[SciPy-Dev] SVDLIBC for sparse SVDs

Fabian Pedregosa fabian.pedregosa@inria...
Wed Dec 12 06:50:45 CST 2012

On Mon, Dec 10, 2012 at 7:29 PM, Jake Vanderplas <
vanderplas@astro.washington.edu> wrote:

> Hi folks,
> I just came across a sparse svd implementation based on SVDLIBC [1] with
> a nice python wrapper utilizing Scipy's csc_matrix type [2]. Scipy
> currently includes a basic iterative sparse svd implementation based on
> ARPACK (scipy.sparse.linalg.svds), but the implementation is somewhat
> hackish.  The SVDLIBC version uses the same principles as ARPACK --
> Lanczos factorization -- and from my quick checks, can be faster than
> the ARPACK version in some cases.  All the code, including python
> wrappers, is released under a BSD license, so it would be fairly
> seamless to include in Scipy.
> On the plus side, incorporating SVDLIBC would add some well-tested
> sparse functionality and gives users more powerful options.  Where our
> current svds function performs iterations within python, the SVDLIBC
> implementation performs the iterations directly within the C code.  It
> uses the csc_matrix format internally, so no data copying is involved.
> It could fairly easily supplement or replace our current sparse svd.

I used this routine for the passed weeks. I frankly saw no improvements in
performance over the current ARPACK implementation, plus I found it
annoying to having to explicitly convert to CSC. Also, the current bindings
do not provide any optional parameters such as tolerance or maxiter.

Part of my applications is large dense matrices, and in that case
converting to CSC kills performance, loosing a factor 2-3 over ARPACK.  But
I'd be interested to see if it has practical advantages (stability?
accuracy?) over ARPACK.


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