Nonharmonic multivariate Fourier transforms and matrices: condition numbers and hyperplane geometry
Weilin Li

TL;DR
This paper establishes lower bounds for the smallest singular value of nonharmonic multivariate Fourier transform operators, revealing geometric influences and showing that typical sets have better-conditioned transforms than worst-case scenarios.
Contribution
It provides new lower bounds on the singular values of nonharmonic Fourier transform matrices based on geometric properties of the support set, highlighting improved conditioning in higher dimensions.
Findings
Lower bounds depend on point separation and local hyperplane structure.
Condition numbers are better for generic sets and higher dimensions.
The bounds reveal a localization effect and improved behavior for typical configurations.
Abstract
Consider an operator that takes the Fourier transform of a discrete measure supported in and restricts it to a compact . We provide lower bounds for its smallest singular value when is either a closed ball of radius or closed cube of side length , and under different types of geometric assumptions on . We first show that if distances between points in are lower bounded by a that is allowed to be arbitrarily small, then the smallest singular value is at least , where is the maximum number of elements in contained within any ball or cube of an explicitly given radius. This estimate communicates a localization effect of the Fourier transform. While it is sharp, the smallest singular value behaves better than…
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Taxonomy
TopicsAdvanced Scientific Research Methods · Morphological variations and asymmetry · Mathematical Analysis and Transform Methods
