A Low-Rank Inexact Newton–Krylov Method for Stochastic Eigenvalue Problems-Reference-Cited by-同舟云学术

A Low-Rank Inexact Newton–Krylov Method for Stochastic Eigenvalue Problems

Published:2018-07-21 Issue:1 Volume:19 Page:5-22
ISSN:1609-9389
Container-title:Computational Methods in Applied Mathematics
language:en
Short-container-title:

Author:

Benner Peter¹,Onwunta Akwum¹,Stoll Martin²

Affiliation:

1. Computational Methods in Systems and Control Theory , Max Planck Institute for Dynamics of Complex Technical Systems , Sandtorstr. 1, 39106 Magdeburg , Germany

2. Faculty of Mathematics , Professorship Scientific Computing , Technische Universität Chemnitz , 09107 Chemnitz , Germany

Abstract

Abstract 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.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Computational Mathematics,Numerical Analysis

Reference44 articles.

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