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improve linear examples

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This commit is contained in:
Laurent Perron
2020-06-25 10:33:13 +02:00
parent a8fc48e1fd
commit 5c7e66d998
2 changed files with 94 additions and 54 deletions
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49
examples/cpp/integer_programming.cc
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@@ -13,46 +13,50 @@
// Integer programming example that shows how to use the API.
#include "ortools/base/commandlineflags.h"
#include "ortools/base/logging.h"
#include "ortools/linear_solver/linear_solver.h"
namespace operations_research {
void RunIntegerProgrammingExample(const std::string& optimization_problem_type) {
void RunIntegerProgrammingExample(
const std::string& optimization_problem_type) {
LOG(INFO) << "---- Integer programming example with "
<< optimization_problem_type << " ----";
std::unique_ptr<MPSolver> solver(MPSolver::CreateSolver(
"IntegerProgrammingExample", optimization_problem_type));
if (solver.get() == nullptr) return;
if (!MPSolver::ParseAndCheckSupportForProblemType(
optimization_problem_type)) {
LOG(INFO) << " support for solver not linked in.";
return;
}
const double infinity = solver->infinity();
MPSolver solver("IntegerProgrammingExample",
MPSolver::ParseSolverTypeOrDie(optimization_problem_type));
const double infinity = solver.infinity();
// x and y are integer non-negative variables.
MPVariable* const x = solver->MakeIntVar(0.0, infinity, "x");
MPVariable* const y = solver->MakeIntVar(0.0, infinity, "y");
MPVariable* const x = solver.MakeIntVar(0.0, infinity, "x");
MPVariable* const y = solver.MakeIntVar(0.0, infinity, "y");
// Maximize x + 10 * y.
MPObjective* const objective = solver->MutableObjective();
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);
MPConstraint* const c0 = solver.MakeRowConstraint(-infinity, 17.5);
c0->SetCoefficient(x, 1);
c0->SetCoefficient(y, 7);
// x <= 3.5
MPConstraint* const c1 = solver->MakeRowConstraint(-infinity, 3.5);
MPConstraint* const c1 = solver.MakeRowConstraint(-infinity, 3.5);
c1->SetCoefficient(x, 1);
c1->SetCoefficient(y, 0);
LOG(INFO) << "Number of variables = " << solver->NumVariables();
LOG(INFO) << "Number of constraints = " << solver->NumConstraints();
LOG(INFO) << "Number of variables = " << solver.NumVariables();
LOG(INFO) << "Number of constraints = " << solver.NumConstraints();
solver->SetNumThreads(8);
solver->EnableOutput();
const MPSolver::ResultStatus result_status = solver->Solve();
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!";
@@ -63,9 +67,9 @@ void RunIntegerProgrammingExample(const std::string& optimization_problem_type)
LOG(INFO) << "Optimal objective value = " << objective->Value();
LOG(INFO) << "";
LOG(INFO) << "Advanced usage:";
LOG(INFO) << "Problem solved in " << solver->wall_time() << " milliseconds";
LOG(INFO) << "Problem solved in " << solver->iterations() << " iterations";
LOG(INFO) << "Problem solved in " << solver->nodes()
LOG(INFO) << "Problem solved in " << solver.wall_time() << " milliseconds";
LOG(INFO) << "Problem solved in " << solver.iterations() << " iterations";
LOG(INFO) << "Problem solved in " << solver.nodes()
<< " branch-and-bound nodes";
}
@@ -77,12 +81,13 @@ void RunAllExamples() {
RunIntegerProgrammingExample("GLPK");
RunIntegerProgrammingExample("CPLEX");
}
} // namespace operations_research
int main(int argc, char** argv) {
google::InitGoogleLogging(argv[0]);
FLAGS_logtostderr = 1;
absl::SetFlag(&FLAGS_logtostderr, true);
absl::SetFlag(&FLAGS_log_prefix, false);
gflags::ParseCommandLineFlags(&argc, &argv, true);
operations_research::RunAllExamples();
return EXIT_SUCCESS;
return 0;
}

99
examples/cpp/linear_programming.cc
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@@ -13,58 +13,82 @@
// Linear programming example that shows how to use the API.
#include "ortools/base/commandlineflags.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 RunLinearProgrammingExample() {
MPSolver solver("LinearProgrammingExample",
MPSolver::GLOP_LINEAR_PROGRAMMING);
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");
void RunLinearProgrammingExample(const std::string& optimization_problem_type) {
LOG(INFO) << "---- Linear programming example with "
<< optimization_problem_type << " ----";
if (!MPSolver::ParseAndCheckSupportForProblemType(
optimization_problem_type)) {
LOG(INFO) << " support for solver not linked in.";
return;
}
// Objectif function: Maximize 3x + 4y.
