南航双语矩阵论 matrix theory第三章部分题解

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南航矩阵论研究生试卷及答案

南航矩阵论研究生试卷及答案
(1)求系数矩阵 的满秩分解;
(2)求广义逆矩阵 ;
(3)求该线性方程组的极小最小二乘解.
解答:(1) 矩阵 , 的满秩分解为
.…………………(5分)
(2) .……………………(10分)
(3)方程组的极小最小二乘解为 .…………(5分)
共6页第5页
四、(20分)已知幂级数 的收敛半径为3,矩阵 .
(1) 求 ;

证明 是 的一个内积;
(3)求 在题(2)所定义的内积下的一组标准正交基;
(4)证明 是 的线性变换,并求 在题(1)所取基下的矩阵.
解答:(1) 的一组基为 维数为3.
……………………………………(5分)
(2)直接验证内积定义的四个条件成立.……………………………(4分)
(3) 标准正交基 .…………(5分)
(4)由于 ,所以 是 的一个变换.又直接验证,知
,
因此 是 的一个线性变换.………………………………(3分)
线性变换 在基 下的矩阵为
.……………………………………………(3分)
二、(20分)设三阶矩阵 , , .
(1)求 的行列式因子、不变因子、初等因子及Jordan标准形;
(2)利用 矩阵的知识,判断矩阵 和 是否相似,并说明理由.
南京航空航天大学2012级硕士研究生
共6页 第1页
2012~2013学年第1学期《矩阵论》课程考试A卷
考试日期:2013年1月15日课程编号:A080001命题教师:阅卷教师:
学院专业学号姓名成绩
一、(20分)设 是 的一个线性子空间,对任意 ,定义: ,其中 .
(1)求 的一组基和维数;
(2)对任意 ,定义:
解答: ( 的行列式因子为 ;…(3分)

南航矩阵论课后习题答案

南航矩阵论课后习题答案

南航矩阵论课后习题答案南航矩阵论课后习题答案矩阵论是数学中的一个重要分支,广泛应用于各个领域,包括物理学、工程学、计算机科学等等。

南航的矩阵论课程是培养学生数学思维和解决实际问题的重要环节。

在课后习题中,学生需要运用所学的矩阵理论知识,解答各种问题。

下面是南航矩阵论课后习题的一些答案和解析。

1. 已知矩阵A = [1 2 3; 4 5 6; 7 8 9],求A的逆矩阵。

解析:要求一个矩阵的逆矩阵,需要先判断该矩阵是否可逆。

一个矩阵可逆的充要条件是其行列式不为零。

计算矩阵A的行列式,得到det(A) = -3。

因此,矩阵A可逆。

接下来,我们可以使用伴随矩阵法求解逆矩阵。

首先,计算矩阵A的伴随矩阵Adj(A),然后将其除以行列式的值,即可得到逆矩阵。

计算得到A的伴随矩阵为Adj(A) = [-3 6 -3; 6 -12 6; -3 6 -3]。

最后,将伴随矩阵除以行列式的值,即可得到矩阵A的逆矩阵A^-1 = [-1 2 -1; 2 -4 2; -1 2 -1]。

2. 已知矩阵A = [2 1; 3 4],求A的特征值和特征向量。

解析:要求一个矩阵的特征值和特征向量,需要先求解其特征方程。

特征方程的形式为|A - λI| = 0,其中A为给定矩阵,λ为特征值,I为单位矩阵。

计算得到特征方程为|(2-λ) 1; 3 (4-λ)| = (2-λ)(4-λ) - 3 = λ^2 - 6λ + 5 = 0。

解这个二次方程,得到特征值λ1 = 1,λ2 = 5。

接下来,我们可以求解对应于每个特征值的特征向量。

将特征值代入(A - λI)x = 0,即可求解出特征向量。

对于特征值λ1 = 1,解得特征向量x1 = [1; -1];对于特征值λ2 = 5,解得特征向量x2 = [1; 3]。

3. 已知矩阵A = [1 2; 3 4],求A的奇异值分解。

解析:奇异值分解是将一个矩阵分解为三个矩阵的乘积:A = UΣV^T,其中U和V是正交矩阵,Σ是对角矩阵。

南京航空航天大学研究生课程《矩阵论》内容总结与习题选讲

南京航空航天大学研究生课程《矩阵论》内容总结与习题选讲

《矩阵论》复习提纲与习题选讲Chapter1 线性空间和内积空间内容总结:z 线性空间的定义、基和维数;z 一个向量在一组基下的坐标;z 线性子空间的定义与判断;z 子空间的交z 内积的定义;z 内积空间的定义;z 向量的长度、距离和正交的概念;z Gram-Schmidt 标准正交化过程;z 标准正交基。

