Motivations and realizations of Krylov subspace methods for large sparse linear systems

Motivations and realizations of Krylov subspace methods for large sparse linear systems
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大型稀疏线性系统 Krylov 子空间方法的动机和实现

DOI:
10.1016/j.cam.2015.01.025
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发表时间:
2015-08
影响因子:
2.4
通讯作者:
Zhong-Zhi Bai
Zhong-Zhi Bai
中科院分区:
数学2区
文献类型:
--
作者:
Zhong-Zhi Bai

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本文简要介绍了求解线性方程组的典型和重要的直接法和迭代法,从理论和应用的角度具体描述了它们的基本特征,并阐明了它们之间的本质区别。特别地,描述了在Krylov子空间中搜索线性方程组解的动机,给出了广义最小残差(GMRES)方法的算法实现,并简要回顾了几类最新的代数预条件子。这有助于正确、深入、全面地理解这些方法的应用范围、理论性质和数值行为,也有助于设计求解线性方程组的新方法。
We briefly introduce typical and important direct and iterative methods for solving systems of linear equations, concretely describe their fundamental characteristics in viewpoints of both theory and applications, and clearly clarify the substantial differences among these methods. In particular, the motivations of searching the solution of a linear system in a Krylov subspace are described and the algorithmic realizations of thegeneralized minimal residual(GMRES) method are shown, and several classes of state-of-the-art algebraic preconditioners are briefly reviewed. All this is useful for correctly, deeply and completely understanding the application scopes, theoretical properties and numerical behaviors of these methods, and is also helpful in designing new methods for solving systems of linear equations.
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