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Main Authors: Arunachalam, Srinivasan, Doriguello, Joao F.
Format: Preprint
Published: 2021
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Online Access:https://arxiv.org/abs/2109.02600
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author Arunachalam, Srinivasan
Doriguello, Joao F.
author_facet Arunachalam, Srinivasan
Doriguello, Joao F.
contents 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 $N = 2^{Ω(\varepsilon^2 n/r^2)}$.
format Preprint
id arxiv_https___arxiv_org_abs_2109_02600
institution arXiv
publishDate 2021
record_format arxiv
spellingShingle Matrix hypercontractivity, streaming algorithms and LDCs: the large alphabet case
Arunachalam, Srinivasan
Doriguello, Joao F.
Computational Complexity
Functional Analysis
Quantum Physics
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 $N = 2^{Ω(\varepsilon^2 n/r^2)}$.
title Matrix hypercontractivity, streaming algorithms and LDCs: the large alphabet case
topic Computational Complexity
Functional Analysis
Quantum Physics
url https://arxiv.org/abs/2109.02600