A Two-Level Preconditioned Helmholtz-Jacobi-Davidson Method for the Maxwell Eigenvalue Problem

Qigang Liang, Xuejun Xu
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引用次数: 6

Abstract

In this paper, based on a domain decomposition (DD) method, we shall propose an efficient two-level preconditioned Helmholtz-Jacobi-Davidson (PHJD) method for solving the algebraic eigenvalue problem resulting from the edge element approximation of the Maxwell eigenvalue problem. In order to eliminate the components in orthogonal complement space of the eigenvalue, we shall solve a parallel preconditioned system and a Helmholtz projection system together in fine space. After one coarse space correction in each iteration and minimizing the Rayleigh quotient in a small dimensional Davidson space, we finally get the error reduction of this two-level PHJD method as γ = c(H)(1 − C δ H2 ), where C is a constant independent of the mesh size h and the diameter of subdomains H , δ is the overlapping size among the subdomains, and c(H) decreasing as H → 0, which means the greater the number of subdomains, the better the convergence rate. Numerical results supporting our theory shall be given.
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求解Maxwell特征值问题的两级预条件Helmholtz-Jacobi-Davidson方法
本文基于域分解(DD)方法,提出了一种有效的两级预条件Helmholtz-Jacobi-Davidson (PHJD)方法,用于求解由Maxwell特征值问题的边元近似引起的代数特征值问题。为了消去特征值在正交补空间中的分量,我们将在精细空间中求解一个平行预条件系统和一个亥姆霍兹投影系统。在每个迭代和最小化一个粗空间校正后的瑞利商小维戴维森空间,我们最后得到的错误减少二级PHJD方法γ= c (H)(1−cδH2), c是一个恒定的独立的筛孔尺寸H和子域的直径H,δ是子域之间的重叠的大小,和c (H)降低H→0时,这意味着更大的子域的数量,收敛速度就越好。将给出支持我们理论的数值结果。
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