
/*
 * Copyright 2017 Darian Sastre darian.sastre@minimaxlabs.com
 * 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.
 *
 * ************************************************************************
 *
 * This model was created by Hakan Kjellerstrand (hakank@gmail.com)
 */
package com.google.ortools.contrib;

import com.google.ortools.linearsolver.MPConstraint;
import com.google.ortools.linearsolver.MPObjective;
import com.google.ortools.linearsolver.MPSolver;
import com.google.ortools.linearsolver.MPVariable;

public class DietMIP {
	static {
		System.loadLibrary("jniortools");
	}

	private static MPSolver createSolver(String solverType) {
		try {
			return new MPSolver("MIPDiet", MPSolver.OptimizationProblemType.valueOf(solverType));
		} catch (java.lang.IllegalArgumentException e) {
			System.err.println("Bad solver type: " + e);
			return null;
		}
	}

	private static void solve(String solverType) {
		MPSolver solver = createSolver(solverType);
		double infinity = MPSolver.infinity();

		int n = 4; // variables number
		int m = 4; // constraints number

		int[] price = { 50, 20, 30, 80 };

		int[] limits = { 500, 6, 10, 8 };

		int[] calories = { 400, 200, 150, 500 };
		int[] chocolate = { 3, 2, 0, 0 };
		int[] sugar = { 2, 2, 4, 4 };
		int[] fat = { 2, 4, 1, 5 };

		int[][] values = { calories, chocolate, sugar, fat };

		MPVariable[] x = solver.makeIntVarArray(n, 0, 100, "x");
		MPObjective objective = solver.objective();
		MPConstraint[] targets = new MPConstraint[4];

		for (int i = 0; i < n; i++) {
			objective.setCoefficient(x[i], price[i]);

			// constraints
			targets[i] = solver.makeConstraint(limits[i], infinity);
			for (int j = 0; j < m; j++) {
				targets[i].setCoefficient(x[j], values[i][j]);
			}
		}

		final MPSolver.ResultStatus resultStatus = solver.solve();

		/** printing */
		if (resultStatus != MPSolver.ResultStatus.OPTIMAL) {
			System.err.println("The problem does not have an optimal solution!");
			return;
		} else {
			System.out.println("Optimal objective value = " + solver.objective().value());

			System.out.print("Item quantities: ");
			System.out.print((int) x[0].solutionValue() + " ");
			System.out.print((int) x[1].solutionValue() + " ");
			System.out.print((int) x[2].solutionValue() + " ");
			System.out.print((int) x[3].solutionValue() + " ");
		}
	}

	public static void main(String[] args) throws Exception {
		solve("CBC_MIXED_INTEGER_PROGRAMMING");
	}
}


/*
 * Copyright 2017 Darian Sastre darian.sastre@minimaxlabs.com
 * 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.
 *
 * ************************************************************************
 *
 * This model was created by Hakan Kjellerstrand (hakank@gmail.com)
 */
package com.google.ortools.contrib;

import com.google.ortools.linearsolver.MPConstraint;
import com.google.ortools.linearsolver.MPObjective;
import com.google.ortools.linearsolver.MPSolver;
import com.google.ortools.linearsolver.MPVariable;

public class DietMIP {
	static {
		System.loadLibrary("jniortools");
	}

	private static void solve(String solverType) {
		MPSolver solver = MPSolver.createSolver(solverType);
		double infinity = MPSolver.infinity();

		int n = 4; // variables number
		int m = 4; // constraints number

		int[] price = { 50, 20, 30, 80 };

		int[] limits = { 500, 6, 10, 8 };

		int[] calories = { 400, 200, 150, 500 };
		int[] chocolate = { 3, 2, 0, 0 };
		int[] sugar = { 2, 2, 4, 4 };
		int[] fat = { 2, 4, 1, 5 };

		int[][] values = { calories, chocolate, sugar, fat };

		MPVariable[] x = solver.makeIntVarArray(n, 0, 100, "x");
		MPObjective objective = solver.objective();
		MPConstraint[] targets = new MPConstraint[4];

		for (int i = 0; i < n; i++) {
			objective.setCoefficient(x[i], price[i]);

			// constraints
			targets[i] = solver.makeConstraint(limits[i], infinity);
			for (int j = 0; j < m; j++) {
				targets[i].setCoefficient(x[j], values[i][j]);
			}
		}

		final MPSolver.ResultStatus resultStatus = solver.solve();

		/** printing */
		if (resultStatus != MPSolver.ResultStatus.OPTIMAL) {
			System.err.println("The problem does not have an optimal solution!");
			return;
		} else {
			System.out.println("Optimal objective value = " + solver.objective().value());

			System.out.print("Item quantities: ");
			System.out.print((int) x[0].solutionValue() + " ");
			System.out.print((int) x[1].solutionValue() + " ");
			System.out.print((int) x[2].solutionValue() + " ");
			System.out.print((int) x[3].solutionValue() + " ");
		}
	}

	public static void main(String[] args) throws Exception {
		solve("CBC");
	}
}

Remove custom createSolver() method