Stage‐parallel preconditioners for implicit Runge–Kutta methods of arbitrarily high order, linear problems

IF 1.8 3区 数学 Q1 MATHEMATICS
Owe Axelsson, Ivo Dravins, Maya Neytcheva
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引用次数: 0

Abstract

Abstract Fully implicit Runge–Kutta methods offer the possibility to use high order accurate time discretization to match space discretization accuracy, an issue of significant importance for many large scale problems of current interest, where we may have fine space resolution with many millions of spatial degrees of freedom and long time intervals. In this work, we consider strongly A‐stable implicit Runge–Kutta methods of arbitrary order of accuracy, based on Radau quadratures. For the arising large algebraic systems we introduce efficient preconditioners, that (1) use only real arithmetic, (2) demonstrate robustness with respect to problem and discretization parameters, and (3) allow for fully stage‐parallel solution. The preconditioners are based on the observation that the lower‐triangular part of the coefficient matrices in the Butcher tableau has larger in magnitude values, compared to the corresponding strictly upper‐triangular part. We analyze the spectrum of the corresponding preconditioned systems and illustrate their performance with numerical experiments. Even though the observation has been made some time ago, its impact on constructing stage‐parallel preconditioners has not yet been done and its systematic study constitutes the novelty of this article.
任意高阶线性问题的隐式龙格-库塔方法的阶段并行预调节器
摘要:全隐式龙格-库塔方法提供了使用高阶精确时间离散化来匹配空间离散化精度的可能性,这对于许多当前感兴趣的大规模问题具有重要意义,在这些问题中,我们可能具有具有数百万空间自由度和长时间间隔的精细空间分辨率。在这项工作中,我们考虑了基于Radau正交的任意精度阶的强A稳定隐式龙格-库塔方法。对于正在出现的大型代数系统,我们引入了有效的预调节器,(1)仅使用实数算法,(2)证明对问题和离散参数的鲁棒性,以及(3)允许完全阶段并行解决。预条件是基于这样的观察,即在Butcher表中,系数矩阵的下三角部分比相应的严格上三角部分具有更大的幅度值。我们分析了相应的预条件系统的频谱,并用数值实验说明了它们的性能。尽管这一观察结果在一段时间前就已出现,但其对级并联预调节器构造的影响尚未得到证实,其系统研究构成了本文的新颖之处。
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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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