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
______________________________________________
R-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-devel
