Worst-case optimal approximation with increasingly flat Gaussian kernels

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2020-03-06
Major/Subject
Mcode
Degree programme
Language
en
Pages
17
Series
ADVANCES IN COMPUTATIONAL MATHEMATICS, Volume 46, issue 2
Abstract
We study worst-case optimal approximation of positive linear functionals in reproducing kernel Hilbert spaces induced by increasingly flat Gaussian kernels. This provides a new perspective and some generalisations to the problem of interpolation with increasingly flat radial basis functions. When the evaluation points are fixed and unisolvent, we show that the worst-case optimal method converges to a polynomial method. In an additional one-dimensional extension, we allow also the points to be selected optimally and show that in this case convergence is to the unique Gaussian quadrature–type method that achieves the maximal polynomial degree of exactness. The proofs are based on an explicit characterisation of the reproducing kernel Hilbert space of the Gaussian kernel in terms of exponentially damped polynomials.
Description
Keywords
Gaussian kernel, Gaussian quadrature, Reproducing kernel Hilbert spaces, Worst-case analysis
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Citation
Karvonen , T & Särkkä , S 2020 , ' Worst-case optimal approximation with increasingly flat Gaussian kernels ' , Advances in Computational Mathematics , vol. 46 , no. 2 , 21 . https://doi.org/10.1007/s10444-020-09767-1