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Ubiquitous Doubling Algorithms for Nonlinear Matrix Equations and Applications

普遍存在的非线性矩阵方程和应用的倍增算法

基本信息

批准号：
1719620
负责人：
Ren-Cang Li
金额：
$ 20.08万
依托单位：
University of Texas at Arlington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1719620&HistoricalAwards=false
关键词：
Ubiquitous Doubling Algorithms Nonlinear Matrix

项目摘要

The principal investigator's (PI's) research is aimed at the solution of practical problems of optimal control theory from areas as diverse as vibration analysis for high speed trains and quantum transport in nano research. Underlying these applications are formulations that require the efficient and accurate solution of important nonlinear matrix equations which critically influence the overall performance and fidelity of the entire simulations. Past experience has shown that practically relevant simulations requiring solutions of nonlinear matrix equations are notoriously challenging to the point that computed solutions may be completely erroneous. This project aims at changing the status quo by developing a complete theory, devising innovative algorithms to better reflect problem structures, and designing more robust implementations. In addition to advancing research in these nonlinear matrix equations, the PI will recruit and train graduate students in computational mathematics and interdisciplinary studies.This project will advance the understanding and solution techniques for high impact nonlinear matrix equations in the context of mathematical theory, computational methods, and software. For decades, computational scientists and engineers have been struggling to compute trustworthy numerical solutions to some of these nonlinear matrix equations with limited success. For example, the entries of the solution matrices to M-matrix algebraic Riccati equations from Markov-modulated fluid flow theory represent probabilities of events, and tiny entries indicate events that are rare but still important, and thus need to be computed accurately. The eigenvalues from vibration analysis of high speed trains vary widely in magnitude beyond the ranges that the IEEE double precision can handle if not dealt with correctly. The doubling algorithm may not converge at all on the unperturbed nonlinear matrix equations for quantum transport in nano research. These difficulties are often not caused by the large sizes of the problems but rather are more due to their inherent mathematical properties. With the successful completion of this project, a complete and coherent unifying framework of doubling algorithms, along with the relevant theory, will be developed. A much deeper understanding of doubling algorithms will be gained, and it is possible that important applications will be identified where doubling algorithms can be utilized and are faster than the current state-of-the-art methods. More significantly, the theory developed will assure that the new algorithms will produce more trustworthy accurate numerical results than is currently possible.

首席研究员（PI）的研究旨在解决最优控制理论的实际问题，这些问题来自不同的领域，如高速列车的振动分析和纳米研究中的量子传输。这些应用程序的基础是配方，需要有效和准确的解决方案的重要的非线性矩阵方程，严重影响整个模拟的整体性能和保真度。过去的经验表明，实际相关的模拟需要解决非线性矩阵方程是众所周知的挑战点，计算的解决方案可能是完全错误的。该项目旨在通过开发一个完整的理论，设计创新的算法来更好地反映问题结构，并设计更强大的实现来改变现状。除了推进这些非线性矩阵方程的研究外，PI还将招募和培养计算数学和跨学科研究的研究生。该项目将在数学理论，计算方法和软件的背景下推进对高影响力非线性矩阵方程的理解和解决技术。几十年来，计算科学家和工程师一直在努力计算这些非线性矩阵方程的可靠数值解，但成功有限。例如，马尔可夫调制流体流动理论的M矩阵代数黎卡提方程的解矩阵的条目表示事件的概率，而微小的条目表示罕见但仍然重要的事件，因此需要精确计算。高速列车振动分析的特征值变化幅度很大，如果处理不当，超出了IEEE双精度可以处理的范围。在纳米研究中，对于量子输运的非线性矩阵方程，倍增算法可能根本不收敛。这些困难通常不是由问题的大尺寸引起的，而是更多地是由于其固有的数学性质。随着本计画的成功完成，将发展出一套完整且连贯的加倍演算法统一架构，并将沿着相关理论。加倍算法将获得更深入的理解，它是可能的，重要的应用将被确定，加倍算法可以利用，并比目前最先进的方法更快。更重要的是，开发的理论将确保新算法将产生比目前可能的更可靠的精确数值结果。