算子序列的最优恢复

Q3 Mathematics
V. Babenko, N. Parfinovych, D. Skorokhodov
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We show that the optimal method of recovery in this problem is either operator $\\Phi^*_m$ with some $m\\in\\mathbb{Z}_+$ ($m\\le n$ in case $y\\in\\ell^n_r$), where \n\\smallskip\\centerline{$\\displaystyle \\Phi^*_m(y) = \\Big\\{y_1\\left(1 - \\frac{t_{m+1}^q}{t_{1}^q}\\Big),\\ldots,y_m\\Big(1 - \\frac{t_{m+1}^q}{t_{m}^q}\\Big),0,\\ldots\\right\\},\\quad y\\in\\mathbb{R}^n\\text{ or } y\\in\\ell_\\infty,$} \n\\smallskip\\noior convex combination $(1-\\lambda) \\Phi^*_{m+1} + \\lambda\\Phi^*_{m}$. \nThe second one is the problem of optimal recovery of the scalar product operator acting on the Cartesian product $W^{T,S}_{p,q}$ of classes $W^T_p$ and $W^S_q$, where $1 < p,q < \\infty$, $\\frac{1}{p} + \\frac{1}{q} = 1$ and $s_1\\ge s_2\\ge \\ldots \\ge 0$ are given. Information available about elements $x\\in W^T_p$ and $y\\in W^S_q$ is provided by elements $z,w\\in \\mathbb{R}^n$ such that the distance between vectors $\\left(x_1y_1, x_2y_2,\\ldots,x_ny_n\\right)$ and $\\left(z_1w_1,\\ldots,z_nw_n\\right)$ in the space $\\ell_r^n$ does not exceed $\\varepsilon$. We show that the optimal method of recovery is delivered either by operator $\\Psi^*_m$ with some $m\\in\\{0,1,\\ldots,n\\}$, where \n\\smallskip\\centerline{$\\displaystyle \\Psi^*_m = \\sum_{k=1}^m z_kw_k\\Big(1 - \\frac{t_{m+1}s_{m+1}}{t_ks_k}\\Big),\\quad z,w\\in\\mathbb{R}^n,$} \n\\smallskip\\noior by convex combination $(1-\\lambda)\\Psi^*_{m+1} + \\lambda\\Psi^*_{m}$. \nAs an application of our results we consider the problem of optimal recovery of classes in Hilbert spaces by the Fourier coefficients of its elements known with an error measured in the space $\\ell_p$ with $p > 2$.","PeriodicalId":37555,"journal":{"name":"Matematychni Studii","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimal recovery of operator sequences\",\"authors\":\"V. Babenko, N. Parfinovych, D. 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引用次数: 0

