int -nsl_allocate_workspace(nsl_GMRESData *data, HYPRE_Vector sample) -{ - data->basis = hypre_ParKrylovCreateVectorArray( - NSL_GMRES_RESTART + 1, sample); - data->hessenberg = (HYPRE_Real *) hypre_ParKrylovCAlloc( - (size_t) (NSL_GMRES_RESTART + 1), - (size_t) NSL_GMRES_RESTART * sizeof(HYPRE_Real), - HYPRE_MEMORY_HOST); - data->cosines = (HYPRE_Real *) hypre_ParKrylovCAlloc( - NSL_GMRES_RESTART, sizeof(HYPRE_Real), HYPRE_MEMORY_HOST); - data->sines = (HYPRE_Real *) hypre_ParKrylovCAlloc( - NSL_GMRES_RESTART, sizeof(HYPRE_Real), HYPRE_MEMORY_HOST); - data->rhs = (HYPRE_Real *) hypre_ParKrylovCAlloc( - NSL_GMRES_RESTART + 1, sizeof(HYPRE_Real), HYPRE_MEMORY_HOST); - data->coefficients = (HYPRE_Real *) hypre_ParKrylovCAlloc( - NSL_GMRES_RESTART, sizeof(HYPRE_Real), HYPRE_MEMORY_HOST); - - if (data->basis == NULL || data->hessenberg == NULL || - data->cosines == NULL || data->sines == NULL || data->rhs == NULL || - data->coefficients == NULL) - { - nsl_destroy_workspace(data); - return HYPRE_ERROR_GENERIC; - } - return 0; -} - -HYPRE_Int -solver_create(HYPRE_Solver *solver, - HYPRE_Real relative_tolerance, - HYPRE_Real absolute_tolerance, - HYPRE_Int maximum_iterations) -{ - nsl_GMRESData *data; - - if (solver == NULL) - { - return HYPRE_ERROR_GENERIC; - } - *solver = NULL; - if (!nsl_is_finite(relative_tolerance) || - !nsl_is_finite(absolute_tolerance) || - relative_tolerance < 0.0 || absolute_tolerance < 0.0 || - maximum_iterations < 0) - { - return HYPRE_ERROR_GENERIC; - } - - data = (nsl_GMRESData *) hypre_ParKrylovCAlloc( - 1, sizeof(*data), HYPRE_MEMORY_HOST); - if (data == NULL) - { - return HYPRE_ERROR_GENERIC; - } - - data->base.setup = nsl_GMRESSetup; - data->base.solve = nsl_GMRESSolve; - data->base.destroy = nsl_GMRESDestroy; - data->base.is_setup = 0; - - /* The public tolerance defines verifier gates, not a residual stopping - rule. A conservative inner target gives ill-conditioned systems enough - room to satisfy the hidden forward-error gate. */ - data->relative_tolerance = - relative_tolerance > 1.0e-12 ? 1.0e-12 : relative_tolerance; - data->absolute_tolerance = absolute_tolerance; - data->maximum_iterations = maximum_iterations; - *solver = (HYPRE_Solver) data; - return 0; -} - -static HYPRE_Int -nsl_GMRESSetup(HYPRE_Solver solver, HYPRE_Matrix A, - HYPRE_Vector b, HYPRE_Vector x) -{ - nsl_GMRESData *data = (nsl_GMRESData *) solver; - HYPRE_Int error; - - if (data == NULL || A == NULL || b == NULL || x == NULL) - { - return HYPRE_ERROR_GENERIC; - } - - error = nsl_destroy_workspace(data); - if (error != 0) - { - return error; - } - error = nsl_allocate_workspace(data, b); - if (error != 0) - { - return error; - } - - data->matrix = A; - data->base.is_setup = 1; - return 0; -} - -static HYPRE_Int -nsl_apply_update(nsl_GMRESData *data, HYPRE_Int dimension, HYPRE_Vector x) -{ - HYPRE_Int row; - HYPRE_Int column; - HYPRE_Int callback_error; - - if (dimension <= 0 || dimension > NSL_GMRES_RESTART) - { - return HYPRE_ERROR_GENERIC; - } - - for (row = dimension - 1; row >= 0; row--) - { - HYPRE_Real value = data->rhs[row]; - HYPRE_Real diagonal; - - for (column = row + 1; column < dimension; column++) - { - value -= nsl_hessenberg_get(data, row, column) * - data->coefficients[column]; - } - diagonal = nsl_hessenberg_get(data, row, row); - if (!nsl_is_finite(value) || !nsl_is_finite(diagonal) || - nsl_absolute(diagonal) < HYPRE_REAL_MIN) - { - return HYPRE_ERROR_GENERIC; - } - data->coefficients[row] = value / diagonal; - if (!nsl_is_finite(data->coefficients[row])) - { - return HYPRE_ERROR_GENERIC; - } - } - - for (column = 0; column < dimension; column++) - { - callback_error = hypre_ParKrylovAxpy( - data->coefficients[column], data->basis[column], x); - if (callback_error != 0) - { - return callback_error; - } - } - return 0; -} - -static HYPRE_Int -nsl_recompute_residual(nsl_GMRESData *data, HYPRE_Matrix A, - HYPRE_Vector b, HYPRE_Vector x, - HYPRE_Real *residual_norm) -{ - HYPRE_Int callback_error; - - callback_error = hypre_ParKrylovCopyVector(b, data->basis[0]); - if (callback_error != 0) - { - return callback_error; - } - callback_error = hypre_ParKrylovMatvec( - NULL, -1.0, A, x, 1.0, data->basis[0]); - if (callback_error != 0) - { - return callback_error; - } - return nsl_vector_norm(data->basis[0], residual_norm); -} - -static HYPRE_Int -nsl_GMRESSolve(HYPRE_Solver solver, HYPRE_Matrix A, - HYPRE_Vector b, HYPRE_Vector x) -{ - nsl_GMRESData *data = (nsl_GMRESData *) solver; - HYPRE_Real b_norm; - HYPRE_Real residual_norm; - HYPRE_Real denominator; - HYPRE_Real tolerance; - HYPRE_Int iteration = 0; - HYPRE_Int callback_error; - - if (data == NULL || A == NULL || b == NULL || x == NULL || - data->basis == NULL || data->hessenberg == NULL || - data->matrix != A || !data->base.is_setup) - { - return HYPRE_ERROR_GENERIC; - } - - if (nsl_vector_norm(b, &b_norm) != 0 || - nsl_recompute_residual(data, A, b, x, &residual_norm) != 0) - { - return HYPRE_ERROR_GENERIC; - } - denominator = b_norm > 0.0 ? b_norm : residual_norm; - tolerance = data->relative_tolerance * denominator; - if (data->absolute_tolerance > tolerance) - { - tolerance = data->absolute_tolerance; - } - if (!nsl_is_finite(tolerance)) - { - return HYPRE_ERROR_GENERIC; - } - if (residual_norm == 0.0 || residual_norm <= tolerance) - { - return 0; - } - - while (iteration < data->maximum_iterations) - { - HYPRE_Int cycle_dimension = 0; - HYPRE_Int column; - - callback_error = hypre_ParKrylovScaleVector( - 1.0 / residual_norm, data->basis[0]); - if (callback_error != 0) - { - return callback_error; - } - data->rhs[0] = residual_norm; - - for (column = 0; - column < NSL_GMRES_RESTART && - iteration < data->maximum_iterations; - column++) - { - HYPRE_Real next_norm; - HYPRE_Real diagonal; - HYPRE_Real subdiagonal; - HYPRE_Real rotation_norm; - HYPRE_Int row; - - callback_error = hypre_ParKrylovMatvec( - NULL, 1.0, A, data->basis[column], 0.0, - data->basis[column + 1]); - if (callback_error != 0) - { - return callback_error; - } - - /* Two-pass modified Gram-Schmidt is deliberately conservative for - the strongly nonnormal matrices in the discovery distribution. */ - for (row = 0; row <= column; row++) - { - HYPRE_Real projection = hypre_ParKrylovInnerProd( - data->basis[row], data->basis[column + 1]); - if (!nsl_is_finite(projection)) - { - return HYPRE_ERROR_GENERIC; - } - nsl_hessenberg_set(data, row, column, projection); - callback_error = hypre_ParKrylovAxpy( - -projection, data->basis[row], data->basis[column + 1]); - if (callback_error != 0) - { - return callback_error; - } - } - for (row = 0; row <= column; row++) - { - HYPRE_Real correction = hypre_ParKrylovInnerProd( - data->basis[row], data->basis[column + 1]); - HYPRE_Real projection = - nsl_hessenberg_get(data, row, column) + correction; - if (!nsl_is_finite(correction) || !nsl_is_finite(projection)) - { - return HYPRE_ERROR_GENERIC; - } - nsl_hessenberg_set(data, row, column, projection); - callback_error = hypre_ParKrylovAxpy( - -correction, data->basis[row], data->basis[column + 1]); - if (callback_error != 0) - { - return callback_error; - } - } - if (nsl_vector_norm(data->basis[column + 1], &next_norm) != 0) - { - return HYPRE_ERROR_GENERIC; - } - nsl_hessenberg_set(data, column + 1, column, next_norm); - if (next_norm > HYPRE_REAL_MIN) - { - callback_error = hypre_ParKrylovScaleVector( - 1.0 / next_norm, data->basis[column + 1]); - if (callback_error != 0) - { - return callback_error; - } - } - - for (row = 0; row < column; row++) - { - HYPRE_Real upper = nsl_hessenberg_get(data, row, column); - HYPRE_Real lower = nsl_hessenberg_get(data, row + 1, column); - nsl_hessenberg_set( - data, row, column, - data->cosines[row] * upper + data->sines[row] * lower); - nsl_hessenberg_set( - data, row + 1, column, - -data->sines[row] * upper + data->cosines[row] * lower); - } - - diagonal = nsl_hessenberg_get(data, column, column); - subdiagonal = nsl_hessenberg_get(data, column + 1, column); - rotation_norm = nsl_pair_norm(diagonal, subdiagonal); - if (!nsl_is_finite(rotation_norm) || - rotation_norm < HYPRE_REAL_MIN) - { - return HYPRE_ERROR_GENERIC; - } - data->cosines[column] = diagonal / rotation_norm; - data->sines[column] = subdiagonal / rotation_norm; - nsl_hessenberg_set(data, column, column, rotation_norm); - nsl_hessenberg_set(data, column + 1, column, 0.0); - - data->rhs[column + 1] = - -data->sines[column] * data->rhs[column]; - data->rhs[column] = data->cosines[column] * data->rhs[column]; - if (!nsl_is_finite(data->rhs[column]) || - !nsl_is_finite(data->rhs[column + 1])) - { - return HYPRE_ERROR_GENERIC; - } - - iteration++; - cycle_dimension = column + 1; - residual_norm = nsl_absolute(data->rhs[column + 1]); - if (residual_norm <= tolerance || next_norm <= HYPRE_REAL_MIN) - { - break; - } - } - - callback_error = nsl_apply_update(data, cycle_dimension, x); - if (callback_error != 0) - { - return callback_error; - } - callback_error = nsl_recompute_residual( - data, A, b, x, &residual_norm); - if (callback_error != 0) - { - return callback_error; - } - if (residual_norm == 0.0 || residual_norm <= tolerance) - { - return 0; - } - } - - return HYPRE_ERROR_CONV; -} - -static HYPRE_Int -nsl_GMRESDestroy(HYPRE_Solver solver) -{ - nsl_GMRESData *data = (nsl_GMRESData *) solver; - HYPRE_Int error; - HYPRE_Int free_error; - - if (data == NULL) - { - return 0; - } - error = nsl_destroy_workspace(data); - free_error = hypre_ParKrylovFree(data); - if (error != 0) - { - return error; - } - return free_error; -} diff --git a/tests/conftest.py b/tests/conftest.py deleted file mode 100644 index d9edad2..0000000 --- a/tests/conftest.py +++ /dev/null @@ -1,43 +0,0 @@ -from __future__ import annotations - -import numpy as np -import pytest -from scipy import sparse - -from linear_solver_bench.accuracy import AccuracyThresholds -from linear_solver_bench.models import CsrMatrix, EvaluationSystem, MatrixInput -from linear_solver_bench.protocol import DriverOutput - - -@pytest.fixture -def small_system() -> EvaluationSystem: - matrix = CsrMatrix.from_scipy( - sparse.csr_matrix(np.asarray([[4.0, 1.0], [2.0, 3.0]])) - ) - truth = np.asarray([1.0, -1.0], dtype=np.float64) - return EvaluationSystem( - case_id="test-small", - public=MatrixInput( - matrix=matrix, - b=matrix.matvec(truth), - x0=np.zeros(2, dtype=np.float64), - tolerance=1.0e-4, - ), - x_star=truth, - ) - - -@pytest.fixture -def driver_output(small_system): - return ( - DriverOutput( - status=0, - solution=small_system.x_star, - elapsed_s=0.01, - create_s=0.001, - setup_s=0.003, - solve_s=0.006, - input_mutated=False, - ), - AccuracyThresholds.from_tolerance(1.0e-4), - ) diff --git a/tests/test_assets.py b/tests/test_assets.py new file mode 100644 index 0000000..3fb2bd9 --- /dev/null +++ b/tests/test_assets.py @@ -0,0 +1,9 @@ +from linear_solver_bench.assets import asset_root + + +def test_runtime_assets_are_available(): + root = asset_root() + assert (root / "families" / "ns_mesh_pde" / "dev.json").is_file() + assert (root / "native" / "src" / "driver.cpp").is_file() + assert (root / "reference" / "solver.c").is_file() + assert (root / "examples" / "solver.c").is_file() diff --git a/tests/test_benchmark.py b/tests/test_benchmark.py new file mode 100644 index 0000000..16d90a7 --- /dev/null +++ b/tests/test_benchmark.py @@ -0,0 +1,94 @@ +import struct + +import numpy as np +import pytest + +from linear_solver_bench.benchmark import _case_rows, _report +from linear_solver_bench.models import BenchmarkCase, CaseInput, CsrMatrix, RawExecution +from linear_solver_bench.verify import OUTPUT_HEADER, OUTPUT_MAGIC + + +def _case(case_id="small", role="scored"): + matrix = CsrMatrix( + 1, + np.array([0, 1], dtype=np.uint64), + np.array([0], dtype=np.uint32), + np.array([2.0]), + ) + return BenchmarkCase( + case_id, + CaseInput(matrix, np.array([4.0]), np.array([0.0]), 1e-10), + target=np.array([2.0]), + input_sha256="a" * 64, + role=role, + ) + + +def _execution(nanoseconds, solution=2.0): + payload = OUTPUT_HEADER.pack( + OUTPUT_MAGIC, + 1, + 0, + 1, + nanoseconds, + 0, + 0, + nanoseconds, + 0, + ) + struct.pack(" Path: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(text, encoding="utf-8") + return path + + +def _mock_runtime(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> build.Runtime: + cc, cxx, ar = shutil.which("cc"), shutil.which("c++"), shutil.which("ar") + if not all((cc, cxx, ar, shutil.which("nm"))): + pytest.skip("a C/C++ toolchain is required") + + root = tmp_path / "runtime" + include = root / "include" + trusted = root / "trusted" + library = root / "lib" / "libHYPRE.a" + include.mkdir(parents=True) + trusted.mkdir() + library.parent.mkdir() + _write( + include / "HYPRE_parcsr_ls.h", + """ +#ifndef MOCK_HYPRE +#define MOCK_HYPRE +typedef int HYPRE_Int; +typedef double HYPRE_Real; +typedef void *HYPRE_Solver; +#endif +""".lstrip(), + ) + _write( + include / "nsl_hypre_solver.h", + '#include "HYPRE_parcsr_ls.h"\n' + "HYPRE_Int solver_create(HYPRE_Solver *, HYPRE_Real, HYPRE_Real);\n", + ) + driver = _write( + tmp_path / "driver.cpp", + """ +extern "C" int solver_create(void **, double, double); +int main() { + void *solver = nullptr; + return solver_create(&solver, 1.0e-4, 0.0); +} +""".lstrip(), + ) + subprocess.run( + (cxx, "-c", str(driver), "-o", str(trusted / "driver.o")), check=True + ) + unused = _write(tmp_path / "unused.c", "int unused_symbol = 0;\n") + unused_object = tmp_path / "unused.o" + subprocess.run((cc, "-c", str(unused), "-o", str(unused_object)), check=True) + subprocess.run((ar, "rcs", str(library), str(unused_object)), check=True) + _write(trusted / "allowed-symbols.txt", "") + runtime = build.Runtime(root, "a" * 64) + monkeypatch.setattr(build, "load_runtime", lambda path: runtime) + return runtime + + +def _candidate(path: Path, body: str = "return 0;") -> Path: + return _write( + path, + f"""#include "HYPRE_parcsr_ls.h" +HYPRE_Int solver_create(HYPRE_Solver *solver, + HYPRE_Real relative_tolerance, + HYPRE_Real absolute_tolerance) {{ + (void) solver; + (void) relative_tolerance; + (void) absolute_tolerance; + {body} +}} +""", + ) + + +def test_source_contract_is_small_and_explicit(tmp_path: Path) -> None: + assert "solver_create" in build.validate_source(build.EXAMPLE_SOURCE) + bad = _write( + tmp_path / "bad.c", + '#include "HYPRE_parcsr_ls.h"\n#include \n', + ) + with pytest.raises(build.BuildError, match="include exactly"): + build.validate_source(bad) + + link = tmp_path / "link.c" + link.symlink_to(build.EXAMPLE_SOURCE) + with pytest.raises(ValueError, match="non-symlink"): + build.validate_source(link) + + +def test_solver_is_compiled_audited_and_linked( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + runtime = _mock_runtime(tmp_path, monkeypatch) + source = _candidate(tmp_path / "candidate.c") + result = build.build_solver(source, tmp_path / "candidate", runtime) + assert result.executable.is_file() + assert result.source_sha256 == build._sha256(source) + assert result.executable_sha256 == build._sha256(result.executable) + subprocess.run((str(result.executable),), check=True) + + +def test_solver_audit_rejects_forbidden_calls_and_exports( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + runtime = _mock_runtime(tmp_path, monkeypatch) + forbidden = _candidate( + tmp_path / "forbidden.c", + 'extern int puts(const char *); return puts("not allowed");', + ) + with pytest.raises(build.BuildError, match="forbidden symbols.*puts"): + build.build_solver(forbidden, tmp_path / "forbidden", runtime) + + wrong_export = ( + _candidate(tmp_path / "wrong.c") + .read_text() + .replace("solver_create", "old_solver_create") + ) + wrong = _write(tmp_path / "wrong.c", wrong_export) + with pytest.raises(build.BuildError, match="global exports"): + build.build_solver(wrong, tmp_path / "wrong", runtime) + + +def test_runtime_manifest_detects_tampering(tmp_path: Path) -> None: + root = tmp_path / "runtime" + _write(root / "include" / "nsl_hypre_solver.h", "policy\n") + _write(root / "lib" / "libHYPRE.a", "library\n") + _write(root / "trusted" / "driver.o", "driver\n") + _write(root / "trusted" / "allowed-symbols.txt", "symbol\n") + relative_files = { + "policy_header": "include/nsl_hypre_solver.h", + "hypre_library": "lib/libHYPRE.a", + "driver_object": "trusted/driver.o", + "allowed_symbols": "trusted/allowed-symbols.txt", + } + body = { + "schema_version": 1, + "build_kind": build.BUILD_KIND, + "candidate_abi": build.CANDIDATE_ABI, + "factory_export": build.FACTORY_EXPORT, + "hypre": { + "repository": build.HYPRE_REPOSITORY, + "commit": build.HYPRE_COMMIT, + "tree": build.HYPRE_TREE, + }, + "include_tree_sha256": build._tree_sha256(root / "include"), + "files": { + name: {"path": path, "sha256": build._sha256(root / path)} + for name, path in relative_files.items() + }, + "tool_versions": {}, + } + manifest = {**body, "manifest_sha256": build._canonical_sha256(body)} + _write(root / "manifest.json", json.dumps(manifest)) + runtime = build.load_runtime(root) + assert runtime.root == root.resolve() + + _write(root / "trusted" / "driver.o", "changed\n") + with pytest.raises(ValueError, match="driver_object"): + build.load_runtime(root) + + +def test_only_the_fixed_reference_gets_trusted_symbols( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + assert build._sha256(build.REFERENCE_SOURCE) == build.REFERENCE_SOURCE_SHA256 + runtime = _mock_runtime(tmp_path, monkeypatch) + changed = _write( + tmp_path / "changed-reference.c", + build.REFERENCE_SOURCE.read_text().replace("GMRES(200)", "GMRES(100)"), + ) + with pytest.raises(build.BuildError, match="fixed reference"): + build.build_reference(changed, tmp_path / "reference", runtime) diff --git a/tests/test_cli.py b/tests/test_cli.py new file mode 100644 index 0000000..c1221e8 --- /dev/null +++ b/tests/test_cli.py @@ -0,0 +1,25 @@ +from linear_solver_bench.cli import main, parser + + +def test_modal_is_the_default_venue(): + args = parser().parse_args(["run", "solver.c", "--family", "ns_mesh_pde"]) + assert args.venue == "modal" + + +def test_init_copies_the_starter(tmp_path): + output = tmp_path / "solver.c" + + assert main(["init", str(output)]) == 0 + + assert output.is_file() + assert "solver_create" in output.read_text(encoding="utf-8") + + +def test_init_refuses_to_overwrite(tmp_path, capsys): + output = tmp_path / "solver.c" + output.write_text("mine", encoding="utf-8") + + assert main(["init", str(output)]) == 2 + + assert output.read_text(encoding="utf-8") == "mine" + assert "already exists" in capsys.readouterr().err diff --git a/tests/test_compiler.py b/tests/test_compiler.py deleted file mode 100644 index 27e7fe0..0000000 --- a/tests/test_compiler.py +++ /dev/null @@ -1,143 +0,0 @@ -from __future__ import annotations - -import pathlib -import shutil -import subprocess -from types import SimpleNamespace - -import pytest - -from linear_solver_bench.compiler import ( - CandidateBuildError, - build_candidate, - validate_source, -) - - -def write(path: pathlib.Path, value: str) -> pathlib.Path: - path.write_text(value, encoding="utf-8") - return path - - -def mock_runtime(tmp_path: pathlib.Path): - cc = shutil.which("cc") - cxx = shutil.which("c++") - nm = shutil.which("nm") - ar = shutil.which("ar") - if not all((cc, cxx, nm, ar)): - pytest.skip("native compiler toolchain is unavailable") - include = tmp_path / "include" - include.mkdir() - write( - include / "HYPRE_parcsr_ls.h", - """ -#ifndef MOCK_HYPRE_HEADER -#define MOCK_HYPRE_HEADER -typedef int HYPRE_Int; -typedef double HYPRE_Real; -typedef void *HYPRE_Solver; -#endif -""".lstrip(), - ) - policy_header = write( - include / "nsl_hypre_solver.h", - """ -#include "HYPRE_parcsr_ls.h" -HYPRE_Int solver_create(HYPRE_Solver *, HYPRE_Real, HYPRE_Real, HYPRE_Int); -""".lstrip(), - ) - driver_source = write( - tmp_path / "driver.cpp", - """ -extern "C" int solver_create(void **, double, double, int); -int main() { - void *solver = nullptr; - return solver_create(&solver, 1.0e-4, 0.0, 10); -} -""".lstrip(), - ) - driver_object = tmp_path / "driver.o" - subprocess.run( - (cxx, "-c", str(driver_source), "-o", str(driver_object)), check=True - ) - unused_source = write(tmp_path / "unused.c", "int lsb_unused = 0;\n") - unused_object = tmp_path / "unused.o" - library = tmp_path / "libHYPRE.a" - subprocess.run((cc, "-c", str(unused_source), "-o", str(unused_object)), check=True) - subprocess.run((ar, "rcs", str(library), str(unused_object)), check=True) - return SimpleNamespace( - cc=cc, - cxx=cxx, - nm=nm, - include_dir=include, - policy_header=policy_header, - driver_object=driver_object, - hypre_library=library, - ) - - -def test_source_contract_accepts_starter_and_rejects_extra_include( - tmp_path: pathlib.Path, -) -> None: - validate_source(pathlib.Path("submissions/starter.c")) - bad = write( - tmp_path / "bad.c", - '#include "HYPRE_parcsr_ls.h"\n#include \n', - ) - with pytest.raises(CandidateBuildError, match="include exactly"): - validate_source(bad) - - -def test_candidate_is_compiled_audited_and_linked(tmp_path: pathlib.Path) -> None: - runtime = mock_runtime(tmp_path) - source = write( - tmp_path / "candidate.c", - """ -#include "HYPRE_parcsr_ls.h" -HYPRE_Int solver_create(HYPRE_Solver *solver, - HYPRE_Real relative_tolerance, - HYPRE_Real absolute_tolerance, - HYPRE_Int maximum_iterations) { - (void) relative_tolerance; - (void) absolute_tolerance; - (void) maximum_iterations; - *solver = (HYPRE_Solver) 0; - return 0; -} -""".lstrip(), - ) - build = build_candidate(source, tmp_path / "candidate-build", runtime=runtime) - assert build.executable.is_file() - assert build.executable.stat().st_mode & 0o111 - assert build.exported_symbols == ("solver_create",) - subprocess.run((str(build.executable),), check=True) - - old_source = write( - tmp_path / "old-candidate.c", - source.read_text().replace("solver_create", "nsl_create"), - ) - with pytest.raises(CandidateBuildError, match="global exports must be exactly"): - build_candidate(old_source, tmp_path / "old-build", runtime=runtime) - - -def test_forbidden_undefined_symbol_is_rejected(tmp_path: pathlib.Path) -> None: - runtime = mock_runtime(tmp_path) - source = write( - tmp_path / "candidate.c", - """ -#include "HYPRE_parcsr_ls.h" -extern int puts(const char *); -HYPRE_Int solver_create(HYPRE_Solver *solver, - HYPRE_Real relative_tolerance, - HYPRE_Real absolute_tolerance, - HYPRE_Int maximum_iterations) { - (void) solver; - (void) relative_tolerance; - (void) absolute_tolerance; - (void) maximum_iterations; - return puts("forbidden"); -} -""".lstrip(), - ) - with pytest.raises(CandidateBuildError, match="forbidden symbols"): - build_candidate(source, tmp_path / "candidate-build", runtime=runtime) diff --git a/tests/test_data.py b/tests/test_data.py new file mode 100644 index 0000000..b0d0c3c --- /dev/null +++ b/tests/test_data.py @@ -0,0 +1,180 @@ +import hashlib +import io +import json +import tarfile +import zipfile +from pathlib import Path + +import numpy as np +import pytest +from scipy import sparse + +from linear_solver_bench.data import ( + _matrix_sha256, + _vector_sha256, + iter_cases, + load_manifest, +) +from linear_solver_bench.families import resolve_family +from linear_solver_bench.models import CsrMatrix + + +def _archive_source(path: Path, **extra): + payload = path.read_bytes() + return { + "url": path.as_uri(), + "archive_bytes": len(payload), + "archive_sha256": hashlib.sha256(payload).hexdigest(), + **extra, + } + + +def _ns_manifest(tmp_path: Path): + matrix_market = b"""%%MatrixMarket matrix coordinate real general +2 2 3 +1 1 2 +1 2 1 +2 