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140 lines
4.8 KiB
140 lines
4.8 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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using MIPLearn
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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.int_vars) == 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.int_vars[1]) == "xab"
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@test BB.name(mip, mip.int_vars[2]) == "xac"
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@test BB.name(mip, mip.int_vars[3]) == "xad"
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@test mip.int_vars_lb[1] == 0.0
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@test mip.int_vars_ub[1] == 1.0
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vals = BB.values(mip, mip.int_vars)
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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.int_vars[1], 0.5, 0.0, 1.0, 1_000_000)
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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.int_vars[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.int_vars[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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# Fix all binary variables to one, making problem infeasible
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N = length(mip.int_vars)
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BB.set_bounds!(mip, mip.int_vars, 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.int_vars)
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BB.set_bounds!(mip, mip.int_vars, 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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BB.StrongBranching(aggregation=:min),
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BB.ReliabilityBranching(aggregation=:min),
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]
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for branch_rule in branch_rules
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for instance in ["bell5", "vpm2"]
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h5 = Hdf5Sample("$basepath/../fixtures/$instance.h5")
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mip_lower_bound = h5.get_scalar("mip_lower_bound")
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mip_upper_bound = h5.get_scalar("mip_upper_bound")
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mip_sense = h5.get_scalar("mip_sense")
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mip_primal_bound = mip_sense == "min" ? mip_upper_bound : mip_lower_bound
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h5.file.close()
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mip = BB.init(optimizer)
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BB.read!(mip, "$basepath/../fixtures/$instance.mps.gz")
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@info optimizer_name, branch_rule, instance
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@time BB.solve!(
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mip,
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initial_primal_bound = mip_primal_bound,
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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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end
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@testset "BB" begin
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# @time runtests(
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# "Clp",
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# optimizer_with_attributes(
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# Clp.Optimizer,
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# ),
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# )
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if is_gurobi_available
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using Gurobi
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@time runtests(
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"Gurobi",
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optimizer_with_attributes(
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Gurobi.Optimizer,
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"Threads" => 1,
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)
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)
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end
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if is_cplex_available
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using CPLEX
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@time runtests(
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"CPLEX",
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optimizer_with_attributes(
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CPLEX.Optimizer,
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"CPXPARAM_Threads" => 1,
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),
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)
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end
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end
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