CAREER: New Techniques for High Dimensional Systems

职业：高维系统新技术

• 批准号: 1552520
• 负责人: Adam Marcus
• 金额: $ 40.22万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2020-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1552520&HistoricalAwards=false
• 关键词: CAREER; New; Techniques; Dimensional; Systems

The onset of "big data" has led to an increased appreciation of the value of large data sets in both industrial and academic settings. Typically, the analysis of such data sets begins by modeling the data as a high dimensional system, and so an understanding of the behavior of such systems (and techniques for dealing with them) has become equally important. This award supports a mathematical investigation of the behavior of such systems using techniques introduced by the researcher in previous work with collaborators. These techniques have led to the resolution of a number of open problems across various mathematical fields, including the Kadison-Singer problem and the existence of Ramanujan graphs of all sizes and degrees. This project aims to further develop substantial connections with research across numerous fields of science, including convex optimization, real algebraic geometry, functional analysis, combinatorics, and probability. The award also supports plans to focus interdisciplinary activities of particular benefit to students associated with the project.On a technical level, the project focuses on two areas of research: (A) to develop a theory of ''finite free probability,'' a collection of ideas lying in the intersection of random matrix theory, convex optimization, real algebraic geometry, and polynomial geometry, and (B) to generalize the previously mentioned techniques so as to widen their potential application. This includes extending the ideas to bivariate polynomials as well as a non-Hermitian setting. In the direction of (B), the project aims to address some of the obstacles that hinder the application of the current techniques (for example, the need for real rootedness of associated polynomials) by investigating analogous behaviors in more general settings. This would be a necessary part of extending (A) to non-Hermitian settings, for example, as such objects no longer satisfy the conditions necessary to apply the current techniques. Extensions in this direction would also open the possibility of application to new areas such as quantitative geometry.

“大数据”的出现使人们越来越重视工业和学术界的大数据集的价值。通常，对这些数据集的分析始于将数据建模为高维系统，因此理解这些系统的行为（以及处理它们的技术）变得同样重要。该奖项支持使用研究人员在以前的工作中与合作者介绍的技术对此类系统的行为进行数学研究。这些技术导致了许多数学领域的开放问题的解决，包括Kadison-Singer问题和所有大小和度数的Ramanujan图的存在性。该项目旨在进一步发展与众多科学领域研究的实质性联系，包括凸优化，真实的代数几何，泛函分析，组合学和概率。该奖项还支持计划集中跨学科的活动，特别有利于与该项目相关的学生。在技术层面上，该项目侧重于两个研究领域：（A）发展“有限自由概率”理论，这是随机矩阵理论、凸优化、真实的代数几何和多项式几何交叉的思想集合，（B）推广上述技术，以拓宽其潜在应用。这包括将这些想法扩展到二元多项式以及非埃尔米特设置。在（B）的方向上，该项目旨在通过调查更一般的设置中的类似行为来解决阻碍当前技术应用的一些障碍（例如，相关多项式的真实的有根性的需要）。这将是将（A）扩展到非厄米特设置的必要部分，例如，因为这样的对象不再满足应用当前技术所必需的条件。在这个方向上的扩展也将打开应用到新的领域，如定量几何的可能性。