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# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2024, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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using JuMP
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using HiGHS
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global MaxWeightStableSetData = PyNULL()
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global MaxWeightStableSetGenerator = PyNULL()
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function __init_problems_stab__()
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copy!(MaxWeightStableSetData, pyimport("miplearn.problems.stab").MaxWeightStableSetData)
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copy!(MaxWeightStableSetGenerator, pyimport("miplearn.problems.stab").MaxWeightStableSetGenerator)
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end
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function build_stab_model_jump(data::Any; optimizer=HiGHS.Optimizer)
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nx = pyimport("networkx")
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if data isa String
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data = read_pkl_gz(data)
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end
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model = Model(optimizer)
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# Variables and objective function
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nodes = data.graph.nodes
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x = @variable(model, x[nodes], Bin)
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@objective(model, Min, sum(-data.weights[i+1] * x[i] for i in nodes))
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# Edge inequalities
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for (i1, i2) in data.graph.edges
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@constraint(model, x[i1] + x[i2] <= 1, base_name = "eq_edge[$i1,$i2]")
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end
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function cuts_separate(cb_data)
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x_val = callback_value.(Ref(cb_data), x)
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violations = []
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for clique in nx.find_cliques(data.graph)
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if sum(x_val[i] for i in clique) > 1.0001
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push!(violations, sort(clique))
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end
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end
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return violations
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end
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function cuts_enforce(violations)
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@info "Adding $(length(violations)) clique cuts..."
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for clique in violations
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constr = @build_constraint(sum(x[i] for i in clique) <= 1)
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submit(model, constr)
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end
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end
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return JumpModel(
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model,
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cuts_separate=cuts_separate,
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cuts_enforce=cuts_enforce,
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)
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end
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export MaxWeightStableSetData, MaxWeightStableSetGenerator, build_stab_model_jump
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# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2024, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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using SCIP
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function gen_stab()
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np = pyimport("numpy")
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uniform = pyimport("scipy.stats").uniform
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randint = pyimport("scipy.stats").randint
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np.random.seed(42)
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gen = MaxWeightStableSetGenerator(
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w=uniform(10.0, scale=1.0),
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n=randint(low=50, high=51),
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p=uniform(loc=0.5, scale=0.0),
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fix_graph=true,
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)
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data = gen.generate(1)
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data_filenames = write_pkl_gz(data, "$BASEDIR/../fixtures", prefix="stab-n50-")
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collector = BasicCollector(write_mps=false)
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collector.collect(
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data_filenames,
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data -> build_stab_model_jump(data, optimizer=SCIP.Optimizer),
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progress=true,
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verbose=true,
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)
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end
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function test_cuts()
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data_filenames = ["$BASEDIR/../fixtures/stab-n50-0000$i.pkl.gz" for i in 0:0]
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clf = pyimport("sklearn.neighbors").KNeighborsClassifier(n_neighbors=1)
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extractor = H5FieldsExtractor(
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instance_fields=["static_var_obj_coeffs"],
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)
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comp = MemorizingCutsComponent(clf=clf, extractor=extractor)
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solver = LearningSolver(components=[comp])
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solver.fit(data_filenames)
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@show comp.n_features_
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@show comp.n_targets_
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stats = solver.optimize(
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data_filenames[1],
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data -> build_stab_model_jump(data, optimizer=SCIP.Optimizer),
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)
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@test stats["Cuts: AOT"] > 0
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end
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@ -0,0 +1,27 @@
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# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2024, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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using PyCall
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using SCIP
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function test_problems_stab()
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test_problems_stab_1()
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test_problems_stab_2()
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end
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function test_problems_stab_1()
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nx = pyimport("networkx")
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data = MaxWeightStableSetData(
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graph=nx.gnp_random_graph(25, 0.5, seed=42),
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weights=repeat([1.0], 25),
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)
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h5 = H5File(tempname(), "w")
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model = build_stab_model_jump(data, optimizer=SCIP.Optimizer)
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model.extract_after_load(h5)
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model.optimize()
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model.extract_after_mip(h5)
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@test h5.get_scalar("mip_obj_value") == -6
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@test h5.get_scalar("mip_cuts")[1:20] == "[[0,8,11,13],[0,8,13"
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h5.close()
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end
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