《线性代数入门》,梁鑫等编著,清华大学出版社,2022 年.
1 线性映射和矩阵
1.1 基本概念
1.1.1
考虑其中任意一个向量 a \boldsymbol{a} a ,每个 a \boldsymbol{a} a 都有一个向量 b \boldsymbol{b} b 与其共线且反向,若令 a = [ x y ] \boldsymbol{a}=\begin{bmatrix}x\\y\\\end{bmatrix} a = [ x y ] ,则 b = [ x cos π 2 − y sin π 2 x sin π 2 + y cos π 2 ] = [ − x − y ] \boldsymbol{b}=\begin{bmatrix}x\cos{\cfrac{\pi}{2}}-y\sin{\cfrac{\pi}{2}}\\x\sin{\cfrac{\pi}{2}}+y\cos{\cfrac{\pi}{2}}\\\end{bmatrix}=\begin{bmatrix}-x\\-y\\\end{bmatrix} b = ⎣ ⎢ ⎡ x cos 2 π − y sin 2 π x sin 2 π + y cos 2 π ⎦ ⎥ ⎤ = [ − x − y ] (旋转变换),故 a + b = [ x y ] + [ − x − y ] = 0 \boldsymbol{a}+\boldsymbol{b}=\begin{bmatrix}x\\y\\\end{bmatrix}+\begin{bmatrix}-x\\-y\\\end{bmatrix}=\boldsymbol{0} a + b = [ x y ] + [ − x − y ] = 0 ,因此 12 个向量之和为 0 \boldsymbol{0} 0 .
按照 1 中所述可知,余下 11 个向量之和为与 2 点方向相反方向的向量,即 8 点方向向量.由 12 点方向向量,可表示 8 点方向向量为 [ − sin π 3 cos π 3 ] = [ − 3 2 − 1 2 ] \begin{bmatrix}-\sin{\cfrac{\pi}{3}}\\\cos{\cfrac{\pi}{3}}\\\end{bmatrix}=\begin{bmatrix}-\cfrac{\sqrt{3}}{2}\\-\cfrac{1}{2}\\\end{bmatrix} ⎣ ⎢ ⎡ − sin 3 π cos 3 π ⎦ ⎥ ⎤ = ⎣ ⎢ ⎡ − 2 3 − 2 1 ⎦ ⎥ ⎤ ,即余下 11 个向量之和为 [ − 3 2 − 1 2 ] \begin{bmatrix}-\cfrac{\sqrt{3}}{2}\\-\cfrac{1}{2}\\\end{bmatrix} ⎣ ⎢ ⎡ − 2 3 − 2 1 ⎦ ⎥ ⎤ .
设原有向量为 [ x 1 y 1 ] , [ x 2 y 2 ] , ⋯ , [ x 12 y 12 ] \begin{bmatrix}x_1\\y_1\\\end{bmatrix},\begin{bmatrix}x_2\\y_2\\\end{bmatrix},\cdots ,\begin{bmatrix}x_{12}\\y_{12}\\\end{bmatrix} [ x 1 y 1 ] , [ x 2 y 2 ] , ⋯ , [ x 1 2 y 1 2 ] ,则 ∑ i = 1 12 [ x i y i ] = [ x 1 + x 2 + ⋯ + x 12 y 1 + y 2 + ⋯ + y 12 ] = 0 \sum\limits_{i=1}^{12}\begin{bmatrix}x_i\\y_i\\\end{bmatrix}=\begin{bmatrix}x_1+x_2+\cdots +x_{12}\\y_1+y_2+\cdots +y_{12}\\\end{bmatrix}=\boldsymbol{0} i = 1 ∑ 1 2 [ x i y i ] = [ x 1 + x 2 + ⋯ + x 1 2 y 1 + y 2 + ⋯ + y 1 2 ] = 0 . 按照题意,可知每个向量的 y y y 分量均加 1,故此时 12 个向量之和为 ∑ i = 1 12 [ x i y i + 1 ] = [ x 1 + x 2 + ⋯ + x 12 y 1 + y 2 + ⋯ + y 12 + 12 ] = [ 0 12 ] \sum\limits_{i=1}^{12}\begin{bmatrix}x_i\\y_i+1\\\end{bmatrix}=\begin{bmatrix}x_1+x_2+\cdots +x_{12}\\y_1+y_2+\cdots +y_{12}+12\\\end{bmatrix}=\begin{bmatrix}0\\12\\\end{bmatrix} i = 1 ∑ 1 2 [ x i y i + 1 ] = [ x 1 + x 2 + ⋯ + x 1 2 y 1 + y 2 + ⋯ + y 1 2 + 1 2 ] = [ 0 1 2 ] .
