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arXiv · 2103.12109

Approximating matrix eigenvalues by subspace iteration with repeated random sparsification

Samuel M. Greene·Robert J. Webber·Timothy C. Berkelbach·Jonathan Weare
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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.

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Samuel M. Greene, Robert J. Webber, Timothy C. Berkelbach, Jonathan Weare. 2021-03-22. Approximating matrix eigenvalues by subspace iteration with repeated random sparsification. https://doi.org/10.1137/21m1422513

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