[R] SVD on very large data matrix

Berend Hasselman bhh at xs4all.nl
Mon Apr 8 21:31:38 CEST 2013

On 08-04-2013, at 16:44, Andy Cooper <andy_cooper83 at yahoo.co.uk> wrote:

> Dear All,
> I need to perform a SVD on a very large data matrix, of dimension ~ 500,000 x 1,000 , and I am looking
> for an efficient algorithm that can perform an approximate (partial) SVD to extract on the order of the top 50
> right and left singular vectors.
> Would be very grateful for any advice on what R-packages are available to perform such a task, what the RAM requirement is, and indeed what would be the state-of-the-art in terms of numerical algorithms and programming
> language to use to accomplish this task.

Info found with package sos and findFn("svd") and scrolling through the list for something relevant.

Have a look at package irlba.
It can work with dense matrices and sparse matrices as provided by package Matrix, according to the documentation.


More information about the R-help mailing list