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Update linear solver build (#4945)

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This commit is contained in:
Guillaume Chatelet
2025-12-12 09:33:50 +01:00
committed by Corentin Le Molgat
parent 6d76575f3d
commit 69dc22f35d
30 changed files with 595 additions and 489 deletions
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68
examples/cpp/integer_programming.cc
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@@ -14,6 +14,7 @@
// Integer programming example that shows how to use the API.
#include <cstdlib>
#include <memory>
#include <string>
#include <vector>
@@ -26,47 +27,40 @@
#include "ortools/linear_solver/linear_solver.h"
namespace operations_research {
void RunIntegerProgrammingExample(absl::string_view solver_id) {
void RunIntegerProgrammingExample(const std::string& solver_id) {
LOG(INFO) << "---- Integer programming example with " << solver_id << " ----";
MPSolver::OptimizationProblemType problem_type;
if (!MPSolver::ParseSolverType(solver_id, &problem_type)) {
LOG(INFO) << "Solver id " << solver_id << " not recognized";
std::unique_ptr<MPSolver> solver(MPSolver::CreateSolver(solver_id));
if (!solver) {
LOG(INFO) << "Unable to create solver : " << solver_id;
return;
}
if (!MPSolver::SupportsProblemType(problem_type)) {
LOG(INFO) << "Supports for solver " << solver_id << " not linked in.";
return;
}
MPSolver solver("IntegerProgrammingExample", problem_type);
const double infinity = solver.infinity();
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();
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!";
@@ -77,21 +71,33 @@ void RunIntegerProgrammingExample(absl::string_view solver_id) {
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";
}
void RunAllExamples() {
RunIntegerProgrammingExample("CBC");
RunIntegerProgrammingExample("SAT");
RunIntegerProgrammingExample("SCIP");
RunIntegerProgrammingExample("GUROBI");
RunIntegerProgrammingExample("GLPK");
RunIntegerProgrammingExample("CPLEX");
RunIntegerProgrammingExample("XPRESS");
RunIntegerProgrammingExample("HIGHS");
std::vector<MPSolver::OptimizationProblemType> supported_problem_types =
MPSolverInterfaceFactoryRepository::GetInstance()
->ListAllRegisteredProblemTypes();
for (MPSolver::OptimizationProblemType type : supported_problem_types) {
const std::string type_name = MPModelRequest::SolverType_Name(
static_cast<MPModelRequest::SolverType>(type));
if (!SolverTypeIsMip(type)) continue;
if (absl::StrContains(type_name, "KNAPSACK")) continue;
if (absl::StrContains(type_name, "BOP")) continue;
if (absl::StrContains(type_name, "HIGHS")) continue;
// ASAN issues a warning in CBC code which cannot be avoided for now:
// AddressSanitizer: float-cast-overflow
// third_party/cbc/Cgl/src/CglPreProcess/CglPreProcess.cpp:1717:36
#ifdef ADDRESS_SANITIZER
if (type_name.find("CBC") != std::string::npos) {
continue;
}
#endif
RunIntegerProgrammingExample(type_name);
}
}
} // namespace operations_research

