{"title":"On the power of standard information for tractability for L∞ approximation of periodic functions in the worst case setting","authors":"Jiaxin Geng, Heping Wang","doi":"10.1016/j.jco.2023.101790","DOIUrl":null,"url":null,"abstract":"<div><p>We study multivariate approximation of periodic functions in the worst case setting with the error measured in the <span><math><msub><mrow><mi>L</mi></mrow><mrow><mo>∞</mo></mrow></msub></math></span> norm. We consider algorithms that use standard information <span><math><msup><mrow><mi>Λ</mi></mrow><mrow><mi>std</mi></mrow></msup></math></span> consisting of function values or general linear information <span><math><msup><mrow><mi>Λ</mi></mrow><mrow><mi>all</mi></mrow></msup></math></span> consisting of arbitrary continuous linear functionals. We investigate equivalences of various notions of algebraic and exponential tractability for <span><math><msup><mrow><mi>Λ</mi></mrow><mrow><mi>std</mi></mrow></msup></math></span> and <span><math><msup><mrow><mi>Λ</mi></mrow><mrow><mi>all</mi></mrow></msup></math></span> under the absolute or normalized error criterion, and show that the power of <span><math><msup><mrow><mi>Λ</mi></mrow><mrow><mi>std</mi></mrow></msup></math></span> is the same as the one of <span><math><msup><mrow><mi>Λ</mi></mrow><mrow><mi>all</mi></mrow></msup></math></span> 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) <span>[40]</span>.</p></div>","PeriodicalId":50227,"journal":{"name":"Journal of Complexity","volume":"80 ","pages":"Article 101790"},"PeriodicalIF":1.8000,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Complexity","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0885064X23000596","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS","Score":null,"Total":0}
引用次数: 0
Abstract
We study multivariate approximation of periodic functions in the worst case setting with the error measured in the norm. We consider algorithms that use standard information consisting of function values or general linear information consisting of arbitrary continuous linear functionals. We investigate equivalences of various notions of algebraic and exponential tractability for and under the absolute or normalized error criterion, and show that the power of is the same as the one of 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].
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.