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integration test: partial solution / subsolver (#1073)
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| # # We want to make sure that when we fix variables, these variables are | ||
| # # removed from the subsolver and the solution returned contains the fixed | ||
| # # variables and the cost of the fixed variables. | ||
| # function test_fixed_variables() | ||
| # env = CL.Env{ClMP.VarId}(CL.Params()) | ||
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| # # Create the following formulation: | ||
| # # min x1 + 2x2 + 3x3 | ||
| # # st. x1 + 2x2 + 3x3 >= 16 | ||
| # # x1 >= 1 | ||
| # # x2 >= 2 | ||
| # # x3 >= 3 | ||
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| # form = ClMP.create_formulation!(env, ClMP.DwMaster()) | ||
| # vars = Dict{String, ClMP.Variable}() | ||
| # for i in 1:3 | ||
| # x = ClMP.setvar!(form, "x$i", ClMP.OriginalVar; cost = i, lb = i) | ||
| # vars["x$i"] = x | ||
| # end | ||
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| # members = Dict{ClMP.VarId,Float64}( | ||
| # ClMP.getid(vars["x1"]) => 1, | ||
| # ClMP.getid(vars["x2"]) => 2, | ||
| # ClMP.getid(vars["x3"]) => 3 | ||
| # ) | ||
| # c = ClMP.setconstr!(form, "c", ClMP.OriginalConstr; | ||
| # rhs = 16, sense = ClMP.Greater, members = members | ||
| # ) | ||
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| # ClMP.push_optimizer!(form, CL._optimizerbuilder(MOI._instantiate_and_check(GLPK.Optimizer))) | ||
| # DynamicSparseArrays.closefillmode!(ClMP.getcoefmatrix(form)) | ||
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| # output = ClA.run!(ClA.SolveLpForm(get_dual_sol = true), env, form, ClA.OptimizationState(form)) | ||
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| # primal_sol = ClA.get_best_lp_primal_sol(output) | ||
| # dual_sol = ClA.get_best_lp_dual_sol(output) | ||
| # @test ClMP.getvalue(primal_sol) == 16 | ||
| # @test ClMP.getvalue(dual_sol) == 16 | ||
| # @test ClMP.getcurrhs(form, c) == 16 | ||
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| # # min x1 + 2x2 + x3 | ||
| # # st. 2x2 + 3x3 >= 14 | ||
| # # x1 == 2 | ||
| # # x2 >= 2 | ||
| # # x3 >= 3 | ||
| # ClMP.fix!(form, vars["x1"], 2) | ||
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| # output = ClA.run!(ClA.SolveLpForm(get_dual_sol = true), env, form, ClA.OptimizationState(form)) | ||
| # primal_sol = ClA.get_best_lp_primal_sol(output) | ||
| # dual_sol = ClA.get_best_lp_dual_sol(output) | ||
| # @test ClMP.getvalue(primal_sol) == 16 | ||
| # @test ClMP.getvalue(dual_sol) == 16 | ||
| # @test ClMP.getcurrhs(form, c) == 14 | ||
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| # # min x1 + 2x2 + x3 | ||
| # # st. 3x3 >= 8 | ||
| # # x1 == 2 | ||
| # # x2 == 3 | ||
| # # x3 >= 3 | ||
| # ClMP.fix!(form, vars["x2"], 3) | ||
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| # output = ClA.run!(ClA.SolveLpForm(get_dual_sol = true), env, form, ClA.OptimizationState(form)) | ||
| # primal_sol = ClA.get_best_lp_primal_sol(output) | ||
| # dual_sol = ClA.get_best_lp_dual_sol(output) | ||
| # @test ClMP.getvalue(primal_sol) == 17 | ||
| # @test ClMP.getvalue(dual_sol) == 17 | ||
| # @test ClMP.getcurrhs(form, c) == 8 | ||
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| # # min x1 + 2x2 + x3 | ||
| # # st. 0 >= -4 | ||
| # # x1 == 2 | ||
| # # x2 == 3 | ||
| # # x3 == 4 | ||
| # ClMP.fix!(form, vars["x3"], 4) | ||
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| # output = ClA.run!(ClA.SolveLpForm(get_dual_sol = true), env, form, ClA.OptimizationState(form)) | ||
| # primal_sol = ClA.get_best_lp_primal_sol(output) | ||
| # dual_sol = ClA.get_best_lp_dual_sol(output) | ||
| # @test ClMP.getvalue(primal_sol) == 20 | ||
| # @test ClMP.getvalue(dual_sol) == 20 | ||
| # @test ClMP.getcurrhs(form, c) == -4 | ||
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| # #@test_warn ClMP.setcurlb!(form, vars["x3"], 0) | ||
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| # ClMP.unfix!(form, vars["x3"]) | ||
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| # output = ClA.run!(ClA.SolveLpForm(get_dual_sol = true), env, form, ClA.OptimizationState(form)) | ||
| # primal_sol = ClA.get_best_lp_primal_sol(output) | ||
| # dual_sol = ClA.get_best_lp_dual_sol(output) | ||
