Encouraged - Linear Algebra - Solved Exam, Exams of Linear Algebra

This is the Solved Exam of Linear Algebra which includes Matrix, System, Row Reduce, System, Solve, Top Left Entry, Appropriate Algorithm, Jump, Last Column etc. Key important points are: Encouraged, Vectors, System, Method, Factorization, Square Matrices, Throughout, Invertible, Symmetric, Reflection Matrix

Typology: Exams

2012/2013

Uploaded on 02/27/2013

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Answer Key for Exam #1
1. The vectors
1
1
1
1
and
1
1
0
1
have lengths p12+ 12+ 12+ 12= 2 and p12+ 12+ 02+ 12=3,
respectively, and their dot pro duct is 1 + 1 + 0 + 1 = 3. So the angle θbetween them satisfies cos θ=3
23=
3
2. Therefore θ=π
6, or 30.
2. We use elimination on an augmented matrix. Subtracting the first row from the second and third, and
then adding the second row to the third, we have
1 1 2 3
1 1 3 7
1 9 1 15
1 1 2 3
0 0 1 4
0 8 1 12
1 1 2 3
0 0 1 4
0 8 0 16
.
The last step wasn’t necessary since the system was already triangular, but it doesn’t hurt. The second
equation gives x3= 4 and the third one says x2= 2. Then the first equation becomes x1+ 2 + 8 = 3, so
x1=7.
3. Here we have to reduce [A I] to £I A1¤by row operations. Using the first pivot to eliminate the other
entries in the first column of Awe have
1 2 3 1 0 0
2 3 1 0 1 0
3 1 2 0 0 1
1 2 3 1 0 0
0152 1 0
0573 0 1
.
Multiplying the second row by 1 to make the second pivot equal 1, and then using it to clear out the other
entries in the second column, we get
1 2 3 1 0 0
0152 1 0
0573 0 1
1 0 73 2 0
0 1 5 2 1 0
0 0 18 7 5 1
.
To put off the fractions as long as possible we can multiply the first and second rows by 18:
1 0 73 2 0
0 1 5 2 1 0
0 0 18 7 5 1
18 0 126 54 36 0
0 18 90 36 18 0
0 0 18 7 5 1
.
If we now add 7 times the third row to the first, and subtract 5 times the third row from the second, we get
18 0 126 54 36 0
0 18 90 36 18 0
0 0 18 7 5 1
18 0 0 5 1 7
0 18 0 1 7 5
0 0 18 7 5 1
.
Therefore A1=1
18
5 1 7
1 7 5
75 1
.
4(a) Step 1: subtract twice the first row of Afrom the second, and thrice the first row from the third. Step
2: subtract 4 times the second row from the third:
A=
1 4 3
2 9 8
3 16 18
1 4 3
0 1 2
0 4 9
1 4 3
0 1 2
0 0 1
=U.
pf3

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Answer Key for Exam #

  1. The vectors

 and

 have lengths^

12 + 1^2 + 1^2 + 1^2 = 2 and

12 + 1^2 + 0^2 + 1^2 =

respectively, and their dot product is 1 + 1 + 0 + 1 = 3. So the angle θ between them satisfies cos θ =

. Therefore θ = π 6 , or 30◦.

  1. We use elimination on an augmented matrix. Subtracting the first row from the second and third, and then adding the second row to the third, we have  

The last step wasn’t necessary since the system was already triangular, but it doesn’t hurt. The second equation gives x 3 = 4 and the third one says x 2 = 2. Then the first equation becomes x 1 + 2 + 8 = 3, so x 1 = −7.

  1. Here we have to reduce [A I] to

[

I A−^1

]

by row operations. Using the first pivot to eliminate the other entries in the first column of A we have  

Multiplying the second row by −1 to make the second pivot equal 1, and then using it to clear out the other entries in the second column, we get  

To put off the fractions as long as possible we can multiply the first and second rows by 18:  

If we now add 7 times the third row to the first, and subtract 5 times the third row from the second, we get  

Therefore A−^1 =

4(a) Step 1: subtract twice the first row of A from the second, and thrice the first row from the third. Step 2: subtract 4 times the second row from the third:

A =

 = U.

Step 1 puts a 2 and 3 respectively in the second and third rows of the first column of L, and step 2 puts a 4

in the middle of the bottom row of L, so L =

 (^) and A = LU.

4(b) We may now solve A~x =

 (^) in two steps. First we solve L~c = ~b, which in this case is

c 1 c 2 c 3

and we get c 1 = 7; 14 + c 2 = 13, so c 2 = −1; and 21 − 4 + c 3 = 15 so c 3 = −2. Finally we solve U~x = ~c, which in this case is (^) 

x 1 x 2 x 3

Here we have x 3 = −2; x 2 − 4 = −1 so x 2 = 3; x 1 + 12 − 6 = 7 so x 1 = 1.

4(c) Here we have to reduce [L I] to

[

I L−^1

]

by row operations, which takes three easy steps:  

So L−^1 =

, and by symmetry U −^1 =

4(d) Since A = LU and L and U are both invertible, we have A−^1 = U −^1 L−^1. Therefore

if A =

 (^) then A−^1 =

5(i) A large family of examples is

( ra rb sa sb

ub vb −ua −va

and

ub vb −ua −va

ra rb sa sb

= (ru + sv)

ab b^2 −a^2 −ab

which is not O unless ru + sv = 0 or a and b are both 0. A specific example is

( 12 20 9 15

but

5(ii) If A is invertible then apply A−^1 to AB = O on the left to get A−^1 AB = A−^1 O, which simplifies to B = O. But if B = O, then BA = OA = O. Similarly, if B is invertible then apply B−^1 to AB = O on the right to get ABB−^1 = OB−^1 , which simplifies to A = O. If A = O, then BA = BO = O. So if either A or B is invertible, then AB = O implies BA = O (and conversely).

5(iii) If we transpose the equation AB = O we get (remember that the order switches) BT^ AT^ = OT^. But OT^ = O, so this says BT^ AT^ = O. If A and B are symmetric then this becomes BA = O.