[Numpy-discussion] N dimensional dichotomy optimization
Tue Nov 23 10:31:10 CST 2010
On Nov 23, 2010, at 10:57 AM, Gael Varoquaux wrote:
> On Tue, Nov 23, 2010 at 04:33:00PM +0100, Sebastian Walter wrote:
>> At first glance it looks as if a relaxation is simply not possible:
>> either there are additional rows or not.
>> But with some technical transformations it is possible to reformulate
>> the problem into a form that allows the relaxation of the integer
>> constraint in a natural way.
>> Maybe this is also possible in your case?
> Well, given that it is a cross-validation score that I am optimizing,
> there is not simple algorithm giving this score, so it's not obvious
> all that there is a possible relaxation. A road to follow would be to
> find an oracle giving empirical risk after estimation of the penalized
> problem, and try to relax this oracle. That's two steps further than
> I am
> (I apologize if the above paragraph is incomprehensible, I am
> getting too
> much in the technivalities of my problem.
>> Otherwise, well, let me know if you find a working solution ;)
> Nelder-Mead seems to be working fine, so far. It will take a few weeks
> (or more) to have a real insight on what works and what doesn't.
Jumping in a little late, but it seems that simulated annealing might
be a decent method here: take random steps (drawing from a
distribution of integer step sizes), reject steps that fall outside
the fitting range, and accept steps according to the standard
Something with a global optimum but spikes along the way is pretty
well-suited to SA in general, and it's also an easy algorithm to make
work on a lattice. If you're in high dimensions, there are also bolt-
on methods for biasing the steps toward "good directions" as opposed
to just taking isotropic random steps. Again, pretty easy to think of
discrete implementations of this...
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