Sponsor
Gopalakrishnan was supported in part by the National Science Foundation under grant DMS-0713833 and by the IMA.
Published In
SIAM Journal on Numerical Analysis
Document Type
Post-Print
Publication Date
2010
Subjects
Hybridization -- Molecular aspects, Approximation theory, Eigenfunctions
Abstract
We introduce hybridization and postprocessing techniques for the Raviart–Thomas approximation of second-order elliptic eigenvalue problems. Hybridization reduces the Raviart–Thomas approximation to a condensed eigenproblem. The condensed eigenproblem is nonlinear, but smaller than the original mixed approximation. We derive multiple iterative algorithms for solving the condensed eigenproblem and examine their interrelationships and convergence rates. An element-by-element postprocessing technique to improve accuracy of computed eigenfunctions is also presented. We prove that a projection of the error in the eigenspace approximation by the mixed method (of any order) superconverges and that the postprocessed eigenfunction approximations converge faster for smooth eigenfunctions. Numerical experiments using a square and an L-shaped domain illustrate the theoretical results.
DOI
10.1137/090765894
Persistent Identifier
http://archives.pdx.edu/ds/psu/10654
Citation Details
Cockburn, Bernardo; Gopalakrishnan, Jay; Li, F.; Nguyen, Ngoc Cuong; and Peraire, Jaume, "Hybridization and Postprocessing Techniques for Mixed Eigenfunctions" (2010). Mathematics and Statistics Faculty Publications and Presentations. 52.
http://archives.pdx.edu/ds/psu/10654
Description
This is an Author's Accepted Manuscript. First Published in SIAM Journal on Numerical Analysis in volume 48 and Issue 3, published by the Society of Industrial and Applied Mathematics (SIAM) . Copyright © by SIAM. Unauthorized reproduction of this article is prohibited.