lp_test.cc

// Copyright 2010-2024 Google LLC
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

// Linear programming example that shows how to use the API.

#include "ortools/base/init_google.h"
#include "ortools/base/logging.h"
#include "ortools/linear_solver/linear_solver.h"
#include "ortools/linear_solver/linear_solver.pb.h"

namespace operations_research {
void SolveAndPrint(MPSolver& solver, std::vector<MPVariable*> variables,
                   std::vector<MPConstraint*> constraints, bool is_continuous) {
  LOG(INFO) << "Number of variables = " << solver.NumVariables();
  LOG(INFO) << "Number of constraints = " << solver.NumConstraints();

  const MPSolver::ResultStatus result_status = solver.Solve();
  // Check that the problem has an optimal solution.
  if (result_status != MPSolver::OPTIMAL) {
    LOG(FATAL) << "The problem does not have an optimal solution!";
  }
  LOG(INFO) << "Solution:";
  for (const auto& i : variables) {
    LOG(INFO) << i->name() << " = " << i->solution_value();
  }
  LOG(INFO) << "Optimal objective value = " << solver.Objective().Value();
  LOG(INFO) << "";
  LOG(INFO) << "Advanced usage:";
  LOG(INFO) << "Problem solved in " << solver.wall_time() << " milliseconds";
  if (solver.ProblemType() != MPSolver::BOP_INTEGER_PROGRAMMING)
    LOG(INFO) << "Problem solved in " << solver.iterations() << " iterations";
  if (is_continuous) {
    for (const auto& i : variables) {
      LOG(INFO) << i->name() << ": reduced cost " << i->reduced_cost();
    }

    const std::vector<double> activities = solver.ComputeConstraintActivities();
    for (const auto& i : constraints) {
      LOG(INFO) << i->name() << ": dual value = " << i->dual_value()
                << " activity = " << activities[i->index()];
    }
  }
}

void RunLinearProgrammingExample(
    MPSolver::OptimizationProblemType optimization_problem_type) {
  MPSolver solver("LinearProgrammingExample", optimization_problem_type);
  const double infinity = solver.infinity();
  // x and y are continuous non-negative variables.
  MPVariable* const x = solver.MakeNumVar(0.0, infinity, "x");
  MPVariable* const y = solver.MakeNumVar(0.0, infinity, "y");

  // Objectif function: Maximize 3x + 4y.
  MPObjective* const objective = solver.MutableObjective();
  objective->SetCoefficient(x, 3);
  objective->SetCoefficient(y, 4);
  objective->SetMaximization();

  // x + 2y <= 14.
  MPConstraint* const c0 = solver.MakeRowConstraint(-infinity, 14.0, "c0");
  c0->SetCoefficient(x, 1);
  c0->SetCoefficient(y, 2);

  // 3x - y >= 0.
  MPConstraint* const c1 = solver.MakeRowConstraint(0.0, infinity, "c1");
  c1->SetCoefficient(x, 3);
  c1->SetCoefficient(y, -1);

  // x - y <= 2.
  MPConstraint* const c2 = solver.MakeRowConstraint(-infinity, 2.0, "c2");
  c2->SetCoefficient(x, 1);
  c2->SetCoefficient(y, -1);

  SolveAndPrint(solver, {x, y}, {c0, c1, c2}, true);
}

void RunMixedIntegerProgrammingExample(
    MPSolver::OptimizationProblemType optimization_problem_type) {
  MPSolver solver("MixedIntegerProgrammingExample", optimization_problem_type);
  const double infinity = solver.infinity();
  // x and y are integers non-negative variables.
  MPVariable* const x = solver.MakeIntVar(0.0, infinity, "x");
  MPVariable* const y = solver.MakeIntVar(0.0, infinity, "y");

  // Objective function: Maximize x + 10 * y.
  MPObjective* const objective = solver.MutableObjective();
  objective->SetCoefficient(x, 1);
  objective->SetCoefficient(y, 10);
  objective->SetMaximization();

  // x + 7 * y <= 17.5
  MPConstraint* const c0 = solver.MakeRowConstraint(-infinity, 17.5, "c0");
  c0->SetCoefficient(x, 1);
  c0->SetCoefficient(y, 7);

  // x <= 3.5
  MPConstraint* const c1 = solver.MakeRowConstraint(-infinity, 3.5, "c1");
  c1->SetCoefficient(x, 1);
  c1->SetCoefficient(y, 0);

  SolveAndPrint(solver, {x, y}, {c0, c1}, false);
}

void RunBooleanProgrammingExample(
    MPSolver::OptimizationProblemType optimization_problem_type) {
  MPSolver solver("BooleanProgrammingExample", optimization_problem_type);
  const double infinity = solver.infinity();
  // x and y are boolean variables.
  MPVariable* const x = solver.MakeBoolVar("x");
  MPVariable* const y = solver.MakeBoolVar("y");

  // Objective function: Minimize 2 * x + y.
  MPObjective* const objective = solver.MutableObjective();
  objective->SetCoefficient(x, 2);
  objective->SetCoefficient(y, 1);
  objective->SetMinimization();

