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.
翻译:高压摩尔捆绑是一个优雅的语句,它代表着Girth、 Hoory 和 Linial[AHL02] 的典型工作,它与领先的常数紧密相连。对于正统的Rightal2 参数来说,一个恰当的概括化由 Feige [Fei08] 以最小周期或甚至覆盖值(一个具有不同程度的子高射线图)或尺寸(一个具有更高程度的子高射线)或高射线(高射线)和高射线(高射线高射线)之间的对比。对于图形(即$221 和Linalial2 的典型分析),它们的分析,尤其是美元标准值的直径,由Feige[Fei] 得出一个适当的概括化。在Gruswami、Kothhar和马诺尔[GM21]最近的工作规模中,一个更简单、更短的Kirku 系数使得高射程的基质的模型得以使用。