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arXiv 0708.2895math.PRmath.SP
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随机矩阵:循环法则

Random Matrices: The circular Law

Terence Tao, Van Vu

摘要:

设$\a$是均值为零且方差有界的复随机变量$σ^{2}$。设$N_{n}$是$n$阶的随机矩阵,其元素是i.i.d.。$\a$的副本。令$λ_{1}, ..., λ_{n}$为$\frac{1}{σ\sqrt n}N_{n}$的特征值。用公式$$ μ_n(s,t) := \frac{1}{n} # \{k \leq n| \Re(λ_k) \leq s; \Im(λ_k) \leq t \}.$$定义$N_{n}$的经验谱分布$μ_{n}$循环定律猜想断言当$n$趋于无穷大时,$μ_{n}$收敛于单位圆盘上的均匀分布$μ_\infty$。我们证明了这个猜想下稍强的假设下,$(2+η)þ$ -时刻的$\a$是有界的,任何$η>0$。我们的方法建立并改进了Girko,Bai,Götze-Tikhomirov和Pan-Zhou的早期工作,并且也适用于稀疏随机矩阵。本文的主要内容是利用加法组合学的工具和思想得到了随机矩阵最小奇异值的一般结果。

英文摘要:

Let $\a$ be a complex random variable with mean zero and bounded variance $σ^{2}$. Let $N_{n}$ be a random matrix of order $n$ with entries being i.i.d. copies of $\a$. Let $λ_{1}, ..., λ_{n}$ be the eigenvalues of $\frac{1}{σ\sqrt n}N_{n}$. Define the empirical spectral distribution $μ_{n}$ of $N_{n}$ by the formula $$ μ_n(s,t) := \frac{1}{n} # \{k \leq n| \Re(λ_k) \leq s; \Im(λ_k) \leq t \}.$$ The Circular law conjecture asserts that $μ_{n}$ converges to the uniform distribution $μ_\infty$ over the unit disk as $n$ tends to infinity. We prove this conjecture under the slightly stronger assumption that the $(2+η)þ$-moment of $\a$ is bounded, for any $η>0$. Our method builds and improves upon earlier work of Girko, Bai, Götze-Tikhomirov, and Pan-Zhou, and also applies for sparse random matrices. The new key ingredient in the paper is a general result about the least singular value of random matrices, which was obtained using tools and ideas from additive combinatorics.

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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