Noisy Computing of the $\mathsf{OR}$ and $\mathsf{MAX}$ Functions

Banghua Zhu, Ziao Wang, Nadim Ghaddar, Jiantao Jiao, Lele Wang
9/7/2023
cs.DScs.AIcs.IT

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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 \[ (1 \pm o(1)) \frac{n\log \frac{1}δ}{D_{\mathsf{KL}}(p \| 1-p)} \] is both sufficient and necessary to compute both functions with a vanishing error probability $δ= o(1)$, 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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Cite this paper

@article{zhu2023noisy,
  title  = {Noisy Computing of the $\mathsf{OR}$ and $\mathsf{MAX}$ Functions},
  author = {Banghua Zhu and Ziao Wang and Nadim Ghaddar and Jiantao Jiao and Lele Wang},
  year   = {2023},
  eprint = {2309.03986},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.03986v1}
}

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