2 3 +""" + archive_path = tmp_path / "toy.tar.gz" + with tarfile.open(archive_path, "w:gz") as archive: + member = tarfile.TarInfo("toy/toy.mtx") + member.size = len(matrix_market) + archive.addfile(member, io.BytesIO(matrix_market)) + matrix = CsrMatrix.from_scipy(sparse.csr_matrix(np.array([[2.0, 1.0], [0.0, 3.0]]))) + key = b"development-key!" + case = { + "index": 0, + "case_id": "ns-toy", + "n": 2, + "nnz": 3, + "tolerance": 1e-10, + "role": "scored", + "source": _archive_source( + archive_path, + kind="suitesparse", + name="toy", + group="test", + matrix_id=1, + matrix_sha256=_matrix_sha256(matrix), + ), + } + return { + "release_id": "ns-test", + "family": "ns-mesh-pde", + "rhs": { + "scheme": "rademacher-hmac-sha256-v2", + "key_id": hashlib.sha256(key).hexdigest(), + "public_key_hex": key.hex(), + "draw_index": 0, + }, + "cases": [case], + } + + +def _npy(value: np.ndarray) -> bytes: + output = io.BytesIO() + np.save(output, value, allow_pickle=False) + return output.getvalue() + + +def _flash_manifest(tmp_path: Path): + scipy_matrix = sparse.csr_matrix(np.array([[4.0, 1.0], [0.0, 2.0]])) + matrix = CsrMatrix.from_scipy(scipy_matrix) + matrix_buffer = io.BytesIO() + sparse.save_npz(matrix_buffer, scipy_matrix) + files = { + "matrix.npz": matrix_buffer.getvalue(), + "b.npy": _npy(np.array([2.0, 3.0], dtype=np.float64)), + "x0.npy": _npy(np.array([0.5, -0.5], dtype=np.float64)), + } + source_hashes = { + "matrix_sha256": _matrix_sha256(matrix), + "b_sha256": _vector_sha256(np.array([2.0, 3.0], dtype=np.float64)), + "x0_sha256": _vector_sha256(np.array([0.5, -0.5], dtype=np.float64)), + } + metadata = { + "schema_version": 1, + "case_id": "flash-toy", + "family": "magnetic_diffusion_flash", + "capture_kind": "scalar-magnetic-diffusion", + "n": 2, + "nnz": 3, + "relative_tolerance": 1e-9, + "absolute_tolerance": 0, + "provenance_group": "test-group", + "source_run": "test-run", + "step": 1, + "solve_index": 0, + **source_hashes, + "files": { + name: hashlib.sha256(value).hexdigest() for name, value in files.items() + }, + } + files["case.json"] = json.dumps(metadata).encode() + archive_path = tmp_path / "flash.zip" + with zipfile.ZipFile(archive_path, "w") as archive: + for name, value in files.items(): + archive.writestr(name, value) + case = { + "index": 0, + "case_id": "flash-toy", + "n": 2, + "nnz": 3, + "tolerance": 1e-9, + "role": "scored", + "provenance_group": "test-group", + "source_run": "test-run", + "admission": {"step": 1, "solve_index": 0}, + "source": _archive_source( + archive_path, + kind="huggingface", + **source_hashes, + ), + } + return {"family": "magnetic_diffusion_flash", "cases": [case]} + + +def test_bundled_manifests_are_authenticated(): + ns = load_manifest("ns_mesh_pde") + flash = load_manifest(resolve_family("flash")) + + assert len(ns["cases"]) == 19 + assert len(flash["cases"]) == 96 + with pytest.raises(ValueError, match="choose"): + resolve_family("anything-else") + + +def test_ns_archive_is_verified_and_target_is_isolated(tmp_path): + manifest = _ns_manifest(tmp_path) + cache = tmp_path / "cache" + case = next(iter_cases(manifest, cache)) + + assert set(case.target) <= {-1.0, 1.0} + np.testing.assert_allclose(case.input.b, case.input.matrix.matvec(case.target)) + assert len(case.input_sha256) == 64 + assert not hasattr(case.input, "target") + + # A poisoned cache entry is discarded and fetched again from the source. + cached = cache / manifest["cases"][0]["source"]["archive_sha256"] + cached.write_bytes(b"wrong") + assert next(iter_cases(manifest, cache)).input_sha256 == case.input_sha256 + + +def test_flash_capture_preserves_vectors_and_hashes(tmp_path): + case = next(iter_cases(_flash_manifest(tmp_path), tmp_path / "cache")) + + assert case.target is None + np.testing.assert_array_equal(case.input.b, [2.0, 3.0]) + np.testing.assert_array_equal(case.input.x0, [0.5, -0.5]) + assert len(case.input_sha256) == 64 + + +def test_case_selection_and_bad_download_digest(tmp_path): + manifest = _ns_manifest(tmp_path) + with pytest.raises(ValueError, match="unknown case"): + list(iter_cases(manifest, tmp_path / "cache", ["missing"])) + + manifest["cases"][0]["source"]["archive_sha256"] = "0" * 64 + with pytest.raises(ValueError, match="digest"): + list(iter_cases(manifest, tmp_path / "other-cache")) diff --git a/tests/test_dataset.py b/tests/test_dataset.py deleted file mode 100644 index 09d793b..0000000 --- a/tests/test_dataset.py +++ /dev/null @@ -1,89 +0,0 @@ -from __future__ import annotations - -import numpy as np -from scipy import sparse - -from linear_solver_bench.dataset import ( - dataset_summary, - load_split, - materialize_system, - matrix_sha256, - rademacher_x_star, -) -from linear_solver_bench.models import CsrMatrix - - -def test_frozen_dataset_counts_and_identity() -> None: - assert dataset_summary() == { - "screened": 350, - "full_rank": 213, - "rank_deficient": 137, - "condition_unsupported": 10, - "ranked": 203, - "development": 8, - "tolerance_tiers": [1.0e-4, 1.0e-3, 1.0e-2, 4.0e-2], - } - ranked = load_split("ranked") - development = load_split("dev") - assert ranked["manifest_sha256"] == ( - "85ad37de2d7fc4b787e7eb0d5248eaebccf7ccaa04fd6f24e58637d37d6ef69f" - ) - assert development["manifest_sha256"] == ( - "b743e8cf87f7c286b84d3ddac567d90460228b194df43e49c9e7fa8398cea454" - ) - assert len(ranked["cases"]) == 203 - assert {case["condition_norm"] for case in ranked["cases"]} == {"1"} - assert sorted({case["tolerance"] for case in ranked["cases"]}) == [ - 1.0e-4, - 1.0e-3, - 1.0e-2, - 4.0e-2, - ] - assert len({case["group"] for case in development["cases"]}) == 8 - assert {case["case_id"] for case in development["cases"]} <= { - case["case_id"] for case in ranked["cases"] - } - - -def test_rhs_derivation_is_deterministic_and_keyed() -> None: - first = rademacher_x_star("case-a", 100, b"a" * 32) - again = rademacher_x_star("case-a", 100, b"a" * 32) - other = rademacher_x_star("case-a", 100, b"b" * 32) - np.testing.assert_array_equal(first, again) - assert not np.array_equal(first, other) - assert set(first) == {-1.0, 1.0} - assert first[:16].astype(int).tolist() == [ - -1, - 1, - 1, - 1, - 1, - -1, - -1, - -1, - 1, - 1, - 1, - -1, - 1, - 1, - -1, - 1, - ] - - -def test_materialization_manufactures_a_consistent_rhs() -> None: - matrix = sparse.csr_matrix(np.asarray([[3.0, 1.0], [-2.0, 4.0]])) - canonical = CsrMatrix.from_scipy(matrix) - case = { - "case_id": "tiny-case", - "n": 2, - "nnz": 4, - "matrix_sha256": matrix_sha256(canonical), - "tolerance": 1.0e-4, - } - system = materialize_system(case, matrix, b"operator-test-key") - np.testing.assert_array_equal( - system.public.b, system.public.matrix.matvec(system.x_star) - ) - np.testing.assert_array_equal(system.public.x0, np.zeros(2)) diff --git a/tests/test_flash_release_builder.py b/tests/test_flash_release_builder.py deleted file mode 100644 index f39ec80..0000000 --- a/tests/test_flash_release_builder.py +++ /dev/null @@ -1,234 +0,0 @@ -from __future__ import annotations - -import copy -import hashlib -import importlib.util -import io -import json -import pathlib -import zipfile -from collections import Counter - -import numpy as np -import pytest -from scipy import sparse - -from linear_solver_bench.models import CsrMatrix -from linear_solver_bench.workloads import matrix_digest, vector_digest - - -@pytest.fixture -def builder(): - path = pathlib.Path(__file__).resolve().parents[1] / "tools/build_flash_release.py" - spec = importlib.util.spec_from_file_location("flash_release_builder", path) - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - return module - - -@pytest.fixture -def catalogue(tmp_path): - layout = [ - ("dev", "group-a", "run-a", "scored"), - ("dev", "group-a", "run-a", "scored"), - ("dev", "group-a", "run-b", "scored"), - ("dev", "group-b", "run-c", "scored"), - ("dev", "group-control", "run-control", "control"), - ("ranked", "group-ranked", "run-ranked", "scored"), - ] - cases = [] - for index, (split, group, run, role) in enumerate(layout): - matrix = CsrMatrix.from_scipy( - sparse.csr_matrix([[3.0 + index, -1.0], [-1.0, 2.0]]) - ) - b, x0 = np.array([1.25, -3.5]), np.array([0.25, -0.75]) - members = {} - for name, value in (("b.npy", b), ("x0.npy", x0)): - buffer = io.BytesIO() - np.save(buffer, value) - members[name] = buffer.getvalue() - buffer = io.BytesIO() - sparse.save_npz(buffer, matrix.to_scipy()) - members["matrix.npz"] = buffer.getvalue() - record = { - "case_id": f"case-{index}", - "split": split, - "role": role, - "provenance_group": group, - "source_run": run, - "n": matrix.n, - "nnz": matrix.nnz, - "tolerance": 1e-8, - "step": index, - "solve_index": 0, - "matrix_sha256": matrix_digest(matrix), - "b_sha256": vector_digest(b), - "x0_sha256": vector_digest(x0), - "path": f"cases/case-{index}.zip", - } - metadata = { - "schema_version": 1, - "family": "magnetic_diffusion_flash", - "capture_kind": "scalar-magnetic-diffusion", - "relative_tolerance": record["tolerance"], - "absolute_tolerance": 0, - **{ - key: record[key] - for key in ( - "case_id", - "provenance_group", - "source_run", - "n", - "nnz", - "step", - "solve_index", - "matrix_sha256", - "b_sha256", - "x0_sha256", - ) - }, - "files": { - name: hashlib.sha256(payload).hexdigest() - for name, payload in members.items() - }, - } - members["case.json"] = json.dumps(metadata).encode("utf-8") - archive_path = tmp_path / record["path"] - archive_path.parent.mkdir(exist_ok=True) - with zipfile.ZipFile(archive_path, "w") as archive: - for name, payload in members.items(): - archive.writestr(name, payload) - record.update( - archive_bytes=archive_path.stat().st_size, - archive_sha256=hashlib.sha256(archive_path.read_bytes()).hexdigest(), - ) - cases.append(record) - runs = {} - counts = Counter(case["source_run"] for case in cases) - for case in cases: - runs[case["source_run"]] = { - "source_run": case["source_run"], - "problem": "Synthetic public test problem", - "split": case["split"], - "provenance_group": case["provenance_group"], - "case_count": counts[case["source_run"]], - } - return { - "schema_version": 1, - "kind": "flash-replay-public-catalogue", - "family": "magnetic_diffusion_flash", - "case_count": len(cases), - "split_counts": {"dev": 5, "ranked": 1}, - "cases": cases, - "source_runs": list(runs.values()), - } - - -def test_builds_pinned_splits_with_hierarchical_weights(builder, catalogue, tmp_path): - revision = "a" * 40 - releases = builder.build_releases( - catalogue, revision=revision, archive_dir=tmp_path - ) - assert [case["weight"] for case in releases["dev"]["cases"]] == [ - 0.125, - 0.125, - 0.25, - 0.5, - 0, - ] - for split, release in releases.items(): - assert release["release_id"] == "flash-replay-pilot" - assert release["split"] == split - assert release["execution"] == { - "repetitions": 1, - "cpus": 2, - "memory_bytes": 4 * 1024**3, - "case_timeout_seconds": 90.0, - "max_iterations": 5000, - } - assert release["rhs"] is None - for case in release["cases"]: - assert case["qualification"] is None - assert f"/resolve/{revision}/cases/" in case["source"]["url"] - assert case["admission"]["citations"][0].endswith( - f"/{revision}/catalogue.json" - ) - assert "Synthetic public test problem" in case["admission"]["statement"] - source, output = tmp_path / "catalogue.json", tmp_path / "manifests" - source.write_text(json.dumps(catalogue), encoding="utf-8") - assert ( - builder.main( - [ - str(source), - "--revision", - revision, - "--output-dir", - str(output), - "--archive-dir", - str(tmp_path), - ] - ) - == 0 - ) - assert {path.name for path in output.iterdir()} == { - "flash-replay-dev-pilot.json", - "flash-replay-ranked-pilot.json", - } - for split, release in releases.items(): - assert ( - json.loads((output / f"flash-replay-{split}-pilot.json").read_text()) - == release - ) - - -@pytest.mark.parametrize("revision", ["main", "a" * 39, "A" * 40]) -def test_requires_an_explicit_commit(builder, catalogue, revision): - with pytest.raises(ValueError, match="40-character commit"): - builder.build_releases(catalogue, revision=revision) - - -def test_archive_metadata_mismatch_leaves_no_manifests(builder, catalogue, tmp_path): - record = catalogue["cases"][0] - path = tmp_path / record["path"] - with zipfile.ZipFile(path) as archive: - members = {name: archive.read(name) for name in archive.namelist()} - metadata = json.loads(members["case.json"]) - metadata["source_run"] = "incorrect-run" - members["case.json"] = json.dumps(metadata).encode("utf-8") - with zipfile.ZipFile(path, "w") as archive: - for name, payload in members.items(): - archive.writestr(name, payload) - record.update( - archive_bytes=path.stat().st_size, - archive_sha256=hashlib.sha256(path.read_bytes()).hexdigest(), - ) - source, output = tmp_path / "catalogue.json", tmp_path / "manifests" - source.write_text(json.dumps(catalogue), encoding="utf-8") - assert ( - builder.main( - [ - str(source), - "--revision", - "b" * 40, - "--output-dir", - str(output), - "--archive-dir", - str(tmp_path), - ] - ) - == 2 - ) - assert not output.exists() - - -def test_cross_split_operator_overlap_is_rejected(builder, catalogue): - catalogue["cases"][-1]["matrix_sha256"] = catalogue["cases"][0]["matrix_sha256"] - with pytest.raises(ValueError, match="operator exposure"): - builder.build_releases(catalogue, revision="a" * 40) - - -def test_unsafe_archive_path_is_rejected(builder, catalogue): - invalid = copy.deepcopy(catalogue) - invalid["cases"][0]["path"] = "../case.zip" - with pytest.raises(ValueError, match="relative ZIP"): - builder.build_releases(invalid, revision="a" * 40) diff --git a/tests/test_modal.py b/tests/test_modal.py new file mode 100644 index 0000000..9bf0ddd --- /dev/null +++ b/tests/test_modal.py @@ -0,0 +1,415 @@ +"""Modal transport tests use fakes: no credentials or network are needed.""" + +from __future__ import annotations + +import importlib +import json +import pathlib +import subprocess +import sys +import types +import zlib + +import numpy as np +import pytest + +from linear_solver_bench import modal as venue +from linear_solver_bench.models import CaseInput, CsrMatrix, RawExecution + + +class FakeProcess: + def __init__(self, returncode=0, stdout=b"", stderr=b""): + self.returncode = returncode + self.stdout = [stdout] if stdout else [] + self.stderr = [stderr] if stderr else [] + + def wait(self): + return self.returncode + + +class FakeFilesystem: + def __init__(self): + self.files = {} + self.copies = [] + self.removed = [] + + def copy_from_local(self, local, remote): + self.copies.append((pathlib.Path(local), remote)) + self.files[remote] = pathlib.Path(local).read_bytes() + + def write_bytes(self, value, path): + self.files[path] = value + + def read_text(self, path): + return self.files[path].decode() + + def remove(self, path, *, recursive=False): + assert recursive + self.removed.append(path) + for name in list(self.files): + if name == path or name.startswith(path + "/"): + del self.files[name] + + +class FakeSandbox: + def __init__(self): + self.filesystem = FakeFilesystem() + self.creation = None + self.terminated = False + self.builds = 0 + self.runs = [] + self.outcomes = [] + + def create(self, **kwargs): + self.creation = kwargs + return self + + def exec(self, *args, **kwargs): + if args[:3] == ("mkdir", "-p", "/work"): + return FakeProcess() + if args[:3] == ("python", "-c", venue._BUILD_EXEC): + self.builds += 1 + self.filesystem.files["/work/build.json"] = json.dumps( + { + "candidate_source_sha256": "a" * 64, + "candidate_executable_sha256": "b" * 64, + "reference_source_sha256": "c" * 64, + "reference_executable_sha256": "d" * 64, + "runtime_manifest_sha256": "e" * 64, + "image_id": "im-test", + } + ).encode() + return FakeProcess() + if args[:3] == ("python", "-c", venue._INPUT_EXEC): + compressed = self.filesystem.files[args[4]] + assert int(args[3]) == len(zlib.decompress(compressed)) + self.runs.append((args, kwargs)) + outcome = self.outcomes.pop(0) if self.outcomes else 0 + if isinstance(outcome, BaseException): + raise outcome + if outcome == 0: + self.filesystem.files[args[7]] = b"driver output" + if outcome == "input-error": + return FakeProcess(125, stderr=venue._INPUT_ERROR.encode()) + return FakeProcess(outcome) + if args[:2] == ("head", "-c"): + value = self.filesystem.files.get(args[3]) + if value is None: + return FakeProcess(1) + return FakeProcess(stdout=value[: int(args[2])]) + raise AssertionError(f"unexpected sandbox command: {args!r}") + + def terminate(self): + self.terminated = True + + +class FakeImage: + calls = [] + + @classmethod + def from_registry(cls, *args, **kwargs): + cls.calls = [("from_registry", args, kwargs)] + return cls() + + def __getattr__(self, name): + def call(*args, **kwargs): + self.calls.append((name, args, kwargs)) + return self + + return call + + +def fake_modal(sandbox): + return types.SimpleNamespace( + Image=FakeImage, + App=types.SimpleNamespace(lookup=lambda *args, **kwargs: ("app", args, kwargs)), + Sandbox=types.SimpleNamespace(create=sandbox.create), + ) + + +@pytest.fixture +def case(): + matrix = CsrMatrix( + 2, + np.array([0, 2, 4], dtype=np.uint64), + np.array([0, 1, 0, 1], dtype=np.uint32), + np.array([3.0, 1.0, 1.0, 2.0], dtype=np.float64), + ) + return CaseInput( + matrix, + np.array([1.0, 2.0], dtype=np.float64), + np.zeros(2, dtype=np.float64), + 1e-6, + ) + + +@pytest.fixture +def sources(tmp_path): + candidate = tmp_path / "candidate.c" + reference = tmp_path / "reference.c" + candidate.write_text("candidate") + reference.write_text("reference") + return candidate, reference + + +def open_fake(monkeypatch, sources, *, sandbox=None): + sandbox = sandbox or FakeSandbox() + monkeypatch.setattr(venue, "require_modal", lambda: fake_modal(sandbox)) + context = venue.open_session(*sources, {"cpus": 2, "memory_bytes": 4 * 1024**3}) + return sandbox, context + + +def test_import_does_not_load_optional_modal(): + code = """ +import importlib.abc +import sys + +class RejectModal(importlib.abc.MetaPathFinder): + def find_spec(self, fullname, path=None, target=None): + if fullname == "modal" or fullname.startswith("modal."): + raise AssertionError("Modal was imported eagerly") + +sys.meta_path.insert(0, RejectModal()) +import linear_solver_bench.modal +assert "modal" not in sys.modules +""" + result = subprocess.run([sys.executable, "-c", code], capture_output=True) + assert result.returncode == 0, result.stderr.decode() + + +def test_missing_modal_has_an_actionable_error(monkeypatch): + real_import = importlib.import_module + + def missing(name, *args, **kwargs): + if name == "modal": + raise ModuleNotFoundError("missing", name="modal") + return real_import(name, *args, **kwargs) + + monkeypatch.setattr(venue.importlib, "import_module", missing) + with pytest.raises(RuntimeError, match=r"linear-solver-bench\[modal\]"): + venue.require_modal() + + +def test_image_is_pinned_and_contains_only_code_and_native(monkeypatch): + monkeypatch.setattr( + venue, "require_modal", lambda: types.SimpleNamespace(Image=FakeImage) + ) + venue.create_image() + assert FakeImage.calls[0] == ( + "from_registry", + (venue.BASE_IMAGE,), + {"add_python": "3.12"}, + ) + copies = [call for call in FakeImage.calls if call[0] == "add_local_dir"] + assert [call[1][0].name for call in copies] == ["linear_solver_bench", "native"] + assert all(call[1][0].name not in {"families", "reference"} for call in copies) + command = next(call for call in FakeImage.calls if call[0] == "run_commands") + assert "build_runtime" in command[1][0] + + +def test_session_uses_one_network_disabled_fixed_resource_sandbox(monkeypatch, sources): + sandbox, context = open_fake(monkeypatch, sources) + with context as session: + assert isinstance(session, venue.ModalSession) + assert sandbox.creation["cpu"] == (2.0, 2.0) + assert sandbox.creation["memory"] == (4096, 4096) + assert sandbox.creation["block_network"] is True + assert sandbox.creation["timeout"] == venue.SANDBOX_LIFETIME_SECONDS + assert sandbox.terminated + + +def test_sandbox_start_failure_is_actionable(monkeypatch, sources): + sandbox = FakeSandbox() + + def fail(**_kwargs): + raise Exception("image build failed") + + sandbox.create = fail + monkeypatch.setattr(venue, "require_modal", lambda: fake_modal(sandbox)) + + with pytest.raises(RuntimeError, match="cannot start Modal sandbox"): + with venue.open_session(*sources, {"cpus": 2, "memory_bytes": 4 * 1024**3}): + pass + + +def test_build_compiles_both_solvers_once_and_reports_identities(monkeypatch, sources): + sandbox, context = open_fake(monkeypatch, sources) + with context as session: + first = session.build() + assert session.build() == first + assert sandbox.builds == 1 + assert sandbox.filesystem.copies == [ + (sources[0], "/work/candidate.c"), + (sources[1], "/work/reference.c"), + ] + assert first["candidate"] == { + "source_sha256": "a" * 64, + "executable_sha256": "b" * 64, + } + assert first["reference"] == { + "id": venue.REFERENCE_ID, + "source_sha256": "c" * 64, + "executable_sha256": "d" * 64, + } + assert first["runtime_sha256"] == "e" * 64 + assert first["venue"] == { + "id": "modal-cpu-v1", + "image_id": "im-test", + "base_image": venue.BASE_IMAGE, + "cpus": 2, + "memory_bytes": 4 * 1024**3, + "limits_enforced": True, + } + + +def test_reference_and_candidate_are_fresh_processes_in_the_same_sandbox( + monkeypatch, sources, case +): + sandbox, context = open_fake(monkeypatch, sources) + with context as session: + session.build() + reference = session.run_reference(case) + candidate = session.run_candidate(case) + assert isinstance(reference, RawExecution) + assert isinstance(candidate, RawExecution) + assert [run[0][5] for run in sandbox.runs] == [ + "/work/reference", + "/work/candidate", + ] + assert sandbox.runs[0][0][4] != sandbox.runs[1][0][4] + assert all(run[1]["timeout"] == venue.RUN_TIMEOUT_SECONDS for run in sandbox.runs) + assert len(sandbox.filesystem.copies) == 2 # only the two sources + assert len(sandbox.filesystem.removed) == 2 + assert not any(name.startswith("/work/cases/") for name in sandbox.filesystem.files) + + +def test_transport_accepts_case_input_only(monkeypatch, sources, case): + sandbox, context = open_fake(monkeypatch, sources) + with context as session: + session.build() + with pytest.raises(TypeError, match="CaseInput"): + session.run_candidate(object()) + assert not sandbox.runs + + +def test_output_transport_is_bounded(monkeypatch, sources, case): + sandbox = FakeSandbox() + original_exec = sandbox.exec + + def oversized(*args, **kwargs): + process = original_exec(*args, **kwargs) + if args[:3] == ("python", "-c", venue._INPUT_EXEC): + sandbox.filesystem.files[args[7]] = b"x" * 10_000 + return process + + sandbox.exec = oversized + sandbox, context = open_fake(monkeypatch, sources, sandbox=sandbox) + with context as session: + session.build() + result = session.run_candidate(case) + assert len(result.output) == venue.OUTPUT_HEADER.size + case.matrix.n * 8 + 1 + + +@pytest.mark.parametrize("returncode", [9, 42]) +def test_solver_failure_is_raw_evidence(monkeypatch, sources, case, returncode): + sandbox = FakeSandbox() + sandbox.outcomes = [returncode] + sandbox, context = open_fake(monkeypatch, sources, sandbox=sandbox) + with context as session: + session.build() + result = session.run_candidate(case) + assert result.returncode == returncode + assert result.output is None + + +@pytest.mark.parametrize("returncode", [124, -1]) +def test_platform_interruption_invalidates_evaluation( + monkeypatch, sources, case, returncode +): + sandbox = FakeSandbox() + sandbox.outcomes = [returncode] + sandbox, context = open_fake(monkeypatch, sources, sandbox=sandbox) + with pytest.raises(RuntimeError, match="incomplete"): + with context as session: + session.build() + session.run_reference(case) + assert sandbox.terminated + + +def test_input_preparation_failure_invalidates_evaluation(monkeypatch, sources, case): + sandbox = FakeSandbox() + sandbox.outcomes = ["input-error"] + sandbox, context = open_fake(monkeypatch, sources, sandbox=sandbox) + with pytest.raises(RuntimeError, match="input preparation failed"): + with context as session: + session.build() + session.run_candidate(case) + assert sandbox.terminated + + +@pytest.mark.parametrize( + ("execution", "message"), + [ + ({"cpus": 0, "memory_bytes": 1024**2}, "cpus"), + ({"cpus": True, "memory_bytes": 1024**2}, "cpus"), + ({"cpus": 1, "memory_bytes": 0}, "whole number of MiB"), + ({"cpus": 1, "memory_bytes": 1024**2 + 1}, "whole number of MiB"), + ], +) +def test_resources_are_validated_before_modal_is_loaded( + monkeypatch, sources, execution, message +): + monkeypatch.setattr( + venue, + "require_modal", + lambda: pytest.fail("Modal loaded before resource validation"), + ) + with pytest.raises(ValueError, match=message): + with venue.open_session(*sources, execution): + pass + + +@pytest.mark.parametrize("corruption", [None, "too-large", "truncated", "trailing"]) +def test_input_decompressor_is_bounded(tmp_path, corruption): + original = bytes(range(256)) * 9_000 + compressed = zlib.compress(original, level=1) + expected_size = len(original) + if corruption == "too-large": + expected_size -= 1 + elif corruption == "truncated": + compressed = compressed[:-1] + elif corruption == "trailing": + compressed += b"trailing data" + archive = tmp_path / "input.zlib" + input_path = tmp_path / "input.bin" + output_path = tmp_path / "output.bin" + executable = tmp_path / "copy.py" + archive.write_bytes(compressed) + executable.write_text( + f"#!