Smoothed-adaptive perturbed inverse iteration for elliptic eigenvalue problems
Numerical Analysis
2021-04-13 v1 Numerical Analysis
Abstract
We present a perturbed subspace iteration algorithm to approximate the lowermost eigenvalue cluster of an elliptic eigenvalue problem. As a prototype, we consider the Laplace eigenvalue problem posed in a polygonal domain. The algorithm is motivated by the analysis of inexact (perturbed) inverse iteration algorithms in numerical linear algebra. We couple the perturbed inverse iteration approach with mesh refinement strategy based on residual estimators. We demonstrate our approach on model problems in two and three dimensions.
Cite
@article{arxiv.2102.06818,
title = {Smoothed-adaptive perturbed inverse iteration for elliptic eigenvalue problems},
author = {Stefano Giani and Luka Grubišić and Luca Heltai and Ornela Mulita},
journal= {arXiv preprint arXiv:2102.06818},
year = {2021}
}
Comments
30 pages, 15 figures