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arXiv 2103.13890math.NAcs.NA
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无Jacobian Newton-Krylov方法的多重网格预处理

A Multigrid Preconditioner for Jacobian-free Newton-Krylov Methods

Hardik Kothari, Alena Kopaničáková, Rolf Krause

摘要:

在这项工作中,我们提出了一个多重网格预处理雅可比自由牛顿克雷洛夫(JFNK)方法。我们的多重网格方法不需要知识的雅可比矩阵在任何级别的多重网格层次结构。由于它是常见的标准多重网格方法,所提出的方法还依赖于三个构建块:传输操作符,平滑器,和一个粗略的水平求解器。除了约束和延拓算子外,我们还使用了一个投影算子来将当前的牛顿迭代法转换到一个更粗糙的层次。采用三层切比雪夫半迭代法作为平滑器,因为它具有良好的平滑特性,并且不需要雅可比矩阵的表示。我们取代了直接求解器的粗层次上的矩阵自由Krylov子空间方法,从而产生一个真正的雅可比自由多重网格预处理。我们将详细讨论我们的多重网格预处理器的所有构建模块,并使用几个数值例子来证明所提出的方法的鲁棒性和效率。

英文摘要:

In this work, we propose a multigrid preconditioner for Jacobian-free Newton-Krylov (JFNK) methods. Our multigrid method does not require knowledge of the Jacobian at any level of the multigrid hierarchy. As it is common in standard multigrid methods, the proposed method also relies on three building blocks: transfer operators, smoothers, and a coarse level solver. In addition to the restriction and prolongation operator, we also use a projection operator to transfer the current Newton iterate to a coarser level. The three-level Chebyshev semi-iterative method is employed as a smoother, as it has good smoothing properties and does not require the representation of the Jacobian matrix. We replace the direct solver on the coarsest level with a matrix-free Krylov subspace method, thus giving rise to a truly Jacobian-free multigrid preconditioner. We will discuss all building blocks of our multigrid preconditioner in detail and demonstrate the robustness and the efficiency of the proposed method using several numerical examples.

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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