c34026b101
note: done using ```sh git grep -l "2010-2024 Google" | xargs sed -i 's/2010-2024 Google/2010-2025 Google/' ```
106 lines
3.9 KiB
C++
106 lines
3.9 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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#include <string>
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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 BuildLinearProgrammingMaxExample(MPSolver::OptimizationProblemType type) {
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const double kObjCoef[] = {10.0, 6.0, 4.0};
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const std::string kVarName[] = {"x1", "x2", "x3"};
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const int numVars = 3;
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const int kNumConstraints = 3;
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const std::string kConstraintName[] = {"c1", "c2", "c3"};
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const double kConstraintCoef1[] = {1.0, 1.0, 1.0};
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const double kConstraintCoef2[] = {10.0, 4.0, 5.0};
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const double kConstraintCoef3[] = {2.0, 2.0, 6.0};
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const double* kConstraintCoef[] = {kConstraintCoef1, kConstraintCoef2,
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kConstraintCoef3};
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const double kConstraintUb[] = {100.0, 600.0, 300.0};
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const double infinity = MPSolver::infinity();
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MPModelProto model_proto;
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model_proto.set_name("Max_Example");
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// Create variables and objective function
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for (int j = 0; j < numVars; ++j) {
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MPVariableProto* x = model_proto.add_variable();
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x->set_name(kVarName[j]); // Could be skipped (optional).
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x->set_lower_bound(0.0);
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x->set_upper_bound(infinity); // Could be skipped (default value).
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x->set_is_integer(false); // Could be skipped (default value).
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x->set_objective_coefficient(kObjCoef[j]);
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}
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model_proto.set_maximize(true);
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// Create constraints
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for (int i = 0; i < kNumConstraints; ++i) {
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MPConstraintProto* constraint_proto = model_proto.add_constraint();
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constraint_proto->set_name(kConstraintName[i]); // Could be skipped.
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constraint_proto->set_lower_bound(-infinity); // Could be skipped.
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constraint_proto->set_upper_bound(kConstraintUb[i]);
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for (int j = 0; j < numVars; ++j) {
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// These two lines may be skipped when the coefficient is zero.
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constraint_proto->add_var_index(j);
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constraint_proto->add_coefficient(kConstraintCoef[i][j]);
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}
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}
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MPModelRequest model_request;
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*model_request.mutable_model() = model_proto;
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#if defined(USE_GLOP)
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if (type == MPSolver::GLOP_LINEAR_PROGRAMMING) {
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model_request.set_solver_type(MPModelRequest::GLOP_LINEAR_PROGRAMMING);
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}
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#endif // USE_GLOP
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#if defined(USE_CLP)
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if (type == MPSolver::CLP_LINEAR_PROGRAMMING) {
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model_request.set_solver_type(MPModelRequest::CLP_LINEAR_PROGRAMMING);
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}
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#endif // USE_CLP
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MPSolutionResponse solution_response;
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MPSolver::SolveWithProto(model_request, &solution_response);
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// The problem has an optimal solution.
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CHECK_EQ(MPSOLVER_OPTIMAL, solution_response.status());
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LOG(INFO) << "objective = " << solution_response.objective_value();
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for (int j = 0; j < numVars; ++j) {
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LOG(INFO) << model_proto.variable(j).name() << " = "
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<< solution_response.variable_value(j);
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}
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}
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void RunAllExamples() {
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#if defined(USE_GLOP)
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LOG(INFO) << "----- Running Max Example with GLOP -----";
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BuildLinearProgrammingMaxExample(MPSolver::GLOP_LINEAR_PROGRAMMING);
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#endif // USE_GLOP
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#if defined(USE_CLP)
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LOG(INFO) << "----- Running Max Example with Coin LP -----";
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BuildLinearProgrammingMaxExample(MPSolver::CLP_LINEAR_PROGRAMMING);
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#endif // USE_CLP
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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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InitGoogle(argv[0], &argc, &argv, true);
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operations_research::RunAllExamples();
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return EXIT_SUCCESS;
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}
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