Ramanujan界之外的割稀疏化：割与谱稀疏化的分离
Cut Sparsification of the Clique Beyond the Ramanujan Bound: A Separation of Cut Versus Spectral Sparsification
摘要：
我们证明了随机$d$ -正则图，有很高的概率，是近似误差至多为$\left(2\sqrt{\frac 2 π} + o_{n,d}(1)\right)/\sqrt d$的团的割稀疏子，其中$2\sqrt{\frac 2 π} = 1.595\ldots$和$o_{n,d}(1)$表示依赖于$n$和$d$的误差项，如果我们先取极限$n\rightarrow \infty$，然后取极限$d \rightarrow \infty$，则该误差项趋于零.这是通过使用Jagannath和Sen从统计物理学思想中获得的技术分析线性大小切割，并通过鞅不等式分析小切割来建立的。我们还证明了新的下界谱稀疏的集团。若$G$是该团的谱稀疏子，且$G$具有平均度$d$，则只要$G$的加权邻接矩阵是一个双随机矩阵（的倍数），或者$G$满足一定的高“奇伪围长”性质，我们证明了逼近误差至少是$d$ -正则Ramanujan图所满足的“Ramanujan界”$(2-o_{n,d}(1))/\sqrt d$.第一种情况可以被看作是“对称双随机矩阵的阿隆-博帕纳定理”，表明具有$dn$非零元素的对称双随机矩阵具有至少$(2-o_{n,d}(1))/\sqrt d$大小的非平凡特征值;第二种情况推广了Srivastava和Trevisan的下界，这需要大围长假设。总之，这些结果意味着频谱稀疏化和切割稀疏化之间的分离。如果$G$是$n$顶点上的随机$\log n$ -正则图，我们证明了$G$以很高的概率存在一个平均度为$d$的（加权子图）割稀疏子和至多$(1.595\ldots + o_{n,d}(1))/\sqrt d$的逼近误差，而$G$的每个平均度为$d$的（加权子图）谱稀疏子至少有$(2-o_{n,d}(1))/\sqrt d$的逼近误差.
英文摘要：
We prove that a random $d$-regular graph, with high probability, is a cut sparsifier of the clique with approximation error at most $\left(2\sqrt{\frac 2 π} + o_{n,d}(1)\right)/\sqrt d$, where $2\sqrt{\frac 2 π} = 1.595\ldots$ and $o_{n,d}(1)$ denotes an error term that depends on $n$ and $d$ and goes to zero if we first take the limit $n\rightarrow \infty$ and then the limit $d \rightarrow \infty$. This is established by analyzing linear-size cuts using techniques of Jagannath and Sen derived from ideas in statistical physics, and analyzing small cuts via martingale inequalities. We also prove new lower bounds on spectral sparsification of the clique. If $G$ is a spectral sparsifier of the clique and $G$ has average degree $d$, we prove that the approximation error is at least the "Ramanujan bound'' $(2-o_{n,d}(1))/\sqrt d$, which is met by $d$-regular Ramanujan graphs, provided that either the weighted adjacency matrix of $G$ is a (multiple of) a doubly stochastic matrix, or that $G$ satisfies a certain high "odd pseudo-girth" property. The first case can be seen as an "Alon-Boppana theorem for symmetric doubly stochastic matrices," showing that a symmetric doubly stochastic matrix with $dn$ non-zero entries has a non-trivial eigenvalue of magnitude at least $(2-o_{n,d}(1))/\sqrt d$; the second case generalizes a lower bound of Srivastava and Trevisan, which requires a large girth assumption. Together, these results imply a separation between spectral sparsification and cut sparsification. If $G$ is a random $\log n$-regular graph on $n$ vertices, we show that, with high probability, $G$ admits a (weighted subgraph) cut sparsifier of average degree $d$ and approximation error at most $(1.595\ldots + o_{n,d}(1))/\sqrt d$, while every (weighted subgraph) spectral sparsifier of $G$ having average degree $d$ has approximation error at least $(2-o_{n,d}(1))/\sqrt d$.
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