An inexact Newton-Krylov method for stochastic eigenvalue problems
For researchers solving stochastic eigenvalue problems, this method offers a more efficient approach by leveraging tensor structure, though it is an incremental improvement over existing techniques.
The paper develops a globalized low-rank inexact Newton method for stochastic eigenvalue problems, exploiting tensor product structure to reduce computational cost. Numerical experiments demonstrate effectiveness, but no concrete performance numbers are provided.
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.