Example 2: Solving a general nonlinear programming problem

subject to

is solved.

import com.imsl.math.*;

public class MinConNLPEx2 implements MinConNLP.Gradient {

    public double f(double[] x, int iact, boolean[] ierr) {
        double result;
        ierr[0] = false;
        if (iact == 0) {
            result = (x[0] - 2.e0) * (x[0] - 2.e0)
                    + (x[1] - 1.e0) * (x[1] - 1.e0);
            return result;
        } else {
            switch (iact) {
                case 1:
                    result = (x[0] - 2.e0 * x[1] + 1.e0);
                    return result;
                case 2:
                    result = (-(x[0] * x[0]) / 4.e0 - (x[1] * x[1]) + 1.e0);
                    return result;
                default:
                    ierr[0] = true;
                    return 0.e0;
            }
        }
    }

    public void gradient(double[] x, int iact, double[] result) {
        if (iact == 0) {
            result[0] = 2.e0 * (x[0] - 2.e0);
            result[1] = 2.e0 * (x[1] - 1.e0);
        } else {
            switch (iact) {
                case 1:
                    result[0] = 1.e0;
                    result[1] = -2.e0;
                    break;
                case 2:
                    result[0] = -0.5e0 * x[0];
                    result[1] = -2.e0 * x[1];
                    break;
            }
        }
    }

    public static void main(String args[]) throws Exception {
        int m = 2;
        int me = 1;
        int n = 2;
        MinConNLP minconnon = new MinConNLP(m, me, n);
        minconnon.setGuess(new double[]{2., 2.});
        MinConNLPEx2 grad = new MinConNLPEx2();
        double x[] = minconnon.solve(grad);
        System.out.println("x is " + x[0] + " " + x[1]);
    }
}

Output

x is 0.8228756555325117 0.9114378277662558