Matrix Analytics and Applications: Positivity, Graphs, and Stability

矩阵分析和应用：积极性、图表和稳定性

• 批准号: RGPIN-2014-06036
• 负责人: Fallat, Shaun
• 金额: $ 1.31万
• 依托单位: University of Regina
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=650422
• 关键词: Matrix; Analytics; Applications; Positivity; Graphs

My research interests are centered on `Positivity' in Linear Algebra and Graph Theory. Along these lines, I have proposed various research projects involving totally positive matrices and properties of certain matrices associated with graphs and their related combinatorial parameters and corresponding algebraic invariants.**The former is a well studied and fundamental class of matrices that arises in numerous applications, including statistics, mathematical biology, and computer aided geometric design. I intend to investigate a number of key problems associated with this class by making use of the combinatorial framework associated with certain matrix factorizations, and explore some new exciting directions involving important connections to an underlying fundamental algebraic structure. The plan that I have developed will lead to a deeper understanding of this class of matrices by setting out a sequence of concrete objectives.**The main issue of interest concerning matrices that are derived from graphs comes from a classical problem known as an inverse eigenvalue problem (that is, the eigenvalues are provided and the corresponding matrix is desired). An important part of this inverse eigenvalue problem is the minimum rank problem. The connection between the two follows since rank and nullity are intimately related. The hope here is to appeal to the algebraic properties of matrices and combine them with the combinatorial characteristics of graphs to yield interesting results about this special collection of matrices. In summation, I will combine existing theoretical knowledge along with new cutting edge sophistication to explore a number of fundamental advances and connections to important applications including communication complexity in computer science, control of quantum systems in mathematical physics and compelling variations on certain searching problems in networks and graphs. This cutting edge research is of current interest across the globe, as it brings together material from the theory of matrices, central notions in combinatorics, and develops a sequence of expanding fundamental issues.

我的研究兴趣集中在线性代数和图论中的“正性”。沿着这些思路，我提出了各种研究项目，涉及全正矩阵和与图有关的某些矩阵的性质及其相关的组合参数和相应的代数不变量。**前者是一类研究得很好的基本矩阵，出现在许多应用中，包括统计学、数学生物学和计算机辅助几何设计。我打算利用与某些矩阵分解相关的组合框架来研究与这类有关的一些关键问题，并探索一些新的激动人心的方向，涉及到基本代数结构的重要联系。我制定的计划将通过列出一系列具体目标来加深对这类矩阵的理解。**关于从图派生的矩阵的主要感兴趣的问题来自一个被称为逆特征值问题的经典问题(即，提供了特征值，并且需要相应的矩阵)。这个逆特征值问题的一个重要部分是最小秩问题。两者之间的联系如下所示，因为等级和无效是密切相关的。这里的希望是吸引矩阵的代数性质，并将它们与图的组合特征相结合，以产生关于这一特殊矩阵集合的有趣结果。总而言之，我将结合现有的理论知识和新的尖端技术来探索一些基本的进步和与重要应用的联系，包括计算机科学中的通信复杂性，数学物理中的量子系统控制，以及网络和图形中某些搜索问题的引人注目的变化。这一前沿研究是当前全球感兴趣的，因为它汇集了来自矩阵理论的材料，即组合学中的中心概念，并开发了一系列扩展的基本问题。