论普罗尼方法恢复指数和的精确性

IF 2.6 2区 数学 Q1 MATHEMATICS, APPLIED
Rami Katz , Nuha Diab , Dmitry Batenkov
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引用次数: 0

摘要

在本文中,我们建立了普罗尼方法(Prony's method,PM)的精度边界,用于从不完整和有噪声的频率测量中恢复稀疏度量,即所谓的超分辨率问题,此时度量支持点之间的最小间隔可能远小于瑞利极限。我们特别指出,在测量带宽恒定、最小间隔为零的情况下,相对于之前建立的最小-最大约束,PM 是最优的。我们的主要技术贡献在于准确分析了 PM 每一步中不同误差之间的相互关系,从而产生了之前未曾注意到的抵消。我们还证明了 PM 在有限精度算术中的数值稳定性。我们相信,我们的分析将为全面准确分析超分辨率问题的已知算法铺平道路。
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 accuracy of Prony's method for recovery of exponential sums with closely spaced exponents

In this paper we establish accuracy bounds of Prony's method (PM) for recovery of sparse measures from incomplete and noisy frequency measurements, or the so-called problem of super-resolution, when the minimal separation between the points in the support of the measure may be much smaller than the Rayleigh limit. In particular, we show that PM is optimal with respect to the previously established min-max bound for the problem, in the setting when the measurement bandwidth is constant, with the minimal separation going to zero. Our main technical contribution is an accurate analysis of the inter-relations between the different errors in each step of PM, resulting in previously unnoticed cancellations. We also prove that PM is numerically stable in finite-precision arithmetic. We believe our analysis will pave the way to providing accurate analysis of known algorithms for the super-resolution problem in full generality.

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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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