Worst-case optimal approximation with increasingly flat Gaussian kernels

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

Tutkijat

Organisaatiot

  • Alan Turing Institute

Kuvaus

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.

Yksityiskohdat

AlkuperäiskieliEnglanti
Artikkeli21
Sivumäärä17
JulkaisuADVANCES IN COMPUTATIONAL MATHEMATICS
Vuosikerta46
Numero2
TilaJulkaistu - 1 huhtikuuta 2020
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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