APPROXIMATING MATRIX EIGENVALUES BY SUBSPACE ITERATION WITH REPEATED RANDOM SPARSIFICATION

Samuel M. Greene

doi:10.1137/21M1422513

2022-09-26

Samuel M. Greene *

Robert J. Webber

Timothy C. Berkelbach

Jonathan Weare

DOI：10.1137/21M1422513

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摘要

En 中文

Traditional numerical methods for calculating matrix eigenvalues are prohibitively expensive for high-dimensional problems. Iterative random sparsification methods allow for the estimation of a single dominant eigenvalue at reduced cost by leveraging repeated random sampling and averaging. We present a general approach to extending such methods for the estimation of multiple eigenvalues and demonstrate its performance for several benchmark problems in quantum chemistry.

Keyword:

eigenvalues

subspace iteration

randomized algorithms

Monte Carlo

期刊

SIAM Journal on Scientific Computing

IF:

2.6

论文数:

5.1K

被引数:

1.8W

机构

Columbia University

学者数:

7.1W

论文数: 6.4W

被引数: 263

New York University

学者数:

4.4W

论文数: 3.9W

被引数: 5.8W

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