110 lines
4.0 KiB
C++
110 lines
4.0 KiB
C++
// Copyright 2010-2021 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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// Testing correctness of the code snippets in the comments of math_opt.h.
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#include <iostream>
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#include <limits>
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#include "absl/flags/parse.h"
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#include "absl/flags/usage.h"
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#include "absl/status/statusor.h"
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#include "ortools/base/logging.h"
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#include "ortools/math_opt/cpp/math_opt.h"
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namespace {
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// Model the problem:
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// max 2.0 * x + y
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// s.t. x + y <= 1.5
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// x in {0.0, 1.0}
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// y in [0.0, 2.5]
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//
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void SolveVersion1() {
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using ::operations_research::math_opt::LinearConstraint;
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using ::operations_research::math_opt::MathOpt;
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using ::operations_research::math_opt::Objective;
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using ::operations_research::math_opt::Result;
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using ::operations_research::math_opt::SolveParametersProto;
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using ::operations_research::math_opt::SolveResultProto;
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using ::operations_research::math_opt::Variable;
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MathOpt optimizer(operations_research::math_opt::SOLVER_TYPE_GSCIP,
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"my_model");
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const Variable x = optimizer.AddBinaryVariable("x");
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const Variable y = optimizer.AddContinuousVariable(0.0, 2.5, "y");
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const LinearConstraint c = optimizer.AddLinearConstraint(
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-std::numeric_limits<double>::infinity(), 1.5, "c");
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c.set_coefficient(x, 1.0);
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c.set_coefficient(y, 1.0);
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const Objective obj = optimizer.objective();
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obj.set_linear_coefficient(x, 2.0);
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obj.set_linear_coefficient(y, 1.0);
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obj.set_maximize();
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const Result result = optimizer.Solve(SolveParametersProto()).value();
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for (const auto& warning : result.warnings) {
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std::cerr << "Solver warning: " << warning << std::endl;
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}
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CHECK_EQ(result.termination_reason, SolveResultProto::OPTIMAL)
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<< result.termination_detail;
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// The following code will print:
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// objective value: 2.5
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// value for variable x: 1
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std::cout << "objective value: " << result.objective_value()
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<< "\nvalue for variable x: " << result.variable_values().at(x)
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<< std::endl;
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}
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void SolveVersion2() {
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using ::operations_research::math_opt::LinearExpression;
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using ::operations_research::math_opt::MathOpt;
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using ::operations_research::math_opt::Result;
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using ::operations_research::math_opt::SolveParametersProto;
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using ::operations_research::math_opt::SolveResultProto;
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using ::operations_research::math_opt::Variable;
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MathOpt optimizer(operations_research::math_opt::SOLVER_TYPE_GSCIP,
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"my_model");
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const Variable x = optimizer.AddBinaryVariable("x");
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const Variable y = optimizer.AddContinuousVariable(0.0, 2.5, "y");
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// We can directly use linear combinations of variables ...
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optimizer.AddLinearConstraint(x + y <= 1.5, "c");
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// ... or build them incrementally.
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LinearExpression objective_expression;
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objective_expression += 2 * x;
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objective_expression += y;
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optimizer.objective().Maximize(objective_expression);
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const Result result = optimizer.Solve(SolveParametersProto()).value();
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for (const auto& warning : result.warnings) {
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std::cerr << "Solver warning: " << warning << std::endl;
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}
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CHECK_EQ(result.termination_reason, SolveResultProto::OPTIMAL)
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<< result.termination_detail;
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// The following code will print:
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// objective value: 2.5
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// value for variable x: 1
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std::cout << "objective value: " << result.objective_value()
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<< "\nvalue for variable x: " << result.variable_values().at(x)
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<< std::endl;
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}
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} // namespace
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int main(int argc, char** argv) {
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google::InitGoogleLogging(argv[0]);
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absl::ParseCommandLine(argc, argv);
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SolveVersion1();
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SolveVersion2();
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return 0;
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}
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