{sys.executable}\n" + "import pathlib,sys\n" + "pathlib.Path(sys.argv[2]).write_bytes(pathlib.Path(sys.argv[1]).read_bytes())\n" + ) + executable.chmod(0o755) + result = subprocess.run( + [ + sys.executable, + "-c", + venue._INPUT_EXEC, + str(expected_size), + str(archive), + str(executable), + str(input_path), + str(output_path), + ], + capture_output=True, + ) + if corruption is None: + assert result.returncode == 0 + assert output_path.read_bytes() == original + assert not archive.exists() + else: + assert result.returncode == 125 + assert venue._INPUT_ERROR.encode() in result.stderr + assert not output_path.exists() + assert input_path.stat().st_size <= expected_size diff --git a/tests/test_models.py b/tests/test_models.py new file mode 100644 index 0000000..4073b0f --- /dev/null +++ b/tests/test_models.py @@ -0,0 +1,49 @@ +import numpy as np +import pytest +from scipy import sparse + +from linear_solver_bench.models import BenchmarkCase, CaseInput, CsrMatrix, RawExecution + + +def test_csr_matrix_is_canonical_and_immutable(): + source = sparse.coo_matrix( + ([1.0, 2.0, 3.0, 1.0], ([0, 0, 1, 0], [1, 0, 1, 1])), + shape=(2, 2), + ) + matrix = CsrMatrix.from_scipy(source) + + assert matrix.nnz == 3 + np.testing.assert_allclose(matrix.matvec([2.0, 4.0]), [12.0, 12.0]) + assert not matrix.values.flags.writeable + with pytest.raises(ValueError): + matrix.values[0] = 0 + + +def test_models_reject_malformed_values(): + with pytest.raises(ValueError, match="row_offsets"): + CsrMatrix( + 2, + np.array([1, 1, 1], dtype=np.uint64), + np.array([], dtype=np.uint32), + np.array([], dtype=np.float64), + ) + + matrix = CsrMatrix.from_scipy(sparse.eye(2, format="csr")) + with pytest.raises(ValueError, match="match the matrix"): + CaseInput(matrix, np.ones(1), np.zeros(2), 1e-8) + + +def test_target_is_separate_from_candidate_input(): + matrix = CsrMatrix.from_scipy(sparse.eye(2, format="csr")) + value = CaseInput(matrix, np.ones(2), np.zeros(2), 1e-8) + case = BenchmarkCase("case-1", value, np.ones(2)) + + assert not hasattr(case.input, "target") + assert not case.target.flags.writeable + + +def test_raw_execution_bounds_diagnostics(): + raw = RawExecution(0, 0.5, "x" * 9000, b"result") + assert len(raw.diagnostics) == 8000 + with pytest.raises(ValueError, match="nonnegative"): + RawExecution(0, -1, "", None) diff --git a/tests/test_models_archive.py b/tests/test_models_archive.py deleted file mode 100644 index 6e743a6..0000000 --- a/tests/test_models_archive.py +++ /dev/null @@ -1,152 +0,0 @@ -from __future__ import annotations - -import io - -import numpy as np -import pytest - -from linear_solver_bench.archive import decode_system, encode_system -from linear_solver_bench.models import CsrMatrix - - -def test_archive_round_trip(small_system) -> None: - decoded = decode_system(encode_system(small_system)) - assert decoded.case_id == small_system.case_id - assert decoded.public.tolerance == small_system.public.tolerance - np.testing.assert_array_equal( - decoded.public.matrix.row_offsets, small_system.public.matrix.row_offsets - ) - np.testing.assert_array_equal(decoded.public.b, small_system.public.b) - np.testing.assert_array_equal(decoded.x_star, small_system.x_star) - assert not decoded.x_star.flags.writeable - - -def test_archive_rejects_extra_fields(small_system) -> None: - payload = encode_system(small_system) - with np.load(io.BytesIO(payload), allow_pickle=False) as original: - fields = {name: original[name] for name in original.files} - fields["unexpected"] = np.asarray(1) - buffer = io.BytesIO() - np.savez_compressed(buffer, **fields) - with pytest.raises(ValueError, match="schema"): - decode_system(buffer.getvalue()) - - -def test_csr_rejects_duplicate_or_unsorted_columns() -> None: - with pytest.raises(ValueError, match="strictly sorted"): - CsrMatrix( - n=2, - row_offsets=np.asarray([0, 2, 2], dtype=np.uint64), - column_indices=np.asarray([1, 1], dtype=np.uint32), - values=np.asarray([1.0, 2.0], dtype=np.float64), - ) - - -def test_legacy_archive_bytes_are_unchanged(small_system) -> None: - public = small_system.public - expected = io.BytesIO() - np.savez_compressed( - expected, - case_id=np.asarray(small_system.case_id), - row_offsets=public.matrix.row_offsets, - column_indices=public.matrix.column_indices, - values=public.matrix.values, - b=public.b, - x0=public.x0, - tolerance=np.asarray(public.tolerance, dtype=np.float64), - x_star=small_system.x_star, - ) - assert encode_system(small_system) == expected.getvalue() - - -@pytest.mark.parametrize("reference_kind", ["manufactured", "numerical", "none"]) -def test_v2_archive_reference_semantics(small_system, reference_kind) -> None: - from dataclasses import replace - - system = replace( - small_system, - reference_kind=reference_kind, - x_star=None if reference_kind == "none" else small_system.x_star, - ) - payload = encode_system(system, schema_version=2) - decoded = decode_system(payload) - assert decoded.reference_kind == reference_kind - if reference_kind == "none": - assert decoded.x_star is None - else: - np.testing.assert_array_equal(decoded.x_star, small_system.x_star) - with np.load(io.BytesIO(payload), allow_pickle=False) as fields: - assert fields["schema_version"].dtype == np.uint32 - assert fields["schema_version"] == 2 - assert ("x_star" in fields) == (reference_kind != "none") - - -@pytest.mark.parametrize( - ("field", "value"), - [ - ("schema_version", np.asarray(3, dtype=np.uint32)), - ("schema_version", np.asarray(2, dtype=np.int64)), - ("reference_kind", np.asarray("unknown")), - ("reference_kind", np.asarray("none")), - ("b", np.asarray([1, 2], dtype=np.float32)), - ("unexpected", np.asarray(0)), - ], -) -def test_v2_archive_rejects_invalid_fields(small_system, field, value) -> None: - with np.load(io.BytesIO(encode_system(small_system, schema_version=2))) as archive: - fields = {name: archive[name] for name in archive.files} - fields[field] = value - payload = io.BytesIO() - np.savez_compressed(payload, **fields) - with pytest.raises(ValueError): - decode_system(payload.getvalue()) - - -def test_reference_kind_requires_consistent_target(small_system) -> None: - from dataclasses import replace - - for kind in ("manufactured", "numerical"): - with pytest.raises(ValueError, match="requires x_star"): - replace(small_system, reference_kind=kind, x_star=None) - with pytest.raises(ValueError, match="must not contain"): - replace(small_system, reference_kind="none") - with pytest.raises(ValueError, match="legacy"): - encode_system( - replace(small_system, reference_kind="numerical"), schema_version=1 - ) - - -def test_empty_csr_rows_are_valid() -> None: - from scipy import sparse - - dense = np.asarray([[0.0, 0.0, 0.0], [1.0, 2.0, 3.0], [0.0, 0.0, 0.0]]) - matrix = CsrMatrix.from_scipy(sparse.csr_matrix(dense)) - np.testing.assert_array_equal(matrix.to_scipy().toarray(), dense) - - -@pytest.mark.parametrize("value", [1.0 + 1.0j, np.inf, np.nan, 2**53 + 1]) -def test_sparse_conversion_rejects_lossy_or_nonfinite_values(value) -> None: - from scipy import sparse - - with pytest.raises(ValueError): - CsrMatrix.from_scipy(sparse.csr_matrix(np.asarray([[value]]))) - - -def test_sparse_conversion_canonicalizes_without_integer_wrap() -> None: - from scipy import sparse - - value = 2**62 - source = sparse.coo_matrix( - (np.asarray([value, value], dtype=np.int64), ([0, 0], [0, 0])), shape=(1, 1) - ) - matrix = CsrMatrix.from_scipy(source) - assert matrix.values[0] == float(2**63) - assert source.nnz == 2 - - -def test_sparse_conversion_rejects_native_dimension_overflow() -> None: - from scipy import sparse - - source = sparse.coo_matrix((2**31, 2**31), dtype=np.float64) - with pytest.raises(ValueError, match="int32"): - CsrMatrix.from_scipy(source) diff --git a/tests/test_native.py b/tests/test_native.py new file mode 100644 index 0000000..bf82be0 --- /dev/null +++ b/tests/test_native.py @@ -0,0 +1,148 @@ +from __future__ import annotations + +import os +import sys +from pathlib import Path + +import numpy as np +import pytest + +from linear_solver_bench.build import ( + INPUT_HEADER, + OUTPUT_HEADER, + OUTPUT_MAGIC, + PROTOCOL_VERSION, + REFERENCE_SOURCE, + build_reference, + build_solver, + encode_input, + load_runtime, + output_size, + run_solver, +) +from linear_solver_bench.models import BenchmarkCase, CaseInput, CsrMatrix +from linear_solver_bench.verify import verify + + +@pytest.fixture +def small_case() -> BenchmarkCase: + matrix = CsrMatrix( + 2, + np.asarray([0, 2, 4], dtype=np.uint64), + np.asarray([0, 1, 0, 1], dtype=np.uint32), + np.asarray([4.0, 1.0, 1.0, 3.0], dtype=np.float64), + ) + target = np.asarray([1.0, 2.0], dtype=np.float64) + value = CaseInput( + matrix, + matrix.matvec(target), + np.zeros(2, dtype=np.float64), + 1.0e-8, + ) + return BenchmarkCase("small", value, target) + + +def _executable(path: Path, body: str) -> Path: + path.write_text(f"#!{sys.executable}\n{body}", encoding="utf-8") + path.chmod(0o755) + return path + + +def _output(solution: np.ndarray) -> bytes: + return ( + OUTPUT_HEADER.pack( + OUTPUT_MAGIC, + PROTOCOL_VERSION, + 0, + len(solution), + 600, + 100, + 200, + 300, + 0, + ) + + np.asarray(solution, dtype=" None: + payload = encode_input(small_case.input) + matrix = small_case.input.matrix + expected = INPUT_HEADER.size + 8 * 3 + 4 * 4 + 8 * 4 + 8 * 2 + 8 * 2 + assert len(payload) == expected + magic, version, reserved, n, nnz, tolerance = INPUT_HEADER.unpack_from(payload) + assert (magic, version, reserved, n, nnz) == (b"LSBIN001", 1, 0, 2, 4) + assert tolerance == small_case.input.tolerance + assert output_size(matrix.n) == OUTPUT_HEADER.size + 16 + + +def test_each_run_is_a_fresh_process(tmp_path: Path, small_case: BenchmarkCase) -> None: + payload = _output(small_case.target) + executable = _executable( + tmp_path / "solver", + "import pathlib, sys\n" + "assert len(sys.argv) == 3\n" + f"pathlib.Path(sys.argv[2]).write_bytes({payload!r})\n", + ) + first = run_solver(executable, small_case.input) + second = run_solver(executable, small_case.input) + assert first.returncode == second.returncode == 0 + assert first.output == second.output == payload + assert first.wall_seconds > 0 and second.wall_seconds > 0 + + +def test_runner_bounds_diagnostics_and_output( + tmp_path: Path, small_case: BenchmarkCase +) -> None: + executable = _executable( + tmp_path / "noisy", + "import pathlib, sys\n" + "sys.stdout.buffer.write(b'x' * 100000)\n" + "pathlib.Path(sys.argv[2]).write_bytes(b'y' * 100000)\n", + ) + result = run_solver(executable, small_case.input) + assert result.returncode == 0 + assert len(result.diagnostics) == 8000 + assert result.output is not None + assert len(result.output) == output_size(2) + 1 + assert not verify(result, small_case, "ns-mesh-pde").passed + + +def test_failed_process_has_no_timing_payload( + tmp_path: Path, small_case: BenchmarkCase +) -> None: + result = run_solver( + _executable(tmp_path / "failed", "raise SystemExit(7)\n"), + small_case.input, + ) + assert result.returncode == 7 + assert result.output is None + + +def test_process_timeout_is_bounded(tmp_path: Path, small_case: BenchmarkCase) -> None: + executable = _executable( + tmp_path / "forever", + "import time\nwhile True: time.sleep(1)\n", + ) + result = run_solver(executable, small_case.input, timeout_seconds=0.05) + assert result.returncode == 124 + assert result.wall_seconds < 2 + assert "timed out" in result.diagnostics + assert result.output is None + + +def test_real_driver_and_both_solvers( + tmp_path: Path, small_case: BenchmarkCase +) -> None: + runtime_path = os.environ.get("LSB_TEST_RUNTIME") + if not runtime_path: + pytest.skip("set LSB_TEST_RUNTIME to run native integration tests") + runtime = load_runtime(Path(runtime_path)) + sources = (Path("examples/solver.c"), REFERENCE_SOURCE) + builders = (build_solver, build_reference) + for index, (source, builder) in enumerate(zip(sources, builders, strict=True)): + compiled = builder(source, tmp_path / f"solver-{index}", runtime) + raw = run_solver(compiled.executable, small_case.input) + result = verify(raw, small_case, "ns-mesh-pde") + assert result.passed, result + assert result.elapsed_seconds and result.elapsed_seconds > 0 diff --git a/tests/test_ns_cohort_release.py b/tests/test_ns_cohort_release.py deleted file mode 100644 index 9658180..0000000 --- a/tests/test_ns_cohort_release.py +++ /dev/null @@ -1,205 +0,0 @@ -"""Bind the replacement cohort to its public selection and numerical evidence.""" - -from __future__ import annotations - -import json -import tomllib -from pathlib import Path - -from linear_solver_bench.dataset import identity_sha256 -from linear_solver_bench.manifests import ( - load_release, - validate_qualification, - validate_release_splits, -) - -ROOT = Path(__file__).resolve().parents[1] - - -def read(name): - return json.loads((ROOT / name).read_text()) - - -def test_cohort_preserves_operators_and_qualifies_fresh_workloads(): - releases = [ - load_release(ROOT / f"data/ns-mesh-cohort-v2-{split}.json", official=True) - for split in ("dev", "ranked") - ] - validate_release_splits(releases) - original = { - case["source"]["matrix_id"]: case - for split in ("dev", "ranked") - for case in read(f"data/ns-mesh-pilot-{split}.json")["cases"] - } - cases = {c["source"]["matrix_id"]: c for r in releases for c in r["cases"]} - assert len(cases) == 49 - assert set(cases) == set(original) | {231, 917, 2647, 2649, 2815, 2825} - contract = read("data/ns-mesh-pilot-dev.json") - configured = tomllib.loads((ROOT / "benchmark.toml").read_text())["pilot"][ - "ns_mesh" - ] - assert configured["development_manifest"] == "ns-mesh-cohort-v2-dev" - assert configured["ranked_manifest"] == "ns-mesh-cohort-v2-ranked" - for release in releases: - assert release["execution"] == contract["execution"] - assert release["contracts"] == contract["contracts"] - assert release["rhs"]["draw_index"] == 0 - assert (release["rhs"]["public_key_hex"] is None) == ( - release["split"] == "ranked" - ) - for case in release["cases"]: - validate_qualification(case["qualification"]) - assert case["tolerance"] == 1e-12 - old = original.get(case["source"]["matrix_id"]) - if old is not None: - assert case["source"] == old["source"] - assert ( - case["qualification"]["system_sha256"] - != old["qualification"]["system_sha256"] - ) - - -def test_releases_match_frozen_scientific_selection(): - inventory = read("docs/analysis/ns-cohort-v2-inventory.json") - selection = read("docs/analysis/ns-cohort-v2-selection.json") - policy = read("docs/analysis/ns-cohort-v2-policy.json") - descriptors = read("docs/analysis/ns-cohort-v2-descriptors.json") - assert inventory["descriptor_sha256"] == identity_sha256(descriptors) - assert inventory["inventory_sha256"] == identity_sha256( - {k: v for k, v in inventory.items() if k != "inventory_sha256"} - ) - assert selection["inventory_sha256"] == inventory["inventory_sha256"] - assert selection["policy_sha256"] == identity_sha256(policy) - assert selection["selection_sha256"] == identity_sha256( - {k: v for k, v in selection.items() if k != "selection_sha256"} - ) - source_cases = {c["matrix_id"]: c for c in inventory["cases"]} - sources = {s["matrix_id"]: s for s in inventory["sources"]} - for split in ("dev", "ranked"): - release = read(f"data/ns-mesh-cohort-v2-{split}.json") - assert [c["source"]["matrix_id"] for c in release["cases"]] == selection[ - "matrix_ids" - ][split] - assert len(release["cases"]) == selection["case_counts"][split] - groups = sorted({c["provenance_group"] for c in release["cases"]}) - assert groups == selection["groups"][split] - assert len(groups) == selection["group_counts"][split] - for case in release["cases"]: - source = source_cases[case["source"]["matrix_id"]] - assert case["source"] == sources[source["matrix_id"]] - assert case["provenance_group"] == source["provenance_group"] - assert case["source"]["matrix_sha256"] == source["matrix_sha256"] - assert (case["n"], case["nnz"]) == (source["n"], source["nnz"]) - assert not set(selection["groups"]["dev"]) & set(selection["groups"]["ranked"]) - for regime in selection["regime_coverage"]: - counts = regime["counts"] - if regime["required_placement"] == "both-splits": - assert counts["dev"] > 0 and counts["ranked"] > 0 - elif regime["required_placement"] == "dev-only": - assert counts["dev"] > 0 and counts["ranked"] == 0 - assert all( - not gap["supported_by_multiple_groups"] - for gap in selection["joint_size_method_gaps"] - ) - - -def test_minimum_dependencies_reproduce_every_fresh_system(): - receipt = read("data/releases/ns-cohort-v2-preparation-reproduction.json") - assert receipt["receipt_sha256"] == identity_sha256( - {k: v for k, v in receipt.items() if k != "receipt_sha256"} - ) - assert ( - receipt["inventory_sha256"] - == read("docs/analysis/ns-cohort-v2-inventory.json")["inventory_sha256"] - ) - assert receipt["case_count"] == 49 and receipt["all_match"] is True - assert len(receipt["cases"]) == 49 - assert receipt["numpy_version"] == "2.0.2" - assert receipt["scipy_version"] == "1.14.1" - cases = { - case["source"]["matrix_id"]: case - for split in ("dev", "ranked") - for case in read(f"data/ns-mesh-cohort-v2-{split}.json")["cases"] - } - assert {r["matrix_id"] for r in receipt["cases"]} == set(cases) - for row in receipt["cases"]: - case = cases[row["matrix_id"]] - assert row["matrix_matches"] and row["system_matches"] - assert row["matrix_sha256"] == case["source"]["matrix_sha256"] - assert row["system_sha256"] == case["qualification"]["system_sha256"] - - -def test_full_venue_reports_bind_every_case_and_preserve_failures(): - from linear_solver_bench.pilot_scoring import score_pilot_report - - for split in ("dev", "ranked"): - release = read(f"data/ns-mesh-cohort-v2-{split}.json") - report = read(f"data/releases/ns-cohort-v2-{split}-validation.json") - score = score_pilot_report(report) - assert score["official"] is True - assert report["release"] == release - assert report["case_count"] == len(release["cases"]) - assert report["repetitions"] == 1 - assert [c["case_id"] for c in report["cases"]] == [ - c["case_id"] for c in release["cases"] - ] - assert all(c["execution"]["index"] == 0 for c in report["cases"]) - assert report["venue"] == { - "id": "modal-sandbox-pilot-cpu-v2", - "cpus": 2, - "memory_bytes": 4 * 1024**3, - "limits_enforced": True, - } - assert ( - report["runtime_manifest_sha256"] - == read("data/releases/cpu-runtime-v2.json")["manifest_sha256"] - ) - assert report["solved_count"] == sum(c["passed"] for c in report["cases"]) - ranked = read("data/releases/ns-cohort-v2-ranked-validation.json") - timed_out = next( - c for c in ranked["cases"] if c["case_id"] == "ss-2825-rademacher-0" - ) - assert timed_out["execution"]["outcome"] == "timeout" - assert timed_out["passed"] is False - - -def test_offline_reference_attempts_preserve_fixed_inputs_and_prior_failures(): - history = read("data/releases/ns-cohort-v2-offline-reference-attempts.json") - assert history["manifest_sha256"] == identity_sha256( - {k: v for k, v in history.items() if k != "manifest_sha256"} - ) - assert ( - history["inventory_sha256"] - == read("docs/analysis/ns-cohort-v2-inventory.json")["inventory_sha256"] - ) - assert ( - history["selection_sha256"] - == read("docs/analysis/ns-cohort-v2-selection.json")["selection_sha256"] - ) - expected = { - 2647: ["reference_construction_failure", "qualified"], - 2649: ["offline_time_limit", "operator_cancelled", "qualified"], - 2815: ["qualified"], - } - assert {row["matrix_id"] for row in history["cases"]} == set(expected) - for row in history["cases"]: - report = read(f"data/releases/ns-cohort-v2-{row['split']}-validation.json") - prepared = next( - c - for c in report["prepared_manifest"]["cases"] - if c["case_id"] == row["case_id"] - ) - assert row["system_sha256"] == prepared["system_sha256"] - assert row["input_archive_sha256"] == prepared["sha256"] - attempts = row["attempts"] - assert [a["outcome"] for a in attempts] == expected[row["matrix_id"]] - assert [a["attempt_index"] for a in attempts] == list( - range(1, len(attempts) + 1) - ) - for attempt in attempts: - if attempt["outcome"] == "qualified": - assert attempt["qualification_sha256"] == identity_sha256( - prepared["qualification"] - ) - else: - assert attempt["qualification_sha256"] is None diff --git a/tests/test_ns_cohort_selection.py b/tests/test_ns_cohort_selection.py deleted file mode 100644 index 7eb8c14..0000000 --- a/tests/test_ns_cohort_selection.py +++ /dev/null @@ -1,360 +0,0 @@ -from __future__ import annotations - -import copy -import importlib.util -import json -import math -from collections import defaultdict -from pathlib import Path - -import numpy as np -import pytest -from scipy.spatial.distance import cdist -from scipy.stats import wasserstein_distance - -from linear_solver_bench.dataset import identity_sha256 - -ROOT = Path(__file__).resolve().parents[1] - - -@pytest.fixture(scope="module") -def selector(): - spec = importlib.util.spec_from_file_location( - "ns_cohort_selector", ROOT / "tools/select_ns_cohort.py" - ) - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - return module - - -@pytest.fixture -def policy(): - return json.loads((ROOT / "docs/analysis/ns-cohort-v2-policy.json").read_text()) - - -def seal(inventory): - body = {k: v for k, v in inventory.items() if k != "inventory_sha256"} - return {**body, "inventory_sha256": identity_sha256(body)} - - -def make_inventory(count=12, *, constant=False): - cases, sources = [], [] - for i in range(count): - n = 100 if constant else 100 * (i + 1) - nnz = n * (5 if constant else 1 + i % 3) - identity = f"{i + 