随机矩阵积的通用性和最小奇异值:一种简化方法

Rohit Chaudhuri, Vishesh Jain, N. Pillai
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引用次数: 1

摘要

本文给出了在研究独立随机矩阵积的局部普适性时,如何对平移线性化矩阵的最小奇异值提供精确的控制。这个问题最早是在Koppel, O'Rourke和Vu最近的工作中提出的,与他们的工作相比,我们的证明要简单得多,并且具有更大的普遍性。特别是,我们只假设集合的项是中心的,并且第二和第四矩均匀地边界远离$0$和无穷大,而以前的工作假设了一个均匀的亚高斯衰减条件,并且乘积的每个因子内的项是相同分布的。我们的最小奇异值界的一个结果是,最近由Koppel, O'Rourke和Vu获得的独立随机矩阵乘积的四矩匹配普适性结果在更弱的假设下成立。我们的证明技术在结构化稀疏矩阵的研究中也具有独立的意义。
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Universality and least singular values of random matrix products: a simplified approach
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.
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