On the convergence of generalized kernel-based interpolation by greedy data selection algorithms

2024/12/27 by Kristof Albrecht, Armin Iske · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Image and Signal Denoising Methods #Model Reduction and Neural Networks #Numerical methods in inverse problems

paper · pdf · doi:10.1007/s10543-024-01048-3

crossref issued 2024/12/27 · crossref published 2024/12/27 · crossref published-online 2024/12/27 · openalex publication_date 2024/12/27 · crossref created 2024/12/27 · crossref published-print 2025/03/01 · crossref deposited 2025/03/20 · openalex created_date 2025/10/10 · crossref indexed 2026/07/29 · openalex updated_date 2026/08/04

Abstract

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.

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• Kernel-based Greedy Approximation of Parametric Elliptic Boundary Value Problems

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