Random matrixes: Eigenvalues distributions and Universality

随机矩阵：特征值分布和普遍性

• 批准号: 1307797
• 负责人: Van Vu
• 金额: $ 34.5万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2016-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1307797&HistoricalAwards=false
• 关键词: Random; matrixes; Eigenvalues; distributions; Universality

One of the main goals of the theory of random matrices is to study distributions concerning the eigenvalues. In recent years, we have witnessed notable progresses on central problems in this area. The PI has been fortunate to participate in some these developments and he would like to propose to continue his research on these topics. In this proposal, he is going to discuss the current state of some of these problems, and propose to study several research problems that would lead to a more complete and deeper understanding of the subject, especially problems related to the universality phenomenon. Beside studying traditional questions on limiting distributions, we will also discuss non-asymptotic aspects of the theory, e.g. problems concerning large deviations. Many of these problems are important in applications in other fields, such as theoretical computer science and data mining.The theory of random matrices is a rich topic in mathematics. Beside being interesting on their own right, random matrices play fundamental role in various areas such as statistics, mathematical physics, combinatorics, theoretical computer science, etc. A famous example here is the study of physicist Wigner, who used the spectrum of random matrices as a model in nuclear physics, and consequently discovered the fundamental semi-circle law. The main project in this proposal is to study limiting distribution the spectrum of random matrices, at the finest scale, motivated by long standing problems from mathematical physics and probability. We also believe that the methods being developed in the project will be useful for other purposes, such as the study of large (random) networks and large data from random samples.

随机矩阵理论的主要目标之一是研究有关特征值的分布。 近年来，我们看到在这一领域的核心问题上取得了显著进展。PI有幸参与了其中的一些发展，他希望继续对这些主题进行研究。在这份提案中，他将讨论其中一些问题的现状，并建议研究几个研究问题，以更全面和更深入地了解这一主题，特别是与普遍性现象有关的问题。除了研究传统的极限分布问题外，我们还将讨论理论的非渐近方面，例如关于大偏差的问题。其中许多问题在其他领域的应用中也很重要，例如 理论计算机科学和数据挖掘。随机矩阵理论是数学中一个丰富的主题。除了有趣的本身权利，随机矩阵发挥了重要作用，在各个领域，如统计，数学物理，组合学，理论计算机科学等一个著名的例子是研究物理学家维格纳，谁使用的频谱随机矩阵作为一个模型在核物理，并因此发现了基本的半圆定律。主要项目在 本研究的目的是在最精细的尺度上研究随机矩阵谱的极限分布，其动机是数学物理和概率的长期问题。我们还相信，该项目中开发的方法将用于其他目的，例如研究大型（随机）网络和来自随机样本的大量数据。