Can anyone compare Theano and openopt for automatic differentiation? On Fri, Sep 28, 2012 at 2:36 PM, Dmitrey <[email protected]> wrote: > Hi all, > nice to hear about another one OpenOpt application. > >> For small non linear problems having an exact SVM/SVR solver >> (not approximated) is very useful IMHO. > > I'm not sure what does this mean "For small non linear problems having > an exact SVM/SVR solver (not approximated) is very useful IMHO" > > ralg cannot search solution with required tolerance, and thus is approximate > solver (maybe in ML "exact/approximate" have a certain meaning? I'm not aware > though). That "ftol" in the code is only a stopping criterion. > For large problems (e.g. 10^4,10^5 variables) using ralg is impossible (it > stores dense matrix of shape nVars x nVars in RAM), but you could try the > constrained solvers like http://openopt.org/IPOPT, http://openopt.org/ALGENCAN > or http://openopt.org/gsubg; latter can handle fTol - required tolerance > abs(f-f*)<fTol, see also my post "routine for linear least norms problems" > http://forum.openopt.org/viewtopic.php?id=598 . All these solvers are > installed > and thus can be tried in oursage server (http://sage.openopt.org), although, > it > has quite low equipment (1 GB RAM, 2 GHz processor). > >>Please put on sunglasses before opening the openopt webpage. > > OpenOpt website will be moved to new engine as soon as we will got > possibilities > to make it done. > > FYI in 2012, after 41 years since initial ralg article in 1971, N.G.Zhurbenko, > co-author of r-algorithm (http://openopt.org/NikolayZhurbenko) seems to have > invented major enhancement for r-algorithm, but I haven't possibilities to > code > it into my implementation of the solver (http://openopt.org/ralg) right now, > mb > it will be done several months later. > > ------------ > Regards, D. > http://openopt.org/Dmitrey > > > ------------------------------------------------------------------------------ > Got visibility? > Most devs has no idea what their production app looks like. > Find out how fast your code is with AppDynamics Lite. > http://ad.doubleclick.net/clk;262219671;13503038;y? > http://info.appdynamics.com/FreeJavaPerformanceDownload.html > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
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