Work

Approximating Matrix Eigenvalues by Subspace Iteration with Repeated Random Sparsification

Year: 2022

Type: article

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.

Source: SIAM Journal on Scientific Computing

DOI: 10.1137/21m1422513

Authors: Samuel M. Greene, Robert J. Webber, Timothy C. Berkelbach, Jonathan Weare

Institutions: Columbia University, Courant Institute of Mathematical Sciences, New York University, Flatiron Institute

Language: English

Topic: Matrix Theory and Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid