Linear Algebra Exercises-n-Answers.pdf
Linear Algebra Exercises-n-Answers.pdf Linear Algebra Exercises-n-Answers.pdf
110 Linear Algebra, by Hefferon (a) Yes; we have both r · (g ◦ h) (⃗v) = r · g( h(⃗v) ) = (r · g) ◦ h (⃗v) and g ◦ (r · h) (⃗v) = g( r · h(⃗v) ) = r · g(h(⃗v)) = r · (g ◦ h) (⃗v) (the second equality holds because of the linearity of g). (b) Both answers are yes. First, f◦(rg+sh) and r·(f◦g)+s·(f◦h) both send ⃗v to r·f(g(⃗v))+s·f(h(⃗v)); the calculation is as in the prior item (using the linearity of f for the first one). For the other, (rf + sg) ◦ h and r · (f ◦ h) + s · (g ◦ h) both send ⃗v to r · f(h(⃗v)) + s · g(h(⃗v)). Three.IV.2.25 We have not seen a map interpretation of the transpose operation, so we will verify these by considering the entries. (a) The i, j entry of GH trans is the j, i entry of GH, which is the dot product of the j-th row of G and the i-th column of H. The i, j entry of H trans G trans is the dot product of the i-th row of H trans and the j-th column of G trans , which is the the dot product of the i-th column of H and the j-th row of G. Dot product is commutative and so these two are equal. (b) By the prior item each equals its transpose, e.g., (HH trans ) trans = H transtrans H trans = HH trans . Three.IV.2.26 Consider r x , r y : R 3 → R 3 rotating all vectors π/2 radians counterclockwise about the x and y axes (counterclockwise in the sense that a person whose head is at ⃗e 1 or ⃗e 2 and whose feet are at the origin sees, when looking toward the origin, the rotation as counterclockwise). Rotating r x first and then r y is different than rotating r y first and then r x . In particular, r x (⃗e 3 ) = −⃗e 2 so r y ◦ r x (⃗e 3 ) = −⃗e 2 , while r y (⃗e 3 ) = ⃗e 1 so r x ◦ r y (⃗e 3 ) = ⃗e 1 , and hence the maps do not commute. Three.IV.2.27 It doesn’t matter (as long as the spaces have the appropriate dimensions). For associativity, suppose that F is m × r, that G is r × n, and that H is n × k. We can take any r dimensional space, any m dimensional space, any n dimensional space, and any k dimensional space — for instance, R r , R m , R n , and R k will do. We can take any bases A, B, C, and D, for those spaces. Then, with respect to C, D the matrix H represents a linear map h, with respect to B, C the matrix G represents a g, and with respect to A, B the matrix F represents an f. We can use those maps in the proof. The second half is done similarly, except that G and H are added and so we must take them to represent maps with the same domain and codomain. Three.IV.2.28 (a) The product of rank n matrices can have rank less than or equal to n but not greater than n. To see that the rank can fall, consider the maps π x , π y : R 2 → R 2 projecting onto the axes. Each is rank one but their composition π x ◦π y , which is the zero map, is rank zero. That can be translated over to matrices representing those maps in this way. ( ) ( ) ( ) 1 0 0 0 0 0 Rep E2,E 2 (π x ) · Rep E2,E 2 (π y ) = = 0 0 0 1 0 0 To prove that the product of rank n matrices cannot have rank greater than n, we can apply the map result that the image of a linearly dependent set is linearly dependent. That is, if h: V → W and g : W → X both have rank n then a set in the range R(g ◦ h) of size larger than n is the image under g of a set in W of size larger than n and so is linearly dependent (since the rank of h is n). Now, the image of a linearly dependent set is dependent, so any set of size larger than n in the range is dependent. (By the way, observe that the rank of g was not mentioned. See the next part.) (b) Fix spaces and bases and consider the associated linear maps f and g. Recall that the dimension of the image of a map (the map’s rank) is less than or equal to the dimension of the domain, and consider the arrow diagram. V f ↦−→ R(f) g ↦−→ R(g ◦ f) First, the image of R(f) must have dimension less than or equal to the dimension of R(f), by the prior sentence. On the other hand, R(f) is a subset of the domain of g, and thus its image has dimension less than or equal the dimension of the domain of g. Combining those two, the rank of a composition is less than or equal to the minimum of the two ranks. The matrix fact follows immediately.
