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Marcinkiewicz-type discretization of L^p-norms under the Nikolskii-type inequality assumption

Journal of Mathematical Analysis and Applications. 2021. Vol. 504. No. 1. Article 125358.
Kosov E.

The paper studies the sampling discretization problem for integral norms on subspaces of L^p(\mu). Several close to optimal results are obtained on subspaces for which certain Nikolskii-type inequality is valid. The problem of norms discretization is connected with the probabilistic question about the approximation with high probability of marginals of a high dimensional random vector by sampling. As a byproduct of our approach we refine the result of  O. Guedon and M. Rudelson concerning the approximation of marginals. In particular, the obtained improvement recovers a theorem of J. Bourgain, J. Lindenstrauss, and V. Milman concerning embeddings of  finite dimensional subspaces of L^p[0, 1] into \ell_p^m. The proofs in the paper use the recent developments of the chaining technique by R. van Handel.

Research target: Mathematics
Language: English
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DOI
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Keywords: EntropySamplingdiscretizationChainingMoments of a random vector
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