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arXiv 2402.12833math.NAcs.NA
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集成加性多重网格与多重预处理共轭梯度法

Integrating Additive Multigrid with Multipreconditioned Conjugate Gradient Method

Hardik Kothari, Maria Giuseppina Chiara Nestola, Marco Favino, Rolf Krause

AI总结:

本研究将加性多重网格与多重预处理共轭梯度法结合,利用MG层级修正作为独立搜索方向,通过能量范数最小化计算最优系数,以提升并行效率并保持收敛性能。

摘要:

由于其最优复杂度,多重网格(MG)方法是求解由偏微分方程离散化产生的大规模线性系统最常用的方法之一。然而,标准MG方法本质上是乘性的,其并行实现面临通信复杂度增加的问题。在这种情况下,MG方法的加性变体因其固有的并行性而提供了良好的替代方案,尽管它们收敛速度较慢。本工作将加性多重网格方法与多重预处理共轭梯度(MPCG)方法相结合。在所提出的方法中,MPCG方法利用MG层级中不同层次的修正作为独立的预处理搜索方向。在该方法中,MPCG方法通过使用预处理搜索方向的线性组合来更新当前迭代,其中线性组合的最优系数通过利用CG方法的能量范数最小化来计算。我们方法背后的想法是将MPCG方法搜索方向的$A$-共轭性与MG层级修正的拟$H_1$-正交性相结合。在数值部分,我们研究了所提出方法相对于用作CG方法预处理器的标准加性和乘性MG方法的性能。

英文摘要:

Due to its optimal complexity, the multigrid (MG) method is one of the most popular approaches for solving large-scale linear systems arising from the discretization of partial differential equations. However, the parallel implementation of standard MG methods, which are inherently multiplicative, suffers from increasing communication complexity. In such cases, the additive variants of MG methods provide a good alternative due to their inherently parallel nature, although they exhibit slower convergence. This work combines the additive multigrid method with the multipreconditioned conjugate gradient (MPCG) method. In the proposed approach, the MPCG method employs the corrections from the different levels of the MG hierarchy as separate preconditioned search directions. In this approach, the MPCG method updates the current iterate by using the linear combination of the preconditioned search directions, where the optimal coefficients for the linear combination are computed by exploiting the energy norm minimization of the CG method. The idea behind our approach is to combine the $A$-conjugacy of the search directions of the MPCG method and the quasi $H_1$-orthogonality of the corrections from the MG hierarchy. In the numerical section, we study the performance of the proposed method compared to the standard additive and multiplicative MG methods used as preconditioners for the CG method.

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