索波列夫和贝索夫空间流形宽度的尖锐下限

IF 1.8 2区 数学 Q1 MATHEMATICS
Jonathan W. Siegel
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引用次数: 0

摘要

我们研究了 Sobolev 和 Besov 空间的流形 n 宽度,误差以 Lp 值衡量。流形宽度衡量连续非线性参数方法逼近这些空间的效率。现有的上界和下界只有在平滑指数 q 满足 q≤p 或 1≤p≤2 时才能匹配。我们缩小了这一差距,获得了所有 1≤p,q≤∞的尖锐边界,对于这些边界,紧凑嵌入是成立的。在此过程中,我们确定了当 p≤q 时,有限维 ℓqM 球在ℓp 规范下的流形宽度的精确值。虽然这个结果并不新颖,但我们提供了一个新的证明,并将其应用于索波列夫和贝索夫空间流形宽度的下界。我们的结果表明,通常用来下限流形宽度的伯恩斯坦宽度在许多情况下会比流形宽度衰减得更快。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sharp lower bounds on the manifold widths of Sobolev and Besov spaces

We study the manifold n-widths of Sobolev and Besov spaces with error measured in the Lp-norm. The manifold widths measure how efficiently these spaces can be approximated by continuous non-linear parametric methods. Existing upper and lower bounds only match when the smoothness index q satisfies qp or 1p2. We close this gap, obtaining sharp bounds for all 1p,q for which a compact embedding holds. In the process, we determine the exact value of the manifold widths of finite dimensional qM-balls in the p-norm when pq. Although this result is not new, we provide a new proof and apply it to lower bounding the manifold widths of Sobolev and Besov spaces. Our results show that the Bernstein widths, which are typically used to lower bound the manifold widths, decay asymptotically faster than the manifold widths in many cases.

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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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