Take the 2-minute tour ×
MathOverflow is a question and answer site for professional mathematicians. It's 100% free, no registration required.

What are the standard methods of computing the rank-k truncated SVD of large dense matrices? My literature search yields results only for large sparse matrices.

I assume for k small that you use a Krylov subspace method (this is what Matlab's svds does). But (empirically) how large can k get before these methods become impractical, and then what should one resort to?

share|improve this question
    
Not sure why this had two downvotes... –  Yemon Choi Mar 15 '13 at 7:12
    

1 Answer 1

When dense matrix is so large that Krylov-based techniques become impractical, one have to rely on certain specific of a matrix to compute its low-rank approximation. Because different matrices have different properties, I doubt there are any standard approaches, which work as a silver bullet.

However, you can try to compute a low-rank approximation of your matrix by a cross interpolation: http://dx.doi.org/10.1007/s006070070031 http://www.researchgate.net/publication/251735015_How_to_find_a_good_submatrix/file/e0b49527254918b220.pdf

share|improve this answer

Your Answer

 
discard

By posting your answer, you agree to the privacy policy and terms of service.

Not the answer you're looking for? Browse other questions tagged or ask your own question.