筑波大学 理工情報生命学術院 システム情報工学研究群 社会工学学位プログラム 2018年2月実施 线性代数
Author
思齐塾 , 祭音Myyura
Description
2次の実正方行列全体の集合をV とする.すなわち,
V = { ( x y z w ) ∣ x , y , z , w ∈ R } V = \left\{ \begin{pmatrix} x & y \\ z & w \end{pmatrix} \mid x, y, z, w \in \mathbb{R} \right\} V = { ( x z y w ) ∣ x , y , z , w ∈ R }
とする。また、2次の実正方行列 X ∈ V X \in V X ∈ V に対して f ( X ) = A X f(X) = AX f ( X ) = A X と定めることにより、写像 f : V → V f: V \to V f : V → V を定義する。ただし、
A = ( 1 2 2 4 ) A = \begin{pmatrix} 1 & 2 \\ 2 & 4 \end{pmatrix} A = ( 1 2 2 4 )
とする。以下の問いに答えよ。
(1) 行列 E 11 , E 12 , E 21 , E 22 E_{11}, E_{12}, E_{21}, E_{22} E 11 , E 12 , E 21 , E 22 を
E 11 = ( 1 0 0 0 ) , E 12 = ( 0 1 0 0 ) , E 21 = ( 0 0 1 0 ) , E 22 = ( 0 0 0 1 ) E_{11} = \begin{pmatrix} 1 & 0 \\ 0 & 0 \end{pmatrix}, E_{12} = \begin{pmatrix} 0 & 1 \\ 0 & 0 \end{pmatrix}, E_{21} = \begin{pmatrix} 0 & 0 \\ 1 & 0 \end{pmatrix}, E_{22} = \begin{pmatrix} 0 & 0 \\ 0 & 1 \end{pmatrix} E 11 = ( 1 0 0 0 ) , E 12 = ( 0 0 1 0 ) , E 21 = ( 0 1 0 0 ) , E 22 = ( 0 0 0 1 )
と定義する。
X = ( x y z w ) ∈ V X = \begin{pmatrix} x & y \\ z & w \end{pmatrix} \in V X = ( x z y w ) ∈ V
を E 11 , E 12 , E 21 , E 22 E_{11}, E_{12}, E_{21}, E_{22} E 11 , E 12 , E 21 , E 22 の線形結合で表せ。
(2) E 11 E_{11} E 11 と E 12 E_{12} E 12 で張られる(生成される) 空間をUとする。以下を証明せよ。
(i) X 1 ∈ U , X 2 ∈ U X_1 \in U, X_2 \in U X 1 ∈ U , X 2 ∈ U ならば X 1 + X 2 ∈ U X_1 + X_2 \in U X 1 + X 2 ∈ U .
(ii) X ∈ U , k ∈ R X \in U, k \in \mathbb{R} X ∈ U , k ∈ R ならば k X ∈ U kX \in U k X ∈ U .
(3) f f f が線形写像であることを線形写像の定義に基づき示せ。
(4) 写像fの核空間 K e r f = { X ∈ V ∣ f ( X ) = O } Kerf = \{ X \in V \mid f(X) = O \} Ker f = { X ∈ V ∣ f ( X ) = O } の基底を一組求め, K e r f Kerf Ker f の次元を答えよ。ただし、 O O O は零行列とする。
(5) 写像fの像空間 I m f Imf I m f の基底を一組求め, I m f Imf I m f の次元を答えよ。
题目描述
令 V V V 为全体二阶实方阵组成的集合,即
V = { ( x y z w ) | x , y , z , w ∈ R } . V=\left\{
\begin{pmatrix}x&y\\z&w\end{pmatrix}
\mathrel{}\middle|\mathrel{}
x,y,z,w\in\mathbb R
\right\}. V = { ( x z y w ) x , y , z , w ∈ R } .
对 X ∈ V X\in V X ∈ V ,以
f ( X ) = A X , A = ( 1 2 2 4 ) f(X)=AX,\qquad
A=\begin{pmatrix}1&2\\2&4\end{pmatrix} f ( X ) = A X , A = ( 1 2 2 4 )
定义映射 f : V → V f:V\to V f : V → V 。回答下列问题:
定义
E 11 = ( 1 0 0 0 ) , E 12 = ( 0 1 0 0 ) , E 21 = ( 0 0 1 0 ) , E 22 = ( 0 0 0 1 ) . E_{11}=\begin{pmatrix}1&0\\0&0\end{pmatrix},\quad
E_{12}=\begin{pmatrix}0&1\\0&0\end{pmatrix},\quad
E_{21}=\begin{pmatrix}0&0\\1&0\end{pmatrix},\quad
E_{22}=\begin{pmatrix}0&0\\0&1\end{pmatrix}. E 11 = ( 1 0 0 0 ) , E 12 = ( 0 0 1 0 ) , E 21 = ( 0 1 0 0 ) , E 22 = ( 0 0 0 1 ) .
