[SciPy-user] [ANN][Automatic Differentiation] Beta Version of PYADOLC
Tue May 12 15:33:22 CDT 2009
Hey guys; I thought I'd chime in here. If you're interested in learning
about automatic differentiation, Justin Domke, who's a grad student in
my department, has written a series of posts on his blog that are really
informative. You can just check out http://justindomke.wordpress.com/.
David Warde-Farley wrote:
> On 12-May-09, at 12:41 PM, Pauli Virtanen wrote:
>> AD typically builds an "implicit" graph expression corresponding to
>> computation, and constructs the Jacobian based on that. So it's not
>> symbolic or numerical differentiation.
> I've never quite understood the difference between what AD does and
> the 'symbolic' way, but from what I'm reading on Wikipedia it's just a
> way of *implementing* the chain rule cleverly using graph operations.
> Is that what you mean Pauli?
> So it is exact differentiation (to the extent the floating point
> hardware can provide) rather than an approximation such as finite
> differences will yield, and thus the resulting code is equivalent in
> function to what you'd get if you symbolically differentiated and then
> coded it up, is that right?
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