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Volume 13 (2017) Article 11 pp. 1-36
Superquadratic Lower Bound for 3-Query Locally Correctable Codes over the Reals
Received: July 20, 2015
Revised: March 20, 2017
Published: October 23, 2017
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Keywords: coding theory, discrete geometry, combinatorics, lower bounds
ACM Classification: E.4
AMS Classification: 94B65, 52C35

Abstract: [Plain Text Version]

$ \newcommand{\R}{{\mathbb R}} \newcommand{\eg}{e.\,g.} \def\R{{\mathbb{R}}} $

We prove that 3-query linear locally correctable codes of dimension $d$ over the reals require block length $n> d^{2+\alpha}$ for some fixed, positive $\alpha > 0$. Geometrically, this means that if $n$ vectors in $\R^d$ are such that each vector is spanned by a linear number of disjoint triples of others, then it must be that $n > d^{2+\alpha}$. This improves the known quadratic lower bounds (e.g., Kerenidis - de Wolf (2004), Woodruff (2007)). While the improvement is modest, we expect that the new techniques introduced in this article will be useful for further progress on lower bounds of locally correctable and decodable codes with more than 2 queries, possibly over other fields as well.

Several of the new ideas in the proof work over every field. At a high level, our proof has two parts, clustering and random restriction.

The clustering step uses a powerful theorem of Barthe from convex geometry. It can be used (after preprocessing our LCC to be balanced), to apply a basis change (and rescaling) of the vectors, so that the resulting unit vectors become nearly isotropic. This together with the fact that any LCC must have many `correlated' pairs of points, lets us deduce that the vectors must have a surprisingly strong geometric clustering, and hence also combinatorial clustering with respect to the spanning triples.

In the restriction step, we devise a new variant of the dimension reduction technique used in previous lower bounds, which is able to take advantage of the combinatorial clustering structure above. The analysis of our random projection method reduces to a simple (weakly) random graph process, and works over any field.

An extended abstract of this paper appeared in the Proceedings of of the Forty-sixth Annual ACM Symposium on Theory of Computing (STOC 2014).