Fully computable a posteriori error bounds for eigenfunctions

0561025 - MÚ 2023 RIV DE eng J - Journal Article
Liu, X. - Vejchodský, Tomáš
Fully computable a posteriori error bounds for eigenfunctions.
Numerische Mathematik. Roč. 152, č. 1 (2022), s. 183-221. ISSN 0029-599X. E-ISSN 0945-3245
R&D Projects: GA ČR(CZ) GA20-01074S
Institutional support: RVO:67985840
Keywords : eigenvalue problems * Laplace eigenvalues * approximation
OECD category: Pure mathematics
Impact factor: 2.1, year: 2022
Method of publishing: Limited access
https://doi.org/10.1007/s00211-022-01304-0

For compact self-adjoint operators in Hilbert spaces, two algorithms are proposed to provide fully computable a posteriori error estimate for eigenfunction approximation. Both algorithms apply well to the case of tight clusters and multiple eigenvalues, under the settings of target eigenvalue problems. Algorithm I is based on the Rayleigh quotient and the min-max principle that characterizes the eigenvalue problems. The formula for the error estimate provided by Algorithm I is easy to compute and applies to problems with limited information of Rayleigh quotients. Algorithm II, as an extension of the Davis–Kahan method, takes advantage of the dual formulation of differential operators along with the Prager–Synge technique and provides greatly improved accuracy of the estimate, especially for the finite element approximations of eigenfunctions. Numerical examples of eigenvalue problems of matrices and the Laplace operators over convex and non-convex domains illustrate the efficiency of the proposed algorithms.
Permanent Link: https://hdl.handle.net/11104/0333781