[SciPy-user] Solver with n-dimentional steps
Sun Jun 15 08:55:47 CDT 2008
I'm suggesting this partly to satisfy my own curiosity, so ignore this
if it is difficult. Since you are running with so many dimensions,
you might want to try minimizing the square of your function using
fmin_l_bfgs_b. lbfgs is written for higher dimensions, and I've heard
it can give incredible speed improvements over many other methods.
I'm curious if it would help in your case.
On Sun, Jun 15, 2008 at 1:41 PM, Yosef Meller <email@example.com> wrote:
> On Sunday 15 June 2008 13:19:54 David Warde-Farley wrote:
>> It's doing finite differences to estimate the gradient. This is pretty
>> much unavoidable if you're only giving it those three arguments. Have
>> you thought about supplying fprime as a function to analytically
>> compute the gradient?
> Havind dug through the fortran code, I realized that I would have to do that.
> Thanks for the answer.
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