Dr Stefano Giani stefano.giani@durham.ac.uk
Associate Professor
Robust error estimates for approximations of non-self-adjoint eigenvalue problems
Giani, S.; Grubišić, L.; Międlar, A.; Ovall, J.
Authors
L. Grubišić
A. Międlar
J. Ovall
Abstract
We present new residual estimates based on Kato’s square root theorem for spectral approximations of non-self-adjoint differential operators of convection–diffusion–reaction type. It is not assumed that the eigenvalue/vector approximations are obtained from any particular numerical method, so these estimates may be applied quite broadly. Key eigenvalue and eigenvector error results are illustrated in the context of an hp-adaptive finite element algorithm for spectral computations, where it is shown that the resulting a posteriori error estimates are reliable. The efficiency of these error estimates is also strongly suggested empirically.
Citation
Giani, S., Grubišić, L., Międlar, A., & Ovall, J. (2016). Robust error estimates for approximations of non-self-adjoint eigenvalue problems. Numerische Mathematik, 133(3), 471-495. https://doi.org/10.1007/s00211-015-0752-3
Journal Article Type | Article |
---|---|
Acceptance Date | Jun 1, 2015 |
Online Publication Date | Jul 9, 2015 |
Publication Date | Jul 1, 2016 |
Deposit Date | Jun 22, 2015 |
Publicly Available Date | Mar 29, 2024 |
Journal | Numerische Mathematik |
Print ISSN | 0029-599X |
Electronic ISSN | 0945-3245 |
Publisher | Springer |
Peer Reviewed | Peer Reviewed |
Volume | 133 |
Issue | 3 |
Pages | 471-495 |
DOI | https://doi.org/10.1007/s00211-015-0752-3 |
Keywords | 65N30, 65N25, 65N15. |
Files
Accepted Journal Article
(703 Kb)
PDF
Copyright Statement
The final publication is available at Springer via http://dx.doi.org/10.1007/s00211-015-0752-3.
You might also like
An hp-adaptive discontinuous Galerkin method for phase field fracture
(2023)
Journal Article
Convolutional neural network framework for wind turbine electromechanical fault detection
(2023)
Journal Article
On Effects of Concentrated Loads on Perforated Sensitive Shells of Revolution
(2023)
Journal Article
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search