[Numpy-discussion] N dimensional dichotomy optimization

Matthieu Brucher matthieu.brucher@gmail....
Mon Nov 22 16:27:32 CST 2010

2010/11/22 Gael Varoquaux <gael.varoquaux@normalesup.org>:
> On Mon, Nov 22, 2010 at 11:12:26PM +0100, Matthieu Brucher wrote:
>> It seems that a simplex is what you need.
> Ha! I am learning new fancy words. Now I can start looking clever.
>> > I realize that maybe I should rephrase my question to try and draw more
>> > out of the common wealth of knowledge on this mailing list: what do
>> > people suggest to tackle this problem? Guided by Matthieu's suggestion, I
>> > have started looking at Powell's algorithm, and given the introduction of
>> > www.damtp.cam.ac.uk/user/na/NA_papers/NA2007_03.pdf I am wondering
>> > whether I should not investigate it. Can people provide any insights on
>> > these problems.
>> Indeed, Powell may also a solution. A simplex is just what is closer
>> to what you hinted as an optimization algorithm.
> I'll do a bit more reading.
>> > PS: The reason I am looking at this optimization problem is that I got
>> > tired of looking at grid searches optimize the cross-validation score on
>> > my 3-parameter estimator (not in the scikit-learn, because it is way too
>> > specific to my problems).
>> Perhaps you may want to combine it with genetic algorithms. We also
>> kind of combine grid search with simplex-based optimizer with
>> simulated annealing in some of our global optimization problems, and I
>> think I'll try at one point to introduce genetic algorithms instead of
>> the grid search.
> Well, in the scikit, in the long run (it will take a little while) I'd
> like to expose other optimization methods then the GridSearchCV, so if
> you have code or advice to give us, we'd certainly be interested.
> Gael

There is scikits.optimization partly in the externals :D But I don't
think they should be in scikits.learn directly. Of course, the scikit
may need access to some global optimization methods, but the most used
one is already there (the grid search).
Then for genetic algorithms, pyevolve is pretty much all you want (I
still have to check the multiprocessing part)

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