1.1.2
共线.
证明 既然 [ a b ] \begin{bmatrix}a\\b\\\end{bmatrix} [ a b ] 与 [ c d ] \begin{bmatrix}c\\d\\\end{bmatrix} [ c d ] 共线,不妨设 [ a b ] = k [ c d ] \begin{bmatrix}a\\b\\\end{bmatrix}=k\begin{bmatrix}c\\d\\\end{bmatrix} [ a b ] = k [ c d ] ,其中 k ∈ R k\in\R k ∈ R ,显然有 a = k c a=kc a = k c ,b = k d b=kd b = k d . 再取 m = c d ∈ R m=\cfrac{c}{d}\in\R m = d c ∈ R ,于是 [ a c ] = [ k c c ] = m [ k d d ] = m [ b d ] \begin{bmatrix}a\\c\\\end{bmatrix}=\begin{bmatrix}kc\\c\\\end{bmatrix}=m\begin{bmatrix}kd\\d\\\end{bmatrix}=m\begin{bmatrix}b\\d\\\end{bmatrix} [ a c ] = [ k c c ] = m [ k d d ] = m [ b d ] ,所以 [ a c ] \begin{bmatrix}a\\c\\\end{bmatrix} [ a c ] 与 [ b d ] \begin{bmatrix}b\\d\\\end{bmatrix} [ b d ] 共线.
1.1.3
验证即可,此处略.
1.1.4
相等表达式有 ( b + a ) + c (\boldsymbol{b}+\boldsymbol{a})+\boldsymbol{c} ( b + a ) + c ,c + ( a + b ) \boldsymbol{c}+(\boldsymbol{a}+\boldsymbol{b}) c + ( a + b ) ,c + ( b + a ) \boldsymbol{c}+(\boldsymbol{b}+\boldsymbol{a}) c + ( b + a ) .
相等表达式有 a + b + c + d \boldsymbol{a}+\boldsymbol{b}+\boldsymbol{c}+\boldsymbol{d} a + b + c + d ,( a + b ) + c + d (\boldsymbol{a}+\boldsymbol{b})+\boldsymbol{c}+\boldsymbol{d} ( a + b ) + c + d ,a + ( b + c ) + d \boldsymbol{a}+(\boldsymbol{b}+\boldsymbol{c})+\boldsymbol{d} a + ( b + c ) + d ,a + b + ( c + d ) \boldsymbol{a}+\boldsymbol{b}+(\boldsymbol{c}+\boldsymbol{d}) a + b + ( c + d ) ,( a + b + c ) + d (\boldsymbol{a}+\boldsymbol{b}+\boldsymbol{c})+\boldsymbol{d} ( a + b + c ) + d ,a + ( b + c + d ) \boldsymbol{a}+(\boldsymbol{b}+\boldsymbol{c}+\boldsymbol{d}) a + ( b + c + d ) ,( a + ( b + c ) ) + d (\boldsymbol{a}+(\boldsymbol{b}+\boldsymbol{c}))+\boldsymbol{d} ( a + ( b + c ) ) + d ,a + ( b + ( c + d ) ) \boldsymbol{a}+(\boldsymbol{b}+(\boldsymbol{c}+\boldsymbol{d})) a + ( b + ( c + d ) ) ,a + ( ( b + c ) + d ) \boldsymbol{a}+((\boldsymbol{b}+\boldsymbol{c})+\boldsymbol{d}) a + ( ( b + c ) + d ) .
1.1.5
不是. 因为 f ( x 1 ) + f ( x 2 ) = x 1 + 1 + x 2 + 1 = x 1 + x 2 + 2 f(x_1)+f(x_2)=x_1+1+x_2+1=x_1+x_2+2 f ( x 1 ) + f ( x 2 ) = x 1 + 1 + x 2 + 1 = x 1 + x 2 + 2 ,而 f ( x 1 + x 2 ) = x 1 + x 2 + 1 f(x_1+x_2)=x_1+x_2+1 f ( x 1 + x 2 ) = x 1 + x 2 + 1 .
是.
是.
不是. 因为 f ( x 1 ) + f ( x 2 ) = 1 + 1 = 2 f(x_1)+f(x_2)=1+1=2 f ( x 1 ) + f ( x 2 ) = 1 + 1 = 2 ,而 f ( x 1 + x 2 ) = 1 f(x_1+x_2)=1 f ( x 1 + x 2 ) = 1 .
