On the convergence of generalized kernel-based interpolation by greedy data selection algorithms
Numerical Analysis
2024-11-26 v2 Numerical Analysis
Abstract
We analyze the convergence of generalized kernel-based interpolation methods. This is done under minimalistic assumptions on both the kernel and the target function. On these grounds, we further prove convergence of popular greedy data selection algorithms for totally bounded sets of sampling functionals. Supporting numerical results concerning computerized tomography are provided for illustration.
Cite
@article{arxiv.2407.03840,
title = {On the convergence of generalized kernel-based interpolation by greedy data selection algorithms},
author = {Kristof Albrecht and Armin Iske},
journal= {arXiv preprint arXiv:2407.03840},
year = {2024}
}