通过细分实现多变量紧凑支持的 C∞ 函数

IF 2.6 2区 数学 Q1 MATHEMATICS, APPLIED Applied and Computational Harmonic Analysis Pub Date : 2024-01-19 DOI:10.1016/j.acha.2024.101630
Maria Charina , Costanza Conti , Nira Dyn
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引用次数: 0

摘要

本文讨论通过细分方案生成具有紧凑小支撑的多元 C∞ 函数。根据基于度数不断增长的样条细分方案掩码的非稳态方案构建的单变量函数(称为Up-函数),我们将生成的多变量函数称为Up-类函数。我们通过基于支持度不断增长的三向盒样条曲线掩码的非稳态方案生成它们。为了分析这些非稳态方案的收敛性和平滑性,我们开发了新的工具,这些工具适用于比我们所研究的方案更广泛的方案类别。用我们的方法实现了小的紧凑支撑,在单变量情况下,我们得到了支撑[0,1+ϵ]的类Up函数,与Up函数的支撑[0,2]相比。本文给出了单变量和双变量类 Up 函数的例子。与单变量情况一样,Up-like 函数的构造可以促使在任何维度上生成 C∞ 紧凑支持的小支持小波。
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Multivariate compactly supported C∞ functions by subdivision

This paper discusses the generation of multivariate C functions with compact small supports by subdivision schemes. Following the construction of such a univariate function, called Up-function, by a non-stationary scheme based on masks of spline subdivision schemes of growing degrees, we term the multivariate functions we generate Up-like functions. We generate them by non-stationary schemes based on masks of three-directional box-splines of growing supports. To analyze the convergence and smoothness of these non-stationary schemes, we develop new tools which apply to a wider class of schemes than the class we study. With our method for achieving small compact supports, we obtain in the univariate case, Up-like functions with supports [0,1+ϵ] in comparison to the support [0,2] of the Up-function. Examples of univariate and bivariate Up-like functions are given. As in the univariate case, the construction of Up-like functions can motivate the generation of C compactly supported wavelets of small support in any dimension.

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来源期刊
Applied and Computational Harmonic Analysis
Applied and Computational Harmonic Analysis 物理-物理:数学物理
CiteScore
5.40
自引率
4.00%
发文量
67
审稿时长
22.9 weeks
期刊介绍: Applied and Computational Harmonic Analysis (ACHA) is an interdisciplinary journal that publishes high-quality papers in all areas of mathematical sciences related to the applied and computational aspects of harmonic analysis, with special emphasis on innovative theoretical development, methods, and algorithms, for information processing, manipulation, understanding, and so forth. The objectives of the journal are to chronicle the important publications in the rapidly growing field of data representation and analysis, to stimulate research in relevant interdisciplinary areas, and to provide a common link among mathematical, physical, and life scientists, as well as engineers.
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