I am looking for an algorithm that produces only some of the singular values, not all of them. I assume that would be cheaper. I didn't see such a function in Julia's documentation.
-erik > On Mar 15, 2015, at 18:22 , Christian Peel <[email protected]> wrote: > > 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] -- 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.
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