数学物理学报, 2026, 46(5): 2055-2074

对偶矩阵两类星序的特征和性质

刘娜,1, 刘晓冀,2,*

1 广西民族大学数学科学学院 南宁 530006

2 广西职业师范学院教育学院 南宁 530007

Properties and Characterizations of Dual Left (Right) Star Partial Orders

Liu Na,1, Liu Xiaoji,2,*

1 School of Mathematical Sciences, Guangxi Minzu University, Nanning 530006

2 School of Education, Guangxi Vocational Normal University, Nanning 530007

通讯作者: * 刘晓冀, E-mail: xiaojiliu72@126.com

收稿日期: 2025-01-10   修回日期: 2025-10-21  

基金资助: 国家自然科学基金(12061015)
国家自然科学基金(12361067)
广西自然科学基金(2024GXNSFAA010503)
广西自然科学基金(2024GXNSFAA010506)

Received: 2025-01-10   Revised: 2025-10-21  

Fund supported: NSFC(12061015)
NSFC(12361067)
Guangxi Natural Science Foundation(2024GXNSFAA010503)
Guangxi Natural Science Foundation(2024GXNSFAA010506)

作者简介 About authors

刘娜,E-mail:470186879@qq.com

摘要

该文利用 DMPGI 和 MPDGI 引入对偶矩阵上的左 (右) D-* 序和左 (右) P-* 序, 给出其等价刻画和性质. 进一步, 给出对偶矩阵上的左 (右) D-* 序和左 (右) P-* 序与 D-* 序, D 序, P-* 序之间的区别联系.

关键词: 左 D-* 序; 右 D-* 序; 左 P-* 序; 右 P-* 序

Abstract

In this paper, we introduce left (right) D-* order and left (right) P-* order on dual matrix by using DMPGI and MPDGI. The equivalent characterizations and properties are presented. Furthermore, we discuss the relationships among the left (right) D-* order, the left (right) P-* order, the minus order, the D-* order and the P-* order on dual matrices.

Keywords: left D-* partial order; right D-* partial order; left P-* partial order; right P-* partial order

PDF (515KB) 元数据 多维度评价 相关文章 导出 EndNote| Ris| Bibtex  收藏本文

本文引用格式

刘娜, 刘晓冀. 对偶矩阵两类星序的特征和性质[J]. 数学物理学报, 2026, 46(5): 2055-2074

Liu Na, Liu Xiaoji. Properties and Characterizations of Dual Left (Right) Star Partial Orders[J]. Acta Mathematica Scientia, 2026, 46(5): 2055-2074

1 引言

本文使用以下记号: $\mathbb{R}^{m\times n}$$\mathbb{D}^{m\times n}$ 表示 $m\times n$ 的实矩阵和对偶矩阵, $A^{T}$, ${\mathcal{R}}\left( {A} \right)$${ rk}\left( A \right)$ 分别代表 $A$ 的转置, 值域和秩. 设 $A\in\mathbb{R}^{m\times n}$, 若存在唯一的 $X\in\mathbb{R}^{n\times m}$ 满足 $ AXA = A, XAX=X, (AX)^{T}=AX $$ (XA)^{T}=XA $, 则称 $X$$A$ 的 Moore-Penrose 逆, 记为 $ X=A^{\dagger} $, 见文献 [4,6,22].

设对偶矩阵 $\widehat{A}=A+\epsilon A_{0}$, 其中 $A, A_{0}\in\mathbb{R}^{m\times n}$; $\epsilon$ 是满足 $\epsilon \neq 0, 0\epsilon=\epsilon 0=0, 1\epsilon=\epsilon 1=\epsilon$$\epsilon^{2}=0$ 的对偶单位元. 进一步, $\widehat{A}^{T}$ 表示 $\widehat{A}$ 的转置, 即 $\widehat{A}^{T}=A^{T}+\epsilon A_{0}^{T}$. ${\mathcal{R}}\left( \widehat{A} \right)$ 代表 $\widehat{A}$ 的值域. 对偶矩阵广泛应用于科学与工程领域, 例如机械的运动学分析、空间机制的动态分析、机器人技术及传感器的校准. 近几年, 对偶矩阵及其应用受到广泛的关注. 更多关于对偶矩阵的理论及其应用可见文献[13,7,10,1315,1820,27,29]

$\widehat{A}=A+\epsilon A_{0}\in\mathbb{D}^{m\times n}$, 则 $\widehat{A}$ 的 Moore-Penrose 对偶广义逆 (简记为: MPDGI) 有 $\widehat{A}^{p}=A^{\dagger}-\epsilon A^{\dagger}A_{0}A^{\dagger} $ 的形式. 显然,每一个对偶矩阵都有 MPDGIs. Pennestrì 等人给出了 MPDGI 的相关性质和刻画[17].

$\widehat A\in\mathbb{D}^{m\times n}$, 若存在唯一的 $\widehat X\in\mathbb{D}^{n\times m}$ 满足 $ \widehat A \widehat X \widehat A = \widehat A, \widehat X \widehat A \widehat X=\widehat X, (\widehat A \widehat X)^{T}=\widehat A \widehat X $, 和 $ (\widehat X \widehat A)^{T}=\widehat X \widehat A $, 则称 $\widehat X$$\widehat A$ 的对偶 Moore-Penrose 逆 (简记为: DMPGI), 记为 $ \widehat X=\widehat A^{\dagger} $. Udwadia 在文献[21] 中指出不是所有的对偶矩阵都有 DMPGIs, 并得到 DMPGI 一些有趣的结果. 王在文献[23] 中给出 DMPGI 的一个紧化公式, 并给出对偶矩阵具有 DMPGI 的必要和充分条件. 王, 李和魏等人在文献[24] 中提出对偶四元数高斯变换, 利用高斯变换得到对偶四元数LU分解和部分旋转对偶四元数LU分解的算法. 魏,丁和魏在文献[28] 中给出对偶复矩阵的奇异值分解 (CDSVD) 以及具有显式表达式存在的充分必要条件. 进一步, 介绍对偶复矩阵在不同度量下的低秩逼近以及 CDSVD 的相关理论应用. 崔和祁等人在文献[8] 中将四元数 Hermite 矩阵的 Chen 行列式和 Moore 行列式推广到对偶四元数 Hermite 矩阵, 并研究对偶四元数 Hermite 矩阵的特征多项式. 崔和祁在文献[9] 中基于对偶矩阵的奇异值分解, 提出一个新的逆 NDMPI, 讨论 NDMPI, DMPGI 和 MPDGI 之间的联系. 进一步, 证明 NDMPI 可以用来生成对偶最小二乘问题的最小范数解. 这些理论是本文研究对偶矩阵偏序理论的主要工具.

对偶广义逆是研究线性对偶方程最小二乘解的有力工具. Belzile 和 Angeles 解决了对偶矩阵方程的最小二乘问题, 并利用对偶 Householder 变换解决了 RCCC 四连杆的近似合成问题 [2]. 魏, 丁和魏利用 CDSVD 算法来模拟小规模的道路监控视频数据和大规模的脑功能磁共振成像数据, 验证有识别行波的脑区与对应的大脑皮层功能之间的一致性 [28]. 这些应用为对偶广义逆理论的深入研究提供了动力.

最近,由于矩阵偏序成为矩阵理论研究的一个热点. 国内外许多学者从事各种类型的矩阵偏序研究. 例如: 减序、* 序、sharp 序和 core 序, 并被应用于数理统计等领域. 值得注意的是, Mitra, Bhimasankaram 和 Malik 的工作为矩阵偏序的研究提供坚实的基础 [16]. 王和黄在文献[25] 中定义了对偶 * 序, 并给出其特征和性质; 王和蒋在文献[26] 中引入对偶 sharp 序, 对偶 D-sharp 序,对偶 G-sharp 序, 并给出等价刻画和性质. 进一步, 王和高在文献[12] 讨论了对偶减序, 对偶 sharp 序, 对偶减星序之间的联系.

本文构造如下: 第二节首先介绍了基本的分解和对偶偏序的一些结果. 第三节利用 DMPGI 引入了左 (右) D-* 二元关系, 并证明左 (右) D-* 二元关系是偏序, 称为左 (右) D-* 序. 进一步, 给出等价刻画和性质. 最后, 讨论了对偶矩阵上左 (右) D-* 序, D 序和 D-* 序之间的联系. 第四节利用 MPDGI 引入了左 (右) P-* 二元关系, 并证明左 (右) P-* 二元关系是偏序, 称为左 (右) P-* 序. 进一步, 给出等价刻画和性质. 最后, 讨论了对偶矩阵上左 (右) P-* 序, D 序和 P-* 序之间的联系.

2 预备知识

在本节给出实矩阵的奇异值分解 (SVD 分解), 左 (右) * 序的刻画和 DMPGI 的基础知识.

引理 2.1(SVD 分解) 设 $A\in\mathbb{R}^{m\times n}$${\mbox{ rk}}(A)=a$, 则存在正交矩阵 $U\in\mathbb{R}^{m\times m}$$V\in\mathbb{R}^{n\times n}$ 使得

$\begin{align*} \nonumber A =U \left( \begin{matrix} T &0 \\ 0 &0 \end{matrix} \right)V^{T}, \end{align*}$

其中 $T\in\mathbb{R}^{a\times a}$ 是对角正定矩阵.

引理 2.2[5]$A, B \in \mathbb{R}^{m \times n}$,${\mbox{ rk}}(A)=a$,${\mbox{ rk}}(B)=b$$b > a$, 则下面条件等价

(1) $A{*}\leq B $;

(2) $A^{T}A=A^{T}B$${\mathcal{R}}\left( {A} \right)$$\subseteq$${\mathcal{R}}\left( {B} \right)$;

(3) $A^{\dagger}A=A^{\dagger}B$${\mathcal{R}}\left( {A} \right)$$\subseteq$${\mathcal{R}}\left( {B} \right)$;

(4) 存在正交矩阵 $U$$V$ 使得

$\begin{align*} \label{1-1} A =U\left( \begin{matrix} T &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T}, B =U\left( \begin{matrix} T &0 &0 \\ RE &E &0 \\ 0 &0 &0 \end{matrix} \right)V^{T}, \end{align*} $

其中 $T\in\mathbb{R}^{a\times a}$$E\in\mathbb{R}^{(b-a)\times(b-a)}$ 是对角正定矩阵.

引理 2.3[5]$A, B\in\mathbb{R}^{m\times n}$, ${\mbox{ rk}}(A)=a$, ${\mbox{ rk}}(B)=b$$b>a$, 则下面条件等价

(1) $ A \leq {*}B $;

(2) $AA^{T}=BA^{T}$${\mathcal{R}}\left( {A^{T}} \right)$$\subseteq$${\mathcal{R}}\left( {B^{T}} \right)$;

(3) $AA^{\dagger}=BA^{\dagger}$${\mathcal{R}}\left( {A^{T}} \right)$$\subseteq$${\mathcal{R}}\left( {B^{T}} \right)$;

(4) 存在正交矩阵 $U$$V$ 使得

$\begin{align*} \label{2-1} A =U\left( \begin{matrix} T &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T}, B =U\left( \begin{matrix} T &ES &0 \\ 0 &E &0 \\ 0 &0 &0 \end{matrix} \right)V^{T}, \end{align*}$

其中 $T\in\mathbb{R}^{a\times a}$$E\in\mathbb{R}^{(b-a)\times(b-a)}$ 是对角正定矩阵.

引理 2.4[23]$\widehat{A}=A+ \epsilon A_{0}\in\mathbb{D}^{m\times n}$, 则下面条件等价

(1) $\widehat{A} $ 的 DMPGI 存在;

(2) $ (I_{m}-AA^{\dagger})A_{0}(I_{n}-A^{\dagger}A)=0$;

(3) $ {\mbox{ rk}}\left( \begin{matrix} A_{0} &A \\ A &0 \end{matrix} \right)=2{\mbox{ rk}}(A).$

$\widehat{A} $ 的 DMPGI 存在, 则

$\widehat{A}^{\dagger}={A}^{\dagger} +\epsilon R, $

其中$R=-{A}^{\dagger}A_{0}{A}^{\dagger}+({A}^{T}A)^{\dagger}A_{0}^{T}(I_{m}-AA^{\dagger})+(I_{n}-A^{\dagger}A)A_{0}^{T}(A{A}^{T})^{\dagger}$.

