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arXiv 0911.5684math-phmath.MP
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大型随机图线性特征值统计量涨落的中心极限定理

Central limit theorem for fluctuations of linear eigenvalue statistics of large random graphs

M. Shcherbina, B. Tirozzi

摘要:

本文研究了大随机图的邻接矩阵$A$,并研究了函数$f_n(z,u)=\frac{1}{n}\sum_{k=1}^n\exp\{-uG_{kk}(z)\}$与$G(z)=(z-iA)^{-1}$的涨落.证明了高斯随机变量在极限$n^{-1/2}$下的涨落矩满足Wick关系。这使我们能够证明中心极限定理$\hbox{Tr}G(z)$,然后扩展结果的线性特征值统计$\hbox{Tr}ϕ(A)$的任何功能$ϕ:\mathbb{R}\to\mathbb{R}$增加,连同其前两个衍生物,在无穷大不快于指数。

英文摘要:

We consider the adjacency matrix $A$ of a large random graph and study fluctuations of the function $f_n(z,u)=\frac{1}{n}\sum_{k=1}^n\exp\{-uG_{kk}(z)\}$ with $G(z)=(z-iA)^{-1}$. We prove that the moments of fluctuations normalized by $n^{-1/2}$ in the limit $n\to\infty$ satisfy the Wick relations for the Gaussian random variables. This allows us to prove central limit theorem for $\hbox{Tr}G(z)$ and then extend the result on the linear eigenvalue statistics $\hbox{Tr}ϕ(A)$ of any function $ϕ:\mathbb{R}\to\mathbb{R}$ which increases, together with its first two derivatives, at infinity not faster than an exponential.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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