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Solutions to exam #2 of the appm 3310: matrix methods course, covering topics such as finding an orthogonal basis for the range of a matrix, determining the dimension of the kernel, finding a basis for the coker, calculating the gram matrix, and finding the least squares solution to a linear system.
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APPM 3310: Matrix Methods — Exam #2 Solutions — April 2, 2010
Problem 1. (25 points) Suppose we have A =
(a) (9 pts) Find an orthogonal basis for range(A). (b) (8 pts) What is the dimension of the kernel of A? (c) (8 pts) Find a basis for coker(A).
Solution:
(a) A =
(^). So range(A) =
span
. Now using Gram Schmidt, starting with ~v 1 =
, we have
~v 2 = w~ 2 −
〈 w~ 2 , ~v 1 〉 ‖~v 1 ‖^2
~v 1 =
so an orthogonal basis of range(A) is
(b) By the Fundamental Theorem of Linear Algebra dim(ker A) = m − rank(A) = 3 − 2 = 1
(c) Solve AT^ y = 0,
−x + y = 0 y + z = 0 z = z
x = −z y = −z z = z
thus coker(A) = span
Problem 2. (25 points)
(a) (15 pts) Find the Gram matrix generated by the vectors p 1 (x) = x and p 2 (x) = x^2 with respect to the L^2 inner product on [0, 1]. Show all work.
(b) (10 pts) Is the Gram matrix described in part (a) positive definite? Why or why not? Justify your answer.
Solution:
(a) Note 〈x, x〉 =
0 x
(^2) dx = x^3 3
0 = 1/3 and^ 〈x, x
0 x
(^3) dx = x^4 4
0 = 1/4 and^ 〈x
(^2) , x (^2) 〉 = ∫ (^1) 0 x
(^4) dx = x^5 5
0 = 1/5 so
K =
〈x, x〉 〈x, x^2 〉 〈x^2 , x〉 〈x^2 , x^2 〉
(b) The Gram matrix in part (a) is positive definite since x and x^2 are linearly independent.
Problem 3. (20 points) Find the least squares solution to the linear system:
x y
Solution: Here K = AT^ A =
and f = AT^ b =
. Now solve Kx = f , ( 2 2 3 2 7 6
→ 2 x^ + 2y^ =^3 5 y = 3
→ x^ =^9 /^10 y = 3 / 5
so the least squares solution is x∗^ =
Problem 4. (20 points) Give a brief answer to each question. Show all work.
(a) Consider the norms ‖ · ‖ 3 and ‖ · ‖ 5 on Rn. Now define the F-norm, ‖ · ‖F , as
‖x‖F =
‖x‖ 5 + ‖x‖ 3
, for any x ∈ Rn.
Show that the F-norm defined above satisfies the Triangle inequality. Show all work.
(b) Does the expression 〈u, v〉 = u 1 v 1 + 2u 1 v 2 define an inner product on R^2? Why or why not? Explicitly justify your answer.
Solution:
(a) Show ‖x + y‖F ≤ ‖x‖F + ‖y‖F , so note
‖x + y‖F =
‖x + y‖ 5 + ‖x + y‖ 3
‖x‖ 5 + ‖y‖ 5 + ‖x‖ 3 + ‖y‖ 3
‖x‖ 5 + ‖x‖ 3
‖y‖ 5 + ‖y‖ 3
= ‖x‖F + ‖y‖F
where the inequality follows from the fact that both ‖ · ‖ 3 and ‖ · ‖ 5 satisfy the Triangle inequality.
(b) No, consider v =
then 〈v, v〉 = 1 · 1 + 2 · 1 · (−1) = − 1 < 0 which violates positivity.
Problem 5. (10 points) Does the set S =
z z
∣ z^ ∈^ C
form a vector subspace of C^2? Why or
why not? Justify your answer.
Solution: No, the set S is not a subspace since closure under scalar multiplication fails. Note, in general, for a complex scalar α ∈ C we have,
α ·
z z
αz αz
αz αz
, which implies α ·
z z