From nashjc at uottawa.ca  Thu Apr 16 02:26:41 2009
From: nashjc at uottawa.ca (John C Nash)
Date: Wed, 15 Apr 2009 20:26:41 -0400
Subject: [Rd] (Not quite) Automatic Differentiation
Message-ID: <49E67B41.7070507@uottawa.ca>

Many thanks to Gabor Grothendieck for responding to my posting about 
Automatic Differentiation (invite from Shaun Forth for interaction with 
R developers) showing how one might use rSymPy and symbolic (rather than 
automatic) differentiation to get a function that computes gradients. 
See 
http://code.google.com/p/rsympy/#Automatic_Differentiation_(well,_sort_of)  
for a worked example on the Broyden test function.

This is a big step forward. There's still a way to go before we can 
produce a vectorized gradient code automatically when the size of the 
problem is variable, but the example may serve to incite some 
imaginative coders to action.

Thanks again Gabor.

JN



[Rd] (Not quite) Automatic Differentiation
John C Nash