Files
ortools-clone/examples/linear_solver_protocol_buffers.cc

114 lines
4.0 KiB
C++
Raw Normal View History

// Copyright 2010-2011 Google
// 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.
#include <string>
#include "base/commandlineflags.h"
#include "base/logging.h"
#include "linear_solver/linear_solver.h"
#include "linear_solver/linear_solver.pb.h"
namespace operations_research {
void BuildLinearProgrammingMaxExample(MPSolver::OptimizationProblemType type) {
const double kObjCoef[] = {10.0, 6.0, 4.0};
const string kVarName[] = {"x1", "x2", "x3"};
const int numVars = 3;
const int kNumConstraints = 3;
const string kConstraintName[] = {"c1", "c2", "c3"};
const double kConstraintCoef1[] = {1.0, 1.0, 1.0};
const double kConstraintCoef2[] = {10.0, 4.0, 5.0};
const double kConstraintCoef3[] = {2.0, 2.0, 6.0};
const double* kConstraintCoef[] = {kConstraintCoef1,
kConstraintCoef2,
kConstraintCoef3};
const double kConstraintUb[] = {100.0, 600.0, 300.0};
MPSolver solver("Max_Example", type);
const double infinity = solver.infinity();
MPModelProto model_proto;
// Create variables and objective function
for (int j = 0; j < numVars; ++j) {
MPVariableProto* x = model_proto.add_variables();
x->set_id(kVarName[j]);
x->set_lb(0.0);
x->set_ub(infinity);
x->set_integer(false);
MPTermProto* obj_term = model_proto.add_objective_terms();
obj_term->set_variable_id(kVarName[j]);
obj_term->set_coefficient(kObjCoef[j]);
}
model_proto.set_maximize(true);
// Create constraints
for (int i = 0; i < kNumConstraints; ++i) {
MPConstraintProto* constraint_proto = model_proto.add_constraints();
constraint_proto->set_id(kConstraintName[i]);
constraint_proto->set_lb(-infinity);
constraint_proto->set_ub(kConstraintUb[i]);
for (int j = 0; j < numVars; ++j) {
MPTermProto* term = constraint_proto->add_terms();
term->set_variable_id(kVarName[j]);
term->set_coefficient(kConstraintCoef[i][j]);
}
}
MPModelRequest model_request;
model_request.mutable_model()->CopyFrom(model_proto);
#if defined(USE_GLPK)
if (type == MPSolver::GLPK_LINEAR_PROGRAMMING) {
model_request.set_problem_type(MPModelRequest::GLPK_LINEAR_PROGRAMMING);
}
#endif // USE_GLPK
#if defined(USE_CLP)
if (type == MPSolver::CLP_LINEAR_PROGRAMMING) {
model_request.set_problem_type(MPModelRequest::CLP_LINEAR_PROGRAMMING);
}
#endif // USE_CLP
MPSolutionResponse solution_response;
solver.SolveWithProtocolBuffers(model_request, &solution_response);
// The problem has an optimal solution.
CHECK_EQ(MPSolver::OPTIMAL, solution_response.result_status());
LOG(INFO) << "objective = " << solution_response.objective_value();
2011-09-12 21:17:15 +00:00
const int num_non_zeros = solution_response.solution_values_size();
for (int j = 0; j < num_non_zeros; ++j) {
MPSolutionValue solution_value = solution_response.solution_values(j);
LOG(INFO) << solution_value.variable_id() << " = "
<< solution_value.value();
}
2011-09-17 13:33:29 +00:00
if (num_non_zeros != numVars) {
LOG(INFO) << "All other variables have zero value";
}
}
void RunAllExamples() {
#if defined(USE_GLPK)
LOG(INFO) << "----- Running Max Example with GLPK -----";
BuildLinearProgrammingMaxExample(MPSolver::GLPK_LINEAR_PROGRAMMING);
#endif // USE_GLPK
#if defined(USE_CLP)
LOG(INFO) << "----- Running Max Example with Coin LP -----";
BuildLinearProgrammingMaxExample(MPSolver::CLP_LINEAR_PROGRAMMING);
#endif // USE_CLP
}
} // namespace operations_research
int main(int argc, char **argv) {
google::ParseCommandLineFlags(&argc, &argv, true);
operations_research::RunAllExamples();
return 0;
}