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[2207.10850] A simple and sharper proof of the hypergraph Moore bound

 1 year ago
source link: https://arxiv.org/abs/2207.10850
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Mathematics > Combinatorics

[Submitted on 22 Jul 2022]

A simple and sharper proof of the hypergraph Moore bound

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The hypergraph Moore bound is an elegant statement that characterizes the extremal trade-off between the girth - the number of hyperedges in the smallest cycle or even cover (a subhypergraph with all degrees even) and size - the number of hyperedges in a hypergraph. For graphs (i.e., 2-uniform hypergraphs), a bound tight up to the leading constant was proven in a classical work of Alon, Hoory and Linial [AHL02]. For hypergraphs of uniformity k>2, an appropriate generalization was conjectured by Feige [Fei08]. The conjecture was settled up to an additional \log^{4k+1} n factor in the size in a recent work of Guruswami, Kothari and Manohar [GKM21]. Their argument relies on a connection between the existence of short even covers and the spectrum of a certain randomly signed Kikuchi matrix. Their analysis, especially for the case of odd k, is significantly complicated.
In this work, we present a substantially simpler and shorter proof of the hypergraph Moore bound. Our key idea is the use of a new reweighted Kikuchi matrix and an edge deletion step that allows us to drop several involved steps in [GKM21]'s analysis such as combinatorial bucketing of rows of the Kikuchi matrix and the use of the Schudy-Sviridenko polynomial concentration. Our simpler proof also obtains tighter parameters: in particular, the argument gives a new proof of the classical Moore bound of [AHL02] with no loss (the proof in [GKM21] loses a \log^3 n factor), and loses only a single logarithmic factor for all k>2-uniform hypergraphs.
As in [GKM21], our ideas naturally extend to yield a simpler proof of the full trade-off for strongly refuting smoothed instances of constraint satisfaction problems with similarly improved parameters.

Subjects: Combinatorics (math.CO); Discrete Mathematics (cs.DM); Data Structures and Algorithms (cs.DS)
Cite as: arXiv:2207.10850 [math.CO]
  (or arXiv:2207.10850v1 [math.CO] for this version)
  https://doi.org/10.48550/arXiv.2207.10850

Submission history

From: Sidhanth Mohanty [view email]
[v1] Fri, 22 Jul 2022 02:55:56 UTC (73 KB)

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