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arXiv 1410.2153math.NAcs.NA
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一种近最优的通用非结构化网格多网格方法

A Nearly Optimal Multigrid Method for General Unstructured Grids

RWTH Aachen(亚琛工业大学);The Pennsylvania State University(宾夕法尼亚州立大学)

Lars Grasedyck, Lu Wang, Jinchao Xu

AI总结:

本文提出了一种适用于非结构化网格的多网格方法,通过构建辅助粗网格层次结构,结合平滑技术,实现了对椭圆型PDE的近最优收敛率。

摘要:

本文开发了一种适用于非结构化形状正则网格的多网格方法。对于一般形状正则非结构化网格,我们提出了一种构造辅助粗网格层次结构的方法,该层次结构可用于应用几何多网格方法,并通过辅助空间预条件技术在原始网格上进行平滑。这种构造通过聚类树实现,可在O(N log N)时间内完成。该树结构用于从粗到细定义网格层次结构。对于所构造的网格层次结构,我们证明了多网格预条件CG方法对椭圆型PDE的收敛率为1 - O(1/log N)。数值实验验证了理论界限,并显示总复杂度为O(N log N)。

英文摘要:

In this paper, we develop a multigrid method on unstructured shape-regular grids. For a general shape-regular unstructured grid of ${\cal O}(N)$ elements, we present a construction of an auxiliary coarse grid hierarchy on which a geometric multigrid method can be applied together with a smoothing on the original grid by using the auxiliary space preconditioning technique. Such a construction is realized by a cluster tree which can be obtained in ${\cal O}(N\log N)$ operations for a grid of $N$ elements. This tree structure in turn is used for the definition of the grid hierarchy from coarse to fine. For the constructed grid hierarchy we prove that the convergence rate of the multigrid preconditioned CG for an elliptic PDE is $1 - {\cal O}({1}/{\log N})$. Numerical experiments confirm the theoretical bounds and show that the total complexity is in ${\cal O}(N\log N)$.

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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