Implementing LOA and its infinite version.

Project.toml

@@ -15,7 +15,10 @@ InfiniteDisjunctiveProgramming = "InfiniteOpt"
 
 [compat]
 Aqua = "0.8"
+InfiniteOpt = "0.6.3"
+Ipopt = "1.9.0"
 JuMP = "1.18"
+Juniper = "0.9.3"
 Reexport = "1"
 julia = "1.10"
 Juniper = "0.9.3"
@@ -25,9 +28,10 @@ InfiniteOpt = "0.6.3"
 [extras]
 Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+InfiniteOpt = "20393b10-9daf-11e9-18c9-8db751c92c57"
 Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
 Juniper = "2ddba703-00a4-53a7-87a5-e8b9971dde84"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
 test = ["Aqua", "HiGHS", "Test", "Juniper", "Ipopt", "InfiniteOpt"]

docs/src/index.md

@@ -61,7 +61,7 @@ A [`GDPModel`](@ref) is a `JuMP Model` with a [`GDPData`](@ref) field in the mod
 - `Logical Constraints`: Selector (cardinality) or proposition (Boolean) constraints describing the relationships between the logical variables.
 - `Disjunct Constraints`: Constraints associated with each disjunct in the model.
 - `Disjunctions`: Disjunction constraints.
-- `Solution Method`: The reformulation technique or solution method. Currently, supported methods include Big-M, Hull, and Indicator Constraints.
+- `Solution Method`: The reformulation technique or solution method. Currently, supported methods include Big-M, Multiple Big-M, Hull, P-Split, Indicator Constraints, and Logic-based Outer Approximation (LOA).
 - `Reformulation Variables`: List of JuMP variables created when reformulating a GDP model into a MIP model.
 - `Reformulation Constraints`: List of constraints created when reformulating a GDP model into a MIP model.
 - `Ready to Optimize`: Flag indicating if the model can be optimized.
@@ -163,9 +163,15 @@ The following reformulation methods are currently supported:
 
 1. [Big-M](https://optimization.cbe.cornell.edu/index.php?title=Disjunctive_inequalities#Big-M_Reformulation[1][2]): The [`BigM`](@ref) struct is used.
 
-2. [Hull](https://optimization.cbe.cornell.edu/index.php?title=Disjunctive_inequalities#Convex-Hull_Reformulation[1][2]): The [`Hull`](@ref) struct is used.
+2. Multiple Big-M: A tighter big-M reformulation that computes a separate big-M value for each disjunct constraint (rather than a single global value) by solving auxiliary mini-models. This is invoked with the [`MBM`](@ref) struct, which requires an optimizer.
 
-3. [Indicator](https://jump.dev/JuMP.jl/stable/manual/constraints/#Indicator-constraints): This method reformulates each disjunct constraint into an indicator constraint with the Boolean reformulation counterpart of the Logical variable used to define the disjunct constraint. This is invoked with [`Indicator`](@ref).
+3. [Hull](https://optimization.cbe.cornell.edu/index.php?title=Disjunctive_inequalities#Convex-Hull_Reformulation[1][2]): The [`Hull`](@ref) struct is used.
+
+4. P-Split: A partitioned reformulation that splits the variables into groups and applies a P-split formulation to each group, giving a relaxation between Big-M and Hull in tightness. This is invoked with the [`PSplit`](@ref) struct.
+
+5. [Indicator](https://jump.dev/JuMP.jl/stable/manual/constraints/#Indicator-constraints): This method reformulates each disjunct constraint into an indicator constraint with the Boolean reformulation counterpart of the Logical variable used to define the disjunct constraint. This is invoked with [`Indicator`](@ref).
+
+6. Logic-based Outer Approximation (LOA): An iterative solution method for nonlinear GDPs, rather than a single-shot reformulation. It alternates between a primal NLP (the model reformulated by an inner method — `BigM`, `MBM`, or `Hull` — with the disjunct binaries fixed at the current selection) and a master MILP that accumulates outer-approximation and no-good cuts until the bound meets the incumbent. This is invoked with the [`LOA`](@ref) struct, e.g. `optimize!(m, gdp_method = LOA(Ipopt.Optimizer))`.
 
 ## Release Notes