>> Gabor G. wrote >> "R does not currently have AD (except for the Ryacas package >> which can do true AD for certain simple one line functions, i..e. >> input the function and output a function representing its >> derivative); however, for specific problems one can get close >> using deriv and associated functions or the approach explained >> below using rSymPy: >> ...
As the instigator of Finlay's participation in this work, I probably didn't express clearly enough the contribution Gabor has made to get as far as he has with Ryacas and rSympy, which may show another pathway for AD/Symbolic diff. development. At UseR all conversations seemed more rushed than I'd like.
Gabor showed Ravi Varadhan and I a way to get some derivatives via his tools that "worked". We need to play with this a bit more to see how general it could be -- Gabor is very fair in his post that some work is needed for each instance. On the other hand, if analytic gradients were straightforward, we wouldn't be exchanging posts about them.
The clear issue in my mind is that users who need gradients/Jacobians for R want to be able to send a function X to some process that will return another function gradX or JacX that computes analytic derivatives. This has to be "easy", which implies a very simple command or GUI interface. I am pretty certain the users have almost no interest in the mechanism, as long as it works. Currently, most use numerical derivatives, not realizing the very large time penalty and quite large loss in accuracy that can compromise some optimization and differential equation codes. I'll try to prepare a few examples to illustrate this and post them somewhere in the next few weeks. Time, as always, ...
However, the topic does appear to be on the table. JN ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
