Work

Smoothed-Adaptive Perturbed Inverse Iteration for Elliptic Eigenvalue Problems

Year: 2021

Type: article

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.

Source: Computational Methods in Applied Mathematics

DOI: 10.1515/cmam-2020-0027

Authors: Stefano Giani, Luka Grubišić, Luca Heltai, Ornela Mulita

Institutions: Durham University, University of Zagreb, Scuola Internazionale Superiore di Studi Avanzati, Humboldt-Universität zu Berlin

Language: English

Topic: Advanced Numerical Methods in Computational Mathematics

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: Hrvatska Zaklada za Znanost

Award: Randomized low rank algorithms and applications to parameter dependent problems