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Linear Algebra: Concepts and Methods (2013) – Solutions Manual – Anthony

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INSTANT PDF DOWNLOAD — Complete step-by-step solutions for Linear Algebra: Concepts and Methods by Martin Anthony & Michele Harvey. Covers all 13 chapters: systems of linear equations, Gaussian elimination, matrices and inverses, determinants, vector spaces, subspaces, linear independence, basis & dimension, linear transformations, rank–nullity, eigenvalues/eigenvectors, diagonalization, inner-product spaces, orthogonality, Gram–Schmidt, least squares, and applications. Searchable, printable PDF with clear proofs, worked examples, and exam-ready problem solving for math, engineering, CS, data science, and economics courses. linear algebra solutions, matrix algebra workbook, gaussian elimination problems, determinants practice, vector spaces exercises, linear independence basis dimension, rank nullity theorem, linear transformations solutions, eigenvalues eigenvectors problems, diagonalization practice, orthogonality least squares, gram schmidt solutions, inner product space, systems of linear equations solved, singular value decomposition intro, spectral theorem exercises, proof based linear algebra, data science math foundations, engineering mathematics textbook help, Anthony Harvey solutions

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ALL 13 CHAPTERS COVERED




SOLUTIONS MANUAL

,Chapter 1


Problem 1.1 (a) Ab is not defined.

(b) Since C is a 3 × 3 matrix and A is 3 × 2, the product CA is defined and is the
3 × 2 matrix
    
1 2 1 2 1 4 6
CA =  3 0 −1   1 1  =  6 0 
4 1 1 0 3 9 8


(c) A + Cb is not defined, since Cb is 3 × 1 and A is 3 × 2.
 
2 2

(d) A + D = 3 6
6 6
 
0 1
(e) bT D = ( 1 1 −1 )  2 5  = ( −4 3)
6 3

(f) The product DAT is a 3 × 3 matrix, as is C, so this expression is defined:
   
0 1 ( ) 1 1 3
2 1 0
DAT =  2 5  =  9 7 15  ,
1 1 3
6 3 15 9 9


5

,6 CHAPTER 1.
     
1 1 3 1 2 1 2 3 4
T
DA + C =  9 7  
15 + 3 0  
−1 = 12 7 14 
15 9 9 4 1 1 19 10 10

 
1
(g) bT b = ( 1 1 −1 )  1  = (3). As mentioned in Section 1.8.2, we
−1
normally just identify a 1 × 1 matrix by its entry. So we may write (3) as 3.
   
1 1 1 −1
(h) bbT =  1  ( 1 1 −1 ) =  1 1 −1 
−1 −1 −1 1
    
1 2 1 1 2

(i) Cb = 3 0 −1   1  = 4

4 1 1 −1 4

Problem 1.2 The matrix aT b is the product of a 1 × n matrix and an n × 1
matrix, so it is a 1 × 1 matrix (which can be identified with the scalar it
represents; that is, the scalar given by the inner product ⟨a, b⟩ ). The product bT a
is also a 1 × 1 matrix. Since the product aT b is a 1 × 1 matrix, it is symmetric,
so that
aT b = (aT b)T = bT a.

Problem 1.3 We have
( )( ) ( ) ( )
3 7 x y 3x + 7z 3y + 7w 1 0
= =
0 −1 z w −z −w 0 1
Hence,

3x + 7z = 1 


3y + 7w = 0  3x + 0 = 1 1 7
=⇒ =⇒ x = , y = , z = 0, w = −1
−z = 0 
 3y − 7 = 0 3 3


−w = 1
(1 7
)
Therefore B −1 = 3 3 .
0 −1

, 7

Using the method in Activity 1.24, we have
( )
−1 1 −1 −7
|B| = −3, so that B =− ,
3 0 3
which, of course, agrees with the previous result for B −1 .

You can now check that this is indeed B −1 by multiplying out BB −1 and B −1 B
to obtain the identity matrix.

Problem 1.4 To obtain A, you can first take the transpose of both sides of the
equation ( )
−1 T 3 5
(A ) =
1 2
to obtain ( )
−1 3 1
A = .
5 2
Now A = (A−1 )−1 , so we need the inverse of the above matrix. This can be
found using the method given in Activity 1.24. You should obtain
( )
2 −1
A= .
−5 3

Problem 1.5 Since A is symmetric, we have aij = aji , and since B is
skew-symmetric we must have bij = −bji . Then the matrices are
   
1 −4 7 0 −3 5
A =  −4 6 0  B= 3 0 2.
7 0 2 −5 −2 0

Problem 1.6 Since (A + AT )T = AT + A = A + AT , the matrix A + AT is
symmetric. Similarly, (A − AT )T = AT − A = −(A − AT ), so this matrix is
skew symmetric.

Adding the matrices A + AT and A − AT we obtain 2A, so we can write
1 1
A = (A + AT ) + (A − AT ),
2 2
which is the sum of a symmetric and a skew-symmetric matrix.

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