Sampling recovery on classes defined by integral operators and sparse approximation with adaptive dictionaries

Abstract: In this paper we continue to develop the following general approach. We study asymptotic behavior of the errors of sampling recovery not for an individual smoothness class, how it is usually done, but for the collection of classes, which are defined by integral operators with kernels coming from a given class of functions. Earlier, such approach was realized for the Kolmogorov widths and very recently for the entropy numbers. It turns out that the above problem is closely related to the sparse approximation problem with respect to different redundant dictionaries. Specifically, the problem of sampling recovery is connected with sparse nonlinear approximation with respect to adaptive dictionaries, which means that the dictionary depends on the function under approximation.