# Worst-case optimal approximation with increasingly flat Gaussian kernels

## TL;DR

This paper investigates the optimal approximation of positive linear functionals in Gaussian kernel-induced spaces, revealing convergence to polynomial and Gaussian quadrature methods as the kernel becomes flatter.

## Contribution

It introduces a new perspective on approximation with flat Gaussian kernels and generalizes the interpolation problem, including optimal point selection and convergence analysis.

## Key findings

- Convergence to polynomial methods with fixed points.
- Extension to optimal point selection leading to Gaussian quadrature.
- Explicit characterization of the RKHS via damped polynomials.

## 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.