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高等代数教学辅导

4.3 生成子空间、极大无关组与秩

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基础题.

1.
\(A=\begin{pmatrix} 1 & -3 & -1\\ 0 & 1 & -2\\ -1 & 1 & 5 \end{pmatrix}\),判断下列向量是否属于\(A\)的列空间,并说明理由。
  1. \(\beta=(1,1,0)^T\)
  2. \(\gamma=(1,-1,1)^T\)
解答.
  1. 对矩阵\(\begin{pmatrix} A & \beta\end{pmatrix}\)进行初等行变换:
    \begin{equation*} \begin{pmatrix} A & \beta\end{pmatrix}=\left(\begin{array}{ccc|c} 1 & -3 & -1 & 1\\ 0 & 1 & -2 & 1\\ -1 & 1 & 5 & 0 \end{array}\right)\rightarrow\left(\begin{array}{ccc|c} 1 & -3 & -1 & 1\\ 0 & 1 & -2 & 1\\ 0 & 0 & 0 & 3 \end{array}\right), \end{equation*}
    由于\(r(\begin{pmatrix} A & \beta\end{pmatrix})=3\neq 2 = r(A)\),线性方程组\(AX=\beta\)无解,因此\(\beta\)不属于\(A\)的列空间。
  2. 对矩阵\(\begin{pmatrix} A & \gamma\end{pmatrix}\)进行初等行变换:
    \begin{equation*} \begin{pmatrix} A & \gamma\end{pmatrix}=\left(\begin{array}{ccc|c} 1 & -3 & -1 & 1\\ 0 & 1 & -2 & -1\\ -1 & 1 & 5 & 1 \end{array}\right)\rightarrow\left(\begin{array}{ccc|c} 1 & -3 & -1 & 1\\ 0 & 1 & -2 & 1\\ 0 & 0 & 0 & 0 \end{array}\right), \end{equation*}
    由于\(r(\begin{pmatrix} A & \gamma\end{pmatrix})= 2 = r(A)\),线性方程组\(AX=\gamma\)有解,因此\(\gamma\)属于\(A\)的列空间。
2.
\begin{equation*} \alpha_1=\begin{pmatrix} 1\\ 0\\ 2\\ -1 \end{pmatrix},\alpha_2=\begin{pmatrix} -2\\ 1\\ -4\\ 6 \end{pmatrix},\alpha_3=\begin{pmatrix} 3\\ 2\\ 7\\ 5 \end{pmatrix},\alpha_4=\begin{pmatrix} 1\\ -2\\ 6\\ -9 \end{pmatrix}, \end{equation*}
  1. \(\alpha_1,\alpha_2,\alpha_3,\alpha_4\)的一个极大线性无关组;
  2. 将其余向量表示为该极大线性无关组的线性组合。
解答.
  1. \(A=(\alpha_1,\alpha_2,\alpha_3,\alpha_4)\),对\(A\)作行初等变换
    \begin{equation*} A=\begin{pmatrix} 1&-2&3&1\\ 0&1&2&-2\\ 2&-4&7&6\\ -1&6&5&-9 \end{pmatrix}\rightarrow {\rm rref}(A)=\begin{pmatrix} 1&0&0&-31\\ 0&1&0&-10\\ 0&0&1&4\\ 0&0&0&0 \end{pmatrix}, \end{equation*}
    因为行初等变换不改变列向量组的线性关系,
    \begin{equation*} \beta_1=(1,0,0,0)^T,\beta_2=(0,1,0,0)^T,\beta_3=(0,0,1,0)^T \end{equation*}
    \({\rm rref}(A)\)的列向量组\(\beta_1,\beta_2,\beta_3,\beta_4\)的一个极大线性无关组,所以\(\alpha_1,\alpha_2,\alpha_3\)\(\alpha_1,\alpha_2,\alpha_3,\alpha_4\)的一个极大线性无关组。
  2. 因为\(\beta_4=-31\beta_1-10\beta_2+4\beta_3\),所以
    \begin{equation*} \alpha_4=-31\alpha_1-10\alpha_2+4\alpha_3. \end{equation*}
3.
\(\alpha_1,\alpha_2,\alpha_3\in\F^m\),若它们两两线性无关但全体线性相关,证明:\(\langle \alpha_1,\alpha_2\rangle=\langle \alpha_2,\alpha_3\rangle=\langle \alpha_1,\alpha_3\rangle\)
解答.
