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Matematicheskie Zametki, 2018, Volume 103, Issue 5, paper published in the English version journal
(Mi mzm12094)
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This article is cited in 2 scientific papers (total in 2 papers)
Papers published in the English version of the journal
Exponential Convergence of an Approximation Problem
for Infinitely Differentiable Multivariate Functions
Yongping Liua, Guiqiao Xub, Jie Zhanga a School of Mathematical Sciences, Beijing Normal University, Beijing, People's Republic of China
b School of Mathematical Sciences, Tianjin Normal University,
Tianjin, People's Republic of China
Abstract:
We study approximation problems for infinitely differentiable multivariate
functions in the worst-case setting.
Using a series of information-based algorithms as approximation tools, in which each
algorithm is constructed by performing finitely many standard information
operations, we prove that the
$L_\infty$-approximation problem is
exponentially convergent.
As a corollary, we show that the
corresponding integral problem is exponentially convergent as well.
Keywords:
infinitely differentiable function class,
standard information, worst-case setting.
Received: 08.11.2017 Revised: 25.03.2018
Citation:
Yongping Liu, Guiqiao Xu, Jie Zhang, “Exponential Convergence of an Approximation Problem
for Infinitely Differentiable Multivariate Functions”, Math. Notes, 103:5 (2018), 769–779
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https://www.mathnet.ru/eng/mzm12094
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