习题选讲:1、设表示实数域3]x [R R 上次数小于3的多项式再添上零多项式构成 的线性空间(按通常多项式的加法和数与多项式的乘法)。

(1) 求的维数;并写出的一组基;求在所取基下的坐标;3]x [R 3]x [R 221x x ++ (2) 在中定义3]x [R , ∫−=11)()(),(dx x g x f g f n x R x g x f ][)(),(∈ 证明:上述代数运算是内积;求出的一组标准正交基;3][x R (3)求与之间的距离;221x x ++2x 2x 1+−(4)证明:是的子空间;2][x R 3]x [R (5)写出2[][]3R x R x ∩的维数和一组基;二、 设22R ×是实数域R 上全体22×实矩阵构成的线性空间(按通常矩阵的加 法和数与矩阵的乘法)。

(1) 求22R ×的维数,并写出其一组基;(2) 在(1)所取基下的坐标; ⎥⎦⎤⎢⎣⎡−−3111(3) 设W 是实数域R 上全体22×实对称矩阵构成的线性空间(按通常矩阵的加法和数与矩阵的乘法)。

证明:W 是22R ×的子空间;并写出W 的维数和一组基;(4) 在W 中定义内积, )A B (tr )B ,A (T =W B ,A ∈求出W 的一组标准正交基;(5)求与之间的距离; ⎥⎦⎤⎢⎣⎡0331⎥⎦⎤⎢⎣⎡−1221 (6)设V 是实数域R 上全体22×实上三角矩阵构成的线性空间(按通常矩阵的加法和数与矩阵的乘法)。

证明:V 也是22R ×的子空间;并写出V 的维数和一组基;(7)写出子空间的一组基和维数。

南航双语矩阵论-matrix-theory第三章部分题解精选全文

南航双语矩阵论-matrix-theory第三章部分题解精选全文

可编辑修改精选全文完整版Solution Key to Some Exercises in Chapter 3 #5. Determine the kernel and range of each of the following linear transformations on 2P(a) (())'()p x xp x σ=(b) (())()'()p x p x p x σ=- (c) (())(0)(1)p x p x p σ=+Solution (a) Let ()p x ax b =+. (())p x ax σ=.(())0p x σ= if and only if 0ax = if and only if 0a =. Thus, ker(){|}b b R σ=∈The range of σis 2()P σ={|}ax a R ∈ (b) Let ()p x ax b =+. (())p x ax b a σ=+-.(())0p x σ= if and only if 0ax b a +-= if and only if 0a =and 0b =. Thus, ker(){0}σ=The range of σis 2()P σ=2{|,}P ax b a a b R +-∈=(c) Let ()p x ax b =+. (())p x bx a b σ=++.(())0p x σ= if and only if 0bx a b ++= if and only if 0a =and 0b =. Thus, ker(){0}σ=The range of σis 2()P σ=2{|,}P bx a b a b R ++∈= 备注: 映射的核以及映射的像都是集合,应该以集合的记号来表达或者用文字来叙述. #7. Let be the linear mapping that maps 2P into 2R defined by10()(())(0)p x dx p x p σ⎛⎫⎪= ⎪⎝⎭⎰ Find a matrix A such that()x A ασαββ⎛⎫+= ⎪⎝⎭.Solution1(1)1σ⎛⎫= ⎪⎝⎭ 1/2()0x σ⎛⎫= ⎪⎝⎭11/211/2()1010x ασαβαββ⎛⎫⎛⎫⎛⎫⎛⎫+=+= ⎪ ⎪⎪⎪⎝⎭⎝⎭⎝⎭⎝⎭Hence, 11/210A ⎛⎫= ⎪⎝⎭#10. Let σ be the transformation on 3P defined by(())'()"()p x xp x p x σ=+a) Find the matrix A representing σ with respect to 2[1,,]x x b) Find the matrix B representing σ with respect to 2[1,,1]x x + c) Find the matrix S such that 1B S AS -=d) If 2012()(1)p x a a x a x =+++, calculate (())n p x σ.Solution (a) (1)0σ= ()x x σ=22()22x x σ=+002010002A ⎛⎫⎪= ⎪ ⎪⎝⎭(b) (1)0σ=()x x σ=22(1)2(1)x x σ+=+000010002B ⎛⎫⎪= ⎪ ⎪⎝⎭(c)2[1,,1]x x +2[1,,]x x =101010001⎛⎫⎪⎪ ⎪⎝⎭The transition matrix from 2[1,,]x x to 2[1,,1]x x + is101010001S ⎛⎫ ⎪= ⎪ ⎪⎝⎭, 1B S AS -=(d) 2201212((1))2(1)n n a a x a x a x a x σ+++=++#11. Let A and B be n n ⨯ matrices. Show that if A is similar to B then there exist n n ⨯ matrices S and T , with S nonsingular, such thatA ST =andB TS =.Proof There exists a nonsingular matrix P such that 1A P BP -=. Let 1S P -=, T BP =. Then A ST =and B TS =.#12. Let σ be a linear transformation on the vector space V of dimension n . If there exist a vector v such that 1()v 0n σ-≠ and ()v 0n σ=, show that(a) 1,(),,()v v v n σσ- are linearly independent.(b) there exists a basis E for V such that the matrix representing σ with respect to the basis E is000010000010⎛⎫⎪⎪⎪⎪⎝⎭Proof(a) Suppose that1011()()v v v 0n n k k k σσ--+++= Then 11011(()())v v v 0n n n k k k σσσ---+++=That is, 12210110()()())()v v v v 0n n n n n k k k k σσσσ----+++==Thus, 0k must be zero since 1()v 0n σ-≠. 211111(()())()v v v 0n n n n k k k σσσσ----++==This will imply that 1k must be zero since 1()v 0n σ-≠.By repeating the process above, we obtain that 011,,,n k k k - must be all zero. Thisproves that1,(),,()v v v n σσ- are linearly independent.(b) Since 1,(),,()v v v n σσ- are n linearly independent, they form a basis for V .Denote 112,(),,()εv εv εv n n σσ-=== 12()εεσ= 23()εεσ= …….1()εεn n σ-= ()ε0n σ=12[(),(),,()]εεεn σσσ121[,,,,]εεεεn n -=000010000010⎛⎫⎪⎪⎪⎪⎝⎭#13. If A is a nonzero square matrix and k A O =for some positive integer k , show that A can not be similar to a diagonal matrix.Proof Suppose that A is similar to a diagonal matrix 12diag(,,,)n λλλ. Then for each i , there exists a nonzero vector x i such that x x i i i A λ= x x x 0k k i i i i i A λλ=== since k A O =.This will imply that 0i λ= for 1,2,,i n =. Thus, matrix A is similar to the zero matrix. Therefore, A O =since a matrix that is similar to the zero matrix must be the zero matrix, whichcontradicts the assumption.This contradiction shows that A can not be similar to a diagonal matrix. OrIf 112diag(,,,)n A P P λλλ-= then 112diag(,,,)k k k k n A P P λλλ-=. k A O = implies that 0i λ= for 1,2,,i n =. Hence, B O =. This will imply that A O =.Contradiction!。

