Wegner estimate and upper bound on the eigenvalue condition number of non-Hermitian random matrices

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-05-03 DOI:10.1002/cpa.22201
László Erdős, Hong Chang Ji
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Abstract

We consider N × N $N\times N$ non-Hermitian random matrices of the form X + A $X+A$ , where A $A$ is a general deterministic matrix and N X $\sqrt {N}X$ consists of independent entries with zero mean, unit variance, and bounded densities. For this ensemble, we prove (i) a Wegner estimate, that is, that the local density of eigenvalues is bounded by N 1 + o ( 1 ) $N^{1+o(1)}$ and (ii) that the expected condition number of any bulk eigenvalue is bounded by N 1 + o ( 1 ) $N^{1+o(1)}$ ; both results are optimal up to the factor N o ( 1 ) $N^{o(1)}$ . The latter result complements the very recent matching lower bound obtained by Cipolloni et al. and improves the N $N$ -dependence of the upper bounds by Banks et al. and Jain et al. Our main ingredient, a near-optimal lower tail estimate for the small singular values of X + A z $X+A-z$ , is of independent interest.

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非ermitian 随机矩阵的韦格纳估计值和特征值条件数上限
我们考虑了非ermitian 随机矩阵的形式 ,其中 , 是一个一般的确定性矩阵,由均值为零、方差为单位且密度有界的独立条目组成。对于这个集合,我们证明了 (i) 韦格纳估计,即特征值的局部密度有界于;(ii) 任何主体特征值的预期条件数有界于 ;这两个结果都是最优的,直到系数 。后一个结果补充了 Cipolloni 等人最近得到的匹配下界,并改进了 Banks 等人和 Jain 等人的上界的-依赖性。我们的主要内容是对小奇异值 , 的近最优下尾估计,这与我们的兴趣无关。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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