﻿ 求解复对称线性系统的改进EPGS迭代法

# 求解复对称线性系统的改进EPGS迭代法Improved EPGS Iterative Method for Solving Complex Symmetric Linear Systems

Abstract: In this paper, we mainly discuss the EPGS iterative method for solving complex symmetric linear systems. Based on the idea of triangular splitting of matrix blocks, we introduce an acceleration parameter to this method and obtain the improved EPGS iterative method (IEPGS). We analyze the convergence of IEPGS iteration method, give the condition of convergence, and obtain the optimal iteration parameters and the corresponding optimal convergence factors. Numerical experiments show that the method is effective.

1. 引言

$Au=\left(W+\text{i}T\right)u=b$, $A\in {C}^{n×n}$, $W,T\in {R}^{n×n}$, $u,b\in {C}^{n}$ (1)

$Bz\equiv \left[\begin{array}{cc}W& -T\\ T& W\end{array}\right]\left[\begin{array}{c}x\\ y\end{array}\right]=\left[\begin{array}{c}f\\ g\end{array}\right]\equiv p$, $B\in {R}^{2n×2n}$, $z,p\in {R}^{2n}$, $x,y,f,g\in {R}^{n}$ (2)

2. IEPGS迭代法

${G}_{\theta }=\left[\begin{array}{cc}\mathrm{cos}\left(\theta \right)I& -\mathrm{sin}\left(\theta \right)I\\ \mathrm{sin}\left(\theta \right)I& \mathrm{cos}\left(\theta \right)I\end{array}\right]$,

$\stackrel{˜}{B}z={G}_{\theta }^{\text{T}}Bz=\left[\begin{array}{cc}{\stackrel{˜}{W}}_{\theta }& -{\stackrel{˜}{T}}_{\theta }\\ {\stackrel{˜}{T}}_{\theta }& {\stackrel{˜}{W}}_{\theta }\end{array}\right]\left[\begin{array}{c}x\\ y\end{array}\right]={G}_{\theta }^{\text{T}}\left[\begin{array}{c}f\\ g\end{array}\right]=\left[\begin{array}{c}\stackrel{˜}{f}\\ \stackrel{˜}{g}\end{array}\right]=\stackrel{˜}{p}$ (3)

$\left\{\begin{array}{l}{\stackrel{˜}{W}}_{\theta }{x}^{k+1}={\stackrel{˜}{T}}_{\theta }{y}^{k}+\stackrel{˜}{f},\\ {\stackrel{˜}{W}}_{\theta }{y}^{k+1}=-{\stackrel{˜}{T}}_{\theta }{x}^{k+1}+\stackrel{˜}{g}\end{array}$ (4)

$\stackrel{˜}{B}=\left[\begin{array}{cc}\alpha {\stackrel{˜}{W}}_{\theta }& 0\\ {\stackrel{˜}{T}}_{\theta }& {\stackrel{˜}{W}}_{\theta }\end{array}\right]-\left[\begin{array}{cc}\left(\alpha -1\right){\stackrel{˜}{W}}_{\theta }& {\stackrel{˜}{T}}_{\theta }\\ 0& 0\end{array}\right]=M\left(\theta ,\alpha \right)-N\left(\theta ,\alpha \right)$

$\left[\begin{array}{c}{x}^{\left(k+1\right)}\\ {y}^{\left(k+1\right)}\end{array}\right]=\Gamma \left(\theta ,\alpha \right)\left[\begin{array}{c}{x}^{\left(k\right)}\\ {y}^{\left(k\right)}\end{array}\right]+\varphi \left(\theta ,\alpha \right)\left[\begin{array}{c}\stackrel{˜}{f}\\ \stackrel{˜}{g}\end{array}\right]$, (5)

