Automatic Differentiation for R
Not sure if this is sufficient for your needs but R does include symbolic differentiation, see ?D, and the Ryacas and rSymPy packages interface R to the yacas and sympy computer algebra systems (CAS) and those system include symbolic differentiation. http://ryacas.googlecode.com http://rsympy.googlecode.com Note that Ryacas communicates with yacas via XML but recent versions of the XML package changed in a way that breaks Ryacas so you will likely have to use an old version of XML and Ryacas if you want to try that one -- see home page. The rsympy interface is early stage but its functional and is easier to install since it includes the entire CAS right in the R package.
On Wed, Apr 15, 2009 at 9:30 AM, John C Nash <nashjc at uottawa.ca> wrote:
In efforts to improve optimization tools for R, one of my interests has been getting automatic differentiation capabilities so that analytic rather than numerical derivatives can be used. They would be helpful in several other areas besides optimization, My timings show factors of the order of 1000s in time improvements by avoiding numerical derivatives in some cases. There has been some work in this e.g., http://code.google.com/p/pbs-software/ is an R interface to ADMB (Automatic Differentiation Model Builder). However, as far as I can see, this is directed essentially to nonlinear least squares modelling, an important but not general problem. Tom Coleman of Waterloo responded favourably with some advice, but the most enthusiastic answer came from Shaun Forth, which I have included below. I read this as an opportunity to develop what could be a profitable collaboration with the AD community. Unfortunately, I cannot take up the invitation to join the AD folk in Oxford due to a pre-existing obligation. Nor am I more than a complete novice with S3 and S4 classes etc. I am, nevertheless, willing to help organize the effort e.g., do some of the communications, chasing grant money, getting Google Summer of Code applications filled in etc. Can the R community come up with a few people who can provide the AD workers with appropriate information? If so, is there a reasonable chance to generate sufficient funding for a student? I suspect the answer in both cases is yes, but that we need some form of "booster cables" to get things going. (In Canada, booster cables are used to get cars started in winter by connecting a running vehicle's battery to that of a dead one.) I suggest communications off-list until there is progress to report. Possibly there is a better forum for this -- suggestions welcome. John Nash ---- included msg from Shaun Forth --- Hi John, My computational statistics colleague Trevor Ringrose has asked me to consider AD in R in the past. As you may or may not be aware AD is implemented in one of two ways: overloading using OO features of the target language, or source transformation using compiler tools (after several man years of development) to read in the target code and spit out the differentiated code. Last time I looked I didn't think the object oriented features of R were up to overloading but on checking today I can see that this might now be possible (I can see overloading of arithmetic operators and functions for example now which I didn't see last time). I'd certainly be interested in following this up particularly on the overloading side but would need to get funding for a PhD student to do the graft. It would be particularly interesting doing this in an open source language because we could then perhaps tweak some of the core language features if they didn't seamlessly support the AD (we can't do this in Matlab and that is a pain!). My immediate suggestion is that you, or some other more local (to UK) R expert talks at the next European AD workshop in Oxford http://www.autodiff.org/?module=Workshops&submenu=EuroAD/8/main We're a very friendly group and I'm sure there are others who might like to tackle R or perhaps we could put together a multigroup project. If someone could give a talk on R, its language features including the OO aspects, and some optimisation examples with associated code, the group there would be able to give you the best feedback on the planet on the possibilities. Please do treat this as a positive response and let's keep in touch on this. Regards Shaun #################################################################### Dr Shaun Forth Applied Mathematics & Scientific Computation Cranfield Defence and Security Cranfield University, Shrivenham Campus Swindon SN6 8LA, England tel: +44 (0)1793 785311 fax: +44 (0)1793 784196 email: S.A.Forth at cranfield.ac.uk http://www.amorg.co.uk #####################################################################
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