Noisy Computing of the $\mathsf{OR}$ and $\mathsf{MAX}$ Functions
September 07, 2023 Β· Declared Dead Β· π IEEE Journal on Selected Areas in Information Theory
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Authors
Banghua Zhu, Ziao Wang, Nadim Ghaddar, Jiantao Jiao, Lele Wang
arXiv ID
2309.03986
Category
cs.DS: Data Structures & Algorithms
Cross-listed
cs.AI,
cs.IT,
cs.LG
Citations
3
Venue
IEEE Journal on Selected Areas in Information Theory
Last Checked
4 months ago
Abstract
We consider the problem of computing a function of $n$ variables using noisy queries, where each query is incorrect with some fixed and known probability $p \in (0,1/2)$. Specifically, we consider the computation of the $\mathsf{OR}$ function of $n$ bits (where queries correspond to noisy readings of the bits) and the $\mathsf{MAX}$ function of $n$ real numbers (where queries correspond to noisy pairwise comparisons). We show that an expected number of queries of \[ (1 \pm o(1)) \frac{n\log \frac{1}Ξ΄}{D_{\mathsf{KL}}(p \| 1-p)} \] is both sufficient and necessary to compute both functions with a vanishing error probability $Ξ΄= o(1)$, where $D_{\mathsf{KL}}(p \| 1-p)$ denotes the Kullback-Leibler divergence between $\mathsf{Bern}(p)$ and $\mathsf{Bern}(1-p)$ distributions. Compared to previous work, our results tighten the dependence on $p$ in both the upper and lower bounds for the two functions.
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