Democracy functions and optimal embeddings for approximation spaces
Functional Analysis
2009-11-26 v1
Abstract
We prove optimal embeddings for nonlinear approximation spaces in terms of weighted Lorentz sequence spaces, with the weights depending on the democracy functions of the basis. As applications we recover known embeddings for -term wavelet approximation in Lebesgue, Orlicz, and Lorentz norms. We also study the "greedy classes" introduced by Gribonval and Nielsen.
Cite
@article{arxiv.0911.4900,
title = {Democracy functions and optimal embeddings for approximation spaces},
author = {Gustavo Garrigós and Eugenio Hernández and Maria de Natividade},
journal= {arXiv preprint arXiv:0911.4900},
year = {2009}
}
Comments
24 pages with references