Two-level Restricted Additive Schwarz preconditioner based on Multiscale Spectral Generalized FEM for Heterogeneous Helmholtz Problems

Chupeng Ma, Christian Alber, Robert Scheichl
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Abstract

We present and analyze a two-level restricted additive Schwarz (RAS) preconditioner for heterogeneous Helmholtz problems, based on a multiscale spectral generalized finite element method (MS-GFEM) proposed in [C. Ma, C. Alber, and R. Scheichl, SIAM. J. Numer. Anal., 61 (2023), pp. 1546--1584]. The preconditioner uses local solves with impedance boundary conditions, and a global coarse solve based on the MS-GFEM approximation space constructed from local eigenproblems. It is derived by first formulating MS-GFEM as a Richardson iterative method, and without using an oversampling technique, reduces to the preconditioner recently proposed and analyzed in [Q. Hu and Z.Li, arXiv 2402.06905]. We prove that both the Richardson iterative method and the preconditioner used within GMRES converge at a rate of $\Lambda$ under some reasonable conditions, where $\Lambda$ denotes the error of the underlying MS-GFEM \rs{approximation}. Notably, the convergence proof of GMRES does not rely on the `Elman theory'. An exponential convergence property of MS-GFEM, resulting from oversampling, ensures that only a few iterations are needed for convergence with a small coarse space. Moreover, the convergence rate $\Lambda$ is not only independent of the fine-mesh size $h$ and the number of subdomains, but decays with increasing wavenumber $k$. In particular, in the constant-coefficient case, with $h\sim k^{-1-\gamma}$ for some $\gamma\in (0,1]$, it holds that $\Lambda \sim k^{-1+\frac{\gamma}{2}}$.
基于多尺度谱广义有限元的两级限制性加法施瓦茨预处理器,用于解决异质亥姆霍兹问题
我们介绍并分析了基于多尺度谱广义有限元法(MS-GFEM)的异质亥姆霍兹(Helmholtz)问题的两级受限加法施瓦茨(RAS)预处理器 [C. Ma, C. Alber and R. Scheichl, SIAM.Ma, C.Alber, and R. Scheichl, SIAM.J. Numer.Anal., 61 (2023), pp.]该预处理使用带有阻抗边界条件的局部求解,以及基于局部特征问题构建的 MS-GFEM 近似空间的全局粗求解。它首先将 MS-GFEM 表述为 Richardson 迭代法,在不使用超采样技术的情况下,简化为最近在 [Q. Hu and Z. Li, arXiv2402.06905] 中提出并分析的预处理方法。我们证明,在一些合理的条件下,Richardson 迭代方法和 GMRES 中使用的预处理都能以 $\Lambda$ 的速度收敛,其中 $\Lambda$ 表示底层 MS-GFEM (rs{approximation})的误差。值得注意的是,GMRES 的收敛证明并不依赖于 "埃尔曼理论"。超采样产生的 MS-GFEM 指数收敛特性确保了只需少量迭代就能在较小的粗空间内实现收敛。此外,收敛速率 $\Lambda$ 不仅与细网格大小 $h$ 和子域数量无关,而且随着波长数 $k$ 的增加而衰减。特别是,在实体-系数情况下,当某个 $\gamma\in(0,1]$ 为 $h\sim k^{-1-\gamma}$ 时,$\Lambda \sim k^{-1+\frac{\gamma}{2}}$ 是成立的。
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