// Copyright 2010-2021 Google LLC
// 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.
package com.google.ortools.java;

import com.google.ortools.Loader;
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 {
  private static void runIntegerProgrammingExample(String solverType) {
    MPSolver solver = MPSolver.createSolver(solverType);
    if (solver == null) {
      System.out.println("Could not create solver " + solverType);
      return;
    }
    double infinity = java.lang.Double.POSITIVE_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, /* log_errors= */ 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 {
    Loader.loadNativeLibraries();
    System.out.println("---- Integer programming example with SCIP (recommended) ----");
    runIntegerProgrammingExample("SCIP");
    System.out.println("---- Integer programming example with CBC ----");
    runIntegerProgrammingExample("CBC");
    System.out.println("---- Integer programming example with CP-SAT ----");
    runIntegerProgrammingExample("SAT");
  }
}
