Solutions to Assignment #6 - Applied Linear Algebra | MATH 415, Assignments of Linear Algebra

Material Type: Assignment; Class: Applied Linear Algebra; Subject: Mathematics; University: University of Illinois - Urbana-Champaign; Term: Summer 2008;

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Pre 2010

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Math 415 - Assignment 6 Solutions
Problems: 4.2.1, 4.2.4, 4.2.5 (c), 4.2.7 (b), 4.3.1, 4.3.3, 4.3.14 (d), 4.3.17, 4.4.3
Problem 4.2.1
First we write this quadratic function in the standard form f(x) = xTKx 2xTf+cwhere
K=
110
131
011
, f =
1
0
3
2
, c = 2.By Gaussian elimination
110
131
011
110
021
011
1 1 0
0 2 1
0 0 1
2
=U
and since the diagonal entries of Uare all positive, Kis positive definite. This shows that the
minimizer is unique. By Theorem 4.1 the minimizer is the solution of Kx =fand we solve this by
elimination:
110
131
011
1
0
3
2
110
021
011
1
1
3
2
1 1 0
0 2 1
0 0 1
2
1
1
1
Therefore 1
2z=1 or z=2,2y=1zor y=1
2(1 + 2) = 1
2, x = 1 y= 1 1
2=1
2. Then the
minimum value is
1
2
1
2
2
T
110
131
011
1
2
1
2
2
2
1
2
1
2
2
T
1
0
3
2
+ 2 = 3
2
Problem 4.2.4
(a) Since the (1,1) entry of Ais positive, we just need the determinant to be positive: 4 b2>0,
i.e. 2< b < 2
(b) A=1b
b41b
0 4 b21 0
0 4 b2 1b
0 1
so D=1 0
0 4 b2and L=1 0
b1
(c) This part of the question is an application of Theorem 4.4. First assume that bis such that A
is positive definite. Then the unique minimizer satisfies Ax =f=0 1 T. Thus x=A1f=
1b
b410
1=4
4+b2
b
4+b2
b
4+b21
4+b2 0
1=b
4+b2
1
4+b2
and so the minimum is b
4+b22
+ 2bb
4+b21
4+b2+ 4 1
4+b22
21
4+b2=1
4+b2.
If b > 2 or b < 2, Ais neither definite or semidefinite, and so the minimum in this case is −∞.
If b= 2, the quadratic function reduces to p(x, y) = (x+ 2y)22yand so p(2y, y) = 2y −∞
as y . If b=2, the quadratic function reduces to p(x, y)=(x2y)22yand so p(2y, y) =
2y −∞ as y . Done!
Problem 4.2.5 (c)
p(x, y, z) =
x
y
z
T
31 1
1 2 1
11 3
x
y
z
2
x
y
z
T
1
0
2
3
= 3x22xy + 2xz + 2y22yz + 3z22x+ 4z3. Now let’s solve Kx =f:
31 1
1 2 1
11 3
1
0
2
31 1
05
32
3
02
3
8
3
1
1
3
7
3
31 1
0 5 2
02
3
8
3
1
1
7
3
31 1
0 5 2
0 0 12
1
1
11
so 12z=11, i.e. z=11
12 ,5y= 1 + 2z, i.e. y=1
5(1 22
12 ) = 1
6,3x= 1 + yz, i.e. x=
pf3
pf4

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Math 415 - Assignment 6 Solutions

Problems: 4.2.1, 4.2.4, 4.2.5 (c), 4.2.7 (b), 4.3.1, 4.3.3, 4.3.14 (d), 4.3.17, 4.4.

Problem 4.2. First we write this quadratic function in the standard form f (x) = xT^ Kx − 2 xT^ f + c where

K =

 (^) , f =

 (^) , c = 2. By Gaussian elimination

 = U

and since the diagonal entries of U are all positive, K is positive definite. This shows that the minimizer is unique. By Theorem 4.1 the minimizer is the solution of Kx = f and we solve this by elimination:

Therefore 12 z = −1 or z = − 2 , 2 y = − 1 − z or y = 12 (−1 + 2) = 12 , x = 1 − y = 1 − 12 = 12. Then the minimum value is

1 (^21) 2 − 2

T 

1 (^21) 2 − 2

1 (^21) 2 − 2

T 

2

Problem 4.2. (a) Since the (1,1) entry of A is positive, we just need the determinant to be positive: 4 − b^2 > 0 , i.e. − 2 < b < 2