不是. 因为 f ( x 1 ) + f ( x 2 ) = x 1 2 + x 2 2 f(x_1)+f(x_2)=x_1^2+x_2^2 f ( x 1 ) + f ( x 2 ) = x 1 2 + x 2 2 ,而 f ( x 1 + x 2 ) = ( x 1 + x 2 ) 2 = x 1 2 + 2 x 1 x 2 + x 2 2 f(x_1+x_2)=(x_1+x_2)^2=x_1^2+2x_1x_2+x_2^2 f ( x 1 + x 2 ) = ( x 1 + x 2 ) 2 = x 1 2 + 2 x 1 x 2 + x 2 2 .
不是. 因为 f ( x 1 ) + f ( x 2 ) = 2 x 1 + 2 x 2 f(x_1)+f(x_2)=2^{x_1}+2^{x_2} f ( x 1 ) + f ( x 2 ) = 2 x 1 + 2 x 2 ,而 f ( x 1 + x 2 ) = 2 x 1 + x 2 f(x_1+x_2)=2^{x_1+x_2} f ( x 1 + x 2 ) = 2 x 1 + x 2 .
是.
不是. 因为 f ( [ x 1 y 1 ] ) + f ( [ x 2 y 2 ] ) = [ x 1 + 1 y 1 − x 1 2 x 1 ] + [ x 2 + 1 y 2 − x 2 2 x 2 ] = [ x 1 + x 2 + 2 y 1 + y 2 − x 1 − x 2 2 x 1 + 2 x 2 ] f(\begin{bmatrix}x_1\\y_1\end{bmatrix})+f(\begin{bmatrix}x_2\\y_2\end{bmatrix})=\begin{bmatrix}x_1+1\\y_1-x_1\\2x_1\end{bmatrix}+\begin{bmatrix}x_2+1\\y_2-x_2\\2x_2\end{bmatrix}=\begin{bmatrix}x_1+x_2+2\\y_1+y_2-x_1-x_2\\2x_1+2x_2\end{bmatrix} f ( [ x 1 y 1 ] ) + f ( [ x 2 y 2 ] ) = ⎣ ⎡ x 1 + 1 y 1 − x 1 2 x 1 ⎦ ⎤ + ⎣ ⎡ x 2 + 1 y 2 − x 2 2 x 2 ⎦ ⎤ = ⎣ ⎡ x 1 + x 2 + 2 y 1 + y 2 − x 1 − x 2 2 x 1 + 2 x 2 ⎦ ⎤ ,而 f ( [ x 1 + x 2 y 1 + y 2 ] ) = [ x 1 + x 2 + 1 y 1 + y 2 − x 1 − x 2 2 x 1 + 2 x 2 ] f(\begin{bmatrix}x_1+x_2\\y_1+y_2\end{bmatrix})=\begin{bmatrix}x_1+x_2+1\\y_1+y_2-x_1-x_2\\2x_1+2x_2\end{bmatrix} f ( [ x 1 + x 2 y 1 + y 2 ] ) = ⎣ ⎡ x 1 + x 2 + 1 y 1 + y 2 − x 1 − x 2 2 x 1 + 2 x 2 ⎦ ⎤ .
1.1.6
证明 因为 f f f 是线性映射,所以 f ( x ) = f ( 1 ⋅ x ) = f ( 1 ) ⋅ x f(x)=f(1\cdot x)=f(1)\cdot x f ( x ) = f ( 1 ⋅ x ) = f ( 1 ) ⋅ x . 显然,f ( 1 ) ∈ R f(1)\in\R f ( 1 ) ∈ R ,那么只需要令 k = f ( 1 ) k=f(1) k = f ( 1 ) ,就找到了一个实数 k k k 使得 f ( x ) = k x f(x)=kx f ( x ) = k x .
