233 lines
8.6 KiB
C++
233 lines
8.6 KiB
C++
// Copyright 2010-2025 Google LLC
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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// Linear programming example that shows how to use the API.
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#include "ortools/base/init_google.h"
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#include "ortools/base/logging.h"
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#include "ortools/linear_solver/linear_solver.h"
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#include "ortools/linear_solver/linear_solver.pb.h"
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namespace operations_research {
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void SolveAndPrint(MPSolver& solver, std::vector<MPVariable*> variables,
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std::vector<MPConstraint*> constraints, bool is_continuous) {
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LOG(INFO) << "Number of variables = " << solver.NumVariables();
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LOG(INFO) << "Number of constraints = " << solver.NumConstraints();
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const MPSolver::ResultStatus result_status = solver.Solve();
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// Check that the problem has an optimal solution.
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if (result_status != MPSolver::OPTIMAL) {
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LOG(FATAL) << "The problem does not have an optimal solution!";
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}
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LOG(INFO) << "Solution:";
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for (const auto& i : variables) {
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LOG(INFO) << i->name() << " = " << i->solution_value();
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}
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LOG(INFO) << "Optimal objective value = " << solver.Objective().Value();
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LOG(INFO) << "";
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LOG(INFO) << "Advanced usage:";
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LOG(INFO) << "Problem solved in " << solver.wall_time() << " milliseconds";
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if (solver.ProblemType() != MPSolver::BOP_INTEGER_PROGRAMMING)
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LOG(INFO) << "Problem solved in " << solver.iterations() << " iterations";
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if (is_continuous) {
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for (const auto& i : variables) {
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LOG(INFO) << i->name() << ": reduced cost " << i->reduced_cost();
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}
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const std::vector<double> activities = solver.ComputeConstraintActivities();
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for (const auto& i : constraints) {
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LOG(INFO) << i->name() << ": dual value = " << i->dual_value()
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<< " activity = " << activities[i->index()];
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}
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}
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}
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void RunLinearProgrammingExample(
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MPSolver::OptimizationProblemType optimization_problem_type) {
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MPSolver solver("LinearProgrammingExample", optimization_problem_type);
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const double infinity = solver.infinity();
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// x and y are continuous non-negative variables.
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MPVariable* const x = solver.MakeNumVar(0.0, infinity, "x");
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MPVariable* const y = solver.MakeNumVar(0.0, infinity, "y");
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// Objectif function: Maximize 3x + 4y.
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MPObjective* const objective = solver.MutableObjective();
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objective->SetCoefficient(x, 3);
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objective->SetCoefficient(y, 4);
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objective->SetMaximization();
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// x + 2y <= 14.
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MPConstraint* const c0 = solver.MakeRowConstraint(-infinity, 14.0, "c0");
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c0->SetCoefficient(x, 1);
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c0->SetCoefficient(y, 2);
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// 3x - y >= 0.
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MPConstraint* const c1 = solver.MakeRowConstraint(0.0, infinity, "c1");
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c1->SetCoefficient(x, 3);
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c1->SetCoefficient(y, -1);
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// x - y <= 2.
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MPConstraint* const c2 = solver.MakeRowConstraint(-infinity, 2.0, "c2");
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c2->SetCoefficient(x, 1);
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c2->SetCoefficient(y, -1);
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SolveAndPrint(solver, {x, y}, {c0, c1, c2}, true);
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}
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void RunMixedIntegerProgrammingExample(
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MPSolver::OptimizationProblemType optimization_problem_type) {
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MPSolver solver("MixedIntegerProgrammingExample", optimization_problem_type);
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const double infinity = solver.infinity();
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// x and y are integers non-negative variables.
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MPVariable* const x = solver.MakeIntVar(0.0, infinity, "x");
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MPVariable* const y = solver.MakeIntVar(0.0, infinity, "y");
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// Objective function: Maximize x + 10 * y.
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MPObjective* const objective = solver.MutableObjective();
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objective->SetCoefficient(x, 1);
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objective->SetCoefficient(y, 10);
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objective->SetMaximization();
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// x + 7 * y <= 17.5
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MPConstraint* const c0 = solver.MakeRowConstraint(-infinity, 17.5, "c0");
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c0->SetCoefficient(x, 1);
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c0->SetCoefficient(y, 7);
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// x <= 3.5
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MPConstraint* const c1 = solver.MakeRowConstraint(-infinity, 3.5, "c1");
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c1->SetCoefficient(x, 1);
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c1->SetCoefficient(y, 0);
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SolveAndPrint(solver, {x, y}, {c0, c1}, false);
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}
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void RunBooleanProgrammingExample(
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MPSolver::OptimizationProblemType optimization_problem_type) {
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MPSolver solver("BooleanProgrammingExample", optimization_problem_type);
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const double infinity = solver.infinity();
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// x and y are boolean variables.
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MPVariable* const x = solver.MakeBoolVar("x");
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MPVariable* const y = solver.MakeBoolVar("y");
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// Objective function: Minimize 2 * x + y.
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MPObjective* const objective = solver.MutableObjective();
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objective->SetCoefficient(x, 2);
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objective->SetCoefficient(y, 1);
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objective->SetMinimization();
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// 1 <= x + 2 * y <= 3.
