Example 6: Usage With Class MinUnconMultiVar

import com.imsl.math.*;
import java.text.*;

public class NumericalDerivativesEx6  {
    static 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)