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| # @test ClMP.getvalue(primal_sol) == 20 | ||
| # @test ClMP.getvalue(dual_sol) == 20 | ||
| # @test ClMP.getcurrhs(form, c) == 8 | ||
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| # ClMP.setcurlb!(form, vars["x3"], 3) | ||
| # ClMP.setcurub!(form, vars["x3"], Inf) | ||
| # output = ClA.run!(ClA.SolveLpForm(get_dual_sol = true), env, form, ClA.OptimizationState(form)) | ||
| # primal_sol = ClA.get_best_lp_primal_sol(output) | ||
| # dual_sol = ClA.get_best_lp_dual_sol(output) | ||
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| # @test ClMP.getvalue(primal_sol) == 17 | ||
| # @test ClMP.getvalue(dual_sol) == 17 | ||
| # @test ClMP.getcurrhs(form, c) == 8 | ||
| # end | ||
| # register!(integration_tests, "MOI - fixed_variables", test_fixed_variables) | ||
| # # We want to make sure that when put variables in the partial solution, these variables are | ||
| # # removed from the subsolver and the solution returned contains the variables in the partial solution | ||
| # # variables and the cost of the partial solution. | ||
| function test_fixed_variables() | ||
| env = CL.Env{ClMP.VarId}(CL.Params()) | ||
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| # Create the following formulation: | ||
| # min x1 + 2x2 + 3x3 | ||
| # st. x1 + 2x2 + 3x3 >= 16 | ||
| # x1 >= 1 | ||
| # x2 >= 2 | ||
| # x3 >= 3 | ||
|
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| form = ClMP.create_formulation!(env, ClMP.DwMaster()) | ||
| vars = Dict{String, ClMP.Variable}() | ||
| for i in 1:3 | ||
| x = ClMP.setvar!(form, "x$i", ClMP.OriginalVar; cost = i, lb = i) | ||
| vars["x$i"] = x | ||
| end | ||
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| members = Dict{ClMP.VarId,Float64}( | ||
| ClMP.getid(vars["x1"]) => 1, | ||
| ClMP.getid(vars["x2"]) => 2, | ||
| ClMP.getid(vars["x3"]) => 3 | ||
| ) | ||
| c = ClMP.setconstr!(form, "c", ClMP.OriginalConstr; | ||
| rhs = 16, sense = ClMP.Greater, members = members | ||
| ) | ||
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| ClMP.push_optimizer!(form, CL._optimizerbuilder(MOI._instantiate_and_check(GLPK.Optimizer))) | ||
| DynamicSparseArrays.closefillmode!(ClMP.getcoefmatrix(form)) | ||
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| output = ClA.run!(ClA.SolveLpForm(get_dual_sol = true), env, form, ClA.OptimizationState(form)) | ||
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| primal_sol = ClA.get_best_lp_primal_sol(output) | ||
| dual_sol = ClA.get_best_lp_dual_sol(output) | ||
| @test ClMP.getvalue(primal_sol) == 16 | ||
| @test ClMP.getvalue(dual_sol) == 16 | ||
| @test ClMP.getcurrhs(form, c) == 16 | ||
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| @test primal_sol[ClMP.getid(vars["x1"])] == 1 | ||
| @test primal_sol[ClMP.getid(vars["x2"])] == 2 | ||
| @test primal_sol[ClMP.getid(vars["x3"])] ≈ 3 + 2/3 | ||
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| # min x1' + 2x2' + 3x3' | ||
| # st. x1' + 2x2' + 3x3' >= 16 - 1 - 4 - 9 >= 2 | ||
| # x1' >= 0 | ||
| # x2' >= 0 | ||
| # x3' >= 0 | ||
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| ClMP.add_to_partial_solution!(form, vars["x1"], 1) | ||
| ClMP.add_to_partial_solution!(form, vars["x2"], 2) | ||
| ClMP.add_to_partial_solution!(form, vars["x3"], 3) | ||
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| # We perform propagation by hand (the preprocessing should do it) | ||
| ClMP.setcurrhs!(form, c, 2.0) | ||
| ClMP.setcurlb!(form, vars["x1"], 0.0) | ||
| ClMP.setcurlb!(form, vars["x2"], 0.0) | ||
| ClMP.setcurlb!(form, vars["x3"], 0.0) | ||
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| output = ClA.run!(ClA.SolveLpForm(get_dual_sol = true), env, form, ClA.OptimizationState(form)) | ||
| primal_sol = ClA.get_best_lp_primal_sol(output) | ||
| dual_sol = ClA.get_best_lp_dual_sol(output) | ||
| @test ClMP.getvalue(primal_sol) == 16 | ||
| @test ClMP.getvalue(dual_sol) == 16 | ||
| @test ClMP.getcurrhs(form, c) == 2 | ||
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| @test primal_sol[ClMP.getid(vars["x1"])] == 1 | ||
| @test primal_sol[ClMP.getid(vars["x2"])] == 2 | ||
| @test primal_sol[ClMP.getid(vars["x3"])] ≈ 3 + 2/3 | ||
| end | ||
| register!(integration_tests, "MOI - fixed_variables", test_fixed_variables) |