[SciPy-Dev] scipy.sparse.linalg.svds

[Ilhan Polat]

Jake Vanderplas also followed up on Fabian's post around those times. For
reference, https://jakevdp.github.io/blog/2012/12/19/sparse-svds-in-python/

My guess is that PROPACK's license was ambiguous for a long time while
sparse library was being implemented and getting mature. There are more
experienced devs that can comment on that.

But maybe during the numpy.matrix revamp (
https://github.com/scipy/scipy/issues/8162) we can consider involving
PROPACK alternatives.


On Fri, Jun 15, 2018 at 1:55 AM, Robert Lucente <rlucente at pipeline.com>
wrote:

> https://docs.scipy.org/doc/scipy/reference/generated/
> scipy.sparse.linalg.svds.html
>
> states "This is a naive implementation using ARPACK as an eigensolver on
> A.H * A or A * A.H, depending on which one is more efficient."
>
> One of my co-workers is struggling to find an implementation for SVD for a
> sparse matrix. I was thinking of suggesting that he look at SciPy. The
> phrase "naive implementation" has made me think twice about making the
> suggestion. I just don't want the first intro of Python/SciPy into the
> organization to be a negative one.
>
> I poked around and found the following 2 articles
>
> http://fa.bianp.net/blog/2012/singular-value-decomposition-in-scipy/
>
> https://simplyml.com/benchmarking-the-singular-value-decomposition/
>
> I realize that I should be suspicious of bench marks and that you have to
> run your own.
>
> I would like to provide my co-worker the link to SciPy and then to 2
> articles which can help guide him in creating his own bench mark. Any
> suggestions would be appreciated.
>
> Please note that I am not an expert in SVD.
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at python.org
> https://mail.python.org/mailman/listinfo/scipy-dev
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scipy-dev/attachments/20180616/6c3c8125/attachment.html>