Answers to Exercises 111 Three.IV.2.29 The ‘commutes with’ relation is reflexive and symmetric. However, it is not transitive: for instance, with ( ) ( ) ( ) 1 2 1 0 5 6 G = H = J = 3 4 0 1 7 8 G commutes with H and H commutes with J, but G does not commute with J. Three.IV.2.30 (a) Either ⎛ ⎞ of these. ⎛ ⎞ ⎛ ⎞ ⎝ x y⎠ ↦−→ πx ⎝ x 0⎠ y ↦−→ ⎝ 0 0⎠ ⎝ x y⎠ y ↦−→ ⎝ 0 y⎠ ↦−→ πx ⎝ 0 0⎠ z 0 0 z 0 0 (b) The composition is the fifth derivative map d 5 /dx 5 on the space of fourth-degree polynomials. (c) With respect to the natural bases, ⎛ ⎞ ⎛ ⎞ 1 0 0 0 0 0 Rep E3,E 3 (π x ) = ⎝0 0 0⎠ Rep E3,E 3 (π y ) = ⎝0 1 0⎠ 0 0 0 0 0 0 and their product (in either order) is the zero matrix. (d) Where B = 〈1, x, x 2 , x 3 , ⎛ x 4 〉, ⎞ ⎛ ⎞ 0 0 2 0 0 0 0 0 6 0 Rep B,B ( d2 dx 2 ) = 0 0 0 6 0 ⎜0 0 0 0 12 ⎟ Rep B,B ( d3 ⎝0 0 0 0 0 ⎠ dx 3 ) = 0 0 0 0 24 ⎜0 0 0 0 0 ⎟ ⎝0 0 0 0 0 ⎠ 0 0 0 0 0 0 0 0 0 0 and their product (in either order) is the zero matrix. Three.IV.2.31 Note that (S + T )(S − T ) = S 2 − ST + T S − T 2 , so a reasonable try is to look at matrices that do not commute so that −ST ( and) T S don’t ( cancel: ) with 1 2 5 6 S = T = 3 4 7 8 we have the desired inequality. ( ) ( ) −56 −56 (S + T )(S − T ) = S 2 − T 2 −60 −68 = −88 −88 −76 −84 Three.IV.2.32 Because the identity map acts on the basis B as β ⃗ 1 ↦→ β ⃗ 1 , . . . , β ⃗ n ↦→ β ⃗ n , the representation is this. ⎛ ⎞ 1 0 0 0 0 1 0 0 0 0 1 0 ⎜ ⎝ . .. ⎟ ⎠ 0 0 0 1 The second part of the question is obvious from Theorem 2.6. Three.IV.2.33 Here are four solutions. T = ⎛ ⎞ ( ) ±1 0 0 ±1 Three.IV.2.34 (a) The vector space M 2×2 has dimension four. The set {T 4 , . . . , T, I} has five elements and thus is linearly dependent. (b) Where T is n × n, generalizing the argument from the prior item shows that there is such a polynomial of degree n 2 or less, since {T n2 , . . . , T, I} is a n 2 +1-member subset of the n 2 -dimensional space M n×n . (c) First compute the powers ( √ ) ( ) ( √ ) T 2 1/2 − 3/2 = √ T 3 0 −1 = T 4 = √ −1/2 − 3/2 3/2 1/2 1 0 3/2 −1/2 (observe that rotating by π/6 three times results in a rotation by π/2, which is indeed what T 3 represents). Then set c 4 T 4 + c 3 T 3 + c 2 T 2 + c 1 T + c 0 I equal to the zero matrix ( √ ) ( ) ( √ ) (√ ) ( ) √ −1/2 − 3/2 0 −1 1/2 − 3/2 c 4 + c 3/2 −1/2 1 0 3 + √ 3/2 −1/2 c 2 + √ 1 0 c 1 + c 3/2 1/2 1/2 3/2 0 1 0 ) = ⎛ ⎞ ⎛ ⎞ ( 0 0 0 0
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<strong>Answers</strong> to <strong>Exercises</strong> 111<br />
Three.IV.2.29 The ‘commutes with’ relation is reflexive and symmetric. However, it is not transitive:<br />
for instance, with ( ) ( ) ( )<br />
1 2 1 0 5 6<br />
G = H = J =<br />
3 4 0 1 7 8<br />
G commutes with H and H commutes with J, but G does not commute with J.<br />
