[SciPy-user] [ANN][Automatic Differentiation] Beta Version of PYADOLC

David Warde-Farley dwf@cs.toronto....
Tue May 12 15:25:21 CDT 2009

On 12-May-09, at 12:41 PM, Pauli Virtanen wrote:

> AD typically builds an "implicit" graph expression corresponding to  
> the
> 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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