Message-ID: <49E67B41.7070507@uottawa.ca>
Date: 2009-04-16T00:26:41Z
From: John C Nash
Subject: (Not quite) Automatic Differentiation
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