进一步, 设 $A$ 的 SVD 有引理 2.1 的形式, 则

$\widehat{A}=U\left(\begin{array}{ll} T & 0 \\ 0 & 0 \end{array}\right) V^{T}+\epsilon U\left(\begin{array}{cc} A_{1} & A_{2} \\ A_{3} & 0 \end{array}\right) V^{T}, $
$\widehat{A}^{\dagger}=V\left(\begin{array}{cc} T^{-1} & 0 \\ 0 & 0 \end{array}\right) U^{T}+\epsilon V\left(\begin{array}{cc} -T^{-1} A_{1} T^{-1} & T^{-2} A_{3}^{T} \\ A_{2}^{T} T^{-2} & 0 \end{array}\right) U^{T} $

其中 $T$ 是对角正定矩阵.

引理 2.5[11]$A, B \in \mathbb{R}^{m \times n}$, 则 $ {A} \leq {B} $ 当且仅当

$\begin{align*} \nonumber {\mbox{ rk}}(B-A)={\mbox{ rk}}(B)-{\mbox{ rk}}(A). \end{align*}$

引理 2.6[12]$\widehat{A}=A+ \epsilon A_{0}, \widehat{B} =B+ \epsilon B_{0}\in\mathbb{R}^{m\times n}$, 且 $\widehat{A}, \widehat{B}$ 的 DMPGIs 存在, 则 $\widehat{A} \mathop\le\limits^{\tiny {D}} \widehat{B}$ 当且仅当

$\begin{align*} \nonumber \left( \begin{matrix} A_{0} &A \\ A &0 \end{matrix} \right) \leq \left( \begin{matrix} B_{0} &B \\ B &0 \end{matrix} \right). \end{align*}$

3 左 (右) D-* 序

在本节利用 DMPGI 引入左 (右) D-* 序,并给出等价刻画和性质. 进一步,讨论左 (右) D-* 序, D 序和 D-* 序之间的关系.

定义 3.1$\widehat{A}=A+ \epsilon A_{0}\in\mathbb{D}^{m\times n}$, $\widehat{B} =B+ \epsilon B_{0}\in\mathbb{D}^{m\times n}$, 且 $ \widehat{A}, \widehat{B}$ 的 DMPGIs 存在. 若 $\widehat{A},\widehat{B}$ 满足

$\begin{align*} \nonumber \widehat{A}^{\dagger}\widehat{A}=\widehat{A}^{\dagger}\widehat{B}, \ \ {\mathcal{R}}\left( {\widehat{A} } \right) \subseteq {\mathcal{R}}\left( {\widehat{B} } \right), \end{align*}$

$\widehat{A}$$\widehat{B}$ 满足左D-* 二元关系, 记为 $\widehat{A}\leq^{\tiny {D-*}}_{L} \widehat{B} $.

定理 3.1$\widehat{A}=A+ \epsilon A_{0}$$\widehat{B} =B+ \epsilon B_{0}$, 其中 $A,A_{0},B,B_{0}\in\mathbb{R}^{m\times n}$.$\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在, 则 $\widehat{A}\leq^{\tiny {D-*}}_{L} \widehat{B}$ 当且仅当

$\begin{align*} \nonumber \left\{ \begin{aligned} &A^{\dagger}A=A^{\dagger}B, \\ &A^{\dagger}A_{0}+RA=A^{\dagger}B_{0}+RB, \\ &{\mbox{ rk}}\left( \begin{matrix} A_{0} &B &B_{0} \\ A &0 &B \end{matrix} \right)=2{\mbox{ rk}}(B), \end{aligned} \right. \end{align*}$

其中$R=-{A}^{\dagger}A_{0}{A}^{\dagger}+({A}^{T}A)^{\dagger}A_{0}^{T}(I_{m}-AA^{\dagger})+(I_{n}-A^{\dagger}A)A_{0}^{T}(A{A}^{T})^{\dagger}$.

$\Rightarrow$$\widehat{A}=A+ \epsilon A_{0}$, $\widehat{B} =B+ \epsilon B_{0}$, 且 $\widehat{A}$ 的 DMPGI 存在, 记为 $\widehat{A}^{\dagger}={A}^{\dagger}+\epsilon R$. 因为 $\widehat{A}^{\dagger}\widehat{A}=\widehat{A}^{\dagger}\widehat{B}$

$\begin{align*} \nonumber \left\{ \begin{aligned} &\widehat{A}^{\dagger}\widehat{A}=(A^{\dagger}+ \epsilon R)(A+ \epsilon A_{0})=A^{\dagger}A+\epsilon(A^{\dagger}A_{0}+RA), \\ &\widehat{A}^{\dagger}\widehat{B}=(A^{\dagger}+ \epsilon R)(B+ \epsilon B_{0})=A^{\dagger}B+\epsilon(A^{\dagger}B_{0}+RB), \end{aligned} \right. \end{align*}$

可得 $A^{\dagger}A=A^{\dagger}B$$A^{\dagger}A_{0}+RA=A^{\dagger}B_{0}+RB$. 又因为 ${\mathcal{R}}\left( {\widehat{A}} \right) \subseteq {\mathcal{R}}\left( {\widehat{B}} \right)$, 存在 $\widehat{C}$ 满足 $\widehat{A}=\widehat{B}\widehat{C}$, 即 $A+\epsilon A_{0} =BC+\epsilon(BC_{0}+B_{0}C)$, 可得 $A=BC$$A_{0}=BC_{0}+B_{0}C$. 应用引理 2.4, 可得

$\begin{align*} \nonumber {\mbox{ rk}}\left( \begin{matrix} A_{0} &B &B_{0} \\ A &0 &B \end{matrix} \right)=2{\mbox{ rk}}(B). \end{align*}$

$\Leftarrow$ 由于 $A^{\dagger}A=A^{\dagger}B$$A^{\dagger}A_{0}+RA=A^{\dagger}B_{0}+RB$, 则 $\widehat{A}^{\dagger}\widehat{A}=\widehat{A}^{\dagger}\widehat{B}$. 通过 ${\mbox{ rk}}\left( \begin{matrix} A_{0} &B &B_{0} \\ A &0 &B \end{matrix} \right)=2{\mbox{ rk}}(B)$, 则存在 ${C}$, ${C_{0}}$ 满足 $A=BC$$A_{0}=BC_{0}+B_{0}C$, 即 $\widehat{A}=\widehat{B}\widehat{C}$. 因此,有 $\widehat{A}\leq^{\tiny {D-*}}_{L}\widehat{B}$.

定理 3.2$\widehat{A}=A+\epsilon A_{0}\in\mathbb{D}^{m\times n}$, $\widehat{B}=B+\epsilon B_{0}\in\mathbb{D}^{m\times n}$, 且 $\widehat{A}$$\widehat{B}$的 DMPGIs 存在, 则 $\widehat{A}\leq^{\tiny {D-*}}_{L}\widehat{B}$ 当且仅当存在正交矩阵 $U$$V$ 使得

$\begin{align*} \nonumber \left\{ \begin{aligned} \nonumber \widehat{A} &=U\left( \begin{matrix} T &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} +\epsilon U\left( \begin{matrix} A_{1} &A_{2} &A_{3} \\ A_{4} &0 &0 \\ A_{7} &0 &0 \end{matrix} \right)V^{T}, \\ \widehat{B} &=U\left( \begin{matrix} T &0 &0 \\ RE &E &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} +\epsilon U\left( \begin{matrix} A_{1}-T^{-1}A^{T}_{4}RE &A_{2}-T^{-1}A^{T}_{4}E &A_{3} \\ B_{4} &B_{5} &B_{6} \\ A_{7}+B_{8}E^{-1}RE &B_{8} &0 \end{matrix} \right)V^{T}, \end{aligned} \right. \end{align*}$

其中 $T$$E$ 是对角正定矩阵.

$\Rightarrow$${\mbox{ rk}}(A)=a$${\mbox{ rk}}(B)=b$. 由于 $\widehat{A}\leq^{\tiny {D-*}}_{L} \widehat{B}$, 利用引理 2.2, 可得 $A {*}\leq B$, 则 $A$$B$ 有 (2.1) 式的形式. 因为 $\widehat{A}$ 的 DMPGI 存在, 可记

$\begin{align*} A_{0} \label{gl3.1} =U\left( \begin{matrix} A_{1} &A_{2} &A_{3} \\ A_{4} &0 &0 \\ A_{7} &0 &0 \end{matrix} \right)V^{T}, \end{align*} $

其中 $A_{1}\in\mathbb{R}^{a\times a}$, $A_{2}\in\mathbb{R}^{a\times (b-a)}$$A_{4}\in\mathbb{R}^{(b-a)\times a}$. 利用引理 2.4, 可得

$\begin{align*} \label{gl3.2} \widehat{A}^{\dagger} &=V\left( \begin{matrix} T^{-1} &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)U^{T} +\epsilon V\left( \begin{matrix} -T^{-1}A_{1}T^{-1} &T^{-2}A^{T}_{4} &T^{-2}A^{T}_{7} \\ A^{T}_{2}T^{-2} &0 &0 \\ A^{T}_{3}T^{-2} &0 &0 \end{matrix} \right)U^{T}. \end{align*}$

又因为 $\widehat{B}$ 的 DMPGI 存在, 记为

$\begin{align*} B_{0} \label{gl3.3} =U\left( \begin{matrix} B_{1} &B_{2} &B_{3} \\ B_{4} &B_{5} &B_{6} \\ B_{7} &B_{8} &0 \end{matrix} \right)V^{T}, \end{align*} $

其中 $B_{1}\in\mathbb{R}^{a\times a}$$B_{2}\in\mathbb{R}^{a\times (b-a)}$. 应用 (2.1) 式, (3.1) 式, (3.2) 式和 (3.3) 式, 可得

$\begin{align*} \nonumber \left\{ \begin{aligned} \widehat{A}^{\dagger}\widehat{A} &=V\left( \begin{matrix} I_{a} &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} + \epsilon V\left( \begin{matrix} 0 &T^{-1}A_{2} &T^{-1}A_{3} \\ A^{T}_{2}T^{-1} &0 &0 \\ A^{T}_{3}T^{-1} &0 &0 \end{matrix} \right)V^{T}, \\ \nonumber \widehat{A}^{\dagger}\widehat{B} &=V\left( \begin{matrix} I_{a} &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} + \epsilon V\left( \begin{matrix} T^{-1}B_{1}-T^{-1}A_{1}+T^{-2}A^{T}_{4}RE &T^{-1}B_{2}+T^{-2}A^{T}_{4}E &T^{-1}B_{3} \\ A^{T}_{2}T^{-1} &0 &0 \\ A^{T}_{3}T^{-1} &0 &0 \end{matrix} \right)V^{T}. \end{aligned} \right. \end{align*}$

由于 $\widehat{A}\leq^{\tiny {D-*}}_{L} \widehat{B}$, 有 $\widehat{A}^{\dagger}\widehat{A}=\widehat{A}^{\dagger}\widehat{B}$, 即

$\begin{align*} \nonumber \left\{ \begin{aligned} &T^{-1}B_{1}-T^{-1}A_{1}+T^{-2}A^{T}_{4}RE=0, \\ &T^{-1}B_{2}+T^{-2}A^{T}_{4}E=T^{-1}A_{2}, \\ &T^{-1}B_{2}=T^{-1}A_{3}. \end{aligned} \right. \end{align*}$

因此, $B_{1}=A_{1}-T^{-1}A^{T}_{4}RE, B_{2}=A_{2}-T^{-1}A^{T}_{4}E$$B_{3}=A_{3}$. 通过 (3.3) 式, 可得

