非精确内外Golub-Kahan双对角化方法:一种松弛策略

IF 1.8 3区 数学 Q1 MATHEMATICS
Vincent Darrigrand, Andrei Dumitrasc, Carola Kruse, Ulrich Rüde
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引用次数: 1

摘要

研究了求解两乘二块结构鞍点问题的非精确内外广义Golub-Kahan算法。在每次外部迭代中,必须求解一个内部系统,这在理论上必须精确地完成。然而,当系统变得越来越大时,内部精确求解器就不再有效甚至可行,必须使用迭代方法。本文着重研究了内迭代解的精度对块系统解精度的影响。进一步强调降低计算成本,计算成本被定义为内部迭代的总次数。我们开发了旨在动态改变每个外部迭代的内部公差的松弛技术,以进一步减少内部迭代的总数。我们在Stokes问题上说明了我们的发现,并在泊松问题的混合公式上验证了它们。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inexact inner–outer Golub–Kahan bidiagonalization method: A relaxation strategy
We study an inexact inner–outer generalized Golub–Kahan algorithm for the solution of saddle-point problems with a two-times-two block structure. In each outer iteration, an inner system has to be solved which in theory has to be done exactly. Whenever the system is getting large, an inner exact solver is, however, no longer efficient or even feasible and iterative methods must be used. We focus this article on a numerical study showing the influence of the accuracy of an inner iterative solution on the accuracy of the solution of the block system. Emphasis is further given on reducing the computational cost, which is defined as the total number of inner iterations. We develop relaxation techniques intended to dynamically change the inner tolerance for each outer iteration to further minimize the total number of inner iterations. We illustrate our findings on a Stokes problem and validate them on a mixed formulation of the Poisson problem.
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来源期刊
CiteScore
3.40
自引率
2.30%
发文量
50
审稿时长
12 months
期刊介绍: Manuscripts submitted to Numerical Linear Algebra with Applications should include large-scale broad-interest applications in which challenging computational results are integral to the approach investigated and analysed. Manuscripts that, in the Editor’s view, do not satisfy these conditions will not be accepted for review. Numerical Linear Algebra with Applications receives submissions in areas that address developing, analysing and applying linear algebra algorithms for solving problems arising in multilinear (tensor) algebra, in statistics, such as Markov Chains, as well as in deterministic and stochastic modelling of large-scale networks, algorithm development, performance analysis or related computational aspects. Topics covered include: Standard and Generalized Conjugate Gradients, Multigrid and Other Iterative Methods; Preconditioning Methods; Direct Solution Methods; Numerical Methods for Eigenproblems; Newton-like Methods for Nonlinear Equations; Parallel and Vectorizable Algorithms in Numerical Linear Algebra; Application of Methods of Numerical Linear Algebra in Science, Engineering and Economics.
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