99 lines
3.6 KiB
Java
99 lines
3.6 KiB
Java
// Copyright 2010-2017 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.samples;
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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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/**
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* Integer programming example that shows how to use the API.
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*
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*/
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public class IntegerProgramming {
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static { System.loadLibrary("jniortools"); }
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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.OptimizationProblemType.valueOf(solverType));
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} catch (java.lang.IllegalArgumentException e) {
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return null;
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}
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}
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private static void runIntegerProgrammingExample(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 = MPSolver.infinity();
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// x1 and x2 are integer non-negative variables.
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MPVariable x1 = solver.makeIntVar(0.0, infinity, "x1");
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MPVariable x2 = solver.makeIntVar(0.0, infinity, "x2");
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// Minimize x1 + 2 * x2.
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MPObjective objective = solver.objective();
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objective.setCoefficient(x1, 1);
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objective.setCoefficient(x2, 2);
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// 2 * x2 + 3 * x1 >= 17.
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MPConstraint ct = solver.makeConstraint(17, infinity);
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ct.setCoefficient(x1, 3);
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ct.setCoefficient(x2, 2);
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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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// 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, /*logErrors=*/true)) {
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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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return;
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}
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System.out.println("Problem solved in " + solver.wallTime() + " milliseconds");
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// The objective value of the solution.
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System.out.println("Optimal objective value = " + solver.objective().value());
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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("Advanced usage:");
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System.out.println("Problem solved in " + solver.nodes() + " branch-and-bound nodes");
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}
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public static void main(String[] args) throws Exception {
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System.out.println("---- Integer programming example with SCIP (recommended) ----");
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runIntegerProgrammingExample("SCIP_MIXED_INTEGER_PROGRAMMING");
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System.out.println("---- Integer programming example with CBC ----");
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runIntegerProgrammingExample("CBC_MIXED_INTEGER_PROGRAMMING");
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System.out.println("---- Integer programming example with GLPK ----");
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runIntegerProgrammingExample("GLPK_MIXED_INTEGER_PROGRAMMING");
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}
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}
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