$\begin{align*} \label{GS3} B_{0}= U\left( \begin{matrix} A_{1}-T^{-1}A^{T}_{4}RE &A_{2}-T^{-1}A^{T}_{4}E &A_{3} \\ B_{4} &B_{5} &B_{6} \\ B_{7} &B_{8} &0 \end{matrix} \right)V^{T}. \end{align*} $

基于 ${\mbox{ rk}}\left( \begin{matrix} A_{0} &B &B_{0} \\ A &0 &B \end{matrix} \right)=2{\mbox{ rk}}(B)$

$\begin{align*} \nonumber &{\mbox{ rk}} \left( \begin{matrix} A_{1} &A_{2} &A_{3} &T &0 &0 &A_{1}-T^{-1}A^{T}_{4}RE &A_{2}-T^{-1}A^{T}_{4}E &A_{3} \\ A_{4} &0 &0 &RE &E &0 &B_{4} &B_{5} &B_{6} \\ A_{7} &0 &0 &0 &0 &0 &B_{7} &B_{8} &0 \\ T &0 &0 &0 &0 &0 &T &0 &0 \\ 0 &0 &0 &0 &0 &0 &RE &E &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \end{matrix} \right) \\ \nonumber = &{\mbox{ rk}}\left( \begin{matrix} 0 &A_{2} &A_{3} &T &0 &0 &-T^{-1}A^{T}_{4}RE &A_{2}- T^{-1}A^{T}_{4}E &A_{3} \\ 0 &0 &0 &RE &E &0 &B_{4}-A_{4} &0 &0 \\ 0 &0 &0 &0 &0 &0 &B_{7}-A_{7}-B_{8}E^{-1}RE &0 &0 \\ T &0 &0 &0 &0 &0 &T &0 &0 \\ 0 &0 &0 &0 &0 &0 &RE &E &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \end{matrix} \right), \end{align*}$

可得 $B_{7}=A_{7} +B_{8}E^{-1}RE$. 利用 (2.1) 式和 (3.4) 式, 可得

$\begin{align*} \nonumber \widehat{B} &=U\left( \begin{matrix} T &0 &0 \\ RE &E &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} +\epsilon U\left( \begin{matrix} A_{1}-T^{-1}A^{T}_{4}RE &A_{2}-T^{-1}A^{T}_{4}E &A_{3} \\ B_{4} &B_{5} &B_{6} \\ A_{7}+B_{8}E^{-1}RE &B_{8} &0 \end{matrix} \right)V^{T}. \end{align*}$

$\Leftarrow$ 设存在正交矩阵 $U$$V$ 使得 $\widehat{A}$$\widehat{B}$ 有定理 3.2 的形式, 可证

$\begin{align*} \nonumber \widehat{A}^{\dagger}\widehat{A}=\widehat{A}^{\dagger}\widehat{B} =V\left( \begin{matrix} I_{a} &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} + \epsilon V\left( \begin{matrix} 0 &T^{-1}A_{2} &T^{-1}A_{3} \\ A^{T}_{2}T^{-1} &0 &0 \\ A^{T}_{3}T^{-1} &0 &0 \end{matrix} \right)V^{T} \end{align*}$

$\begin{align*} \nonumber {\mbox{ rk}}\left( \begin{matrix} A_{0} &B &B_{0} \\ A &0 &B \end{matrix} \right) =2{\mbox{ rk}}(B). \end{align*}$

由上述方程,可得 $\widehat{A}^{\dagger}\widehat{A}=\widehat{A}^{\dagger}\widehat{B}$${\mathcal{R}}\left( {\widehat{A}} \right) \subseteq {\mathcal{R}}\left( {\widehat{B}} \right)$. 因此, 利用定义 3.1 有 $\widehat{A}\leq^{\tiny {D-*}}_{L} \widehat{B}$.

定理 3.3$\widehat A, \widehat B\in\mathbb{D}^{m\times n}$, ${\mbox{ rk}}(\widehat A)=a$, ${\mbox{ rk}}(\widehat B)=b$$b>a$.$\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在, 则下面条件等价

(1) $\widehat{A}\leq^{\tiny {D-*}}_{L} \widehat{B} $;

(2) $\widehat{A}^{T}\widehat{A}=\widehat{A}^{T}\widehat{B}$${\mathcal{R}}\left( {\widehat{A}} \right) \subseteq {\mathcal{R}}\left( {\widehat{B}} \right)$.

$(1)\Rightarrow(2)$$ \widehat{A}\leq^{\tiny {D-*}}_{L}\widehat{B} $, 则 $\widehat{A}$$\widehat{B}$ 有定理 3.2 的形式, 很容易证明

$\begin{align*} \nonumber \widehat{A}^{T}\widehat{A}=\widehat{A}^{T}\widehat{B} =V\left( \begin{matrix} T^{2} &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} + \epsilon V\left( \begin{matrix} TA_{1}+A^{T}_{1}T &TA_{2} &TA_{3} \\ A^{T}_{2}T &0 &0 \\ A^{T}_{3}T &0 &0 \end{matrix} \right)V^{T} \end{align*}$

$\begin{align*} \nonumber &{\mbox{ rk}} \left( \begin{matrix} A_{1} &A_{2} &A_{3} &T &0 &0 &A_{1}-T^{-1}A^{T}_{4}RE &A_{2}-T^{-1}A^{T}_{4}E &A_{3} \\ A_{4} &0 &0 &RE &E &0 &B_{4} &B_{5} &B_{6} \\ A_{7} &0 &0 &0 &0 &0 &A_{7}+B_{8}E^{-1}RE &B_{8} &0 \\ T &0 &0 &0 &0 &0 &T &0 &0 \\ 0 &0 &0 &0 &0 &0 &RE &E &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \end{matrix} \right)\\ = &{\mbox{ rk}}\left( \begin{matrix} 0 &A_{2} &A_{3} &T &0 &0 &-T^{-1}A^{T}_{4}RE &A_{2}-T^{-1}A^{T}_{4}E &A_{3} \\ 0 &0 &0 &RE &E &0 &B_{4}-A_{4} &0 &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \\ T &0 &0 &0 &0 &0 &T &0 &0 \\ 0 &0 &0 &0 &0 &0 &RE &E &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \end{matrix} \right). \end{align*}$

由上述等式, 可得 ${\mbox{ rk}}\left( \begin{matrix} A_{0} &B &B_{0} \\ A &0 &B \end{matrix} \right) =2{\mbox{ rk}}(B)$. 因此, 有 $\widehat{A}^{T}\widehat{A}=\widehat{A}^{T}\widehat{B}$${\mathcal{R}}\left( {\widehat{A}} \right) \subseteq {\mathcal{R}}\left( {\widehat{B}} \right)$.

$(1)\Leftarrow(2)$ 应用引理 2.2, $A$$B$ 有 (2.1) 式的形式. 由于 $\widehat{A}$ 的 DMPGI 存在, 记

$\begin{align*} \label{GS1} A_{0} =U \left( \begin{matrix} A_{1} &A_{2} &A_{3} \\ A_{4} &0 &0 \\ A_{7} &0 &0 \end{matrix} \right)V^{T}, \end{align*}$

其中 $A_{1}\in\mathbb{R}^{a\times a}$, $A_{2}\in\mathbb{R}^{a\times(b-a)}$$A_{4}\in\mathbb{R}^{(b-a)\times a}$. 由于 $\widehat{B}$ 的 DMPGI 存在, 记

$\begin{align*} \label{GS2} B_{0} =U \left( \begin{matrix} B_{1} &B_{2} &B_{3} \\ B_{4} &B_{5} &B_{6} \\ B_{7} &B_{8} &0 \end{matrix} \right)V^{T}, \end{align*} $

其中 $B_{1}\in\mathbb{R}^{a\times a}$, $B_{2}\in\mathbb{R}^{a\times(b-a)}$. 通过 (2.1) 式, (3.5) 式和 (3.6) 式, 可以证明

$\begin{align*} \nonumber \left\{ \begin{aligned} \widehat{A}^{T}\widehat{A} &=V\left( \begin{matrix} T^{2} &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} + \epsilon V\left( \begin{matrix} TA_{1}+A^{T}_{1}T &TA_{2} &TA_{3} \\ A^{T}_{2}T &0 &0 \\ A^{T}_{3}T &0 &0 \end{matrix} \right)V^{T}, \\ \nonumber \widehat{A}^{T}\widehat{B} &=V\left( \begin{matrix} T^{2} &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} + \varepsilon V\left( \begin{matrix} TB_{1}+A^{T}_{1}T+A^{T}_{4}RE &TB_{2}+A^{T}_{4}E &TB_{3} \\ A^{T}_{2}T &0 &0 \\ A^{T}_{3}T &0 &0 \end{matrix} \right)V^{T}. \end{aligned} \right. \end{align*}$

利用 $\widehat{A}^{T}\widehat{A}=\widehat{A}^{T}\widehat{B}$, 即

$\begin{align*} \nonumber \left\{ \begin{aligned} &TA_{1}+A^{T}_{1}T=TB_{1}+A^{T}_{1}T+A^{T}_{4}RE, \\ &TA_{2} =TB_{2}+A^{T}_{4}E, \\ &TA_{3} =TB_{3}. \end{aligned} \right. \end{align*}$

由于 $T$ 是可逆的, 则 $B_{1}=A_{1}-T^{-1}A^{T}_{4}RE$, $B_{2}=A_{2}-T^{-1}A^{T}_{4}E$$B_{3}=A_{3}$. 进一步, 记为

$\begin{align*} \label{GS-6} B_{0}= U\left( \begin{matrix} A_{1}-T^{-1}A^{T}_{4}RE &A_{2}-T^{-1}A^{T}_{4}E &A_{3} \\ B_{4} &B_{5} &B_{6} \\ B_{7} &B_{8} &0 \end{matrix} \right)V^{T}. \end{align*} $

又因为

$\begin{align*} \nonumber &{\mbox{ rk}}\left( \begin{matrix} A_{1} &A_{2} &A_{3} &T &0 &0 &A_{1}-T^{-1}A^{T}_{4}RE &A_{2}-T^{-1}A^{T}_{4}E &A_{3} \\ A_{4} &0 &0 &RE &E &0 &B_{4} &B_{5} &B_{6} \\ A_{7} &0 &0 &0 &0 &0 &B_{7} &B_{8} &0 \\ T &0 &0 &0 &0 &0 &T &0 &0 \\ 0 &0 &0 &0 &0 &0 &RE &E &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \end{matrix} \right) \\ \nonumber = &{\mbox{ rk}}\left( \begin{matrix} 0 &A_{2} &A_{3} &T &0 &0 &-T^{-1}A^{T}_{4}RE &A_{2}-T^{-1}A^{T}_{4}E &A_{3} \\ 0 &0 &0 &RE &E &0 &B_{4}-A_{4} &0 &0 \\ 0 &0 &0 &0 &0 &0 &B_{7}-A_{7}-B_{8}E^{-1}RE &0 &0 \\ T &0 &0 &0 &0 &0 &T &0 &0 \\ 0 &0 &0 &0 &0 &0 &RE &E &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \end{matrix} \right), \end{align*}$

可得 ${\mbox{ rk}}\left( \begin{matrix} A_{0} &B &B_{0} \\ A &0 &B \end{matrix} \right)=2{\mbox{ rk}}(B)$, 即

$\begin{align*} \label{3-4} B_{7}=A_{7}+B_{8}E^{-1}RE. \end{align*} $

利用 (2.1) 式, (3.7) 式和 (3.8) 式, 可得

$\begin{align*} \nonumber \widehat{B} &=U\left( \begin{matrix} T &0 &0 \\ RE &E &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} +\epsilon U\left( \begin{matrix} A_{1}-T^{-1}A^{T}_{4}RE &A_{2}-T^{-1}A^{T}_{4}E &A_{3} \\ B_{4} &B_{5} &B_{6} \\ A_{7}+B_{8}E^{-1}RE &B_{8} &0 \end{matrix} \right)V^{T}. \end{align*}$

根据定理 3.2, 有 $\widehat{A} \leq^{\tiny {D-*}}_{L} \widehat{B} $.

