Mathematical Sciences: Preconditioned Parallel Methods for Large Symmetric Eigenproblems

9501507 Knyazev 该项目将研究用于计算具有非常大的对称矩阵的广义特征值问题的几个极端特征对的预处理迭代方法。 这些问题在许多重大挑战的科学和工程应用中非常重要：结构动力学和屈曲、海洋建模、量子化学和磁流体动力学。它们通常源自连续模型的离散化，由偏微分方程组描述。 矩阵可能太大，以至于标准数值方法变得不令人满意，甚至在最强大的现代超级计算机上也无法实现，因为它们太慢或需要太多内存。 预处理迭代方法是专门为此类问题而设计的，现在它们已被很好地理解用于求解大型病态线性代数方程组。对于特征问题，理论仍然很差，并且这些方法很少被使用。理想情况下，这些方法将以与求解相应线性代数系统相同的计算成本来计算分离良好的特征值簇和相应的特征空间。这样的过程可以是有效的并且可并行的。域分解和迭代子结构方法特别有前途。 该研究的最终目标是发展现在已知的对称特征值问题的预处理方法的完整理论，并找到新的快速、准确和鲁棒的迭代方法。这将为高性能并行计算系统上的先进软件和算法的开发提供基础。预计将编写一本关于大型对称特征值问题的预处理迭代方法的研究专着来总结该项目的成果。

9501507 Knyazev This project will study preconditioned iterative methods for computing several extreme eigenpairs of generalized eigenvalue problems, with very large symmetric matrices. Such problems are of major importance in many grand challenge scientific and engineering applications: structural dynamics and buckling, ocean modeling, quantum chemistry, and magnetohydrodynamics. They arise typically from the discretization of continuous models, described by systems of partial differential equations. Matrices may be so large that the standard numerical methods become unsatisfactory and cannot be implemented even on the most powerful modern supercomputers as they are too slow or require too much memory. Preconditioned iterative methods were specially designed for problems of that kind and now they are well understood for solving large ill-conditioned linear algebraic systems of equations. For eigenproblems the theory is still poor, and these methods are rarely used. Ideally, the methods would compute well-separated clusters of eigenvalues and corresponding eigenspaces at the same order of computational cost as that for solution of the corresponding linear algebraic system. Such processes can be effective and parallelizable. Domain decomposition and iterative substructuring methods are particularly promising. The ultimate goal of the research is to develop a complete theory of the preconditioned methods now known for symmetric eigenproblems and to find new fast, accurate, and robust iterative methods. This would provide a basis for the development of advanced software and algorithms on high performance parallel computing systems. It is expected that a research monograph on preconditioned iterative methods for large symmetric eigenvalue problems will be prepared to summarize the results of the project.