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

2024/07/04 by Kristof Albrecht, Armin Iske, Albrecht, Kristof +1 · 1 citation
Computer Science · Engineering · #Image and Signal Denoising Methods #Advanced Image Processing Techniques #Advanced Numerical Analysis Techniques

paper · pdf · doi:10.48550/arxiv.2407.03840

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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