2010/11/23 Zachary Pincus <[email protected]>: > > 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 >> at >> 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 > annealing formula.
There is also a simulated-annealing modification of Nelder Mead that can be of use. Matthieu -- Information System Engineer, Ph.D. Blog: http://matt.eifelle.com LinkedIn: http://www.linkedin.com/in/matthieubrucher _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