摘要

本文解决了两个基于带有误差的给定信息的最优恢复问题。首先是类的最优恢复问题 $W^T_q = \{(t_1h_1,t_2h_2,\ldots)\,\colon \,\|h\|_{\ell_q}\le 1\}$,其中 $1\le q < \infty$ 和 $t_1\ge t_2\ge \ldots \ge 0$ 是给定的,在空间中 $\ell_q$. 关于序列的可用信息 $x\in W^T_q$ 是由元素提供的(i) $y\in\mathbb{R}^n$, $n\in\mathbb{N}$,谁的距离到第一个 $n$ 坐标 $\left(x_1,\ldots,x_n\right)$ 的 $x$ 在太空中 $\ell_r^n$, $0 < r \le \infty$,不超过给定 $\varepsilon\ge 0$,或(ii)按顺序 $y\in\ell_\infty$ 谁的距离 $x$ 在太空中 $\ell_r$ 不超过 $\varepsilon$. 我们证明了该问题的最优恢复方法是任一算子 $\Phi^*_m$ 有一些 $m\in\mathbb{Z}_+$ ($m\le n$ 以防万一 $y\in\ell^n_r$),其中 \smallskip\centerline{$\displaystyle \Phi^*_m(y) = \Big\{y_1\left(1 - \frac{t_{m+1}^q}{t_{1}^q}\Big),\ldots,y_m\Big(1 - \frac{t_{m+1}^q}{t_{m}^q}\Big),0,\ldots\right\},\quad y\in\mathbb{R}^n\text{ or } y\in\ell_\infty,$} \smallskip\noior 凸组合 $(1-\lambda) \Phi^*_{m+1} + \lambda\Phi^*_{m}$. 第二个问题是作用于笛卡尔积的标量积算子的最优恢复问题 $W^{T,S}_{p,q}$ 类的 $W^T_p$ 和 $W^S_q$,其中 $1 < p,q < \infty$, $\frac{1}{p} + \frac{1}{q} = 1$ 和 $s_1\ge s_2\ge \ldots \ge 0$ 是给定的。有关元素的可用信息 $x\in W^T_p$ 和 $y\in W^S_q$ 由元素提供 $z,w\in \mathbb{R}^n$ 使得向量之间的距离 $\left(x_1y_1, x_2y_2,\ldots,x_ny_n\right)$ 和 $\left(z_1w_1,\ldots,z_nw_n\right)$ 在太空中 $\ell_r^n$ 不超过 $\varepsilon$. 结果表明,最佳的采收率方法是由作业者提出的 $\Psi^*_m$ 有一些 $m\in\{0,1,\ldots,n\}$,其中 \smallskip\centerline{$\displaystyle \Psi^*_m = \sum_{k=1}^m z_kw_k\Big(1 - \frac{t_{m+1}s_{m+1}}{t_ks_k}\Big),\quad z,w\in\mathbb{R}^n,$} \smallskip\noior 通过凸组合 $(1-\lambda)\Psi^*_{m+1} + \lambda\Psi^*_{m}$. 作为我们的结果的一个应用,我们考虑了Hilbert空间中类的最优恢复问题,该问题是通过在空间中测量误差的已知元素的傅里叶系数来实现的 $\ell_p$ 有 $p > 2$.
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal recovery of operator sequences
In this paper we solve two problems of optimal recovery based on information given with an error. First is the problem of optimal recovery of the class $W^T_q = \{(t_1h_1,t_2h_2,\ldots)\,\colon \,\|h\|_{\ell_q}\le 1\}$, where $1\le q < \infty$ and $t_1\ge t_2\ge \ldots \ge 0$ are given, in the space $\ell_q$. Information available about a sequence $x\in W^T_q$ is provided either (i) by an element $y\in\mathbb{R}^n$, $n\in\mathbb{N}$, whose distance to the first $n$ coordinates $\left(x_1,\ldots,x_n\right)$ of $x$ in the space $\ell_r^n$, $0 < r \le \infty$, does not exceed given $\varepsilon\ge 0$, or (ii) by a sequence $y\in\ell_\infty$ whose distance to $x$ in the space $\ell_r$ does not exceed $\varepsilon$. We show that the optimal method of recovery in this problem is either operator $\Phi^*_m$ with some $m\in\mathbb{Z}_+$ ($m\le n$ in case $y\in\ell^n_r$), where \smallskip\centerline{$\displaystyle \Phi^*_m(y) = \Big\{y_1\left(1 - \frac{t_{m+1}^q}{t_{1}^q}\Big),\ldots,y_m\Big(1 - \frac{t_{m+1}^q}{t_{m}^q}\Big),0,\ldots\right\},\quad y\in\mathbb{R}^n\text{ or } y\in\ell_\infty,$} \smallskip\noior convex combination $(1-\lambda) \Phi^*_{m+1} + \lambda\Phi^*_{m}$. The second one is the problem of optimal recovery of the scalar product operator acting on the Cartesian product $W^{T,S}_{p,q}$ of classes $W^T_p$ and $W^S_q$, where $1 < p,q < \infty$, $\frac{1}{p} + \frac{1}{q} = 1$ and $s_1\ge s_2\ge \ldots \ge 0$ are given. Information available about elements $x\in W^T_p$ and $y\in W^S_q$ is provided by elements $z,w\in \mathbb{R}^n$ such that the distance between vectors $\left(x_1y_1, x_2y_2,\ldots,x_ny_n\right)$ and $\left(z_1w_1,\ldots,z_nw_n\right)$ in the space $\ell_r^n$ does not exceed $\varepsilon$. We show that the optimal method of recovery is delivered either by operator $\Psi^*_m$ with some $m\in\{0,1,\ldots,n\}$, where \smallskip\centerline{$\displaystyle \Psi^*_m = \sum_{k=1}^m z_kw_k\Big(1 - \frac{t_{m+1}s_{m+1}}{t_ks_k}\Big),\quad z,w\in\mathbb{R}^n,$} \smallskip\noior by convex combination $(1-\lambda)\Psi^*_{m+1} + \lambda\Psi^*_{m}$. As an application of our results we consider the problem of optimal recovery of classes in Hilbert spaces by the Fourier coefficients of its elements known with an error measured in the space $\ell_p$ with $p > 2$.
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来源期刊
Matematychni Studii
Matematychni Studii Mathematics-Mathematics (all)
CiteScore
1.00
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0.00%
发文量
38
期刊介绍: Journal is devoted to research in all fields of mathematics.
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