We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.

Durham Research Online
You are in:

Smoothed-Adaptive Perturbed Inverse Iteration for Elliptic Eigenvalue Problems

Giani, S. and Grubišić, L. and Heltai, L. and Mulita, O. (2021) 'Smoothed-Adaptive Perturbed Inverse Iteration for Elliptic Eigenvalue Problems.', Computational methods in applied mathematics., 21 (2). pp. 385-405.


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.

Item Type:Article
Full text:(AM) Accepted Manuscript
Download PDF
Publisher Web site:
Date accepted:16 February 2021
Date deposited:23 February 2021
Date of first online publication:12 March 2021
Date first made open access:12 March 2022

Save or Share this output

Look up in GoogleScholar