Linear Response Eigenvalue Problem: New Minimization Principles and Efficient Algorithms

线性响应特征值问题：新的最小化原理和高效算法

• 批准号: 1317330
• 负责人: Ren-Cang Li
• 金额: $ 21.45万
• 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: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1317330&HistoricalAwards=false
• 关键词: Linear; Response; Eigenvalue; Problem; New

The linear response eigenvalue problem, also known as the random phase approximation eigenvalue problem, arises from computing excitation states (energies) of physical systems. Such an eigenvalue problem is usually of large scale -- the matrix dimensions for a molecule of reasonable size can easily get up to tens of millions. It is considered much more difficult than the symmetric eigenvalue problem because it is non-Hermitian in nature. But it has internal symmetric structures in each of the submatrix blocks, which previously have been largely unexploited. This project involves the development of new theory and advanced computational methods through fully exploiting the internal symmetric structures. A systematic study will be thoroughly conducted to uncover new min/maximization principles. These principles should mirror those for the symmetric eigenvalue problem, and will make it possible and guide the investigator to transform some of existing and successful techniques for the symmetric eigenvalue problem for use in the linear response eigenvalue problem research, It is highly expected that new efficient algorithms that are capable of computing several smallest positive eigenvalues simultaneously will emerge as the result of this project. In addition to advancing research in the linear response eigenvalue problem, the investigator will recruit and train graduate students in computational mathematics and interdisciplinary studies. The linear response eigenvalue problem is a major tool in computing energy excitation states of electrons and molecules. This project will critically advance current understanding and solution techniques for the eigenvalue problem in the context of mathematical theory, computational methods, and software. With the successful completion of the project, a significant contribution will be made to the state-of-the-art physical excitation energy computations via random phase approximations, a proven technique that is widely used in computational quantum chemistry and physics.

线性响应本征值问题，也称为随机相位近似本征值问题，源于计算物理系统的激发态（能量）。这样的特征值问题通常是大规模的--对于一个合理大小的分子，矩阵维数可以很容易地达到数千万。它被认为比对称特征值问题困难得多，因为它本质上是非厄米特的。但它在每个子矩阵块中具有内部对称结构，这在以前很大程度上未被利用。该项目涉及通过充分利用内部对称结构来发展新理论和先进的计算方法。一个系统的研究将彻底进行，以发现新的最小/最大化原则。这些原则应反映对称特征值问题，并将使它成为可能，并指导研究人员将一些现有的和成功的对称特征值问题的技术用于线性响应特征值问题的研究，这是高度期望的新的高效算法，能够同时计算几个最小的正特征值将出现作为本项目的结果。除了推进线性响应特征值问题的研究外，研究人员还将招募和培训计算数学和跨学科研究的研究生。 线性响应本征值问题是计算电子和分子能量激发态的主要工具。本项目将在数学理论、计算方法和软件的背景下，批判性地推进当前对特征值问题的理解和解决技术。随着该项目的成功完成，将通过随机相位近似对最先进的物理激发能计算做出重大贡献，这是一种在计算量子化学和物理学中广泛使用的成熟技术。