$\Gamma \left(\theta ,\alpha \right)=\left[\begin{array}{cc}\frac{1}{\alpha }{\stackrel{˜}{W}}_{\theta }^{-1}& 0\\ -\frac{1}{\alpha }{\stackrel{˜}{W}}_{\theta }^{-1}{\stackrel{˜}{T}}_{\theta }{\stackrel{˜}{W}}_{\theta }^{-1}& {\stackrel{˜}{W}}_{\theta }^{-1}\end{array}\right]\left[\begin{array}{cc}\left(\alpha -1\right){\stackrel{˜}{W}}_{\theta }& {\stackrel{˜}{T}}_{\theta }\\ 0& 0\end{array}\right]=\left[\begin{array}{cc}\left(1-\frac{1}{\alpha }\right)I& \frac{1}{\alpha }{\stackrel{˜}{W}}_{\theta }^{-1}{\stackrel{˜}{T}}_{\theta }\\ -\left(1-\frac{1}{\alpha }\right){\stackrel{˜}{W}}_{\theta }^{-1}{\stackrel{˜}{T}}_{\theta }& -\frac{1}{\alpha }{\stackrel{˜}{W}}_{\theta }^{-1}{\stackrel{˜}{T}}_{\theta }{\stackrel{˜}{W}}_{\theta }^{-1}{\stackrel{˜}{T}}_{\theta }\end{array}\right]$ (6)

$\left\{\begin{array}{l}\alpha {\stackrel{˜}{W}}_{\theta }{x}^{k+1}=\left(\alpha -1\right){\stackrel{˜}{W}}_{\theta }{x}^{k}+{\stackrel{˜}{T}}_{\theta }{y}^{k}+\stackrel{˜}{f},\\ {\stackrel{˜}{W}}_{\theta }{y}^{k+1}=-{\stackrel{˜}{T}}_{\theta }{x}^{k+1}+\stackrel{˜}{g}.\end{array}$ (7)

3. IEPGS迭代法的收敛性分析

1) 迭代矩阵 $\Gamma \left(\theta ,\alpha \right)$ 的特征值 $\lambda$ 取值如下：

$\lambda =0$, $\lambda =1-\frac{1}{\alpha }$, $\lambda =1-\frac{1+{\eta }_{i}^{2}}{\alpha }$ (8)

2) 谱半径 $\rho \left(\Gamma \left(\theta ,\alpha \right)\right)<1$ 当且仅当 $\alpha >\frac{1+{\eta }_{\mathrm{max}}^{2}}{2}$

3) $\rho \left(\Gamma \left(\theta ,\alpha \right)\right)$ 可表示为：

$\rho \left(\Gamma \left(\theta ,\alpha \right)\right)=\mathrm{max}\left\{|1-\frac{1+{\eta }_{\mathrm{max}}^{2}}{\alpha }|,|1-\frac{1+{\eta }_{\mathrm{min}}^{2}}{\alpha }|,|1-\frac{1}{\alpha }|\right\}$ (9)

${P}_{1}=\left[\begin{array}{cc}{\stackrel{˜}{W}}_{\theta }^{-\frac{1}{2}}& 0\\ 0& {\stackrel{˜}{W}}_{\theta }^{-\frac{1}{2}}\end{array}\right]$ (10)

$\stackrel{^}{\Gamma }\left(\theta ,\alpha \right)=\left[\begin{array}{cc}{\stackrel{˜}{W}}_{\theta }^{\frac{1}{2}}& 0\\ 0& {\stackrel{˜}{W}}_{\theta }^{\frac{1}{2}}\end{array}\right]\left[\begin{array}{cc}\left(1-\frac{1}{\alpha }\right)I& \frac{1}{\alpha }{\stackrel{˜}{W}}_{\theta }^{-1}{\stackrel{˜}{T}}_{\theta }\\ -\left(1-\frac{1}{\alpha }\right){\stackrel{˜}{W}}_{\theta }^{-1}{\stackrel{˜}{T}}_{\theta }& -\frac{1}{\alpha }{\stackrel{˜}{W}}_{\theta }^{-1}{\stackrel{˜}{T}}_{\theta }{\stackrel{˜}{W}}_{\theta }^{-1}{\stackrel{˜}{T}}_{\theta }\end{array}\right]\left[\begin{array}{cc}{\stackrel{˜}{W}}_{\theta }^{-\frac{1}{2}}& 0\\ 0& {\stackrel{˜}{W}}_{\theta }^{-\frac{1}{2}}\end{array}\right]=\left[\begin{array}{cc}\left(1-\frac{1}{\alpha }\right)I& \frac{1}{\alpha }Q\\ -\left(1-\frac{1}{\alpha }\right)Q& -\frac{1}{\alpha }{Q}^{2}\end{array}\right]$

