학술논문

Noisy Computing of the OR and MAX Functions
Document Type
Periodical
Source
IEEE Journal on Selected Areas in Information Theory IEEE J. Sel. Areas Inf. Theory Selected Areas in Information Theory, IEEE Journal on. 5:302-313 2024
Subject
Communication, Networking and Broadcast Technologies
Noise measurement
Noise
Error probability
Random variables
Upper bound
Information theory
Input variables
Noisy computing
active learning
online learning
Language
ISSN
2641-8770
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 $\textsf {OR}$ function of n bits (where queries correspond to noisy readings of the bits) and the $\textsf {MAX}$ function of n real numbers (where queries correspond to noisy pairwise comparisons). We show that an expected number of queries of $(1 \pm o(1)) {}\frac {n\log {}\frac {1}{\delta }}{D_{\textsf {KL}}(p \| 1-p)}$ is both sufficient and necessary to compute both functions with a vanishing error probability $\delta = o(1)$ , where $D_{\textsf {KL}}(p \| 1-p)$ denotes the Kullback-Leibler divergence between $\textsf {Bern}(p)$ and $\textsf {Bern}(1-p)$ distributions. Compared to previous work, our results tighten the dependence on p in both the upper and lower bounds for the two functions.