On Tue, Nov 23, 2010 at 2:50 PM, Gael Varoquaux
On Tue, Nov 23, 2010 at 02:47:10PM +0100, Sebastian Walter wrote:
Well, I don't know what the best method is to solve your problem, so take the following with a grain of salt: Wouldn't it be better to change the model than modifying the optimization algorithm?
In this case, that's not possible. You can think of this parameter as the number of components in a PCA (it's actually a more complex dictionnary learning framework), so it's a parameter that is discrete, and I can't do anything about it :).
In optimum experimental design one encounters MINLPs where integers define the number of rows of a matrix. 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? Otherwise, well, let me know if you find a working solution ;)
It sounds as if the resulting objective function is piecewise constant.
AFAIK most optimization algorithms for continuous problems require at least Lipschitz continuous functions to work ''acceptable well''. Not sure if this is also true for Nelder-Mead.
Yes correct. We do have a problem.
I have a Nelder-Mead that seems to be working quite well on a few toy problems.
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