推荐回答(5个)
特征值 λ = -2, 3, 3,特征向量: (1 0 -1)^T、(3 0 2)^T。
解:
|λE-A| =
|λ-1 -1 -3|
| 0 λ-3 0|
|-2 -2 λ|
|λE-A| = (λ-3)*
|λ-1 -3|
|-2 λ|
|λE-A| = (λ-3)(λ^2-λ-6) = (λ+2)(λ-3)^2
特征值 λ = -2, 3, 3
对于 λ = -2, λE-A =
[-3 -1 -3]
[ 0 -5 0]
[-2 -2 -2]
行初等变换为
[ 1 1 1]
[ 0 1 0]
[ 0 2 0]
行初等变换为
[ 1 0 1]
[ 0 1 0]
[ 0 0 0]
得特征向量 (1 0 -1)^T。
对于重特征值 λ = 3, λE-A =
[ 2 -1 -3]
[ 0 0 0]
[-2 -2 3]
行初等变换为
[ 2 -1 -3]
[ 0 -3 0]
[ 0 0 0]
行初等变换为
[ 2 0 -3]
[ 0 1 0]
[ 0 0 0]
得特征向量 (3 0 2)^T。
答:特征值 λ = -2, 3, 3,特征向量: (1 0 -1)^T、(3 0 2)^T。
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扩展资料
特征值是线性代数中的一个重要概念。在数学、物理学、化学、计算机等领域有着广泛的应用
设 A 是n阶方阵,如果存在数m和非零n维列向量 x,使得 Ax=mx 成立,则称 m 是A的一个特征值(characteristic value)或本征值(eigenvalue)。
非零n维列向量x称为矩阵A的属于(对应于)特征值m的特征向量或本征向量,简称A的特征向量或A的本征向量。
矩阵的特征向量是矩阵理论上的重要概念之一,它有着广泛的应用。数学上,线性变换的特征向量(本征向量)是一个非简并的向量,其方向在该变换下不变。该向量在此变换下缩放的比例称为其特征值(本征值)。
|λI-A| =
λ-1 -1 -3
0 λ-3 0
-2 -2 λ
= (λ-1)(λ-3)λ-2×3×(λ-3) = (λ-3)(λ+2)(λ-3) = 0
解得λ=-2,3(两重)
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|λE-A| =
|λ-1 -1 -3|
| 0 λ-3 0|
|-2 -2 λ|
|λE-A| = (λ-3)*
|λ-1 -3|
|-2 λ|
|λE-A| = (λ-3)(λ^2-λ-6) = (λ+2)(λ-3)^2
特征值 λ = -2, 3, 3
对于 λ = -2, λE-A =
[-3 -1 -3]
[ 0 -5 0]
[-2 -2 -2]
行初等变换为
[ 1 1 1]
[ 0 1 0]
[ 0 2 0]
行初等变换为
[ 1 0 1]
[ 0 1 0]
[ 0 0 0]
得特征向量 (1 0 -1)^T
对于重特征值 λ = 3, λE-A =
[ 2 -1 -3]
[ 0 0 0]
[-2 -2 3]
行初等变换为
[ 2 -1 -3]
[ 0 -3 0]
[ 0 0 0]
行初等变换为
[ 2 0 -3]
[ 0 1 0]
[ 0 0 0]
得特征向量 (3 0 2)^T.
你好,满意请采纳哦!
|A-λE|=
2-λ 3 2
1 8-λ 2
-2 -14 -3-λ
= -(λ-1)(λ-3)^2=0
解得特征值为1,3,3
1对应的特征向量:
(A-E)x=0
系数矩阵:
1 3 2
1 7 2
-2 -14 -4
初等行变换结果是:
1 0 2
0 1 0
0 0 0
所以特征向量是[-2 0 1]^T
3对应的特征向量:
(A-3E)x=0
系数矩阵:
-1 3 2
1 5 2
-2 -14 -6
初等行变换结果是:
1 1 0
0 2 1
0 0 0
所以特征向量是[1 -1 2]^T
一个线性方程组的基础解系是这样的一个解向量组:
1.这个解向量组是线性无关的;
2.方程组的任意一个解向量都可由这个线性无关的解向量组线性表示.
在线性方程组Ax=0中,若矩阵A的秩为r, 则基础解系所含解向量的个数是n-r.
在这里,矩阵A的秩为1,则基础解系所含解向量的个数应该是2个解向量,一个解向量是不能构成一个基础解系的.
另外你给出的那个解正好可以用X1,X2这个基础解系线性表示.X1+X2就是你给出的那个解.
原方程组和x1-x2+x3=0是同解的,若令x2=k1,x3=k2,则x1=k1-k2,
于是方程组的通解为(k1-k2,k1,k2)=k1(1,1,0)+k2(-1,0,1).
可见基础解系就是让自由未知量x2和x3轮流取1,0后再确定出x1所得到的.
一般情况下若线性方程组Ax=0的系数矩阵的秩为r,则线性方程组有n-r个自由未知量,故基础解系所含解向量的个数是n-r个.让这n-r个自由未知时中的一个取1,其它自由未知时取0,这样可以得到一个解向量。用这样的方法可以得到n-r个解向量,也就得到了基础解系.
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