例 3.1

$\begin{align*} \nonumber \widehat{A} = \left( \begin{matrix} 1 &0 \\ 0 &0 \end{matrix} \right)+ \epsilon \left( \begin{matrix} 1 &0 \\ 0 &0 \end{matrix} \right), \widehat{B} = \left( \begin{matrix} 1 &0 \\ 0 &1 \end{matrix} \right)+ \epsilon \left( \begin{matrix} 1 &0 \\ 0 &1 \end{matrix} \right), \end{align*}$

$\begin{align*} \nonumber \widehat{A}^{\dagger} = \left( \begin{matrix} 1 &0 \\ 0 &0 \end{matrix} \right)+ \epsilon \left( \begin{matrix} -1 &0 \\ 0 &0 \end{matrix} \right). \end{align*}$

利用定理 3.2, 可得 $\widehat{A} \leq^{\tiny {D-*}}_{L} \widehat{B} $. 因为

$\begin{align*} \nonumber \widehat{A}^{T}\widehat{A}= \widehat{A}^{T}\widehat{B}= \left( \begin{matrix} 1 &0 \\ 0 &0 \end{matrix} \right)+ \epsilon \left( \begin{matrix} 2 &0 \\ 0 &0 \end{matrix} \right) \end{align*}$

$\begin{align*} \nonumber {\mbox{ rk}}\left( \begin{matrix} 1 &0 &1 &0 &1 &0 \\ 0 &0 &0 &1 &0 &1 \\ 1 &0 &0 &0 &1 &0 \\ 0 &0 &0 &0 &0 &1 \end{matrix} \right) = 2{ rk}\left( \begin{matrix} 1 &0 \\ 0 &1 \end{matrix} \right), \end{align*}$

可得 ${\mathcal{R}}\left( {\widehat{A}} \right) \subseteq {\mathcal{R}}\left( {\widehat{B}} \right)$.

定理 3.4 左 D-* 二元关系是一个偏序.

$\widehat{A}=A+ \epsilon A_{0}$, $\widehat{B} =B+ \epsilon B_{0}$, 且 $\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在. 若 $\widehat{A}$$\widehat{B}$ 满足左 D-* 二元关系, 即 $\widehat{A}^{\dagger}\widehat{A}=\widehat{A}^{\dagger}\widehat{B}$${\mathcal{R}}\left( {\widehat{A}} \right) \subseteq {\mathcal{R}}\left( {\widehat{B}} \right)$. 接下来, 证明左 D-* 二元关系满足自反性, 反对称性和传递性.

${\bf(1)}$ 自反性显然成立.

${\bf(2)}$$\widehat{A}\leq^{\tiny {D-*}}_{L} \widehat{B}$$\widehat{B}\leq^{\tiny {D-*}}_{L}\widehat{A}$, 则 $\widehat{A}^{\dagger}\widehat{A}=\widehat{A}^{\dagger}\widehat{B}$, ${\mathcal{R}}\left( {\widehat{A}} \right) \subseteq {\mathcal{R}}\left( {\widehat{B}} \right)$$\widehat{B}^{\dagger}\widehat{B}=\widehat{B}^{\dagger}\widehat{A}$, ${\mathcal{R}}\left( {\widehat{B}} \right) \subseteq {\mathcal{R}}\left( {\widehat{A}} \right)$. 由于 $\widehat{A}^{*}=(\widehat{A}\widehat{A}^{\dagger}\widehat{A})^{*}=(\widehat{A}^{\dagger}\widehat{A})^{*}\widehat{A}^{*} =(\widehat{A}^{\dagger}\widehat{B})^{*}\widehat{A}^{*}=\widehat{B}^{*}(\widehat{A}^{\dagger})^{*}\widehat{A}^{*}=\widehat{B}^{*}$, 可得 $\widehat{A}=\widehat{B}$. 因此, 反对称性成立.

${\bf(3)}$$\widehat{A}\leq^{\tiny {D-*}}_{L} \widehat{B}$$\widehat{B}\leq^{\tiny {D-*}}_{L} \widehat{C}$, 则 $\widehat{A}^{\dagger}\widehat{A}=\widehat{A}^{\dagger}\widehat{B}$, ${\mathcal{R}}\left( {\widehat{A}} \right) \subseteq {\mathcal{R}}\left( {\widehat{B}} \right)$$\widehat{B}^{\dagger}\widehat{B}=\widehat{B}^{\dagger}\widehat{C}$, ${\mathcal{R}}\left( {\widehat{B}} \right) \subseteq {\mathcal{R}}\left( {\widehat{C}} \right)$. 又因为 $\widehat{A}^{\dagger}\widehat{A}=\widehat{A}^{\dagger}\widehat{B}=\widehat{A}^{\dagger}\widehat{B}\widehat{B}^{\dagger}\widehat{B} =\widehat{A}^{\dagger}\widehat{B}\widehat{B}^{\dagger}\widehat{C}=\widehat{A}^{\dagger}\widehat{C}$${\mathcal{R}}\left( {\widehat{A}} \right) \subseteq {\mathcal{R}}\left( {\widehat{C}} \right)$, 可得 $\widehat{A}\leq^{\tiny {D-*}}_{L} \widehat{C}$. 因此, 传递性成立.

由上述讨论,可得左 D-* 二元关系是一个偏序.

注 3.1 根据定理 3.4 可知左 D-* 二元关系是一个偏序, 称之为左 D-* 序.

定理 3.5$\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在, 若 $\widehat{A}\leq^{\tiny {D-*}}_{L} \widehat{B}$, 则 $ \widehat{A} \mathop\le\limits^{\tiny {D}} \widehat{B} $.

$\widehat{A}\leq^{\tiny {D-*}}_{L} \widehat{B}$, 则 $\widehat{A}$$\widehat{B}$ 有定理 3.2 的形式,

$\begin{align*} \nonumber &{\mbox{ rk}} \left( \begin{matrix} -T^{-1}A^{T}_{4}RE &-T^{-1}A^{T}_{4}E &0 &0 &0 &0 \\ B_{4}-A_{4} &B_{5} &B_{6} &RE &E &0 \\ B_{8}E^{-1}RE &B_{8} &0 &0 &0 &0 \\ 0 &0 &0 &0 &0 &0 \\ RE &E &0 &0 &0 &0 \\ 0 &0 &0 &0 &0 &0 \end{matrix} \right) = {\mbox{ rk}} \left( \begin{matrix} 0 &0 &0 &0 &0 &0 \\ 0 &0 &0 &RE &E &0 \\ 0 &0 &0 &0 &0 &0 \\ 0 &0 &0 &0 &0 &0 \\ RE &E &0 &0 &0 &0 \\ 0 &0 &0 &0 &0 &0 \end{matrix} \right) =2{\mbox{ rk}} \left( \begin{matrix} 0 &0 &0 \\ RE &E &0 \\ 0 &0 &0 \end{matrix} \right). \end{align*}$

由上述等式, 可得

$\begin{align*} \label{3-7} {\mbox{ rk}}\left( \begin{matrix} B_{0}-A_{0} &B-A \\ B-A &0 \end{matrix} \right) =2{\mbox{ rk}}(B-A). \end{align*}$

又因为 $\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在, 显然有 $ {\mbox{ rk}}\left( \begin{matrix} A_{0} &A \\ A &0 \end{matrix} \right) =2{\mbox{ rk}}(A), $$ {\mbox{ rk}}\left( \begin{matrix} B_{0} &B \\ B &0 \end{matrix} \right) =2{\mbox{ rk}}(B) $

$\begin{align*} \label{3-8} {\mbox{ rk}}\left( \begin{matrix} T &0 &0 \\ RE &E &0 \\ 0 &0 &0 \end{matrix} \right) - {\mbox{ rk}}\left( \begin{matrix} T &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right) ={\mbox{ rk}}(B)-{\mbox{ rk}}(A). \end{align*}$

应用 (3.9) 式和 (3.10) 式, 可得

$\begin{align*} \nonumber {\mbox{ rk}}\left( \begin{matrix} B_{0}-A_{0} &B-A \\ B-A &0 \end{matrix} \right) ={\mbox{ rk}}\left( \begin{matrix} B_{0} &B \\ B &0 \end{matrix} \right) - {\mbox{ rk}}\left( \begin{matrix} A_{0} &A \\ A &0 \end{matrix} \right). \end{align*}$

根据引理2.5, 可得 $\widehat{A} \mathop\le\limits^{\tiny {D}} \widehat{B}$.

定理 3.6$\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在, 若 $ \widehat{A} \mathop\le\limits^{\tiny {D-*}} \widehat{B} $, 则 $\widehat{A}\leq^{\tiny {D-*}}_{L} \widehat{B}$.

由于 $\widehat{A} \mathop\le\limits^{\tiny {D-*}} \widehat{B} $, 则 $\widehat{A}$$\widehat{B}$ 有文献 [定理 6] 的形式,

$\begin{align*} \nonumber \widehat{A}^{\dagger}\widehat{A}=\widehat{A}^{\dagger}\widehat{B} =V\left( \begin{matrix} I_{a} &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} +\epsilon V\left( \begin{matrix} 0 &T_{1}^{-1}A_{2} &T_{1}^{-1}A_{3} \\ A_{2}^{T}T_{1}^{-1} &0 &0 \\ T_{3}^{T}T_{1}^{-1} &0 &0 \end{matrix} \right)V^{T} \end{align*}$

$\begin{align*} \nonumber &{\mbox{ rk}} \left( \begin{matrix} A_{1} &A_{2} &A_{3}&T_{1} &0 &0 &A_{1} &A_{2}-T_{1}^{-1}A^{T}_{4}T_{2} &A_{3} \\ A_{4} &0 &0 &0 &T_{2} &0 &A_{4}-T_{2}A^{T}_{2}T_{1}^{-1} &B_{5} &B_{6} \\ A_{7} &0 &0 &0 &0 &0 &A_{7} &B_{8} &0 \\ T_{1} &0 &0 &0 &0 &0 &T_{1} &0 &0 \\ 0 &0 &0 &0 &0 &0 &0 &T_{2} &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \end{matrix} \right)\\ = &{\mbox{ rk}} \left( \begin{matrix} 0 &A_{2} &A_{3} &T_{1} &0 &0 &0 &A_{2}-T_{1}^{-1}A^{T}_{4}T_{2} &A_{3} \\ 0 &0 &0 &0 &T_{2} &0 &-T_{2}A^{T}_{2}T_{1}^{-1} &0 &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \\ T &0 &0 &0 &0 &0 &T_{1} &0 &0 \\ 0 &0 &0 &0 &0 &0 &0 &T_{2} &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \end{matrix} \right). \end{align*}$

由上述等式,可得 $\widehat{A}^{\dagger}\widehat{A}=\widehat{A}^{\dagger}\widehat{B}$${\mbox{ rk}}\left( \begin{matrix} A_{0} &B &B_{0} \\ A &0 &B \end{matrix} \right) =2{\mbox{ rk}}(B)$. 利用定理 3.1, 有 $\widehat{A}\leq ^{\tiny {D-*}}_{L} \widehat{B} $.

下面的证明过程类似.

定义 3.2$\widehat{A}=A+ \epsilon A_{0}\in\mathbb{D}^{m\times n}$, $\widehat{B} =B+ \epsilon B_{0}\in\mathbb{D}^{m\times n}$, 且 $\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在. 若 $\widehat{A},\widehat{B}$ 满足

$\begin{align*} \nonumber \widehat{A}\widehat{A}^{\dagger}=\widehat{B}\widehat{A}^{\dagger}, \ \ {\mathcal{R}}\left( {\widehat{A^{T}} } \right) \subseteq {\mathcal{R}}\left( {\widehat{B^{T}} } \right), \end{align*}$

$\widehat{A}$$\widehat{B}$ 是右 D-* 二元关系. 记为 $\widehat{A}\leq^{\tiny {D-*}}_{R} \widehat{B} $.

