Approximating Matrix Eigenvalues by Subspace Iteration with Repeated Random Sparsification
Samuel M. Greene, Robert J. Webber, Timothy C. Berkelbach, Jonathan Weare · SIAM Journal on Scientific Computing · 2022
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.