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Material Type: Assignment; Class: Applied Linear Algebra; Subject: Mathematics; University: University of Illinois - Urbana-Champaign; Term: Summer 2008;
Typology: Assignments
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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
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
1 (^21) 2 − 2
1 (^21) 2 − 2
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 =
. 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
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
x y z
x y z
= 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
7 12 − (^16) − (^1112)
7 12 − (^16) − (^1112)
7 12 − (^16) − (^1112)
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
x y
x y
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
, f =
, 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
227 35
227 35
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:
, f =
, 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
(^35) 6
(^35) 6
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