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 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
