|Subject:||Re: SV: [Help-gsl] On conjugate gradient algorithms in multidimentional minimisation problems.|
|Date:||Wed, 14 Dec 2005 15:56:45 +0100|
|User-agent:||Thunderbird 1.5 (X11/20051025)|
Hi list,I've ran into a funny issue. I have a certain point, not too far from a minimum, which I use as a starting point for fits.
A simple random walk routine (pick a random direction in which the chi squared is smaller by throwing parameters inside a small sphere around the current point) works ok. James Bergstra's mm_hess routine works fine.
Built-in GSL conjugate gradient algorithms (Fletcher-Reeves, Polak-Ribiere and the vector gradient algorithm) all bail out after 15-20 iterations claiming iterations are not making progress toward a solution.
Can someone explain what's up with that? Why are the advanced conjugate gradient methods failing when even the simplest random walk does the job? Slowly, yes, but it is, at least, going on.
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