Approximation in Hilbert spaces of the Gaussian and other weighted power series kernels

Toni Karvonen

Abstract: This article considers linear approximation based on function evaluations in reproducing kernel Hilbert spaces of the Gaussian kernel and a more general class of weighted power series kernels on the interval $[-1, 1]$. We derive almost matching upper and lower bounds on the worst-case error, measured both in the uniform and $L^2([-1,1])$-norm, in these spaces. The results show that if the power series kernel expansion coefficients $\alpha_n^{-1}$ decay at least factorially, their rate of decay controls that of the worst-case error. Specifically, (i) the $n$th minimal error decays as $\alpha_n^{{ -1/2}}$ up to a sub-exponential factor and (ii) for any $n$ sampling points in $[-1, 1]$ there exists a linear algorithm whose error is $\alpha_n^{{ -1/2}}$ up to an exponential factor. For the Gaussian kernel the dominating factor in the bounds is $(n!)^{-1/2}$.