[SciPy-dev] Abstract vectors in optimization

[David Cournapeau]

Robert Kern wrote:
> On Mon, Jan 5, 2009 at 23:27, David Cournapeau
> <david at ar.media.kyoto-u.ac.jp> wrote:
>> I want to ask: why would you *not* want to use numpy ? What does it
>> bring to you ?
>
> If you want to implement optimization in a curved space with a
> non-Euclidean metric (say in the space of correlation matrices or
> SO(3)), you usually can't just use numpy arrays in the optimization
> implementations that we have. You really do need to rewrite the
> algorithm in terms of the abstract operations. For example,
>
> http://www-math.mit.edu/~lippert/sgmin.html

Sure, I did not mean more abstract are never useful, I understood the op
question as why not writing the whole scipy without numpy. For more
specific algorithms, I don't see much difference between "abstract
arrays" for optimization and another kind of classes - unless you need
the whole numpy interface and linear algebra ?

David

P.S: Thanks for the link, BTW, I did not know about this work, it looks
quite interesting; did you implement some of the methods mentioned in
the paper "Nonlinear Eigenvalue Problems With Orthogonality Constraints
<http://www-math.mit.edu/%7Elippert/research/sgchapter.ps.gz>" ?