$Q={U}^{\text{T}}\left[\begin{array}{cc}{\Sigma }_{r}& 0\\ 0& 0\end{array}\right]U$, (11)

${P}_{2}=\left[\begin{array}{cc}{U}^{\text{T}}& 0\\ 0& {U}^{\text{T}}\end{array}\right]$ (12)

$\stackrel{˜}{\Gamma }\left(\theta ,\alpha \right)=\left[\begin{array}{cc}U& 0\\ 0& U\end{array}\right]\left[\begin{array}{cc}\left(1-\frac{1}{\alpha }\right)I& \frac{1}{\alpha }Q\\ -\left(1-\frac{1}{\alpha }\right)Q& -\frac{1}{\alpha }{Q}^{2}\end{array}\right]\left[\begin{array}{cc}{U}^{\text{T}}& 0\\ 0& {U}^{\text{T}}\end{array}\right]=\left[\begin{array}{cc}\left(1-\frac{1}{\alpha }\right)I& \frac{1}{\alpha }UQ{U}^{\text{T}}\\ -\left(1-\frac{1}{\alpha }\right)UQ{U}^{\text{T}}& -\frac{1}{\alpha }UQ{U}^{\text{T}}UQ{U}^{\text{T}}\end{array}\right]$

$\stackrel{˜}{\Gamma }\left(\theta ,\alpha \right)=\left[\begin{array}{cccc}\left(1-\frac{1}{\alpha }\right){I}_{r}& 0& \frac{1}{\alpha }{\Sigma }_{r}& 0\\ 0& \left(1-\frac{1}{\alpha }\right){I}_{n-r}& 0& 0\\ -\left(1-\frac{1}{\alpha }\right){\Sigma }_{r}& 0& -\frac{1}{\alpha }{\Sigma }_{r}^{2}& 0\\ 0& 0& 0& 0\end{array}\right]$

$\begin{array}{c}|\lambda I-\stackrel{˜}{\Gamma }\left(\theta ,\alpha \right)|=|\begin{array}{cccc}\left(\lambda -1+\frac{1}{\alpha }\right){I}_{r}& 0& -\frac{1}{\alpha }{\Sigma }_{r}& 0\\ 0& \left(\lambda -1+\frac{1}{\alpha }\right){I}_{n-r}& 0& 0\\ \left(1-\frac{1}{\alpha }\right){\Sigma }_{r}& 0& \lambda {I}_{r}-\frac{1}{\alpha }{\Sigma }_{r}^{2}& 0\\ 0& 0& 0& \lambda {I}_{n-r}\end{array}|\\ ={\left(-1\right)}^{r}\cdot {\lambda }^{r}\cdot \left[\left(\lambda -1+\frac{1}{\alpha }\right)\frac{\alpha }{{\eta }_{1}}+{\eta }_{1}\right]\cdot \cdots \cdot \left[\left(\lambda -1+\frac{1}{\alpha }\right)\frac{\alpha }{{\eta }_{r}}+{\eta }_{r}\right]\cdot {\left(\lambda -1+\frac{1}{\alpha }\right)}^{n-r}\cdot {\left(-1\right)}^{r}\cdot \frac{{\eta }_{1}}{\alpha }\cdot \cdots \cdot \frac{{\eta }_{r}}{\alpha }\cdot {\lambda }^{n-r}\\ ={\lambda }^{n}\cdot \left[\left(\lambda -1+\frac{1}{\alpha }\right)\frac{\alpha }{{\eta }_{1}}+{\eta }_{1}\right]\cdot \cdots \cdot \left[\left(\lambda -1+\frac{1}{\alpha }\right)\frac{\alpha }{{\eta }_{r}}+{\eta }_{r}\right]\cdot {\left(\lambda -1+\frac{1}{\alpha }\right)}^{n-r}\cdot \frac{{\eta }_{1}}{\alpha }\cdot \cdots \cdot \frac{{\eta }_{r}}{\alpha }\end{array}$

