A note on superconvergence in projection-based numerical approximations of eigenvalue problems for Fredholm integral operators
This work addresses convergence issues in numerical methods for integral operators, which is incremental for computational mathematics and applied analysis.
The paper tackled the eigenvalue problem for Fredholm integral operators by analyzing convergence in numerical approximations, showing that a modified collocation method achieves faster convergence rates for eigenvalues and eigenfunctions, with superconvergence demonstrated in numerical experiments.
This paper studies the eigenvalue problem $K Ï= λÏ$ associated with a Fredholm integral operator $K$ defined by a smooth kernel. The focus is on analyzing the convergence behaviour of numerical approximations to eigenvalues and their corresponding spectral subspaces. The interpolatory projection methods are employed on spaces of piecewise polynomials of even degree, using $2r+1$ collocation points that are not restricted to Gauss nodes. Explicit convergence rates are established, and the modified collocation method attains faster convergence of approximation of eigenvalues and associated eigenfunctions than the classical collocation scheme. Moreover, it is shown that the iteration yields superconvergent approximations of eigenfunctions. Numerical experiments are presented to validate the theoretical findings.