Abstract
We prove optimal embeddings for nonlinear approximation spaces \(\mathcal{A}^{\alpha}_q\), 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 N-term wavelet approximation in L p, Orlicz, and Lorentz norms. We also study the “greedy classes” \({\mathcal{G}_{q}^{\alpha}}\) introduced by Gribonval and Nielsen, obtaining new counterexamples which show that \({\mathcal{G}_{q}^{\alpha}}\not=\mathcal{A}^{\alpha}_q\) for most non-democratic unconditional bases.
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Communicated by Volodya Temlyakov.
Research supported by Grants MTM2007-60952 and MTM2010-16518 (Spain). Research of M. de Natividade supported by Instituto Nacional de Bolsas de Estudos de Angola, INABE.
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Garrigós, G., Hernández, E. & de Natividade, M. Democracy functions and optimal embeddings for approximation spaces. Adv Comput Math 37, 255–283 (2012). https://doi.org/10.1007/s10444-011-9197-0
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DOI: https://doi.org/10.1007/s10444-011-9197-0
Keywords
- Non-linear approximation
- Greedy algorithm
- Democratic bases
- Jackson and Bernstein inequalities
- Discrete Lorentz spaces
- Wavelets