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Worst-case optimal approximation with increasingly flat Gaussian kernels

Published: 06 March 2020 Publication History

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.

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Cited By

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  • (2023)Exponential tractability of L2-approximation with function valuesAdvances in Computational Mathematics10.1007/s10444-023-10021-749:2Online publication date: 7-Mar-2023

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Published In

cover image Advances in Computational Mathematics
Advances in Computational Mathematics  Volume 46, Issue 2
Apr 2020
725 pages

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 06 March 2020
Accepted: 24 January 2020
Received: 05 June 2019

Author Tags

  1. Worst-case analysis
  2. Reproducing kernel Hilbert spaces
  3. Gaussian kernel
  4. Gaussian quadrature

Author Tags

  1. 41A05
  2. 41A30
  3. 46E22
  4. 65D05
  5. 65D32

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  • Research-article

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  • Academy of Finland
  • Aalto University

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  • (2023)Exponential tractability of L2-approximation with function valuesAdvances in Computational Mathematics10.1007/s10444-023-10021-749:2Online publication date: 7-Mar-2023

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