[SciPy-user] [ANN][Automatic Differentiation] Beta Version of PYADOLC
Tue May 12 09:35:10 CDT 2009
the topic of automatic differentiation is very interesting for me (also
in light of my announcement here not so long ago...). Does ADOL-C derive
the code so that analytical formulas for the jacobians are obtained, or
does it use some finite differencing scheme? I am not familiar with AD,
so pardon my ignorance.
Sebastian Walter wrote:
> I am pleased to announce the release of PYADOLC (beta version).
> Homepage: http://github.com/b45ch1/pyadolc/
> For download and instructions check the homepage.
> About the package
> PYADOLC is a wrapper of the C++ software ADOL-C.
> It computes derivatives of arbitrarily complex algorithms (with loops
> and if then else) efficiently on the C++ side.
> 0) easy and pythonic user interface
> 1) efficient computation of _gradients_ g, _Hessians_ H and _higher_
> order tensors T
> 2) efficient computation of products dot(u.T, H), dot(H,v) as they
> are needed in optimization algorithms
> 3) well documented by docstrings. For more information one can read
> the C++ documentation.
> 4) extensive unit test and many examples, including constrained
> optimization by projected gradients, etc ...
> 5) should be suitable for derivative generation of rather large scale
> optimization problems.
> E.g. optimal control problems, inverse problems, This is not tested though.
> 6) Sparse Jacobian support.
> Suggestions and Bugs:
> Please report any bugs or inconveniences that you encounter!
> E.g. just write me if you have troubles with the installation.
> Everything *should* work as you expect.
> Sparse Jacobian support is experimental and the build process needs a
> little user assistance but should work.
> The API is not completely fixed. However, changes to the API will be
> backward compatible.
> Hope someone can make use of it.
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