  // 1 <= x + 2 * y <= 3.
  MPConstraint* const c0 = solver.MakeRowConstraint(1, 3, "c0");
  c0->SetCoefficient(x, 1);
  c0->SetCoefficient(y, 2);

  SolveAndPrint(solver, {x, y}, {c0}, false);
}

void MutableObjectiveCrash() {
  LOG(INFO) << "MutableObjectiveCrash";
  // Create the linear solver with the GLOP backend.
  std::unique_ptr<MPSolver> solver(MPSolver::CreateSolver("GLOP"));

  // Create the variables x and y.
  MPVariable* const x = solver->MakeNumVar(0.0, 1, "x");
  MPVariable* const y = solver->MakeNumVar(0.0, 2, "y");

  LOG(INFO) << "Number of variables = " << solver->NumVariables();

  // Create a linear constraint, 0 <= x + y <= 2.
  MPConstraint* const ct = solver->MakeRowConstraint(0.0, 2.0, "ct");
  ct->SetCoefficient(x, 1);
  ct->SetCoefficient(y, 1);

  LOG(INFO) << "Number of constraints = " << solver->NumConstraints();

  // Create the objective function, 3 * x + y.
  MPObjective* const objective = solver->MutableObjective();
  objective->SetCoefficient(x, 3);
  objective->SetCoefficient(y, 1);
  objective->SetMaximization();

  solver->Solve();

  LOG(INFO) << "Solution:" << std::endl;
  LOG(INFO) << "Objective value = " << objective->Value();
  LOG(INFO) << "x = " << x->solution_value();
  LOG(INFO) << "y = " << y->solution_value();
}

void RunAllExamples() {
  // Linear programming problems
#if defined(USE_CLP)
  LOG(INFO) << "---- Linear programming example with CLP ----";
  RunLinearProgrammingExample(MPSolver::CLP_LINEAR_PROGRAMMING);
#endif  // USE_CLP
#if defined(USE_GLPK)
  LOG(INFO) << "---- Linear programming example with GLPK ----";
  RunLinearProgrammingExample(MPSolver::GLPK_LINEAR_PROGRAMMING);
#endif  // USE_GLPK
#if defined(USE_GLOP)
  LOG(INFO) << "---- Linear programming example with GLOP ----";
  RunLinearProgrammingExample(MPSolver::GLOP_LINEAR_PROGRAMMING);
#endif  // USE_GLOP
#if defined(USE_PDLP)
  LOG(INFO) << "---- Linear programming example with PDLP ----";
  RunLinearProgrammingExample(MPSolver::PDLP_LINEAR_PROGRAMMING);
#endif  // USE_PDLP
#if defined(USE_GUROBI)
  LOG(INFO) << "---- Linear programming example with Gurobi ----";
  RunLinearProgrammingExample(MPSolver::GUROBI_LINEAR_PROGRAMMING);
#endif  // USE_GUROBI
#if defined(USE_CPLEX)
  LOG(INFO) << "---- Linear programming example with CPLEX ----";
  RunLinearProgrammingExample(MPSolver::CPLEX_LINEAR_PROGRAMMING);
#endif  // USE_CPLEX

  // Integer programming problems
#if defined(USE_SCIP)
  LOG(INFO) << "---- Mixed Integer programming example with SCIP ----";
  RunMixedIntegerProgrammingExample(MPSolver::SCIP_MIXED_INTEGER_PROGRAMMING);
#endif  // USE_SCIP
#if defined(USE_GLPK)
  LOG(INFO) << "---- Mixed Integer programming example with GLPK ----";
  RunMixedIntegerProgrammingExample(MPSolver::GLPK_MIXED_INTEGER_PROGRAMMING);
#endif  // USE_GLPK
#if defined(USE_CBC)
  LOG(INFO) << "---- Mixed Integer programming example with CBC ----";
  RunMixedIntegerProgrammingExample(MPSolver::CBC_MIXED_INTEGER_PROGRAMMING);
#endif  // USE_CBC
#if defined(USE_GUROBI)
  LOG(INFO) << "---- Mixed Integer programming example with GUROBI ----";
  RunMixedIntegerProgrammingExample(MPSolver::GUROBI_MIXED_INTEGER_PROGRAMMING);
#endif  // USE_GUROBI
#if defined(USE_CPLEX)
  LOG(INFO) << "---- Mixed Integer programming example with CPLEX ----";
  RunMixedIntegerProgrammingExample(MPSolver::CPLEX_MIXED_INTEGER_PROGRAMMING);
#endif  // USE_CPLEX

  // Boolean integer programming problems
#if defined(USE_BOP)
  LOG(INFO) << "---- Boolean Integer programming example with BOP ----";
  RunBooleanProgrammingExample(MPSolver::BOP_INTEGER_PROGRAMMING);
#endif  // USE_BOP

  MutableObjectiveCrash();
}
}  // namespace operations_research

int main(int argc, char** argv) {
  google::InitGoogleLogging(argv[0]);
  absl::SetFlag(&FLAGS_stderrthreshold, 0);
  operations_research::RunAllExamples();
  return 0;
}