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arXiv 1608.02567math.NAcs.CEcs.NA
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DPG系统矩阵的几何多重网格预处理策略

A Geometric Multigrid Preconditioning Strategy for DPG System Matrices

Nathan V. Roberts, Jesse Chan

摘要:

Demkowicz和Gopalakrishnan [15,17]的不连续Petrov-Galerkin(DPG)方法保证了能量范数下解的最优性,并提供了几个便于自适应方案的特征。一个关键的问题,尚未回答一般-虽然有一些结果,泊松,例如-是如何最好地预处理的DPG系统矩阵,使迭代求解器可以用来解决大规模的问题。在本文中,我们详细介绍了预处理的DPG系统矩阵使用几何多重网格,我们已经实现了Camellia [26]的一部分,并通过数值实验证明其有效性的上下文中的几个变分公式。我们观察到,在我们的一些实验中,预条件的行为是紧密联系在一起的离散测试空间丰富。我们包括实验,涉及自适应网格与悬挂节点的盖子驱动的空腔流,证明了预条件可以应用在具有挑战性的问题的背景下。我们还包括一个可扩展性的研究表明,该方法-和我们的实现-规模以及许多MPI的行列。

英文摘要:

The discontinuous Petrov-Galerkin (DPG) methodology of Demkowicz and Gopalakrishnan [15,17] guarantees the optimality of the solution in an energy norm, and provides several features facilitating adaptive schemes. A key question that has not yet been answered in general - though there are some results for Poisson, e.g. - is how best to precondition the DPG system matrix, so that iterative solvers may be used to allow solution of large-scale problems. In this paper, we detail a strategy for preconditioning the DPG system matrix using geometric multigrid which we have implemented as part of Camellia [26], and demonstrate through numerical experiments its effectiveness in the context of several variational formulations. We observe that in some of our experiments, the behavior of the preconditioner is closely tied to the discrete test space enrichment. We include experiments involving adaptive meshes with hanging nodes for lid-driven cavity flow, demonstrating that the preconditioners can be applied in the context of challenging problems. We also include a scalability study demonstrating that the approach - and our implementation - scales well to many MPI ranks.

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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