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ortools-clone/ortools/math_opt/samples/integer_programming.cc
2021-05-22 19:00:14 +02:00

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C++

// Copyright 2010-2021 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.
// Simple linear programming example
#include <iostream>
#include <limits>
#include "absl/flags/parse.h"
#include "absl/flags/usage.h"
#include "absl/status/statusor.h"
#include "absl/time/time.h"
#include "ortools/base/logging.h"
#include "ortools/math_opt/cpp/math_opt.h"
namespace {
using ::operations_research::math_opt::MathOpt;
using ::operations_research::math_opt::Result;
using ::operations_research::math_opt::SolveParametersProto;
using ::operations_research::math_opt::SOLVER_TYPE_GSCIP;
using ::operations_research::math_opt::SolveResultProto;
using ::operations_research::math_opt::SolveStatsProto;
using ::operations_research::math_opt::Variable;
using ::operations_research::math_opt::VariableMap;
constexpr double kInf = std::numeric_limits<double>::infinity();
// Model and solve the problem:
// max x + 10 * y
// s.t. x + 7 * y <= 17.5
// x <= 3.5
// x in {0.0, 1.0, 2.0, ...,
// y in {0.0, 1.0, 2.0, ...,
//
void SolveSimpleMIP() {
MathOpt optimizer(SOLVER_TYPE_GSCIP, "Integer programming example");
// Variables
const Variable x = optimizer.AddIntegerVariable(0.0, kInf, "x");
const Variable y = optimizer.AddIntegerVariable(0.0, kInf, "y");
// Constraints
optimizer.AddLinearConstraint(x + 7 * y <= 17.5, "c1");
optimizer.AddLinearConstraint(x <= 3.5, "c2");
// Objective
optimizer.objective().Maximize(x + 10 * y);
std::cout << "Num variables: " << optimizer.num_variables() << std::endl;
std::cout << "Num constraints: " << optimizer.num_linear_constraints()
<< std::endl;
const Result result = optimizer.Solve(SolveParametersProto()).value();
// Check for warnings.
for (const auto& warning : result.warnings) {
LOG(ERROR) << "Solver warning: " << warning << std::endl;
}
// Check that the problem has an optimal solution.
QCHECK_EQ(result.termination_reason, SolveResultProto::OPTIMAL)
<< "Failed to find an optimal solution: " << result.termination_detail;
std::cout << "Problem solved in " << result.solve_time() << std::endl;
std::cout << "Objective value: " << result.objective_value() << std::endl;
const double x_val = result.variable_values().at(x);
const double y_val = result.variable_values().at(y);
std::cout << "Variable values: [x=" << x_val << ", y=" << y_val << "]"
<< std::endl;
const SolveStatsProto& stat = result.solve_stats;
std::cout << "Simplex iterations: " << stat.simplex_iterations() << std::endl;
std::cout << "Barrier iterations: " << stat.barrier_iterations() << std::endl;
std::cout << "Branch and bound nodes: " << stat.node_count() << std::endl;
}
} // namespace
int main(int argc, char** argv) {
google::InitGoogleLogging(argv[0]);
absl::ParseCommandLine(argc, argv);
SolveSimpleMIP();
return 0;
}