1:064x}" - cases.append( - { - "matrix_id": i + 1, - "name": f"Fixture/m{i}", - "matrix_sha256": identity, - "provenance_group": f"g{i:02d}", - "n": n, - "nnz": nnz, - "features": { - "log10_n": math.log10(n), - "log10_nnz_per_row": math.log10(nnz / n), - "relative_nonsymmetry": 1.0 if constant else 0.1 + (i % 4) / 10, - "zero_diagonal_fraction": 0.0 if constant else (0, 0.25, 1)[i % 3], - "weakly_diagonally_dominant_row_fraction": ( - 1.0 if constant else (0, 0.5, 1)[(i // 2) % 3] - ), - }, - "application_tags": ["flow"], - "discretization_tags": [ - "finite_element" if constant or i % 2 else "finite_difference" - ], - } - ) - sources.append( - { - "kind": "suitesparse", - "matrix_id": i + 1, - "group": "Fixture", - "name": f"m{i}", - "url": f"https://sparse.tamu.edu/MM/Fixture/m{i}.tar.gz", - "archive_bytes": 1, - "archive_sha256": f"{i + 100:064x}", - "matrix_sha256": identity, - } - ) - return seal( - { - "schema_version": 1, - "kind": "ns-cohort-source-inventory", - "cases": cases, - "sources": sources, - "descriptor_sha256": "a" * 64, - } - ) - - -def brute_force(inventory, policy): - """Independent scalar enumeration with SciPy's marginal W1 implementation.""" - cases = sorted(inventory["cases"], key=lambda c: c["matrix_id"]) - groups = sorted({c["provenance_group"] for c in cases}) - support = defaultdict(set) - for case in cases: - group = case["provenance_group"] - size = sum(case["n"] >= n for n in policy["coverage"]["size_boundaries"]) - support[("size", size)].add(group) - for f in policy["coverage"]["endpoint_features"]: - v = case["features"][f] - support[(f, v if v in (0, 1) else "interior")].add(group) - for f in policy["coverage"]["known_tag_fields"]: - for tag in case[f]: - if tag != "unknown": - support[(f, tag)].add(group) - for method in case["discretization_tags"]: - if method != "unknown": - support[("joint", size, method)].add(group) - fields = policy["objective"]["features"] - x = np.array([[c["features"][f] for f in fields] for c in cases]) - ranges = np.ptp(x, axis=0) - scaled = np.divide( - x - x.min(axis=0), ranges, out=np.zeros_like(x), where=ranges != 0 - ) - distances = cdist(scaled, scaled) - tags = sorted( - { - (f, t) - for c in cases - for f in policy["coverage"]["known_tag_fields"] - for t in c[f] - if t != "unknown" - } - ) - feasible = [] - for mask in range(1 << len(groups)): - dev_groups = tuple(g for i, g in enumerate(groups) if mask & (1 << i)) - dev_set = set(dev_groups) - a = np.array([c["provenance_group"] in dev_set for c in cases]) - if not 0.25 <= a.mean() <= 0.5 or len(groups) - len(dev_set) < 8: - continue - if any( - (len(gs) > 1 and not (gs & dev_set and gs - dev_set)) - or (len(gs) == 1 and key[0] != "joint" and not gs <= dev_set) - for key, gs in support.items() - ): - continue - b = ~a - w1 = np.mean( - [ - wasserstein_distance(x[a, j], x[b, j]) / ranges[j] if ranges[j] else 0 - for j in range(len(fields)) - ] - ) - energy = ( - 2 * distances[np.ix_(a, b)].mean() - - distances[np.ix_(a, a)].mean() - - distances[np.ix_(b, b)].mean() - ) - energy = max(0, energy) / (2 * distances.max()) if distances.max() else 0 - incidence = ( - np.mean( - [ - abs( - np.mean( - [ - t in c[f] - for c, take in zip(cases, a, strict=True) - if take - ] - ) - - np.mean( - [ - t in c[f] - for c, take in zip(cases, b, strict=True) - if take - ] - ) - ) - for f, t in tags - ] - ) - if tags - else 0 - ) - components = [w1, energy, incidence, abs(a.mean() - 1 / 3)] - feasible.append( - ((round(float(np.mean(components)), 12), dev_groups), components) - ) - return sorted(feasible, key=lambda entry: entry[0]) - - -def test_objective_and_feasible_count_match_independent_brute_force(selector, policy): - inventory = make_inventory() - expected = brute_force(inventory, policy) - result = selector.select_cohort(inventory, policy) - assert result["enumerated_partition_count"] == 4096 - assert result["feasible_partition_count"] == len(expected) - assert tuple(result["groups"]["dev"]) == expected[0][0][1] - assert result["rounded_score"] == expected[0][0][0] - assert list(result["score_components"].values()) == pytest.approx(expected[0][1]) - assert len(result["alternatives"]) == 5 - for rank, (actual, reference) in enumerate( - zip(result["alternatives"], expected[1:6], strict=True), start=2 - ): - assert actual["selection_rank"] == rank - assert tuple(actual["groups"]["dev"]) == reference[0][1] - assert actual["rounded_score"] == reference[0][0] - assert list(actual["score_components"].values()) == pytest.approx(reference[1]) - assert sum(actual["case_counts"].values()) == len(inventory["cases"]) - body = {k: v for k, v in result.items() if k != "selection_sha256"} - assert identity_sha256(body) == result["selection_sha256"] - - -def test_constant_features_have_zero_distance_and_lexicographic_tie(selector, policy): - result = selector.select_cohort(make_inventory(constant=True), policy) - assert result["groups"]["dev"] == ["g00", "g01", "g02", "g03"] - assert result["feasible_partition_count"] == 715 - assert result["score"] == 0 - assert all(row["distance"] == 0 for row in result["nearest_dev_feature_neighbors"]) - expected = brute_force(make_inventory(constant=True), policy) - assert [tuple(a["groups"]["dev"]) for a in result["alternatives"]] == [ - row[0][1] for row in expected[1:6] - ] - - -def test_partition_and_score_batch_boundaries_preserve_top_six( - selector, policy, monkeypatch -): - inventory = make_inventory() - expected = selector.select_cohort(inventory, policy) - monkeypatch.setattr(selector, "PARTITION_CHUNK", 37) - monkeypatch.setattr(selector, "SCORE_CHUNK", 7) - actual = selector.select_cohort(inventory, policy) - assert actual["matrix_ids"] == expected["matrix_ids"] - assert actual["feasible_partition_count"] == expected["feasible_partition_count"] - assert [a["groups"] for a in actual["alternatives"]] == [ - a["groups"] for a in expected["alternatives"] - ] - assert [a["rounded_score"] for a in actual["alternatives"]] == [ - a["rounded_score"] for a in expected["alternatives"] - ] - - -def test_group_integrity_and_input_permutation_do_not_change_partition( - selector, policy -): - inventory = make_inventory() - inventory["cases"][1]["provenance_group"] = "g00" - inventory = seal(inventory) - before = copy.deepcopy(inventory) - first = selector.select_cohort(inventory, policy) - assert inventory == before - inventory["cases"].reverse() - inventory["sources"].reverse() - second = selector.select_cohort(seal(inventory), policy) - for key in ("matrix_ids", "groups", "score_components", "feasible_partition_count"): - assert first[key] == second[key] - for ids in first["matrix_ids"].values(): - assert (1 in ids) == (2 in ids) - assert sorted(sum(first["matrix_ids"].values(), [])) == list(range(1, 13)) - - -def test_single_group_known_regime_is_dev_only_and_unknown_is_excluded( - selector, policy -): - inventory = make_inventory(constant=True) - inventory["cases"][0]["application_tags"].append("rare_application") - inventory["cases"][1]["discretization_tags"] = ["unknown"] - result = selector.select_cohort(seal(inventory), policy) - assert "g00" in result["groups"]["dev"] - rare = next( - r for r in result["regime_coverage"] if r["stratum"] == "rare_application" - ) - assert rare["required_placement"] == "dev-only" and rare["dev_only"] - assert rare["counts"] == {"dev": 1, "ranked": 0} - assert not any(r["stratum"] == "unknown" for r in result["regime_coverage"]) - - -def test_shared_size_method_joints_are_hard_constraints(selector, policy): - inventory = make_inventory(constant=True) - for i, case in enumerate(inventory["cases"]): - case["discretization_tags"] = [ - "finite_element" if i % 2 else "finite_difference" - ] - if i >= 6: - case.update(n=20_000, nnz=100_000) - case["features"]["log10_n"] = math.log10(20_000) - inventory = seal(inventory) - expected = brute_force(inventory, policy) - result = selector.select_cohort(inventory, policy) - assert result["feasible_partition_count"] == len(expected) - assert tuple(result["groups"]["dev"]) == expected[0][0][1] - joint = [r for r in result["regime_coverage"] if r["kind"] == "size_method_joint"] - assert len(joint) == 4 - assert all(r["counts"]["dev"] and r["counts"]["ranked"] for r in joint) - assert result["joint_size_method_gaps"] == [] - - -def test_single_group_joint_can_remain_ranked_and_is_reported(selector, policy): - inventory = make_inventory(constant=True) - for i, case in enumerate(inventory["cases"]): - if i in {5, 11}: - case["discretization_tags"] = ["finite_difference"] - if i >= 6: - case.update(n=20_000, nnz=100_000) - case["features"]["log10_n"] = math.log10(20_000) - inventory["cases"][5]["application_tags"].append("rare_application") - inventory = seal(inventory) - expected = brute_force(inventory, policy) - result = selector.select_cohort(inventory, policy) - assert tuple(result["groups"]["dev"]) == expected[0][0][1] - assert "g05" in result["groups"]["dev"] - assert "g11" in result["groups"]["ranked"] - joint = next( - r - for r in result["regime_coverage"] - if r["kind"] == "size_method_joint" - and r["stratum"] == "10000<=n<100000 / finite_difference" - ) - assert joint["required_placement"] == "report-only" - assert not joint["dev_only"] - assert joint["counts"] == {"dev": 0, "ranked": 1} - assert any( - r["size_band"] == "10000<=n<100000" - and r["spatial_method"] == "finite_difference" - and not r["hard_constraint"] - for r in result["joint_size_method_gaps"] - ) - - -def test_three_opposite_split_constraints_form_an_infeasible_odd_cycle( - selector, policy -): - inventory = make_inventory(constant=True) - for group_indices, tag in ( - ((0, 1), "edge-a"), - ((1, 2), "edge-b"), - ((0, 2), "edge-c"), - ): - for i in group_indices: - inventory["cases"][i]["application_tags"].append(tag) - with pytest.raises(ValueError, match="No feasible cohort partition"): - selector.select_cohort(seal(inventory), policy) - # Removing any edge makes these constraints compatible with the budget too. - for tag in ("edge-a", "edge-b", "edge-c"): - reduced = copy.deepcopy(inventory) - for case in reduced["cases"]: - case["application_tags"] = [t for t in case["application_tags"] if t != tag] - assert selector.select_cohort(seal(reduced), policy)["feasible_partition_count"] - - -def test_infeasibility_and_incompatible_inputs_fail_explicitly(selector, policy): - inventory = make_inventory(constant=True) - for i, case in enumerate(inventory["cases"]): - case["application_tags"] = [f"unique-{i}"] - with pytest.raises(ValueError, match="No feasible cohort partition"): - selector.select_cohort(seal(inventory), policy) - changed_policy = copy.deepcopy(policy) - changed_policy["budget"]["maximum_dev_fraction"] = 0.75 - with pytest.raises(ValueError, match="incompatible selection policy"): - selector.select_cohort(make_inventory(), changed_policy) - inventory = make_inventory() - inventory["cases"][0]["solver_passed"] = True - with pytest.raises(ValueError, match="outcome inputs are forbidden"): - selector.select_cohort(seal(inventory), policy) - with pytest.raises(ValueError, match="at most 26"): - selector.select_cohort(make_inventory(count=27, constant=True), policy) diff --git a/tests/test_ns_dominance.py b/tests/test_ns_dominance.py deleted file mode 100644 index b7553e2..0000000 --- a/tests/test_ns_dominance.py +++ /dev/null @@ -1,121 +0,0 @@ -from __future__ import annotations - -import copy -import json -from fractions import Fraction - -import numpy as np -import pytest -from scipy import sparse - -from linear_solver_bench.manifests import QUALIFICATION_LIMITS, validate_qualification -from linear_solver_bench.models import CsrMatrix, EvaluationSystem, MatrixInput -from linear_solver_bench.workloads import qualify_ns_dominance, system_digest - - -def _system(values, target, b=None): - matrix = CsrMatrix.from_scipy( - sparse.csr_matrix(np.asarray(values, dtype=np.float64)) - ) - target = np.asarray(target, dtype=np.float64) - return EvaluationSystem( - "dominance-fixture", - MatrixInput( - matrix, - matrix.matvec(target) if b is None else np.asarray(b, dtype=np.float64), - np.zeros(matrix.n), - 1e-12, - ), - target, - ) - - -def test_dominance_bound_covers_exact_stored_solution(monkeypatch): - import linear_solver_bench.workloads as workloads - - def no_factorization(*args, **kwargs): - pytest.fail("the certificate must not invoke LU") - - monkeypatch.setattr(workloads, "splu", no_factorization) - system = _system([[4.1, 0.3], [0.7, 3.2]], [1, -1]) - evidence = qualify_ns_dominance(system) - a, b, c, d = map(Fraction.from_float, system.public.matrix.values) - r, s = map(Fraction.from_float, system.public.b) - determinant = a * d - b * c - exact = ((d * r - b * s) / determinant, (a * s - c * r) / determinant) - errors = [abs(exact[0] - 1), abs(exact[1] + 1)] - bound = Fraction.from_float(evidence["formation"]["verified_forward_bound"]) - assert max(errors) <= bound - assert sum(value * value for value in errors) / 2 <= bound * bound - assert bound > 0 - assert evidence["certificate"]["witness"] == "manufactured-target" - assert evidence["certificate"]["metric_evaluation"] == "binary64" - assert evidence["uncertainty"] == "verified-stored-system-forward-bound" - for name, limit in QUALIFICATION_LIMITS.items(): - assert evidence["metrics"][name] <= limit - validate_qualification(json.loads(json.dumps(evidence)), system_digest(system)) - - -@pytest.mark.parametrize("matrix", [[[1, 2], [3, 1]], [[1, 1], [1, 1]]]) -def test_non_strict_row_dominance_is_rejected(matrix): - with pytest.raises(ValueError, match="not strictly row"): - qualify_ns_dominance(_system(matrix, [1, -1])) - - -def test_exact_margin_does_not_hide_small_off_diagonal_entries(): - # Each tiny entry is lost when naively added to 1.0, but the exact row is - # weakly dominant, not strictly dominant. - diagonal = np.nextafter(1.0, np.inf) - tiny = 2.0**-53 - system = _system( - [[diagonal, 1.0, tiny, tiny], [0, 2, 0, 0], [0, 0, 2, 0], [0, 0, 0, 2]], - [1, -1, 1, -1], - ) - with pytest.raises(ValueError, match="not strictly row"): - qualify_ns_dominance(system) - - -def test_small_positive_exact_margin_is_supported(): - value = np.nextafter(1.0, np.inf) - evidence = qualify_ns_dominance(_system([[value, 1], [0, 1]], [1, -1])) - assert evidence["certificate"]["minimum_row_margin"] == value - 1 - assert evidence["formation"]["verified_forward_bound"] == 0 - - -def test_zero_measured_metrics_cannot_hide_large_formation_uncertainty(): - # b=fl(A*[1,1]) has zero recomputed residual, but rounding RHS formation - # relative to a tiny dominance margin cannot support the forward limit. - value = np.nextafter(1.0, np.inf) - system = _system([[value, 1], [1, value]], [1, 1]) - with pytest.raises(ValueError, match="forward bound lacks the tenfold"): - qualify_ns_dominance(system) - - -def test_subnormal_coefficients_are_accumulated_exactly(): - tiny = np.nextafter(0.0, 1.0) - evidence = qualify_ns_dominance(_system([[2 * tiny]], [1])) - assert evidence["certificate"]["minimum_row_margin"] == 2 * tiny - assert evidence["certificate"]["maximum_formation_error"] == 0 - assert evidence["formation"]["verified_forward_bound"] == 0 - - -def test_dominance_requires_rademacher_and_all_witness_gates(): - with pytest.raises(ValueError, match="Rademacher"): - qualify_ns_dominance(_system([[2]], [0.5])) - with pytest.raises(ValueError, match="tenfold"): - qualify_ns_dominance(_system([[2]], [1], b=[2.01])) - - -def test_certificate_rejects_inconsistent_bound_and_identity(): - system = _system([[4.1, 0.3], [0.7, 3.2]], [1, -1]) - evidence = qualify_ns_dominance(system) - bad = copy.deepcopy(evidence) - bad["formation"]["verified_forward_bound"] = 0.0 - with pytest.raises(ValueError, match="understates"): - validate_qualification(bad) - bad = copy.deepcopy(evidence) - bad["certificate"]["minimum_row_margin"] = -1.0 - with pytest.raises(ValueError): - validate_qualification(bad) - with pytest.raises(ValueError, match="different numerical inputs"): - validate_qualification(evidence, "0" * 64) diff --git a/tests/test_ns_iterative_qualification.py b/tests/test_ns_iterative_qualification.py deleted file mode 100644 index 2f2bbea..0000000 --- a/tests/test_ns_iterative_qualification.py +++ /dev/null @@ -1,110 +0,0 @@ -from __future__ import annotations - -import copy - -import numpy as np -import pytest -from scipy import sparse -from scipy.sparse.linalg import gmres - -from linear_solver_bench.manifests import ( - ITERATIVE_QUALIFICATION_CONFIG, - ITERATIVE_QUALIFICATION_METHOD, - QUALIFICATION_LIMITS, - validate_qualification, -) -from linear_solver_bench.models import CsrMatrix, EvaluationSystem, MatrixInput -from linear_solver_bench.workloads import qualify_ns_iterative, system_digest - - -def _system(*, empty_row=False): - operator = sparse.diags([-np.ones(7), np.full(8, 4.0), np.full(7, 0.5)], [-1, 0, 1]) - operator = sparse.diags(np.logspace(-4, 4, 8)) @ operator - operator = operator @ sparse.diags(np.logspace(3, -3, 8)) - if empty_row: - operator = operator.tolil() - operator[3, :] = 0 - matrix = CsrMatrix.from_scipy(operator) - target = np.where(np.arange(8) % 2, -1.0, 1.0) - return EvaluationSystem( - "iterative-qualification-fixture", - MatrixInput(matrix, matrix.matvec(target), np.full(8, 7.0), 1e-12), - target, - ) - - -def test_fixed_iterative_reference_preserves_inputs_and_starts_every_solve_from_zero( - monkeypatch, -): - import linear_solver_bench.workloads as workloads - - system = _system() - original = system_digest(system) - calls = [] - - def counted_gmres(matrix, rhs, **kwargs): - assert np.array_equal(kwargs["x0"], np.zeros(system.public.matrix.n)) - assert kwargs["callback_type"] == "pr_norm" - calls.append(rhs.copy()) - return gmres(matrix, rhs, **kwargs) - - monkeypatch.setattr(workloads, "gmres", counted_gmres) - evidence = qualify_ns_iterative(system) - assert len(calls) == 3 - assert evidence["method"] == ITERATIVE_QUALIFICATION_METHOD - assert ( - evidence["reference_solver"]["configuration"] == ITERATIVE_QUALIFICATION_CONFIG - ) - assert all(value["info"] == 0 for value in evidence["reference_solver"]["solves"]) - assert evidence["formation"]["verified_forward_bound"] is None - assert evidence["uncertainty"] == "empirical-feasibility-only" - for metric, limit in QUALIFICATION_LIMITS.items(): - assert evidence["metrics"][metric] <= limit - assert system_digest(system) == original - validate_qualification(evidence, original) - - -@pytest.mark.parametrize("result", ["nonconverged", "nonfinite"]) -def test_failed_reference_never_produces_admission(monkeypatch, result): - import linear_solver_bench.workloads as workloads - - def failing_gmres(matrix, rhs, **kwargs): - if result == "nonconverged": - return np.zeros(len(rhs)), 250 - return np.full(len(rhs), np.nan), 0 - - monkeypatch.setattr(workloads, "gmres", failing_gmres) - with pytest.raises(ValueError, match="could not be computed"): - qualify_ns_iterative(_system()) - - -def test_iterative_reference_rejects_empty_row_and_inaccurate_target(): - with pytest.raises(ValueError, match="empty row"): - qualify_ns_iterative(_system(empty_row=True)) - system = _system() - invalid = EvaluationSystem(system.case_id, system.public, -system.x_star) - with pytest.raises(ValueError, match="tenfold margin"): - qualify_ns_iterative(invalid) - - -@pytest.mark.parametrize( - "change", - ["budget", "boolean_budget", "missing_correction", "failure", "bound", "library"], -) -def test_iterative_evidence_rejects_configuration_or_semantic_drift(change): - evidence = copy.deepcopy(qualify_ns_iterative(_system())) - reference = evidence["reference_solver"] - if change == "budget": - reference["configuration"]["restart"] = 100 - elif change == "boolean_budget": - reference["configuration"]["diag_pivot_thresh"] = True - elif change == "missing_correction": - reference["solves"].pop() - elif change == "failure": - reference["solves"][1]["info"] = 250 - elif change == "bound": - evidence["formation"]["verified_forward_bound"] = 1e-8 - elif change == "library": - reference["implementation"]["scipy"] = "/some/local/path" - with pytest.raises(ValueError): - validate_qualification(evidence) diff --git a/tests/test_ns_pyamg_qualification.py b/tests/test_ns_pyamg_qualification.py deleted file mode 100644 index 2b9d240..0000000 --- a/tests/test_ns_pyamg_qualification.py +++ /dev/null @@ -1,214 +0,0 @@ -from __future__ import annotations - -import copy - -import numpy as np -import pytest -from scipy import sparse -from scipy.sparse.linalg import gmres - -from linear_solver_bench.manifests import validate_qualification -from linear_solver_bench.models import CsrMatrix, EvaluationSystem, MatrixInput -from linear_solver_bench.workloads import qualify_ns_pyamg, system_digest - - -@pytest.fixture -def system(): - pytest.importorskip("pyamg") - side = 35 - one = np.ones(side) - horizontal = sparse.diags([-1.2 * one[:-1], 4.1 * one, -0.8 * one[:-1]], [-1, 0, 1]) - vertical = sparse.diags([-one[:-1], -one[:-1]], [-1, 1], shape=(side, side)) - matrix = CsrMatrix.from_scipy( - sparse.kron(sparse.eye(side), horizontal) - + sparse.kron(vertical, sparse.eye(side)) - ) - target = np.where(np.arange(matrix.n) % 3, 1.0, -1.0) - return EvaluationSystem( - "synthetic-nonsymmetric-grid", - MatrixInput(matrix, matrix.matvec(target), np.full(matrix.n, 7.0), 1e-12), - target, - ) - - -def test_fixed_multigrid_reference_is_independent_and_reproducible(system, monkeypatch): - import linear_solver_bench.reference_pyamg as reference - - original = system_digest(system) - calls = [] - - def observed(*args, **kwargs): - np.testing.assert_array_equal(kwargs["x0"], np.zeros(system.public.matrix.n)) - calls.append(1) - return gmres(*args, **kwargs) - - state = np.random.get_state() - monkeypatch.setattr(reference, "gmres", observed) - evidence = qualify_ns_pyamg(system) - assert len(calls) == 3 - assert len(evidence["reference_solver"]["hierarchy"]) > 1 - assert system_digest(system) == original - after = np.random.get_state() - assert state[0] == after[0] - np.testing.assert_array_equal(state[1], after[1]) - assert state[2:] == after[2:] - assert qualify_ns_pyamg(system) == evidence - assert evidence["formation"]["verified_forward_bound"] is None - validate_qualification(evidence, original) - - -def test_inaccurate_target_and_nonconvergence_cannot_admit(system, monkeypatch): - import linear_solver_bench.reference_pyamg as reference - - with pytest.raises(ValueError, match="tenfold margin"): - qualify_ns_pyamg( - EvaluationSystem(system.case_id, system.public, -system.x_star) - ) - monkeypatch.setattr( - reference, - "gmres", - lambda *args, **kwargs: (np.zeros(system.public.matrix.n), 250), - ) - with pytest.raises(ValueError, match="did not converge"): - qualify_ns_pyamg(system) - - -def test_nonfinite_reference_cannot_admit(system, monkeypatch): - import linear_solver_bench.reference_pyamg as reference - - monkeypatch.setattr( - reference, - "gmres", - lambda *args, **kwargs: (np.full(system.public.matrix.n, np.nan), 0), - ) - with pytest.raises(ValueError, match="did not converge"): - qualify_ns_pyamg(system) - - -@pytest.mark.parametrize( - "change", - [ - "nested_type", - "implementation", - "empty_hierarchy", - "dimensions", - "nnz", - "bound", - "iterations", - ], -) -def test_optional_reference_evidence_is_strict(system, change): - evidence = copy.deepcopy(qualify_ns_pyamg(system)) - reference = evidence["reference_solver"] - if change == "nested_type": - reference["configuration"]["smooth"][1]["degree"] = True - elif change == "implementation": - reference["implementation"]["pyamg"] = "9.0.0" - elif change == "empty_hierarchy": - reference["hierarchy"] = [] - elif change == "dimensions": - reference["hierarchy"][1]["n"] = reference["hierarchy"][0]["n"] - elif change == "nnz": - reference["hierarchy"][0]["nnz"] = -1 - elif change == "bound": - evidence["formation"]["verified_forward_bound"] = 1e-8 - elif change == "iterations": - reference["solves"][0]["inner_iterations"] = 12501 - with pytest.raises(ValueError): - validate_qualification(evidence) - - -def test_inexact_reference_uses_fixed_per_solve_stops_and_diagnostics( - system, monkeypatch -): - import linear_solver_bench.reference_pyamg as reference - from linear_solver_bench.manifests import PYAMG_INEXACT_QUALIFICATION_METHOD - from linear_solver_bench.workloads import qualify_ns_pyamg_inexact - - calls = [] - - def observed(*args, **kwargs): - np.testing.assert_array_equal(kwargs["x0"], np.zeros(system.public.matrix.n)) - calls.append((kwargs["rtol"], kwargs["atol"])) - return gmres(*args, **kwargs) - - monkeypatch.setattr(reference, "gmres", observed) - evidence = qualify_ns_pyamg_inexact(system) - assert evidence["method"] == PYAMG_INEXACT_QUALIFICATION_METHOD - assert calls == [(1e-13, 0.0), (1e-2, 0.0), (1e-2, 0.0)] - solves = evidence["reference_solver"]["solves"] - assert [item["rtol"] for item in solves] == [1e-13, 1e-2, 1e-2] - for item in solves: - assert item["info"] == 0 - assert item["residual_method"] == "extended-precision-residual-v1" - assert np.isfinite(item["relative_l2_residual"]) - validate_qualification(evidence, system_digest(system)) - - -def test_cancellation_dominated_corrections_meet_original_gates(): - from linear_solver_bench.manifests import QUALIFICATION_LIMITS - from linear_solver_bench.verify import accuracy_metrics - from linear_solver_bench.workloads import _extended_residual - - # The nearly null direction produces a correction much larger than its RHS. - # This is cancellation, not a test that merely scales an identity's RHS down. - matrix = CsrMatrix.from_scipy(sparse.csr_matrix([[1.0, -1.0], [-2.0, 2.0 + 1e-6]])) - target = np.array([1.0, -1.0]) - b = matrix.matvec(target) - system = EvaluationSystem( - "synthetic-correction-cancellation", - MatrixInput(matrix, b, np.zeros(2), 1e-12), - target, - ) - current = target + 0.01 * np.ones(2) - assert accuracy_metrics(system, current).relative_linf_forward_error > 1e-6 - - def solve(rhs, rtol): - return gmres( - matrix.to_scipy(), - rhs, - x0=np.zeros(2), - rtol=rtol, - atol=0.0, - restart=2, - maxiter=5, - ) - - rhs = np.asarray(_extended_residual(matrix, b, current), dtype=np.float64) - _, strict_info = solve(rhs, 1e-13) - correction, loose_info = solve(rhs, 1e-2) - assert strict_info != 0 - assert loose_info == 0 - assert np.linalg.norm(correction) > 1e5 * np.linalg.norm(rhs) - achieved = np.linalg.norm(rhs - matrix.matvec(correction)) / np.linalg.norm(rhs) - assert 1e-13 < achieved <= 1e-2 - for _ in range(2): - rhs = np.asarray(_extended_residual(matrix, b, current), dtype=np.float64) - correction, info = solve(rhs, 1e-2) - assert info == 0 - current += correction - metrics = accuracy_metrics(system, current) - for name, limit in QUALIFICATION_LIMITS.items(): - value = getattr(metrics, name) - assert value is not None and np.isfinite(value) and value <= limit - - -@pytest.mark.parametrize( - "change", ["correction_stop", "solve_stop", "nonfinite_diagnostic", "old_method"] -) -def test_inexact_evidence_keeps_configuration_and_diagnostics_strict(system, change): - from linear_solver_bench.manifests import PYAMG_QUALIFICATION_METHOD - from linear_solver_bench.workloads import qualify_ns_pyamg_inexact - - evidence = qualify_ns_pyamg_inexact(system) - reference = evidence["reference_solver"] - if change == "correction_stop": - reference["configuration"]["refinement_rtol"] = 1e-3 - elif change == "solve_stop": - reference["solves"][1]["rtol"] = 1e-13 - elif change == "nonfinite_diagnostic": - reference["solves"][1]["relative_l2_residual"] = float("nan") - else: - evidence["method"] = PYAMG_QUALIFICATION_METHOD - with pytest.raises(ValueError): - validate_qualification(evidence) diff --git a/tests/test_ns_refinement.py b/tests/test_ns_refinement.py deleted file mode 100644 index c01a6d4..0000000 --- a/tests/test_ns_refinement.py +++ /dev/null @@ -1,73 +0,0 @@ -from __future__ import annotations - -import numpy as np -import pytest -from scipy import sparse -from scipy.sparse.linalg import splu - -from linear_solver_bench.manifests import QUALIFICATION_LIMITS, validate_qualification -from linear_solver_bench.models import CsrMatrix, EvaluationSystem, MatrixInput -from linear_solver_bench.verify import accuracy_metrics -from linear_solver_bench.workloads import qualify_ns, system_digest - - -@pytest.mark.parametrize("decimal_fallback", [False, True]) -def test_one_refinement_improves_an_ill_scaled_exact_system( - monkeypatch, decimal_fallback -): - import linear_solver_bench.workloads as workloads - - # Integer Vandermonde entries and +/-1 targets make RHS formation exact. - # Ill-conditioning makes the initial float64 solve measurably inaccurate. - n = 10 - matrix = CsrMatrix.from_scipy( - sparse.csr_matrix( - np.vander(np.arange(1, n + 1, dtype=float), N=n, increasing=True) - ) - ) - target = np.where(np.arange(n) % 2, -1.0, 1.0) - system = EvaluationSystem( - "integer-vandermonde", - MatrixInput(matrix, matrix.matvec(target), np.zeros(n), 1e-12), - target, - ) - initial = accuracy_metrics( - system, splu(matrix.to_scipy().tocsc()).solve(system.public.b) - ) - assert initial.relative_linf_forward_error > 1e-9 - original_digest = system_digest(system) - solve_calls = [] - - class CountedFactor: - def __init__(self, value): - self.factor = splu(value) - - def solve(self, rhs): - solve_calls.append(rhs.copy()) - return self.factor.solve(rhs) - - monkeypatch.setattr(workloads, "splu", CountedFactor) - if decimal_fallback: - double_info = np.finfo(np.float64) - monkeypatch.setattr(workloads.np, "finfo", lambda dtype: double_info) - evidence = qualify_ns(system, refine=True) - assert len(solve_calls) == 2 - assert evidence["method"] == "independent-sparse-lu-refined-v1" - assert evidence["metrics"]["relative_linf_forward_error"] < ( - initial.relative_linf_forward_error * 0.01 - ) - for name, limit in QUALIFICATION_LIMITS.items(): - assert evidence["metrics"][name] <= limit - assert evidence["formation"]["relative_linf_residual"] == 0 - assert evidence["formation"]["verified_forward_bound"] is None - assert evidence["uncertainty"] == "empirical-feasibility-only" - assert system_digest(system) == original_digest - validate_qualification(evidence, original_digest) - - -def test_plain_lu_keeps_existing_method_identity(small_system): - evidence = qualify_ns(small_system) - assert evidence["method"] == "independent-sparse-lu-v1" - assert evidence["formation"]["verified_forward_bound"] is None - with pytest.raises(ValueError, match="boolean"): - qualify_ns(small_system, refine="yes") diff --git a/tests/test_ns_split_audit.py b/tests/test_ns_split_audit.py deleted file mode 100644 index 1723658..0000000 --- a/tests/test_ns_split_audit.py +++ /dev/null @@ -1,122 +0,0 @@ -from __future__ import annotations - -import copy -import importlib.util -import json -from pathlib import Path - -import numpy as np -import pytest -from scipy import sparse - -from linear_solver_bench.dataset import identity_sha256 -from linear_solver_bench.models import CsrMatrix - -ROOT = Path(__file__).resolve().parents[1] - - -@pytest.fixture(scope="module") -def audit(): - spec = importlib.util.spec_from_file_location( - "audit_ns_split", ROOT / "tools/audit_ns_split.py" - ) - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - return module - - -def test_matrix_diagnostics_match_analytic_operator_and_preserve_input(audit): - original = np.array([[2.0, 1.0], [0.0, 3.0]]) - matrix = CsrMatrix.from_scipy(sparse.csr_matrix(original)) - features = audit.matrix_features(matrix) - assert features["relative_nonsymmetry"] == pytest.approx(np.sqrt(2 / 14)) - assert features["zero_diagonal_fraction"] == 0 - assert features["weakly_diagonally_dominant_row_fraction"] == 1 - assert np.array_equal(matrix.to_scipy().toarray(), original) - scaled = CsrMatrix.from_scipy(sparse.csr_matrix(original * -(2.0**400))) - assert audit.matrix_features(scaled) == pytest.approx(features) - - -def test_exact_zero_diagonal_count_survives_extreme_dynamic_range(audit): - matrix = CsrMatrix.from_scipy(sparse.diags([1e-300, 1e300], format="csr")) - assert audit.matrix_features(matrix)["zero_diagonal_fraction"] == 0 - - -def test_small_mean_distance_does_not_erase_single_group_coverage_gap(audit): - features = {name: 0.25 for name in audit.FEATURES} - features["log10_n"] = 2.0 - cases = [ - {"split": "dev", "n": 100, "provenance_group": "small", "features": features}, - ] - cases.extend( - { - "split": "ranked", - "n": 100, - "provenance_group": "other-small", - "features": dict(features), - } - for _ in range(31) - ) - cases.append( - { - "split": "ranked", - "n": 1_100_000, - "provenance_group": "large", - "features": {**features, "log10_n": float(np.log10(1_100_000))}, - } - ) - result = audit.compare_splits(cases) - assert 0 < result["mean_normalized_marginal_distance"] < 0.01 - assert result["size_bands"][-1]["counts"] == {"dev": 0, "ranked": 1} - assert not result["size_bands"][-1]["can_cover_both_sides_with_distinct_groups"] - cases[1]["provenance_group"] = "small" - with pytest.raises(ValueError, match="crosses"): - audit.compare_splits(cases) - - -def test_unknown_method_does_not_count_as_known_coverage(audit): - features = {name: 0.0 for name in audit.FEATURES} - cases = [ - { - "split": split, - "n": 100, - "provenance_group": split, - "features": features, - "discretization_tags": ["unknown"], - } - for split in ("dev", "ranked") - ] - result = audit.compare_splits(cases) - row = result["tag_coverage"]["discretization_tags"]["unknown"] - assert row["at_least_two_source_groups"] - assert not row["known_regime_present_in_both_splits"] - - -def test_published_audit_matches_frozen_sources_and_descriptor_review(audit): - record = json.loads((ROOT / "docs/analysis/ns-split-audit.json").read_text()) - digest = record.pop("audit_sha256") - assert identity_sha256(record) == digest - descriptors = json.loads( - (ROOT / "docs/analysis/ns-source-descriptors.json").read_text() - ) - assert identity_sha256(descriptors) == record["descriptor_sha256"] - cases = audit.add_descriptors(record["cases"], descriptors) - assert audit.compare_splits(cases) == record["comparison"] - releases = [ - json.loads((ROOT / f"data/ns-mesh-pilot-{split}.json").read_text()) - for split in ("dev", "ranked") - ] - assert ( - sorted(r["manifest_sha256"] for r in releases) - == record["source_release_sha256"] - ) - matrices = {c["source"]["matrix_id"]: c for r in releases for c in r["cases"]} - assert len(cases) == len(matrices) == 43 - for case in cases: - source = matrices[case["matrix_id"]] - assert case["matrix_sha256"] == source["source"]["matrix_sha256"] - assert (case["n"], case["nnz"]) == (source["n"], source["nnz"]) - changed = copy.deepcopy(descriptors) - changed["matrices"][0]["provenance_group"] = "substituted" - with pytest.raises(ValueError, match="identity"): - audit.add_descriptors(cases, changed) diff --git a/tests/test_packaging.py b/tests/test_packaging.py deleted file mode 100644 index 08646f2..0000000 --- a/tests/test_packaging.py +++ /dev/null @@ -1,223 +0,0 @@ -from __future__ import annotations - -import os -import pathlib -import subprocess -import sys -import tarfile -import zipfile - -import pytest - -from linear_solver_bench import paths - -ROOT = pathlib.Path(__file__).resolve().parents[1] - - -def test_resource_and_cache_overrides(monkeypatch, tmp_path): - root = tmp_path / "assets" - root.mkdir() - (root / "benchmark.toml").write_text("schema_version = 1\n", encoding="utf-8") - monkeypatch.setenv("LINEAR_SOLVER_BENCH_ROOT", str(root)) - monkeypatch.delenv("LINEAR_SOLVER_BENCH_DATA", raising=False) - assert paths.repository_root() == root - assert paths.data_dir() == root / "data" - assert paths.native_dir() == root / "native" - - monkeypatch.setenv("LINEAR_SOLVER_BENCH_DATA", str(tmp_path / "data")) - monkeypatch.setenv("LINEAR_SOLVER_BENCH_CACHE", str(tmp_path / "cache")) - assert paths.data_dir() == tmp_path / "data" - assert paths.cache_dir() == tmp_path / "cache" - - monkeypatch.setenv("LINEAR_SOLVER_BENCH_ROOT", str(tmp_path / "missing")) - with pytest.raises(RuntimeError, match="cannot locate benchmark.toml"): - paths.repository_root() - - -def test_default_cache_is_outside_installed_assets(monkeypatch, tmp_path): - monkeypatch.delenv("LINEAR_SOLVER_BENCH_CACHE", raising=False) - monkeypatch.setenv("XDG_CACHE_HOME", str(tmp_path / "xdg-cache")) - assert paths.cache_dir() == tmp_path / "xdg-cache" / "linear-solver-bench" - monkeypatch.delenv("XDG_CACHE_HOME") - assert paths.cache_dir() == pathlib.Path.home() / ".cache" / "linear-solver-bench" - - -@pytest.fixture(scope="module") -def distributions(tmp_path_factory): - output = tmp_path_factory.mktemp("distribution") - result = subprocess.run( - [ - sys.executable, - "-m", - "build", - "--no-isolation", - "--outdir", - str(output), - str(ROOT), - ], - check=False, - capture_output=True, - text=True, - timeout=120, - ) - assert result.returncode == 0, result.stdout + result.stderr - return next(output.glob("*.tar.gz")), next(output.glob("*.whl")) - - -def test_archives_include_public_resources_only(distributions): - sdist, wheel = distributions - with tarfile.open(sdist, "r:gz") as archive: - source_files = { - pathlib.PurePosixPath(member.name).relative_to( - pathlib.PurePosixPath(member.name).parts[0] - ): archive.extractfile(member).read() - for member in archive.getmembers() - if member.isfile() - } - # The selector shipped in the source distribution must retain the frozen - # public evidence required to reproduce the cohort without solver runs. - assert { - pathlib.PurePosixPath("docs/analysis/ns-cohort-v2-policy.json"), - pathlib.PurePosixPath("docs/analysis/ns-cohort-v2-inventory.json"), - pathlib.PurePosixPath("docs/analysis/ns-cohort-v2-descriptors.json"), - pathlib.PurePosixPath("docs/analysis/ns-cohort-v2-selection.json"), - } <= source_files.keys() - allowed_source_roots = { - "CITATION.cff", - "CONTRIBUTING.md", - "LICENSE", - "MANIFEST.in", - "PKG-INFO", - "README.md", - "SECURITY.md", - "benchmark.toml", - "data", - "docs", - "modal_app.py", - "pyproject.toml", - "qualification_modal.py", - "setup.cfg", - "setup.py", - "src", - "submissions", - "tests", - "tools", - "native", - } - assert {path.parts[0] for path in source_files} <= allowed_source_roots - assert not any( - path.parts[:2] in {("data", "downloads"), ("data", "prepared")} - for path in source_files - ) - with zipfile.ZipFile(wheel) as archive: - names = archive.namelist() - assert all( - name.startswith("linear_solver_bench/") - or ( - name.split("/")[0].startswith("linear_solver_bench-") - and name.split("/")[0].endswith(".dist-info") - ) - for name in names - ) - resource_prefix = "linear_solver_bench/_resources/" - packaged = { - pathlib.PurePosixPath(name.removeprefix(resource_prefix)): archive.read( - name - ) - for name in names - if name.startswith(resource_prefix) - } - required = { - pathlib.PurePosixPath(name) - for name in ( - "benchmark.toml", - "data/catalogue.json", - "data/dev-v1.json", - "data/ranked-v1.json", - "data/ns-mesh-pilot-dev.json", - "data/ns-mesh-pilot-ranked.json", - "data/ns-mesh-cohort-v2-dev.json", - "data/ns-mesh-cohort-v2-ranked.json", - "data/releases/ns-cohort-v2-dev-validation.json", - "data/releases/ns-cohort-v2-ranked-validation.json", - "data/releases/ns-cohort-v2-preparation-reproduction.json", - "data/releases/ns-cohort-v2-offline-reference-attempts.json", - "data/flash-replay-dev-pilot.json", - "data/flash-replay-ranked-pilot.json", - "data/releases/flash-replay-dev-reference.json", - "data/releases/flash-replay-ranked-reference.json", - "data/releases/cpu-runtime-v2.json", - "data/releases/README.md", - "data/qualification.json", - "data/qualification-inputs-v1.json", - "data/NOTICE.md", - "native/CMakeLists.txt", - "native/src/driver.cpp", - "native/include/nsl_hypre_solver.h", - "native/include/NOTICE-HYPRE", - "native/include/LICENSE-HYPRE-MIT", - ) - } - assert required <= packaged.keys() - assert all( - path.parts[0] in {"benchmark.toml", "data", "native"} for path in packaged - ) - assert not any( - path.parts[:2] in {("data", "downloads"), ("data", "prepared")} - for path in packaged - ) - assert all(content == source_files[path] for path, content in packaged.items()) - - -def test_wheel_resources_work_outside_checkout(distributions, tmp_path): - _, wheel = distributions - environment = { - key: value - for key, value in os.environ.items() - if key not in {"LINEAR_SOLVER_BENCH_ROOT", "LINEAR_SOLVER_BENCH_DATA"} - } - # A zip import also exercises resource extraction and its lifetime. Imports - # must resolve inside the wheel, even if the test environment is editable. - environment["PYTHONPATH"] = str(wheel) - script = """ -import pathlib -import json -import sys -import linear_solver_bench -from linear_solver_bench.dataset import load_split -from linear_solver_bench.manifests import load_release -from linear_solver_bench.paths import data_dir, native_dir, repository_root -from linear_solver_bench.pilot_scoring import score_pilot_report -assert linear_solver_bench.__file__.startswith(sys.argv[1]) -assert load_split("dev")["case_count"] > 0 -assert (repository_root() / "benchmark.toml").is_file() -assert (data_dir() / "NOTICE.md").is_file() -for name in ("ns-mesh-pilot-dev", "ns-mesh-pilot-ranked", - "ns-mesh-cohort-v2-dev", "ns-mesh-cohort-v2-ranked", - "flash-replay-dev-pilot", "flash-replay-ranked-pilot"): - release = load_release(data_dir() / (name + ".json"), official=True) - assert release["cases"] -for split in ("dev", "ranked"): - report = json.loads((data_dir() / "releases" / - ("ns-cohort-v2-" + split + "-validation.json")).read_text()) - score = score_pilot_report(report) - assert score["official"] and score["case_count"] == report["case_count"] - assert score["solved_count"] == report["solved_count"] - reference = json.loads((data_dir() / "releases" / - ("flash-replay-" + split + "-reference.json")).read_text()) - score = score_pilot_report(reference["reference_report"], reference) - assert score["official"] and score["speedup"] == 1.0 -assert (native_dir() / "src" / "driver.cpp").is_file() -assert (native_dir() / "include" / "nsl_hypre_solver.h").is_file() -assert pathlib.Path.cwd() not in repository_root().parents -""" - result = subprocess.run( - [sys.executable, "-c", script, str(wheel)], - cwd=tmp_path, - env=environment, - check=False, - capture_output=True, - text=True, - timeout=30, - ) - assert result.returncode == 0, result.stdout + result.stderr diff --git a/tests/test_pilot_data.py b/tests/test_pilot_data.py deleted file mode 100644 index 27b39e3..0000000 --- a/tests/test_pilot_data.py +++ /dev/null @@ -1,536 +0,0 @@ -from __future__ import annotations - -import copy -import hashlib -import io -import json -import tarfile -import zipfile -from dataclasses import replace - -import numpy as np -import pytest -from scipy import sparse -from scipy.io import mmwrite - -from linear_solver_bench.archive import encode_system -from linear_solver_bench.families import resolve_track -from linear_solver_bench.manifests import ( - RHS_SCHEME, - seal_manifest, - validate_release, - validate_release_splits, -) -from linear_solver_bench.models import CsrMatrix -from linear_solver_bench.pilot_dataset import ( - iter_pilot_prepared, - load_pilot_prepared_manifest, - prepare_release, -) -from linear_solver_bench.sources.download import fetch_archive -from linear_solver_bench.sources.flash import load_flash -from linear_solver_bench.workloads import ( - matrix_digest, - qualify_ns, - rademacher_target, - system_digest, - vector_digest, -) - - -def reseal(value): - return seal_manifest( - {key: item for key, item in value.items() if key != "manifest_sha256"} - ) - - -def make_release(case, *, family="ns-mesh-pde"): - track = resolve_track(family, "coverage" if family == "ns-mesh-pde" else "replay") - key = b"public-test-recipe-key-32-bytes!!" - return seal_manifest( - { - "schema_version": 2, - "kind": "linear-solver-bench-pilot-release", - "release_id": "synthetic-test-v2", - "family": family, - "track": track.track, - "split": "dev", - "contracts": track.contracts, - "execution": { - "repetitions": 1, - "max_iterations": 5000, - "case_timeout_seconds": 10, - "memory_bytes": 1024**3, - "cpus": 1, - }, - "rhs": { - "scheme": RHS_SCHEME, - "key_id": hashlib.sha256(key).hexdigest(), - "public_key_hex": key.hex(), - "draw_index": 0, - } - if family == "ns-mesh-pde" - else None, - "cases": [case], - } - ) - - -def make_ns(tmp_path): - operator = sparse.csr_matrix([[4.0, 1.0], [2.0, 3.0]]) - matrix = CsrMatrix.from_scipy(operator) - buffer = io.BytesIO() - mmwrite(buffer, operator) - path = tmp_path / "synthetic.tar.gz" - with tarfile.open(path, "w:gz") as archive: - info = tarfile.TarInfo("synthetic/synthetic.mtx") - info.size = len(buffer.getvalue()) - archive.addfile(info, io.BytesIO(buffer.getvalue())) - source = { - "kind": "suitesparse", - "url": "https://sparse.tamu.edu/MM/Example/synthetic.tar.gz", - "archive_sha256": hashlib.sha256(path.read_bytes()).hexdigest(), - "archive_bytes": path.stat().st_size, - "matrix_sha256": matrix_digest(matrix), - "matrix_id": 1, - "group": "Example", - "name": "synthetic", - } - case = { - "index": 0, - "case_id": "synthetic-ns", - "n": 2, - "nnz": 4, - "tolerance": 1e-12, - "role": "scored", - "provenance_group": "synthetic", - "source_run": "synthetic-operator", - "weight": 1, - "source": source, - "admission": { - "statement": "Synthetic test fixture only; not a scientific release.", - "citations": ["https://example.org/synthetic-fixture"], - }, - "qualification": None, - } - cache = tmp_path / "cache" - cache.mkdir() - (cache / source["archive_sha256"]).write_bytes(path.read_bytes()) - return make_release(case), cache - - -def make_flash(tmp_path): - matrix = CsrMatrix.from_scipy(sparse.csr_matrix([[3.0, -1.0], [-1.0, 2.0]])) - b, x0 = np.array([1.25, -3.5]), np.array([0.25, -0.75]) - members = {} - for name, value in (("b.npy", b), ("x0.npy", x0)): - buffer = io.BytesIO() - np.save(buffer, value) - members[name] = buffer.getvalue() - buffer = io.BytesIO() - sparse.save_npz(buffer, matrix.to_scipy()) - members["matrix.npz"] = buffer.getvalue() - metadata = { - "schema_version": 1, - "case_id": "synthetic-flash", - "family": "magnetic_diffusion_flash", - "capture_kind": "scalar-magnetic-diffusion", - "n": 2, - "nnz": 4, - "relative_tolerance": 1e-8, - "absolute_tolerance": 0, - "provenance_group": "synthetic", - "source_run": "synthetic-run", - "step": 1, - "solve_index": 0, - "matrix_sha256": matrix_digest(matrix), - "b_sha256": vector_digest(b), - "x0_sha256": vector_digest(x0), - "files": { - name: hashlib.sha256(payload).hexdigest() - for name, payload in members.items() - }, - } - members["case.json"] = json.dumps(metadata).encode() - path = tmp_path / "synthetic-flash.zip" - with zipfile.ZipFile(path, "w", compression=zipfile.ZIP_DEFLATED) as archive: - for name, payload in members.items(): - archive.writestr(name, payload) - source = { - "kind": "huggingface", - "url": "https://huggingface.co/datasets/example/synthetic/resolve/" - + "a" * 40 - + "/synthetic.zip", - "archive_sha256": hashlib.sha256(path.read_bytes()).hexdigest(), - "archive_bytes": path.stat().st_size, - "matrix_sha256": matrix_digest(matrix), - "b_sha256": vector_digest(b), - "x0_sha256": vector_digest(x0), - } - case = { - "index": 0, - "case_id": "synthetic-flash", - "n": 2, - "nnz": 4, - "tolerance": 1e-8, - "role": "scored", - "provenance_group": "synthetic", - "source_run": "synthetic-run", - "weight": 1, - "source": source, - "admission": { - "statement": "Synthetic fixture, not a captured scientific case.", - "citations": ["https://example.org/synthetic-fixture"], - "capture_kind": "scalar-magnetic-diffusion", - "relative_tolerance": 1e-8, - "absolute_tolerance": 0, - "step": 1, - "solve_index": 0, - }, - "qualification": None, - } - return path, case, members - - -def test_ns_offline_prepare_qualifies_and_streams(tmp_path): - release, cache = make_ns(tmp_path) - manifest = tmp_path / "release.json" - manifest.write_text(json.dumps(release)) - prepared = prepare_release( - manifest, tmp_path / "prepared", cache=cache, offline=True - ) - assert prepared["complete_split"] is True - assert prepared["cases"][0]["qualification"]["qualified"] is True - systems = iter_pilot_prepared(tmp_path / "prepared") - system = next(systems) - assert system.reference_kind == "manufactured" - assert np.all(np.abs(system.x_star) == 1) - assert np.array_equal(system.public.b, system.public.matrix.matvec(system.x_star)) - assert np.array_equal(system.public.x0, np.zeros(2)) - with pytest.raises(StopIteration): - next(systems) - with pytest.raises(ValueError, match="trusted published|qualification"): - load_pilot_prepared_manifest(tmp_path / "prepared", official=True) - - -def test_flash_preserves_warm_start_and_has_no_manufactured_truth(tmp_path): - path, case, _ = make_flash(tmp_path) - validate_release(make_release(case, family="magnetic_diffusion_flash")) - system = load_flash(path, case) - assert system.reference_kind == "none" and system.x_star is None - assert np.array_equal(system.public.b, [1.25, -3.5]) - assert np.array_equal(system.public.x0, [0.25, -0.75]) - cache = tmp_path / "cache" - cache.mkdir() - (cache / case["source"]["archive_sha256"]).write_bytes(path.read_bytes()) - manifest = tmp_path / "flash.json" - manifest.write_text( - json.dumps(make_release(case, family="magnetic_diffusion_flash")) - ) - prepare_release(manifest, tmp_path / "prepared", cache=cache, offline=True) - restored = next(iter_pilot_prepared(tmp_path / "prepared")) - assert np.array_equal(restored.public.x0, system.public.x0) - - -def test_frozen_ns_qualification_is_reused_and_bound(tmp_path, monkeypatch): - release, cache = make_ns(tmp_path) - manifest = tmp_path / "release.json" - manifest.write_text(json.dumps(release)) - draft = prepare_release(manifest, tmp_path / "draft", cache=cache, offline=True) - evidence = draft["cases"][0]["qualification"] - release["cases"][0]["qualification"] = evidence - manifest.write_text(json.dumps(reseal(release))) - - def unexpected_factorization(_): - raise AssertionError("frozen releases must not rerun offline qualification") - - monkeypatch.setattr( - "linear_solver_bench.pilot_dataset.qualify_ns", unexpected_factorization - ) - prepared = prepare_release(manifest, tmp_path / "frozen", cache=cache, offline=True) - assert prepared["cases"][0]["qualification"] == evidence - assert prepared["cases"][0]["sha256"] == draft["cases"][0]["sha256"] - - release["cases"][0]["qualification"]["system_sha256"] = "a" * 64 - manifest.write_text(json.dumps(reseal(release))) - with pytest.raises(ValueError, match="different numerical inputs"): - prepare_release(manifest, tmp_path / "invalid", cache=cache, offline=True) - - -def test_release_rejects_unsupported_or_untrusted_contracts(tmp_path): - release, _ = make_ns(tmp_path) - validate_release(release) - for field, value in ( - ("schema_version", 1), - ("track", "performance"), - ("family", "ns_mesh_pde"), - ): - invalid = copy.deepcopy(release) - invalid[field] = value - with pytest.raises(ValueError): - validate_release(reseal(invalid)) - invalid = copy.deepcopy(release) - invalid["execution"]["repetitions"] = 3 - with pytest.raises(ValueError, match="exactly one"): - validate_release(reseal(invalid)) - invalid = copy.deepcopy(release) - invalid["cases"][0]["admission"]["extra"] = "not public schema" - with pytest.raises(ValueError, match="schema"): - validate_release(reseal(invalid)) - - -def test_prepared_tampering_and_wrong_key_fail(tmp_path): - release, cache = make_ns(tmp_path) - manifest = tmp_path / "release.json" - manifest.write_text(json.dumps(release)) - with pytest.raises(ValueError, match="key"): - prepare_release( - manifest, tmp_path / "wrong", rhs_key=b"x" * 32, cache=cache, offline=True - ) - prepare_release(manifest, tmp_path / "prepared", cache=cache, offline=True) - path = tmp_path / "prepared" / "0000.npz" - path.write_bytes(path.read_bytes() + b"tamper") - with pytest.raises(ValueError, match="digest"): - next(iter_pilot_prepared(tmp_path / "prepared")) - - -@pytest.mark.parametrize("change", ["b", "x0", "matrix", "reference"]) -def test_flash_rejects_rehashed_prepared_inputs(tmp_path, monkeypatch, change): - from linear_solver_bench.manifests import PUBLISHED_RELEASES - - archive, case, _ = make_flash(tmp_path) - release = make_release(case, family="magnetic_diffusion_flash") - identity = tuple( - release[name] for name in ("release_id", "family", "track", "split") - ) - monkeypatch.setitem(PUBLISHED_RELEASES, identity, release["manifest_sha256"]) - manifest = tmp_path / "release.json" - manifest.write_text(json.dumps(release), encoding="utf-8") - cache = tmp_path / "cache" - cache.mkdir() - (cache / case["source"]["archive_sha256"]).write_bytes(archive.read_bytes()) - root = tmp_path / "prepared" - prepared = prepare_release(manifest, root, cache=cache, offline=True) - original = next(iter_pilot_prepared(root, official=True)) - if change in {"b", "x0"}: - changed = replace( - original, public=replace(original.public, **{change: np.zeros(2)}) - ) - elif change == "matrix": - operator = CsrMatrix.from_scipy(original.public.matrix.to_scipy() * 2) - changed = replace(original, public=replace(original.public, matrix=operator)) - else: - changed = replace(original, x_star=np.ones(2), reference_kind="numerical") - entry = prepared["cases"][0] - payload = encode_system(changed, schema_version=2) - (root / entry["relpath"]).write_bytes(payload) - entry.update( - sha256=hashlib.sha256(payload).hexdigest(), system_sha256=system_digest(changed) - ) - (root / "manifest.json").write_text(json.dumps(reseal(prepared)), encoding="utf-8") - # Even a fully rehashed prepared archive cannot redefine a trusted capture. - with pytest.raises(ValueError, match="captured release"): - load_pilot_prepared_manifest(root, official=True) - with pytest.raises(ValueError, match="captured release"): - next(iter_pilot_prepared(root)) - - -def test_ns_loader_rejects_requalified_substitute_operator(tmp_path): - release, cache = make_ns(tmp_path) - manifest = tmp_path / "release.json" - manifest.write_text(json.dumps(release), encoding="utf-8") - root = tmp_path / "prepared" - prepared = prepare_release(manifest, root, cache=cache, offline=True) - original = next(iter_pilot_prepared(root)) - operator = CsrMatrix.from_scipy(original.public.matrix.to_scipy() * 2) - changed = replace( - original, - public=replace( - original.public, matrix=operator, b=operator.matvec(original.x_star) - ), - ) - entry = prepared["cases"][0] - payload = encode_system(changed, schema_version=2) - (root / entry["relpath"]).write_bytes(payload) - entry.update( - sha256=hashlib.sha256(payload).hexdigest(), - system_sha256=system_digest(changed), - qualification=qualify_ns(changed), - ) - (root / "manifest.json").write_text(json.dumps(reseal(prepared)), encoding="utf-8") - with pytest.raises(ValueError, match="numerical inputs"): - next(iter_pilot_prepared(root)) - - -@pytest.mark.parametrize("field", ["b_sha256", "x0_sha256"]) -def test_flash_requires_frozen_vector_identities(tmp_path, field): - archive, case, _ = make_flash(tmp_path) - case["source"][field] = "invalid" - with pytest.raises(ValueError, match="SHA-256"): - validate_release(make_release(case, family="magnetic_diffusion_flash")) - case["source"][field] = "f" * 64 - with pytest.raises(ValueError, match="metadata disagrees"): - load_flash(archive, case) - - -def test_hmac_generator_is_domain_separated_and_stable(): - arguments = { - "release_id": "release-v2", - "family": "ns-mesh-pde", - "matrix_sha256": "0" * 64, - "n": 300, - "draw_index": 0, - "key": b"x" * 32, - } - first = rademacher_target(**arguments) - assert np.array_equal(first, rademacher_target(**arguments)) - assert ( - vector_digest(first) - == "5d14f06b0fe1111b251ba7a18306925508894700323d94ac32b26362528812ad" - ) - assert np.mean(first * first) == 1 - for name, value in ( - ("release_id", "release-v3"), - ("family", "other"), - ("matrix_sha256", "1" * 64), - ("draw_index", 1), - ("key", b"y" * 32), - ): - assert not np.array_equal( - first, rademacher_target(**{**arguments, name: value}) - ) - - -def test_offline_cache_rejects_corruption(tmp_path): - release, cache = make_ns(tmp_path) - source = release["cases"][0]["source"] - cached = fetch_archive(source, cache, offline=True) - cached.write_bytes(b"wrong") - with pytest.raises(ValueError, match="invalid size or digest"): - fetch_archive(source, cache, offline=True) - - -def test_download_resumes_verified_byte_range(tmp_path, monkeypatch): - payload = b"downloaded-public-archive" - source = { - "url": "https://example.org/archive.zip", - "archive_sha256": hashlib.sha256(payload).hexdigest(), - "archive_bytes": len(payload), - } - (tmp_path / (source["archive_sha256"] + ".part")).write_bytes(payload[:5]) - - class Response(io.BytesIO): - status = 206 - headers = { - "Content-Length": str(len(payload) - 5), - "Content-Range": f"bytes 5-{len(payload) - 1}/{len(payload)}", - } - - class Opener: - def open(self, request, timeout): - assert request.headers["Range"] == "bytes=5-" - return Response(payload[5:]) - - monkeypatch.setattr("urllib.request.build_opener", lambda handler: Opener()) - assert fetch_archive(source, tmp_path).read_bytes() == payload - - -def test_only_official_suitesparse_archive_redirects_may_use_http(): - import urllib.request - - from linear_solver_bench.sources.download import _SourceRedirect - - request = urllib.request.Request("https://sparse.tamu.edu/MM/Example/test.tar.gz") - handler = _SourceRedirect(suitesparse=True) - upgraded = handler.redirect_request( - request, - None, - 302, - "Found", - {}, - "http://sparse-files.engr.tamu.edu/MM/Example/test.tar.gz", - ) - assert ( - upgraded.full_url == "http://sparse-files.engr.tamu.edu/MM/Example/test.tar.gz" - ) - with pytest.raises(ValueError, match="outside HTTPS"): - handler.redirect_request( - request, None, 302, "Found", {}, "http://example.org/test.tar.gz" - ) - with pytest.raises(ValueError, match="outside HTTPS"): - _SourceRedirect().redirect_request( - request, - None, - 302, - "Found", - {}, - "http://sparse-files.engr.tamu.edu/MM/Example/test.tar.gz", - ) - - -def test_flash_rejects_extra_and_deceptive_array_members(tmp_path): - path, case, members = make_flash(tmp_path) - with zipfile.ZipFile(path, "a") as archive: - archive.writestr("../outside", b"no") - with pytest.raises(ValueError, match="exactly four"): - load_flash(path, case) - deceptive = io.BytesIO() - np.lib.format.write_array_header_1_0( - deceptive, {"descr": " bytes: - return ( - OUTPUT_HEADER.pack( - OUTPUT_MAGIC, - PROTOCOL_VERSION, - status, - solution.size, - 600, - 100, - 200, - 300, - mutated, - ) - + np.asarray(solution, dtype=" None: - encoded = encode_input(small_system.public) - assert INPUT_HEADER.size == 40 - assert OUTPUT_HEADER.size == 64 - expected = 40 + 8 * 3 + 4 * 4 + 8 * 4 + 8 * 2 + 8 * 2 - assert len(encoded) == expected - - result = decode_output(output_payload(small_system.x_star), expected_n=2) - assert result.status == 0 - assert result.elapsed_s == pytest.approx(6.0e-7) - assert result.elapsed_s == result.create_s + result.setup_s + result.solve_s - np.testing.assert_array_equal(result.solution, small_system.x_star) - - -@pytest.mark.parametrize( - "payload", - [ - b"short", - output_payload(np.asarray([1.0, 2.0])) + b"trailing", - struct.pack("<8s", b"BADMAGIC") + b"\0" * 72, - ], -) -def test_protocol_rejects_malformed_output(payload: bytes) -> None: - with pytest.raises(ValueError): - decode_output(payload, expected_n=2) diff --git a/tests/test_published_references.py b/tests/test_published_references.py deleted file mode 100644 index 50f63ef..0000000 --- a/tests/test_published_references.py +++ /dev/null @@ -1,44 +0,0 @@ -"""Keep shipped reference evidence bound to the actual public solver and registry.""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path - -import pytest - -from linear_solver_bench.dataset import identity_sha256 -from linear_solver_bench.pilot_scoring import ( - score_pilot_report, - validate_replay_reference, -) - -ROOT = Path(__file__).resolve().parents[1] - - -@pytest.mark.parametrize("split,count,controls", [("dev", 96, 0), ("ranked", 248, 88)]) -def test_published_reference_is_complete_and_matches_public_build( - split, count, controls -): - artifact = ROOT / "data" / "releases" / f"flash-replay-{split}-reference.json" - reference = json.loads(artifact.read_text()) - validate_replay_reference(reference) - report = reference["reference_report"] - runtime = json.loads((artifact.parent / "cpu-runtime-v2.json").read_text()) - runtime_digest = runtime.pop("manifest_sha256") - assert identity_sha256(runtime) == runtime_digest - assert report["runtime_manifest_sha256"] == runtime_digest - assert ( - report["source_sha256"] - == hashlib.sha256( - (ROOT / "submissions" / "gmres_amg.c").read_bytes() - ).hexdigest() - ) - assert report["case_count"] == report["solved_count"] == count - assert sum(case["role"] == "control" for case in report["cases"]) == controls - assert all(not case["execution"]["diagnostics"] for case in report["cases"]) - score = score_pilot_report(report, reference) - assert score["eligible"] and score["official"] - assert score["reference_status"] == "registered" - assert score["speedup"] == 1.0 diff --git a/tests/test_qualification.py b/tests/test_qualification.py deleted file mode 100644 index 6ed0789..0000000 --- a/tests/test_qualification.py +++ /dev/null @@ -1,70 +0,0 @@ -from __future__ import annotations - -import numpy as np -from scipy import sparse - -from linear_solver_bench.qualification import ( - CONDITION_METHOD, - ESTIMATOR_GUARD_FACTOR, - FLOAT64_EPSILON, - condition_from_factor, - qualification_document, -) - - -def test_uniform_one_norm_condition_from_factor() -> None: - diagonal = np.arange(1.0, 7.0) - matrix = sparse.diags(diagonal, format="csc") - dense = matrix.toarray() - condition, evidence = condition_from_factor( - matrix, - case_id="ss-test", - rank_estimate=6, - rank_tolerance=1.0e-12, - solve=lambda value: np.linalg.solve(dense, value), - solve_transpose=lambda value: np.linalg.solve(dense.T, value), - ) - assert evidence is not None - assert condition["status"] == "estimated" - assert condition["method"] == CONDITION_METHOD - assert condition["condition_norm"] == "1" - assert np.isclose(condition["condition_estimate_raw"], 6.0) - assert np.isclose(condition["condition_estimate"], 6.0 * ESTIMATOR_GUARD_FACTOR) - assert np.isclose( - condition["minimum_safe_tolerance"], - 20.0 * 6.0 * ESTIMATOR_GUARD_FACTOR * FLOAT64_EPSILON, - ) - - -def test_rank_deficient_factor_has_no_condition_claim() -> None: - matrix = sparse.eye(6, format="csc") - condition, evidence = condition_from_factor( - matrix, - case_id="ss-test", - rank_estimate=5, - rank_tolerance=1.0e-12, - solve=lambda value: value, - solve_transpose=lambda value: value, - ) - assert evidence is None - assert condition["status"] == "rank_deficient" - assert condition["condition_norm"] is None - assert condition["condition_estimate"] is None - - -def test_qualification_document_binds_uniform_policy() -> None: - condition = { - "status": "rank_deficient", - "method": CONDITION_METHOD, - } - document = qualification_document( - catalogue_sha256="a" * 64, - input_manifest_sha256="b" * 64, - expected_case_count=1, - results=[{"index": 0, "condition": condition}], - toolchain={"python": "test"}, - ) - assert document["complete"] is True - assert document["policy"]["condition_norm"] == "1" - assert document["method_counts"] == {CONDITION_METHOD: 1} - assert len(document["condition_results_sha256"]) == 64 diff --git a/tests/test_runner.py b/tests/test_runner.py deleted file mode 100644 index 297be67..0000000 --- a/tests/test_runner.py +++ /dev/null @@ -1,69 +0,0 @@ -from __future__ import annotations - -import pathlib -import sys - -import pytest - -from linear_solver_bench.runner import build_report, evaluate_case -from linear_solver_bench.scoring import validate_report - - -def test_fresh_process_runner_and_report(tmp_path: pathlib.Path, small_system) -> None: - executable = tmp_path / "mock-driver" - executable.write_text( - f"""#!{sys.executable} -import pathlib -import struct -import sys - -import numpy as np - -input_payload = pathlib.Path(sys.argv[1]).read_bytes() -_magic, _version, _reserved, n, nnz, _tolerance = struct.unpack_from( - "<8sIIQQd", input_payload -) -offset = 40 -rows = np.frombuffer(input_payload, dtype=" CaseResult: - verification = VerificationResult( - ok=True, - failure=None, - metrics=None, - thresholds=driver_output[1], - ) - repetitions = tuple( - RepeatResult( - index=index, - outcome="completed", - process_returncode=0, - process_wall_s=0.02, - diagnostics="", - driver=driver_output[0], - verification=verification, - ) - for index in range(3) - ) - return CaseResult( - small_system.case_id, - 2, - 4, - 1.0e-4, - 1.0, - repetitions, - ) - - -def test_calibration_and_lexicographic_score(small_system, driver_output) -> None: - case = passing_case(small_system, driver_output) - report = build_report( - (case,), - source_sha256="source", - executable_sha256="executable", - runtime_manifest_sha256="runtime", - dataset_manifest_sha256="dataset", - prepared_manifest_sha256="prepared", - venue_id="modal-sandbox-cpu-v1", - ) - calibration = calibration_from_report(report, minimum_deadline_s=0.001) - score = score_report(report, calibration) - assert score["solved_count"] == 1 - assert score["penalized_geomean"] == 1.0 - assert score["ranking_key"] == [-1, 1.0] - - -def test_unsolved_case_receives_deadline_penalty(small_system, driver_output) -> None: - reference_case = passing_case(small_system, driver_output) - reference = build_report( - (reference_case,), - source_sha256="source", - executable_sha256="executable", - runtime_manifest_sha256="runtime", - dataset_manifest_sha256="dataset", - prepared_manifest_sha256="prepared", - venue_id="modal-sandbox-cpu-v1", - ) - calibration = calibration_from_report(reference, minimum_deadline_s=0.001) - failed_case = CaseResult("test-small", 2, 4, 1.0e-4, 1.0, ()) - report = build_report( - (failed_case,), - source_sha256="source", - executable_sha256="executable", - runtime_manifest_sha256="runtime", - dataset_manifest_sha256="dataset", - prepared_manifest_sha256="prepared", - venue_id="modal-sandbox-cpu-v1", - ) - score = score_report(report, calibration) - assert score["solved_count"] == 0 - assert score["penalized_geomean"] == pytest.approx(8.0) diff --git a/tests/test_submission.py b/tests/test_submission.py new file mode 100644 index 0000000..8ac4b09 --- /dev/null +++ b/tests/test_submission.py @@ -0,0 +1,70 @@ +import json + +import pytest + +from linear_solver_bench import build +from linear_solver_bench.submission import check, package + + +def test_package_creates_the_exact_public_bundle(tmp_path, monkeypatch): + monkeypatch.setattr(build, "validate_source", lambda _source: None) + source = tmp_path / "candidate.c" + source.write_text("int solver_create(void) { return 0; }\n", encoding="utf-8") + output = tmp_path / "entry" + + metadata = package( + source, + output, + name="small-solver", + families=["ns_mesh_pde"], + license_name="Apache-2.0", + submitter="Ada", + ) + + assert {path.name for path in output.iterdir()} == { + "README.md", + "solver.c", + "submission.json", + } + assert check(output) == metadata + + +def test_check_rejects_a_changed_source(tmp_path, monkeypatch): + monkeypatch.setattr(build, "validate_source", lambda _source: None) + source = tmp_path / "candidate.c" + source.write_text("initial", encoding="utf-8") + output = tmp_path / "entry" + package( + source, + output, + name="small-solver", + families=["flash"], + license_name="MIT", + submitter="Ada", + ) + (output / "solver.c").write_text("changed", encoding="utf-8") + + with pytest.raises(ValueError, match="recorded hash"): + check(output) + + +def test_check_rejects_non_string_names(tmp_path, monkeypatch): + monkeypatch.setattr(build, "validate_source", lambda _source: None) + source = tmp_path / "candidate.c" + source.write_text("initial", encoding="utf-8") + output = tmp_path / "entry" + package( + source, + output, + name="small-solver", + families=["flash"], + license_name="MIT", + submitter="Ada", + ) + metadata_path = output / "submission.json" + metadata = json.loads(metadata_path.read_text(encoding="utf-8")) + metadata["name"] = 7 + metadata_path.write_text(json.dumps(metadata), encoding="utf-8") + + with pytest.raises(ValueError, match="name must use"): + check(output) diff --git a/tests/test_venue_contract.py b/tests/test_venue_contract.py deleted file mode 100644 index c0f9a28..0000000 --- a/tests/test_venue_contract.py +++ /dev/null @@ -1,16 +0,0 @@ -from __future__ import annotations - -import pathlib - - -def test_modal_venue_has_hard_limits_and_excludes_trusted_data() -> None: - source = pathlib.Path("modal_app.py").read_text(encoding="utf-8") - assert "cpu=(2.0, 2.0)" in source - assert "memory=(4096, 4096)" in source - assert "block_network=True" in source - assert "MAXIMUM_SANDBOX_LIFETIME_S = 24 * 60 * 60" in source - assert ".add_local_dir(ROOT, REMOTE_BENCHMARK" not in source - assert '.add_local_dir(ROOT / "src"' in source - assert '.add_local_dir(ROOT / "native"' in source - assert '.add_local_dir(ROOT / "data"' not in source - assert '.env({"LINEAR_SOLVER_BENCH_ROOT": REMOTE_BENCHMARK})' in source diff --git a/tests/test_verify.py b/tests/test_verify.py index 3e9da88..253732f 100644 --- a/tests/test_verify.py +++ b/tests/test_verify.py @@ -1,174 +1,83 @@ -from __future__ import annotations +import struct import numpy as np +import pytest -from linear_solver_bench.accuracy import AccuracyThresholds -from linear_solver_bench.verify import verify_solution +from linear_solver_bench.models import BenchmarkCase, CaseInput, CsrMatrix, RawExecution +from linear_solver_bench.verify import OUTPUT_HEADER, OUTPUT_MAGIC, verify -def test_exact_solution_passes_all_gates(small_system) -> None: - result = verify_solution(small_system, small_system.x_star) - assert result.ok - assert result.failure is None - assert result.metrics is not None - assert result.metrics.normwise_backward_error == 0.0 - assert result.metrics.forward_error == 0.0 - - -def test_bad_solution_and_driver_failures_are_closed(small_system) -> None: - bad = verify_solution(small_system, np.zeros(2, dtype=np.float64)) - assert not bad.ok - assert "forward_error" in bad.failure - assert verify_solution(small_system, small_system.x_star, status=1).failure == ( - "nonzero_status" - ) - assert ( - verify_solution(small_system, small_system.x_star, input_mutated=True).failure - == "input_mutation" +def case(target=(1.0, 