Local multilevel preconditioned Jacobi-Davidson method for elliptic eigenvalue problems on adaptive meshes

IF 2.6 3区 数学 Q2 MATHEMATICS, APPLIED
Jianing Guo, Qigang Liang, Xuejun Xu
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引用次数: 0

Abstract

In this work, we propose an efficient adaptive multilevel preconditioned Jacobi-Davidson (PJD) method for eigenvalue problems with singularity. Our multilevel method utilizes a local smoothing strategy to solve the preconditioned Jacobi-Davidson algebraic systems arising from adaptive finite element methods (AFEM). As a result, the algorithm holds optimal computational complexity \(\varvec{O(N)}\). The theoretical analysis reveals that our method has a uniform convergence rate with respect to mesh levels and degrees of freedom. Further, the convergence rate is not affected by highly discontinuous coefficients within the domain. Numerical results verify our theoretical findings.

自适应网格上椭圆特征值问题的局部多水平预条件Jacobi-Davidson方法
在本文中,我们提出了一种有效的自适应多水平预条件Jacobi-Davidson (PJD)方法来求解具有奇异性的特征值问题。我们的多层方法利用局部平滑策略来求解由自适应有限元法(AFEM)产生的预置Jacobi-Davidson代数系统。因此,该算法具有最佳的计算复杂度\(\varvec{O(N)}\)。理论分析表明,该方法在网格层次和自由度方面具有一致的收敛速度。此外,收敛速度不受区域内高度不连续系数的影响。数值结果验证了我们的理论发现。
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来源期刊
CiteScore
3.00
自引率
5.90%
发文量
68
审稿时长
3 months
期刊介绍: Advances in Computational Mathematics publishes high quality, accessible and original articles at the forefront of computational and applied mathematics, with a clear potential for impact across the sciences. The journal emphasizes three core areas: approximation theory and computational geometry; numerical analysis, modelling and simulation; imaging, signal processing and data analysis. This journal welcomes papers that are accessible to a broad audience in the mathematical sciences and that show either an advance in computational methodology or a novel scientific application area, or both. Methods papers should rely on rigorous analysis and/or convincing numerical studies.
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