矩阵论第三章答案

矩阵论第三章答案
d1 (λ ) = L = d n −1 (λ ) = 1 , d n (λ ) = (λ − a )
n
因此初等因子只有一个,即有 (λ − a )n .
11. 证:
A( λ )与 B( λ )相抵当且仅当它们有相同的不变因
子,当且仅当它们的各阶行列式因子相同.
1 1 ⎤ ⎡λ − 2 ⎢ 12. 解 : ( 1 ) 因 为 λI − A = ⎢ − 2 λ + 1 2 ⎥ ⎥ 的初等因子为 ⎢ − 1 λ − 2⎥ ⎣ 1 ⎦
0 0 ⎤ r2 − (− 1)r3 ⎡1 0 0 ⎤ c 2 − (2λ − 1)c1 ⎡1 ⎢0 ⎥ ⎢ 2 λ − λ ⎥ ⎯⎯ ⎯ λ2 ⎥ ⎯⎯ ⎯ ⎯→ ⎢ ⎯→ ⎢0 λ ⎥ 2 2 2 ⎥ ⎢ ⎥ c3 + (− λ )c1 ⎢ ( ) r + 1 − λ r 0 λ − λ − λ − λ 0 0 − λ − λ 3 2 ⎣ ⎦ ⎣ ⎦
2. 解 : ( 1)因为 A 的特征矩阵为
⎡λ + 1 ⎤ ⎢ ⎥ λ+2 ⎢ ⎥ A(λ ) = λI − A = ⎢ ⎥ λ −1 ⎢ ⎥ λ − 2⎦ ⎣
所以 A( λ )的行列式因子为
⎡1⎤ A=⎢ ⎥ ⎣1⎦
不变因子为
d 1 (λ ) = D1 (λ ) = 1, d 4 (λ ) = D4 (λ ) D3 (λ ) d 2 (λ ) = d 3 (λ ) = 1,
10. 解:
因为 A(λ ) = (λ − a )n ,所以 Dn (λ ) = (λ − a )n ,又因
c1 λ − a c2 O
O
= c1c 2 L c n −1 ≠ 0 ,
λ − a c n −1

matrix theory(矩阵论)

matrix theory(矩阵论)