1.1.7
证明 先证充分性. 既然 f f f 是线性映射,那么,f ( [ x 1 y 1 z 1 ] + [ x 2 y 2 z 2 ] ) = [ g ( x 1 + x 2 , y 1 + y 2 , z 1 + z 2 ) h ( x 1 + x 2 , y 1 + y 2 , z 1 + z 2 ) ] = f ( [ x 1 y 1 z 1 ] ) + f ( [ x 2 y 2 z 2 ] ) = [ g ( x 1 , y 1 , z 1 ) h ( x 1 , y 1 , z 1 ) ] + [ g ( x 2 , y 2 , z 2 ) h ( x 2 , y 2 , z 2 ) ] f(\begin{bmatrix}x_1\\y_1\\z_1\end{bmatrix}+\begin{bmatrix}x_2\\y_2\\z_2\end{bmatrix})=\begin{bmatrix}g(x_1+x_2,y_1+y_2,z_1+z_2)\\h(x_1+x_2,y_1+y_2,z_1+z_2)\end{bmatrix}=f(\begin{bmatrix}x_1\\y_1\\z_1\end{bmatrix})+f(\begin{bmatrix}x_2\\y_2\\z_2\end{bmatrix})=\begin{bmatrix}g(x_1,y_1,z_1)\\h(x_1,y_1,z_1)\end{bmatrix}+\begin{bmatrix}g(x_2,y_2,z_2)\\h(x_2,y_2,z_2)\end{bmatrix} f ( ⎣ ⎡ x 1 y 1 z 1 ⎦ ⎤ + ⎣ ⎡ x 2 y 2 z 2 ⎦ ⎤ ) = [ g ( x 1 + x 2 , y 1 + y 2 , z 1 + z 2 ) h ( x 1 + x 2 , y 1 + y 2 , z 1 + z 2 ) ] = f ( ⎣ ⎡ x 1 y 1 z 1 ⎦ ⎤ ) + f ( ⎣ ⎡ x 2 y 2 z 2 ⎦ ⎤ ) = [ g ( x 1 , y 1 , z 1 ) h ( x 1 , y 1 , z 1 ) ] + [ g ( x 2 , y 2 , z 2 ) h ( x 2 , y 2 , z 2 ) ] ,f ( k [ x y z ] ) = [ g ( k x , k y , k z ) h ( k x , k y , k z ) ] = k f ( [ x y z ] ) = k [ g ( x , y , z ) h ( x , y , z ) ] f(k\begin{bmatrix}x\\y\\z\end{bmatrix})=\begin{bmatrix}g(kx,ky,kz)\\h(kx,ky,kz)\end{bmatrix}=kf(\begin{bmatrix}x\\y\\z\end{bmatrix})=k\begin{bmatrix}g(x,y,z)\\h(x,y,z)\end{bmatrix} f ( k ⎣ ⎡ x y z ⎦ ⎤ ) = [ g ( k x , k y , k z ) h ( k x , k y , k z ) ] = k f ( ⎣ ⎡ x y z ⎦ ⎤ ) = k [ g ( x , y , z ) h ( x , y , z ) ] . 综合两式,显然 g , h g,h g , h 均为线性映射.
再证必要性. 根据上述证明中的等式易得 f f f 是线性映射.
1.1.8
不存在. 考虑 f ( x 2 ) = b 2 f(x_2)=b_2 f ( x 2 ) = b 2 ,有 f ( x 2 ) = f ( x 1 + x 3 ) = f ( x 1 ) + f ( x 3 ) = b 1 + b 3 f(x_2)=f(x_1+x_3)=f(x_1)+f(x_3)=b_1+b_3 f ( x 2 ) = f ( x 1 + x 3 ) = f ( x 1 ) + f ( x 3 ) = b 1 + b 3 ,显然 b 2 ≠ b 1 + b 3 b_2\not ={b_1+b_3} b 2 = b 1 + b 3 ,因而这不是线性映射.
1.1.9
是.
是.
不是.
