Sharp $L_p$-error estimates for sampling operators

TL;DR

This paper introduces a new smoothness measure using Steklov averages to improve approximation estimates of linear sampling operators in $L_p$ spaces, providing exact error decay rates and extending classical results.

Contribution

It develops a novel smoothness measure based on Steklov averages, enabling sharper approximation inequalities and precise error decay analysis for sampling operators in $L_p$ spaces.

Findings

Established matching direct and inverse approximation inequalities.

Determined the exact order of decay of $L_p$-errors for specific function classes.

Introduced a new $K$-functional for analyzing sampling operator smoothness.

Abstract

We study approximation properties of linear sampling operators in the spaces $L_p$ for $1\le p<\infty$. By means of the Steklov averages, we introduce a new measure of smoothness that simultaneously contains information on the smoothness of a function in $L_p$ and discrete information on the behaviour of a function at sampling points. The new measure of smoothness enables us to improve and extend several classical results of approximation theory to the case of linear sampling operators. In particular, we obtain matching direct and inverse approximation inequalities for sampling operators in $L_p$, find the exact order of decay of the corresponding $L_p$-errors for particular classes of functions, and introduce a special $K$-functional and its realization suitable for studying smoothness properties of sampling operators.