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111 lines
4.1 KiB
111 lines
4.1 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, 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 Clp
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using JuMP
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using Test
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using MIPLearn.BB
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basepath = @__DIR__
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function runtests(optimizer_name, optimizer; large = true)
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@testset "Solve ($optimizer_name)" begin
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@testset "interface" begin
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filename = "$basepath/../fixtures/danoint.mps.gz"
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mip = BB.init(optimizer)
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BB.read!(mip, filename)
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@test mip.sense == 1.0
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@test length(mip.binary_variables) == 56
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status, obj = BB.solve_relaxation!(mip)
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@test status == :Optimal
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@test round(obj, digits = 6) == 62.637280
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@test BB.name(mip, mip.binary_variables[1]) == "xab"
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@test BB.name(mip, mip.binary_variables[2]) == "xac"
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@test BB.name(mip, mip.binary_variables[3]) == "xad"
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vals = BB.values(mip, mip.binary_variables)
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@test round(vals[1], digits = 6) == 0.046933
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@test round(vals[2], digits = 6) == 0.000841
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@test round(vals[3], digits = 6) == 0.248696
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# Probe (up and down are feasible)
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probe_up, probe_down = BB.probe(mip, mip.binary_variables[1])
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@test round(probe_down, digits = 6) == 62.690000
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@test round(probe_up, digits = 6) == 62.714100
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# Fix one variable to zero
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BB.set_bounds!(mip, mip.binary_variables[1:1], [0.0], [0.0])
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status, obj = BB.solve_relaxation!(mip)
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@test status == :Optimal
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@test round(obj, digits = 6) == 62.690000
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# Fix one variable to one and another variable variable to zero
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BB.set_bounds!(mip, mip.binary_variables[1:2], [1.0, 0.0], [1.0, 0.0])
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status, obj = BB.solve_relaxation!(mip)
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@test status == :Optimal
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@test round(obj, digits = 6) == 62.714777
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# Probe (up is infeasible, down is feasible)
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BB.set_bounds!(mip, mip.binary_variables[1:3], [1.0, 1.0, 0.0], [1.0, 1.0, 1.0])
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status, obj = BB.solve_relaxation!(mip)
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@test status == :Optimal
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probe_up, probe_down = BB.probe(mip, mip.binary_variables[3])
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@test round(probe_up, digits = 6) == Inf
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@test round(probe_down, digits = 6) == 63.073992
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# Fix all binary variables to one, making problem infeasible
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N = length(mip.binary_variables)
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BB.set_bounds!(mip, mip.binary_variables, ones(N), ones(N))
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status, obj = BB.solve_relaxation!(mip)
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@test status == :Infeasible
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@test obj == Inf
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# Restore original problem
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N = length(mip.binary_variables)
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BB.set_bounds!(mip, mip.binary_variables, zeros(N), ones(N))
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status, obj = BB.solve_relaxation!(mip)
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@test status == :Optimal
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@test round(obj, digits = 6) == 62.637280
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end
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@testset "varbranch" begin
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branch_rules = [
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BB.RandomBranching(),
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BB.FirstInfeasibleBranching(),
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BB.LeastInfeasibleBranching(),
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BB.MostInfeasibleBranching(),
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BB.PseudocostBranching(),
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BB.StrongBranching(),
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BB.ReliabilityBranching(),
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BB.HybridBranching(),
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]
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for branch_rule in branch_rules
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filename = "$basepath/../fixtures/vpm2.mps.gz"
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mip = BB.init(optimizer)
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BB.read!(mip, filename)
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@info optimizer_name, branch_rule
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@time BB.solve!(
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mip,
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initial_primal_bound = 13.75,
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print_interval = 10,
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node_limit = 100,
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branch_rule = branch_rule,
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)
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end
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end
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end
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end
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@testset "BB" begin
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@time runtests("Clp", Clp.Optimizer)
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if is_gurobi_available
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using Gurobi
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@time runtests("Gurobi", Gurobi.Optimizer)
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end
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end
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