How to use joptimizer for linear programming

Question

I am working on solving a linear programming problem using joptimizer.

My problem is:

Maximize (x1*f1 + x2*f2 + x3*f3)

such that (x1*v1 + x2*v2 + x3*v3) <= h

I need to find x1, x2 and x3.

I do not know how to create a joptimizer input from the above equation.

Answer 1

Java doc is available here http://www.joptimizer.com/apidocs/index.html

simple example minimize 3x+4y such that 2x+3y >= 8, 5x+2y >= 12, x >= 0, y >= 0

My sample code for solving simple linear programming question is below:

package test_joptimizer;

import com.joptimizer.functions.ConvexMultivariateRealFunction;
import com.joptimizer.functions.LinearMultivariateRealFunction;
import com.joptimizer.optimizers.JOptimizer;
import com.joptimizer.optimizers.OptimizationRequest;
import org.apache.log4j.BasicConfigurator;

/**
 * @author K.P.L.Kanchana
 */

public class Main {

    public static void main(String[] args) throws Exception {

        // Objective function (plane)
        LinearMultivariateRealFunction objectiveFunction = new LinearMultivariateRealFunction(new double[] {3.0, 4.0}, 0); //minimize 3x+4y

        //inequalities (polyhedral feasible set G.X<H )
        ConvexMultivariateRealFunction[] inequalities = new ConvexMultivariateRealFunction[4];
        // x >= 0
        inequalities[0] = new LinearMultivariateRealFunction(new double[]{-1.0, 0.00}, 0.0);  // focus: -x+0 <= 0 
        // y >= 0
        inequalities[1] = new LinearMultivariateRealFunction(new double[]{0.0, -1.00}, 0.0);  // focus: -y+0 <= 0
        // 2x+3y >= 8
        inequalities[2] = new LinearMultivariateRealFunction(new double[]{-2.0, -3.00}, 8.0); // focus: -2x-3y+8 <= 0
        // 5x+2y >= 12
        inequalities[3] = new LinearMultivariateRealFunction(new double[]{-5.0, -2.00}, 12.0);// focus: -5x-2y+12 <= 0

        //optimization problem
        OptimizationRequest or = new OptimizationRequest();
        or.setF0(objectiveFunction);
        or.setFi(inequalities);
        //or.setInitialPoint(new double[] {0.0, 0.0});//initial feasible point, not mandatory
        or.setToleranceFeas(1.E-9);
        or.setTolerance(1.E-9);

        //optimization
        JOptimizer opt = new JOptimizer();
        opt.setOptimizationRequest(or);
        int returnCode = opt.optimize();

        double[] sol = opt.getOptimizationResponse().getSolution();

        System.out.println("Length: " + sol.length);
        for (int i=0; i<sol.length/2; i++){
            System.out.println( "X" + (i+1) + ": " + Math.round(sol[i]) + "\ty" + (i+1) + ": " + Math.round(sol[i+1]) );
        }
    }

}