因为向量组\(\alpha_1,\alpha_2,\alpha_3\)线性相关,\(\alpha_1,\alpha_2\)线性无关,所以\(\alpha_3\)可由向量组\(\alpha_1,\alpha_2\)线性表出。易见\(\alpha_2\)可由向量组\(\alpha_1,\alpha_2\)线性表出,因此向量组\(\alpha_2,\alpha_3\)可由向量组\(\alpha_1,\alpha_2\)线性表出。同理,由\(\alpha_1,\alpha_2,\alpha_3\)线性相关、\(\alpha_2,\alpha_3\)线性无关可推出向量组\(\alpha_1,\alpha_2\)可由向量组\(\alpha_2,\alpha_3\)线性表出,由此可知向量组\(\alpha_1,\alpha_2\)\(\alpha_2,\alpha_3\)等价,从而\(\langle \alpha_1,\alpha_2 \rangle =\langle \alpha_2,\alpha_3 \rangle \)。同理可证\(\langle \alpha_1,\alpha_2 \rangle =\langle \alpha_1,\alpha_3 \rangle \),结论成立。
4.
设向量\(\beta\)可以由\(\alpha_1,\dots ,\alpha_s\)线性表示,但不能由\(\alpha_1,\dots ,\alpha_{s-1}\)线性表示。证明:
  1. 向量组\(\alpha_1,\dots ,\alpha_{s-1},\alpha_s\)与向量组\(\alpha_1,\dots ,\alpha_{s-1},\beta\)等价;
  2. \(r(\alpha_1,\dots ,\alpha_{s-1},\alpha_s)=r(\alpha_1,\dots ,\alpha_{s-1},\beta)\)
解答.
  1. 因为\(\beta\)可以由\(\alpha_1,\dots ,\alpha_s\)线性表示,所以存在\(a_1,\dots ,a_s\in\mathbb{F}\),使得
    \begin{equation*} \beta=a_1\alpha_1+\cdots +a_s\alpha_s. \end{equation*}
    \(a_s=0\),则\(\beta=a_1\alpha_1+\cdots +a_{s-1}\alpha_{s-1}\),与条件\(\beta\)不能由向量组\(\alpha_1,\dots ,\alpha_{s-1}\)线性表出相矛盾,因此\(a_s\neq 0\)。从而
    \begin{equation*} \alpha_s=-\frac{a_1}{a_s}\alpha_1-\cdots -\frac{a_{s-1}}{a_{s}}\alpha_{s-1}+\frac{1}{a_{s-1}}\beta, \end{equation*}
    则向量组\(\alpha_1,\dots ,\alpha_s\)可由向量组\(\alpha_1,\dots ,\alpha_{s-1},\beta\)线性表出。另一方面,由于\(\beta\)可由向量组\(\alpha_1,\dots ,\alpha_s\)线性表出,所以向量组\(\alpha_1,\dots ,\alpha_{s-1},\beta\)可由向量组\(\alpha_1,\dots ,\alpha_s\)线性表出。因此向量组\(\alpha_1,\dots ,\alpha_{s-1},\alpha_s\)与向量组 \(\alpha_1,\dots ,\alpha_{s-1},\beta\)等价。
  2. 因为向量组\(\alpha_1,\dots ,\alpha_{s-1},\alpha_s\)与向量组 \(\alpha_1,\dots ,\alpha_{s-1},\beta\)等价,所以
    \begin{equation*} r(\alpha_1,\dots ,\alpha_{s-1},\alpha_s)=r(\alpha_1,\dots ,\alpha_{s-1},\beta). \end{equation*}
5.
设向量组\(\alpha_1,\dots ,\alpha_r\)与向量组\(\beta_1,\dots ,\beta_s\)等价,且\(\alpha_1,\dots ,\alpha_r\)线性无关,试问\(\beta_1,\dots ,\beta_s\)是否一定线性无关?如果结论成立,请证明;如果不成立,请举出反例。
解答.
不一定成立。比如,向量组
\begin{equation*} \alpha_1=(1,0)^T,\alpha_2=(0,1)^T \end{equation*}
与向量组
\begin{equation*} \beta_1=(1,0)^T,\beta_2=(0,1)^T,\beta_3=(1,1)^T \end{equation*}
等价,\(\alpha_1,\alpha_2\)线性无关,但\(\beta_1,\beta_2,\beta_3\)线性相关。
6.
\(A\)是一个对角矩阵,证明:\(r(A)\)等于\(A\)的非\(0\)对角元个数。
解答.