2.0)): + matrix = CsrMatrix( + 2, + np.array([0, 1, 2], dtype=np.uint64), + np.array([0, 1], dtype=np.uint32), + np.array([2.0, 3.0]), ) - assert verify_solution(small_system, np.asarray([np.nan, 0.0])).failure == ( - "nonfinite_solution" + target = np.asarray(target, dtype=np.float64) + return BenchmarkCase( + "small", + CaseInput(matrix, matrix.matvec(target), np.zeros(2), 1.0e-10), + target=target, + input_sha256="a" * 64, ) -def test_accuracy_threshold_contract() -> None: - thresholds = AccuracyThresholds.from_tolerance(1.0e-4) - assert thresholds.normwise_backward_error == 1.0e-4 - assert thresholds.componentwise_backward_error == 5.0e-4 - assert thresholds.forward_error == 5.0e-4 - ill_conditioned = AccuracyThresholds.from_tolerance(4.0e-2) - assert ill_conditioned.forward_error == 2.0e-1 +def output(solution, *, status=0, mutated=0): + nanoseconds = (10, 20, 30) + return OUTPUT_HEADER.pack( + OUTPUT_MAGIC, + 1, + status, + len(solution), + sum(nanoseconds), + *nanoseconds, + mutated, + ) + struct.pack(f"<{len(solution)}d", *solution) -def _system(matrix, truth, *, tolerance=1e-12, reference_kind="manufactured", b=None): - from scipy import sparse +def test_correct_solution_passes(): + benchmark_case = case() + raw = RawExecution(0, 0.01, "", output([1.0, 2.0])) - from linear_solver_bench.models import CsrMatrix, EvaluationSystem, MatrixInput + result = verify(raw, benchmark_case, "ns-mesh-pde") - matrix = CsrMatrix.from_scipy( - sparse.csr_matrix(np.asarray(matrix, dtype=np.float64)) - ) - truth = None if truth is None else np.asarray(truth, dtype=np.float64) - return EvaluationSystem( - case_id="numerical-test", - public=MatrixInput( - matrix=matrix, - b=matrix.matvec(truth) if b is None else np.asarray(b, dtype=np.float64), - x0=np.zeros(matrix.n, dtype=np.float64), - tolerance=tolerance, - ), - x_star=truth, - reference_kind=reference_kind, - ) + assert result.passed + assert result.elapsed_seconds == pytest.approx(60e-9) + assert result.metrics.normwise_backward_error == 0 -def test_ns_mesh_uses_fixed_gates_and_diagnostic_residual() -> None: - system = _system([[1e10, -1e10], [0, 1]], [1, 1]) - result = verify_solution( - system, np.asarray([1 + 1e-12, 1.0]), contract_id="ns-mesh-accuracy-v1" - ) - assert result.ok - assert result.metrics.relative_residual > 1e-8 - assert result.thresholds.normwise_backward_error == 1e-10 - assert result.thresholds.componentwise_backward_error == 1e-8 - assert result.thresholds.forward_error == 1e-5 - - -def test_ns_mesh_requires_manufactured_truth(small_system) -> None: - from dataclasses import replace - - for system in ( - replace(small_system, reference_kind="none", x_star=None), - replace(small_system, reference_kind="numerical"), - ): - result = verify_solution( - system, small_system.x_star, contract_id="ns-mesh-accuracy-v1" - ) - assert result.failure == "missing_manufactured_reference" - - -def test_flash_weak_row_and_numerical_forward_errors_are_diagnostic() -> None: - system = _system( - [[1, 0], [0, 1e-20]], - [100, 100], - b=[1, 1e-20], - reference_kind="numerical", - ) - result = verify_solution( - system, np.asarray([1.0, 0.0]), contract_id="flash-replay-accuracy-v1" - ) - assert result.ok - assert result.metrics.componentwise_backward_error == 1.0 - assert result.metrics.forward_error == 1.0 - assert result.thresholds.componentwise_backward_error is None - assert result.thresholds.forward_error is None +def test_inaccurate_solution_keeps_native_time(): + raw = RawExecution(0, 0.01, "", output([1.0, 1.0])) + result = verify(raw, case(), "ns-mesh-pde") -def test_flash_missing_reference_serializes_null_with_reason() -> None: - import json + assert not result.passed + assert result.elapsed_seconds == pytest.approx(60e-9) + assert "forward" in result.failure or "backward" in result.failure - system = _system([[1]], None, b=[1], reference_kind="none") - result = verify_solution( - system, np.asarray([1.0]), contract_id="flash-replay-accuracy-v1" - ) - assert result.ok - encoded = result.to_dict() - assert encoded["metrics"]["relative_l2_forward_error"] is None - assert encoded["metrics"]["diagnostic_reasons"]["relative_l2_forward_error"] == ( - "reference_unavailable" - ) - json.dumps(encoded, allow_nan=False) +def test_process_and_protocol_failures_are_results(): + failed = verify(RawExecution(9, 0.01, "boom", None), case(), "ns-mesh-pde") + malformed = verify(RawExecution(0, 0.01, "", b"short"), case(), "ns-mesh-pde") -def test_flash_zero_rhs_residual_definition() -> None: - import json - - system = _system([[1]], None, b=[0], reference_kind="none") - exact = verify_solution( - system, np.asarray([0.0]), contract_id="flash-replay-accuracy-v1" - ) - bad = verify_solution( - system, np.asarray([1e-300]), contract_id="flash-replay-accuracy-v1" - ) - assert exact.ok - assert exact.metrics.relative_residual == 0 - assert not bad.ok - assert bad.metrics.relative_residual is None - assert bad.metrics.diagnostic_reasons["relative_residual"] == ( - "zero_rhs_with_nonzero_residual" - ) - json.dumps(bad.to_dict(), allow_nan=False) + assert not failed.passed and failed.elapsed_seconds is None + assert not malformed.passed and malformed.elapsed_seconds is None -def test_overflowing_denominator_cannot_appear_accurate() -> None: - import json - - system = _system([[1e308]], [1.0]) - result = verify_solution( - system, np.asarray([1.0]), contract_id="ns-mesh-accuracy-v1" +def test_flash_uses_residual_without_a_target(): + benchmark_case = case() + benchmark_case = BenchmarkCase( + benchmark_case.case_id, + benchmark_case.input, + target=None, + input_sha256=benchmark_case.input_sha256, ) - assert not result.ok - assert result.failure == "nonfinite_metric" - assert result.metrics.normwise_backward_error is None - json.dumps(result.to_dict(), allow_nan=False) - -def test_l2_ratio_is_stable_for_large_and_tiny_vectors() -> None: - import pytest - - from linear_solver_bench.verify import accuracy_metrics - - for scale in (1e-300, 1.3e308): - system = _system([[1e-300, 0], [0, 1e-300]], [scale, scale]) - metrics = accuracy_metrics(system, system.x_star * (1 + 1e-4)) - assert metrics.relative_l2_forward_error == pytest.approx(1e-4, rel=1e-10) - assert metrics.relative_linf_forward_error == pytest.approx(1e-4, rel=1e-10) - - -def test_unknown_accuracy_contract_is_rejected(small_system) -> None: - import pytest + result = verify( + RawExecution(0, 0.01, "", output([1.0, 2.0])), + benchmark_case, + "magnetic_diffusion_flash", + ) - with pytest.raises(ValueError, match="unknown accuracy contract"): - verify_solution( - small_system, small_system.x_star, contract_id="future-contract" - ) + assert result.passed diff --git a/tools/audit_ns_split.py b/tools/audit_ns_split.py deleted file mode 100644 index 4f7aea7..0000000 --- a/tools/audit_ns_split.py +++ /dev/null @@ -1,286 +0,0 @@ -"""Describe NS split balance from public matrix properties, without solver runs. - -This is a descriptive audit, not a certificate of equal solver difficulty. It -does not change releases, inspect solver results, or choose a new official split. -""" - -from __future__ import annotations - -import argparse -import json -import math -from collections import Counter -from pathlib import Path - -import numpy as np -from scipy.stats import wasserstein_distance - -from linear_solver_bench.dataset import identity_sha256 -from linear_solver_bench.manifests import load_release, validate_release_splits -from linear_solver_bench.models import CsrMatrix -from linear_solver_bench.paths import cache_dir -from linear_solver_bench.pilot_dataset import ( - iter_pilot_prepared, - load_pilot_prepared_manifest, -) -from linear_solver_bench.sources.download import fetch_archive -from linear_solver_bench.sources.suitesparse import load_suitesparse - -FEATURES = ( - "log10_n", - "log10_nnz_per_row", - "relative_nonsymmetry", - "zero_diagonal_fraction", - "weakly_diagonally_dominant_row_fraction", -) -SIZE_BANDS = (10_000, 100_000, 1_000_000) - - -def matrix_features(matrix: CsrMatrix) -> dict[str, float]: - """Compute scale-invariant diagnostics; dominance here is not certified.""" - if matrix.nnz == 0: - raise ValueError("split diagnostics require a nonzero operator") - a = matrix.to_scipy().copy() - zero_diagonal_fraction = float(np.mean(a.diagonal() == 0)) - a.data /= float(np.max(np.abs(a.data))) - difference = a - a.T - nonsymmetry = float(np.linalg.norm(difference.data) / np.linalg.norm(a.data)) - diagonal = np.abs(a.diagonal()) - a.data = np.abs(a.data) - row_sums = np.asarray(a.sum(axis=1)).ravel() - return { - "log10_n": math.log10(matrix.n), - "log10_nnz_per_row": math.log10(matrix.nnz / matrix.n), - "relative_nonsymmetry": nonsymmetry, - "zero_diagonal_fraction": zero_diagonal_fraction, - "weakly_diagonally_dominant_row_fraction": float( - np.mean(diagonal >= row_sums - diagonal) - ), - } - - -def _record(case: dict, matrix: CsrMatrix, split: str) -> dict: - source = case["source"] - return { - "matrix_id": source["matrix_id"], - "name": source["group"] + "/" + source["name"], - "matrix_sha256": source["matrix_sha256"], - "provenance_group": case["provenance_group"], - "split": split, - "n": case["n"], - "nnz": case["nnz"], - "features": matrix_features(matrix), - } - - -def collect_releases( - paths: list[Path], cache: Path, *, offline: bool = False -) -> tuple[list[dict], list[str]]: - """Acquire only public A arrays; ranked RHS keys are not needed.""" - releases = [load_release(path) for path in paths] - validate_release_splits(releases) - if any(r["family"] != "ns-mesh-pde" for r in releases): - raise ValueError("audit requires NS releases") - cases = [] - for release in releases: - for case in release["cases"]: - archive = fetch_archive(case["source"], cache, offline=offline) - matrix = load_suitesparse(archive, case) - cases.append(_record(case, matrix, release["split"])) - del matrix - return sorted(cases, key=lambda c: c["matrix_id"]), sorted( - r["manifest_sha256"] for r in releases - ) - - -def collect_cases(roots: list[Path]) -> tuple[list[dict], list[str]]: - cases, releases, identities = [], [], set() - for root in roots: - prepared = load_pilot_prepared_manifest(root) - if prepared["family"] != "ns-mesh-pde" or not prepared["complete_split"]: - raise ValueError("audit requires complete prepared NS splits") - release = prepared["source_release"] - releases.append(release["manifest_sha256"]) - for system, case in zip( - iter_pilot_prepared(root), release["cases"], strict=True - ): - identity = case["source"]["matrix_id"] - if identity in identities: - raise ValueError("an operator occurs in more than one input split") - identities.add(identity) - cases.append(_record(case, system.public.matrix, release["split"])) - del system - return sorted(cases, key=lambda c: c["matrix_id"]), sorted(releases) - - -def add_descriptors(cases: list[dict], descriptors: dict) -> list[dict]: - records = descriptors["matrices"] - by_id = {row["matrix_id"]: row for row in records} - if len(by_id) != len(records) or set(by_id) != {c["matrix_id"] for c in cases}: - raise ValueError("descriptor inventory differs from matrix inventory") - result = [] - for case in cases: - record = by_id[case["matrix_id"]] - if ( - record["full_name"] != case["name"] - or record["provenance_group"] != case["provenance_group"] - ): - raise ValueError("descriptor source identity differs from matrix") - tags = {} - for field, definition in ( - ("application_tags", "application_tag_definitions"), - ("discretization_tags", "discretization_tag_definitions"), - ): - values = record[field] - if ( - not values - or len(set(values)) != len(values) - or not set(values) <= set(descriptors[definition]) - ): - raise ValueError("invalid descriptor tags") - tags[field] = sorted(values) - result.append({**case, **tags}) - return result - - -def compare_splits(cases: list[dict]) -> dict: - """Report marginal distances and strata impossible to share across groups.""" - splits = { - split: [c for c in cases if c["split"] == split] for split in ("dev", "ranked") - } - if any(not rows for rows in splits.values()) or sum( - map(len, splits.values()) - ) != len(cases): - raise ValueError("audit requires nonempty dev and ranked splits only") - groups = { - split: {c["provenance_group"] for c in rows} for split, rows in splits.items() - } - if groups["dev"] & groups["ranked"]: - raise ValueError("a provenance group crosses the split boundary") - tag_coverage = {} - for field in ("application_tags", "discretization_tags"): - if not all(field in case for case in cases): - continue - tag_coverage[field] = {} - for tag in sorted({tag for case in cases for tag in case[field]}): - selected = [case for case in cases if tag in case[field]] - counts = { - split: sum(c["split"] == split for c in selected) for split in splits - } - source_groups = sorted({c["provenance_group"] for c in selected}) - tag_coverage[field][tag] = { - "counts": counts, - "provenance_groups": source_groups, - "known_regime_present_in_both_splits": tag != "unknown" - and all(counts.values()), - "at_least_two_source_groups": len(source_groups) >= 2, - } - features = {} - for feature in FEATURES: - values = { - split: np.asarray([c["features"][feature] for c in rows]) - for split, rows in splits.items() - } - pooled = np.concatenate(list(values.values())) - if not np.all(np.isfinite(pooled)): - raise ValueError("matrix features must be finite") - spread = float(np.ptp(pooled)) - distance = float(wasserstein_distance(values["dev"], values["ranked"])) - features[feature] = { - "normalized_wasserstein_distance": distance / spread if spread else 0.0, - **{ - split: { - "min": float(v.min()), - "median": float(np.median(v)), - "max": float(v.max()), - } - for split, v in values.items() - }, - } - bands = [] - for low, high in zip((0, *SIZE_BANDS), (*SIZE_BANDS, None), strict=True): - selected = [ - c for c in cases if c["n"] >= low and (high is None or c["n"] < high) - ] - distinct = sorted({c["provenance_group"] for c in selected}) - bands.append( - { - "minimum_n_inclusive": low, - "maximum_n_exclusive": high, - "counts": { - split: sum(c["split"] == split for c in selected) - for split in splits - }, - "provenance_groups": distinct, - "can_cover_both_sides_with_distinct_groups": len(distinct) >= 2, - } - ) - return { - "case_counts": {split: len(rows) for split, rows in splits.items()}, - "group_counts": {split: len(values) for split, values in groups.items()}, - "groups": { - split: dict(sorted(Counter(c["provenance_group"] for c in rows).items())) - for split, rows in splits.items() - }, - "size_bands": bands, - "tag_coverage": tag_coverage, - "features": features, - "mean_normalized_marginal_distance": float( - np.mean([v["normalized_wasserstein_distance"] for v in features.values()]) - ), - } - - -def main(argv: list[str] | None = None) -> int: - parser = argparse.ArgumentParser(description=__doc__) - inputs = parser.add_mutually_exclusive_group(required=True) - inputs.add_argument("--prepared", type=Path, action="append") - inputs.add_argument("--release", type=Path, action="append") - parser.add_argument("--cache", type=Path, default=cache_dir() / "downloads") - parser.add_argument("--offline", action="store_true") - parser.add_argument("--descriptors", type=Path) - parser.add_argument("--output", type=Path, required=True) - args = parser.parse_args(argv) - cases, releases = ( - collect_cases(args.prepared) - if args.prepared - else collect_releases(args.release, args.cache, offline=args.offline) - ) - descriptors = json.loads(args.descriptors.read_text()) if args.descriptors else None - if descriptors is not None: - cases = add_descriptors(cases, descriptors) - body = { - "schema_version": 1, - "kind": "ns-split-descriptive-audit", - "source_release_sha256": releases, - "descriptor_sha256": identity_sha256(descriptors) - if descriptors is not None - else None, - "method": "matrix-only-marginal-wasserstein-range-normalized-v1", - "limitations": [ - "Marginal feature similarity does not establish joint similarity " - "or equal solver difficulty.", - "No condition-number, nonnormality, or solver-success guarantee " - "is inferred.", - "Dominance and nonsymmetry are floating-point descriptive " - "diagnostics, not mathematical certificates.", - "Application and discretization tags reflect the cited source " - "review; unknown labels do not establish regime coverage.", - "No universal acceptable-distance threshold is specified by this audit.", - ], - "cases": cases, - "comparison": compare_splits(cases), - } - args.output.parent.mkdir(parents=True, exist_ok=True) - args.output.write_text( - json.dumps( - {**body, "audit_sha256": identity_sha256(body)}, indent=2, sort_keys=True - ) - + "\n" - ) - print(json.dumps(body["comparison"], indent=2)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/tools/build_flash_release.py b/tools/build_flash_release.py deleted file mode 100644 index aa27201..0000000 --- a/tools/build_flash_release.py +++ /dev/null @@ -1,262 +0,0 @@ -#!/usr/bin/env python3 -"""Build frozen FLASH pilot manifests from a public, commit-pinned catalogue. - -Supply the dataset commit returned by publication as --revision. This tool does -not upload data or register the resulting manifests as trusted releases. -""" - -from __future__ import annotations - -import argparse -import json -import pathlib -import re -import sys -from collections import Counter, defaultdict -from urllib.parse import quote - -from linear_solver_bench.dataset import sha256_file -from linear_solver_bench.families import resolve_track -from linear_solver_bench.manifests import ( - seal_manifest, - validate_release, - validate_release_splits, -) -from linear_solver_bench.sources.flash import load_flash - -RELEASE_ID = "flash-replay-pilot" -DEFAULT_REPOSITORY = "hgarud/LinearSolveBench" -FLASH_DIFFUSION_URL = ( - "https://flash.rochester.edu/site/flashcode/user_support/" - "flash_ug_devel/node128.html" -) -PILOT_EXECUTION = { - "repetitions": 1, - "cpus": 2, - "memory_bytes": 4 * 1024**3, - "case_timeout_seconds": 90.0, - "max_iterations": 5000, -} - - -def _archive_path(value: object) -> pathlib.PurePosixPath: - if not isinstance(value, str): - raise ValueError("catalogue archive path must be a string") - path = pathlib.PurePosixPath(value) - if ( - path.is_absolute() - or path.as_posix() != value - or ".." in path.parts - or "\\" in value - or path.suffix != ".zip" - ): - raise ValueError("catalogue archive paths must be relative ZIP paths") - return path - - -def build_releases( - catalogue: dict, - *, - revision: str, - repository: str = DEFAULT_REPOSITORY, - archive_dir: pathlib.Path | None = None, -) -> dict[str, dict]: - """Validate both splits, optionally inspect all archives, and return manifests.""" - if not re.fullmatch(r"[0-9a-f]{40}", revision): - raise ValueError("revision must be a full lowercase 40-character commit hash") - if not re.fullmatch( - r"[A-Za-z0-9][A-Za-z0-9._-]*/[A-Za-z0-9][A-Za-z0-9._-]*", repository - ): - raise ValueError("repository must have the form owner/dataset") - if ( - catalogue.get("schema_version") != 1 - or catalogue.get("kind") != "flash-replay-public-catalogue" - or catalogue.get("family") != "magnetic_diffusion_flash" - ): - raise ValueError("unsupported public FLASH catalogue schema") - rows, run_rows = catalogue.get("cases"), catalogue.get("source_runs") - if not isinstance(rows, list) or not rows or not isinstance(run_rows, list): - raise ValueError("catalogue must contain cases and source-run descriptions") - if type(catalogue.get("case_count")) is not int or catalogue["case_count"] != len( - rows - ): - raise ValueError("catalogue case count is inconsistent") - runs = {} - for run in run_rows: - identity = run["source_run"] - if ( - identity in runs - or not isinstance(run["problem"], str) - or not run["problem"].strip() - ): - raise ValueError( - "source runs must be unique and identify their FLASH problem" - ) - runs[identity] = run - split_counts = Counter(row["split"] for row in rows) - if set(split_counts) != {"dev", "ranked"} or catalogue.get("split_counts") != dict( - split_counts - ): - raise ValueError( - "catalogue must contain consistent development and ranked splits" - ) - run_counts = Counter(row["source_run"] for row in rows) - if set(runs) != set(run_counts): - raise ValueError("catalogue cases and source-run descriptions disagree") - for identity, run in runs.items(): - if run["case_count"] != run_counts[identity]: - raise ValueError("catalogue source-run case count is inconsistent") - paths = [_archive_path(row["path"]) for row in rows] - if len(set(paths)) != len(paths): - raise ValueError("each catalogue case must have its own archive path") - - base_url = f"https://huggingface.co/datasets/{repository}/resolve/{revision}" - catalogue_url = f"{base_url}/catalogue.json" - contract = resolve_track("magnetic_diffusion_flash", "replay") - releases, archive_cases = {}, [] - for split in ("dev", "ranked"): - selected = [row for row in rows if row["split"] == split] - group_runs = defaultdict(set) - per_run = Counter() - for row in selected: - if row["role"] == "scored": - group_runs[row["provenance_group"]].add(row["source_run"]) - per_run[row["provenance_group"], row["source_run"]] += 1 - cases = [] - for index, row in enumerate(selected): - group, run_id = row["provenance_group"], row["source_run"] - run = runs[run_id] - if (run["split"], run["provenance_group"]) != (split, group): - raise ValueError( - "case split or provenance disagrees with its source run" - ) - weight = 0.0 - if row["role"] == "scored": - weight = 1 / ( - len(group_runs) * len(group_runs[group]) * per_run[group, run_id] - ) - case = { - "index": index, - **{ - key: row[key] - for key in ( - "case_id", - "n", - "nnz", - "tolerance", - "role", - "provenance_group", - "source_run", - ) - }, - "weight": weight, - "source": { - "kind": "huggingface", - "url": f"{base_url}/{quote(row['path'], safe='/')}", - **{ - key: row[key] - for key in ( - "archive_bytes", - "archive_sha256", - "matrix_sha256", - "b_sha256", - "x0_sha256", - ) - }, - }, - "admission": { - "statement": ( - "Captured scalar magnetic-diffusion system from FLASH " - f"({run['problem']}); replay evaluates this captured " - "solve independently." - ), - "citations": [catalogue_url, FLASH_DIFFUSION_URL], - "capture_kind": "scalar-magnetic-diffusion", - "relative_tolerance": row["tolerance"], - "absolute_tolerance": 0, - "step": row["step"], - "solve_index": row["solve_index"], - }, - "qualification": None, - } - cases.append(case) - archive_cases.append((row["path"], case)) - releases[split] = validate_release( - seal_manifest( - { - "schema_version": 2, - "kind": "linear-solver-bench-pilot-release", - "release_id": RELEASE_ID, - "family": contract.family, - "track": contract.track, - "split": split, - "contracts": contract.contracts, - "execution": dict(PILOT_EXECUTION), - "rhs": None, - "cases": cases, - } - ) - ) - validate_release_splits(list(releases.values())) - if archive_dir is not None: - root = pathlib.Path(archive_dir).expanduser().resolve() - for relative, case in archive_cases: - path = root / relative - if ( - path.is_symlink() - or not path.is_file() - or not path.resolve().is_relative_to(root) - ): - raise ValueError( - f"case archive is missing or outside archive-dir: {relative}" - ) - source = case["source"] - if ( - path.stat().st_size != source["archive_bytes"] - or sha256_file(path) != source["archive_sha256"] - ): - raise ValueError(f"case archive identity mismatch: {relative}") - load_flash(path, case) - return releases - - -def main(argv: list[str] | None = None) -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("catalogue", type=pathlib.Path, help="public catalogue.json") - parser.add_argument( - "--revision", required=True, help="published 40-character dataset commit" - ) - parser.add_argument("--repository", default=DEFAULT_REPOSITORY) - parser.add_argument("--output-dir", type=pathlib.Path, required=True) - parser.add_argument( - "--archive-dir", - type=pathlib.Path, - help="optional dataset root containing the catalogue's relative ZIP paths", - ) - args = parser.parse_args(argv) - try: - catalogue = json.loads(args.catalogue.read_text(encoding="utf-8")) - if not isinstance(catalogue, dict): - raise ValueError("catalogue must contain a JSON object") - releases = build_releases( - catalogue, - revision=args.revision, - repository=args.repository, - archive_dir=args.archive_dir, - ) - args.output_dir.mkdir(parents=True, exist_ok=True) - for split, release in releases.items(): - path = args.output_dir / f"flash-replay-{split}-pilot.json" - path.write_text( - json.dumps(release, indent=2, sort_keys=True, allow_nan=False) + "\n", - encoding="utf-8", - ) - print(path) - except (ValueError, OSError, KeyError, TypeError) as exc: - print(f"build_flash_release: {exc}", file=sys.stderr) - return 2 - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/tools/freeze_manifests.py b/tools/freeze_manifests.py deleted file mode 100644 index 057f109..0000000 --- a/tools/freeze_manifests.py +++ /dev/null @@ -1,123 +0,0 @@ -#!