${\lambda }_{{i}_{1}}=0$, ${\lambda }_{{i}_{2}}=1-\frac{1+{\eta }_{i}^{2}}{\alpha }$, ${\lambda }_{{i}_{3}}=1-\frac{1}{\alpha }$, (13)

$\rho \left(\Gamma \left(\theta ,\alpha \right)\right)<1⇔\left\{\begin{array}{l}|{\lambda }_{{i}_{2}}|<1\\ |{\lambda }_{{i}_{3}}|<1\end{array}⇔\left\{\begin{array}{l}|1-\frac{1+{\eta }_{i}^{2}}{\alpha }|<1\\ |1-\frac{1}{\alpha }|<1\end{array}⇔\left\{\begin{array}{l}-1<1-\frac{1+{\eta }_{i}^{2}}{\alpha }<1\\ -1<1-\frac{1}{\alpha }<1\end{array}⇔\left\{\begin{array}{l}\alpha >\frac{1+{\eta }_{i}^{2}}{2}\\ \alpha >\frac{1}{2}\end{array}⇔\alpha >\frac{1+{\eta }_{\mathrm{max}}^{2}}{2}$

$x\ge 0$ 时， $S\left(x\right)=1-\frac{1+x}{\alpha }$ 关于x单调递减知

$\underset{{\eta }_{i}}{\mathrm{max}}|1-\frac{1+{\eta }_{i}^{2}}{\alpha }|=\mathrm{max}\left\{|1-\frac{1+{\eta }_{\mathrm{max}}^{2}}{\alpha }|,|1-\frac{1+{\eta }_{\mathrm{min}}^{2}}{\alpha }|\right\}$,

$\rho \left(\Gamma \left(\theta ,\alpha \right)\right)=\mathrm{max}\left\{|1-\frac{1+{\eta }_{\mathrm{max}}^{2}}{\alpha }|,|1-\frac{1+{\eta }_{\mathrm{min}}^{2}}{\alpha }|,|1-\frac{1}{\alpha }|\right\}$

$\lambda +\frac{{\left({\mu }_{i}\mathrm{cos}\theta -\mathrm{sin}\theta \right)}^{2}}{{\left(\mathrm{cos}\theta +{\mu }_{i}\mathrm{sin}\theta \right)}^{2}}=0$, $\left(1\le i\le n\right)$,

${\mu }_{\mathrm{max}},{\mu }_{\mathrm{min}}$ 分别为矩阵 $S={W}^{-1}T$ 的最大和最小特征值，则有如下定理成立。

${\theta }_{\ast }=\mathrm{arctan}\frac{{\mu }_{\mathrm{min}}{\mu }_{\mathrm{max}}-1+\sqrt{\left(1+{\mu }_{\mathrm{min}}^{2}\right)\left(1+{\mu }_{\mathrm{max}}^{2}\right)}}{{\mu }_{\mathrm{min}}+{\mu }_{\mathrm{max}}}$,

$\rho \left(\Gamma \left({\theta }_{\text{*}},{\alpha }_{\text{*}}\right)\right)=\frac{{\eta }_{\mathrm{max}}^{2}}{2+{\eta }_{\mathrm{max}}^{2}}$ (14)