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MPConstraint* const c0 = solver.MakeRowConstraint(1, 3, "c0");
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c0->SetCoefficient(x, 1);
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c0->SetCoefficient(y, 2);
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SolveAndPrint(solver, {x, y}, {c0}, false);
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}
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void MutableObjectiveCrash() {
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LOG(INFO) << "MutableObjectiveCrash";
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// Create the linear solver with the GLOP backend.
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std::unique_ptr<MPSolver> solver(MPSolver::CreateSolver("GLOP"));
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// Create the variables x and y.
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MPVariable* const x = solver->MakeNumVar(0.0, 1, "x");
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MPVariable* const y = solver->MakeNumVar(0.0, 2, "y");
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LOG(INFO) << "Number of variables = " << solver->NumVariables();
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// Create a linear constraint, 0 <= x + y <= 2.
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MPConstraint* const ct = solver->MakeRowConstraint(0.0, 2.0, "ct");
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ct->SetCoefficient(x, 1);
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ct->SetCoefficient(y, 1);
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LOG(INFO) << "Number of constraints = " << solver->NumConstraints();
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// Create the objective function, 3 * x + y.
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MPObjective* const objective = solver->MutableObjective();
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objective->SetCoefficient(x, 3);
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objective->SetCoefficient(y, 1);
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objective->SetMaximization();
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solver->Solve();
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LOG(INFO) << "Solution:" << std::endl;
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LOG(INFO) << "Objective value = " << objective->Value();
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LOG(INFO) << "x = " << x->solution_value();
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LOG(INFO) << "y = " << y->solution_value();
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}
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void RunAllExamples() {
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// Linear programming problems
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#if defined(USE_CLP)
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LOG(INFO) << "---- Linear programming example with CLP ----";
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RunLinearProgrammingExample(MPSolver::CLP_LINEAR_PROGRAMMING);
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#endif // USE_CLP
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#if defined(USE_GLPK)
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LOG(INFO) << "---- Linear programming example with GLPK ----";
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RunLinearProgrammingExample(MPSolver::GLPK_LINEAR_PROGRAMMING);
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#endif // USE_GLPK
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#if defined(USE_GLOP)
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LOG(INFO) << "---- Linear programming example with GLOP ----";
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RunLinearProgrammingExample(MPSolver::GLOP_LINEAR_PROGRAMMING);
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#endif // USE_GLOP
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#if defined(USE_PDLP)
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LOG(INFO) << "---- Linear programming example with PDLP ----";
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RunLinearProgrammingExample(MPSolver::PDLP_LINEAR_PROGRAMMING);
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#endif // USE_PDLP
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#if defined(USE_GUROBI)
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LOG(INFO) << "---- Linear programming example with Gurobi ----";
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RunLinearProgrammingExample(MPSolver::GUROBI_LINEAR_PROGRAMMING);
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#endif // USE_GUROBI
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#if defined(USE_CPLEX)
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LOG(INFO) << "---- Linear programming example with CPLEX ----";
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RunLinearProgrammingExample(MPSolver::CPLEX_LINEAR_PROGRAMMING);
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#endif // USE_CPLEX
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// Integer programming problems
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#if defined(USE_SCIP)
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LOG(INFO) << "---- Mixed Integer programming example with SCIP ----";
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RunMixedIntegerProgrammingExample(MPSolver::SCIP_MIXED_INTEGER_PROGRAMMING);
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#endif // USE_SCIP
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#if defined(USE_GLPK)
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LOG(INFO) << "---- Mixed Integer programming example with GLPK ----";
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RunMixedIntegerProgrammingExample(MPSolver::GLPK_MIXED_INTEGER_PROGRAMMING);
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#endif // USE_GLPK
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#if defined(USE_CBC)
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LOG(INFO) << "---- Mixed Integer programming example with CBC ----";
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RunMixedIntegerProgrammingExample(MPSolver::CBC_MIXED_INTEGER_PROGRAMMING);
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#endif // USE_CBC
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#if defined(USE_GUROBI)
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LOG(INFO) << "---- Mixed Integer programming example with GUROBI ----";
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RunMixedIntegerProgrammingExample(MPSolver::GUROBI_MIXED_INTEGER_PROGRAMMING);
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#endif // USE_GUROBI
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#if defined(USE_CPLEX)
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LOG(INFO) << "---- Mixed Integer programming example with CPLEX ----";
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RunMixedIntegerProgrammingExample(MPSolver::CPLEX_MIXED_INTEGER_PROGRAMMING);
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#endif // USE_CPLEX
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// Boolean integer programming problems
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#if defined(USE_BOP)
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LOG(INFO) << "---- Boolean Integer programming example with BOP ----";
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RunBooleanProgrammingExample(MPSolver::BOP_INTEGER_PROGRAMMING);
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#endif // USE_BOP
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MutableObjectiveCrash();
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}
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} // namespace operations_research
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int main(int argc, char** argv) {
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google::InitGoogleLogging(argv[0]);
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absl::SetStderrThreshold(absl::LogSeverityAtLeast::kInfo);
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operations_research::RunAllExamples();
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return 0;
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}
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