Jacobi-Davidson method on low-rank matrix manifolds

2017/03/27 by Rakhuba, Maxim, Oseledets, Ivan · 1 citation
#15A18 #15A69 #53B21 #65F15 #FOS: Mathematics #Numerical Analysis (math.NA)

paper · doi:10.48550/arxiv.1703.09096

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.

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