1.1.10
f f f 是线性映射. 由题意,f ( [ b 1 b 2 b 3 ] ) = [ a 1 a 2 a 3 ] ⋅ [ b 1 b 2 b 3 ] = a 1 b 1 + a 2 b 2 + a 3 b 3 f(\begin{bmatrix}b_1\\b_2\\b_3\end{bmatrix})=\begin{bmatrix}a_1\\a_2\\a_3\end{bmatrix}\cdot\begin{bmatrix}b_1\\b_2\\b_3\end{bmatrix}=a_1b_1+a_2b_2+a_3b_3 f ( ⎣ ⎡ b 1 b 2 b 3 ⎦ ⎤ ) = ⎣ ⎡ a 1 a 2 a 3 ⎦ ⎤ ⋅ ⎣ ⎡ b 1 b 2 b 3 ⎦ ⎤ = a 1 b 1 + a 2 b 2 + a 3 b 3 . 那么 f ( [ b 1 b 2 b 3 ] + [ c 1 c 2 c 3 ] ) = [ a 1 a 2 a 3 ] ⋅ [ b 1 + c 1 b 2 + c 2 b 3 + c 3 ] = a 1 b 1 + a 2 b 2 + a 3 b 3 + a 1 c 1 + a 2 c 2 + a 3 c 3 = [ a 1 a 2 a 3 ] ⋅ [ b 1 b 2 b 3 ] + [ a 1 a 2 a 3 ] ⋅ [ c 1 c 2 c 3 ] = f ( [ b 1 b 2 b 3 ] ) + f ( [ c 1 c 2 c 3 ] ) f(\begin{bmatrix}b_1\\b_2\\b_3\end{bmatrix}+\begin{bmatrix}c_1\\c_2\\c_3\end{bmatrix})=\begin{bmatrix}a_1\\a_2\\a_3\end{bmatrix}\cdot\begin{bmatrix}b_1+c_1\\b_2+c_2\\b_3+c_3\end{bmatrix}=a_1b_1+a_2b_2+a_3b_3+a_1c_1+a_2c_2+a_3c_3=\begin{bmatrix}a_1\\a_2\\a_3\end{bmatrix}\cdot\begin{bmatrix}b_1\\b_2\\b_3\end{bmatrix}+\begin{bmatrix}a_1\\a_2\\a_3\end{bmatrix}\cdot\begin{bmatrix}c_1\\c_2\\c_3\end{bmatrix}=f(\begin{bmatrix}b_1\\b_2\\b_3\end{bmatrix})+f(\begin{bmatrix}c_1\\c_2\\c_3\end{bmatrix}) f ( ⎣ ⎡ b 1 b 2 b 3 ⎦ ⎤ + ⎣ ⎡ c 1 c 2 c 3 ⎦ ⎤ ) = ⎣ ⎡ a 1 a 2 a 3 ⎦ ⎤ ⋅ ⎣ ⎡ b 1 + c 1 b 2 + c 2 b 3 + c 3 ⎦ ⎤ = a 1 b 1 + a 2 b 2 + a 3 b 3 + a 1 c 1 + a 2 c 2 + a 3 c 3 = ⎣ ⎡ a 1 a 2 a 3 ⎦ ⎤ ⋅ ⎣ ⎡ b 1 b 2 b 3 ⎦ ⎤ + ⎣ ⎡ a 1 a 2 a 3 ⎦ ⎤ ⋅ ⎣ ⎡ c 1 c 2 c 3 ⎦ ⎤ = f ( ⎣ ⎡ b 1 b 2 b 3 ⎦ ⎤ ) + f ( ⎣ ⎡ c 1 c 2 c 3 ⎦ ⎤ ) ,f ( k [ b 1 b 2 b 3 ] ) = [ a 1 a 2 a 3 ] ⋅ [ k b 1 k b 2 k b 3 ] = k a 1 b 1 + k a 2 b 2 + k a 3 b 3 = k ( a 1 b 1 + a 2 b 2 + a 3 b 3 ) = k f ( [ b 1 b 2 b 3 ] ) f(k\begin{bmatrix}b_1\\b_2\\b_3\end{bmatrix})=\begin{bmatrix}a_1\\a_2\\a_3\end{bmatrix}\cdot\begin{bmatrix}kb_1\\kb_2\\kb_3\end{bmatrix}=ka_1b_1+ka_2b_2+ka_3b_3=k(a_1b_1+a_2b_2+a_3b_3)=kf(\begin{bmatrix}b_1\\b_2\\b_3\end{bmatrix}) f ( k ⎣ ⎡ b 1 b 2 b 3 ⎦ ⎤ ) = ⎣ ⎡ a 1 a 2 a 3 ⎦ ⎤ ⋅ ⎣ ⎡ k b 1 k b 2 k b 3 ⎦ ⎤ = k a 1 b 1 + k a 2 b 2 + k a 3 b 3 = k ( a 1 b 1 + a 2 b 2 + a 3 b 3 ) = k f ( ⎣ ⎡ b 1 b 2 b 3 ⎦ ⎤ ) . 所以 f f f 是线性映射.
