2025-01-10 11:33:35 +01:00
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// Copyright 2010-2025 Google LLC
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2020-12-07 14:57:42 +01:00
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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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// Minimal example to call the GLOP solver.
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// [START program]
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package com.google.ortools.linearsolver.samples;
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2024-03-21 18:04:22 +01:00
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2020-12-07 14:57:42 +01:00
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// [START import]
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import com.google.ortools.Loader;
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2024-03-21 18:04:22 +01:00
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import com.google.ortools.init.OrToolsVersion;
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2020-12-07 14:57:42 +01:00
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import com.google.ortools.linearsolver.MPConstraint;
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import com.google.ortools.linearsolver.MPObjective;
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import com.google.ortools.linearsolver.MPSolver;
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import com.google.ortools.linearsolver.MPVariable;
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// [END import]
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2020-12-17 20:02:42 +01:00
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/** Minimal Linear Programming example to showcase calling the solver. */
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public final class BasicExample {
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public static void main(String[] args) {
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// [START loader]
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Loader.loadNativeLibraries();
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// [END loader]
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System.out.println("Google OR-Tools version: " + OrToolsVersion.getVersionString());
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2020-12-07 14:57:42 +01:00
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// [START solver]
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// Create the linear solver with the GLOP backend.
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MPSolver solver = MPSolver.createSolver("GLOP");
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2024-03-21 18:04:22 +01:00
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if (solver == null) {
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System.out.println("Could not create solver GLOP");
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return;
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}
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// [END solver]
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// [START variables]
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// Create the variables x and y.
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MPVariable x = solver.makeNumVar(0.0, 1.0, "x");
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MPVariable y = solver.makeNumVar(0.0, 2.0, "y");
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System.out.println("Number of variables = " + solver.numVariables());
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// [END variables]
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// [START constraints]
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double infinity = Double.POSITIVE_INFINITY;
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// Create a linear constraint, x + y <= 2.
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MPConstraint ct = solver.makeConstraint(-infinity, 2.0, "ct");
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2020-12-07 14:57:42 +01:00
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ct.setCoefficient(x, 1);
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ct.setCoefficient(y, 1);
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System.out.println("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, 3 * x + y.
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MPObjective objective = solver.objective();
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objective.setCoefficient(x, 3);
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objective.setCoefficient(y, 1);
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objective.setMaximization();
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// [END objective]
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// [START solve]
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System.out.println("Solving with " + solver.solverVersion());
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final MPSolver.ResultStatus resultStatus = solver.solve();
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// [END solve]
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// [START print_solution]
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System.out.println("Status: " + resultStatus);
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if (resultStatus != MPSolver.ResultStatus.OPTIMAL) {
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System.out.println("The problem does not have an optimal solution!");
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if (resultStatus == MPSolver.ResultStatus.FEASIBLE) {
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System.out.println("A potentially suboptimal solution was found");
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} else {
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System.out.println("The solver could not solve the problem.");
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return;
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}
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}
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2020-12-07 14:57:42 +01:00
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System.out.println("Solution:");
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System.out.println("Objective value = " + objective.value());
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System.out.println("x = " + x.solutionValue());
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System.out.println("y = " + y.solutionValue());
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// [END print_solution]
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// [START advanced]
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System.out.println("Advanced usage:");
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System.out.println("Problem solved in " + solver.wallTime() + " milliseconds");
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System.out.println("Problem solved in " + solver.iterations() + " iterations");
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// [END advanced]
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2020-12-07 14:57:42 +01:00
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}
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2020-12-17 20:02:42 +01:00
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private BasicExample() {}
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2020-12-07 14:57:42 +01:00
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}
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// [END program]
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