定理 3.7$\widehat{A}=A+ \epsilon A_{0}$$\widehat{B} =B+ \epsilon B_{0}$, 其中 $A,A_{0},B,B_{0}\in\mathbb{R}^{m\times n}$.$\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在, 则 $\widehat{A} \leq^{\tiny {D-*}}_{R}\widehat{B}$ 成立当且仅当

$\begin{align*} \nonumber \left\{ \begin{aligned} &AA^{\dagger}=BA^{\dagger}, \\ &A_{0}A^{\dagger}+AR=B_{0}A^{\dagger}+BR, \\ &{\mbox{ rk}}\left( \begin{matrix} A^{T}_{0} &B^{T} &B^{T}_{0} \\ A^{T} &0 &B^{T} \end{matrix} \right)=2{\mbox{ rk}}(B^{T}), \end{aligned} \right. \end{align*}$

其中 $R=-{A}^{\dagger}A_{0}{A}^{\dagger}+({A}^{T}A)^{\dagger}A_{0}^{T}(I_{m}-AA^{\dagger})+(I_{n}-A^{\dagger}A)A_{0}^{T}(A{A}^{T})^{\dagger}$.

定理 3.8$\widehat{A}=A+ \epsilon A_{0}\in\mathbb{D}^{m\times n}$, $\widehat{B} =B+ \epsilon B_{0}\in\mathbb{D}^{m\times n}$, 且 $ \widehat{A}$$\widehat{B}$ 的 DMPGIs 存在. 若 $\widehat{A}\leq ^{\tiny {D-*}}_{R}\widehat{B}$ 当且仅当存在正交矩阵 $U$$V$ 使得

$\begin{align*} \nonumber \left\{ \begin{aligned} \nonumber \widehat{A} &=U\left( \begin{matrix} T &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} +\epsilon U\left( \begin{matrix} A_{1} &A_{2} &A_{3} \\ A_{4} &0 &0 \\ A_{7} &0 &0 \end{matrix} \right)V^{T}, \\ \widehat{B} &=U\left( \begin{matrix} T &ES &0 \\ 0 &E &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} +\epsilon U\left( \begin{matrix} A_{1}-ESA^{T}_{2}T^{-1} &B_{2} &A_{3}+ESE^{-1}B_{6} \\ A_{4}-EA^{T}_{2}T^{-1} &B_{5} &B_{6} \\ A_{7} &B_{8} &0 \end{matrix} \right)V^{T}, \end{aligned} \right. \end{align*}$

其中 $T$$E$ 是对角正定矩阵.

定理 3.9$\widehat A, \widehat B\in\mathbb{D}^{m\times n}$, ${\mbox{ rk}}(\widehat A)=a$, ${\mbox{ rk}}(\widehat B)=b$, $b>a$, 且 $\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在, 则下面条件等价

(1) $\widehat{A} \leq^{\tiny {D-*}}_{R} \widehat{B} $;

(2) $\widehat{A}\widehat{A}^{T}=\widehat{A}\widehat{B}^{T}$ and ${\mathcal{R}}\left( {\widehat{A}^{T}} \right) \subseteq {\mathcal{R}}\left( {\widehat{B}^{T}} \right)$.

定理 3.10 右 D-* 二元关系是一个偏序.

注 3.2 根据定理 3.10 可知, 右 D-* 二元关系是一个偏序, 称之为右 D-* 偏序.

定理 3.11$\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在, 若 $\widehat{A}\leq^{\tiny {D-*}}_{R} \widehat{B}$, 则 $\widehat{A} \mathop\le\limits^{\tiny {D}} \widehat{B}$.

定理 3.12$\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在, 若 $\widehat{A} \mathop\le\limits^{\tiny {D-*}}\widehat{B} $, 则 $\widehat{A}\leq ^{\tiny {D-*}}_{R} \widehat{B}$.

定理 3.13$\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在, 若 $\widehat{A} \mathop\le\limits^{\tiny {D-*}}\widehat{B}$ 当且仅当 $\widehat{A}\leq^{\tiny {D-*}}_{L} \widehat{B}$$\widehat{A}\leq ^{\tiny {D-*}}_{R} \widehat{B}$.

$\Rightarrow$ 因为 $\widehat{A}\mathop\le\limits^{\tiny {D-*}}\widehat{B}$, 则 $\widehat{A}$$\widehat{B}$ 有文献 [定理 6]的形式,

$\begin{align*} \nonumber \widehat{A}^{\dagger}\widehat{A}=\widehat{A}^{\dagger}\widehat{B} =V\left( \begin{matrix} I_{a} &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} +\epsilon V\left( \begin{matrix} 0 &T_{1}^{-1}A_{2} &T_{1}^{-1}A_{3} \\ A_{2}^{T}T_{1}^{-1} &0 &0 \\ A_{3}^{T}T_{1}^{-1} &0 &0 \end{matrix} \right)V^{T} \end{align*}$

$\begin{align*} \nonumber &{\mbox{ rk}} \left( \begin{matrix} A_{1} &A_{2} &A_{3} &T_{1} &0 &0 &A_{1} &A_{2}-T_{1}^{-1}A^{T}_{4}T_{2} &A_{3} \\ A_{4} &0 &0 &0 &T_{2} &0 &A_{4}-T_{2}A^{T}_{2}T_{1}^{-1} &B_{5} &B_{6} \\ A_{7} &0 &0 &0 &0 &0 &A_{7} &B_{8} &0 \\ T_{1} &0 &0 &0 &0 &0 &T_{1} &0 &0 \\ 0 &0 &0 &0 &0 &0 &0 &T_{2} &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \end{matrix} \right)\\ = &{\mbox{ rk}} \left( \begin{matrix} 0 &A_{2} &A_{3} &T_{1} &0 &0 &0 &A_{2}-T_{1}^{-1}A^{T}_{4}T_{2} &A_{3} \\ 0 &0 &0 &0 &T_{2} &0 &-T_{2}A^{T}_{2}T_{1}^{-1} &0 &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \\ T_{1} &0 &0 &0 &0 &0 &T_{1} &0 &0 \\ 0 &0 &0 &0 &0 &0 &0 &T_{2} &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \end{matrix} \right). \end{align*}$

由上述等式可得 $\widehat{A}^{\dagger}\widehat{A}=\widehat{A}^{\dagger}\widehat{B}$${\mbox{ rk}}\left( \begin{matrix} A_{0} &B &B_{0} \\ A &0 &B \end{matrix} \right) =2{\mbox{ rk}}(B)$, 故$\widehat{A}\leq^{\tiny {D-*}}_{L}\widehat{B}$.

利用

$\begin{align*} \nonumber \widehat{A}\widehat{A}^{\dagger}=\widehat{B}\widehat{A}^{\dagger} =U\left( \begin{matrix} I_{a} &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)U^{T} +\epsilon U\left( \begin{matrix} 0 &T_{1}^{-1}A_{2} &T_{1}^{-1}A_{3} \\ A_{2} T_{1}^{-1} &0 &0 \\ A_{3} T_{1}^{-1} &0 &0 \end{matrix} \right)U^{T} \end{align*}$

$\begin{align*} \nonumber &{\mbox{ rk}} \left( \begin{matrix} A_{1}^{T} &A_{4}^{T} &A_{7}^{T} &T_{1} &0 &0 &A_{1}^{T} &A_{4}^{T}-T_{1}^{-1}A_{2}T_{2} &A_{7}^{T} \\ A_{2}^{T} &0 &0 &0 &T_{2} &0 &A_{2}^{T}-T_{2}A_{4}T_{1}^{-1} &B^{T}_{5} &B_{8}^{T} \\ A_{3}^{T} &0 &0 &0 &0 &0 &A_{3}^{T} &B^{T}_{6} &0 \\ T_{1} &0 &0 &0 &0 &0 &T_{1} &0 &0 \\ 0 &0 &0 &0 &0 &0 &0 &T_{2} &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \end{matrix} \right)\\ = &{\mbox{ rk}} \left( \begin{matrix} 0 &A_{4}^{T} &A_{7}^{T} &T_{1} &0 &0 &0 &A_{4}^{T}-T_{1}^{-1}A_{2}T_{2} &A_{7}^{T} \\ 0 &0 &0 &0 &T_{2} &0 &-T_{2}A_{4}T_{1}^{-1} &B^{T}_{5} &B_{8}^{T} \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \\ T_{1} &0 &0 &0 &0 &0 &T_{1} &0 &0 \\ 0 &0 &0 &0 &0 &0 &0 &T_{2} &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \end{matrix} \right), \end{align*}$

显然有 $\widehat{A}\widehat{A}^{\dagger}=\widehat{B}\widehat{A}^{\dagger}$${\mbox{ rk}}\left( \begin{matrix} A_{0}^{T} &B^{T} &B_{0}^{T} \\ A^{T} &0 &B^{T} \end{matrix} \right) =2{\mbox{ rk}}(B^{T})$, 故 $\widehat{A}\leq ^{\tiny {D-*}}_{R} \widehat{B} $.

$\Leftarrow$ 因为 $\widehat{A}\leq^{\tiny {D-*}}_{L} \widehat{B}$$\widehat{A}\leq ^{\tiny {D-*}}_{R}\widehat{B}$, 则有 $\widehat{A}^{\dagger}\widehat{A}=\widehat{A}^{\dagger}\widehat{B}$$\widehat{A}\widehat{A}^{\dagger}=\widehat{B}\widehat{A}^{\dagger}$, 即 $ \widehat{A} \mathop\le\limits^{\tiny {D-*}}\widehat{B} $.

定理 3.14$\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在, 若 $\widehat{A}\leq ^{\tiny {D-*}}_{R}\widehat{B}$ 当且仅当 $\widehat{A}^{\dagger}\leq^{\tiny {D-*}}_{L} \widehat{B}^{\dagger}$.

$\Rightarrow$$\widehat{A}\leq ^{\tiny {D-*}}_{R}\widehat{B}$, 则 $\widehat{A}\widehat{A}^{\dagger}=\widehat{A}\widehat{B}^{\dagger}$${\mathcal{R}}\left( {\widehat{A}^{T}} \right) \subseteq {\mathcal{R}}\left( {\widehat{B}^{T}} \right)$, 即$(\widehat{A}^{\dagger})^{\dagger}\widehat{A}^{\dagger}=(\widehat{A}^{\dagger})^{\dagger}\widehat{B}^{\dagger}$${\mathcal{R}}\left( {\widehat{A}^{\dagger}} \right) \subseteq {\mathcal{R}}\left( {\widehat{B}^{\dagger}} \right)$. 通过 $(\widehat{A}^{\dagger})^{\dagger}={\widehat{A}}$ 和定义3.1, 可得 $\widehat{A}^{\dagger}\leq^{\tiny {D-*}}_{L} \widehat{B}^{\dagger}$.

$\Leftarrow$$\widehat{A}^{\dagger}\leq^{\tiny {D-*}}_{L} \widehat{B}^{\dagger}$, 则 $(\widehat{A}^{\dagger})^{\dagger}\widehat{A}^{\dagger}=(\widehat{A}^{\dagger})^{\dagger}\widehat{B}^{\dagger}$${\mathcal{R}}\left( {\widehat{A}^{\dagger}} \right) \subseteq {\mathcal{R}}\left( {\widehat{B}^{\dagger}} \right)$, 即 $\widehat{A}\widehat{A}^{\dagger}=\widehat{A}\widehat{B}^{\dagger}$${\mathcal{R}}\left( {\widehat{A}^{T}} \right) \subseteq {\mathcal{R}}\left( {\widehat{B}^{T}} \right)$. 利用定义 3.2, 可得 $\widehat{A}\leq^{\tiny {D-*}}_{R}\widehat{B}$.

4 左 (右) P-* 序

在本节, 使用 MPDGI介绍了左 (右) P-* 序, 给出左 (右) P-* 序的等价刻画和性质. 进一步, 讨论了对偶矩阵上左 (右) P-* 序, D 序和 P-* 序之间的联系.