\(A={\rm diag}(a_1,\ldots,a_n)\),其中\(a_{i_1},\dots,a_{i_r}\)\(a_1,\ldots,a_n\)中的非\(0\)数,则\(A\)的列向量组\(a_1\varepsilon_1,\ldots,a_n\varepsilon_n\)存在线性无关子向量组\(a_{i_1}\varepsilon_{i_1},\dots,a_{i_r}\varepsilon_{i_r}\)。易见对任意\(1\leq j\leq n\)\(a_j\varepsilon_j\)可由\(a_{i_1}\varepsilon_{i_1},\dots,a_{i_r}\varepsilon_{i_r}\)线性表出,因此\(a_{i_1}\varepsilon_{i_1},\dots,a_{i_r}\varepsilon_{i_r}\)\(A\)的列向量组\(a_1\varepsilon_1,\ldots,a_n\varepsilon_n\)的一个极大线性无关组,\(A\)的列秩等于\(r\),从而\(r(A)\)等于\(A\)的非\(0\)对角元个数\(r\)
7.
\(A\)\(m\times n\)矩阵,\(B\)\(n\times m\)矩阵。证明:若\(m > n\),则
\begin{equation*} \det (AB)=0. \end{equation*}
解答.
由于\(r(AB)\leq r(A)\leq n\),又\(n< m\),所以\(m\)阶方阵\(AB\)的秩小于\(m\)。因此\(\det (AB)=0\)

提高题.

8.
\(\alpha_1,\dots ,\alpha_n\)是一组\(n\)维列向量,已知单位向量\(\varepsilon_1,\dots ,\varepsilon_n\)可被它们线性表出,证明:\(\alpha_1,\dots ,\alpha_n\)线性无关。
解答.
因为\(n\)维列向量\(\alpha_1,\dots ,\alpha_n\)必可由\(n\)维标准单位列向量\(\varepsilon_1,\dots ,\varepsilon_n\)线性表出,而由题设\(\varepsilon_1,\dots ,\varepsilon_n\)可由\(\alpha_1,\dots ,\alpha_n\)线性表出,因而\(\alpha_1,\dots ,\alpha_n\)\(\varepsilon_1,\varepsilon_2,\cdots ,\varepsilon_n\)等价。由此得
\begin{equation*} r(\alpha_1,\dots ,\alpha_n)=r(\varepsilon_1,\varepsilon_2,\cdots ,\varepsilon_n). \end{equation*}
注意到\(r(\varepsilon_1,\varepsilon_2,\cdots ,\varepsilon_n)=n\),故 \(r(\alpha_1,\dots ,\alpha_n)=n\)。从而\(\alpha_1,\dots ,\alpha_n\)线性无关。
9.
\(\alpha_1,\dots ,\alpha_n\)是一组\(n\)维列向量,证明:\(\alpha_1,\dots ,\alpha_n\)线性无关的充要条件是任一\(n\)维列向量都可被它们线性表出。
解答.
充分性:由题设,\(n\)维标准单位列向量\(\varepsilon_1,\dots ,\varepsilon_n\)可由向量组\(\alpha_1,\dots ,\alpha_n\)线性表出,根据练习 4.3.8,向量组\(\alpha_1,\dots ,\alpha_n\)线性无关。
必要性:设\(\beta\)为任一\(n\)维列向量,则\(\alpha_1,\dots ,\alpha_n,\beta\)\(n+1\)\(n\)维列向量,必线性相关。而\(\alpha_1,\dots ,\alpha_n\)线性无关,因此\(\beta\)可由向量组\(\alpha_1,\dots ,\alpha_n\)线性表出。
10.
用向量组的知识证明:数域\(\mathbb{F}\)上的\(n\)个方程的\(n\)元线性方程组
\begin{equation*} x_1\alpha_1+\cdots +x_n\alpha_n=\beta \end{equation*}
对任意\(\beta\in\mathbb{F}^n\)都有解的充分必要条件是\(\det (\alpha_1,\dots ,\alpha_n)\neq 0\)
解答.
线性方程组\(x_1\alpha_1+\cdots +x_n\alpha_n=\beta\)对任意\(\beta\in\mathbb{F}^n\)都有解当且仅当任一\(n\)维向量\(\beta\)都可由向量组\(\alpha_1,\dots ,\alpha_n\)线性表出。由练习 4.3.9 可知,其充分必要条件为\(\alpha_1,\dots ,\alpha_n\)线性无关,即\(\det (\alpha_1,\dots ,\alpha_n)\neq 0\)
11.
\(\alpha_1,\dots,\alpha_s\)的秩为\(r\),证明:\(\alpha_1,\dots,\alpha_s\)中任意\(r\)个线性无关的向量都构成它的一个极大无关组。
解答.
