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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