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

被引：14

作者：

Benner, Peter ^{[1
]}

Onwunta, Akwum ^{[1
]}

Stoll, Martin ^{[2
]}

机构：

[1] Max Planck Inst Dynam Complex Tech Syst, Computat Methods Syst & Control Theory, Sandtorstr 1, D-39106 Magdeburg, Germany

[2] Tech Univ Chemnitz, Fac Math, Sci Comp, D-09107 Chemnitz, Germany

来源：

COMPUTATIONAL METHODS IN APPLIED MATHEMATICS | 2019年 / 19卷 / 01期

关键词：

Stochastic Galerkin System; Krylov Methods; Eigenvalues; Eigenvectors; Low-Rank Solution; Preconditioning; FULLY COUPLED SOLUTION; DAVIDSON TYPE METHOD; FORCING TERMS; EQUATIONS; GMRES; PDES;

D O I：

10.1515/cmam-2018-0030

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

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.

引用

页码：5 / 22

页数：18

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