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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.

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