Sharp inverse statements for kernel approximation: Superconvergence and saturation

Fuente: arXiv
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Autor principal: Wenzel, Tizian
Formato: Preprint
Publicado: 2026
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author Wenzel, Tizian
author_facet Wenzel, Tizian
contents This article establishes sharp inverse and saturation statements for kernel-based approximation using finitely smooth Sobolev kernels on bounded Lipschitz regions. The analysis focuses on the superconvergence regime, for which direct statements have only recently been obtained. The resulting theory yields a one-to-one correspondence between the smoothness of a target function - quantified in terms of power spaces - and the achievable approximation rates by kernel-based approximation. In this way, we extend existing results beyond the escaping-the-native-space regime and provide a unified characterization covering the full scale of admissible smoothness spaces.
format Preprint
id arxiv_https___arxiv_org_abs_2601_01808
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Sharp inverse statements for kernel approximation: Superconvergence and saturation
Wenzel, Tizian
Numerical Analysis
This article establishes sharp inverse and saturation statements for kernel-based approximation using finitely smooth Sobolev kernels on bounded Lipschitz regions. The analysis focuses on the superconvergence regime, for which direct statements have only recently been obtained. The resulting theory yields a one-to-one correspondence between the smoothness of a target function - quantified in terms of power spaces - and the achievable approximation rates by kernel-based approximation. In this way, we extend existing results beyond the escaping-the-native-space regime and provide a unified characterization covering the full scale of admissible smoothness spaces.
title Sharp inverse statements for kernel approximation: Superconvergence and saturation
topic Numerical Analysis
url https://arxiv.org/abs/2601.01808