${\alpha }_{\text{*}}=\mathrm{arg}\underset{\alpha }{\mathrm{min}}\rho \left(\Gamma \left(\theta ,\alpha \right)\right)=\mathrm{arg}\underset{\alpha }{\mathrm{min}}\underset{}{\mathrm{max}}\left\{|1-\frac{1+{\eta }_{\mathrm{max}}^{2}}{\alpha }|,|1-\frac{1+{\eta }_{\mathrm{min}}^{2}}{\alpha }|,|1-\frac{1}{\alpha }|\right\}$

${f}_{\eta }\left(\alpha \right)=1-\frac{1+{\eta }^{2}}{\alpha }$，其中 $\eta$ 分别取 $0,{\eta }_{\mathrm{min}},{\eta }_{\mathrm{max}}$

${\alpha }_{\text{*}}=\mathrm{arg}\underset{\alpha }{\mathrm{min}}\underset{\eta }{\mathrm{max}}\left\{|{f}_{\eta }\left(\alpha \right)|\right\}$.

Figure 1. The graph of $|{f}_{\eta }\left(\alpha \right)|$, where $\eta =0,{\eta }_{\mathrm{min}},{\eta }_{\mathrm{max}}$

$g\left({\eta }_{\text{max}}^{2}\right)=\frac{{\eta }_{\mathrm{max}}^{2}}{2+{\eta }_{\mathrm{max}}^{2}}$，则 $g\left({\eta }_{\text{max}}^{2}\right)$ 关于 ${\eta }_{\text{max}}^{2}$ 单调递增，故要极小化 $\rho \left(\Gamma \left(\theta ,{\alpha }_{\text{*}}\right)\right)$，我们选取最优参数 ${\theta }_{\text{*}}$ 极小化 ${\eta }_{\text{max}}^{2}$ 即可。由 ${\eta }_{i}=\frac{{\mu }_{i}\mathrm{cos}\theta -\mathrm{sin}\theta }{\mathrm{cos}\theta +{\mu }_{i}\mathrm{sin}\theta }$

${\theta }_{\text{*}}=\mathrm{arg}\underset{\theta }{\mathrm{min}}{\eta }_{\text{max}}^{2}\left(\theta \right)=\mathrm{arg}\underset{\theta }{\mathrm{min}}\underset{{\mu }_{i}\in \sigma \left({W}^{-1}T\right)}{\mathrm{max}}\frac{{\left(\mathrm{cos}\theta {\mu }_{i}-\mathrm{cos}\theta \right)}^{2}}{{\left(\mathrm{sin}\theta +{\mu }_{i}\mathrm{sin}\theta \right)}^{2}}$.

${\theta }_{\text{*}}=\mathrm{arctan}\frac{{\mu }_{\mathrm{min}}{\mu }_{\mathrm{max}}-1+\sqrt{\left(1+{\mu }_{\mathrm{min}}^{2}\right)\left(1+{\mu }_{\mathrm{max}}^{2}\right)}}{{\mu }_{\mathrm{min}}+{\mu }_{\mathrm{max}}}$,

4. 数值实验

$RES=\frac{{‖b-A{u}^{\left(k\right)}‖}_{2}}{{‖b‖}_{2}}\le {10}^{-9}$

$\left[\left(-{\omega }^{2}M+K\right)+i\left(\omega {C}_{V}+{C}_{H}\right)\right]u=b$ (15)

$K=I\otimes {V}_{m}+{V}_{m}\otimes I$，其中 ${V}_{m}={h}^{-2}tridiag\left(-1,2,-1\right)\in {R}^{m×m}$$n={m}^{2}$。此外，设置 $\omega =\pi$ 并选择右侧向量 $b=\left(1+i\right)A1$，1表示分量全为1的n维列向量，并通过两边同时乘以 ${h}^{2}$ 来正规化(15)式。

Table 1. Selection of experimental parameters for MHSS, E-HS, EPGS and IEPGS iterative methods

Figure 2. Relative residual curves with m = 16 (left) and m = 32 (right)

Figure 3. Relative residual curves with m = 64 (left) and m = 96 (right)

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