What's the reason that you don't want to use Julia's SVD directly? Is your matrix especially large? Some links that may be useful:
Approximating the SVD using Julia: http://beowulf.lcs.mit.edu/18.337/projects/Turner-Presentation_SVD-Julia.pdf https://github.com/alexjturner/SVDapprox Approximating the SVD using the power method http://www.sci.ccny.cuny.edu/~szlam/npisvdnipsshort.pdf https://en.wikipedia.org/wiki/Power_iteration On Sun, Mar 15, 2015 at 1:10 PM, Erik Schnetter <[email protected]> wrote: > I am looking for a routine that calculate the SVD (singular value > decomposition) of a square, complex, dense, non-symmetric matrix. I am > aware of Julia's SVD routine (and the respective LAPACK routines that one > could call directly). However, I don't need all of the singular values -- I > need only the largest one (or some of the largest ones), as well as the > associated entries of U. Is there such a routine? I didn't find one in > Julia. > > I've looked for other packages that could be wrapped, but couldn't find > any that offers this feature. The only thing I found is a description of > the "svds" Matlab routine, which is apparently based on "eigs". > > -erik > > -- > Erik Schnetter <[email protected]> > http://www.perimeterinstitute.ca/personal/eschnetter/ > > My email is as private as my paper mail. I therefore support encrypting > and signing email messages. Get my PGP key from https://sks-keyservers.net > . > > -- [email protected]
