107 lines
3.2 KiB
C++
107 lines
3.2 KiB
C++
// Copyright 2010-2018 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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// [START program]
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// [START import]
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#include "ortools/linear_solver/linear_solver.h"
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// [END import]
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// [START program_part1]
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namespace operations_research {
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// [START data_model]
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struct DataModel {
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const std::vector<std::vector<double>> constraint_coeffs{
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{5, 7, 9, 2, 1},
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{18, 4, -9, 10, 12},
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{4, 7, 3, 8, 5},
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{5, 13, 16, 3, -7},
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};
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const std::vector<double> bounds{250, 285, 211, 315};
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const std::vector<double> obj_coeffs{7, 8, 2, 9, 6};
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const int num_vars = 5;
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const int num_constraints = 4;
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};
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// [END data_model]
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void MipVarArray() {
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// [START data]
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DataModel data;
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// [END data]
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// [END program_part1]
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// [START solver]
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// Create the mip solver with the SCIP backend.
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std::unique_ptr<MPSolver> solver(MPSolver::CreateSolver("SCIP"));
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if (!solver) {
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LOG(WARNING) << "SCIP solver unavailable.";
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return;
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}
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// [END solver]
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// [START program_part2]
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// [START variables]
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const double infinity = solver->infinity();
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// x[j] is an array of non-negative, integer variables.
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std::vector<const MPVariable*> x(data.num_vars);
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for (int j = 0; j < data.num_vars; ++j) {
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x[j] = solver->MakeIntVar(0.0, infinity, "");
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}
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LOG(INFO) << "Number of variables = " << solver->NumVariables();
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// [END variables]
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// [START constraints]
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// Create the constraints.
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for (int i = 0; i < data.num_constraints; ++i) {
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MPConstraint* constraint = solver->MakeRowConstraint(0, data.bounds[i], "");
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for (int j = 0; j < data.num_vars; ++j) {
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constraint->SetCoefficient(x[j], data.constraint_coeffs[i][j]);
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}
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}
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LOG(INFO) << "Number of constraints = " << solver->NumConstraints();
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// [END constraints]
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// [START objective]
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// Create the objective function.
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MPObjective* const objective = solver->MutableObjective();
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for (int j = 0; j < data.num_vars; ++j) {
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objective->SetCoefficient(x[j], data.obj_coeffs[j]);
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}
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objective->SetMaximization();
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// [END objective]
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// [START solve]
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const MPSolver::ResultStatus result_status = solver->Solve();
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// [END solve]
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// [START print_solution]
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// Check that the problem has an optimal solution.
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if (result_status != MPSolver::OPTIMAL) {
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LOG(FATAL) << "The problem does not have an optimal solution.";
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}
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LOG(INFO) << "Solution:";
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LOG(INFO) << "Optimal objective value = " << objective->Value();
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for (int j = 0; j < data.num_vars; ++j) {
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LOG(INFO) << "x[" << j << "] = " << x[j]->solution_value();
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}
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// [END print_solution]
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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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operations_research::MipVarArray();
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return EXIT_SUCCESS;
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}
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// [END program_part2]
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// [END program]
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