[SciPy-Dev] Randomized linear algebra functionality

[Andreas Kloeckner]

Touqir,

Touqir Sajed <touqir at ualberta.ca> writes:
> There seems to be some interest in implementing randomized linear algebra
> algorithms such as JL transformation, low-rank approximation, etc as
> written in  https://github.com/scipy/scipy/wiki/GSoC-2018-project-ideas
> <https://ml-trckr.com/link/https%3A%2F%2Fgithub.com%2Fscipy%2Fscipy%2Fwiki%2FGSoC-2018-project-ideas/cBzmzxqJ6V7Fi58kyipk>
> . To the best of my knowledge, they are not implemented yet. If there still
> exists interests I would like to implement the Fast JL transformation and
> the randomized SVD for low-rank approximation. The pseudo code of Fast JL
> transformation is given here:
> https://pdfs.semanticscholar.org/ccf0/1e0375d11df5335b645161b4833e32380d89.pdf
> <https://ml-trckr.com/link/https%3A%2F%2Fpdfs.semanticscholar.org%2Fccf0%2F1e0375d11df5335b645161b4833e32380d89.pdf/cBzmzxqJ6V7Fi58kyipk>
> . The randomized SVD is based on https://arxiv.org/pdf/0909.4061.pdf
> <https://ml-trckr.com/link/https%3A%2F%2Farxiv.org%2Fpdf%2F0909.4061.pdf/cBzmzxqJ6V7Fi58kyipk>

scipy.linalg.interpolative is in fact based on randomized linear
algebra. In particular, this function:

https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.interpolative.svd.html#scipy.linalg.interpolative.svd

already does implement a randomized SVD with a number of acceleration
bells and whistles.

Andreas
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