[Numpy-discussion] N dimensional dichotomy optimization
Matthieu Brucher
matthieu.brucher@gmail....
Tue Nov 23 01:21:55 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. It uses the barycenter (more
>> or less) to find a new point in the simplex. And it works well only in
>> convex functions (but in fact almost all functions have an issue with
>> this :D)
>
> One last question, now that I know that what I am looking for is a
> simplex algorithm (it indeed corresponds to what I was after), is there a
> reason not to use optimize.fmin? It implements a Nelder-Mead. I must
> admit that I don't see how I can use it to specify the convex hull of the
> parameters in which it operates, or restrict it to work only on integers,
> which are two things that I may want to do.
optimize.fmin can be enough, I don't know it well enough. Nelder-Mead
is not a constrained optimization algorithm, so you can't specify an
outer hull. As for the integer part, I don't know if optimize.fmin is
type consistent, I don't know if scikits.optimization is either, but I
can check it. It should, as there is nothing impeding it.
Matthieu
--
Information System Engineer, Ph.D.
Blog: http://matt.eifelle.com
LinkedIn: http://www.linkedin.com/in/matthieubrucher
More information about the NumPy-Discussion
mailing list