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# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2023, 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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import ..H5File
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using OrderedCollections
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using Statistics
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function collect_gmi(mps_filename; optimizer, max_rounds=10, max_cuts_per_round=100, atol=1e-4)
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@info mps_filename
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reset_timer!()
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# Open HDF5 file
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h5_filename = replace(mps_filename, ".mps.gz" => ".h5")
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h5 = H5File(h5_filename)
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# Read optimal solution
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sol_opt_dict = Dict(
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zip(
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h5.get_array("static_var_names"),
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convert(Array{Float64}, h5.get_array("mip_var_values")),
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),
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)
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# Read optimal value
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obj_mip = h5.get_scalar("mip_lower_bound")
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if obj_mip === nothing
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obj_mip = h5.get_scalar("mip_obj_value")
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end
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obj_lp = h5.get_scalar("lp_obj_value")
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h5.file.close()
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# Define relative MIP gap
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gap(v) = 100 * abs(obj_mip - v) / abs(v)
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# Initialize stats
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stats_obj = []
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stats_gap = []
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stats_ncuts = []
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stats_time_convert = 0
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stats_time_solve = 0
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stats_time_select = 0
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stats_time_tableau = 0
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stats_time_gmi = 0
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all_cuts = nothing
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# Read problem
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model = read_from_file(mps_filename)
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for round = 1:max_rounds
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@info "Round $(round)..."
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stats_time_convert = @elapsed begin
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# Extract problem data
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data = ProblemData(model)
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# Construct optimal solution vector (with correct variable sequence)
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sol_opt = [sol_opt_dict[n] for n in data.var_names]
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# Assert optimal solution is feasible for the original problem
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assert_leq(data.constr_lb, data.constr_lhs * sol_opt)
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assert_leq(data.constr_lhs * sol_opt, data.constr_ub)
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# Convert to standard form
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data_s, transforms = convert_to_standard_form(data)
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model_s = to_model(data_s)
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set_optimizer(model_s, optimizer)
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relax_integrality(model_s)
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# Convert optimal solution to standard form
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sol_opt_s = forward(transforms, sol_opt)
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# Assert converted solution is feasible for standard form problem
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assert_eq(data_s.constr_lhs * sol_opt_s, data_s.constr_lb)
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end
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# Optimize standard form
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optimize!(model_s)
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stats_time_solve += solve_time(model_s)
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obj = objective_value(model_s) + data_s.obj_offset
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if round == 1
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# Assert standard form problem has same value as original
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assert_eq(obj, obj_lp)
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push!(stats_obj, obj)
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push!(stats_gap, gap(obj))
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push!(stats_ncuts, 0)
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end
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if termination_status(model_s) != MOI.OPTIMAL
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return
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end
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# Select tableau rows
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basis = get_basis(model_s)
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sol_frac = get_x(model_s)
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stats_time_select += @elapsed begin
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selected_rows =
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select_gmi_rows(data_s, basis, sol_frac, max_rows=max_cuts_per_round)
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end
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# Compute selected tableau rows
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stats_time_tableau += @elapsed begin
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tableau = compute_tableau(data_s, basis, sol_frac, rows=selected_rows)
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# Assert tableau rows have been computed correctly
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assert_eq(tableau.lhs * sol_frac, tableau.rhs)
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assert_eq(tableau.lhs * sol_opt_s, tableau.rhs)
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end
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# Compute GMI cuts
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stats_time_gmi += @elapsed begin
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cuts_s = compute_gmi(data_s, tableau)
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# Assert cuts have been generated correctly
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assert_cuts_off(cuts_s, sol_frac)
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assert_does_not_cut_off(cuts_s, sol_opt_s)
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# Abort if no cuts are left
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if length(cuts_s.lb) == 0
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@info "No cuts generated. Stopping."
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break
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end
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end
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# Add GMI cuts to original problem
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cuts = backwards(transforms, cuts_s)
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assert_does_not_cut_off(cuts, sol_opt)
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constrs = add_constraint_set(model, cuts)
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# Optimize original form
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set_optimizer(model, optimizer)
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undo_relax = relax_integrality(model)
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optimize!(model)
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obj = objective_value(model)
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push!(stats_obj, obj)
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push!(stats_gap, gap(obj))
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# Store useful cuts; drop useless ones from the problem
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useful = [abs(shadow_price(c)) > atol for c in constrs]
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drop = findall(useful .== false)
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keep = findall(useful .== true)
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delete.(model, constrs[drop])
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if all_cuts === nothing
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all_cuts = cuts
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else
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all_cuts.lhs = [all_cuts.lhs; cuts.lhs[keep, :]]
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all_cuts.lb = [all_cuts.lb; cuts.lb[keep]]
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all_cuts.lb = [all_cuts.lb; cuts.lb[keep]]
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end
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push!(stats_ncuts, length(all_cuts.lb))
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undo_relax()
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end
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# Store cuts
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if all_cuts !== nothing
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@info "Storing $(length(all_cuts.ub)) GMI cuts..."
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h5 = H5File(h5_filename)
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h5.put_sparse("cuts_lhs", all_cuts.lhs)
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h5.put_array("cuts_lb", all_cuts.lb)
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h5.put_array("cuts_ub", all_cuts.ub)
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h5.file.close()
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end
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return OrderedDict(
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"instance" => mps_filename,
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"max_rounds" => max_rounds,
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"rounds" => length(stats_obj) - 1,
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"time_convert" => stats_time_convert,
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"time_solve" => stats_time_solve,
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"time_tableau" => stats_time_tableau,
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"time_gmi" => stats_time_gmi,
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"obj_mip" => obj_mip,
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"stats_obj" => stats_obj,
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"stats_gap" => stats_gap,
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"stats_ncuts" => stats_ncuts,
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)
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end
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export collect_gmi
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