定义 4.1$\widehat{A}=A+ \epsilon A_{0}\in\mathbb{D}^{m\times n}$, $\widehat{B} =B+ \epsilon B_{0}\in\mathbb{D}^{m\times n}$, 且 $ \widehat{A}$$\widehat{B}$ 的 DMPGIs 存在. 若 $\widehat{A},\widehat{B}$ 满足

$\begin{align*} \nonumber \widehat{A}^{p}\widehat{A}=\widehat{A}^{p}\widehat{B}, \ \ {\mathcal{R}}\left( {\widehat{A} } \right) \subseteq {\mathcal{R}}\left( {\widehat{B} } \right), \end{align*} $

$\widehat{A}$$\widehat{B}$ 满足左 P-* 二元关系, 记为 $\widehat{A}\leq^{P-*}_{L}\widehat{B}$.

定理 4.1$\widehat{A}=A+ \epsilon A_{0}$, $\widehat{B} =B+ \epsilon B_{0}$, 其中 $A,A_{0},B,B_{0}\in\mathbb{R}^{m\times n}$.$\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在, 则 $\widehat{A}\leq^{P-*}_{L}\widehat{B}$ 当且仅当

$\begin{align*} \nonumber \left\{ \begin{aligned} &A^{\dagger}A=A^{\dagger}B, \\ &A^{\dagger}A_{0}+R_{p}A=A^{\dagger}B_{0}+R_{p}B, \\ &{\mbox{ rk}}\left( \begin{matrix} A_{0} &B &B_{0} \\ A &0 &B \end{matrix} \right)=2{\mbox{ rk}}(B), \end{aligned} \right. \end{align*}$

其中 $R_{p}=-{A}^{\dagger}A_{0}{A}^{\dagger}$.

$\Rightarrow$$\widehat{A}=A+ \epsilon A_{0}$, $\widehat{B} =B+ \epsilon B_{0}$, 且 $\widehat{A}$ 的 DMPGI 存在. 因为 $\widehat{A}^{p}\widehat{A}=\widehat{A}^{p}\widehat{B}$

$\begin{align*} \nonumber \left\{ \begin{aligned} &\widehat{A}^{p}\widehat{A}=(A^{\dagger}+ \epsilon R_{p})(A+ \epsilon A_{0})=A^{\dagger}A+\epsilon(A^{\dagger}A_{0}+R_{p}A), \\ &\widehat{A}^{p}\widehat{B}=(A^{\dagger}+ \epsilon R_{p})(B+ \epsilon B_{0})=A^{\dagger}B+\epsilon(A^{\dagger}B_{0}+R_{p}B), \end{aligned} \right. \end{align*}$

可得 $A^{\dagger}A=A^{\dagger}B$$A^{\dagger}A_{0}+R_{p}A=A^{\dagger}B_{0}+R_{p}B$. 由于 ${\mathcal{R}}\left( {\widehat{A}} \right) \subseteq {\mathcal{R}}\left( {\widehat{B}} \right)$, 则存在 $\widehat{C}$ 满足 $\widehat{A}=\widehat{B}\widehat{C}$, 即 $A+\epsilon A_{0} =BC+\epsilon(BC_{0}+B_{0}C)$. 可得 $A=BC$$A_{0}=BC_{0}+B_{0}C$. 应用引理 2.4, 可得

$\begin{align*} \nonumber {\mbox{ rk}}\left( \begin{matrix} A_{0} &B &B_{0} \\ A &0 &B \end{matrix} \right)=2{\mbox{ rk}}(B). \end{align*}$

$\Leftarrow$ 利用 $A^{\dagger}A=A^{\dagger}B$$A^{\dagger}A_{0}+R_{p}A=A^{\dagger}B_{0}+R_{p}B$, 则 $\widehat{A}^{p}\widehat{A}=\widehat{A}^{p}\widehat{B}$. 根据 ${\mbox{ rk}}\left( \begin{matrix} A_{0} &B &B_{0} \\ A &0 &B \end{matrix} \right)=2{\mbox{ rk}}(B)$ 和引理 2.4, 可得 ${\mathcal{R}}\left( {\widehat{A}} \right) \subseteq {\mathcal{R}}\left( {\widehat{B}} \right)$. 因此, 可得 $\widehat{A}\leq^{P-*}_{L} \widehat{B}$.

定理 4.2$\widehat{A}=A+ \epsilon A_{0}\in\mathbb{D}^{m\times n}$, $\widehat{B} =B+ \epsilon B_{0}\in\mathbb{D}^{m\times n}$, 且 $\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在, 则 $\widehat{A}\leq^{\tiny {P-*}}_{L}\widehat{B}$ 当且仅当存在正交矩阵 $U$$V$ 使得

$\begin{align*} \nonumber \left\{ \begin{aligned} \nonumber \widehat{A} &=U\left( \begin{matrix} T &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} +\epsilon U\left( \begin{matrix} A_{1} &A_{2} &A_{3} \\ A_{4} &0 &0 \\ A_{7} &0 &0 \end{matrix} \right)V^{T}, \\ \widehat{B} &=U\left( \begin{matrix} T &0 &0 \\ RE &E &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} +\epsilon U\left( \begin{matrix} A_{1} &A_{2} &A_{3} \\ B_{4} &B_{5} &B_{6} \\ A_{7}+B_{8}E^{-1}RE &B_{8} &0 \end{matrix} \right)V^{T}. \end{aligned} \right. \end{align*}$

其中 $T$$E$ 是对角正定矩阵.

$\Rightarrow$$\widehat{A}\leq^{P-*}_{L} \widehat{B}$, 利用引理 2.2, 有 $A {*}\leq B$. 由于 $A {*}\leq B$ 以及 $\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在, 则 $A$, $B$, $A_{0}$$B_{0}$ 分别有 (2.1) 式, (3.1) 式和 (3.3) 式的形式.

进一步, 可得

$\begin{align*} \nonumber \widehat{A}^{p} =V\left( \begin{matrix} T^{-1} &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)U^{T} +\epsilon V\left( \begin{matrix} -T^{-1}A_{1}T^{-1} &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)U^{T} \end{align*}$

$\begin{align*} \left\{ \begin{aligned} \widehat{A}^{p}\widehat{A} \nonumber &=V\left( \begin{matrix} I_{a} &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} + \epsilon V\left( \begin{matrix} 0 &T^{-1}A_{2} &T^{-1}A_{3} \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T}, \\ \nonumber \widehat{A}^{p}\widehat{B} &=V\left( \begin{matrix} I_{a} &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} + \epsilon V\left( \begin{matrix} T^{-1}B_{1}-T^{-1}A_{1} &T^{-1}B_{2} &T^{-1}B_{3} \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T}. \end{aligned} \right. \end{align*}$

由于 $\widehat{A}^{p}\widehat{A}=\widehat{A}^{p}\widehat{B}$, 可得 $B_{1}=A_{1}, B_{2}=A_{2}, B_{3}=A_{3}$. 进一步,

$\begin{align*} \nonumber B= U\left( \begin{matrix} T &0 &0 \\ RE &E &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} +\epsilon U\left( \begin{matrix} A_{1} &A_{2} &A_{3} \\ B_{4} &B_{5} &B_{6} \\ B_{7} &B_{8} &0 \end{matrix} \right)V^{T}. \end{align*}$

根据 ${\mbox{ rk}}\left( \begin{matrix} A_{0} &B &B_{0} \\ A &0 &B \end{matrix} \right)=2{\mbox{ rk}}(B)$

$\begin{align*} \nonumber &{\mbox{ rk}} \left( \begin{matrix} A_{1} &A_{2} &A_{3} &T &0 &0 &A_{1} &A_{2} &A_{3} \\ A_{4} &0 &0 &RE &E &0 &B_{4} &B_{5} &B_{6} \\ A_{7} &0 &0 &0 &0 &0 &B_{7} &B_{8} &0 \\ T &0 &0 &0 &0 &0 &T &0 &0 \\ 0 &0 &0 &0 &0 &0 &RE &E &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \end{matrix} \right) = &{\mbox{ rk}} \left( \begin{matrix} 0 &A_{2} &A_{3} &T &0 &0 &0 &A_{2} &A_{3} \\ 0 &0 &0 &RE &E &0 &B_{4}-A_{4} &B_{5} &B_{6} \\ 0 &0 &0 &0 &0 &0 &B_{7}-A_{7}-B_{8}E^{-1}RE &0 &0 \\ T &0 &0 &0 &0 &0 &T &0 &0 \\ 0 &0 &0 &0 &0 &0 &RE &E &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \end{matrix} \right), \end{align*}$

可得

$\begin{align*} \label{GS4-5} B_{7}=A_{7}+B_{8}E^{-1}RE. \end{align*} $

利用 (4.1) 式, 可得

$\begin{align*} \nonumber \widehat{B} =U\left( \begin{matrix} T &0 &0 \\ RE &E &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} +\epsilon U\left( \begin{matrix} A_{1} &A_{2} &A_{3} \\ B_{4} &B_{5} &B_{6} \\ A_{7}+B_{8}E^{-1}RE &B_{8} &0 \end{matrix} \right)V^{T}. \end{align*}$

$\Leftarrow$ 设存在正交矩阵 $U$$V$ 使得 $\widehat{A}$$\widehat{B}$ 有定理 4.2 的形式, 可得

$\begin{align*} \nonumber \widehat{A}^{p}\widehat{A}=\widehat{A}^{p}\widehat{B} =V\left( \begin{matrix} I_{a} &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} + \epsilon V\left( \begin{matrix} 0 &T^{-1}A_{2} &T^{-1}A_{3} \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} \end{align*}$

$\begin{align*} \nonumber {\mbox{ rk}}\left( \begin{matrix} A_{0} &B &B_{0} \\ A &0 &B \end{matrix} \right) =2{\mbox{ rk}}(B). \end{align*}$

根据上述等式和定理 2.4, 可得 $\widehat{A}^{p}\widehat{A}=\widehat{A}^{p}\widehat{B}$${\mathcal{R}}\left( {\widehat{A}} \right) \subseteq {\mathcal{R}}\left( {\widehat{B}} \right)$, 故 $\widehat{A}\leq^{\tiny {P-*}}_{L} \widehat{B}$.

定理 4.3 左 P-* 二元关系是一个偏序.

$\widehat{A}=A+ \epsilon A_{0}$, $\widehat{B} =B+ \epsilon B_{0}$, 且 $\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在. 若 $\widehat{A}$$\widehat{B}$ 满足左 P-* 二元关系, 即 $\widehat{A}^{p}\widehat{A}=\widehat{A}^{p}\widehat{B}$${\mathcal{R}}\left( {\widehat{A}} \right) \subseteq {\mathcal{R}}\left( {\widehat{B}} \right)$.

接下来, 证明左 P-* 二元关系满足自反性, 反对称性和传递性.

${\bf(1)}$ 自反性显然成立.

${\bf(2)}$$\widehat{A}\leq^{\tiny {P-*}}_{L} \widehat{B}$$\widehat{B}\leq^{\tiny {P-*}}_{L} \widehat{A}$, 则 $\widehat{A}^{P}\widehat{A}=\widehat{A}^{P}\widehat{B}$, ${\mathcal{R}}\left( {\widehat{A}} \right) \subseteq {\mathcal{R}}\left( {\widehat{B}} \right)$$\widehat{B}^{P}\widehat{B}=\widehat{B}^{P}\widehat{A}$, ${\mathcal{R}}\left( {\widehat{B}} \right) \subseteq {\mathcal{R}}\left( {\widehat{A}} \right)$. 又因为 $\widehat{A}^{*}=(\widehat{A}\widehat{A}^{P}\widehat{A})^{*}=(\widehat{A}^{P}\widehat{A})^{*}(\widehat{A})^{*} =(\widehat{A}^{P}\widehat{B})^{*}\widehat{A}^{*}=\widehat{B}^{*}(\widehat{A}^{P})^{*}\widehat{A}^{*}=\widehat{B}^{*}$, 可得 $\widehat{A}=\widehat{B}$. 因此, 反对称性成立.

