110 lines
3.2 KiB
C++
110 lines
3.2 KiB
C++
// 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.
|
|
|
|
// [START program]
|
|
// [START import]
|
|
#include <memory>
|
|
#include <vector>
|
|
|
|
#include "ortools/linear_solver/linear_solver.h"
|
|
// [END import]
|
|
|
|
// [START program_part1]
|
|
namespace operations_research {
|
|
// [START data_model]
|
|
struct DataModel {
|
|
const std::vector<std::vector<double>> constraint_coeffs{
|
|
{5, 7, 9, 2, 1},
|
|
{18, 4, -9, 10, 12},
|
|
{4, 7, 3, 8, 5},
|
|
{5, 13, 16, 3, -7},
|
|
};
|
|
const std::vector<double> bounds{250, 285, 211, 315};
|
|
const std::vector<double> obj_coeffs{7, 8, 2, 9, 6};
|
|
const int num_vars = 5;
|
|
const int num_constraints = 4;
|
|
};
|
|
// [END data_model]
|
|
|
|
void MipVarArray() {
|
|
// [START data]
|
|
DataModel data;
|
|
// [END data]
|
|
// [END program_part1]
|
|
|
|
// [START solver]
|
|
// Create the mip solver with the SCIP backend.
|
|
std::unique_ptr<MPSolver> solver(MPSolver::CreateSolver("SCIP"));
|
|
if (!solver) {
|
|
LOG(WARNING) << "SCIP solver unavailable.";
|
|
return;
|
|
}
|
|
// [END solver]
|
|
|
|
// [START program_part2]
|
|
// [START variables]
|
|
const double infinity = solver->infinity();
|
|
// x[j] is an array of non-negative, integer variables.
|
|
std::vector<const MPVariable*> x(data.num_vars);
|
|
for (int j = 0; j < data.num_vars; ++j) {
|
|
x[j] = solver->MakeIntVar(0.0, infinity, "");
|
|
}
|
|
LOG(INFO) << "Number of variables = " << solver->NumVariables();
|
|
// [END variables]
|
|
|
|
// [START constraints]
|
|
// Create the constraints.
|
|
for (int i = 0; i < data.num_constraints; ++i) {
|
|
MPConstraint* constraint = solver->MakeRowConstraint(0, data.bounds[i], "");
|
|
for (int j = 0; j < data.num_vars; ++j) {
|
|
constraint->SetCoefficient(x[j], data.constraint_coeffs[i][j]);
|
|
}
|
|
}
|
|
LOG(INFO) << "Number of constraints = " << solver->NumConstraints();
|
|
// [END constraints]
|
|
|
|
// [START objective]
|
|
// Create the objective function.
|
|
MPObjective* const objective = solver->MutableObjective();
|
|
for (int j = 0; j < data.num_vars; ++j) {
|
|
objective->SetCoefficient(x[j], data.obj_coeffs[j]);
|
|
}
|
|
objective->SetMaximization();
|
|
// [END objective]
|
|
|
|
// [START solve]
|
|
const MPSolver::ResultStatus result_status = solver->Solve();
|
|
// [END solve]
|
|
|
|
// [START print_solution]
|
|
// 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) << "Optimal objective value = " << objective->Value();
|
|
|
|
for (int j = 0; j < data.num_vars; ++j) {
|
|
LOG(INFO) << "x[" << j << "] = " << x[j]->solution_value();
|
|
}
|
|
// [END print_solution]
|
|
}
|
|
} // namespace operations_research
|
|
|
|
int main(int argc, char** argv) {
|
|
operations_research::MipVarArray();
|
|
return EXIT_SUCCESS;
|
|
}
|
|
// [END program_part2]
|
|
// [END program]
|