MATH 425 Hour Exam I Solution: Orthogonal Projections and Eigenvectors, Exams of Linear Algebra

Solutions to hour exam i of math 425, focusing on orthogonal projections and eigenvectors. Topics covered include finding the closest vector in a subspace, proving properties of the closest vector, and calculating eigenvectors and eigenvalues.

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2011/2012

Uploaded on 05/18/2012

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MATH 425 Hour Exam I Solution Radford 02/22/08
1. (20 points) First
(a) observe that a+bx+cx2Sif and only if 0 = <a +bx +cx2, x> =Z2
0
(a+bx +cx2)x dx
=Z2
0
(ax +bx2+cx3)dx = (ax2
2+bx3
3+cx4
4)¯
¯
¯
¯
¯
2
0
= 2a+8
3b+ 4c; 0 = a+4
3b+ 2c. (10)
(b) Solutions to this equation, which is equivalent to the system
a=4
3b2c
b=b
c=c
, is
given by
a
b
c
=b
4/3
1
0
+c
2
0
1
. A basis for Sis {−4
3+x, 2 + x2}. (10)
2. (20 points) Recall that s0is a closest vector in Sto vmeans sSand ||vs0|| ||vs||
for all sS.
(a) Let sS. Then
||vs||2=||(vs0)+(s0s)||2=||vs0||2+||s0s||2 ||vs0||2,(1)
where the second equation follows since s0sS. By (1) we deduce ||vs||2 ||vs0||2,
hence ||vs|| ||vs0||. (10)
(b) ||vs|| ||vs1|| for all sS; in particular for s=s0. Thus by (1), with s=s1,
we deduce ||vs0||2 ||vs1||2=||vs0||2+||s0s1||2 ||vs0||2,from which
||vs0||2=||vs0||2+||s0s1||2,or equivalently ||s0s1||2= 0, follows. Therefore
||s0s1|| = 0 and consequently s0=s1. (10)
3. (20 points) Let {q1,q2}be the orthonormal basis, and let v=
2
1
1
1
.
(a) By inspection <v,q1>=2
13 and <v,q2>=7
13. Thus the closest vector is
<v,q1>q1+<v,q2>q2=1
169
628
24 + 0
8 + 21
0 + 84
=1
169
34
24
13
84
.
(10)
1
pf3

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MATH 425 Hour Exam I Solution Radford 02/22/

  1. (20 points) First

(a) observe that a+bx+cx^2 ∈ S⊥^ if and only if 0 = <a + bx + cx^2 , x> =

∫ (^2)

0

(a + bx + cx^2 )x dx

∫ (^2)

0

(ax + bx^2 + cx^3 ) dx = (a

x^2 2

  • b

x^3 3

  • c

x^4 4

∣∣ ∣∣ ∣

2

0

= 2a +

b + 4c; 0 = a +

b + 2c. ( 10 )

(b) Solutions to this equation, which is equivalent to the system

a = −

b − 2 c b = b c = c

, is

given by

 

a b c

  = b

 

  + c

 

 . A basis for S⊥ (^) is {−^4 3

  • x, −2 + x^2 }. ( 10 )
  1. (20 points) Recall that s′^ is a closest vector in S to v means s ∈ S and ||v−s′|| ≤ ||v−s|| for all s ∈ S.

(a) Let s ∈ S. Then

||v − s||^2 = ||(v − s 0 ) + (s 0 − s)||^2 = ||v − s 0 ||^2 + ||s 0 − s||^2 ≥ ||v − s 0 ||^2 , (1)

where the second equation follows since s 0 − s ∈ S. By (1) we deduce ||v − s||^2 ≥ ||v − s 0 ||^2 , hence ||v − s|| ≥ ||v − s 0 ||. ( 10 )

(b) ||v − s|| ≥ ||v − s 1 || for all s ∈ S; in particular for s = s 0. Thus by (1), with s = s 1 , we deduce ||v − s 0 ||^2 ≥ ||v − s 1 ||^2 = ||v − s 0 ||^2 + ||s 0 − s 1 ||^2 ≥ ||v − s 0 ||^2 , from which ||v − s 0 ||^2 = ||v − s 0 ||^2 + ||s 0 − s 1 ||^2 , or equivalently ||s 0 − s 1 ||^2 = 0, follows. Therefore ||s 0 − s 1 || = 0 and consequently s 0 = s 1. ( 10 )

  1. (20 points) Let {q 1 , q 2 } be the orthonormal basis, and let v =

   

   

(a) By inspection <v, q 1 > = −

and <v, q 2 > =

. Thus the closest vector is

<v, q 1 >q 1 + <v, q 2 >q 2 =

  

   =

  

  .

(b) A = q 1 qt 1 + q 2 qt 2 =

  

  

   (3 − 12 4 0) +

  

   (−4 0 3 12)

  

  

  

   +

  

  

  

   

   

  1. (20 points) The characteristic polynomial of A is cA(x) =

∣∣ ∣∣ ∣∣ ∣

3 − x 8 4 0 5 − x 1 0 0 3 − x

∣∣ ∣∣ ∣∣ ∣

(3 − x)(5 − x)(3 − x). Thus the eigenvalues for A are λ = 3, 5.

λ = 3: A−λI 3 =

 

  −→ · · · −→

 

  −→ · · · −→

 

 ; eigen-

vectors in vector form are

 

x y z

  = x

 

  + z

 

 . Thus {

 

  ,

 

 } is

a basis for the space of eigenvectors for A belonging to λ = 3.

λ = 5: A − λI 3 =

 

  −→ · · · −→

 

  −→ · · · −→

 

 ;

eigenvectors in vector form are

  

x y z

   =^ y

  

  . Thus^ {

  

  }^ is a basis for the space

of eigenvectors for A belonging to λ = 5.

Take S =

  

   (^10 ) and^ D^ =

  

   (^10 ).

  1. (20 points) Note that A is a transition matrix.

(a) This is the nullspace of A−I 3. By row redutcion A−I 3 =

  

   −→