64
examples/cpp/linear_programming.cc
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@@ -14,6 +14,7 @@
// Linear programming example that shows how to use the API.
#include <cstdlib>
#include <memory>
#include <string>
#include <vector>
@@ -22,71 +23,64 @@
#include "absl/log/log.h"
#include "absl/strings/match.h"
#include "absl/strings/string_view.h"
#include "ortools/base/commandlineflags.h"
#include "ortools/base/init_google.h"
#include "ortools/linear_solver/linear_solver.h"
#include "ortools/linear_solver/linear_solver.pb.h"
namespace operations_research {
void RunLinearProgrammingExample(absl::string_view solver_id) {
void RunLinearProgrammingExample(const std::string& solver_id) {
LOG(INFO) << "---- Linear programming example with " << solver_id << " ----";
MPSolver::OptimizationProblemType problem_type;
if (!MPSolver::ParseSolverType(solver_id, &problem_type)) {
LOG(INFO) << "Solver id " << solver_id << " not recognized";
std::unique_ptr<MPSolver> solver(MPSolver::CreateSolver(solver_id));
if (!solver) {
LOG(INFO) << "Unable to create solver : " << solver_id;
return;
}
if (!MPSolver::SupportsProblemType(problem_type)) {
LOG(INFO) << "Supports for solver " << solver_id << " not linked in.";
return;
}
MPSolver solver("IntegerProgrammingExample", problem_type);
const double infinity = solver.infinity();
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");
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();
MPObjective* const objective = solver->MutableObjective();
objective->SetCoefficient(x1, 10);
objective->SetCoefficient(x2, 6);
objective->SetCoefficient(x3, 4);
objective->SetMaximization();
// x1 + x2 + x3 <= 100.
MPConstraint* const c0 = solver.MakeRowConstraint(-infinity, 100.0);
MPConstraint* const c0 = solver->MakeRowConstraint(-infinity, 100.0);
c0->SetCoefficient(x1, 1);
c0->SetCoefficient(x2, 1);
c0->SetCoefficient(x3, 1);
// 10 * x1 + 4 * x2 + 5 * x3 <= 600.
MPConstraint* const c1 = solver.MakeRowConstraint(-infinity, 600.0);
MPConstraint* const c1 = solver->MakeRowConstraint(-infinity, 600.0);
c1->SetCoefficient(x1, 10);
c1->SetCoefficient(x2, 4);
c1->SetCoefficient(x3, 5);
// 2 * x1 + 2 * x2 + 6 * x3 <= 300.
MPConstraint* const c2 = solver.MakeRowConstraint(-infinity, 300.0);
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();
LOG(INFO) << "Number of variables = " << solver->NumVariables();
LOG(INFO) << "Number of constraints = " << solver->NumConstraints();
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!";
}
LOG(INFO) << "Problem solved in " << solver.wall_time() << " milliseconds";
LOG(INFO) << "Problem solved in " << solver->wall_time() << " milliseconds";
// The objective value of the solution.
LOG(INFO) << "Optimal objective value = " << objective->Value();
@@ -97,11 +91,11 @@ void RunLinearProgrammingExample(absl::string_view solver_id) {
LOG(INFO) << "x3 = " << x3->solution_value();
LOG(INFO) << "Advanced usage:";
LOG(INFO) << "Problem solved in " << solver.iterations() << " iterations";
LOG(INFO) << "Problem solved in " << solver->iterations() << " iterations";
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();
const std::vector<double> activities = solver->ComputeConstraintActivities();
LOG(INFO) << "c0: dual value = " << c0->dual_value()
<< " activity = " << activities[c0->index()];
LOG(INFO) << "c1: dual value = " << c1->dual_value()
@@ -111,14 +105,16 @@ void RunLinearProgrammingExample(absl::string_view solver_id) {
}
void RunAllExamples() {
RunLinearProgrammingExample("GLOP");
RunLinearProgrammingExample("CLP");
RunLinearProgrammingExample("GUROBI_LP");
RunLinearProgrammingExample("CPLEX_LP");
RunLinearProgrammingExample("GLPK_LP");
RunLinearProgrammingExample("XPRESS_LP");
RunLinearProgrammingExample("PDLP");
RunLinearProgrammingExample("HIGHS_LP");
std::vector<MPSolver::OptimizationProblemType> supported_problem_types =
MPSolverInterfaceFactoryRepository::GetInstance()
->ListAllRegisteredProblemTypes();
for (MPSolver::OptimizationProblemType type : supported_problem_types) {
const std::string type_name = MPModelRequest::SolverType_Name(
static_cast<MPModelRequest::SolverType>(type));
if (!absl::StrContains(type_name, "LINEAR_PROGRAMMING")) continue;
if (absl::StrContains(type_name, "HIGHS")) continue;
RunLinearProgrammingExample(type_name);
}
}
} // namespace operations_research

23
examples/cpp/strawberry_fields_with_column_generation.cc
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@@ -610,36 +610,29 @@ int main(int argc, char** argv) {
operations_research::MPSolver::OptimizationProblemType solver_type;
bool found = false;
#if defined(USE_CLP)
if (absl::GetFlag(FLAGS_colgen_solver) == "clp") {
const std::string solver_name = absl::GetFlag(FLAGS_colgen_solver);
if (solver_name == "clp") {
solver_type = operations_research::MPSolver::CLP_LINEAR_PROGRAMMING;
found = true;
}
#endif // USE_CLP
if (absl::GetFlag(FLAGS_colgen_solver) == "glop") {
if (solver_name == "glop") {
solver_type = operations_research::MPSolver::GLOP_LINEAR_PROGRAMMING;
found = true;
}
#if defined(USE_XPRESS)
if (absl::GetFlag(FLAGS_colgen_solver) == "xpress") {
if (solver_name == "xpress") {
solver_type = operations_research::MPSolver::XPRESS_LINEAR_PROGRAMMING;
// solver_type = operations_research::MPSolver::CPLEX_LINEAR_PROGRAMMING;
found = true;
}
#endif
#if defined(USE_CPLEX)
if (absl::GetFlag(FLAGS_colgen_solver) == "cplex") {
if (solver_name == "cplex") {
solver_type = operations_research::MPSolver::CPLEX_LINEAR_PROGRAMMING;
found = true;
}
#endif
if (!found) {
LOG(ERROR) << "Unknown solver " << absl::GetFlag(FLAGS_colgen_solver);
return 1;
LOG(ERROR) << "Unknown solver " << solver_name;
return EXIT_FAILURE;
}
LOG(INFO) << "Chosen solver: " << absl::GetFlag(FLAGS_colgen_solver)
<< std::endl;
LOG(INFO) << "Chosen solver: " << solver_name << std::endl;
if (absl::GetFlag(FLAGS_colgen_instance) == -1) {
for (int i = 0; i < operations_research::kInstanceCount; ++i) {