Compression using Quasi-Interpolation
Martin Buhmann, Feng Dai
TL;DR
The paper develops a comprehensive framework for compression and pointwise convergence in multivariate approximation via quasi-interpolation, with a focus on radial basis functions and wavelet-based nonlinear approximations. It establishes decay and polynomial-reproduction properties for quasi-interpolants, introduces a modified operator for handling rough integrands, and derives pointwise error bounds that scale with smoothness and local regularity. It also analyzes compression in both $C({\mathbb R}^d)$ and $L^p({\mathbb R}^d)$, showing negative results without decay at infinity and positive, rate-optimal results under decay conditions, aided by a wavelet/triebel-Lizorkin framework for nonlinear approximation. The results collectively provide practical guidance for N-term compression and high-dimensional approximation using radial basis functions and wavelet-based methods in spaces with varying smoothness.
Abstract
We consider quasi-interpolation with a main application in radial basis function approximations and compression in this article. Constructing and using these quasi-interpolants, we consider wavelet and compression-type approximations from their linear spaces and provide convergence estimates. The results include an error estimate for nonlinear approximation by quasi-interpolation, results about compression in the space of continuous functions and a pointwise convergence estimate for approximands of low smoothness.
