Probabilistic aspects of asymptotic geometric analysis

渐近几何分析的概率方面

• 批准号: 0902203
• 负责人: Mark Meckes
• 金额: $ 11.9万
• 依托单位: Case Western Reserve University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-06-15 至 2013-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0902203&HistoricalAwards=false
• 关键词: Probabilistic; aspects; asymptotic; geometric; analysis

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).This project is in the field of asymptotic geometric analysis, which deals with high-dimensional phenomena in convex geometry and functional analysis, and more specifically in applications of probability theory in this field. The project has two separate areas of emphasis, the spectral behavior of large random matrices and the distribution of volume in high-dimensional convex bodies. The proposed techniques for studying these problems include both traditional probabilistic tools of random matrix theory and asymptotic geometric analysis, like the method of moments and the concentration of measure phenomenon; as well as novel techniques from theoretical probability, like Stein's method. In the area of random matrices the proposer studies the fluctuations of norms and eigenvalues of random matrices. One main goal here is to sharpen and extend concentration results due to the proposer and others. The proposer will also continue his study of the behavior of large random Toeplitz matrices, a class of random matrices which has only recently been investigated in the literature, and related random matrix ensembles. In the area of convex geometry the proposer will continue his work on Gaussian approximation theorems for volumes of sections of high-dimensional convex bodies, as well as related problems about how the volume is distributed in high-dimensional convex bodies. An important feature of the problems considered here, which is typical of the field, is their intrinsically high-dimensional nature. For example, many quantitative geometric questions are trivial in a sufficiently low number of dimensions, but deep and unexpected phenomena can arise when the dimension becomes very large. In the context of random matrices, the primary interest is in results which are nontrivial for large finite matrices, as opposed to more-traditional limit results as the size becomes infinite. This high- but finite-dimensional aspect is crucial for potential applications to fields like geometry, statistics, or computer science.High-dimensional phenomena arise whenever one studies quantitative problems involving a large number of parameters. Besides areas in pure mathematics like convex geometry and functional analysis which are the focus of this project, such problems naturally arise in fields as diverse as statistics, computer science, mathematical biology, and physics. The so-called curse of dimensionality is familiar in many quantitative fields, indicating the potential difficulty of dealing with such problems. The goal of asymptotic geometric analysis, on the other hand, is to identify regularity or patterns that arise in systems that depend on a very large number of parameters, but which are not apparent for a small number of parameters. Thus high-dimensionality in some ways becomes a blessing rather than a curse. Probability theory is a central tool in this field, both because many problems involve randomness in an explicit way, and because many a priori deterministic problems can be clarified by introducing a probabilistic viewpoint. That is, patterns that arise in high dimensions become apparent when one looks at things in an appropriately random way. This project deals with both these types of applications of probability to high-dimensional phenomena.

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。该项目涉及渐近几何分析领域，涉及凸几何和泛函分析中的高维现象，更具体地说，涉及概率论在该领域的应用。该项目有两个独立的重点领域，大型随机矩阵的谱行为和高维凸体中的体积分布。研究这些问题的建议技术包括传统的随机矩阵理论和渐近几何分析的概率工具，如矩量法和测量现象的浓度;以及理论概率的新技术，如Stein的方法。在随机矩阵领域，研究了随机矩阵的范数和特征值的涨落。这里的一个主要目标是锐化和扩展由于提议者和其他人的浓度结果。提议者还将继续他的行为的研究大型随机Toeplitz矩阵，一类随机矩阵，最近才被调查的文献，和相关的随机矩阵合奏。在凸几何领域的提议者将继续他的工作高斯近似定理的体积部分的高维凸体，以及有关问题的体积是如何分布在高维凸体。 这里考虑的问题，这是典型的领域的一个重要特征，是其固有的高维性质。例如，许多定量几何问题在足够低的维数下是微不足道的，但当维数变得非常大时，可能会出现深刻的和意想不到的现象。在随机矩阵的背景下，主要的兴趣是在结果是非平凡的大型有限矩阵，而不是更传统的限制结果的大小变得无限。 这种高维但有限维的特性对于几何学、统计学或计算机科学等领域的潜在应用至关重要。当人们研究涉及大量参数的定量问题时，就会出现高维现象。 除了在纯数学领域，如凸几何和功能分析，这是这个项目的重点，这样的问题自然出现在不同的领域，如统计，计算机科学，数学生物学和物理学。 所谓的维数灾难在许多定量领域都很常见，这表明了处理此类问题的潜在困难。 另一方面，渐近几何分析的目标是识别依赖于大量参数的系统中出现的规律性或模式，但这些规律性或模式对于少量参数并不明显。 因此，高维在某些方面成为一种祝福而不是诅咒。 概率论是这一领域的核心工具，这既是因为许多问题以明确的方式涉及随机性，也是因为许多先验确定性问题可以通过引入概率观点来澄清。也就是说，当人们以适当的随机方式看待事物时，在高维中出现的模式变得明显。 这个项目涉及这两种类型的应用概率高维现象。