LEAPS-MPS: Some Applications of Free Probability and Random Matrix Theory

LEAPS-MPS：自由概率和随机矩阵理论的一些应用

• 批准号: 2316836
• 负责人: Ping Zhong
• 金额: $ 24.75万
• 依托单位: University of Wyoming
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2316836&HistoricalAwards=false
• 关键词: LEAPS; MPS; Some; Applications; Free

Matrices with random entries arise naturally in physics, statistics, and engineering, and they are designed to describe complicated systems. As the dimensions of these matrices are usually very large, classical tools in linear algebra are inadequate to tackle this situation. One common theme of random matrix theory is that a large family of random matrices shares the same limiting distribution due to universality phenomena. This is an analogue of the central limit theorem in classical probability, where the only requirements for the i.i.d. random variables are some moment conditions. Hence, random matrix theory can make sense of large-scale data under very mild assumptions. Random matrices of large dimension can often be modeled by nonrandom operators living in some abstract operator algebras, where these operators satisfy some highly nontrivial relations characterized by Voiculescu’s free independence. These nonrandom operators are free random variables in free probability theory. The principal investigator will study probability distributions of free random variables and the convergence of suitable random matrix models. The project provides research opportunities for both undergraduate and graduate students. This project, supported by a LEAPS-MPS award, aims to develop analytic tools for studying fundamental questions regarding the limiting distributions of important random matrix models. These questions are motivated by questions from mathematics, statistics, combinatorics, and quantum information. The Brown measure of a free random variable is a spectral measure that generalizes the eigenvalue distribution of square matrices. One major objective is to develop new techniques for calculating Brown measures, which provide predictions for the limits of non-Hermitian random matrices. The Hermitian reduction method and subordination functions are powerful tools for deriving Brown measure formulas. The new results on Brown measures open the door to the study of random matrix models that were previously inaccessible. Free probability theory offers a conceptual approach to studying random matrices of large dimensions. The principal investigator will identify the limiting free random variables for various random matrix models arising from high-dimensional statistics and quantum information theory. In particular, the principal investigator will examine the spectrum of the full rank deformed single-ring random matrix model, the autocovariance matrix of time series, and the k-positivity of random tensor networks. Additionally, the PI will explore the theory of epsilon-freeness and investigate its applications to quantum information. This project is funded in part by the NSF Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

具有随机条目的矩阵自然出现在物理学、统计学和工程学中，它们被设计用来描述复杂的系统，由于这些矩阵的维度通常非常大，线性代数中的经典工具不足以解决这种情况。随机矩阵理论是由于普遍性现象，一大群随机矩阵共享相同的极限分布，这是经典概率中的中心极限定理的模拟，其中独立同分布的唯一要求是一些矩条件。随机矩阵理论可以在非常温和的假设下理解大规模数据。大维随机矩阵通常可以通过一些抽象算子代数中的非随机算子来建模，其中这些算子满足一些以 Voiculescu 自由非随机算子为特征的非平凡关系。是自由概率论中的自由随机变量，该项目为本科生和研究生提供了研究机会。该项目由 LEAPS-MPS 奖项支持，旨在开发分析工具来研究有关重要随机矩阵模型的极限分布的基本问题，这些问题的动机来自数学、统计学、组合学和量子信息的布朗度量。自由随机变量是一种概括方阵特征值分布的谱测度，一个主要目标是开发计算布朗测度的新技术，该技术为非厄米特随机矩阵的极限提供预测。埃尔米特约简法和从属函数是推导布朗测度公式的有力工具，布朗测度的新结果为研究以前无法获得的随机矩阵模型打开了大门，自由概率论提供了研究大维随机矩阵的概念方法。首席研究员将确定由高维统计和量子信息理论产生的各种随机矩阵模型的限制自由随机变量，特别是，首席研究员将检查满秩变形单环随机矩阵模型的谱，即自协方差。此外，PI 将探索 epsilon-freeness 理论并研究其在量子信息中的应用。该项目部分由 NSF 刺激竞争性研究计划资助。 (EPSCoR)。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。