2 %> \f[\min f(x)=100(x_2-x_1^2)^2+(1-x_1)^2\f] subject to \f[x_1-x_2^2-\frac{1}{2} \f]
3 %> @author <a href=
"http://people.epfl.ch/michel.bierlaire">Michel Bierlaire</a>
4 %> @date Wed Mar 25 17:53:06 2015
8 %> @param x value of the variables
9 %> @param index If 0, the objective
function is evaluated. If not, the constraint number index is evaluated.
10 %> @
return f value of the
function 11 %> @
return g value of the gradient
12 %> @
return H value of the hessian
13 function [f,g,H] =
ex1906(x,index)
15 f = 100.0 * (x(2) - x(1) * x(1))^2 + (1.0 - x(1))^2;
16 g = [ -400.0 * x(1) * x(2) + 400 * x(1)^3 - 2.0 + 2 * x(1) ;
17 200.0 * (x(2) - x(1)*x(1)) ];
18 H = [ -400.0 * x(2) + 1200.0 * x(1) * x(1) + 2.0 -400.0 * x(1) ;
19 -400.0 * x(1) 200.0] ;
24 f = x(1) - x(2) * x(2) - 0.5 ;
25 g = [ 1 ; -2.0 * x(2) ] ;
29 error(
"There is only one constraint") ;
function ex1906(in x, in index)