预处理非连续伽勒金方法和对流-扩散-反应问题与结果谱的保证边界

IF 1.8 3区 数学 Q1 MATHEMATICS
Liya Gaynutdinova, Martin Ladecký, Ivana Pultarová, Miloslav Vlasák, Jan Zeman
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引用次数: 0

摘要

本文重点介绍线性二阶偏微分方程新预处理概念的设计、分析和实施,包括采用 Galerkin 或非连续 Galerkin 方法离散化的对流-扩散-反应问题。我们扩展了 Gergelits 等人提出的方法,并将其应用于更一般的环境,假设原始矩阵和预处理矩阵都是由秩非常低的稀疏矩阵组成,代表对全局矩阵的局部贡献。当应用于对称问题时,该方法为预处理矩阵的所有单个特征值提供了边界。我们证明,这种预处理策略不仅适用于 Galerkin 离散化,也适用于非连续 Galerkin 离散化,其中局部贡献与三角形的各个边相关。在非对称问题的情况下,该方法能保证对所得到的特征值的实部和虚部进行约束。我们通过一些数值实验说明了该方法及其实现,展示了该方法在离散化(对流)扩散反应问题的两种变体中的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Preconditioned discontinuous Galerkin method and convection-diffusion-reaction problems with guaranteed bounds to resulting spectra
This paper focuses on the design, analysis and implementation of a new preconditioning concept for linear second order partial differential equations, including the convection-diffusion-reaction problems discretized by Galerkin or discontinuous Galerkin methods. We expand on the approach introduced by Gergelits et al. and adapt it to the more general settings, assuming that both the original and preconditioning matrices are composed of sparse matrices of very low ranks, representing local contributions to the global matrices. When applied to a symmetric problem, the method provides bounds to all individual eigenvalues of the preconditioned matrix. We show that this preconditioning strategy works not only for Galerkin discretization, but also for the discontinuous Galerkin discretization, where local contributions are associated with individual edges of the triangulation. In the case of nonsymmetric problems, the method yields guaranteed bounds to real and imaginary parts of the resulting eigenvalues. We include some numerical experiments illustrating the method and its implementation, showcasing its effectiveness for the two variants of discretized (convection-)diffusion-reaction problems.
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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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