\(\alpha_{i_1},\dots,\alpha_{i_r}\)\(\alpha_1,\dots,\alpha_s\)中任意\(r\)个线性无关向量,则\(\forall 1\leq j\leq s\),向量组\(\alpha_{i_1},\dots,\alpha_{i_r},\alpha_j\)必线性相关(否则\(\alpha_1,\dots,\alpha_s\)的秩不小于\(r+1\)。)因此,\(\alpha_{i_1},\dots,\alpha_{i_r}\)\(\alpha_1,\dots,\alpha_s\)的一个极大无关组。
12.
\(\alpha_1,\dots,\alpha_s\)的秩为\(r\)\(\alpha_{i_1},\dots,\alpha_{i_r}\)\(\alpha_1,\dots,\alpha_s\)\(r\)个向量,使得\(\alpha_1,\dots,\alpha_s\)中每个向量都可被它们线性表出,证明:\(\alpha_{i_1},\dots,\alpha_{i_r}\)\(\alpha_1,\dots,\alpha_s\)的一个极大无关组。
解答.
根据题设,向量组\(\alpha_1,\dots,\alpha_s\)可由\(\alpha_{i_1},\dots,\alpha_{i_r}\)线性表出,而显然子向量组\(\alpha_{i_1},\dots,\alpha_{i_r}\)可由\(\alpha_1,\dots,\alpha_s\)线性表出,因此向量组\(\alpha_1,\dots,\alpha_s\)\(\alpha_{i_1},\dots,\alpha_{i_r}\)等价,从而\(r(\alpha_{i_1},\dots,\alpha_{i_r})=r(\alpha_1,\dots,\alpha_s)=r \),由此推出\(\alpha_{i_1},\dots,\alpha_{i_r}\)线性无关,因此\(\alpha_{i_1},\dots,\alpha_{i_r}\)是向量组\(\alpha_1,\dots,\alpha_s\)的一个极大无关组。
13.
\(A\)是一个上三角矩阵,证明:\(r(A)\)大于等于\(A\)的非\(0\)对角元个数。
解答.
\begin{equation*} A=\begin{pmatrix} a_{11} & a_{12} & \cdots & a_{1n}\\ 0 & a_{22} & \cdots & a_{2n}\\ \vdots & \vdots & \ddots & \vdots\\ 0 & 0 & \cdots & a_{nn} \end{pmatrix} \end{equation*}
是上三角矩阵,其中\(a_{i_1,i_1},a_{i_2,i_2},\dots,a_{i_r,i_r}\)是对角元中的所有非\(0\)元,则
\begin{equation*} \begin{array}{ll} A\begin{bmatrix} i_1 & i_2 & \dots & i_r\\ i_1 & i_2 & \dots & i_r \end{bmatrix} & =\begin{vmatrix} a_{i_1,i_1} & a_{i_1,i_2} & \cdots & a_{i_1,i_r}\\ 0 & a_{i_2,i_2} & \cdots & a_{i_2,i_r}\\ \vdots & \vdots & \ddots & \vdots\\ 0 & 0 & \cdots & a_{i_r,i_r} \end{vmatrix}\\ & =a_{i_1,i_1}a_{i_2,i_2}\cdots a_{i_r,i_r}\neq 0, \end{array} \end{equation*}
\(A\)存在非\(0\)\(r\)阶子式,因此\(r(A)\geq r\)
14.
\(A\)是秩为\(r\)\(m\times n\)矩阵,从\(A\)中任意取\(s\)行作一个\(s\times n\)矩阵\(B\)。证明:\(r(B)\geq r+s-m\)
解答.
\(A\)的行向量组为\(\alpha_1,\dots ,\alpha_m\)\(B\)的行向量组为\(\alpha_{i_1},\dots ,\alpha_{i_s}\)。若\(r(B)=t\),则向量组\(\alpha_{i_1},\dots ,\alpha_{i_s}\)的秩为\(t\)。因为\(\alpha_{i_1},\dots ,\alpha_{i_s}\)的极大无关组为向量组\(\alpha_1,\dots ,\alpha_m\)的线性无关组,故可将\(\alpha_{i_1},\dots ,\alpha_{i_s}\)的一个极大无关组(含\(t\)个向量)扩充为\(\alpha_1,\dots ,\alpha_m\)的一个极大线性无关组(扩充了\(r-t\)个向量)。注意到上述扩充过程中,扩充的向量均取自\(\alpha_{i_1},\dots ,\alpha_{i_s}\)以外的向量,而\(\alpha_1,\dots ,\alpha_m\)中除\(\alpha_{i_1},\dots ,\alpha_{i_s}\)外的向量个数为\(m-s\),故\(r-t\leq m-s\)。因此\(r(B)=t\geq r+s-m\)
15.