将任意
X = ( x y z w ) ∈ V X=\begin{pmatrix}x&y\\z&w\end{pmatrix}\in V X = ( x z y w ) ∈ V
表示为 E 11 , E 12 , E 21 , E 22 E_{11},E_{12},E_{21},E_{22} E 11 , E 12 , E 21 , E 22 的线性组合。
令 U = span { E 11 , E 12 } U=\operatorname{span}\{E_{11},E_{12}\} U = span { E 11 , E 12 } ,证明:
若 X 1 , X 2 ∈ U X_1,X_2\in U X 1 , X 2 ∈ U ,则 X 1 + X 2 ∈ U X_1+X_2\in U X 1 + X 2 ∈ U ;
若 X ∈ U X\in U X ∈ U 、k ∈ R k\in\mathbb R k ∈ R ,则 k X ∈ U kX\in U k X ∈ U 。
直接依据线性映射的定义,证明 f f f 是线性映射。
求核空间
ker f = { X ∈ V ∣ f ( X ) = O } \ker f=\{X\in V\mid f(X)=O\} ker f = { X ∈ V ∣ f ( X ) = O }
的一组基,并求 dim ( ker f ) \dim(\ker f) dim ( ker f ) ,其中 O O O 为零矩阵。
求像空间 Im f \operatorname{Im}f Im f 的一组基,并求 dim ( Im f ) \dim(\operatorname{Im}f) dim ( Im f ) 。
Kai
(1) X = x E 11 + y E 12 + z E 21 + w E 22 X = xE_{11} + yE_{12} + zE_{21} + wE_{22} X = x E 11 + y E 12 + z E 21 + w E 22
(2) U is the set of matrices of the form ( a b 0 0 ) \begin{pmatrix} a & b \\ 0 & 0 \end{pmatrix} ( a 0 b 0 ) where a, b are real numbers.
(i) Let X 1 = ( a 1 b 1 0 0 ) X_1 = \begin{pmatrix} a_1 & b_1 \\ 0 & 0 \end{pmatrix} X 1 = ( a 1 0 b 1 0 ) and X 2 = ( a 2 b 2 0 0 ) X_2 = \begin{pmatrix} a_2 & b_2 \\ 0 & 0 \end{pmatrix} X 2 = ( a 2 0 b 2 0 ) . Then X 1 + X 2 = ( a 1 + a 2 b 1 + b 2 0 0 ) ∈ U X_1 + X_2 = \begin{pmatrix} a_1 + a_2 & b_1 + b_2 \\ 0 & 0 \end{pmatrix} \in U X 1 + X 2 = ( a 1 + a 2 0 b 1 + b 2 0 ) ∈ U since a 1 + a 2 a_1+a_2 a 1 + a 2 and b 1 + b 2 b_1+b_2 b 1 + b 2 are real numbers.
(ii) Let X = ( a b 0 0 ) ∈ U X = \begin{pmatrix} a & b \\ 0 & 0 \end{pmatrix} \in U X = ( a 0 b 0 ) ∈ U and k ∈ R k \in \mathbb{R} k ∈ R . Then k X = ( k a k b 0 0 ) ∈ U kX = \begin{pmatrix} ka & kb \\ 0 & 0 \end{pmatrix} \in U k X = ( ka 0 kb 0 ) ∈ U since k a ka ka and k b kb kb are real numbers.
(3) To prove f f f is a linear transformation, we must show that f ( X 1 + X 2 ) = f ( X 1 ) + f ( X 2 ) f(X_1 + X_2) = f(X_1) + f(X_2) f ( X 1 + X 2 ) = f ( X 1 ) + f ( X 2 ) and f ( c X ) = c f ( X ) f(cX) = c f(X) f ( c X ) = c f ( X ) for all X 1 , X 2 , X ∈ V X_1, X_2, X \in V X 1 , X 2 , X ∈ V and c ∈ R c \in \mathbb{R} c ∈ R .
f ( X 1 + X 2 ) = A ( X 1 + X 2 ) = A X 1 + A X 2 = f ( X 1 ) + f ( X 2 ) f(X_1 + X_2) = A(X_1 + X_2) = AX_1 + AX_2 = f(X_1) + f(X_2) f ( X 1 + X 2 ) = A ( X 1 + X 2 ) = A X 1 + A X 2 = f ( X 1 ) + f ( X 2 ) .
f ( c X ) = A ( c X ) = c ( A X ) = c f ( X ) f(cX) = A(cX) = c(AX) = cf(X) f ( c X ) = A ( c X ) = c ( A X ) = c f ( X ) .
Thus, f f f is a linear transformation.
