Example 6: Usage With Class MinUnconMultiVar

import com.imsl.math.*;

public class NumericalDerivativesEx6 {

    static private int m = 1, n = 2;

    static double fcnEvaluation(double[] x) {
        return 100. * ((x[1] - x[0] * x[0]) * (x[1] - x[0] * x[0]))
                + (1. - x[0]) * (1. - x[0]);
    }

    static class MyFunction implements MinUnconMultiVar.Gradient {

        public double f(double[] x) {
            return fcnEvaluation(x);
        }

        public void gradient(double[] x, double[] gp) {
            NumericalDerivatives.Function fcn
                    = new NumericalDerivatives.Function() {
                        public double[] f(int varIndex, double[] y) {
                            double[] tmp = new double[m];
                            tmp[0] = fcnEvaluation(y);
                            return tmp;
                        }
                    };

            NumericalDerivatives nderv = new NumericalDerivatives(fcn);
            double[][] jacobian = nderv.evaluateJ(x);

            gp[0] = jacobian[0][0];
            gp[1] = jacobian[0][1];
        }
    }

    public static void main(String args[]) throws Exception {
        MinUnconMultiVar solver = new MinUnconMultiVar(n);
        solver.setGuess(new double[]{-1.2, 1.0});
        double x[] = solver.computeMin(new MyFunction());
        System.out.println("Minimum point is (" + x[0] + ", " + x[1] + ")");
    }
}

Output

Minimum point is (0.9999986118580241, 0.9999972746481575)