Uniform Substructuring Preconditioners for High Order FEM on Triangles and the Influence of Nodal Basis Functions

IF 2.8 2区 数学 Q1 MATHEMATICS, APPLIED
Mark Ainsworth, Shuai Jiang
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引用次数: 0

Abstract

SIAM Journal on Numerical Analysis, Volume 62, Issue 4, Page 1465-1491, August 2024.
Abstract. A robust substructuring type preconditioner is developed for high order approximation of problem for which the element matrix takes the form [math] where [math] and [math] are the mass and stiffness matrices, respectively. A standard preconditioner for the pure stiffness matrix results in a condition number bounded by [math] where [math] blows up as [math]. It is shown that the best uniform bound in [math] that one can hope for is [math]. More precisely, we show that the upper envelope of the bound [math] is [math]. What, then, can be done to obtain a preconditioner that is robust for all [math]? The solution turns out to be a relatively minor modification of the basic substructuring algorithm of [I. Babuška et al., SIAM J. Numer. Anal., 28 (1991), pp. 624–661]: one can simply augment the preconditioner with a suitable Jacobi smoothener over the coarse grid degrees of freedom. This is shown to result in a condition number bounded by [math] where the constant is independent of [math]. Numerical results are given which shows that the simple expedient of augmentation with nodal smoothening reduces the condition number by a factor of up to two orders of magnitude.
三角形上高阶有限元的均匀子结构预处理以及节点基函数的影响
SIAM 数值分析期刊》第 62 卷第 4 期第 1465-1491 页,2024 年 8 月。 摘要。针对元素矩阵为 [math] 形式(其中 [math] 和 [math] 分别为质量矩阵和刚度矩阵)的高阶近似问题,开发了一种鲁棒次结构类型预处理。纯刚度矩阵的标准预处理会导致条件数以[math]为界,其中[math]会以[math]的形式爆炸。结果表明,[math] 的最佳统一约束是 [math]。更准确地说,我们证明了[math]边界的上包络是[math]。那么,怎样才能获得对所有 [math] 都稳健的预处理呢?答案是对[I. Babuška et al., SIAM J. Numer. Anal., 28 (1991), pp.结果表明,这将产生一个以 [math] 为界的条件数,其中常数与 [math] 无关。给出的数值结果表明,用节点平滑增强这一简单的权宜之计最多可将条件数降低两个数量级。
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来源期刊
CiteScore
4.80
自引率
6.90%
发文量
110
审稿时长
4-8 weeks
期刊介绍: SIAM Journal on Numerical Analysis (SINUM) contains research articles on the development and analysis of numerical methods. Topics include the rigorous study of convergence of algorithms, their accuracy, their stability, and their computational complexity. Also included are results in mathematical analysis that contribute to algorithm analysis, and computational results that demonstrate algorithm behavior and applicability.
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