(4) K e r f = { X ∈ V ∣ f ( X ) = A X = 0 } Ker f = \{ X \in V | f(X) = AX = 0 \} Ker f = { X ∈ V ∣ f ( X ) = A X = 0 } .
Let X = ( x y z w ) X = \begin{pmatrix} x & y \\ z & w \end{pmatrix} X = ( x z y w ) . Then A X = ( 1 2 2 4 ) ( x y z w ) = ( x + 2 z y + 2 w 2 x + 4 z 2 y + 4 w ) = ( 0 0 0 0 ) AX = \begin{pmatrix} 1 & 2 \\ 2 & 4 \end{pmatrix} \begin{pmatrix} x & y \\ z & w \end{pmatrix} = \begin{pmatrix} x + 2z & y + 2w \\ 2x + 4z & 2y + 4w \end{pmatrix} = \begin{pmatrix} 0 & 0 \\ 0 & 0 \end{pmatrix} A X = ( 1 2 2 4 ) ( x z y w ) = ( x + 2 z 2 x + 4 z y + 2 w 2 y + 4 w ) = ( 0 0 0 0 ) .
This implies x + 2 z = 0 x + 2z = 0 x + 2 z = 0 and y + 2 w = 0 y + 2w = 0 y + 2 w = 0 . Thus x = − 2 z x = -2z x = − 2 z and y = − 2 w y = -2w y = − 2 w .
X = ( − 2 z − 2 w z w ) = z ( − 2 0 1 0 ) + w ( 0 − 2 0 1 ) X = \begin{pmatrix} -2z & -2w \\ z & w \end{pmatrix} = z \begin{pmatrix} -2 & 0 \\ 1 & 0 \end{pmatrix} + w \begin{pmatrix} 0 & -2 \\ 0 & 1 \end{pmatrix} X = ( − 2 z z − 2 w w ) = z ( − 2 1 0 0 ) + w ( 0 0 − 2 1 ) .
So, a basis for Ker f is { ( − 2 0 1 0 ) , ( 0 − 2 0 1 ) } \{\begin{pmatrix} -2 & 0 \\ 1 & 0 \end{pmatrix}, \begin{pmatrix} 0 & -2 \\ 0 & 1 \end{pmatrix} \} { ( − 2 1 0 0 ) , ( 0 0 − 2 1 ) } .
The dimension of Ker f is 2.
(5) I m f = { A X ∣ X ∈ V } Im f = \{ AX | X \in V \} I m f = { A X ∣ X ∈ V } . Let X = ( x y z w ) X = \begin{pmatrix} x & y \\ z & w \end{pmatrix} X = ( x z y w ) .
A X = ( 1 2 2 4 ) ( x y z w ) = ( x + 2 z y + 2 w 2 x + 4 z 2 y + 4 w ) = ( x + 2 z ) ( 1 2 ) ( 1 0 ) + ( y + 2 w ) ( 1 2 ) ( 0 1 ) = ( x + 2 z ) ( 1 0 2 0 ) + ( y + 2 w ) ( 0 1 0 2 ) AX = \begin{pmatrix} 1 & 2 \\ 2 & 4 \end{pmatrix} \begin{pmatrix} x & y \\ z & w \end{pmatrix} = \begin{pmatrix} x + 2z & y + 2w \\ 2x + 4z & 2y + 4w \end{pmatrix} = (x+2z) \begin{pmatrix} 1 \\ 2 \end{pmatrix} \begin{pmatrix} 1 & 0 \end{pmatrix} + (y+2w) \begin{pmatrix} 1 \\ 2 \end{pmatrix} \begin{pmatrix} 0 & 1 \end{pmatrix} = (x+2z)\begin{pmatrix} 1 & 0 \\ 2 & 0 \end{pmatrix} + (y+2w) \begin{pmatrix} 0& 1 \\ 0& 2 \end{pmatrix} A X = ( 1 2 2 4 ) ( x z y w ) = ( x + 2 z 2 x + 4 z y + 2 w 2 y + 4 w ) = ( x + 2 z ) ( 1 2 ) ( 1 0 ) + ( y + 2 w ) ( 1 2 ) ( 0 1 ) = ( x + 2 z ) ( 1 2 0 0 ) + ( y + 2 w ) ( 0 0 1 2 ) .
The two coefficients x + 2 z x+2z x + 2 z and y + 2 w y+2w y + 2 w can be chosen independently as arbitrary real numbers. Thus, a basis for I m f Im f I m f is
{ ( 1 0 2 0 ) , ( 0 1 0 2 ) } . \left\{
\begin{pmatrix} 1 & 0 \\ 2 & 0 \end{pmatrix},
\begin{pmatrix} 0 & 1 \\ 0 & 2 \end{pmatrix}
\right\}. { ( 1 2 0 0 ) , ( 0 0 1 2 ) } .
Therefore, dim ( I m f ) = 2 \dim(Im f)=2 dim ( I m f ) = 2 .