g g g 是线性映射. 由题意. g ( [ b 1 b 2 b 3 ] ) = [ a 1 a 2 a 3 ] × [ b 1 b 2 b 3 ] = [ a 2 b 3 − a 3 b 2 a 3 b 1 − a 1 b 3 a 1 b 2 − a 2 b 1 ] g(\begin{bmatrix}b_1\\b_2\\b_3\end{bmatrix})=\begin{bmatrix}a_1\\a_2\\a_3\end{bmatrix}\times\begin{bmatrix}b_1\\b_2\\b_3\end{bmatrix}=\begin{bmatrix}a_2b_3-a_3b_2\\a_3b_1-a_1b_3\\a_1b_2-a_2b_1\end{bmatrix} g ( ⎣ ⎡ b 1 b 2 b 3 ⎦ ⎤ ) = ⎣ ⎡ a 1 a 2 a 3 ⎦ ⎤ × ⎣ ⎡ b 1 b 2 b 3 ⎦ ⎤ = ⎣ ⎡ a 2 b 3 − a 3 b 2 a 3 b 1 − a 1 b 3 a 1 b 2 − a 2 b 1 ⎦ ⎤ . 那么 g ( [ b 1 b 2 b 3 ] + [ c 1 c 2 c 3 ] ) = [ a 1 a 2 a 3 ] × [ b 1 + c 1 b 2 + c 2 b 3 + c 3 ] = [ a 2 ( b 3 + c 3 ) − a 3 ( b 2 + c 2 ) a 3 ( b 1 + c 1 ) − a 1 ( b 3 + c 3 ) a 1 ( b 2 + c 2 ) − a 2 ( b 1 + c 1 ) ] = [ a 2 b 3 − a 3 b 2 a 3 b 1 − a 1 b 3 a 1 b 2 − a 2 b 1 ] + [ a 2 c 3 − a 3 c 2 a 3 c 1 − a 1 c 3 a 1 c 2 − a 2 c 1 ] = g ( [ b 1 b 2 b 3 ] ) + g ( [ c 1 c 2 c 3 ] ) g(\begin{bmatrix}b_1\\b_2\\b_3\end{bmatrix}+\begin{bmatrix}c_1\\c_2\\c_3\end{bmatrix})=\begin{bmatrix}a_1\\a_2\\a_3\end{bmatrix}\times\begin{bmatrix}b_1+c_1\\b_2+c_2\\b_3+c_3\end{bmatrix}=\begin{bmatrix}a_2(b_3+c_3)-a_3(b_2+c_2)\\a_3(b_1+c_1)-a_1(b_3+c_3)\\a_1(b_2+c_2)-a_2(b_1+c_1)\end{bmatrix}=\begin{bmatrix}a_2b_3-a_3b_2\\a_3b_1-a_1b_3\\a_1b_2-a_2b_1\end{bmatrix}+\begin{bmatrix}a_2c_3-a_3c_2\\a_3c_1-a_1c_3\\a_1c_2-a_2c_1\end{bmatrix}=g(\begin{bmatrix}b_1\\b_2\\b_3\end{bmatrix})+g(\begin{bmatrix}c_1\\c_2\\c_3\end{bmatrix}) g ( ⎣ ⎡ b 1 b 2 b 3 ⎦ ⎤ + ⎣ ⎡ c 1 c 2 c 3 ⎦ ⎤ ) = ⎣ ⎡ a 1 a 2 a 3 ⎦ ⎤ × ⎣ ⎡ b 1 + c 1 b 2 + c 2 b 3 + c 3 ⎦ ⎤ = ⎣ ⎡ a 2 ( b 3 + c 3 ) − a 3 ( b 2 + c 2 ) a 3 ( b 1 + c 1 ) − a 1 ( b 3 + c 3 ) a 1 ( b 2 + c 2 ) − a 2 ( b 1 + c 1 ) ⎦ ⎤ = ⎣ ⎡ a 2 b 3 − a 3 b 2 a 3 b 1 − a 1 b 3 a 1 b 2 − a 2 b 1 ⎦ ⎤ + ⎣ ⎡ a 2 c 3 − a 3 c 2 a 3 c 1 − a 1 c 3 a 1 c 2 − a 2 c 1 ⎦ ⎤ = g ( ⎣ ⎡ b 1 b 2 b 3 ⎦ ⎤ ) + g ( ⎣ ⎡ c 1 c 2 c 3 ⎦ ⎤ ) ,g ( k [ b 1 b 2 b 3 ] ) = [ a 1 a 2 a 3 ] × [ k b 1 k b 2 k b 3 ] = [ k a 2 b 3 − k a 3 b 2 k a 3 b 1 − k a 1 b 3 k a 1 b 2 − k a 2 b 1 ] = k [ a 2 b 3 − a 3 b 2 a 3 b 1 − a 1 b 3 a 1 b 2 − a 2 b 1 ] = k g ( [ b 1 b 2 b 3 ] ) g(k\begin{bmatrix}b_1\\b_2\\b_3\end{bmatrix})=\begin{bmatrix}a_1\\a_2\\a_3\end{bmatrix}\times\begin{bmatrix}kb_1\\kb_2\\kb_3\end{bmatrix}=\begin{bmatrix}ka_2b_3-ka_3b_2\\ka_3b_1-ka_1b_3\\ka_1b_2-ka_2b_1\end{bmatrix}=k\begin{bmatrix}a_2b_3-a_3b_2\\a_3b_1-a_1b_3\\a_1b_2-a_2b_1\end{bmatrix}=kg(\begin{bmatrix}b_1\\b_2\\b_3\end{bmatrix}) g ( k ⎣ ⎡ b 1 b 2 b 3 ⎦ ⎤ ) = ⎣ ⎡ a 1 a 2 a 3 ⎦ ⎤ × ⎣ ⎡ k b 1 k b 2 k b 3 ⎦ ⎤ = ⎣ ⎡ k a 2 b 3 − k a 3 b 2 k a 3 b 1 − k a 1 b 3 k a 1 b 2 − k a 2 b 1 ⎦ ⎤ = k ⎣ ⎡ a 2 b 3 − a 3 b 2 a 3 b 1 − a 1 b 3 a 1 b 2 − a 2 b 1 ⎦ ⎤ = k g ( ⎣ ⎡ b 1 b 2 b 3 ⎦ ⎤ ) . 所以 g g g 是线性映射.
