[SciPy-user] [ANN][Automatic Differentiation] Beta Version of PYADOLC
Wed May 13 03:15:29 CDT 2009
On Tue, May 12, 2009 at 10:33 PM, Rob Patro <firstname.lastname@example.org> wrote:
> 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/.
The tutorials are very nice indeed: concise, informative and an
interesting read in general.
> 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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