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首页> 外文期刊>SIAM Journal on Numerical Analysis >Restricted additive Schwarz preconditioners with harmonic overlap for symmetric positive definite linear systems
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Restricted additive Schwarz preconditioners with harmonic overlap for symmetric positive definite linear systems

机译:对称正定线性系统的具有谐波重叠的受限加性Schwarz预调节器

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A restricted additive Schwarz (RAS) preconditioning technique was introduced recently for solving general nonsymmetric sparse linear systems. In this paper, we provide one-level and two-level extensions of RAS for symmetric positive definite problems using the so-called harmonic overlaps (RASHO). Both RAS and RASHO outperform their counterparts of the classical additive Schwarz variants (AS). The design of RASHO is based on a much deeper understanding of the behavior of Schwarz-type methods in overlapping subregions and in the construction of the overlap. In RASHO, the overlap is obtained by extending the nonoverlapping subdomains only in the directions that do not cut the boundaries of other subdomains, and all functions are made harmonic in the overlapping regions. As a result, the subdomain problems in RASHO are smaller than those of AS, and the communication cost is also smaller when implemented on distributed memory computers, since the right-hand sides of discrete harmonic systems are always zero and therefore do not need to be communicated. We also show numerically that RASHO-preconditioned CG takes fewer iterations than the corresponding AS-preconditioned CG. A nearly optimal theory is included for the convergence of RASHO-preconditioned CG for solving elliptic problems discretized with a finite element method. [References: 25]
机译:最近,为了解决一般的非对称稀疏线性系统,引入了受限加性Schwarz(RAS)预处理技术。在本文中,我们使用所谓的谐波重叠(RASHO)为对称正定问题提供了RAS的一级扩展和二级扩展。 RAS和RASHO的性能均优于经典加性Schwarz变体(AS)。 RASHO的设计基于对重叠子区域和重叠结构中Schwarz型方法的行为的更深入的了解。在RASHO中,通过仅在不切开其他子域边界的方向上扩展非重叠子域来获得重叠,并且使所有功能在重叠区域中谐和。结果,RASHO中的子域问题比AS中的子域问题小,并且在分布式存储计算机上实现时通信成本也较小,这是因为离散谐波系统的右侧始终为零,因此不需要沟通。我们还从数字上显示,RASHO预处理的CG比相应的AS预处理的CG需要更少的迭代。包含了一个几乎最优的理论,用于RASHO预处理的CG的收敛,以解决用有限元方法离散化的椭圆问题。 [参考:25]

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