mr
, B bij
r n
,则
r
mn
, 其中cij ai1b1 j ai 2b2 j air brj aik bkj
k 1
4、转置与共轭转置
a11 a21 设A am1 aij
mn
a12 a22 am 2
a1n a11 a2 n T a12 ,则A amn a1n
B * A*
例题
1、求方阵的逆阵
求逆矩阵的基本方法有: (1)定义法
由 AB E或BA E , 可得A1 B
(2)公式法
A* A- 1 = A
-1
但当n ³ 3时计算A 较复杂,此时一般采用:
(3)初等变换法
(A
E) 揪 揪 揪 E 揪 揪 井
初等行变换
(
A
-1
)
例1:已知n阶方阵A满足A2 + 5 A - 4 E = 0, 求( A - 3E ) - 1
解:
A* 由A- 1 = , 得A* = A A- 1 , A \
( A ) =( A A )
* -1
-1 -1
A = = A- 1 A A
轾 1 1 1 犏 = 2犏 2 1 1 犏 犏 1 3 1 臌
-1
轾 -2 -1 5 犏 = 犏2 2 0 犏 犏1 0 1 臌
四、 矩阵的块运算 1、加法,减法
(
)(
E + XY T = E + 2 XY T + XY T XY T = E + 4 XY T
)
骣1 所以,A ( A - 4 E) = - 3E,即,A 琪 ( A - 4 E ) = E 琪 桫3 1 -1 故,A可逆,且A = - ( A - 4E) . 3

南京航空航天大学2007-2014硕士研究生矩阵论matrixTheory试题

南京航空航天大学2007-2014硕士研究生矩阵论matrixTheory试题

2 3 4 A 4 6 8 6 7 8 。 一(20 分) (1)设
2010 ~ 2011 学年《矩阵论》 课程考试 A 卷
(i)求 A 的特征多项式和 A 的全部特征值; (ii)求 A 的行列式因子,不变因子和初等因子; (iii)写出 A 的 Jordan 标准形;
1 A* A2 A* (3)证明: n 。
1 1 1 1 A 0 0 0 0 四、 (20 分)已知矩阵
(1)求矩阵 A 的 QR 分解;
1 2 0 1 b 1 1 2 1 ,向量 ,
(2)计算 A ;
17 6 14 60 A , B 45 16 3 13 ,试问 A 和 B 是否相似?并说明 (2)设
原因。
2 1 A 1 2 3 1 ,求 A 1 , A 2 , A , A F ; 二(20 分) (1)设

(3)用广义逆判断方程组 Ax b 是否相容?若相容,求其通解;若不相容,求其极小最小二乘解。
五、 (20 分)
(1)设矩阵
问当 t 满足什么条件时, A B 成立?
5 3 2 0 1 A 3 2 t , B 1 1 2 t 2 2 0 .5 t
五(20 分)设
A ( a ij )
为 n 阶 Hermite 矩阵,证明:
3
存在唯一 Hermite 矩阵 B 使得 A B ;
2
(2)
(3) 如果 A 0 ,则 tr ( A)tr ( A ) n 。
1
如果 A 0 ,则 tr ( A ) (tr ( A)) ;
2

南航双语矩阵论matrix theory第4章部分习题参考答案

南航双语矩阵论matrix theory第4章部分习题参考答案

)
If i is a root of p( ) 0 , then p(i ) 0 . We obtain that eigenvalue of C T with eigenvector x (1, i ,, in 2 , in 1 )T .
Exercise 16
Let be an orthogonal transformation on a Euclidean space V (an inner product space over the real number field). If W is a -invariant subspace of V, show that the orthogonal complement of W is also -invariant. Proof Let V W W , where W is -invariant. Let {u1 , u2 ,, uk } be an orthonormal basis for
0 1 T C x 0 0 0 0 1 0 0 0 0 0 0 an 0 an 1 0 an 2 1 a1
T
i i 1 2 2 i i i n2 n 1 n 1 i i i n 1 n n 1 a a a p ( i n i n 1 i 1 i i
C T x i x . Then i is an
(b) If p( ) has n distinct roots, then all roots of p( ) are eigenvalues of C T . We obtain that the characteristic polynomial of C T and p( ) have the same n distinct roots. And also they have the same degree and the same leading coefficient. Hence, the characteristic polynomial of C T is the same as p( ) . Since C and C T have the same characteristic polynomial, we know that p( ) is the characteristic polynomial of C.

矩阵论第3章3-4节

矩阵论第3章3-4节
在一个点 x0 [a, b], 使
P( x0 ) f ( x0 ) ,
所以说 P (x ) 的偏差点总是存在的.
4
定理5
P( x) H n 是 f C[a, b] 的最佳逼近多项式
的充分必要条件是 P (x ) 在 [a, b] 上至少有n 2 个轮流为“正”、 “负”的偏差点, 即有 n 2个点 a x1 x2 xn 2 b ,

n b I 2 ( x)[ a j j ( x) f ( x)] k ( x)dx a ak j 0
(k 0,1,, n),
21
于是有
(
j 0
n
k
( x), j ( x)) a j ( f ( x), k ( x)) (k 0,1, , n). (4.3)
En inf {( f , Pn )} inf max f ( x) Pn ( x) , (3.2)
Pn H n
Pn H n a x b
则称之为 f (x) 在 [a, b]上的最小偏差.
2
定义8 假定 f C[a, b], 若存在 Pn* ( x) H n 使得
且点 xk cos
k π(k 0,1, , n) 是 Tn ( x)的切比雪夫交错点组, n
8
由定理5可知,区间 [1, 1] 上 x n 在 H n 1 中最佳逼近多项式
* 为 Pn1 ( x), 即 n (x) 是与零的偏差最小的多项式.定理得证.
9
例3 求 f ( x) 2 x3 x 2 2 x 1 在 [1, 1]上的最佳2次逼 近多项式. 解 由题意,所求最佳逼近多项式 P2* ( x) 应满足