${\bf(3)}$$\widehat{A}\leq^{\tiny {P-*}}_{L} \widehat{B}$$\widehat{B}\leq^{\tiny {P-*}}_{L} \widehat{C}$, 则 $\widehat{A}^{P}\widehat{A}=\widehat{A}^{P}\widehat{B}$, ${\mathcal{R}}\left( {\widehat{A}} \right) \subseteq {\mathcal{R}}\left( {\widehat{B}} \right)$$\widehat{B}^{P}\widehat{B}=\widehat{B}^{P}\widehat{C}$, ${\mathcal{R}}\left( {\widehat{B}} \right) \subseteq {\mathcal{R}}\left( {\widehat{C}} \right)$. 由于 $\widehat{A}^{P}\widehat{A}=\widehat{A}^{P}\widehat{B}=\widehat{A}^{P}\widehat{B}\widehat{B}^{P}\widehat{B} =\widehat{A}^{P}\widehat{B}\widehat{B}^{P}\widehat{C}=\widehat{A}^{P}\widehat{C}$${\mathcal{R}}\left( {\widehat{A}} \right) \subseteq {\mathcal{R}}\left( {\widehat{C}} \right)$, 可得 $\widehat{A}\leq^{\tiny {P-*}}_{L} \widehat{C}$. 因此, 传递性成立.

由上述讨论,可得左 P-* 二元关系是一个偏序.

注 4.1 根据定理 4.3 可知, 左P-*二元关系是一个偏序, 称之为左 P-* 序.

定理 4.4$\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在, 若 $\widehat{A}\leq^{\tiny {P-*}}_{L} \widehat{B}$, 则 $ \widehat{A} \mathop\le\limits^{\tiny {D}} \widehat{B} $.

由于 $\widehat{A}\leq^{\tiny {P-*}}_{L} \widehat{B}$, 则 $\widehat{A}$$\widehat{B}$ 有定理 4.2 的形式,

$\begin{align*} \nonumber &{\mbox{ rk}} \left( \begin{matrix} 0 &0 &0 &0 &0 &0 \\ B_{4}-A_{4} &B_{5} &B_{6} &RE &E &0 \\ B_{8}E^{-1}RE &B_{8} &0 &0 &0 &0 \\ 0 &0 &0 &0 &0 &0 \\ RE &E &0 &0 &0 &0 \\ 0 &0 &0 &0 &0 &0 \end{matrix} \right) = {\mbox{ rk}} \left( \begin{matrix} 0 &0 &0 &0 &0 &0 \\ 0 &0 &0 &RE &E &0 \\ 0 &0 &0 &0 &0 &0 \\ 0 &0 &0 &0 &0 &0 \\ RE &E &0 &0 &0 &0 \\ 0 &0 &0 &0 &0 &0 \end{matrix} \right) =2{\mbox{ rk}} \left( \begin{matrix} 0 &0 &0 \\ RE &E &0 \\ 0 &0 &0 \end{matrix} \right). \end{align*}$

由上述等式, 可得

$\begin{align*} \label{4-6} {\mbox{ rk}}\left( \begin{matrix} B_{0}-A_{0} &B-A \\ B-A &0 \end{matrix} \right) =2{\mbox{ rk}}(B-A). \end{align*}$

由于 $\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在, 有 $ {\mbox{ rk}}\left( \begin{matrix} A_{0} &A \\ A &0 \end{matrix} \right) =2{\mbox{ rk}}(A), $$ {\mbox{ rk}}\left( \begin{matrix} B_{0} &B \\ B &0 \end{matrix} \right) =2{\mbox{ rk}}(B) $

$\begin{align*} \label{4-7} {\mbox{ rk}} \left( \begin{matrix} T &0 &0 \\ RE &E &0 \\ 0 &0 &0 \end{matrix} \right) - {\mbox{ rk}} \left( \begin{matrix} T &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right) ={\mbox{ rk}}(B)-{\mbox{ rk}}(A). \end{align*} $

利用 (4.2) 和 (4.3) 式, 可得

$\begin{align*} \nonumber {\mbox{ rk}}\left( \begin{matrix} B_{0}-A_{0} &B-A \\ B-A &0 \end{matrix} \right) ={\mbox{ rk}}\left( \begin{matrix} B_{0} &B \\ B &0 \end{matrix} \right) - {\mbox{ rk}}\left( \begin{matrix} A_{0} &A \\ A &0 \end{matrix} \right). \end{align*}$

根据引理2.5, 可得 $\widehat{A} \mathop\le\limits^{\tiny {D}} \widehat{B}$.

定理 4.5$\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在, 若 $\widehat{A} \mathop\le\limits^{\tiny {P-*}} \widehat{B}$, 则 $\widehat{A}\leq^{\tiny {P-*}}_{L}\widehat{B}$.

由于 $\widehat{A} \mathop\le\limits^{P-*} \widehat{B}$, 则 $\widehat{A}$$\widehat{B}$ 有文献 [定理 13]的形式,

$\begin{align*} \nonumber \widehat{A}^{p}\widehat{A}=\widehat{A}^{p}\widehat{B} =V\left( \begin{matrix} I_{a} &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} +\epsilon V\left( \begin{matrix} 0 &T_{1}^{-1}A_{2} &T_{1}^{-1}A_{3} \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} \end{align*}$

$\begin{align*} \nonumber {\mbox{ rk}} \left( \begin{matrix} A_{1} &A_{2} &A_{3} &T_{1} &0 &0 &A_{1} &A_{2} &A_{3} \\ A_{4} &0 &0 &0 &T_{2} &0 &A_{4} &B_{5} &B_{6} \\ A_{7} &0 &0 &0 &0 &0 &A_{7} &B_{8} &0 \\ T_{1} &0 &0 &0 &0 &0 &T_{1} &0 &0 \\ 0 &0 &0 &0 &0 &0 &0 &T_{2} &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \end{matrix} \right) = {\mbox{ rk}} \left( \begin{matrix} 0 &A_{2} &A_{3} &T_{1} &0 &0 &0 &A_{2} &A_{3} \\ 0 &0 &0 &0 &T_{2} &0 &0 &B_{5} &B_{6} \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \\ T_{1} &0 &0 &0 &0 &0 &T_{1} &0 &0 \\ 0 &0 &0 &0 &0 &0 &0 &T_{2} &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \end{matrix} \right). \end{align*}$

由上述等式, 可得 $\widehat{A}^{p}\widehat{A}=\widehat{A}^{p}\widehat{B}$${\mbox{ rk}}\left( \begin{matrix} A_{0} &B &B_{0} \\ A &0 &B \end{matrix} \right) =2{\mbox{ rk}}(B)$, 故 $\widehat{A}\leq^{\tiny {P-*}}_{L} \widehat{B} $.

下面的证明过程类似.

定义 4.2$\widehat{A}=A+ \epsilon A_{0}\in\mathbb{D}^{m\times n}$, $\widehat{B} =B+ \epsilon B_{0}\in\mathbb{D}^{m\times n}$, 且 $\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在. 若 $\widehat{A},\widehat{B}$ 满足

$\begin{align*} \nonumber \widehat{A}\widehat{A}^{p}=\widehat{B}\widehat{A}^{p}, \ \ {\mathcal{R}}\left( {\widehat{A^{T}} } \right) \subseteq {\mathcal{R}}\left( {\widehat{B^{T}} } \right) \end{align*}$

$\widehat{A}$$\widehat{B}$ 满足右 P-* 二元关系, 记为 $\widehat{A}\leq^{P-*}_{R}\widehat{B} $.

定理 4.6$\widehat{A}=A+\epsilon A_{0}$$\widehat{B} =B+\epsilon B_{0}$, 其中 $A,A_{0},B,B_{0}\in\mathbb{R}^{m\times n}$.$\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在, 则 $\widehat{A}\leq^{\tiny {P-*}}_{R} \widehat{B}$ 当且仅当

$\begin{align*} \nonumber \left\{ \begin{aligned} &AA^{\dagger}=BA^{\dagger}, \\ &A_{0}A^{\dagger}+AR_{p}=B_{0}A^{\dagger}+BR_{p}, \\ &{\mbox{ rk}}\left( \begin{matrix} A_{0}^{T} &B^{T} &B_{0}^{T} \\ A^{T} &0 &B^{T} \end{matrix} \right)=2{\mbox{ rk}}(B^{T}). \end{aligned} \right. \end{align*}$

其中 $R_{p}=-{A}^{\dagger}A_{0}{A}^{\dagger}$.

定理 4.7$\widehat{A}=A+ \epsilon A_{0}\in\mathbb{D}^{m\times n}$, $\widehat{B} =B+ \epsilon B_{0}\in\mathbb{D}^{m\times n}$, 且 $\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在. 则 $\widehat{A}\leq^{\tiny {P-*}}_{R}\widehat{B}$ 当且仅当存在正交矩阵 $U$$V$ 使得

$\begin{align*} \nonumber \left\{ \begin{aligned} \nonumber \widehat{A} &=U\left( \begin{matrix} T &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} +\epsilon U\left( \begin{matrix} A_{1} &A_{2} &A_{3} \\ A_{4} &0 &0 \\ A_{7} &0 &0 \end{matrix} \right)V^{T}, \\ \widehat{B} &=U\left(\begin{matrix} T &ES &0 \\ 0 &E &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} +\epsilon U\left( \begin{matrix} A_{1} &B_{2} &A_{3}+E^{-1}SEB_{6} \\ A_{4} &B_{5} &B_{6} \\ A_{7} &B_{8} &0 \end{matrix} \right)V^{T}. \end{aligned} \right. \end{align*}$

其中 $T$$E$ 是对角正定矩阵.

定理 4.8 右 P-* 二元关系是一个偏序.

注 4.2 根据定理 4.8, 右 P-* 二元关系是一个偏序, 称之为右 P-* 序.

定理 4.9$\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在. 若 $\widehat{A}\leq ^{\tiny {P-*}}_{R}\widehat{B}$, 则 $\widehat{A} \mathop\le\limits^{\tiny {D}} \widehat{B} $.

定理 4.10$\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在, 若 $\widehat{A} \mathop\le\limits^{\tiny {P-*}} \widehat{B} $, 则 $\widehat{A}\leq ^{\tiny {P-*}}_{R}\widehat{B}$.

定理 4.11$\widehat{A}$$\widehat{B}$ 的 DMPGIs 存在, 若 $\widehat{A} \mathop\le\limits^{\tiny {P-*}} \widehat{B}$ 当且仅当 $\widehat{A}\leq^{\tiny {P-*}}_{L} \widehat{B}$$\widehat{A}\leq ^{\tiny {P-*}}_{R} \widehat{B}$.

$\Rightarrow$ 由于 $\widehat{A}\mathop\le\limits^{P-*} \widehat{B}$, 则 $\widehat{A}$$\widehat{B}$ 有文献 [定理 13] 的形式, 可得

$\begin{align*} \nonumber \widehat{A}^{p}\widehat{A}=\widehat{A}^{p}\widehat{B} =V\left( \begin{matrix} I_{a} &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} +\epsilon V\left( \begin{matrix} 0 &T_{1}^{-1}A_{2} &T_{1}^{-1}A_{3} \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)V^{T} \end{align*}$

$\begin{align*} \nonumber {\mbox{ rk}}\left( \begin{matrix} A_{1} &A_{2} &A_{3} &T_{1} &0 &0 &A_{1} &A_{2} &A_{3} \\ A_{4} &0 &0 &0 &T_{2} &0 &A_{4} &B_{5} &B_{6} \\ A_{7} &0 &0 &0 &0 &0 &A_{7} &B_{8} &0 \\ T_{1} &0 &0 &0 &0 &0 &T_{1} &0 &0 \\ 0 &0 &0 &0 &0 &0 &0 &T_{2} &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \end{matrix} \right) = {\mbox{ rk}}\left( \begin{matrix} 0 &A_{2} &A_{3} &T_{1} &0 &0 &0 &A_{2} &A_{3} \\ 0 &0 &0 &0 &T_{2} &0 &0 &0 &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \\ T &0 &0 &0 &0 &0 &T_{1} &0 &0 \\ 0 &0 &0 &0 &0 &0 &0 &T_{2} &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \end{matrix} \right). \end{align*}$

由上述等式有 $\widehat{A}^{p}\widehat{A}=\widehat{A}^{p}\widehat{B}$${\mbox{ rk}}\left( \begin{matrix} A_{0} &B &B_{0} \\ A &0 &B \end{matrix} \right) =2{\mbox{ rk}}(B)$, 故 $\widehat{A}\leq ^{\tiny {P-*}}_{L}\widehat{B} $.

