Hi All

My question is about the well known and well studied singular value decomposition (SVD). What I am working on right now requires performing an SVD repeatedly on a slowly varying matrix. Since I don't need an exact decomposition at every iteration, I was wondering whether there are any statistical estimations of the SVD. I found a paper on estimating the maximal eigenvalue of a matrix with non-negative values using Markov chains (http://www.maik.ru/full/rusmath/97/5/rusmath5_97p46full.pdf). Are there any more general statistical estimation techniques?

Thanks

ab initioper iteration), but without knowing how your matrix changes, I wouldn't know if they're applicable. – J. M. Dec 27 '10 at 3:50