证明:\(r\left(\begin{array}{cc} A&0\\0&B \end{array}\right)=r(A)+r(B);\quad r\left(\begin{array}{cc} A&0\\ C &B \end{array}\right) \geq r(A)+r(B)\)
解答 1.
\(\alpha_{i_1},\dots,\alpha_{i_r}\)\(A\)的列向量组\(\alpha_1,\dots,\alpha_s\)的一个极大无关组,\(\beta_{j_1},\dots,\beta_{j_p}\)\(B\)的列向量组\(\beta_1,\dots,\beta_t\)的一个极大无关组。
  1. 下证
    \begin{equation*} \begin{pmatrix}\alpha_{i_1}\\0\end{pmatrix},\dots,\begin{pmatrix}\alpha_{i_r}\\0\end{pmatrix},\begin{pmatrix}0\\\beta_{j_1}\end{pmatrix},\dots,\begin{pmatrix}0\\\beta_{j_p}\end{pmatrix} \end{equation*}
    \(\begin{pmatrix} A & 0\\ 0 & B \end{pmatrix}\)列向量组\(\begin{pmatrix}\alpha_1\\0\end{pmatrix},\dots,\begin{pmatrix}\alpha_s\\0\end{pmatrix},\begin{pmatrix}0\\\beta_1\end{pmatrix},\dots,\begin{pmatrix}0\\\beta_t\end{pmatrix}\)的一个极大无关组。事实上,若
    \begin{equation*} a_1\begin{pmatrix}\alpha_{i_1}\\0\end{pmatrix} + \cdots + a_r\begin{pmatrix}\alpha_{i_r}\\0\end{pmatrix} + b_1\begin{pmatrix}0\\\beta_{j_1}\end{pmatrix} + \cdots + b_p\begin{pmatrix}0\\\beta_{j_p}\end{pmatrix}=0, \end{equation*}
    \(\begin{pmatrix}a_1\alpha_{i_1} + \cdots + a_r\alpha_{i_r}\\b_1\beta_{j_1} + \cdots + b_p\beta_{j_p}\end{pmatrix}=0\),则
    \begin{equation*} a_1\alpha_{i_1} + \cdots + a_r\alpha_{i_r}=0\ \text{且}\ b_1\beta_{j_1} + \cdots + b_p\beta_{j_p}=0. \end{equation*}
    由向量组\(\alpha_{i_1},\dots,\alpha_{i_r}\)\(\beta_{j_1},\dots,\beta_{j_p}\)线性无关知
    \begin{equation*} a_1=\cdots=a_r=b_1=\cdots=b_p=0, \end{equation*}
    因此向量组\(\begin{pmatrix}\alpha_{i_1}\\0\end{pmatrix},\dots,\begin{pmatrix}\alpha_{i_r}\\0\end{pmatrix},\begin{pmatrix}0\\\beta_{j_1}\end{pmatrix},\dots,\begin{pmatrix}0\\\beta_{j_p}\end{pmatrix}\)线性无关。对任意\(1\leq k\leq s\),由\(\alpha_{i_1},\dots,\alpha_{i_r}\)\(A\)的列向量组\(\alpha_1,\dots,\alpha_s\)的一个极大无关组知存在\(c_{1k},\dots,c_{rk}\in\mathbb{F}\),使得
    \begin{equation*} \alpha_k=c_{1k}\alpha_{i_1} + \cdots + c_{rk}\alpha_{i_r}, \end{equation*}
    于是
    \begin{equation*} \begin{pmatrix} \alpha_k \\ 0 \end{pmatrix}=c_{1k}\begin{pmatrix} \alpha_{i_1} \\ 0 \end{pmatrix}+ \cdots + c_{rk}\begin{pmatrix} \alpha_{i_r} \\ 0\end{pmatrix}+0\begin{pmatrix}0\\\beta_{j_1}\end{pmatrix} + \cdots + 0\begin{pmatrix}0\\\beta_{j_p}\end{pmatrix}. \end{equation*}
    同理可证对任意\(1\leq l\leq t\),向量\(\begin{pmatrix}0\\\beta_l\end{pmatrix}\)可由向量组\(\begin{pmatrix}\alpha_{i_1}\\0\end{pmatrix},\dots,\begin{pmatrix}\alpha_{i_r}\\0\end{pmatrix},\begin{pmatrix}0\\\beta_{j_1}\end{pmatrix},\dots,\begin{pmatrix}0\\\beta_{j_p}\end{pmatrix}\)线性表出。因此\(\begin{pmatrix}\alpha_{i_1}\\0\end{pmatrix},\dots,\begin{pmatrix}\alpha_{i_r}\\0\end{pmatrix},\begin{pmatrix}0\\\beta_{j_1}\end{pmatrix},\dots,\begin{pmatrix}0\\\beta_{j_p}\end{pmatrix}\)\(\begin{pmatrix} A & 0\\ 0 & B \end{pmatrix}\)列向量组的一个极大无关组,从而