南京航空航天大学MatrixTheory双语矩阵论期末考试

南京航空航天大学MatrixTheory双语矩阵论期末考试
(2) The Jordan canonical form is
--------------------------------------------------------------------------------------------------------------------------
(2) Find a basis for such that with respect to this basis,thematrixBrepresenting is diagonal.
(3) Find thekernel(核)andrange(值域)of this transformation.
Solution:
南京航空航天大学Matrix-Theory双语矩阵论期末考试
———————————————————————————————— 作者:
———————————————————————————————— 日期:
Part I (必做题,共5题,70分)
第1题(15分)
得分
Let denote the set of all real polynomials of degree less than 3 withdomain(定义域) .The addition and scalar multiplication are defined intheusual way.Definean inner product on by
第2题(15分)
得分
Let be the linear transformation on (the vector space of real polynomials of degree less than 3) defined by

矩阵论1-2-3

矩阵论1-2-3

备注 4:
1. 单个非零向量必 线性无关. 2. 含有零向量的向量组必 线性相关. 3. 两个向量线性相关, 则对应分量成比例. 例4:
1 0 0 1 0 0 线性空间R 上一组矩阵 , 0 0 , 1 0 , 0 0 0 0 0 1 是线性无关的。
2.V中所定义的加法及数乘运算统称为V的线性运算. 在不致产生混淆时,将数域P上的线性空间简称为 线性空间.
3.不管V的元素如何,当P为实数,就称V为复线性空间.
备注2. 向量(线性)空间中的元素称为向量, 但 不一定是有序数组. 备注3. 判别线性空间的方法: 一个集合, 对 于定义的加法和数乘运算不封闭, 或者运算不 满足八条性质的任一条, 则此集合就不能构成 线性空间.
1.2.1 线性空间及其基本性质 1.2.2 向量的线性相关性
1.2.3 线性空间的维数
线性空间是线性代数的中心内容,也是学习矩 阵论的重要基础,它是几何空间的抽象和推广. 在解析几何中讨论的三维向量,它们的加法和数 量乘法可以描述一些几何和力学问题的有关属性.为
了研究一般线性方程组解的理论,我们把三维向量推
算满足的规则抽象出来,就形成了抽象的线性空间的概念,
线性方程组理论和矩阵代数也有非常重要的指导意义.
1.2.1 线性空间及其基本性质
定义1.2.1 设V 是一个非空集合,P 是一个数 域。在V上定义了一种代数运算,称为加法, 记为“+”;定义了P 与V 到V 的一种代数运算, 称为数量乘法(简称数乘),记为“· ”。如果 加法与数量乘法满足如下规则:
定理1.2.1 设V是数域 P上的线性空间, 则 (1) V 中零元素是唯一的; (2) V 中任一元素α的负元素是唯一的;

南京航空航天大学MatrixTheory双语矩阵论期末考试

南京航空航天大学MatrixTheory双语矩阵论期末考试

第6题 第7题
Let P4 be the vector space consisting of all real polynomials of degree less than 4 with usual addition and scalar multiplication. Let x1, x2 , x3 be three distinct real numbers. For each pair of polynomials f and g in P4 , define
Explain.
Solution:
(1) An annihilating polynomial of A is x2 5x 6 .
The minimal polynomial of A divides any annihilating polynomial of A. The possible minimal polynomials are
x 6 , x 1, and x2 5x 6 . --------------------------------------------------------------------------------------------------------------(2) The minimal polynomial of A divides the characteristic polynomial of A. Since A is a matrix of order 3, the characteristic polynomial of A is of degree 3. The minimal polynomial of A and the
(1)
(1) 0 (x) x (x2) 2 2x2

