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早稲田大学 創造理工学研究科 経営システム工学専攻 2014年8月実施 线性代数

Author​

思齐塾, 祭音Myyura

Description​

次の行列(matrix)の固有値 (eigenvalue) と固有ベクトル (eigenvector) をすべて求めよ。

[−1001203−41]\begin{bmatrix} -1 & 0 & 0 \\ 1 & 2 & 0 \\ 3 & -4 & 1 \end{bmatrix}

题目描述​

求矩阵

[−1001203−41]\begin{bmatrix} -1&0&0\\ 1&2&0\\ 3&-4&1 \end{bmatrix}

的全部特征值及其对应的全部特征向量。

Kai​

Let

A=[−1001203−41]A = \begin{bmatrix} -1 & 0 & 0 \\ 1 & 2 & 0 \\ 3 & -4 & 1 \end{bmatrix}

.

To find the eigenvalues, we need to solve the characteristic equation ∣A−λI∣=0|A - \lambda I| = 0 , where II is the identity matrix.

A−λI=[−1−λ0012−λ03−41−λ]A - \lambda I = \begin{bmatrix} -1 - \lambda & 0 & 0 \\ 1 & 2 - \lambda & 0 \\ 3 & -4 & 1 - \lambda \end{bmatrix}

∣A−λI∣=(−1−λ)(2−λ)(1−λ)=0|A - \lambda I| = (-1 - \lambda)(2 - \lambda)(1 - \lambda) = 0

So, the eigenvalues are λ1=−1\lambda_1 = -1 , λ2=2\lambda_2 = 2 , and λ3=1\lambda_3 = 1 .

Now, let's find the eigenvectors for each eigenvalue.

For λ1=−1\lambda_1 = -1 : (A−λ1I)v1=0(A - \lambda_1 I)v_1 = 0

[0001303−42][x1x2x3]=[000]\begin{bmatrix} 0 & 0 & 0 \\ 1 & 3 & 0 \\ 3 & -4 & 2 \end{bmatrix} \begin{bmatrix} x_1 \\ x_2 \\ x_3 \end{bmatrix} = \begin{bmatrix} 0 \\ 0 \\ 0 \end{bmatrix}

x1+3x2=0⇒x1=−3x2x_1 + 3x_2 = 0 \Rightarrow x_1 = -3x_2 3x1−4x2+2x3=0⇒3(−3x2)−4x2+2x3=0⇒−13x2+2x3=0⇒x3=132x23x_1 - 4x_2 + 2x_3 = 0 \Rightarrow 3(-3x_2) - 4x_2 + 2x_3 = 0 \Rightarrow -13x_2 + 2x_3 = 0 \Rightarrow x_3 = \frac{13}{2}x_2

Let x2=2x_2 = 2 , then x1=−6x_1 = -6 and x3=13x_3 = 13 . So,

v1=[−6213]v_1 = \begin{bmatrix} -6 \\ 2 \\ 13 \end{bmatrix}

For λ2=2\lambda_2 = 2 : (A−λ2I)v2=0(A - \lambda_2 I)v_2 = 0

[−3001003−4−1][x1x2x3]=[000]\begin{bmatrix} -3 & 0 & 0 \\ 1 & 0 & 0 \\ 3 & -4 & -1 \end{bmatrix} \begin{bmatrix} x_1 \\ x_2 \\ x_3 \end{bmatrix} = \begin{bmatrix} 0 \\ 0 \\ 0 \end{bmatrix}

−3x1=0⇒x1=0-3x_1 = 0 \Rightarrow x_1 = 0 x1=0x_1 = 0 3x1−4x2−x3=0⇒−4x2−x3=0⇒x3=−4x23x_1 - 4x_2 - x_3 = 0 \Rightarrow -4x_2 - x_3 = 0 \Rightarrow x_3 = -4x_2

Let x2=1x_2 = 1 , then x3=−4x_3 = -4 . So,

v2=[01−4]v_2 = \begin{bmatrix} 0 \\ 1 \\ -4 \end{bmatrix}

For λ3=1\lambda_3 = 1 : (A−λ3I)v3=0(A - \lambda_3 I)v_3 = 0

[−2001103−40][x1x2x3]=[000]\begin{bmatrix} -2 & 0 & 0 \\ 1 & 1 & 0 \\ 3 & -4 & 0 \end{bmatrix} \begin{bmatrix} x_1 \\ x_2 \\ x_3 \end{bmatrix} = \begin{bmatrix} 0 \\ 0 \\ 0 \end{bmatrix}

−2x1=0⇒x1=0-2x_1 = 0 \Rightarrow x_1 = 0 x1+x2=0⇒x2=0x_1 + x_2 = 0 \Rightarrow x_2 = 0 3x1−4x2=0⇒0=03x_1 - 4x_2 = 0 \Rightarrow 0 = 0

Let x3=1x_3 = 1 . So,

v3=[001]v_3 = \begin{bmatrix} 0 \\ 0 \\ 1 \end{bmatrix}

Therefore, the eigenvalues are -1, 2, and 1, with corresponding eigenvectors

[−6213]\begin{bmatrix} -6 \\ 2 \\ 13 \end{bmatrix}

,

[01−4]\begin{bmatrix} 0 \\ 1 \\ -4 \end{bmatrix}

, and

[001]\begin{bmatrix} 0 \\ 0 \\ 1 \end{bmatrix}

.

For each eigenvalue, all eigenvectors are the nonzero scalar multiples of its displayed vector.