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Functional Donoho-Stark Approximate Support Uncertainty Principle

Functional Analysis 2023-07-06 v1 Information Theory math.IT

Abstract

Let ({fj}j=1n,{τj}j=1n)(\{f_j\}_{j=1}^n, \{\tau_j\}_{j=1}^n) and ({gk}k=1n,{ωk}k=1n)(\{g_k\}_{k=1}^n, \{\omega_k\}_{k=1}^n) be two p-orthonormal bases for a finite dimensional Banach space X\mathcal{X}. If xX{0} x \in \mathcal{X}\setminus\{0\} is such that θfx\theta_fx is ε\varepsilon-supported on M{1,,n}M\subseteq \{1,\dots, n\} w.r.t. p-norm and θgx\theta_gx is δ\delta-supported on N{1,,n}N\subseteq \{1,\dots, n\} w.r.t. p-norm, then we show that \begin{align}\label{ME} (1) \quad \quad \quad \quad &o(M)^\frac{1}{p}o(N)^\frac{1}{q}\geq \frac{1}{\displaystyle \max_{1\leq j,k\leq n}|f_j(\omega_k) |}\max \{1-\varepsilon-\delta, 0\},\\ (2) \quad \quad \quad \quad&o(M)^\frac{1}{q}o(N)^\frac{1}{p}\geq \frac{1}{\displaystyle \max_{1\leq j,k\leq n}|g_k(\tau_j) |}\max \{1-\varepsilon-\delta, 0\},\label{ME2} \end{align} where \begin{align*} \theta_f: \mathcal{X} \ni x \mapsto (f_j(x) )_{j=1}^n \in \ell^p([n]); \quad \theta_g: \mathcal{X} \ni x \mapsto (g_k(x) )_{k=1}^n \in \ell^p([n]) \end{align*} and qq is the conjugate index of pp. We call Inequalities (1) and (2) as \textbf{Functional Donoho-Stark Approximate Support Uncertainty Principle}. Inequalities (1) and (2) improve the finite approximate support uncertainty principle obtained by Donoho and Stark \textit{[SIAM J. Appl. Math., 1989]}.

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Cite

@article{arxiv.2307.01215,
  title  = {Functional Donoho-Stark Approximate Support Uncertainty Principle},
  author = {K. Mahesh Krishna},
  journal= {arXiv preprint arXiv:2307.01215},
  year   = {2023}
}

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R2 v1 2026-06-28T11:21:03.078Z