Grocer Function
NAME
dfp_min - DFP minimization
CALLING SEQUENCE
[result]=dfp_min(func,pin[,arg1,...argn])
PARAMETERS
Input
-
func = (minus likelihood) function to minimize
-
pin = parameter vector fed to func
-
argi =
-
'maxit=x' to set the maximum # of iterations (default = 500)
-
arguments of function func others that the parameters
Output
-
a result list with:
- result('meth') = 'dfp'
- result('b') = (kx1) minimizing vector
- result('f') = value of func at solution values
- result('hess') = hessian at solution values
- result('iter') = # number of iterations
- result('flag') = 0 for convergence, 1 for non-convergence
DESCRIPTION
DFP minimization routine to minimize func (Converted from Numerical Recipes book dfpmin routine). func must take the form func(b,varargin) where:
b = parameter vector (k x 1)
varargin = arguments passed to the function
EXAMPLE
1) res = ols2(y,x);
2) result4 = dfp_min('to_llike', [res('beta'); res('sige')],'maxit=1000',y,x);
This example, taken from optim1_d, realises the estimation of a tobit model, with initial values taken from on ols estimation. Maximum # of iterations is set to 1000.
AUTHOR
Eric Dubois 2002