Approximating Matrix Eigenvalues by Subspace Iteration with Repeated Random Sparsification

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