124 lines
4.4 KiB
Java
124 lines
4.4 KiB
Java
// Copyright 2010-2011 Google
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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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package com.google.ortools.linearsolver.samples;
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import com.google.ortools.linearsolver.MPConstraint;
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import com.google.ortools.linearsolver.MPSolver;
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import com.google.ortools.linearsolver.MPVariable;
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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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*/
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public class LinearProgramming {
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static {
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System.loadLibrary("jnilinearsolver");
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}
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private static MPSolver createSolver (String solverType) {
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try {
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return new MPSolver("IntegerProgrammingExample",
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MPSolver.getSolverEnum(solverType));
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} catch (java.lang.ClassNotFoundException e) {
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throw new Error(e);
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} catch (java.lang.NoSuchFieldException e) {
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return null;
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} catch (java.lang.IllegalAccessException e) {
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throw new Error(e);
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}
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}
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private static void runLinearProgrammingExample(String solverType) {
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MPSolver solver = createSolver(solverType);
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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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double infinity = solver.infinity();
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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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solver.setObjectiveCoefficient(x1, 10);
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solver.setObjectiveCoefficient(x2, 6);
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solver.setObjectiveCoefficient(x3, 4);
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solver.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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int resultStatus = solver.solve();
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// Check that the problem has an optimal solution.
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if (resultStatus != MPSolver.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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System.out.println("Problem solved in " + solver.wallTime() +
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" milliseconds");
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// The objective value of the solution.
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System.out.println("Optimal objective value = " + solver.objectiveValue());
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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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System.out.println("Advanced usage:");
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System.out.println("Problem solved in " + solver.iterations() +
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" iterations");
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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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System.out.println(" activity = " + c0.activity());
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System.out.println("c1: dual value = " + c1.dualValue());
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System.out.println(" activity = " + c1.activity());
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System.out.println("c2: dual value = " + c2.dualValue());
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System.out.println(" activity = " + c2.activity());
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}
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public static void main(String[] args) throws Exception {
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System.out.println("---- Linear programming example with GLPK ----");
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runLinearProgrammingExample("GLPK_LINEAR_PROGRAMMING");
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System.out.println("---- Linear programming example with CLP ----");
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runLinearProgrammingExample("CLP_LINEAR_PROGRAMMING");
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}
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}
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