Trading information complexity for error

TL;DR

This paper investigates how allowing errors in two-party communication reduces information complexity, providing bounds and specific results for functions like AND and set disjointness, and addressing open questions in the field.

Contribution

It establishes tight bounds on information complexity reduction due to errors for arbitrary functions and specific functions like AND and set disjointness, answering several open questions.

Findings

Bounds of order (h()) and (h()) for arbitrary functions.

Exact (h())) gain for the AND function.

A new protocol achieving (())) gain for set disjointness.

Abstract

We consider the standard two-party communication model. The central problem studied in this article is how much one can save in information complexity by allowing an error of $\epsilon$. For arbitrary functions, we obtain lower bounds and upper bounds indicating a gain that is of order $\Omega(h(\epsilon))$ and $O(h(\sqrt{\epsilon}))$. Here $h$ denotes the binary entropy function. We analyze the case of the two-bit AND function in detail to show that for this function the gain is $\Theta(h(\epsilon))$. This answers a question of [M. Braverman, A. Garg, D. Pankratov, and O. Weinstein, From information to exact communication (extended abstract), STOC'13]. We obtain sharp bounds for the set disjointness function of order $n$. For the case of the distributional error, we introduce a new protocol that achieves a gain of $\Theta(\sqrt{h(\epsilon)})$ provided that $n$ is sufficiently large. We apply these results to answer another of question of Braverman et al. regarding the randomized communication complexity of the set disjointness function. Answering a question of [Mark Braverman, Interactive information complexity, STOC'12], we apply our analysis of the set disjointness function to establish a gap between the two different notions of the prior-free information cost. This implies that amortized randomized communication complexity is not necessarily equal to the amortized distributional communication complexity with respect to the hardest distribution.