[SciPy-User] Using leastsq(), fmin(), anneal() to do a least squares fit

[Andreas]

Hi there,

I have an optimization problem in 12 variables.

I first wrote a functino toBeMinimized(), which outputs these 12
variables as one array. Trying to solve this problem with leastsq(), I
noticed that however i play around with the parameters, the function
does not seem to find the global optimum.

So I figured I'd try some other functions from scipy.optimize, starting
with anneal(). I wrote a wrapper function around my original
toBeMinimized(), doing nothing but call
np.sum(toBeMinimized(params)**2). Now, however, the results I get from
anneal vary widely, and don't seem to have anything in common with the
results from leastsq().

Basically the same happens when I use fmin() instead of anneal().

I'm somewhat at a loss here. leastsq() seems to give the most consistent
results, but still they vary too much to be too useful for me.

Any ideas?

Thanks for your insight,

Andreas.