[Numpy-discussion] N dimensional dichotomy optimization

josef.pktd@gmai... josef.pktd@gmai...
Mon Nov 22 17:14:14 CST 2010


On Mon, Nov 22, 2010 at 5:27 PM, Matthieu Brucher
<matthieu.brucher@gmail.com> wrote:
> 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)

Is that license http://pyevolve.sourceforge.net/license.html BSD compatible ?

Josef

>
> Matthieu
> --
> Information System Engineer, Ph.D.
> Blog: http://matt.eifelle.com
> LinkedIn: http://www.linkedin.com/in/matthieubrucher
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