Multilevel Interpolation and Approximation

Interpolation by translates of a given radial basis function (RBF) has become a well-recognized means of tting functions sampled at scattered sites in R d. A major drawback of these methods is their inability to interpolate very large data sets in a numerically stable way while maintaining a good t. To circumvent this problem, a multilevel interpolation (ML) method for scattered data was presented by Floater and Iske. Their approach involves m levels of interpolation where at the j th level, the residual of the previous level is interpolated. On each level, the RBF is scaled to match the data density. In this paper, we provide some theoretical underpinnings to the ML method by establishing rates of approximation for a technique that deviates somewhat from the Floater-Iske setting. The nal goal of the ML method will be to provide a numerically stable method for interpolating several thousand points rapidly.