早稲田大学 創造理工学研究科 経営システム工学専攻 2021年8月実施 线性代数
Author
思齐塾 , 祭音Myyura
Description
R R R を実数集合 (set of real numbers) とし、
A = [ 1 1 2 2 − 1 1 3 1 3 2 − 1 0 ] A = \begin{bmatrix} 1 & 1 & 2 \\ 2 & -1 & 1 \\ 3 & 1 & 3 \\ 2 & -1 & 0 \end{bmatrix} A = 1 2 3 2 1 − 1 1 − 1 2 1 3 0
とし、線形写像 (linear transformation) f : R 3 → R 4 f: R^3 \to R^4 f : R 3 → R 4 が
f ( x ) = A x , x ∈ R 3 f(\mathbf{x}) = A\mathbf{x}, \quad \mathbf{x} \in R^3 f ( x ) = A x , x ∈ R 3
と表せるとする。このとき、 f ( x ) = f ( y ) f(\mathbf{x}) = f(\mathbf{y}) f ( x ) = f ( y ) ならば x = y \mathbf{x} = \mathbf{y} x = y であることを示せ。
题目描述
记实数集为 R \mathbb R R ,令
A = [ 1 1 2 2 − 1 1 3 1 3 2 − 1 0 ] , A=\begin{bmatrix}
1&1&2\\
2&-1&1\\
3&1&3\\
2&-1&0
\end{bmatrix}, A = 1 2 3 2 1 − 1 1 − 1 2 1 3 0 ,
并定义线性映射
f : R 3 → R 4 , f ( x ) = A x ( x ∈ R 3 ) . f:\mathbb R^3\to\mathbb R^4,\qquad
f(\mathbf x)=A\mathbf x\quad(\mathbf x\in\mathbb R^3). f : R 3 → R 4 , f ( x ) = A x ( x ∈ R 3 ) .
证明:若 f ( x ) = f ( y ) f(\mathbf x)=f(\mathbf y) f ( x ) = f ( y ) ,则 x = y \mathbf x=\mathbf y x = y 。
Kai
We want to show that if f ( x ) = f ( y ) f(\mathbf{x}) = f(\mathbf{y}) f ( x ) = f ( y ) , then x = y \mathbf{x} = \mathbf{y} x = y . This is equivalent to showing that f ( x ) = A x f(\mathbf{x}) = A\mathbf{x} f ( x ) = A x is injective (one-to-one).
If f ( x ) = f ( y ) f(\mathbf{x}) = f(\mathbf{y}) f ( x ) = f ( y ) , then A x = A y A\mathbf{x} = A\mathbf{y} A x = A y .
Thus, A x − A y = 0 A\mathbf{x} - A\mathbf{y} = \mathbf{0} A x − A y = 0 , which means A ( x − y ) = 0 A(\mathbf{x} - \mathbf{y}) = \mathbf{0} A ( x − y ) = 0 .
Let z = x − y \mathbf{z} = \mathbf{x} - \mathbf{y} z = x − y . We want to show that A z = 0 A\mathbf{z} = \mathbf{0} A z = 0 implies z = 0 \mathbf{z} = \mathbf{0} z = 0 .
In other words, we want to show that the kernel (null space) of A A A is only the zero vector. We can determine this by row reducing A A A .
A = [ 1 1 2 2 − 1 1 3 1 3 2 − 1 0 ] ∼ [ 1 1 2 0 − 3 − 3 0 − 2 − 3 0 − 3 − 4 ] ∼ [ 1 1 2 0 1 1 0 − 2 − 3 0 − 3 − 4 ] ∼ [ 1 1 2 0 1 1 0 0 − 1 0 0 − 1 ] ∼ [ 1 1 2 0 1 1 0 0 1 0 0 0 ] ∼ [ 1 1 0 0 1 0 0 0 1 0 0 0 ] ∼ [ 1 0 0 0 1 0 0 0 1 0 0 0 ] A = \begin{bmatrix} 1 & 1 & 2 \\ 2 & -1 & 1 \\ 3 & 1 & 3 \\ 2 & -1 & 0 \end{bmatrix} \sim \begin{bmatrix} 1 & 1 & 2 \\ 0 & -3 & -3 \\ 0 & -2 & -3 \\ 0 & -3 & -4 \end{bmatrix} \sim \begin{bmatrix} 1 & 1 & 2 \\ 0 & 1 & 1 \\ 0 & -2 & -3 \\ 0 & -3 & -4 \end{bmatrix} \sim \begin{bmatrix} 1 & 1 & 2 \\ 0 & 1 & 1 \\ 0 & 0 & -1 \\ 0 & 0 & -1 \end{bmatrix} \sim \begin{bmatrix} 1 & 1 & 2 \\ 0 & 1 & 1 \\ 0 & 0 & 1 \\ 0 & 0 & 0 \end{bmatrix} \sim \begin{bmatrix} 1 & 1 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \\ 0 & 0 & 0 \end{bmatrix} \sim \begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \\ 0 & 0 & 0 \end{bmatrix} A = 1 2 3 2 1 − 1 1 − 1 2 1 3 0 ∼ 1 0 0 0 1 − 3 − 2 − 3 2 − 3 − 3 − 4 ∼ 1 0 0 0 1 1 − 2 − 3 2 1 − 3 − 4 ∼ 1 0 0 0 1 1 0 0 2 1 − 1 − 1 ∼ 1 0 0 0 1 1 0 0 2 1 1 0 ∼ 1 0 0 0 1 1 0 0 0 0 1 0 ∼ 1 0 0 0 0 1 0 0 0 0 1 0
Since the row reduced echelon form of A A A has rank 3, which is the number of columns, the kernel of A A A is { 0 } \{\mathbf{0}\} { 0 } . Thus, A z = 0 A\mathbf{z} = \mathbf{0} A z = 0 implies z = 0 \mathbf{z} = \mathbf{0} z = 0 . Since z = x − y \mathbf{z} = \mathbf{x} - \mathbf{y} z = x − y , we have x − y = 0 \mathbf{x} - \mathbf{y} = \mathbf{0} x − y = 0 , so x = y \mathbf{x} = \mathbf{y} x = y .