Prefer a chat interface with context about you and your work?
Approximating Matrix Eigenvalues by Subspace Iteration with Repeated Random Sparsification
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 …