南京航空航天大学Matrix-Theory双语矩阵论期末考试2015

南京航空航天大学Matrix-Theory双语矩阵论期末考试2015

NUAALet 3P (the vector space of real polynomials of degree less than 3) defined by(())'()''()p x xp x p x σ=+.(1) Find the matrix A representing σ with respect to the ordered basis [21,,x x ] for 3P .(2) Find a basis for 3P such that with respect to this basis, the matrix B representing σ is diagonal.(3) Find the kernel (核) and range (值域)of this transformation. Solution: (1)221022x x x x σσσ===+()()() 002010002A ⎛⎫⎪= ⎪ ⎪⎝⎭----------------------------------------------------------------------------------------------------------------- (2)101010001T ⎛⎫ ⎪= ⎪ ⎪⎝⎭(The column vectors of T are the eigenvectors of A)The corresponding eigenvectors in 3P are 1000010002T AT -⎛⎫⎪= ⎪ ⎪⎝⎭(T diagonalizes A ) 22[1,,1][1,,]x x x x T += . With respect to this new basis 2[1,,1]x x +, the representingmatrix of σis diagonal.------------------------------------------------------------------------------------------------------------------- (3) The kernel is the subspace consisting of all constant polynomials.The range is the subspace spanned by the vectors 2,1x x +-----------------------------------------------------------------------------------------------------------------------Let 020012A ⎛⎫⎪= ⎪ ⎪-⎝⎭.(1) Find all determinant divisors and elementary divisors of A .(2) Find a Jordan canonical form of A .(3) Compute At e . (Give the details of your computations.) Solution: (1)110020012I A λλλλ-⎛⎫ ⎪-=- ⎪ ⎪-⎝⎭,(特征多项式 2()(1)(2)p λλλ=--. Eigenvalues are 1, 2, 2.)Determinant divisor of order 1()1D λ=, 2()1D λ=, 23()()(1)(2)D p λλλλ==-- Elementary divisors are 2(1) and (2)λλ-- .---------------------------------------------------------------------------------------------------------------------- (2) The Jordan canonical form is100021002J ⎛⎫ ⎪= ⎪ ⎪⎝⎭--------------------------------------------------------------------------------------------------------------------------(3) For eigenvalue 1, 010010011I A ⎛⎫⎪-=- ⎪ ⎪-⎝⎭ , An eigenvector is 1(1,0,0)T p = For eigenvalue 2, 1102000010I A ⎛⎫⎪-= ⎪ ⎪⎝⎭, An eigenvector is 2(0,0,1)T p =Solve 32(2)A I p p -=, 331100(2)00000101A I p p --⎛⎫⎛⎫⎪ ⎪-== ⎪ ⎪ ⎪ ⎪-⎝⎭⎝⎭we obtain that3(1,1,0)T p =-101001010P ⎛⎫ ⎪=- ⎪ ⎪⎝⎭, 1110001010P -⎛⎫⎪= ⎪ ⎪-⎝⎭ 1At J e Pe P -=22210100110001000101000010tt t t e e te e ⎛⎫⎛⎫⎛⎫⎪ ⎪ ⎪=- ⎪ ⎪ ⎪ ⎪ ⎪ ⎪-⎝⎭⎝⎭⎝⎭22220000t t t t t t e e e e tee ⎛⎫-⎪= ⎪ ⎪-⎝⎭ --------------------------------------------------------------------------------------------------------------------Suppose that ∈R A and O I A A =--65.(1) What are the possible minimal polynomials of A ? Explain.(2) In each case of part (1), what are the possible characteristic polynomials of A ? Explain.Solution:(1) An annihilating polynomial of A is 256x x --.The minimal polynomial of A divides any annihilating polynomial of A. The possible minimal polynomials are6x -, 1x +, and 256x x --.---------------------------------------------------------------------------------------------------------------(2) The minimal polynomial of A divides the characteristic polynomial of A. Since A is a matrix of order 3, the characteristic polynomial of A is of degree 3. The minimal polynomial of A and the characteristic polynomial of A have the same linear factors. Case 6x -, the characteristic polynomial is 3(6)x - Case 1x +, the characteristic polynomial is 3(1)x + Case 256x x --, the characteristic polynomial is 2(1)(6)x x +- or 2(6)(1)x x -+-------------------------------------------------------------------------------------------------------------------Let 120000A ⎛⎫=⎪⎝⎭. Find the Moore-Penrose inverse A +of A .Solution: ()12011200000A PG ⎛⎫⎛⎫=== ⎪ ⎪⎝⎭⎝⎭1()(1,0)T T P P P P +-==, 111()250T T G G GG +-⎛⎫⎪== ⎪ ⎪⎝⎭110112(1,0)2055000A G P +++⎛⎫⎛⎫ ⎪⎪=== ⎪ ⎪ ⎪ ⎪⎝⎭⎝⎭也可以用SVD 求.------------------------------------------------------------------------------------------------------------------Part II (选做题, 每题10分)请在以下题目中(第6至第9题)选择三题解答. 如果你做了四题,请在题号上画圈标明需要批改的三题. 否则,阅卷者会随意挑选三题批改,这可能影响你的成绩.Let 4P be the vector space consisting of all real polynomials of degree lessthan 4 with usual addition and scalar multiplication. Let 123,,x x x be three distinct real numbers. For each pair of polynomials f and g in 4P , define 31,()()i i i f g f x g x =<>=∑.Determine whether ,f g <> defines an inner product on 4P or not. Explain.Let n n A ⨯∈R . Show that if x x A =)(σis the orthogonal projection fromn R to )(A R , then A is symmetric and the eigenvalues ofA are all 1’s and 0’s.n n A ⨯∈C . Show that x x A H is real-valued for all n C x ∈if and only if Ais Hermitian.Let n n B A ⨯∈C , be Hermitian matrices, and A bepositive definite. Show thatAB is similar to BA , and is similar to a real diagonal matrix.若正面不够书写,请写在反面.123()()()x x x x x x ---. Then ,0f f <>=. But 0f ≠. This does not define an inner product. For any x , ()()x x T A R A N A ⊥-∈=, ()x x 0T A A -=. Hence, T T A A A =. Thus. T A A =.From above, we have 2A A =. This will imply that λλ-2is an annihilating polynomial of A. The eigenvalue of A must be the roots of 02=-λλ. Thus, the eigenvalues of A are1’s and 0’s.See Thm 7.1.1, page 182. 也可以用其它方法.Since A is nonsingular, 1()AB A BA A -=. Hence, A is similar to BASince A is positive definite, there is a nonsingular hermitian matrix P such that H A PP =. 1()H H AB PP B P P BP P -==Since H P BP is Hermitian, it is similar to a real diagonal matrix.is similar to H AB P BP , H P BP is similar to a real diagonal matrix. Thus AB is similar to a real diagonal matrix.。

