Sharp $L_p$-error estimates for sampling operators

Abstract

We study approximation properties of linear sampling operators in the spaces Lp for 1 ≤ p < ∞. By means of the Steklov averages, we introduce a new measure of smoothness that simultaneously contains information on the smoothness of a function in Lp 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 Lp, find the exact order of decay of the corresponding Lp-errors for particular classes of functions, and introduce a special K-functional and its realization suitable for studying smoothness properties of sampling operators.