1.1.11
确定. S = 1 S=1 S = 1 .
确定. S = 1 S=1 S = 1 .
确定. S = 0 S=0 S = 0 .
确定. S = 3 S=3 S = 3 .
确定. S = 1 S=1 S = 1 .(错切变换不会改变图形的面积 )
1.1.12
证明 f ( [ x 1 y 1 ] ) + f ( [ x 2 y 2 ] ) = [ − y 1 + 1 x 1 + 2 ] + [ − y 2 + 1 x 2 + 2 ] = [ − y 1 − y 2 + 2 x 1 + x 2 + 4 ] f(\begin{bmatrix}x_1\\y_1\end{bmatrix})+f(\begin{bmatrix}x_2\\y_2\end{bmatrix})=\begin{bmatrix}-y_1+1\\x_1+2\end{bmatrix}+\begin{bmatrix}-y_2+1\\x_2+2\end{bmatrix}=\begin{bmatrix}-y_1-y_2+2\\x_1+x_2+4\end{bmatrix} f ( [ x 1 y 1 ] ) + f ( [ x 2 y 2 ] ) = [ − y 1 + 1 x 1 + 2 ] + [ − y 2 + 1 x 2 + 2 ] = [ − y 1 − y 2 + 2 x 1 + x 2 + 4 ] ,然而,f ( [ x 1 y 1 ] + [ x 2 y 2 ] ) = [ − y 1 − y 2 + 1 x 1 + x 2 + 2 ] f(\begin{bmatrix}x_1\\y_1\end{bmatrix}+\begin{bmatrix}x_2\\y_2\end{bmatrix})=\begin{bmatrix}-y_1-y_2+1\\x_1+x_2+2\end{bmatrix} f ( [ x 1 y 1 ] + [ x 2 y 2 ] ) = [ − y 1 − y 2 + 1 x 1 + x 2 + 2 ] ,由 f ( [ x 1 y 1 ] ) + f ( [ x 2 y 2 ] ) ≠ f ( [ x 1 y 1 ] + [ x 2 y 2 ] ) f(\begin{bmatrix}x_1\\y_1\end{bmatrix})+f(\begin{bmatrix}x_2\\y_2\end{bmatrix})\not ={f(\begin{bmatrix}x_1\\y_1\end{bmatrix}+\begin{bmatrix}x_2\\y_2\end{bmatrix})} f ( [ x 1 y 1 ] ) + f ( [ x 2 y 2 ] ) = f ( [ x 1 y 1 ] + [ x 2 y 2 ] ) 可知 f f f 不是线性映射.
不妨令 g ( [ x y ] ) = [ x + 1 y + 1 ] , h ( [ x y ] ) = [ k x y ] g(\begin{bmatrix}x\\y\end{bmatrix})=\begin{bmatrix}x+1\\y+1\end{bmatrix},h(\begin{bmatrix}x\\y\end{bmatrix})=\begin{bmatrix}kx\\y\end{bmatrix} g ( [ x y ] ) = [ x + 1 y + 1 ] , h ( [ x y ] ) = [ k x y ] ,那么 f ( [ x y ] ) = g ∘ h ( [ x y ] ) = g ( [ k x y ] ) = [ k x + 1 y + 1 ] f(\begin{bmatrix}x\\y\end{bmatrix})=g\circ h(\begin{bmatrix}x\\y\end{bmatrix})=g(\begin{bmatrix}kx\\y\end{bmatrix})=\begin{bmatrix}kx+1\\y+1\end{bmatrix} f ( [ x y ] ) = g ∘ h ( [ x y ] ) = g ( [ k x y ] ) = [ k x + 1 y + 1 ] .(这种平移变换和线性变换的复合叫做仿射变换 )
1.1.13
是线性映射.
