On the eigenvalue distribution of spatio-spectral limiting operators in higher dimensions

IF 2.6 2区 数学 Q1 MATHEMATICS, APPLIED
Arie Israel, Azita Mayeli
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引用次数: 0

Abstract

Prolate spheroidal wave functions are an orthogonal family of bandlimited functions on R that have the highest concentration within a specific time interval. They are also identified as the eigenfunctions of a time-frequency limiting operator (TFLO), and the associated eigenvalues belong to the interval [0,1]. Previous work has studied the asymptotic distribution and clustering behavior of the TFLO eigenvalues.

In this paper, we extend these results to multiple dimensions. We prove estimates on the eigenvalues of a spatio-spectral limiting operator (SSLO) on L2(Rd), which is an alternating product of projection operators associated to given spatial and frequency domains in Rd. If one of the domains is a hypercube, and the other domain is convex body satisfying a symmetry condition, we derive quantitative bounds on the distribution of the SSLO eigenvalues in the interval [0,1].

To prove our results, we design an orthonormal system of wave packets in L2(Rd) that are highly concentrated in the spatial and frequency domains. We show that these wave packets are “approximate eigenfunctions” of a spatio-spectral limiting operator. To construct the wave packets, we use a variant of the Coifman-Meyer local sine basis for L2[0,1], and we lift the basis to higher dimensions using a tensor product.

论高维空间谱限制算子的特征值分布
长球面波函数是R上的正交带限函数族,在特定时间间隔内具有最高的浓度。它们也被识别为时频限制算子(TFLO)的特征函数,其相关特征值属于区间[0,1]。前人研究了TFLO特征值的渐近分布和聚类行为。在本文中,我们将这些结果扩展到多个维度。我们证明了L2(Rd)上空间-频谱极限算子(SSLO)特征值的估计,它是与给定空间域和频率域相关联的投影算子的交替积。如果其中一个域是超立方体,另一个域是满足对称条件的凸体,我们导出了SSLO特征值在区间[0,1]内分布的定量界。为了证明我们的结果,我们在L2(Rd)中设计了一个高度集中在空间和频域的正交波包系统。我们证明了这些波包是空间谱极限算子的“近似特征函数”。为了构造波包,我们对L2[0,1]使用Coifman-Meyer局部正弦基的一种变体,并使用张量积将基提升到更高的维度。
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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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