# Fully computable a posteriori error bounds for eigenfunctions

## TL;DR

This paper introduces two fully computable a posteriori error bounds for eigenfunction approximation of compact self-adjoint operators, applicable to clustered and multiple eigenvalues, with improved accuracy demonstrated through numerical examples.

## Contribution

It proposes two novel algorithms for a posteriori error estimation in eigenfunction approximation, extending existing methods to handle complex eigenvalue problems with enhanced precision.

## Key findings

- Algorithms effectively estimate errors in eigenfunction approximations.
- The methods perform well on matrix and Laplace operator problems.
- Numerical results confirm the efficiency and accuracy of the proposed algorithms.

## Abstract

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.