42 citations to https://www.mathnet.ru/rus/sm2126
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M. Gnewuch, M. Wnuk, “Explicit error bounds for randomized Smolyak algorithms and an application to infinite-dimensional integration”, Journal of Approximation Theory, 251 (2020), 105342
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Erich Novak, Springer Proceedings in Mathematics & Statistics, 163, Monte Carlo and Quasi-Monte Carlo Methods, 2016, 161
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В. Н. Темляков, “Конструктивные разреженные тригонометрические приближения и другие задачи для функций смешанной гладкости”, Матем. сб., 206:11 (2015), 131–160 ; V. N. Temlyakov, “Constructive sparse trigonometric approximation and other problems for functions with mixed smoothness”, Sb. Math., 206:11 (2015), 1628–1656
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Е. Д. Нурсултанов, Н. Т. Тлеуханова, “О восстановлении мультипликативных преобразований функций из анизотропных пространств”, Сиб. матем. журн., 55:3 (2014), 592–609 ; E. D. Nursultanov, N. T. Tleukhanova, “On reconstruction of multiplicative transformations of functions in anisotropic spaces”, Siberian Math. J., 55:3 (2014), 482–497
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Matthew Plumlee, “Fast Prediction of Deterministic Functions Using Sparse Grid Experimental Designs”, Journal of the American Statistical Association, 109:508 (2014), 1581
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Nurlan Nauryzbayev, Nurlan Temirgaliyev, “An Exact Order of Discrepancy of the Smolyak Grid and Some General Conclusions in the Theory of Numerical Integration”, Found Comput Math, 2012
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Sickel W., Ullrich T., “Spline interpolation on sparse grids”, Appl Anal, 90:3–4 (2011), 337–383
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Dinh Dung, “B-spline quasi-interpolant representations and sampling recovery of functions with mixed smoothness”, J Complexity, 27:6 (2011), 541–567
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Yuan Xiuhua, Ye Peixin, “Monte Carlo Approximation and Integration for Sobolev Classes”, High Performance Networking, Computing, and Communication Systems, Communications in Computer and Information Science, 163, ed. Wu Y., Springer-Verlag Berlin, 2011, 103–110
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Ye Peixin, Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology, 2011, 797