    \begin{equation*} r\left(\begin{array}{cc} A&0\\0&B \end{array}\right)=r+p=r(A)+r(B). \end{equation*}
  2. \(\gamma_1,\dots,\gamma_s\)\(C\)的列向量组。我们断言,\(\left(\begin{array}{cc} A&0\\ C &B \end{array}\right)\)的列向量组存在线性无关的子向量组
    \begin{equation*} \begin{pmatrix}\alpha_{i_1}\\\gamma_{i_1}\end{pmatrix},\dots,\begin{pmatrix}\alpha_{i_r}\\\gamma_{i_r}\end{pmatrix},\begin{pmatrix}0\\\beta_{j_1}\end{pmatrix},\dots,\begin{pmatrix}0\\\beta_{j_p}\end{pmatrix}. \end{equation*}
    事实上,若
    \begin{equation*} a_1\begin{pmatrix}\alpha_{i_1}\\\gamma_{i_1}\end{pmatrix} + \cdots + a_r\begin{pmatrix}\alpha_{i_r}\\\gamma_{i_r}\end{pmatrix} + b_1\begin{pmatrix}0\\\beta_{j_1}\end{pmatrix} + \cdots + b_0\begin{pmatrix}0\\\beta_{j_p}\end{pmatrix}=0, \end{equation*}
    \begin{equation} a_1\alpha_{i_1} + \cdots + a_r\alpha_{i_r}=0\tag{4.3.1} \end{equation}
    \begin{equation} a_1\gamma_{i_1} + \cdots + a_r\gamma_{i_r}+b_1\beta_{j_1} + \cdots + b_p\beta_{j_p}=0.\tag{4.3.2} \end{equation}
    \(\alpha_{i_1},\dots,\alpha_{i_r}\)线性无关知\(a_1=\cdots=a_r=0\),代入 (4.3.2)\(b_1\beta_{j_1} + \cdots + b_p\beta_{j_p}=0,\) 再由\(\beta_{j_1},\dots,\beta_{j_p}\)线性无关知\(b_1=\cdots=b_p=0\),因此向量组
    \begin{equation*} \begin{pmatrix}\alpha_{i_1}\\\gamma_{i_1}\end{pmatrix},\dots,\begin{pmatrix}\alpha_{i_r}\\\gamma_{i_r}\end{pmatrix},\begin{pmatrix}0\\\beta_{j_1}\end{pmatrix},\dots,\begin{pmatrix}0\\\beta_{j_p}\end{pmatrix} \end{equation*}
    线性无关,从而\(\begin{pmatrix}A&0\\ C &B\end{pmatrix}\)的列秩大于等于\(r+p\),于是
    \begin{equation*} r\left(\begin{array}{cc} A&0\\ C &B \end{array}\right) \geq r(A)+r(B). \end{equation*}
解答 2.
\(r(A)=r\)\(r(B)=p\),则\(A\)存在非\(0\)\(r\)阶子式\(A\begin{bmatrix}i_1 &\cdots & i_r\\ j_1 & \cdots & j_r\end{bmatrix}\)\(B\)存在非\(0\)\(p\)阶子式\(B\begin{bmatrix}k_1 &\cdots & k_p\\ l_1 & \cdots & l_p\end{bmatrix}\)。于是\(\begin{pmatrix}A&0\\ C &B\end{pmatrix}\)存在\(r+p\)阶子式
\begin{equation*} \begin{array}{ll} & \begin{vmatrix} a_{i_1,j_1} & \cdots & a_{i_1,j_r} & 0 & \cdots &0\\ \vdots & & \vdots & \vdots & & \vdots\\ a_{i_r,j_1} & \cdots & a_{i_r,j_r} & 0 & \cdots &0\\ c_{k_1,j_1} & \cdots & c_{k_1,j_r} & b_{k_1,l_1} & \cdots &b_{k_1,l_p}\\ \vdots & & \vdots & \vdots & & \vdots\\ c_{k_p,j_1} & \cdots & c_{k_p,j_r} & b_{k_p,l_1} & \cdots &b_{k_p,l_p} \end{vmatrix}\\ = & A\begin{bmatrix}i_1 &\cdots & i_r\\ j_1 & \cdots & j_r\end{bmatrix}B\begin{bmatrix}k_1 &\cdots & k_p\\ l_1 & \cdots & l_p\end{bmatrix}\neq 0, \end{array} \end{equation*}
因此 \(r\begin{pmatrix}A&0\\ C &B\end{pmatrix}\geq r+p =r(A)+r(B)\)
16.
