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pulsipher merged 1 commit into from
Jan 6, 2026
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Prep for v0.6 #131

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4 changes: 2 additions & 2 deletions Project.toml
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
name = "DisjunctiveProgramming"
uuid = "0d27d021-0159-4c7d-b4a7-9ccb5d9366cf"
authors = ["hdavid16 <[email protected]>"]
version = "0.5.0"
version = "0.6.0"

[deps]
JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
Expand Down Expand Up @@ -30,4 +30,4 @@ Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
Juniper = "2ddba703-00a4-53a7-87a5-e8b9971dde84"

[targets]
test = ["Aqua", "HiGHS", "Test", "Juniper", "Ipopt","InfiniteOpt"]
test = ["Aqua", "HiGHS", "Test", "Juniper", "Ipopt", "InfiniteOpt"]
31 changes: 30 additions & 1 deletion README.md
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Expand Up @@ -4,7 +4,7 @@ A Generalized Disjunctive Programming (GDP) extension to JuMP.

![logo](logo.png)

[![codecov](https://codecov.io/gh/hdavid16/DisjunctiveProgramming.jl/graph/badge.svg?token=3FRPGMWF0J)](https://codecov.io/gh/hdavid16/DisjunctiveProgramming.jl)
[![codecov](https://codecov.io/github/infiniteopt/DisjunctiveProgramming.jl/graph/badge.svg?token=z2CQwBiWzU)](https://codecov.io/github/infiniteopt/DisjunctiveProgramming.jl)
[![Docs](https://img.shields.io/badge/docs-stable-blue.svg)](https://infiniteopt.github.io/DisjunctiveProgramming.jl/stable/)
[![Docs](https://img.shields.io/badge/docs-latest-blue.svg)](https://infiniteopt.github.io/DisjunctiveProgramming.jl/dev/)

Expand Down Expand Up @@ -192,7 +192,36 @@ The following reformulation methods are currently supported:
- `final_reform_method`: Reformulation method to apply after cutting plane iterations. Default: `BigM()`.
- `M_value`: Big-M value to use in the relaxed Big-M reformulation during iterations. Default: `1e9`.

## Infinite-Dimensional GDP
To model disjunctions, logical variables, and logical constraints with infinite-dimensional optimization problems (e.g., dynamic and stochastic optimization), DisjunctiveProgramming is also compatible with [InfiniteOpt.jl](https://github.com/infiniteopt/InfiniteOpt.jl). For this, the syntax is largely the same, users simply need to import `InfiniteOpt` and use `InfiniteGDPModel`. They also can use `InfiniteLogical` to declare infinite logical variables as shown below:
```julia
using DisjunctiveProgramming, InfiniteOpt, HiGHS

# Create the model
model = InfiniteGDPModel(HiGHS.Optimizer)

# Create the infinite variables
I = 1:4
@infinite_parameter(model, t ∈ [0, 1], num_supports = 100)
@variable(model, 0 <= g[I] <= 10, Infinite(t))

# Add the disjunctions and their indicator variables
@variable(model, G[I, 1:2], InfiniteLogical(t))
@constraint(model, [i ∈ I, j ∈ 1:2], 0 <= g[i], Disjunct(G[i, 1]))
@constraint(model, [i ∈ I, j ∈ 1:2], g[i] <= 0, Disjunct(G[i, 2]))
@disjunction(model, [i ∈ I], G[i, :])

# Add the logical propositions
@variable(model, W, InfiniteLogical(t))
@constraint(model, G[1, 1] ∨ G[2, 1] ∧ G[3, 1] == W := true)
@constraint(model, 𝔼(binary_variable(W), t) >= 0.95) # incorporate binary variable of logical variable in a nonlogical constraint

# Reformulate and solve
optimize!(model, gdp_method = Hull())

# check the results
value(W)
```

## Release Notes

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