Re: Function minimizing

[Joao Cardoso]

Francesco Potorti` wrote:
> 
> Took from Matlab's website, and adapted for octave:
> ftp://fly.cnuce.cnr.it/pub/software/octave/leasqr/

with very small modifications the following also run in Octave:

<http://solon.cma.univie.ac.at/~neum/software/ls/> public domain

Matlab Line Search Routines

ELS, Efficient Line Search

ELS is an implementation of a very efficient and robust line search that
finds along each ray x+alp*p with
g(x)^Tp<0 (where g(x) denotes the gradient of the objective function
f(x)) a step size alp>0 such that 
                        f(x+alp*p) <= f(x) - delta (g(x)^Tp/||p||)^2 
with a positive constant delta depending only on f. 

Apart from the initial directional derivative g(x)^Tp, ELS only uses
function evaluations at points along the ray. 

and <ftp://eivind.imm.dtu.dk/pub/cyril/> non-comercial use, I believe.
Search for `conjgrad.m' `steepdes.m'

%CONJGRAD Conjugate Gradient Minimisation.
%
%       CONJGRAD implements the conjugate gradient algorithm for
minimising
%       a multidimensional function.
%       It uses the Hestenes-Stiefel update by default (but
Polak-Ribiere
%       and Fletcher-Reeves are also coded) and an approximate line
search
%       explained in Carl Rasmussen's thesis.

%STEEPDES Steepest descent Minimisation.
%
%       STEEPDES implements the method of steepest descent with
approximate
%       line search.


> 
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-- 
Joao Cardoso                |   e-mail: address@hidden
INESC, R. Jose Falcao 110   |   tel:    + 351 2 2094322
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