利用 练习 4.3.15中的结论证明:设\(A\)\(m\times n\)矩阵,\(B\)\(n\times s\)矩阵,则
\begin{equation*} r(AB)\geq r(A)+r(B)-n. \end{equation*}
提示.
利用辅助矩阵\(\begin{pmatrix} A_{m\times n} & 0_{m\times s}\\ E_n & B_{n\times s} \end{pmatrix} \)\(\begin{pmatrix} AB &\\ 0 & E_n \end{pmatrix} \)
解答.
\(C=\begin{pmatrix} E_n&0\\0&AB \end{pmatrix}\),则
\begin{equation} r(C)=n+r(AB).\tag{4.3.3} \end{equation}
由于
\begin{equation*} \begin{pmatrix} 0&E_m\\E_n&0 \end{pmatrix}\begin{pmatrix} E_n&0\\A&E_m \end{pmatrix}C\begin{pmatrix} E_n&-B\\0&E_m \end{pmatrix}=\begin{pmatrix} A&0\\E_n&-B \end{pmatrix}, \end{equation*}
\(\begin{pmatrix} 0&E_m\\E_n&0 \end{pmatrix}, \begin{pmatrix} E_n&0\\A&E_m \end{pmatrix} , \begin{pmatrix} E_n&-B\\0&E_m \end{pmatrix}\)均可逆,所以
\begin{equation} r(C)=r\begin{pmatrix} A&0\\E_n&-B \end{pmatrix}.\tag{4.3.4} \end{equation}
结合(4.3.3)(4.3.4)
\begin{equation*} r(AB)=r\begin{pmatrix} A&0\\E_n&-B \end{pmatrix}-n. \end{equation*}
根据练习 4.3.15结论知
\begin{equation*} r\begin{pmatrix} A&0\\E_n&-B \end{pmatrix}\geq r(A)+r(-B)=r(A)+r(B), \end{equation*}
因此\(r(AB)\geq r(A)+r(B)-n\)
17.
证明Frobenius不等式:
\begin{equation*} r(ABC)\geq r(AB)+r(BC)-r(B). \end{equation*}
提示.
利用辅助矩阵\(\begin{pmatrix} AB & 0\\ B & BC \end{pmatrix} \)\(\begin{pmatrix} ABC & 0\\ 0 & B \end{pmatrix} \)
解答.
由于
\begin{equation*} \begin{pmatrix}0 & -E\\ E & 0\end{pmatrix}\begin{pmatrix}E & A\\ 0 & E\end{pmatrix}\begin{pmatrix} ABC&0\\0&B \end{pmatrix}\begin{pmatrix}E & 0\\ -C & E\end{pmatrix}=\begin{pmatrix} BC&-B\\0&AB \end{pmatrix}, \end{equation*}
\(\begin{pmatrix}0 & -E\\ E & 0\end{pmatrix},\begin{pmatrix}E & A\\ 0 & E\end{pmatrix},\begin{pmatrix}E & 0\\ -C & E\end{pmatrix}\)可逆, 所以
\begin{equation*} r\begin{pmatrix} ABC&0\\0&B \end{pmatrix}=r\begin{pmatrix} BC&-B\\0&AB \end{pmatrix}, \end{equation*}
\begin{equation*} r(ABC)=r\begin{pmatrix} BC&-B\\0&AB \end{pmatrix}-r(B). \end{equation*}
注意到\(r\begin{pmatrix} BC&-B\\0&AB \end{pmatrix}\geq r(AB)+r(BC)\),因此
\begin{equation*} r(ABC)\geq r(AB)+r(BC)-r(B). \end{equation*}
18.
\(A\)\(n\)阶方阵,证明:\(A^2=E_n\)的充要条件是
\begin{equation*} r(A+E_n)+r(A-E_n)=n. \end{equation*}
提示.
利用辅助矩阵\(\begin{pmatrix} A+E_n & 0\\ 0 & A-E_n \end{pmatrix} \)
解答.