证明 形如 f ( x + y ) = f ( x ) + f ( y ) f(x+y)=f(x)+f(y) f ( x + y ) = f ( x ) + f ( y ) 的方程被称为加性 Cauchy 方程 ,下面给出求解过程,显然其求解结果可以证明本题:
若 x , y ∈ Z + x,y \in \Z^+ x , y ∈ Z + ,不妨取 x = 1 , y = 1 x=1,y=1 x = 1 , y = 1 ,容易有 f ( 2 ) = 2 f ( 1 ) f(2)=2f(1) f ( 2 ) = 2 f ( 1 ) . 再取 x = 2 , y = 1 x=2,y=1 x = 2 , y = 1 ,则有 f ( 3 ) = f ( 2 ) + f ( 1 ) = 3 f ( 1 ) f(3)=f(2)+f(1)=3f(1) f ( 3 ) = f ( 2 ) + f ( 1 ) = 3 f ( 1 ) . 如此递归,可以得到 f ( n ) = n f ( 1 ) f(n)=nf(1) f ( n ) = n f ( 1 ) .
若 x , y ∈ Z x,y \in \Z x , y ∈ Z ,取 x = 1 , y = 0 x=1,y=0 x = 1 , y = 0 ,于是有 f ( 1 ) = f ( 0 ) + f ( 1 ) f(1)=f(0)+f(1) f ( 1 ) = f ( 0 ) + f ( 1 ) ,所以 f ( 0 ) = 0 f(0)=0 f ( 0 ) = 0 . 显然有 0 = f ( 0 ) = f ( x + ( − x ) ) = f ( x ) + f ( − x ) 0=f(0)=f(x+(-x))=f(x)+f(-x) 0 = f ( 0 ) = f ( x + ( − x ) ) = f ( x ) + f ( − x ) ,所以 f ( x ) f(x) f ( x ) 是一个奇函数,于是 ∀ x ∈ Z \forall x\in\Z ∀ x ∈ Z ,有 f ( x ) = x f ( 1 ) f(x)=xf(1) f ( x ) = x f ( 1 ) 成立.
若 x , y ∈ Q x,y \in \mathbb{Q} x , y ∈ Q ,取 x = y = p q x=y=\cfrac{p}{q} x = y = q p ,其中 p , q ∈ Z p,q\in\Z p , q ∈ Z ,容易有 f ( 2 p q ) = 2 f ( p q ) f(\cfrac{2p}{q})=2f(\cfrac{p}{q}) f ( q 2 p ) = 2 f ( q p ) . 按照已有结论,可以知道先证中的取值 1 具有一般性,故 q f ( p q ) = f ( p q q ) = f ( p ) = p f ( 1 ) qf(\cfrac{p}{q})=f(\cfrac{pq}{q})=f(p)=pf(1) q f ( q p ) = f ( q p q ) = f ( p ) = p f ( 1 ) ,所以 f ( p q ) = p q f ( 1 ) f(\cfrac{p}{q})=\cfrac{p}{q}f(1) f ( q p ) = q p f ( 1 ) .
接下来推广至 R \R R . 因为 f f f 是连续的,自然有 lim x → x 0 f ( x ) = f ( x 0 ) \lim\limits_{x\rightarrow x_0}f(x)=f(x_0) x → x 0 lim f ( x ) = f ( x 0 ) . 其中 x 0 ∈ R x_0\in\R x 0 ∈ R . 取一个收敛于 x 0 x_0 x 0 的有理 Cauchy 列,记其为 q 1 , q 2 , ⋯ , q n q_1, q_2,\cdots,q_n q 1 , q 2 , ⋯ , q n ,那么有 f ( x 0 ) = lim n → + ∞ f ( q n ) = lim n → + ∞ q n f ( 1 ) = x 0 f ( 1 ) f(x_0)=\lim\limits_{n\rightarrow+\infin}f(q_n)=\lim\limits_{n\rightarrow+\infin}q_nf(1)=x_0f(1) f ( x 0 ) = n → + ∞ lim f ( q n ) = n → + ∞ lim q n f ( 1 ) = x 0 f ( 1 ) . 此函数符合线性映射定义,显然是线性映射.