Version 1
: Received: 1 July 2023 / Approved: 3 July 2023 / Online: 4 July 2023 (02:15:57 CEST)
How to cite:
KRISHNA, K. M. Functional Donoho-Stark Approximate Support Uncertainty Principle. Preprints2023, 2023070025. https://doi.org/10.20944/preprints202307.0025.v1
KRISHNA, K. M. Functional Donoho-Stark Approximate Support Uncertainty Principle. Preprints 2023, 2023070025. https://doi.org/10.20944/preprints202307.0025.v1
KRISHNA, K. M. Functional Donoho-Stark Approximate Support Uncertainty Principle. Preprints2023, 2023070025. https://doi.org/10.20944/preprints202307.0025.v1
APA Style
KRISHNA, K. M. (2023). Functional Donoho-Stark Approximate Support Uncertainty Principle. Preprints. https://doi.org/10.20944/preprints202307.0025.v1
Chicago/Turabian Style
KRISHNA, K. M. 2023 "Functional Donoho-Stark Approximate Support Uncertainty Principle" Preprints. https://doi.org/10.20944/preprints202307.0025.v1
Abstract
Let $(\{f_j\}_{j=1}^n, \{\tau_j\}_{j=1}^n)$ and $(\{g_k\}_{k=1}^n, \{\omega_k\}_{k=1}^n)$ be two p-orthonormal bases for a finite dimensional Banach space $\mathcal{X}$. If $ x \in \mathcal{X}\setminus\{0\}$ is such that $\theta_fx$ is $\varepsilon$-supported on $M\subseteq \{1,\dots, n\}$ w.r.t. p-norm and $\theta_gx$ is $\delta$-supported on $N\subseteq \{1,\dots, n\}$ w.r.t. p-norm, then we show that \begin{align}\label{ME} &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\},\\ &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 $q$ is the conjugate index of $p$. We call Inequalities (\ref{ME}) and (\ref{ME2}) as \textbf{Functional Donoho-Stark Approximate Support Uncertainty Principle}. Inequalities (\ref{ME}) and (\ref{ME2}) improve the finite approximate support uncertainty principle obtained by Donoho and Stark \textit{[SIAM J. Appl. Math., 1989]}.
Keywords
Uncertainty Principle; Orthonormal Basis; Hilbert space; Banach space
Subject
Computer Science and Mathematics, Analysis
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.