Work

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

Year: 2024

Type: article

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.

Source: BIT Numerical Mathematics

DOI: 10.1007/s10543-024-01048-3

Authors: Kristof Albrecht, Armin Iske

Institution: Universität Hamburg

Language: English

Topic: Mathematical Approximation and Integration

Subfield: Numerical Analysis

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,090

Funders: Deutsche Forschungsgemeinschaft, Universität Hamburg