On Mon, Nov 22, 2010 at 09:12:45PM +0100, Matthieu Brucher wrote: > Hi ;)
Hi bro > > does anybody have, or knows where I can find some N dimensional > > dichotomy optimization code in Python (BSD licensed, or equivalent)? > I don't know any code, but it should be too difficult by bgoing > through a KdTree. I am not in terribly high-dimensional spaces, so I don't really need to use a KdTree (but we do happen to have a nice BallTree available in the scikit-learn, so I could use it just to play :>). > > Worst case, it does not look too bad to code, but I am interested by > > any advice. I haven't done my reading yet, and I don't know how > > ill-posed a problem it is. I had in mind starting from a set of > > points and iterating the computation of the objective function's > > value at the barycenters of these points, and updating this list of > > points. This does raise a few questions on what are the best possible > > updates. > In this case, you may want to check Nelder-Mead algotihm (also known > as down-hill simplex or polytope), which is available in > scikits.optimization, but there are other implementations out there. Interesting reference. I had never looked at the Nelder-Mead algorithm. I am wondering if it does what I want, thought. The reason I am looking at dichotomy optimization is that the objective function that I want to optimize has local roughness, but is globally pretty much a bell-shaped curve. Dichotomy looks like it will get quite close to the top of the curve (and I have been told that it works well on such problems). One thing that is nice with dichotomy for my needs is that it is not based on a gradient, and it works in a convex of the parameter space. Will the Nelder-Mead display such properties? It seems so to me, but I don't trust my quick read through of Wikipedia. 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. Many thanks, Gael 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). _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion