[SciPy-User] Using leastsq(), fmin(), anneal() to do a least squares fit
Wed Jun 30 13:55:47 CDT 2010
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.
Thanks for your insight,
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