NDcost - generic external for optim computing gradient using finite differences

Calling Sequence

[f,g,ind]=CDcost(x,ind,fun,varargin)

Parameters

• x : real vector or matrix
• ind : integer parameter (see optim)
• fun : Scilab function with calling sequence F=fun(x,varargin) varargin may be use to pass parameters p1,...pn
• f : criterion value at point x (see optim)
• g : gradient value at point x (see optim)

Description

This function can be used as an external for optim to minimize problem where gradient is too complicated to be programmed. only the function fun which computes the criterion is required.

This function should be used as follow: [f,xopt,gopt]=optim(list(CDcost,fun,p1,...pn),x0,...)

Examples

   //function to minimize
   function f=rosenbrock(x,varagin)
     p=varargin(1)
     f=1+sum( p*(x(2:$)-x(1:$-1)^2)^2 + (1-x(2:$))^2)
   endfunction

   x0=[1;2;3;4];
   [f,xopt,gopt]=optim(list(NDcost,rosenbrock,200),x0)
 

See Also

optim  external  derivative