[SciPy-dev] eig, eigh, and symeig in scipy
Dominique Orban
dominique.orban@gmail....
Tue Oct 28 15:46:53 CDT 2008
On Tue, Oct 28, 2008 at 3:12 PM, Nils Wagner
<nwagner@iam.uni-stuttgart.de> wrote:
> On Tue, 28 Oct 2008 14:19:45 -0400
> "Nathan Bell" <wnbell@gmail.com> wrote:
>> On Tue, Oct 28, 2008 at 2:03 PM, Robert Kern
>><robert.kern@gmail.com> wrote:
>>>
>>> Not sure. My uses for them were statistical in nature,
>>>not just for
>>> generating arbitrary-within-constraints test cases. I
>>>guess I'd prefer
>>> them to live "primarily" in linalg over gallery. A
>>>gallery package
>>> would be useful, though.
>>>
>>
>>Fair enough. We should return to this question after 0.7
>>is released.
>>
> I look forward to seeing the improvements in the trunk.
>
> The following tickets are inherently connected
>
> http://projects.scipy.org/scipy/scipy/ticket/632
> http://projects.scipy.org/scipy/scipy/ticket/630
> http://projects.scipy.org/scipy/scipy/ticket/456
>
> Cheers,
> Nils
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>
Sorry if I'm catching this conversation a bit late and sorry if this
has been mentioned before. I'm wondering whether dense linear algebra
users would not be better served if all LAPACK and BLAS-related
interfaces were in Numpy instead of Scipy. After all, it is Numpy that
defines the array and dense matrix types. Why not make sure that Numpy
provides a complete and consistent interface to all BLAS 1, 2, 3 and
LAPACK functions?
I mention this for several reasons:
1. (as mentioned earlier), there are inconsistencies between the Numpy
and Scipy interfaces to LAPACK. It seems to me that a Numpy user is
also a good candidate to be a dense linear algebra user.
2. In my experience, Scipy is substantially more complex to install
than Numpy. As a user, if I can have access to all my dense linear
algebra in Numpy, it can save me time and trouble, especially if I
don't need the more advanced packages that Scipy offers.
3. As a wild guess, this might also avoid work duplication. I see that
other packages define new dense matrix types and BLAS and LAPACK
interfaces (such as cvxopt). Might it be because they don't want to
require from their users that they install Scipy?
Also, is there a profound reason to have a trimmed down version of
LAPACK only in Numpy?
Thanks for any comments that might help me understand all this.
--
Dominique
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