Multilevel local defect-correction method for the non-selfadjoint Steklov eigenvalue problems

IF 1.6 3区 数学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Fei Xu, Bingyi Wang, Manting Xie
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引用次数: 0

Abstract

In this paper, we design a multilevel local defect-correction method to solve the non-selfadjoint Steklov eigenvalue problems. Since the computation work needed for solving the non-selfadjoint Steklov eigenvalue problems increases exponentially as the scale of the problems increase, the main idea of our algorithm is to avoid solving large-scale equations especially large-scale Steklov eigenvalue problems directly. Firstly, we transform the non-selfadjoint Steklov eigenvalue problem into some symmetric boundary value problems defined in a multilevel finite element space sequence, and some small-scale non-selfadjoint Steklov eigenvalue problems defined in a low-dimensional auxiliary subspace. Next, the local defect-correction method is used to solve the symmetric boundary value problems, then the difficulty of solving these symmetric boundary value problems is further reduced by decomposing these large-scale problems into a series of small-scale subproblems. Overall, our algorithm can obtain the optimal error estimates with linear computational complexity, and the conclusions are proved by strict theoretical analysis which are different from the developed conclusions for equations with the Dirichlet boundary conditions.

Abstract Image

非自洽斯特克洛夫特征值问题的多级局部缺陷校正方法
本文设计了一种多级局部缺陷校正方法来求解非自洽斯特克洛夫特征值问题。由于求解非自交 Steklov 特征值问题所需的计算工作量会随着问题规模的增大而呈指数级增长,因此我们算法的主要思想是避免直接求解大规模方程,尤其是大规模 Steklov 特征值问题。首先,我们将非自交 Steklov 特征值问题转化为一些定义在多级有限元空间序列中的对称边界值问题,以及一些定义在低维辅助子空间中的小尺度非自交 Steklov 特征值问题。接下来,利用局部缺陷校正法求解对称边界值问题,然后通过将这些大规模问题分解为一系列小规模子问题,进一步降低求解这些对称边界值问题的难度。总之,我们的算法能以线性计算复杂度获得最佳误差估计,并通过严格的理论分析证明了这一结论,这与针对具有 Dirichlet 边界条件的方程所得出的结论是不同的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BIT Numerical Mathematics
BIT Numerical Mathematics 数学-计算机:软件工程
CiteScore
2.90
自引率
0.00%
发文量
38
审稿时长
6 months
期刊介绍: The journal BIT has been published since 1961. BIT publishes original research papers in the rapidly developing field of numerical analysis. The essential areas covered by BIT are development and analysis of numerical methods as well as the design and use of algorithms for scientific computing. Topics emphasized by BIT include numerical methods in approximation, linear algebra, and ordinary and partial differential equations.
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