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October 19, 2026
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Some improved bounds in sampling discretization of integral norms

Journal of Functional Analysis. 2023. Vol. 285. No. 4. Article 109951.
Dai F., E. Kosov, Temlyakov V.

The paper addresses a problem of sampling discretization of integral norms of elements of finite-dimensional subspaces satisfying some conditions. We prove sampling discretization results under a standard assumption formulated in terms of the Nikol’skii-type inequality. In particular, we obtain some upper bounds on the number of sample points sufficient for good discretization of the integral Lp norms, 1 < p < 2, of functions from finite-dimensional subspaces of continuous functions. Our new results improve upon the known results in this direction. We use a new technique based on deep results of Talagrand from functional analysis.

Research target: Mathematics
Language: English
Full text
DOI
Keywords: framesSampling discretizationNikolskii inequality
Publication based on the results of:
Structural learning and its applications (2023)
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