Approximating matrix eigenvalues by subspace iteration with repeated random sparsification
Numerical Analysis
2023-10-03 v3 Strongly Correlated Electrons
Numerical Analysis
Chemical Physics
Abstract
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.
Cite
@article{arxiv.2103.12109,
title = {Approximating matrix eigenvalues by subspace iteration with repeated random sparsification},
author = {Samuel M. Greene and Robert J. Webber and Timothy C. Berkelbach and Jonathan Weare},
journal= {arXiv preprint arXiv:2103.12109},
year = {2023}
}
Comments
31 pages, 8 figures