(b) A =

1 b b 4

1 b 0 4 − b^2

0 4 − b^2

1 b 0 1

so D =

0 4 − b^2

and L =

b 1

(c) This part of the question is an application of Theorem 4.4. First assume that b is such that A

is positive definite. Then the unique minimizer satisfies Ax = f =

)T

. Thus x = A−^1 f = ( 1 b b 4

− (^) −4+^4 b (^2) −4+^ bb 2 b −4+b^2 −^

1 −4+b^2

( (^) b −4+b^2 − (^) −4+^1 b 2

and so the minimum is

b −4+b^2

  • 2b

b −4+b^2

− (^) −4+^1 b 2

− (^) −4+^1 b 2

− (^) −4+^1 b 2

= (^) −4+^1 b 2.

If b > 2 or b < −2, A is neither definite or semidefinite, and so the minimum in this case is −∞. If b = 2, the quadratic function reduces to p(x, y) = (x + 2y)^2 − 2 y and so p(− 2 y, y) = − 2 y → −∞ as y → ∞. If b = −2, the quadratic function reduces to p(x, y) = (x − 2 y)^2 − 2 y and so p(2y, y) = − 2 y → −∞ as y → ∞. Done!

Problem 4.2.5 (c)

p(x, y, z) =

x y z

T 

x y z

x y z

T 

= 3 x^2 − 2 xy + 2xz + 2y^2 − 2 yz + 3z^2 − 2 x + 4z − 3. Now let’s solve Kx = f :

1 3 − (^73)

so 12z = −11, i.e. z = − 1112 , 5 y = 1 + 2z, i.e. y = 15 (1 − 2212 ) = − 16 , 3 x = 1 + y − z, i.e. x =

1 3 (1^ −^

1 6 +^

11 12 ) =^

7

  1. The upper diagonal reduced matrix has only positive entries on the diagonal, and therefore K is positive definite. Hence there is a unique minimizer as given above, and the minimum is

7 12 − (^16) − (^1112)

T 

7 12 − (^16) − (^1112)

7 12 − (^16) − (^1112)

T 

12

Problem 4.2.7 (b) To maximixe a quadratic function, you minimize the negative of the function, i.e minimize p(x, y) = 2 x^2 − 6 xy + 3y^2 − 4 x + 3y = ( x y

)T (

x y

x y

)T (

But the K matrix here is indefinite, i.e the (1,1) entry is positive but the determinant is negative: 2 × 3 − (−3)^2 = −3. Thus the minimum of p is −∞, and so the maximum of the original quatric function is ∞.

Problem 4.3. This is a standard closest point problem. Let A be the matrix with the two spanning vectors as columns and define K = AT^ A, f = AT^ b and c = ‖b‖^2. This gives us

K =

T 

, f =

T 

, c =

‖b‖^2 = 3 So now we need to solve Kx = f , i.e. x = A−^1 f :

x =

7

1 1 7 7

6 35

227 35

The closest point is the vector

v = (^67)

6 387 (^3536) 35

and the minimum distance is( 6 227 35

)T (

227 35

227 35

)T (

Problem 4.3. First we need to find a basis spanning the plane: x = − 2 y + z, so

x y z

− 2 y + z y z

 (^) = y

 (^) + z

Now we repeat the calculation in 4.3.1 using these two spanning vectors:

K =

T 

, f =

T 

, c = ‖b‖^2 =

1 So now we need to solve Kx = f , i.e. x = A−^1 f :

x =

3

1 1 3 3

5 6

(^35) 6

The closest point is the the vector

v = (^13)

1 (^61) (^35) 6

and the minimum distance is( 1 (^35) 6

)T (

(^35) 6

(^35) 6

)T (

By equation (4.38) we have β = (ty − ¯ty¯)/(t^2 − (¯t)^2 ) = (560. 27 − (5)(104.21))/(35 − 25) = 3. 922 , α = y¯ − tβ¯ = 104. 21 − 5(3.922) = 84.6. Thus the least squares line for these data is y = 84.6 + 3. 922 t. (b) The year 2005 corresponds to t = 16, so the median price in that year should be y = 84.6 + 3 .922(16) = 147. 35 The year 2010 corresponds to t = 21, so the median price in that year should be y = 84.6 + 3 .922(21) = 166. 96