Functional Deutsch Uncertainty Principle
K. Mahesh Krishna

TL;DR
This paper establishes a new functional uncertainty principle for Banach spaces involving Parseval p-frames, generalizing Deutsch's uncertainty principle from Hilbert spaces and providing a dual inequality.
Contribution
It introduces the Functional Deutsch Uncertainty Principle for Banach spaces with Parseval p-frames, extending existing uncertainty principles to a broader setting.
Findings
Reduces to Deutsch's uncertainty principle in Hilbert spaces
Derives a dual inequality for the main result
Provides bounds involving frame coefficients and entropy-like sums
Abstract
Let and be Parseval p-frames for a finite dimensional Banach space . Then we show that \begin{align} (1) \quad\quad\quad\quad \log (nm)\geq S_f (x)+S_g (x)\geq -p \log \left(\displaystyle\sup_{y \in \mathcal{X}_f\cap \mathcal{X}_g, \|y\|=1}\left(\max_{1\leq j\leq n, 1\leq k\leq m}|f_j(y)g_k(y)|\right)\right), \quad \forall x \in \mathcal{X}_f\cap \mathcal{X}_g, \end{align} where \begin{align*} &\mathcal{X}_f:= \{z\in \mathcal{X}: f_j(z)\neq 0, 1\leq j \leq n\}, \quad \mathcal{X}_g:= \{w\in \mathcal{X}: g_k(w)\neq 0, 1\leq k \leq m\},\\ &S_f (x):= -\sum_{j=1}^{n}\left|f_j\left(\frac{x}{\|x\|}\right)\right|^p\log \left|f_j\left(\frac{x}{\|x\|}\right)\right|^p, \quad S_g (x):= -\sum_{k=1}^{m}\left|g_k\left(\frac{x}{\|x\|}\right)\right|^p\log \left|g_k\left(\frac{x}{\|x\|}\right)\right|^p, \quad \forall x \in \mathcal{X}_g. \end{align*} We…
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Taxonomy
TopicsDigital Filter Design and Implementation · Mathematical Analysis and Transform Methods · Stochastic processes and financial applications
