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arXiv 1601.04095math.NAcs.NA
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混合有限元方法中向量拉普拉斯算子的多网格预条件器

MultiGrid Preconditioners for Mixed Finite Element Methods of Vector Laplacian

University of California at Irvine(加州大学尔湾分校);University of Electronic Science and Technology of China(电子科技大学);Xiangtan University(湘潭大学)

Long Chen, Yongke Wu, Lin Zhong, Jie Zhou

AI总结:

本文提出基于多网格方法的块对角和块三角预条件器,用于求解向量拉普拉斯算子的鞍点系统,利用点wise高斯-塞德尔平滑器提高求解效率。

摘要:

由于向量拉普拉斯算子的离散线性代数系统具有不定性和较差的谱性质,混合有限元方法难以求解。本文提出了一种块对角预条件器,并证明了变量V-循环多网格方法结合标准点wise高斯-塞德尔平滑器可作为离散向量拉普拉斯算子的有效预条件器。该多网格求解器将进一步用于构建向量拉普拉斯算子和具有无散约束的麦克斯韦方程组的鞍点系统预条件器。本文方法的主要优势在于点wise高斯-塞德尔平滑器更具代数性,易于作为黑盒平滑器实现。

英文摘要:

Due to the indefiniteness and poor spectral properties, the discretized linear algebraic system of the vector Laplacian by mixed finite element methods is hard to solve. A block diagonal preconditioner has been developed and shown to be an effective preconditioner by Arnold, Falk, and Winther [Acta Numerica, 15:1--155, 2006]. The purpose of this paper is to propose alternative and effective block diagonal and block triangular preconditioners for solving this saddle point system. A variable V-cycle multigrid method with the standard point-wise Gauss-Seidel smoother is proved to be a good preconditioner for a discrete vector Laplacian operator. This multigrid solver will be further used to build preconditioners for the saddle point systems of the vector Laplacian and the Maxwell equations with divergent free constraint. The major benefit of our approach is that the point-wise Gauss-Seidel smoother is more algebraic and can be easily implemented as a black-box smoother.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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