Nonlinear approximation in bounded orthonormal product bases

TL;DR

This paper introduces a dimension-incremental algorithm for nonlinear high-dimensional function approximation using bounded orthonormal product bases, focusing on adaptive basis support detection from point evaluations.

Contribution

It presents a novel adaptive algorithm with a proven detection guarantee for selecting significant basis coefficients in high-dimensional approximation.

Findings

Algorithm effectively detects significant basis coefficients.

Numerical examples demonstrate high accuracy and efficiency.

Theoretical guarantee supports practical applicability.

Abstract

We present a dimension-incremental algorithm for the nonlinear approximation of high-dimensional functions in an arbitrary bounded orthonormal product basis. Our goal is to detect a suitable truncation of the basis expansion of the function, where the corresponding basis support is assumed to be unknown. Our method is based on point evaluations of the considered function and adaptively builds an index set of a suitable basis support such that the approximately largest basis coefficients are still included. For this purpose, the algorithm only needs a suitable search space that contains the desired index set. Throughout the work, there are various minor modifications of the algorithm discussed as well, which may yield additional benefits in several situations. For the first time, we provide a proof of a detection guarantee for such an index set in the function approximation case under certain assumptions on the sub-methods used within our algorithm, which can be used as a foundation for similar statements in various other situations as well. Some numerical examples in different settings underline the effectiveness and accuracy of our method.