An Optimal Lower Bound on the Communication Complexity of Gap-Hamming-Distance

TL;DR

This paper establishes an optimal linear lower bound on the randomized communication complexity of the Gap-Hamming-Distance problem, resolving a long-standing conjecture and impacting data stream space lower bounds.

Contribution

It proves the first optimal lower bound for the randomized communication complexity of Gap-Hamming-Distance, confirming the naive protocol's optimality.

Findings

Proves an n lower bound on communication complexity.

Derives near-optimal space lower bounds for data stream algorithms.

Introduces a new geometric Gaussian space correlation result.

Abstract

We prove an optimal $\Omega(n)$ lower bound on the randomized communication complexity of the much-studied Gap-Hamming-Distance problem. As a consequence, we obtain essentially optimal multi-pass space lower bounds in the data stream model for a number of fundamental problems, including the estimation of frequency moments. The Gap-Hamming-Distance problem is a communication problem, wherein Alice and Bob receive $n$-bit strings $x$ and $y$, respectively. They are promised that the Hamming distance between $x$ and $y$ is either at least $n/2+\sqrt{n}$ or at most $n/2-\sqrt{n}$, and their goal is to decide which of these is the case. Since the formal presentation of the problem by Indyk and Woodruff (FOCS, 2003), it had been conjectured that the naive protocol, which uses $n$ bits of communication, is asymptotically optimal. The conjecture was shown to be true in several special cases, e.g., when the communication is deterministic, or when the number of rounds of communication is limited. The proof of our aforementioned result, which settles this conjecture fully, is based on a new geometric statement regarding correlations in Gaussian space, related to a result of C. Borell (1985). To prove this geometric statement, we show that random projections of not-too-small sets in Gaussian space are close to a mixture of translated normal variables.