Example 2: Linear Constrained Optimization

subject to

is solved with an initial guess of , and .

import com.imsl.math.*;

public class MinConGenLinEx2 {

    public static void main(String args[]) throws Exception {
        int neq = 0;
        int ncon = 2;
        int nvar = 3;
        double a[] = {-1.0, -2.0, -2.0, 1.0, 2.0, 2.0};
        double xlb[] = {0.0, 0.0, 0.0};
        double xub[] = {20.0, 11.0, 42.0};
        double xguess[] = {10.0, 10.0, 10.0};
        double b[] = {0.0, 72.0};

        MinConGenLin.Gradient grad = new MinConGenLin.Gradient() {
            public double f(double[] x) {
                return -x[0] * x[1] * x[2];
            }

            public void gradient(double[] x, double[] g) {
                g[0] = -x[1] * x[2];
                g[1] = -x[0] * x[2];
                g[2] = -x[0] * x[1];
            }
        };

        MinConGenLin zf
                = new MinConGenLin(grad, nvar, ncon, neq, a, b, xlb, xub);

        zf.setGuess(xguess);
        zf.solve();
        new PrintMatrix("Solution").print(zf.getSolution());
        System.out.println("Objective value = " + zf.getObjectiveValue());
    }
}

Output

Solution
   0   
0  20  
1  11  
2  15  

Objective value = -3300.0