利用

$\begin{align*} \nonumber \widehat{A}\widehat{A}^{p}=\widehat{B}\widehat{A}^{p} =U\left( \begin{matrix} I_{a} &0 &0 \\ 0 &0 &0 \\ 0 &0 &0 \end{matrix} \right)U^{T} +\epsilon U\left( \begin{matrix} 0 &0 &0 \\ A_{4} T_{1}^{-1} &0 &0 \\ A_{7} T_{1}^{-1} &0 &0 \end{matrix} \right)U^{T} \end{align*}$

$\begin{align*} \nonumber {\mbox{ rk}}\left( \begin{matrix} A_{1}^{T} &A_{4}^{T} &A_{7}^{T} &T_{1} &0 &0 &A_{1}^{T} &A_{4}^{T} &A_{7}^{T} \\ A_{2}^{T} &0 &0 &0 &T_{2} &0 &A_{2}^{T} &B^{T}_{5} &B_{8}^{T} \\ A_{3}^{T} &0 &0 &0 &0 &0 &A_{3}^{T} &B^{T}_{6} &0 \\ T_{1} &0 &0 &0 &0 &0 &T_{1} &0 &0 \\ 0 &0 &0 &0 &0 &0 &0 &T_{2} &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \end{matrix} \right) = {\mbox{ rk}}\left( \begin{matrix} 0 &A_{4}^{T} &A_{7}^{T} &T_{1} &0 &0 &0 &A_{4}^{T} &A_{7}^{T} \\ 0 &0 &0 &0 &T_{2} &0 &0 &B^{T}_{5} &B_{8}^{T} \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \\ T_{1} &0 &0 &0 &0 &0 &T_{1} &0 &0 \\ 0 &0 &0 &0 &0 &0 &0 &T_{2} &0 \\ 0 &0 &0 &0 &0 &0 &0 &0 &0 \end{matrix} \right), \end{align*}$

可得 $\widehat{A}\widehat{A}^{p}=\widehat{B}\widehat{A}^{p}$${\mbox{ rk}}\left( \begin{matrix} A_{0}^{T} &B^{T} &B_{0}^{T} \\ A^{T} &0 &B^{T} \end{matrix} \right) =2{\mbox{ rk}}(B^{T})$, 故 $\widehat{A}\leq ^{\tiny {P-*}}_{R} \widehat{B} $.

$\Leftarrow$ 又因为 $\widehat{A}\leq^{\tiny {P-*}}_{L} \widehat{B}$$\widehat{A}\leq ^{\tiny {P-*}}_{R} \widehat{B}$, 则 $\widehat{A}^{p}\widehat{A}=\widehat{A}^{p}\widehat{B}$$\widehat{A}\widehat{A}^{p}=\widehat{B}\widehat{A}^{p}$, 即 $\widehat{A}\mathop\le\limits^{\tiny {P-*}} \widehat{B} $.

5 总结

本文提出了对偶矩阵上的左 (右) D-* 序和左 (右) D-* 序, 主要讨论对偶矩阵上左 (右) D-* 序, 左 (右) P-* 序, 减序, D-* 序和 P-* 序之间的关系. 这些结果丰富了对偶矩阵和对偶矩阵偏序的理论, 据我们所知, 这是第一篇讨论对偶单边 * 序的文章, 为对偶广义逆的单边序的研究提供了技术和方法.

参考文献

Angeles J. Angeles J, Zakhariev E. The Application of Dual Algebra to Kinematic Analysis.//Angeles J, Zakhariev E. Computational Methods in Mechanical Systems: Mechanism Analysis, Synthesis and Optimization. Berlin: Springer: 1998

[本文引用: 1]

Belzile B, Angeles J.

Reflections over the dual ring-applications to kinematic synthesis

Journal of Mechanical Design, 2019, 141(7): Art 072302

DOI:10.1115/1.4043204      URL     [本文引用: 1]

Belzile B, Angeles J. Dual Least Squares and the Characteristic Length:Applications to Kinematic Synthesis.//Joint International Conference of the International Conference on Mechanisms and Mechanical Transmissions and the International Conference on Robotics. Cham: Springer, 2021

[本文引用: 1]

Ben-Israel A, Greville T N E. Generalized Inverses:Theory and Applications. New York: Springer, 2003

[本文引用: 1]

Baksalary J K, Baksalary O M, Liu X.

Further properties of the star, left-star, right-star, and minus partial orderings

Linear Algebra and its Applications, 2003, 375: 83-94

DOI:10.1016/S0024-3795(03)00609-8      URL     [本文引用: 2]

Campbell S L, Meyer C D.

Generalized Inverses of Linear Transformations

Philadephia: Society for Industrial and Applied Mathematics, 2009

[本文引用: 1]

Cao S, Wang X, Zhang R, Peng Y, Yu H.

Aerobatic Maneuvering flight control of fixed-wing UAVs: an SE (3) approach using dual quaternion

IEEE Transactions on Industrial Electronics, 2024, 71(11): 14362-14372

DOI:10.1109/TIE.2024.3368151      URL     [本文引用: 1]

Cui C, Qi L, Song G, Wang Q.

Moore determinant of dual quaternion Hermitian matrices

Computational and Applied Mathematics, 2024, 43: Art 365

DOI:10.1007/s40314-024-02884-3      [本文引用: 1]

Cui C, Qi L.

A genuine extension of the Moore-Penrose inverse to dual matrices

Journal of Computational and Applied Mathematics, 2025, 454: Art 116185

DOI:10.1016/j.cam.2024.116185      URL     [本文引用: 1]

Cui C, Wang H, Wei Y.

Perturbations of Moore-Penrose inverse and dual Moore-Penrose generalized inverse

Journal of Applied Mathematics and Computing, 2023, 69: 4163-4186

DOI:10.1007/s12190-023-01920-5      [本文引用: 1]

Hartwig, R E.

How to partially order regular elements

Japanese Journal of Mathematics, 1980, 25: 1-13

[本文引用: 1]

Gao J, Wang H, Liu X.

Dual minus partial order

arXiv: 2407.06453v1

[本文引用: 2]

Kumar A, Shekhar V, Mishra D.

On generalized-Drazin inverses and GD-star matrices

Journal of Applied Mathematics and Computing, 2023, 69: 4553-4585

DOI:10.1007/s12190-023-01938-9      [本文引用: 1]

Ling C, He H, Qi L.

Singular values of dual quaternion matrices and their low-rank approximations

Numerical Functional Analysis and Optimization, 2022, 43(12): 1423-1458

DOI:10.1080/01630563.2022.2108835      URL    

Liu Y, Ma H.

Dual core generalized inverse of third-order dual tensor based on the T-product

Computational and Applied Mathematics, 2022, 41: Art 391

DOI:10.1007/s40314-022-02114-8      [本文引用: 1]

Mitra S K, Bhimasankaram P, Malik S B.

Matrix Partial Orders, Shorted Operators and Applications

Hackensack, World Scientific, 2010

[本文引用: 1]

Pennestrí E, Valentini P P, De Falco D.

The Moore-Penrose dual generalized inverse matrix with application to kinematic synthesis of spatial linkages

Journal of Mechanical Design, 2018, 140(10): Art 102303

DOI:10.1115/1.4040882      URL     [本文引用: 1]

The paper initially reports about the properties of an expression of dual generalized inverse matrix currently available in the literature. It is demonstrated that such a matrix does not fulfill all the Penrose conditions. Hence, novel and computationally efficient algorithms/formulas for the computation of the Moore–Penrose dual generalized inverse (MPDGI) are herein proposed. The paper also contains a new algorithm for the singular value decomposition (SVD) of a dual matrix. The availability of these formulas allows the simultaneous solution of overdetermined systems of dual linear equations without requiring the traditional separation in primal and dual parts. This should prove useful for the solution of many kinematic problems. The algorithms/formulas herein deduced have been also tested on the kinematic synthesis of the constant transmission ratio RCCC spatial linkage.

Pennestrí E, Stefanelli R.

Linear algebra and numerical algorithms using dual numbers

Multibody System Dynamics, 2007, 18: 323-344

DOI:10.1007/s11044-007-9088-9      URL     [本文引用: 1]

Qi L, Cui C.

Eigenvalues of dual Hermitian matrices with application in formation control

SIAM Journal on Matrix Analysis and Applications, 2024, 45(4): 2135-2154

DOI:10.1137/24M1652234      URL    

Qi L, Luo Z, Wang Q, Zhang X.

Quaternion matrix optimization: motivation and analysis

Journal of Optimization Theory and Applications, 2022, 193(1): 621-648

DOI:10.1007/s10957-021-01906-y      [本文引用: 1]

Udwadia F E, Pennestri E, Falco D.

Do all dual matrices have dual Moore-Penrose generalized inverses?

Mechanism and Machine Theory, 2020, 151(1): Art 103878

DOI:10.1016/j.mechmachtheory.2020.103878      URL     [本文引用: 1]

Wang G, Wei Y, Qiao S. Generalized Inverses:Theory and Computations. Singapore: Springer, 2018

[本文引用: 1]

Wang H.

Characterizations and properties of the MPDGI and DMPGI

Mechanism and Machine Theory, 2021, 158(7): Art 104212

DOI:10.1016/j.mechmachtheory.2020.104212      URL     [本文引用: 2]

Wang T, Li Y, Wei M, Xi Y, Zhang M.

Algebraic method for LU decomposition of dual quaternion matrix and its corresponding structure-preserving algorithm

Numerical Algorithms, 2024, 97(3): 1367-1382

DOI:10.1007/s11075-024-01753-8      [本文引用: 1]

Wang H, Huang P.

Characterizations and properties of dual matrix star orders

Communications on Applied Mathematics and Computation, 2025, 7: 179-202

DOI:10.1007/s42967-023-00255-z      [本文引用: 1]

Wang H, Jiang T.

Properties and characterizations of dual sharp orders

Journal of Computational and Applied Mathematics, 2023, 433: Art 115321

DOI:10.1016/j.cam.2023.115321      URL     [本文引用: 1]

Wang H, Jiang T, Ling Q, Wei Y.

Dual core-nilpotent decomposition and dual binary relation

Linear Algebra and its Applications, 2024, 684: 127-157

DOI:10.1016/j.laa.2023.12.014      URL     [本文引用: 1]

Wei T, Ding W, Wei Y.

Singular value decomposition of dual matrices and its application to traveling wave identification in the brain

SIAM Journal on Matrix Analysis and Applications, 2024, 45(1): 634-660

DOI:10.1137/23M1556642      URL     [本文引用: 2]

.

Zhong J, Zhang Y.

Dual Drazin inverses of dual matrices and dual Drazin-inverse solutions of systems of linear dual equations

Filomat, 2023, 37(10): 3075-3089

DOI:10.2298/FIL2310075Z      URL     [本文引用: 1]

In this paper, we study a kind of dual generalized inverse, which is called the dual Drazin inverse. Unlike the real matrices case, the dual Drazin inverse of a square dual matrix may not exist. It is shown that the dual Drazin inverse is unique when it exists. Some necessary and sufficient conditions for the existence of the dual Drazin inverse are presented. A compact formula for the computation of the dual Drazin inverse is given when it exists. Moreover, we find an unexpected result that the dual Drazin inverse can be obtained by computing the Drazin inverse of a 2 ? 2 upper triangular block matrix. We also introduce the dual Drazin-inverse solution of systems of linear dual equations. Some characterizations of the dual Drazin-inverse solution are given. In addition, some numerical examples are provided to illustrate the results.

/