南京航空航天大学2009_矩阵论考试考题及答案

南京航空航天大学2009_矩阵论考试考题及答案

二、 (1பைடு நூலகம் 分)设矩阵
考试试卷 A
(考试时间:2009 年 11 月?日 晚 7:00-9:00 考试方式:闭卷 A)
成绩:
一、 (15 分)在 R 4 中有两组基,
1 0 2 A 0 1 1 , 0 1 0
计算: 2 A8 3 A5 A4 A2 4E 。

1 1 4 4
(3) , 因 容 易 验 证 AA b b , 故 方 程 组 Ax b 相 容 , 最 小 范 数 解 为
1 1 3 3 y1 0 1 1 x A b E2 A A y 3 1 3 y 2 34 0 3 3 3 1 y3 4
个基有相同坐标的非零向量为 k x1 x2 x3 x4 , k 非零常数。
(5 分)
共 4 页,第 1 页
学院 年级 班 学号 姓名 ------------------------------线--------------------------------- ---------- -----------------------封--------------------------------------- --------------------------------------密--------------------------------
(3),判断方程组 Ax b 是否相容?若相容,求其最小范数解;若不相容,求其极小最小二乘 解。(4 分)
解:
2 0 0 8 1 0 0 4 行 (1): A 0 2 8 0 0 1 4 0 ,故矩阵 A 的满秩分解为: 2 2 8 8 0 0 0 0 2 0 2 0 1 0 0 4 1 0 0 4 A 0 2 CD, C 0 2 , D 。 0 1 4 0 0 1 4 0 2 2 2 2

南航双语矩阵论matrix theory第3章部分习题参考答案

南航双语矩阵论matrix theory第3章部分习题参考答案

1
Exercise 8
Let S be the subspace of C[a, b] spanned by e x , xe x , and x 2 e x . Let D be the differentiation operation of S, i.e., D( f ) f ' . Find the matrix representing D with respect to [e x , xe x , x2 e x ] Solution
3
This will imply that k1 must be zero since n 1 ( v) 0 . By repeating the process above, we obtain that k0 , k1 , , kn 1 must be all zero. This proves that
Solution
(1) 1
1
1
( x)
1 / 2 0
( x) 1 0 1
Hence, A
1 1/ 2 1 0
1/ 2 1 1/ 2 0
4
If B is nonsingular, then AB B1 ( BA) B . AB and BA are similar.
A P diБайду номын сангаасg(1 , 2 , , n ) P1 then Ak P diag(1k , 2k ,
, n . Hence, diag(1 , 2 ,
, n k ) P1 .
Ak O implies that i 0 for i 1, 2,
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Solution Key to Some Exercises in Chapter 3 #5. Determine the kernel and range of each of the following linear transformations on 2P
(a) (())'()p x xp x σ=
(b) (())()'()p x p x p x σ=- (c) (())(0)(1)p x p x p σ=+
Solution (a) Let ()p x ax b =+. (())p x ax σ=.
(())0p x σ= if and only if 0ax = if and only if 0a =. Thus,
ker(){|}b b R σ=∈
The range of σis 2()P σ={|}ax a R ∈ (b) Let ()p x ax b =+. (())p x ax b a σ=+-.
(())0p x σ= if and only if 0ax b a +-= if and only if 0a =and 0b =. Thus, ker(){0}σ=
The range of σis 2()P σ=2{|,}P ax b a a b R +-∈=
(c) Let ()p x ax b =+. (())p x bx a b σ=++.
(())0p x σ= if and only if 0bx a b ++= if and only if 0a =and 0b =. Thus, ker(){0}σ=
The range of σis 2()P σ=2{|,}P bx a b a b R ++∈= 备注: 映射的核以及映射的像都是集合,应该以集合的记号来表达或者用文字来叙述. #7. Let be the linear mapping that maps 2P into 2R defined by
10
()(())(0)p x dx p x p σ⎛⎫
⎪= ⎪⎝⎭
⎰ Find a matrix A such that
()x A ασαββ⎛⎫
+= ⎪⎝⎭
.
Solution
1(1)1σ⎛⎫
= ⎪⎝⎭ 1/2()0x σ⎛⎫
= ⎪⎝⎭
11/211/2()1010x ασαβαββ⎛⎫⎛⎫
⎛⎫⎛⎫
+=+= ⎪ ⎪
⎪⎪⎝⎭⎝⎭⎝⎭⎝⎭
Hence, 11/210A ⎛⎫
=
⎪⎝⎭
#10. Let σ be the transformation on 3P defined by
(())'()"()p x xp x p x σ=+
a) Find the matrix A representing σ with respect to 2[1,,]x x b) Find the matrix B representing σ with respect to 2[1,,1]x x + c) Find the matrix S such that 1B S AS -=
d) If 2012()(1)p x a a x a x =+++, calculate (())n p x σ. Solution (a) (1)0σ=
()x x σ=
22()22x x σ=+
002010002A ⎛⎫

