  This paper aims at the efficient numerical solution of stochastic eigenvalue
problems. Such problems often lead to prohibitively high dimensional systems
with tensor product structure when discretized with the stochastic Galerkin
method. Here, we exploit this inherent tensor product structure to develop a
globalized low-rank inexact Newton method with which we tackle the stochastic
eigenproblem. We illustrate the effectiveness of our solver with numerical
experiments.
