2025-01-10 11:35:44 +01:00
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// Copyright 2010-2025 Google LLC
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2011-11-07 15:29:46 +00:00
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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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2022-10-05 18:33:10 +02:00
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2022-10-06 21:50:57 +02:00
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package com.google.ortools.java;
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2020-06-24 18:11:12 +02:00
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2020-09-11 21:17:23 +02:00
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import com.google.ortools.Loader;
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2011-11-07 15:29:46 +00:00
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import com.google.ortools.linearsolver.MPConstraint;
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2014-07-09 11:26:55 +00:00
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import com.google.ortools.linearsolver.MPObjective;
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2011-11-07 15:29:46 +00:00
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import com.google.ortools.linearsolver.MPSolver;
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import com.google.ortools.linearsolver.MPVariable;
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2019-05-06 10:31:03 +02:00
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/**
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* Linear programming example that shows how to use the API.
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*/
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2011-11-07 15:29:46 +00:00
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public class LinearProgramming {
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2018-11-10 23:56:52 +01:00
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private static void runLinearProgrammingExample(String solverType, boolean printModel) {
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2020-08-18 17:16:10 +02:00
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MPSolver solver = MPSolver.createSolver(solverType);
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2016-01-26 13:58:53 +01:00
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if (solver == null) {
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System.out.println("Could not create solver " + solverType);
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return;
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}
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2025-03-07 10:33:36 +01:00
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double infinity = Double.POSITIVE_INFINITY;
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2011-12-08 16:47:57 +00:00
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// x1, x2 and x3 are continuous non-negative variables.
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MPVariable x1 = solver.makeNumVar(0.0, infinity, "x1");
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MPVariable x2 = solver.makeNumVar(0.0, infinity, "x2");
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MPVariable x3 = solver.makeNumVar(0.0, infinity, "x3");
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// Maximize 10 * x1 + 6 * x2 + 4 * x3.
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2014-07-09 11:26:55 +00:00
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MPObjective objective = solver.objective();
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objective.setCoefficient(x1, 10);
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objective.setCoefficient(x2, 6);
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objective.setCoefficient(x3, 4);
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objective.setMaximization();
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// x1 + x2 + x3 <= 100.
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MPConstraint c0 = solver.makeConstraint(-infinity, 100.0);
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c0.setCoefficient(x1, 1);
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c0.setCoefficient(x2, 1);
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c0.setCoefficient(x3, 1);
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// 10 * x1 + 4 * x2 + 5 * x3 <= 600.
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MPConstraint c1 = solver.makeConstraint(-infinity, 600.0);
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c1.setCoefficient(x1, 10);
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c1.setCoefficient(x2, 4);
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c1.setCoefficient(x3, 5);
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// 2 * x1 + 2 * x2 + 6 * x3 <= 300.
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MPConstraint c2 = solver.makeConstraint(-infinity, 300.0);
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c2.setCoefficient(x1, 2);
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c2.setCoefficient(x2, 2);
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c2.setCoefficient(x3, 6);
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System.out.println("Number of variables = " + solver.numVariables());
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System.out.println("Number of constraints = " + solver.numConstraints());
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2014-07-24 18:12:50 +00:00
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if (printModel) {
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String model = solver.exportModelAsLpFormat(/* obfuscate= */ false);
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2014-07-24 18:12:50 +00:00
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System.out.println(model);
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}
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2015-06-18 15:47:08 +02:00
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final MPSolver.ResultStatus resultStatus = solver.solve();
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// Check that the problem has an optimal solution.
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if (resultStatus != MPSolver.ResultStatus.OPTIMAL) {
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System.err.println("The problem does not have an optimal solution!");
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return;
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}
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2014-07-09 11:26:55 +00:00
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// Verify that the solution satisfies all constraints (when using solvers
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// others than GLOP_LINEAR_PROGRAMMING, this is highly recommended!).
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if (!solver.verifySolution(/* tolerance= */ 1e-7, /* log_errors= */ true)) {
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2019-05-06 10:31:03 +02:00
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System.err.println("The solution returned by the solver violated the"
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+ " problem constraints by at least 1e-7");
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2014-07-09 11:26:55 +00:00
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return;
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}
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2018-11-10 23:56:52 +01:00
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System.out.println("Problem solved in " + solver.wallTime() + " milliseconds");
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2011-12-08 16:47:57 +00:00
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// The objective value of the solution.
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2018-11-10 23:56:52 +01:00
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System.out.println("Optimal objective value = " + solver.objective().value());
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2011-12-08 16:47:57 +00:00
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// The value of each variable in the solution.
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System.out.println("x1 = " + x1.solutionValue());
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System.out.println("x2 = " + x2.solutionValue());
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System.out.println("x3 = " + x3.solutionValue());
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2015-06-18 15:47:08 +02:00
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final double[] activities = solver.computeConstraintActivities();
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2011-12-08 16:47:57 +00:00
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System.out.println("Advanced usage:");
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System.out.println("Problem solved in " + solver.iterations() + " iterations");
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2011-12-08 16:47:57 +00:00
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System.out.println("x1: reduced cost = " + x1.reducedCost());
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System.out.println("x2: reduced cost = " + x2.reducedCost());
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System.out.println("x3: reduced cost = " + x3.reducedCost());
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System.out.println("c0: dual value = " + c0.dualValue());
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2015-06-18 15:47:08 +02:00
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System.out.println(" activity = " + activities[c0.index()]);
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2011-12-08 16:47:57 +00:00
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System.out.println("c1: dual value = " + c1.dualValue());
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System.out.println(" activity = " + activities[c1.index()]);
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2011-12-08 16:47:57 +00:00
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System.out.println("c2: dual value = " + c2.dualValue());
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2015-06-18 15:47:08 +02:00
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System.out.println(" activity = " + activities[c2.index()]);
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2011-12-08 16:47:57 +00:00
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}
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2011-11-07 15:29:46 +00:00
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public static void main(String[] args) throws Exception {
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2020-09-11 21:17:23 +02:00
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Loader.loadNativeLibraries();
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2018-11-10 23:56:52 +01:00
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System.out.println("---- Linear programming example with GLOP (recommended) ----");
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2020-06-26 09:35:26 +02:00
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runLinearProgrammingExample("GLOP", true);
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2011-11-07 15:29:46 +00:00
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System.out.println("---- Linear programming example with CLP ----");
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2020-06-26 09:35:26 +02:00
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runLinearProgrammingExample("CLP", false);
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2023-11-13 15:29:08 +01:00
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System.out.println("---- Linear programming example with XPRESS ----");
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2023-11-20 12:43:41 +01:00
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runLinearProgrammingExample("XPRESS_LP", false);
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2011-11-07 15:29:46 +00:00
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}
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}
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