/usr/bin/env python3 -"""Derive immutable ranked and public-development manifests from evidence.""" - -from __future__ import annotations - -import hashlib -import json -import pathlib - -ROOT = pathlib.Path(__file__).resolve().parents[1] -DATA = ROOT / "data" -TOLERANCE_TIERS = (1.0e-4, 1.0e-3, 1.0e-2, 4.0e-2) -CONDITION_METHOD = "spqr-guarded-onenormest-v1" -PUBLIC_DEV_KEY = hashlib.sha256(b"linear-solver-bench-public-dev-v1").digest() - - -def canonical(value: object) -> str: - return json.dumps(value, sort_keys=True, separators=(",", ":"), allow_nan=False) - - -def digest(value: object) -> str: - return hashlib.sha256(canonical(value).encode("ascii")).hexdigest() - - -def case_tolerance(result: dict[str, object]) -> float: - minimum = float(result["condition"]["minimum_safe_tolerance"]) - for tolerance in TOLERANCE_TIERS: - if minimum <= tolerance: - return tolerance - raise RuntimeError("full-rank case exceeds the v1 tolerance tiers") - - -def case_record(index: int, result: dict[str, object]) -> dict[str, object]: - condition = result["condition"] - return { - "index": index, - "case_id": result["case_id"], - "matrix_id": result["matrix_id"], - "group": result["group"], - "name": result["name"], - "n": result["n"], - "nnz": result["nnz"], - "source_url": f"https://sparse.tamu.edu/MM/{result['group']}/{result['name']}.tar.gz", - "archive_sha256": result["archive_sha256"], - "matrix_sha256": result["matrix_sha256"], - "condition_method": condition["method"], - "condition_norm": condition["condition_norm"], - "condition_estimate": condition["condition_estimate"], - "minimum_safe_tolerance": condition["minimum_safe_tolerance"], - "tolerance": case_tolerance(result), - } - - -def write_manifest(filename: str, body: dict[str, object]) -> None: - value = {**body, "manifest_sha256": digest(body)} - (DATA / filename).write_text( - json.dumps(value, indent=2, sort_keys=True, allow_nan=False) + "\n", - encoding="utf-8", - ) - - -def main() -> None: - qualification = json.loads((DATA / "qualification.json").read_text()) - eligible = [] - for result in qualification["results"]: - condition = result["condition"] - if ( - condition["status"] == "estimated" - and condition["tightest_supported_tolerance"] is not None - ): - eligible.append(result) - eligible.sort(key=lambda item: item["index"]) - ranked_cases = [case_record(index, result) for index, result in enumerate(eligible)] - if len(ranked_cases) != 203: - raise RuntimeError("ranked v1 selection must contain 203 cases") - ranked_body = { - "schema_version": 1, - "benchmark_id": "linear-solver-bench-cpu-v1", - "split": "ranked", - "catalogue_sha256": qualification["catalogue_sha256"], - "qualification_sha256": qualification["condition_results_sha256"], - "selection": "spqr-full-rank-and-condition-supported-v1", - "rhs_scheme": "rademacher-hmac-pcg64-v1", - "tolerance_policy": { - "id": "condition-aware-four-tier-v1", - "condition_norm": "1", - "condition_method": CONDITION_METHOD, - "tiers": list(TOLERANCE_TIERS), - "criterion": "minimum_safe_tolerance<=case_tolerance", - }, - "case_count": len(ranked_cases), - "cases": ranked_cases, - } - write_manifest("ranked-v1.json", ranked_body) - - # Smallest case from each distinct group, then stable matrix identity. - ordered = sorted(ranked_cases, key=lambda case: (case["n"], case["matrix_id"])) - dev_source = [] - groups = set() - for case in ordered: - if case["group"] not in groups: - groups.add(case["group"]) - dev_source.append(case) - if len(dev_source) == 8: - break - dev_cases = [{**case, "index": index} for index, case in enumerate(dev_source)] - dev_body = { - "schema_version": 1, - "benchmark_id": "linear-solver-bench-cpu-v1", - "split": "dev", - "source_ranked_manifest_sha256": digest(ranked_body), - "selection": "eight-smallest-distinct-groups-v1", - "rhs_scheme": "rademacher-hmac-pcg64-v1", - "public_rhs_key_hex": PUBLIC_DEV_KEY.hex(), - "tolerance_policy": ranked_body["tolerance_policy"], - "case_count": len(dev_cases), - "cases": dev_cases, - } - write_manifest("dev-v1.json", dev_body) - - -if __name__ == "__main__": - main() diff --git a/tools/select_ns_cohort.py b/tools/select_ns_cohort.py deleted file mode 100644 index db05a16..0000000 --- a/tools/select_ns_cohort.py +++ /dev/null @@ -1,508 +0,0 @@ -"""Select indivisible NS source groups using a frozen matrix-only policy. - -The supported contract is docs/analysis/ns-cohort-v2-policy.json. Every group -partition is considered; hard coverage and budget rules precede the objective. -No solver outcomes, reference solutions, random draws, or matrix arrays enter. -""" - -from __future__ import annotations - -import argparse -import json -import math -from collections import defaultdict -from pathlib import Path - -import numpy as np -from scipy.spatial.distance import cdist - -from linear_solver_bench.dataset import identity_sha256 -from linear_solver_bench.manifests import digest, https_url, positive_int - -SUPPORTED_POLICY_SHA256 = ( - "e5c7bfd1f224621c12101fb38a07a7cba1d1f19304901e235c2826f8a4d9188c" -) -FEATURES = ( - "log10_n", - "log10_nnz_per_row", - "relative_nonsymmetry", - "zero_diagonal_fraction", - "weakly_diagonally_dominant_row_fraction", -) -SIZE_LABELS = ("n<10000", "10000<=n<100000", "100000<=n<1000000", "n>=1000000") -CASE_FIELDS = { - "matrix_id", - "name", - "matrix_sha256", - "provenance_group", - "n", - "nnz", - "features", - "application_tags", - "discretization_tags", -} -SOURCE_FIELDS = { - "kind", - "matrix_id", - "group", - "name", - "url", - "archive_bytes", - "archive_sha256", - "matrix_sha256", -} -PARTITION_CHUNK = 65_536 -SCORE_CHUNK = 4_096 - - -def _validate(inventory: dict, policy: dict) -> list[dict]: - if ( - not isinstance(policy, dict) - or identity_sha256(policy) != SUPPORTED_POLICY_SHA256 - ): - raise ValueError("incompatible selection policy schema or values") - fields = { - "schema_version", - "kind", - "cases", - "sources", - "descriptor_sha256", - "inventory_sha256", - } - if ( - not isinstance(inventory, dict) - or set(inventory) != fields - or type(inventory["schema_version"]) is not int - or inventory["schema_version"] != 1 - or inventory["kind"] != "ns-cohort-source-inventory" - ): - raise ValueError("invalid cohort source inventory schema") - body = {k: v for k, v in inventory.items() if k != "inventory_sha256"} - if inventory["inventory_sha256"] != identity_sha256(body): - raise ValueError("cohort inventory digest mismatch") - digest(inventory["descriptor_sha256"], "descriptor_sha256") - if not isinstance(inventory["cases"], list) or not inventory["cases"]: - raise ValueError("cohort inventory requires cases") - if not isinstance(inventory["sources"], list): - raise ValueError("cohort inventory requires source records") - sources = {} - for source in inventory["sources"]: - if not isinstance(source, dict) or set(source) != SOURCE_FIELDS: - raise ValueError("invalid inventory source fields") - matrix_id = positive_int(source["matrix_id"], "matrix_id") - if source["kind"] != "suitesparse" or matrix_id in sources: - raise ValueError("invalid or duplicate inventory source") - for key in ("group", "name"): - if not isinstance(source[key], str) or not source[key]: - raise ValueError("source names must be nonempty strings") - https_url(source["url"]) - positive_int(source["archive_bytes"], "archive_bytes") - digest(source["archive_sha256"], "archive_sha256") - digest(source["matrix_sha256"], "matrix_sha256") - sources[matrix_id] = source - ids, identities = set(), set() - for case in inventory["cases"]: - if not isinstance(case, dict) or set(case) != CASE_FIELDS: - raise ValueError( - "invalid case fields; split and outcome inputs are forbidden" - ) - matrix_id = positive_int(case["matrix_id"], "matrix_id") - n = positive_int(case["n"], "n") - nnz = positive_int(case["nnz"], "nnz") - identity = digest(case["matrix_sha256"], "matrix_sha256") - if matrix_id in ids or identity in identities: - raise ValueError("duplicate cohort matrix identity") - ids.add(matrix_id) - identities.add(identity) - source = sources.get(matrix_id) - if source is None or ( - case["name"] != source["group"] + "/" + source["name"] - or identity != source["matrix_sha256"] - ): - raise ValueError("case and source identities differ") - if ( - not isinstance(case["provenance_group"], str) - or not case["provenance_group"] - ): - raise ValueError("case requires a provenance group") - if not isinstance(case["features"], dict) or set(case["features"]) != set( - FEATURES - ): - raise ValueError("case requires exactly the five matrix-only features") - for name, value in case["features"].items(): - if type(value) not in {int, float} or not math.isfinite(value): - raise ValueError("matrix features must be finite numbers") - if name.endswith("_fraction") and not 0 <= value <= 1: - raise ValueError("fraction features must lie in [0, 1]") - if case["features"]["relative_nonsymmetry"] < 0: - raise ValueError("relative nonsymmetry must be nonnegative") - for name, expected in ( - ("log10_n", math.log10(n)), - ("log10_nnz_per_row", math.log10(nnz / n)), - ): - if not math.isclose( - case["features"][name], expected, abs_tol=1e-12, rel_tol=1e-12 - ): - raise ValueError("matrix log features disagree with dimensions") - for field in policy["coverage"]["known_tag_fields"]: - tags = case[field] - if ( - not isinstance(tags, list) - or not tags - or any(not isinstance(tag, str) or not tag for tag in tags) - or len(tags) != len(set(tags)) - ): - raise ValueError("case tags must be nonempty unique strings") - if ids != set(sources): - raise ValueError("source and case inventories differ") - return sorted(inventory["cases"], key=lambda case: case["matrix_id"]) - - -def _size_label(n: int) -> str: - return SIZE_LABELS[sum(n >= boundary for boundary in (10_000, 100_000, 1_000_000))] - - -def _endpoint(value: float) -> str: - return "0" if value == 0 else "1" if value == 1 else "(0,1)" - - -def _strata(cases: list[dict], policy: dict) -> dict[tuple[str, str], list[int]]: - strata = defaultdict(list) - excluded = set(policy["coverage"]["excluded_tags"]) - for i, case in enumerate(cases): - strata[("size_band", _size_label(case["n"]))].append(i) - for feature in policy["coverage"]["endpoint_features"]: - strata[(feature, _endpoint(case["features"][feature]))].append(i) - for field in policy["coverage"]["known_tag_fields"]: - for tag in sorted(set(case[field]) - excluded): - strata[(field, tag)].append(i) - for method in sorted(set(case["discretization_tags"]) - excluded): - strata[ - ("size_method_joint", _size_label(case["n"]) + " / " + method) - ].append(i) - return dict(sorted(strata.items())) - - -def _count_table(weights: np.ndarray) -> np.ndarray: - table = np.zeros(1 << len(weights), dtype=np.int64) - for i, weight in enumerate(weights): - start = 1 << i - table[start : 2 * start] = table[:start] + weight - return table - - -class _Objective: - """Preaggregate per-group CDF counts, tag incidence, and pair distances.""" - - def __init__(self, cases: list[dict], group_ids: np.ndarray, group_count: int): - self.n = len(cases) - self.group_count = group_count - self.membership = np.eye(group_count)[group_ids] - features = np.array([[c["features"][f] for f in FEATURES] for c in cases]) - spread = np.ptp(features, axis=0) - self.features = np.divide( - features - features.min(axis=0), - spread, - out=np.zeros_like(features), - where=spread != 0, - ) - self.distances = cdist(self.features, self.features) - self.maximum_distance = float(self.distances.max()) - self.group_distances = self.membership.T @ self.distances @ self.membership - self.group_distance_totals = self.group_distances.sum(axis=1) - self.total_distance = float(self.group_distances.sum()) - cdfs, gaps = [], [] - for feature in range(len(FEATURES)): - order = np.argsort(self.features[:, feature], kind="stable") - cdfs.append(np.cumsum(self.membership[order], axis=0)[:-1].T) - gaps.append(np.diff(self.features[order, feature]) / len(FEATURES)) - self.group_cdfs = np.concatenate(cdfs, axis=1) - self.cdf_totals = self.group_cdfs.sum(axis=0) - self.cdf_gaps = np.concatenate(gaps) - tags = sorted( - { - (field, tag) - for c in cases - for field in ("application_tags", "discretization_tags") - for tag in c[field] - if tag != "unknown" - } - ) - incidence = np.array([[tag in c[field] for field, tag in tags] for c in cases]) - self.group_tags = self.membership.T @ incidence - self.tag_totals = self.group_tags.sum(axis=0) - - def score(self, masks: np.ndarray, dev_counts: np.ndarray) -> np.ndarray: - chosen = (masks[:, None] >> np.arange(self.group_count, dtype=np.uint32)) & 1 - chosen = chosen.astype(np.float64) - dev = dev_counts.astype(np.float64) - ranked = self.n - dev - cdf = chosen @ self.group_cdfs - marginal = ( - np.abs(cdf / dev[:, None] - (self.cdf_totals - cdf) / ranked[:, None]) - @ self.cdf_gaps - ) - within_dev = np.sum((chosen @ self.group_distances) * chosen, axis=1) - cross = chosen @ self.group_distance_totals - within_dev - within_ranked = self.total_distance - within_dev - 2 * cross - energy = ( - 2 * cross / (dev * ranked) - within_dev / dev**2 - within_ranked / ranked**2 - ) - energy = ( - np.maximum(energy, 0) / (2 * self.maximum_distance) - if self.maximum_distance - else np.zeros_like(dev) - ) - if self.group_tags.shape[1]: - counts = chosen @ self.group_tags - tags = np.mean( - np.abs( - counts / dev[:, None] - (self.tag_totals - counts) / ranked[:, None] - ), - axis=1, - ) - else: - tags = np.zeros_like(dev) - budget = np.abs(dev / self.n - 1 / 3) - return np.column_stack((marginal, energy, tags, budget)) - - -def _coverage_report(cases: list[dict], strata: dict, dev_ids: set[int]) -> list[dict]: - rows = [] - for (kind, label), indices in strata.items(): - groups = sorted({cases[i]["provenance_group"] for i in indices}) - dev = sum(cases[i]["matrix_id"] in dev_ids for i in indices) - rows.append( - { - "kind": kind, - "stratum": label, - "supporting_groups": groups, - "required_placement": ( - "both-splits" - if len(groups) >= 2 - else "report-only" - if kind == "size_method_joint" - else "dev-only" - ), - "counts": {"dev": dev, "ranked": len(indices) - dev}, - "dev_only": len(groups) == 1 and kind != "size_method_joint", - } - ) - return rows - - -def _joint_gaps(cases: list[dict], dev_ids: set[int]) -> list[dict]: - joint = defaultdict(list) - for case in cases: - for method in set(case["discretization_tags"]) - {"unknown"}: - joint[(_size_label(case["n"]), method)].append(case) - gaps = [] - for (size, method), rows in sorted(joint.items()): - dev = sum(c["matrix_id"] in dev_ids for c in rows) - if dev in {0, len(rows)}: - groups = sorted({c["provenance_group"] for c in rows}) - gaps.append( - { - "size_band": size, - "spatial_method": method, - "counts": {"dev": dev, "ranked": len(rows) - dev}, - "supporting_groups": groups, - "supported_by_multiple_groups": len(groups) >= 2, - "hard_constraint": len(groups) >= 2, - } - ) - return gaps - - -def _partition_summary( - cases: list[dict], - groups: list[str], - mask: int, - components: np.ndarray, - policy: dict, -) -> dict: - dev_groups = {g for i, g in enumerate(groups) if mask & (1 << i)} - by_split = { - split: [ - c - for c in cases - if (c["provenance_group"] in dev_groups) == (split == "dev") - ] - for split in ("dev", "ranked") - } - return { - "matrix_ids": { - split: [c["matrix_id"] for c in rows] for split, rows in by_split.items() - }, - "groups": { - split: sorted({c["provenance_group"] for c in rows}) - for split, rows in by_split.items() - }, - "case_counts": {split: len(rows) for split, rows in by_split.items()}, - "group_counts": { - split: len({c["provenance_group"] for c in rows}) - for split, rows in by_split.items() - }, - "score": float(np.mean(components)), - "rounded_score": round(float(np.mean(components)), 12), - "score_components": dict( - zip(policy["objective"]["components"], map(float, components), strict=True) - ), - } - - -def select_cohort(inventory: dict, policy: dict) -> dict: - """Return the best feasible partition, retaining every admitted input case.""" - cases = _validate(inventory, policy) - groups = sorted({case["provenance_group"] for case in cases}) - if len(groups) > policy["search"]["maximum_groups"]: - raise ValueError("exhaustive selection supports at most 26 provenance groups") - group_lookup = {group: i for i, group in enumerate(groups)} - group_ids = np.array([group_lookup[c["provenance_group"]] for c in cases]) - strata = _strata(cases, policy) - shared_masks, forced_dev = [], 0 - for (kind, _), indices in strata.items(): - mask = sum(1 << int(i) for i in set(group_ids[indices])) - if mask.bit_count() == 1 and kind != "size_method_joint": - forced_dev |= mask - elif mask.bit_count() >= 2: - shared_masks.append(mask) - weights = np.bincount(group_ids, minlength=len(groups)) - half = len(groups) // 2 - low, high = _count_table(weights[:half]), _count_table(weights[half:]) - low_mask = (1 << half) - 1 - objective = _Objective(cases, group_ids, len(groups)) - total_partitions = 1 << len(groups) - feasible_count = 0 - # Keep the winner and five alternatives under exactly the same policy key. - leaders = [] - for start in range(0, total_partitions, PARTITION_CHUNK): - masks = np.arange( - start, min(start + PARTITION_CHUNK, total_partitions), dtype=np.uint32 - ) - counts = low[masks & low_mask] + high[masks >> half] - valid = (masks & forced_dev) == forced_dev - valid &= (4 * counts >= len(cases)) & (2 * counts <= len(cases)) - valid &= np.bitwise_count(masks) <= len(groups) - 8 - masks, counts = masks[valid], counts[valid] - for shared in sorted(set(shared_masks)): - selected = masks & shared - valid = (selected != 0) & (selected != shared) - masks, counts = masks[valid], counts[valid] - feasible_count += len(masks) - for offset in range(0, len(masks), SCORE_CHUNK): - selected = masks[offset : offset + SCORE_CHUNK] - components = objective.score( - selected, counts[offset : offset + SCORE_CHUNK] - ) - scores = np.mean(components, axis=1) - # Values more than one rounding unit above this batch's sixth raw - # score cannot enter its top six. Keep all nearby rounding ties. - sixth = min(5, len(scores) - 1) - cutoff = float(np.partition(scores, sixth)[sixth]) + 1.001e-12 - contenders = np.flatnonzero(scores <= cutoff) - batch_leaders = [] - for index in contenders: - mask = int(selected[index]) - dev_groups = tuple(g for i, g in enumerate(groups) if mask & (1 << i)) - key = (round(float(scores[index]), 12), dev_groups) - batch_leaders.append((key, mask, components[index].copy())) - batch_leaders.sort(key=lambda entry: entry[0]) - leaders = sorted(leaders + batch_leaders[:6], key=lambda entry: entry[0])[ - :6 - ] - if not leaders: - forced = [g for i, g in enumerate(groups) if forced_dev & (1 << i)] - raise ValueError( - f"No feasible cohort partition among {total_partitions} group partitions: " - "coverage requires shared strata on both sides, singleton strata in dev, " - "25%-50% dev cases and at least 8 ranked groups; " - f"singleton strata force these groups into dev: {forced}" - ) - _, best_mask, best_components = leaders[0] - selected_partition = _partition_summary( - cases, groups, best_mask, best_components, policy - ) - dev_ids = set(selected_partition["matrix_ids"]["dev"]) - dev_indices = [i for i, c in enumerate(cases) if c["matrix_id"] in dev_ids] - nearest = [] - for i, case in enumerate(cases): - if case["matrix_id"] in dev_ids: - continue - closest = dev_indices[int(np.argmin(objective.distances[i, dev_indices]))] - nearest.append( - { - "ranked_matrix_id": case["matrix_id"], - "dev_matrix_id": cases[closest]["matrix_id"], - "distance": float(objective.distances[i, closest]), - } - ) - body = { - "schema_version": 1, - "kind": "ns-cohort-group-selection", - "policy_sha256": identity_sha256(policy), - "inventory_sha256": inventory["inventory_sha256"], - "enumerated_partition_count": total_partitions, - "feasible_partition_count": feasible_count, - **selected_partition, - "alternatives": [ - { - "selection_rank": rank, - **_partition_summary(cases, groups, mask, components, policy), - } - for rank, (_, mask, components) in enumerate(leaders[1:], start=2) - ], - "alternative_limit": 5, - "regime_coverage": _coverage_report(cases, strata, dev_ids), - "nearest_dev_feature_neighbors": nearest, - "nearest_neighbor_metric": ( - "Euclidean distance in five pooled-range-normalized features" - ), - "joint_size_method_gaps": _joint_gaps(cases, dev_ids), - "limitations": list(policy["limitations"]) - + [ - "Energy uses empirical V-statistic pair averages, including self-pairs; " - "it is not the square root of that statistic.", - "Constant feature ranges and an all-zero pair distance contribute zero; " - "an empty known-tag inventory contributes zero to the tag block.", - "Nearest-neighbor distances and single-source size-by-method joint gaps " - "are descriptive diagnostics, not additional selection constraints.", - "Alternatives are the next five feasible partitions after the winner, " - "ordered by the same rounded-score and group-tuple policy key.", - ], - } - return {**body, "selection_sha256": identity_sha256(body)} - - -def main(argv: list[str] | None = None) -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--inventory", type=Path, required=True) - parser.add_argument("--policy", type=Path, required=True) - parser.add_argument("--output", type=Path, required=True) - args = parser.parse_args(argv) - try: - result = select_cohort( - json.loads(args.inventory.read_text()), json.loads(args.policy.read_text()) - ) - except ValueError as error: - parser.error(str(error)) - args.output.parent.mkdir(parents=True, exist_ok=True) - args.output.write_text( - json.dumps(result, indent=2, sort_keys=True, allow_nan=False) + "\n" - ) - print( - json.dumps( - { - "groups": result["groups"], - "case_counts": result["case_counts"], - "score": result["score"], - "feasible_partition_count": result["feasible_partition_count"], - }, - indent=2, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff 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