Univariate interpolation by exponential functions and gaussian RBFs for generic sets of nodes

We consider interpolation of univariate functions on arbitrary sets of nodes by Gaussian radial basis functions or by exponential functions. We derive closed-form expressions for the interpolation error based on the Harish-Chandra-Itzykson-Zuber formula. We then prove the exponential convergence of interpolation for functions analytic in a sufficiently large domain. As an application, we prove the global exponential convergence of optimization by expected improvement for such functions.Comment: Some stylistic improvements and added references following feedback from the reviewer

How fast do radial basis function interpolants of analytic functions converge?

The question in the title is answered using tools of potential theory. Convergence and divergence rates of interpolants of analytic functions on the unit interval are analyzed. The starting point is a complex variable contour integral formula for the remainder in RBF interpolation. We study a generalized Runge phenomenon and explore how the location of centers and affects convergence. Special attention is given to Gaussian and inverse quadratic radial functions, but some of the results can be extended to other smooth basis functions. Among other things, we prove that, under mild conditions, inverse quadratic RBF interpolants of functions that are analytic inside the strip $|Im(z)| < (1/2\epsilon)$, where $\epsilon$ is the shape parameter, converge exponentially

Error saturation in Gaussian radial basis functions on a finite interval

AbstractRadial basis function (RBF) interpolation is a “meshless” strategy with great promise for adaptive approximation. One restriction is “error saturation” which occurs for many types of RBFs including Gaussian RBFs of the form ϕ(x;α,h)=exp(−α2(x/h)2): in the limit h→0 for fixed α, the error does not converge to zero, but rather to ES(α). Previous studies have theoretically determined the saturation error for Gaussian RBF on an infinite, uniform interval and for the same with a single point omitted. (The gap enormously increases ES(α).) We show experimentally that the saturation error on the unit interval, x∈[−1,1], is about 0.06exp(−0.47/α2)‖f‖∞ — huge compared to the O(2π/α2)exp(−π2/[4α2]) saturation error for a grid with one point omitted. We show that the reason the saturation is so large on a finite interval is that it is equivalent to an infinite grid which is uniform except for a gap of many points. The saturation error can be avoided by choosing α≪1, the “flat limit”, but the condition number of the interpolation matrix explodes as O(exp(π2/[4α2])). The best strategy is to choose the largest α which yields an acceptably small saturation error: If the user chooses an error tolerance δ, then αoptimum(δ)=1/−2log(δ/0.06)

Convergence of linear barycentric rational interpolation for analytic functions

Polynomial interpolation to analytic functions can be very accurate, depending on the distribution of the interpolation nodes. However, in equispaced nodes and the like, besides being badly conditioned, these interpolants fail to converge even in exact arithmetic in some cases. Linear barycentric rational interpolation with the weights presented by Floater and Hormann can be viewed as blended polynomial interpolation and often yields better approximation in such cases. This has been proven for differentiable functions and indicated in several experiments for analytic functions. So far, these rational interpolants have been used mainly with a constant parameter usually denoted by d, the degree of the blended polynomials, which leads to small condition numbers but to merely algebraic convergence. With the help of logarithmic potential theory we derive asymptotic convergence results for analytic functions when this parameter varies with the number of nodes. Moreover, we present suggestions on how to choose d in order to observe fast and stable convergence, even in equispaced nodes where stable geometric convergence is provably impossible. We demonstrate our results with several numerical examples

Directional Wind Spectrum Description using Bivariate L1 Norm RBFs

In this paper, the simplest directional wind spectrum description is given using surrogate bivariate polynomial radial basis functions (PRBF) with L1 norm smoothed by dense boundary points distribution, which enables an accurate description of the geometry and the calculation of the volume below the observed surface when belonging double integral is known. For that purpose, the direct solution of double integral below the descriptive surface is given for bivariate polynomial RBFs with integer exponents, which is examined for accuracy on two examples, for Franke’s 2D function and upper hemisphere. After proven accurate in those examples, the direct description of the directional wind spectrum and the calculation of the joint density function of the wind spectrum is done in the paper, thus proving PRBFs as an efficient method for wind spectrum description. In that way, it is possible to calculate the joint density function (JDF) of the actual measured directional wind spectrum analytically, instead of the theoretical calculations used so far