Automatic differentiation (was Re: second-order gradient)
Maybe we should focus on writing a decent 'deriv' function then. I know Konrad Hinsen's Scientific had a derivatives package (Scientific.Functions.Derivatives) that implemented automatic differentiation:
That would be great, but wouldn't that be best suited as a utility requiring Sympy? You'll want to take advantage of all sorts of symbolic classes, especially for any source code transformation approach. IMO Hinsen's implementation isn't a very efficient or attractive solution to AD given the great existing C/C++ codes out there. Maybe we should be looking to provide a python interface to an existing open source package such as ADOL-C, but I'm all in favour of a new pure python approach too. What would be perfect is to have a single interface to a python AD package that would support a faster implementation if the user wished to install a C/C++ package, otherwise would default to a pure python equivalent. -Rob
2008/10/30 Rob Clewley
That would be great, but wouldn't that be best suited as a utility requiring Sympy? You'll want to take advantage of all sorts of symbolic classes, especially for any source code transformation approach. IMO Hinsen's implementation isn't a very efficient or attractive solution to AD given the great existing C/C++ codes out there. Maybe we should be looking to provide a python interface to an existing open source package such as ADOL-C, but I'm all in favour of a new pure python approach too. What would be perfect is to have a single interface to a python AD package that would support a faster implementation if the user wished to install a C/C++ package, otherwise would default to a pure python equivalent.
In your experience, is this functionality enough to start a separate package, or should we try to include it somewhere else? Otherwise we could think of a new SciKit. Regards Stéfan
In your experience, is this functionality enough to start a separate package, or should we try to include it somewhere else? Otherwise we could think of a new SciKit.
I confess to knowing no details about scikits so I don't know what the difference really is between a "new package" and a scikit. To do this properly you'd end up with a sizable body of code, and the potential dependency on Sympy would also suggest making it somewhat separate. I'd defer to others on that point, although I don't really see what other package it would naturally fit with because AD has multiple applications. -Rob
Hi, I just saw this python package : PyDX which may answer your needs. The original URL is not working, but the svn location exists. http://gr.anu.edu.au/svn/people/sdburton/pydx/doc/user-guide.html svn co http://gr.anu.edu.au/svn/people/sdburton/pydx br -osman
There are several possibilities, some of them are listed on
http://en.wikipedia.org/wiki/Automatic_differentiation
== pycppad
http://www.seanet.com/~bradbell/pycppad/index.xml
pycppad is a wrapper of the C++ library CppAD ( http://www.coin-or.org/CppAD/ )
the wrapper can do up to second order derivatives very efficiently in
the so-called reverse mode of AD
requires boost::python
== pyadolc
http://github.com/b45ch1/pyadolc
which is a wrapper for the C++ library ADOL-C (
http://www.math.tu-dresden.de/~adol-c/ )
this can do abritrary degree of derivatives and works quite well with
numpy, i.e. you can work with numpy arrays
also quite efficient in the so-called reverse mode of AD
requires boost::python
== ScientificPython
http://dirac.cnrs-orleans.fr/ScientificPython/ScientificPythonManual/
can provide first order derivatives. But as far as I understand only
first order derivatives of functions
f: R -> R
and only in the usually not so efficient forward mode of AD
pure python
== Algopy
http://github.com/b45ch1/algopy/tree/master
pure python, arbitrary derivatives in forward and reverse mode
still quite experimental.
Offers also the possibility to differentiate functions that make heavy
use of matrix operations.
== sympy
this is not automatic differentiation but symbolic differentiation but
is sometimes useful
hope that helps,
Sebastian
On Wed, Mar 11, 2009 at 4:13 AM, Osman
Hi,
I just saw this python package : PyDX which may answer your needs. The original URL is not working, but the svn location exists.
http://gr.anu.edu.au/svn/people/sdburton/pydx/doc/user-guide.html
svn co http://gr.anu.edu.au/svn/people/sdburton/pydx
br -osman
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participants (4)
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Osman -
Rob Clewley -
Sebastian Walter -
Stéfan van der Walt