在最坏情况下周期函数L∞逼近的可跟踪性的标准信息幂

IF 1.8 2区 数学 Q1 MATHEMATICS
Jiaxin Geng, Heping Wang
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引用次数: 0

摘要

研究了周期函数在最坏情况下的多元逼近,误差测量在L∞范数上。我们考虑使用由函数值组成的标准信息Λstd或由任意连续线性泛函组成的一般线性信息Λall的算法。研究了Λstd和Λall在绝对误差或归一化误差判据下各种代数可溯性和指数可溯性概念的等价性,并证明了Λstd的幂次与Λall的幂次对于各种代数可溯性和指数可溯性的幂次是相同的。我们的结果可以应用于加权Korobov空间和指数权重的Korobov空间。这给出了由Novak和Woźniakowski(2012)[40]提出的开放问题145的一个特殊解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the power of standard information for tractability for L∞ approximation of periodic functions in the worst case setting

We study multivariate approximation of periodic functions in the worst case setting with the error measured in the L norm. We consider algorithms that use standard information Λstd consisting of function values or general linear information Λall consisting of arbitrary continuous linear functionals. We investigate equivalences of various notions of algebraic and exponential tractability for Λstd and Λall under the absolute or normalized error criterion, and show that the power of Λstd is the same as the one of Λall for various notions of algebraic and exponential tractability. Our results can be applied to weighted Korobov spaces and Korobov spaces with exponential weights. This gives a special solution to Open Problem 145 as posed by Novak and Woźniakowski (2012) [40].

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来源期刊
Journal of Complexity
Journal of Complexity 工程技术-计算机:理论方法
CiteScore
3.10
自引率
17.60%
发文量
57
审稿时长
>12 weeks
期刊介绍: The multidisciplinary Journal of Complexity publishes original research papers that contain substantial mathematical results on complexity as broadly conceived. Outstanding review papers will also be published. In the area of computational complexity, the focus is on complexity over the reals, with the emphasis on lower bounds and optimal algorithms. The Journal of Complexity also publishes articles that provide major new algorithms or make important progress on upper bounds. Other models of computation, such as the Turing machine model, are also of interest. Computational complexity results in a wide variety of areas are solicited. Areas Include: • Approximation theory • Biomedical computing • Compressed computing and sensing • Computational finance • Computational number theory • Computational stochastics • Control theory • Cryptography • Design of experiments • Differential equations • Discrete problems • Distributed and parallel computation • High and infinite-dimensional problems • Information-based complexity • Inverse and ill-posed problems • Machine learning • Markov chain Monte Carlo • Monte Carlo and quasi-Monte Carlo • Multivariate integration and approximation • Noisy data • Nonlinear and algebraic equations • Numerical analysis • Operator equations • Optimization • Quantum computing • Scientific computation • Tractability of multivariate problems • Vision and image understanding.
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