MPSolver solver("IntegerProgrammingExample",
MPSolver::ParseSolverTypeOrDie(optimization_problem_type));
const double infinity = solver.infinity();
// x1, x2 and x3 are continuous non-negative variables.
MPVariable* const x1 = solver.MakeNumVar(0.0, infinity, "x1");
MPVariable* const x2 = solver.MakeNumVar(0.0, infinity, "x2");
MPVariable* const x3 = solver.MakeNumVar(0.0, infinity, "x3");
// Maximize 10 * x1 + 6 * x2 + 4 * x3.
MPObjective* const objective = solver.MutableObjective();
objective->SetCoefficient(x, 3);
objective->SetCoefficient(y, 4);
objective->SetCoefficient(x1, 10);
objective->SetCoefficient(x2, 6);
objective->SetCoefficient(x3, 4);
objective->SetMaximization();
// x + 2y <= 14.
MPConstraint* const c0 = solver.MakeRowConstraint(-infinity, 14.0);
c0->SetCoefficient(x, 1);
c0->SetCoefficient(y, 2);
// x1 + x2 + x3 <= 100.
MPConstraint* const c0 = solver.MakeRowConstraint(-infinity, 100.0);
c0->SetCoefficient(x1, 1);
c0->SetCoefficient(x2, 1);
c0->SetCoefficient(x3, 1);
// 3x - y >= 0.
MPConstraint* const c1 = solver.MakeRowConstraint(0.0, infinity);
c1->SetCoefficient(x, 3);
c1->SetCoefficient(y, -1);
// 10 * x1 + 4 * x2 + 5 * x3 <= 600.
MPConstraint* const c1 = solver.MakeRowConstraint(-infinity, 600.0);
c1->SetCoefficient(x1, 10);
c1->SetCoefficient(x2, 4);
c1->SetCoefficient(x3, 5);
// x - y <= 2.
MPConstraint* const c2 = solver.MakeRowConstraint(-infinity, 2.0);
c2->SetCoefficient(x, 1);
c2->SetCoefficient(y, -1);
// 2 * x1 + 2 * x2 + 6 * x3 <= 300.
MPConstraint* const c2 = solver.MakeRowConstraint(-infinity, 300.0);
c2->SetCoefficient(x1, 2);
c2->SetCoefficient(x2, 2);
c2->SetCoefficient(x3, 6);
// TODO(user): Change example to show = and >= constraints.
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:";
LOG(INFO) << "x = " << x->solution_value();
LOG(INFO) << "y = " << y->solution_value();
LOG(INFO) << "Optimal objective value = " << objective->Value();
LOG(INFO) << "";
LOG(INFO) << "Advanced usage:";
LOG(INFO) << "Problem solved in " << solver.wall_time() << " milliseconds";
// The objective value of the solution.
LOG(INFO) << "Optimal objective value = " << objective->Value();
// The value of each variable in the solution.
LOG(INFO) << "x1 = " << x1->solution_value();
LOG(INFO) << "x2 = " << x2->solution_value();
LOG(INFO) << "x3 = " << x3->solution_value();
LOG(INFO) << "Advanced usage:";
LOG(INFO) << "Problem solved in " << solver.iterations() << " iterations";
LOG(INFO) << "x: reduced cost = " << x->reduced_cost();
LOG(INFO) << "y: reduced cost = " << y->reduced_cost();
LOG(INFO) << "x1: reduced cost = " << x1->reduced_cost();
LOG(INFO) << "x2: reduced cost = " << x2->reduced_cost();
LOG(INFO) << "x3: reduced cost = " << x3->reduced_cost();
const std::vector<double> activities = solver.ComputeConstraintActivities();
LOG(INFO) << "c0: dual value = " << c0->dual_value()
<< " activity = " << activities[c0->index()];
@@ -73,11 +97,22 @@ void RunLinearProgrammingExample() {
LOG(INFO) << "c2: dual value = " << c2->dual_value()
<< " activity = " << activities[c2->index()];
}
void RunAllExamples() {
RunLinearProgrammingExample("GLOP");
RunLinearProgrammingExample("CLP");
RunLinearProgrammingExample("GUROBI_LP");
RunLinearProgrammingExample("CPLEX_LP");
RunLinearProgrammingExample("GLPK_LP");
RunLinearProgrammingExample("XPRESS_LP");
}
} // namespace operations_research
int main(int argc, char** argv) {
google::InitGoogleLogging(argv[0]);
FLAGS_logtostderr = 1;
operations_research::RunLinearProgrammingExample();
absl::SetFlag(&FLAGS_logtostderr, true);
absl::SetFlag(&FLAGS_log_prefix, false);
gflags::ParseCommandLineFlags(&argc, &argv, true);
operations_research::RunAllExamples();
return EXIT_SUCCESS;
}