I am encountering a problem with the svd solver of the KylovKit.jl package. I am trying to compute the SVD of a 90000X90000 sparse matrix, with a fraction of 2.4E-4 elements non-zero. However, the following execution using KrylovKit; K=deserialize(open( "K.dat" )); Lambda,Phi,Gamma,info = KrylovKit…

I was under the impression that it is sufficient for the krylov dimension needs to be 2L+1, where L is the number of singular values/vectors required. I will try out with the verbosity command. Can you suggest any other tried and tested package. I had earlier reported a problem / bug with the svds…

KrylovKit svd solver not running properly

New to Julia

iamsuddhasattwa September 17, 2019, 9:17pm 12

Thank you. I am having some problems with this package, which I have posted on a separate thread

Topic		Replies	Views
KrylovKit.svdsolve() terminating too early, with non-orthogonal singular vectors Performance	0	345	December 15, 2021
KrylovKit svdsolve not working New to Julia	3	97	November 24, 2024
[ANN] KrylovKit: Community announcement	8	1676	September 7, 2018
SVD solver for Sparse Matrices General Usage question	7	1000	October 4, 2022
Non-orthogonality in SVD solver! General Usage	0	336	January 21, 2021

KrylovKit svd solver not running properly

Related topics