Towards stability results for global radial basis function based quadrature formulas

TL;DR

This paper advances the stability theory of radial basis function-based quadrature formulas, providing new conditions for stability and proposing a least-squares method to enhance stability without altering the shape parameter.

Contribution

It introduces novel stability conditions for global RBF quadrature formulas and proposes a least-squares approach to improve stability independent of polynomial terms.

Findings

Stability of compactly supported RBF-QFs under specific conditions.

Least-squares method enables stable RBF-QFs with more data points than centers.

Asymptotic stability is independent of polynomial terms in many cases.

Abstract

Quadrature formulas (QFs) based on radial basis functions (RBFs) have become an essential tool for multivariate numerical integration of scattered data. Although numerous works have been published on RBF-QFs, their stability theory can still be considered as underdeveloped. Here, we strive to pave the way towards a more mature stability theory for global and function-independent RBF-QFs. In particular, we prove stability of these for compactly supported RBFs under certain conditions on the shape parameter and the data points. As an alternative to changing the shape parameter, we demonstrate how the least-squares approach can be used to construct stable RBF-QFs by allowing the number of data points used for numerical integration to be larger than the number of centers used to generate the RBF approximation space. Moreover, it is shown that asymptotic stability of many global RBF-QFs is independent of polynomial terms, which are often included in RBF approximations. While our findings provide some novel conditions for stability of global RBF-QFs, the present work also demonstrates that there are still many gaps to fill in future investigations.