  Traditional numerical methods for calculating matrix eigenvalues are
prohibitively expensive for high-dimensional problems. Randomized iterative
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 problems in quantum chemistry with matrices
as large as 28 million by 28 million.
