Matrix hypercontractivity, streaming algorithms and LDCs: the large alphabet case

TL;DR

This paper establishes a matrix hypercontractive inequality for large alphabets and applies it to derive bounds in communication complexity, streaming algorithms, and locally decodable codes, advancing understanding in large alphabet settings.

Contribution

It introduces a new hypercontractive inequality for matrix-valued functions over large alphabets and applies it to various problems in complexity and coding theory.

Findings

Derived bounds for Hidden Hypermatching problem communication complexity

Proved quantum space lower bounds for streaming algorithms approximating Unique Games

Established exponential lower bounds for large alphabet locally decodable codes

Abstract

We prove a hypercontractive inequality for matrix-valued functions defined over large alphabets. In order to do so, we prove a generalization of the powerful $2$-uniform convexity inequality for trace norms of Ball, Carlen, Lieb (Inventiones Mathematicae'94). Using our hypercontractive~inequality, we present upper and lower bounds for the communication complexity of the Hidden Hypermatching problem defined over large alphabets. We then consider streaming algorithms for approximating the value of Unique Games on a hypergraph with $t$-size hyperedges. By using our communication lower bound, we show that every streaming algorithm in the adversarial model achieving an $(r-\varepsilon)$-approximation of this value requires $\Omega(n^{1-2/t})$ quantum space, where $r$ is the alphabet size. We next present a lower bound for locally decodable codes (LDC) $\mathbb{Z}_r^n\to \mathbb{Z}_r^N$ over large alphabets with recoverability probability at least $1/r + \varepsilon$. Using hypercontractivity, we give an exponential lower bound $N = 2^{\Omega(\varepsilon^4 n/r^4)}$ for $2$-query (possibly non-linear) LDCs over $\mathbb{Z}_r$ and using the non-commutative Khintchine inequality we prove an improved lower bound of $N = 2^{\Omega(\varepsilon^2 n/r^2)}$.