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

TL;DR

A hypercontractive inequality for matrix-valued functions defined over large alphabets is proved and a generalization of the powerful 2-uniform convexity inequality for trace norms of Ball, Carlen, Lieb is proved.

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 $Ω(n^{1-2/t})$ 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 $N = 2^{Ω(\varepsilon^4 n/r^4)}$ 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 $N = 2^{Ω(\varepsilon^2 n/r^2)}$.

Theorems & Definitions (33)

• Conjecture 1
• Lemma 3
• Theorem 4: Optimal 2-uniform convexity
• Theorem 5
• Theorem 6: A generalization of ball1994sharp
• Remark 7
• Theorem 8
• Remark 10: Comparison with hypercontractivity for real numbers
• Definition 11
• Theorem 12