Sampling based approximation of linear functionals in Reproducing Kernel Hilbert Spaces

Abstract

In this paper we analyze a greedy procedure to approximate a linear functional defined in a Reproducing Kernel Hilbert Space by nodal values. This procedure computes a quadrature rule which can be applied to general functionals, including integration functionals. For a large class of functionals, we prove convergence results for the approximation by means of uniform and greedy points which generalize in various ways several known results. A perturbation analysis of the weights and node computation is also discussed. Beyond the theoretical investigations, we demonstrate numerically that our algorithm is effective in treating various integration densities, and that it is even very competitive when compared to existing methods for Uncertainty Quantification.

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Cited by in corpus (1)

• Analysis of target data-dependent greedy kernel algorithms: Convergence rates for $f$-, $f\cdot P$- and $f/P$-greedy