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@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.
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