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On sampling discretization in $L_2$

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arxiv 2009.10789 v2 pith:I3GHGHLS submitted 2020-09-22 math.FA cs.NAmath.NA

Irina Limonova , Vladimir Temlyakov This is my paper · ORCID
classification math.FAcs.NAmath.NA
keywords samplingdiscretizationsubspacebounddimensiondimensionalfinitefunctions
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We prove a sampling discretization theorem for the square norm of functions from a finite dimensional subspace satisfying Nikol'skii's inequality with an upper bound on the number of sampling points of the order of the dimension of the subspace

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  1. Nonlinear approximation with adaptive dictionaries

    math.NA 2026-07 unverdicted novelty 5.0 of 10

    Sparse approximation of kernels with adaptive, kernel-dependent dictionaries controls sampling-recovery errors for families of integral-operator function classes.

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