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Add support for MIQPs; implement max cut model

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This commit is contained in:
Alinson S. Xavier
2025-06-11 15:38:06 -05:00
parent 9ac2f74856
commit 2ea0043c03
6 changed files with 113 additions and 9 deletions
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2
test/src/MIPLearnT.jl
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@@ -24,6 +24,7 @@ include("Cuts/tableau/test_gmi_dual.jl")
include("problems/test_setcover.jl")
include("problems/test_stab.jl")
include("problems/test_tsp.jl")
include("problems/test_maxcut.jl")
include("solvers/test_jump.jl")
include("test_io.jl")
include("test_usage.jl")
@@ -37,6 +38,7 @@ function runtests()
test_problems_setcover()
test_problems_stab()
test_problems_tsp()
test_problems_maxcut()
test_solvers_jump()
test_usage()
test_cuts()

54
test/src/problems/test_maxcut.jl Normal file
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@@ -0,0 +1,54 @@
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020-2025, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using PyCall
function test_problems_maxcut()
np = pyimport("numpy")
random = pyimport("random")
scipy_stats = pyimport("scipy.stats")
randint = scipy_stats.randint
uniform = scipy_stats.uniform
# Set random seed
random.seed(42)
np.random.seed(42)
# Build random instance
data = MaxCutGenerator(
n = randint(low = 10, high = 11),
p = uniform(loc = 0.5, scale = 0.0),
fix_graph = false,
).generate(
1,
)[1]
# Build model
model = build_maxcut_model_jump(data, optimizer = SCIP.Optimizer)
# Check static features
h5 = H5File(tempname(), "w")
model.extract_after_load(h5)
obj_linear = h5.get_array("static_var_obj_coeffs")
obj_quad = h5.get_array("static_var_obj_coeffs_quad")
@test obj_linear == [3.0, 1.0, 3.0, 1.0, -1.0, 0.0, -1.0, 0.0, -1.0, 0.0]
@test obj_quad == [
0.0 0.0 -1.0 1.0 -1.0 0.0 0.0 0.0 -1.0 -1.0
0.0 0.0 1.0 -1.0 0.0 -1.0 -1.0 0.0 0.0 1.0
0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 -1.0 -1.0
0.0 0.0 0.0 0.0 0.0 -1.0 1.0 -1.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -1.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
]
# Check optimal solution
model.optimize()
model.extract_after_mip(h5)
@test h5.get_scalar("mip_obj_value") == -4
h5.close()
end