Move java examples

examples/java/IntegerProgramming.java

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+// 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;
+
+/**
+ * Integer programming example that shows how to use the API.
+ *
+ */
+
+public class IntegerProgramming {
+  static { System.loadLibrary("jniortools"); }
+
+  private static MPSolver createSolver (String solverType) {
+    try {
+      return new MPSolver("IntegerProgrammingExample",
+                          MPSolver.OptimizationProblemType.valueOf(solverType));
+    } catch (java.lang.IllegalArgumentException e) {
+	    System.err.println("Bad solver type: " + e);
+      return null;
+    }
+  }
+
+  private static void runIntegerProgrammingExample(String solverType) {
+    MPSolver solver = createSolver(solverType);
+    if (solver == null) {
+      System.out.println("Could not create solver " + solverType);
+      return;
+    }
+    double infinity = MPSolver.infinity();
+    // x1 and x2 are integer non-negative variables.
+    MPVariable x1 = solver.makeIntVar(0.0, infinity, "x1");
+    MPVariable x2 = solver.makeIntVar(0.0, infinity, "x2");
+
+    // Minimize x1 + 2 * x2.
+    MPObjective objective = solver.objective();
+    objective.setCoefficient(x1, 1);
+    objective.setCoefficient(x2, 2);
+
+    // 2 * x2 + 3 * x1 >= 17.
+    MPConstraint ct = solver.makeConstraint(17, infinity);
+    ct.setCoefficient(x1, 3);
+    ct.setCoefficient(x2, 2);
+
+    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("Advanced usage:");
+    System.out.println("Problem solved in " + solver.nodes() + " branch-and-bound nodes");
+  }
+
+
+  public static void main(String[] args) throws Exception {
+    System.out.println("---- Integer programming example with SCIP (recommended) ----");
+    runIntegerProgrammingExample("SCIP_MIXED_INTEGER_PROGRAMMING");
+    System.out.println("---- Integer programming example with CBC ----");
+    runIntegerProgrammingExample("CBC_MIXED_INTEGER_PROGRAMMING");
+    System.out.println("---- Integer programming example with GLPK ----");
+    runIntegerProgrammingExample("GLPK_MIXED_INTEGER_PROGRAMMING");
+  }
+}