\(B=\begin{pmatrix} A+E_n&0\\0&A-E_n \end{pmatrix}\),则
\begin{equation*} r(B)=r(A+E_n)+r(A-E_n). \end{equation*}
由于
\begin{equation*} \begin{array}{ll} & \begin{pmatrix}E_n & 0\\ \frac{1}{2}(E_n-A) & E_n\end{pmatrix}\begin{pmatrix}E_n & E_n\\ 0 & E_n\end{pmatrix}B\begin{pmatrix}E_n & 0\\ -E_n & E_n\end{pmatrix}\begin{pmatrix}E_n & \frac{1}{2}(E_n-A)\\ 0 & E_n\end{pmatrix}\\ = & \begin{pmatrix} 2E_n&0\\0&\frac{1}{2}(A^2-E_n), \end{pmatrix}\end{array} \end{equation*}
所以
\begin{equation*} r(B)=r\begin{pmatrix} 2E_n&0\\0&\frac{1}{2}(A^2-E_n), \end{pmatrix}=n+r(A^2-E_n), \end{equation*}
于是
\begin{equation*} r(A+E_n)+r(A-E_n)=n+r(A^2-E). \end{equation*}
因此\(A^2=E_n\)的充要条件是\(r(A+E_n)+r(A-E_n)=n\)

挑战题.

19.
设数域\(\F\)\(n\)阶方阵\(A=(a_{ij})_{n\times n}\)满足
\begin{equation*} |a_{ii}| > \sum\limits_{\begin{array}{c}j=1\\j\neq i\end{array}}^n |a_{ij}|,\ i=1,\ldots , n, \end{equation*}
则称\(A\)严格主对角占优矩阵。证明:若\(A\)\(n\)阶严格主对角占优矩阵,则\(r(A)=n\)
解答.
(反证法)假设\(r(A) \neq n\),则\(r(A) < n\),齐次线性方程组\(AX={\bf 0}\)存在非\(0\)解。设\((c_1,\dots,c_n)^T\)\(AX=0\)的一个非\(0\)解,则对任意\(1\leq i\leq n\)
\begin{equation} a_{i1}c_1+a_{i2}c_2+\cdots+a_{in}c_n=0.\tag{4.3.5} \end{equation}
\(|c_k|=\max\{|c_1|,\dots,|c_n|\}\),则\(|c_k|\neq 0\)。 由(4.3.5)
\begin{equation*} |a_{kk}|= |-\frac{1}{c_{k}}\sum\limits_{\begin{array}{c}1\leq j\leq n\\j\neq k\end{array}} a_{kj}c_j |\leq \sum\limits_{\begin{array}{c}1\leq j\leq n\\j\neq k\end{array}} |\frac{a_{kj}c_j}{c_{k}} |\leq \sum\limits_{\begin{array}{c}1\leq j\leq n\\j\neq k\end{array}} |a_{kj}|, \end{equation*}
与题设矛盾。因此\(r(A)=n\)
20. 第五届全国大学生数学竞赛预赛.
\(n\)阶方阶\(B(t)\)\(n\times 1\)矩阵\(b(t)\)分别为\(B(t)=\left(b_{ij}(t)\right),\ b(t)=(b_1(t),\cdots,b_n(t))^T\),其中\(b_{ij}(t),b_i(t)\)均为关于\(t\)的实系数多项式,\(i,j=1,\ldots,n\)。记\(d(t)\)\(B(t)\)的行列式,\(d_i(t)\)为用\(b(t)\)替代\(B(t)\)的第\(i\)列后所得的\(n\)阶矩阵的行列式。若\(d(t)\)有实根\(t_0\),使得线性方程组\(B(t_0)X=b(t_0)\)有解,证明:\(d(t),d_1(t),\ldots,d_n(t)\)必有次数大等于\(1\)的公因式。
解答.
\(\widetilde{B}(t_0)=\left(B(t_0), b(t_0)\right)\)。因为线性方程组\(B(t_0)X=b(t_0)\)有解,所以
\begin{equation*} r(\widetilde{B}(t_0))=r(B(t_0)) . \end{equation*}
注意到\(d(t_0)=0\),即\(\det B(t_0)=0\),则\(r(B(t_0)) < n\),因此
\begin{equation*} r(\widetilde{B}(t_0))=r(B(t_0)) < n. \end{equation*}
对任意\(i=1,\dots, n\),记\(B_i(t)\)是用\(b(t)\)替代\(B(t)\)的第\(i\)列后所得的\(n\)阶方阵,易见 \(r(B(t_0))\leq r(\widetilde{B}(t_0))\),则\(r(B(t_0))< n\),因此\(\det B_i(t_0)=0\),即\(d_i(t_0)=0\)。从而\(t-t_0\)\(d(t),d_1(t),\dots ,d_n(t)\)的一个公因式,结论成立。