On Universal Sampling Recovery in the Uniform Norm
Trudy Matematicheskogo Instituta imeni V.A. Steklova, Theory of Functions of Several Real Variables and Its Applications, Tome 323 (2023), pp. 213-223.

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It is known that results on universal sampling discretization of the square norm are useful in sparse sampling recovery with error measured in the square norm. In this paper we demonstrate how known results on universal sampling discretization of the uniform norm and recent results on universal sampling representation allow us to provide good universal methods of sampling recovery for anisotropic Sobolev and Nikol'skii classes of periodic functions of several variables. The sharpest results are obtained in the case of functions of two variables, where the Fibonacci point sets are used for recovery.
Keywords: sampling discretization, universality, recovery.
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V. N. Temlyakov. On Universal Sampling Recovery in the Uniform Norm. Trudy Matematicheskogo Instituta imeni V.A. Steklova, Theory of Functions of Several Real Variables and Its Applications, Tome 323 (2023), pp. 213-223. http://geodesic.mathdoc.fr/item/TM_2023_323_a12/

[1] Binev P., Cohen A., Dahmen W., DeVore R., Temlyakov V., “Universal algorithms for learning theory. I: Piecewise constant functions”, J. Mach. Learn. Res., 6 (2005), 1297–1321 | MR | Zbl

[2] F. Dai, A. Prymak, V. N. Temlyakov, and S. Yu. Tikhonov, “Integral norm discretization and related problems”, Russ. Math. Surv., 74:4 (2019), 579–630 | DOI | DOI | MR

[3] Dai F., Temlyakov V., “Universal sampling discretization”, Constr. Approx., 58:3 (2023), 589–613 ; arXiv: 2107.11476v1 [math.FA] | DOI | MR

[4] Dai F., Temlyakov V., Universal discretization and sparse sampling recovery, E-print, 2023, arXiv: 2301.05962v1 [math.NA] | MR

[5] Dai F., Temlyakov V., “Random points are good for universal discretization”, J. Math. Anal. Appl., 529:1 (2024), 127570 ; arXiv: 2301.12536v1 [math.FA] | DOI | MR

[6] Györfy L., Kohler M., Krzyżak A., Walk H., A distribution-free theory of nonparametric regression, Springer, New York, 2002

[7] Jahn T., Ullrich T., Voigtlaender F., “Sampling numbers of smoothness classes via $\ell ^1$-minimization”, J. Complexity, 79 (2023), 101786 ; arXiv: 2212.00445v1 [math.NA] | DOI | MR

[8] Kashin B., Konyagin S., Temlyakov V., “Sampling discretization of the uniform norm”, Constr. Approx., 57:2 (2023), 663–694 ; arXiv: 2104.01229v2 [math.NA] | DOI | MR | Zbl

[9] Kashin B., Kosov E., Limonova I., Temlyakov V., “Sampling discretization and related problems”, J. Complexity, 71 (2022), 101653 ; arXiv: 2109.07567v1 [math.FA] | DOI | MR | Zbl

[10] Niederreiter H., Xing C., “Low-discrepancy sequences and global function fields with many rational places”, Finite Fields Appl., 2:3 (1996), 241–273 | DOI | MR | Zbl

[11] V. N. Temlyakov, “Approximation by elements of a finite-dimensional subspace of functions from various Sobolev or Nikol'skii spaces”, Math. Notes, 43:6 (1988), 444–454 | DOI | MR | Zbl

[12] V. N. Temlyakov, “On universal cubature formulas”, Sov. Math., Dokl., 43:1 (1991), 39–42 | MR | Zbl

[13] V. N. Temlyakov, “On universal estimators in learning theory”, Proc. Steklov Inst. Math., 255 (2006), 244–259 | DOI | MR | Zbl

[14] Temlyakov V., Multivariate approximation, Cambridge Monogr. Appl. Comput. Math., 32, Cambridge Univ. Press, Cambridge, 2018 | MR | Zbl

[15] Temlyakov V.N., “Universal discretization”, J. Complexity, 47 (2018), 97–109 | DOI | MR

[16] Temlyakov V., “On optimal recovery in $L_2$”, J. Complexity, 65 (2021), 101545 ; arXiv: 2010.03103v1 [math.NA] | DOI | MR | Zbl

[17] Temlyakov V., “On universal sampling representation”, Pure Appl. Funct. Anal., 8:2 (2023), 627–645 ; arXiv: 2201.00415v1 [math.NA] | MR | Zbl

[18] A. Zygmund, Trigonometric Series, v. 1, Univ. Press, Cambridge, 1959 | MR | Zbl