On 8 Apr 2013, at 23:21, Andy Cooper <[email protected]> wrote:
> So, no one has direct experience running irlba on a data matrix as large as > 500,000 x 1,000 or larger? I haven't used irlba in production code, but ran a few benchmarks on much smaller matrices. My impression was (also from the documentation, I think) was that irlba is designed for use cases where only a few singular values are needed, up to 10 or so. With 50 singular values, I found randomized SVD to be faster than irlba. If you're working with a dense 500,000 x 1000 matrix, you'll need a lot of RAM. Have you tried the svd() function? Most good BLAS libraries include highly optimised SVD code; if your machine has enough CPU cores, even a high-dimensional SVD might be fast enough. Best, Stefan ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
