Jacobi-Davidson method on low-rank matrix manifolds
Numerical Analysis
2017-03-28 v1
Abstract
In this work we generalize the Jacobi-Davidson method to the case when eigenvector can be reshaped into a low-rank matrix. In this setting the proposed method inherits advantages of the original Jacobi-Davidson method, has lower complexity and requires less storage. We also introduce low-rank version of the Rayleigh quotient iteration which naturally arises in the Jacobi-Davidson method.
Cite
@article{arxiv.1703.09096,
title = {Jacobi-Davidson method on low-rank matrix manifolds},
author = {Maxim Rakhuba and Ivan Oseledets},
journal= {arXiv preprint arXiv:1703.09096},
year = {2017}
}
Comments
18 pages, 7 figures