Move java examples

2018-07-06 21:03:33 +02:00
parent 6485017458
commit 13a566bed4
58 changed files with 340 additions and 693 deletions
--- a/examples/java/LinearProgramming.java
+++ b/examples/java/LinearProgramming.java
@@ -0,0 +1,131 @@
+// Copyright 2010-2017 Google
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+import com.google.ortools.linearsolver.MPConstraint;
+import com.google.ortools.linearsolver.MPObjective;
+import com.google.ortools.linearsolver.MPSolver;
+import com.google.ortools.linearsolver.MPVariable;
+
+/**
+ * Linear programming example that shows how to use the API.
+ *
+ */
+
+public class LinearProgramming {
+  static { System.loadLibrary("jniortools"); }
+
+  private static MPSolver createSolver (String solverType) {
+    try {
+      return new MPSolver("LinearProgrammingExample",
+                          MPSolver.OptimizationProblemType.valueOf(solverType));
+    } catch (java.lang.IllegalArgumentException e) {
+      return null;
+    }
+  }
+
+  private static void runLinearProgrammingExample(String solverType,
+                                                  boolean printModel) {
+    MPSolver solver = createSolver(solverType);
+    if (solver == null) {
+      System.out.println("Could not create solver " + solverType);
+      return;
+    }
+    double infinity = MPSolver.infinity();
+    // x1, x2 and x3 are continuous non-negative variables.
+    MPVariable x1 = solver.makeNumVar(0.0, infinity, "x1");
+    MPVariable x2 = solver.makeNumVar(0.0, infinity, "x2");
+    MPVariable x3 = solver.makeNumVar(0.0, infinity, "x3");
+
+    // Maximize 10 * x1 + 6 * x2 + 4 * x3.
+    MPObjective objective = solver.objective();
+    objective.setCoefficient(x1, 10);
+    objective.setCoefficient(x2, 6);
+    objective.setCoefficient(x3, 4);
+    objective.setMaximization();
+
+    // x1 + x2 + x3 <= 100.
+    MPConstraint c0 = solver.makeConstraint(-infinity, 100.0);
+    c0.setCoefficient(x1, 1);
+    c0.setCoefficient(x2, 1);
+    c0.setCoefficient(x3, 1);
+
+    // 10 * x1 + 4 * x2 + 5 * x3 <= 600.
+    MPConstraint c1 = solver.makeConstraint(-infinity, 600.0);
+    c1.setCoefficient(x1, 10);
+    c1.setCoefficient(x2, 4);
+    c1.setCoefficient(x3, 5);
+
+    // 2 * x1 + 2 * x2 + 6 * x3 <= 300.
+    MPConstraint c2 = solver.makeConstraint(-infinity, 300.0);
+    c2.setCoefficient(x1, 2);
+    c2.setCoefficient(x2, 2);
+    c2.setCoefficient(x3, 6);
+
+    System.out.println("Number of variables = " + solver.numVariables());
+    System.out.println("Number of constraints = " + solver.numConstraints());
+
+    if (printModel) {
+      String model = solver.exportModelAsLpFormat(false);
+      System.out.println(model);
+    }
+
+    final MPSolver.ResultStatus resultStatus = solver.solve();
+
+    // Check that the problem has an optimal solution.
+    if (resultStatus != MPSolver.ResultStatus.OPTIMAL) {
+      System.err.println("The problem does not have an optimal solution!");
+      return;
+    }
+
+    // Verify that the solution satisfies all constraints (when using solvers
+    // others than GLOP_LINEAR_PROGRAMMING, this is highly recommended!).
+    if (!solver.verifySolution(/*tolerance=*/1e-7, /*logErrors=*/true)) {
+      System.err.println("The solution returned by the solver violated the"
+                         + " problem constraints by at least 1e-7");
+      return;
+    }
+
+    System.out.println("Problem solved in " + solver.wallTime() + " milliseconds");
+
+    // The objective value of the solution.
+    System.out.println("Optimal objective value = " + solver.objective().value());
+
+    // The value of each variable in the solution.
+    System.out.println("x1 = " + x1.solutionValue());
+    System.out.println("x2 = " + x2.solutionValue());
+    System.out.println("x3 = " + x3.solutionValue());
+
+    final double[] activities = solver.computeConstraintActivities();
+
+    System.out.println("Advanced usage:");
+    System.out.println("Problem solved in " + solver.iterations() + " iterations");
+    System.out.println("x1: reduced cost = " + x1.reducedCost());
+    System.out.println("x2: reduced cost = " + x2.reducedCost());
+    System.out.println("x3: reduced cost = " + x3.reducedCost());
+    System.out.println("c0: dual value = " + c0.dualValue());
+    System.out.println("    activity = " + activities[c0.index()]);
+    System.out.println("c1: dual value = " + c1.dualValue());
+    System.out.println("    activity = " + activities[c1.index()]);
+    System.out.println("c2: dual value = " + c2.dualValue());
+    System.out.println("    activity = " + activities[c2.index()]);
+  }
+
+  public static void main(String[] args) throws Exception {
+    System.out.println("---- Linear programming example with GLOP (recommended) ----");
+    runLinearProgrammingExample("GLOP_LINEAR_PROGRAMMING", true);
+    System.out.println("---- Linear programming example with CLP ----");
+    runLinearProgrammingExample("CLP_LINEAR_PROGRAMMING", false);
+    System.out.println("---- Linear programming example with GLPK ----");
+    runLinearProgrammingExample("GLPK_LINEAR_PROGRAMMING", false);
+  }
+}