Anti-concentration and the Exact Gap-Hamming Problem

TL;DR

This paper establishes anti-concentration bounds for inner products of random vectors and applies these results to prove that the exact gap-hamming problem requires linear communication, resolving a key open problem.

Contribution

It introduces new anti-concentration bounds for inner products and uses them to prove a linear lower bound for the gap-hamming problem in communication complexity.

Findings

Inner product of independent random vectors is anti-concentrated with probability at most O(1/√n).

Exact gap-hamming problem requires linear communication complexity.

Anti-concentration extends to structured distributions with low entropy.

Abstract

We prove anti-concentration bounds for the inner product of two independent random vectors, and use these bounds to prove lower bounds in communication complexity. We show that if $A,B$ are subsets of the cube $\{\pm 1\}^n$ with $|A| \cdot |B| \geq 2^{1.01 n}$, and $X \in A$ and $Y \in B$ are sampled independently and uniformly, then the inner product $\langle X,Y \rangle$ takes on any fixed value with probability at most $O(1/\sqrt{n})$. In fact, we prove the following stronger "smoothness" statement: $$ \max_{k } \big| \Pr[\langle X,Y \rangle = k] - \Pr[\langle X,Y \rangle = k+4]\big| \leq O(1/n).$$ We use these results to prove that the exact gap-hamming problem requires linear communication, resolving an open problem in communication complexity. We also conclude anti-concentration for structured distributions with low entropy. If $x \in \mathcal{Z}^n$ has no zero coordinates, and $B \subseteq \{\pm 1\}^n$ corresponds to a subspace of $\mathcal{F}_2^n$ of dimension $0.51n$, then $\max_k \Pr[\langle x,Y \rangle = k] \leq O(\sqrt{\ln (n)/n})$.