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An introduction to multi-trace formulations and associated domain decomposition solvers

机译:多迹线公式化和相关域分解求解器简介

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Multi trace formulations (MTFs) are based on a decomposition of the problem domain into subdomains, and thus domain decomposition solvers are of interest. The fully rigorous mathematical MTF can however be daunting for the non-specialist. The first aim of the present contribution is to provide a gentle introduction to MTFs. We introduce these formulations on a simple model problem using concepts familiar to researchers in domain decomposition. This allows us to get a new understanding of MTFs and a natural block Jacobi iteration, for which we determine optimal relaxation parameters. We then show how iterative multi-trace formulation solvers are related to a well known domain decomposition method called optimal Schwarz method: a method which used Dirichlet to Neumann maps in the transmission condition. We finally show that the insight gained from the simple model problem leads to remarkable identities for Calderon projectors and related operators, and the convergence results and optimal choice of the relaxation parameter we obtained is independent of the geometry, the space dimension of the problem, and the precise form of the spatial elliptic operator, like for optimal Schwarz methods. We illustrate our analysis with numerical experiments. (C) 2018 IMACS. Published by Elsevier B.V. All rights reserved.
机译:多轨迹公式(MTF)基于问题域分解为子域的过程,因此感兴趣的是域分解求解器。但是,对于非专业人士而言,严格的数学MTF可能会令人望而生畏。本文稿的首要目标是对MTF进行简要介绍。我们使用域分解中研究人员熟悉的概念,在一个简单的模型问题上介绍这些公式。这使我们对MTF和自然块Jacobi迭代有了新的了解,为此我们确定了最佳松弛参数。然后,我们说明迭代多迹线公式求解器如何与称为最佳Schwarz方法的众所周知的域分解方法相关:该方法在传输条件下使用Dirichlet到Neumann映射。我们最终证明,从简单模型问题中获得的见识导致Calderon投影机和相关算子具有显着的身份,并且我们获得的弛豫参数的收敛结果和最佳选择与几何形状,问题的空间维数和空间椭圆算子的精确形式,例如最佳Schwarz方法。我们通过数值实验来说明我们的分析。 (C)2018年IMACS。由Elsevier B.V.发布。保留所有权利。

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