Low-Rank Solution Methods for Stochastic Eigenvalue Problems

Inexact Methods for Symmetric Stochastic Eigenvalue Problems

“…Meidani and Ghanem [22,23] formulated stochastic subspace iteration using a stochastic version of the modified Gram-Schmidt algorithm. Sousedík and Elman [33] introduced stochastic inverse subspace iteration by combining the two techniques, they showed that deflation of the mean matrix can be used to compute expansions of the interior eigenvalues, and they also showed that the stochastic Rayleigh quotient alone provides a good approximation of an eigenvalue expansion; see also [3,4,27] for closely related methods. The authors of [23,33] used a quadrature-based normalization of eigenvectors.…”