Example 1: Linear Constrained Optimization

subject to

is solved.

import com.imsl.math.*;

public class MinConGenLinEx1 {
    public static void main(String args[]) throws Exception {
        int neq = 2;
        int ncon = 2;
        int nvar = 5;
        double a[] = {1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, -2.0, -2.0};
        double b[] = {5.0, -3.0};
        double xlb[] = {0.0, 0.0, 0.0, 0.0, 0.0};
        double xub[] = {10.0, 10.0, 10.0, 10.0, 10.0};
        
        MinConGenLin.Function fcn = new MinConGenLin.Function() {
            public double f(double[] x) {
                return x[0]*x[0] + x[1]*x[1] + x[2]*x[2] + x[3]*x[3] +
                x[4]*x[4] - 2.0*x[1]*x[2] - 2.0*x[3] * x[4] - 2.0*x[0];
            }
        };
        
        MinConGenLin zf =
        new MinConGenLin(fcn, nvar, ncon, neq, a, b, xlb, xub);
        
        zf.solve();
        new PrintMatrix("Solution").print(zf.getSolution());
    }
}

Output

Solution
   0  
0  1  
1  1  
2  1  
3  1  
4  1