Linear Algebra Exercises-n-Answers.pdf
Linear Algebra Exercises-n-Answers.pdf
Linear Algebra Exercises-n-Answers.pdf
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194 <strong>Linear</strong> <strong>Algebra</strong>, by Hefferon<br />
and P is the inverse of that.<br />
⎛<br />
⎞<br />
1 0 0 0 0<br />
−1 1 1 −1 1<br />
P = Rep E5,B(id) = (P −1 ) −1 =<br />
⎜−1 0 1 0 0<br />
⎟<br />
⎝ 1 0 −1 1 −1⎠<br />
0 0 0 0 1<br />
(c) The calculation to check this is routine.<br />
Five.III.2.22<br />
(a) The calculation<br />
p<br />
(<br />
N p ) (<br />
N<br />
)<br />
(N p )<br />
1/2 −1/2 u ∣∣<br />
1<br />
{ u ∈ C}<br />
1/2 −1/2 u<br />
2 –zero matrix– C 2<br />
shows that any map represented by the matrix must act on the string basis in this way<br />
⃗β 1 ↦→ ⃗ β 2 ↦→ ⃗0<br />
because the nullspace after one application has dimension one and exactly one basis vector, β ⃗ 2 , is<br />
sent to zero. Therefore, this representation with<br />
(<br />
respect<br />
)<br />
to 〈 β ⃗ 1 , β ⃗ 2 〉 is the canonical form.<br />
0 0<br />
1 0<br />
(b) The calculation here is similar to the prior one.<br />
p<br />
⎛<br />
N p N (N p )<br />
1 ⎝ 0 0 0<br />
⎞ ⎛<br />
0 −1 1⎠ { ⎝ u ⎞<br />
v⎠ ∣ u, v ∈ C}<br />
0 −1 1 v<br />
2 –zero matrix– C 3<br />
The table shows that the string basis is of the form<br />
⃗β 1 ↦→ β ⃗ 2 ↦→ ⃗0<br />
⃗β 3 ↦→ ⃗0<br />
because the nullspace after one application of the map has dimension two — β ⃗ 2 and β ⃗ 3 are both<br />
sent to zero — and one more iteration results in the additional vector being brought to zero.<br />
(c) The calculation<br />
p<br />
⎛<br />
N p ⎞ ⎛<br />
N (N p )<br />
−1 1 −1<br />
1 ⎝ 1 0 1 ⎠ { ⎝ u ⎞<br />
0 ⎠ ∣ u ∈ C}<br />
⎛1 −1 1<br />
⎝ 1 0 1<br />
⎞<br />
0 0 0<br />
−1 0 −1<br />
⎛ −u⎞<br />
2<br />
⎠ { ⎝ u v ⎠ ∣ u, v ∈ C}<br />
−u<br />
3 –zero matrix– C 3<br />
shows that any map represented by this basis must act on a string basis in this way.<br />
⃗β 1 ↦→ β ⃗ 2 ↦→ β ⃗ 3 ↦→ ⃗0<br />
Therefore, this is the canonical form. ⎛<br />
⎝ 0 0 0<br />
⎞<br />
1 0 0⎠<br />
0 1 0<br />
Five.III.2.23 A couple of examples<br />
⎛<br />
( ) ( ) ( )<br />
0 0 a b 0 0<br />
=<br />
⎝ 0 0 0<br />
⎞ ⎛<br />
1 0 0⎠<br />
⎝ a b c<br />
⎞ ⎛<br />
d e f⎠ = ⎝ 0 0 0<br />
⎞<br />
a b c⎠<br />
1 0 c d a b<br />
0 1 0 g h i d e f<br />
suggest that left multiplication by a block of subdiagonal ones shifts the rows of a matrix downward.<br />
Distinct blocks ⎛ ⎞ ⎛<br />
⎞ ⎛<br />
⎞<br />
0 0 0 0 a b c d 0 0 0 0<br />
⎜1 0 0 0<br />
⎟ ⎜ e f g h<br />
⎟<br />
⎝0 0 0 0⎠<br />
⎝ i j k l ⎠ = ⎜ a b c d<br />
⎟<br />
⎝0 0 0 0⎠<br />
0 0 1 0 m n o p i j k l<br />
act to shift down distinct parts of the matrix.<br />
Right multiplication does an analgous thing to columns. See Exercise 17.