🚀 go-pugleaf

Hi,
	I hope this isn't the wrong place to post. I am working on a
project where I have to do non-linear least-squares fitting with large
data sets to a complicated function. Up until now I have been using the
Downhill Simplex Method of Nelder and Mead (see Numerical Recipes) to
perform the minimization. This has given me good results, but is
extremely processor-time intensive. I have tried using Powell's method
(also see Numerical Recipes) but found that the Downhill Simplex is
better at finding deeper 'global' minima-Powell's tends to find a stable
local minimum and stay there. However, you may have noticed my problem,
I only have Numerical Recipes as a source. Does anyone have any
suggestions of references which will give a selection of minimization
techniques I can try which are a little more state-of-the-art than those
in NR? I am presuming that minimization techniques have emerged
which are not mentioned in NR which may be suitable.

Yours Hopefully,
Conor.

P.S. I'm only an experimental physicist, so please be gentle!