A Jacobi-Davidson type SVD method

doi:10.1137/S1064827500372973

2001-01-01

Michiel E. Hochstenbach *

DOI：10.1137/S1064827500372973

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摘要

En 中文

We discuss a new method for the iterative computation of a portion of the singular values and vectors of a large sparse matrix. Similar to the Jacobi Davidson method for the eigenvalue problem, we compute in each step a correction by (approximately) solving a correction equation. We give a few variants of this Jacobi Davidson SVD (JDSVD) method with their theoretical properties. It is shown that the JDSVD can be seen as an accelerated (inexact) Newton scheme. We experimentally compare the method with some other iterative SVD methods.

Keyword:

Jacobi-Davidson

singular value decomposition ( SVD)

singular values

singular vectors

norm

augmented matrix

correction equation

(inexact) accelerated Newton

improving singular values

期刊

SIAM Journal on Scientific Computing

IF:

2.6

论文数:

5.1K

被引数:

1.8W

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暂无机构信息

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