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arXiv 2007.03595math.PR
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随机矩阵乘积的普适性和最小奇异值:一种简化方法

Universality and least singular values of random matrix products: a simplified approach

Rohit Chaudhuri, Vishesh Jain, Natesh S. Pillai

摘要:

本文讨论了在研究独立随机矩阵乘积的局部普适性时,如何对平移线性化矩阵的最小奇异值进行精确控制。这个问题首先被认为是在最近的工作Koppel,O'Rourke和武,并与他们的工作相比,我们的证明是大大简单,并建立在更大的一般性。特别是,我们只假设的集合的条目是中心,并有二阶和四阶矩均匀有界远离$0$和无穷大,而以前的工作假设一个均匀的亚高斯衰减条件,并在每个产品的因素内的条目是相同的分布。我们的最小奇异值界的一个后果是,独立随机矩阵的产品,最近获得的Koppel,O'Rourke,和Vu的四个时刻匹配的普遍性结果,在弱得多的假设下。我们的证明技术也是结构稀疏矩阵的研究中的独立利益。

英文摘要:

In this note, we show how to provide sharp control on the least singular value of a certain translated linearization matrix arising in the study of the local universality of products of independent random matrices. This problem was first considered in a recent work of Koppel, O'Rourke, and Vu, and compared to their work, our proof is substantially simpler and established in much greater generality . In particular, we only assume that the entries of the ensemble are centered, and have second and fourth moments uniformly bounded away from $0$ and infinity, whereas previous work assumed a uniform subgaussian decay condition and that the entries within each factor of the product are identically distributed. A consequence of our least singular value bound is that the four moment matching universality results for the products of independent random matrices, recently obtained by Koppel, O'Rourke, and Vu, hold under much weaker hypotheses. Our proof technique is also of independent interest in the study of structured sparse matrices.

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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