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

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arxiv 2403.13092 v1 pith:EPSXPFML submitted 2024-03-19 math.CA math.SP

classification math.CAmath.SP
keywords mathbblimitingrightarrowspatio-spectralcitecompactdecompositiondomains
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abstract

Let $F$, $S$ be bounded measurable sets in $\mathbb{R}^d$. Let $P_F : L^2(\mathbb{R}^d) \rightarrow L^2(\mathbb{R}^d) $ be the orthogonal projection on the subspace of functions with compact support on $F$, and let $B_S : L^2(\mathbb{R}^d) \rightarrow L^2(\mathbb{R}^d)$ be the orthogonal projection on the subspace of functions with Fourier transforms having compact support on $S$. In this paper, we derive improved distributional estimates on the eigenvalue sequence $1 \geq \lambda_1(F,S) \geq \lambda_2(F,S) \geq \cdots > 0$ of the \emph{spatio-spectral limiting operator} $B_S P_F B_S : L^2(\mathbb{R}^d) \rightarrow L^2(\mathbb{R}^d)$. The significance of such estimates lies in their diverse applications in medical imaging, signal processing, geophysics and astronomy. Our proof is based on the decomposition techniques developed in \cite{MaRoSp23}. The novelty of our approach is in the use of a two-stage dyadic decomposition with respect to both the spatial and frequency domains, and the application of the results in \cite{ArieAzita23} on the eigenvalues of spatio-spectral limiting operators associated to cubical domains.

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Cited by 2 Pith papers

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  1. Tensor factorization and explicit spectral bounds for product-box concentration operators

    math.FA 2026-07 accept novelty 6.0 of 10

    Finite box unions admit a fully explicit all-parameter plunge-count bound; cubes have an Omega((log c)^d) lower block and fixed-order trace asymptotics, proved via exact tensor structure.

  2. Eigenvalue distribution analysis of multidimensional prolate matrices

    math.CA 2025-07 conditional novelty 5.0 of 10

    For d-dimensional Cartesian discrete prolate matrices, the number of eigenvalues above a threshold ε is approximately (2MW)^d with an explicit error bound B_d(MW, ε).

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