= ⎪ ⎪⎝⎭
(b) (1)0σ=
()x x σ=
22(1)2(1)x x σ+=+
000010002B ⎛⎫

= ⎪ ⎪⎝⎭
(c)
2[1,,1]x x +2[1,,]x x =101010001⎛⎫

⎪ ⎪⎝⎭
The transition matrix from 2[1,,]x x to 2[1,,1]x x + is
101010001S ⎛⎫ ⎪= ⎪ ⎪⎝⎭
, 1
B S AS -=
(d) 2201212((1))2(1)n n a a x a x a x a x σ+++=++
#11. Let A and B be n n ⨯ matrices. Show that if A is similar to B then there exist
n n ⨯ matrices S and T , with S nonsingular, such that A ST =and B TS =. Proof There exists a nonsingular matrix P such that 1A P BP -=. Let 1S P -=, T BP =. Then
A ST =and
B TS =.
#12. Let σ be a linear transformation on the vector space V of dimension n . If there exist a vector v such that 1()v 0n σ-≠ and ()v 0n σ=, show that
(a) 1,(),,()v v v n σσ-L are linearly independent.
(b) there exists a basis E for V such that the matrix representing σ with respect to the basis E is
00001
0000
010⎛⎫


⎪ ⎪⎝⎭
L L M M M M L
Proof
(a) Suppose that
1011()()v v v 0n n k k k σσ--+++=L
Then 11011(()())v v v 0n n n k k k σσσ---+++=L
That is, 12210110()()())()v v v v 0n n n n n k k k k σσσσ----+++==L Thus, 0k must be zero since 1()v 0n σ-≠. 211111(()())()v v v 0n n n n k k k σσσσ----++==L
This will imply that 1k must be zero since 1()v 0n σ-≠.
By repeating the process above, we obtain that 011,,,n k k k -L must be all zero.
This proves that
1,(),,()v v v n σσ-L are linearly independent.
(b) Since 1,(),,()v v v n σσ-L are n linearly independent, they form a basis for V .
Denote 112,(),,()εv εv εv n n σσ-===L 12()εεσ= 23()εεσ= …….
1()εεn n σ-= ()ε0n σ=
12[(),(),,()]εεεn σσσL 121[,,,,]εεεεn n -=L 00001
0000
010⎛⎫


⎪ ⎪⎝⎭
L L M M M M L
#13. If A is a nonzero square matrix and k A O =for some positive integer k , show that A can not be similar to a diagonal matrix.
Proof Suppose that A is similar to a diagonal matrix 12diag(,,,)n λλλL . Then for each i , there exists a nonzero vector x i such that x x i i i A λ= x x x 0k k i i i i i A λλ=== since k A O =.
This will imply that 0i λ= for 1,2,,i n =L . Thus, matrix A is similar to the zero matrix. Therefore, A O =since a matrix that is similar to the zero matrix must be
the zero matrix, which contradicts the assumption.
This contradiction shows that A can not be similar to a diagonal matrix. Or
If 112diag(,,,)n A P P λλλ-=L then 112diag(,,,)k k k k n A P P λλλ-=L .
k A O = implies that 0i λ= for 1,2,,i n =L . Hence, B O =. This will imply that
A O =. Contradiction!。

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