Three.IV.2.30<br />
(a) Either<br />
⎛ ⎞<br />
of these.<br />
⎛ ⎞<br />
⎛<br />
⎞<br />
⎝ x y⎠ ↦−→<br />
πx ⎝ x 0⎠ y<br />
↦−→ ⎝ 0 0⎠<br />
⎝ x y⎠ y<br />
↦−→ ⎝ 0 y⎠ ↦−→<br />
πx ⎝ 0 0⎠<br />
z 0 0 z 0 0<br />
(b) The composition is the fifth derivative map d 5 /dx 5 on the space of fourth-degree polynomials.<br />
(c) With respect to the natural bases,<br />
⎛ ⎞<br />
⎛ ⎞<br />
1 0 0<br />
0 0 0<br />
Rep E3,E 3<br />
(π x ) = ⎝0 0 0⎠ Rep E3,E 3<br />
(π y ) = ⎝0 1 0⎠<br />
0 0 0<br />
0 0 0<br />
and their product (in either order) is the zero matrix.<br />
(d) Where B = 〈1, x, x 2 , x 3 ,<br />
⎛<br />
x 4 〉,<br />
⎞<br />
⎛<br />
⎞<br />
0 0 2 0 0<br />
0 0 0 6 0<br />
Rep B,B ( d2<br />
dx 2 ) = 0 0 0 6 0<br />
⎜0 0 0 0 12<br />
⎟ Rep B,B ( d3<br />
⎝0 0 0 0 0 ⎠<br />
dx 3 ) = 0 0 0 0 24<br />
⎜0 0 0 0 0<br />
⎟<br />
⎝0 0 0 0 0 ⎠<br />
0 0 0 0 0<br />
0 0 0 0 0<br />
and their product (in either order) is the zero matrix.<br />
Three.IV.2.31 Note that (S + T )(S − T ) = S 2 − ST + T S − T 2 , so a reasonable try is to look at<br />
matrices that do not commute so that −ST ( and)<br />
T S don’t ( cancel: ) with<br />
1 2 5 6<br />
S = T =<br />
3 4 7 8<br />
we have the desired inequality.<br />
( )<br />
( )<br />
−56 −56<br />
(S + T )(S − T ) =<br />
S 2 − T 2 −60 −68<br />
=<br />
−88 −88<br />
−76 −84<br />
Three.IV.2.32 Because the identity map acts on the basis B as β ⃗ 1 ↦→ β ⃗ 1 , . . . , β ⃗ n ↦→ β ⃗ n , the representation<br />
is this.<br />
⎛<br />
⎞<br />
1 0 0 0<br />
0 1 0 0<br />
0 0 1 0<br />
⎜<br />
⎝<br />
. ..<br />
⎟<br />
⎠<br />
0 0 0 1<br />
The second part of the question is obvious from Theorem 2.6.<br />
Three.IV.2.33<br />
Here are four solutions.<br />
T =<br />
⎛<br />
⎞<br />
( )<br />
±1 0<br />
0 ±1<br />
Three.IV.2.34 (a) The vector space M 2×2 has dimension four. The set {T 4 , . . . , T, I} has five elements<br />
and thus is linearly dependent.<br />
(b) Where T is n × n, generalizing the argument from the prior item shows that there is such a<br />
polynomial of degree n 2 or less, since {T n2 , . . . , T, I} is a n 2 +1-member subset of the n 2 -dimensional<br />
space M n×n .<br />
(c) First compute the powers<br />
( √ ) ( ) ( √ )<br />
T 2 1/2 − 3/2<br />
= √ T 3 0 −1<br />
=<br />
T 4 = √ −1/2 − 3/2<br />
3/2 1/2 1 0<br />
3/2 −1/2<br />
(observe that rotating by π/6 three times results in a rotation by π/2, which is indeed what T 3<br />
represents). Then set c 4 T 4 + c 3 T 3 + c 2 T 2 + c 1 T + c 0 I equal to the zero matrix<br />
( √ ) ( ) ( √ ) (√ ) ( )<br />
√ −1/2 − 3/2 0 −1 1/2 − 3/2<br />
c 4 + c 3/2 −1/2 1 0 3 + √ 3/2 −1/2<br />
c 2 + √ 1 0<br />
c 1 + c 3/2 1/2 1/2 3/2 0 1 0<br />
)<br />
=<br />
⎛<br />
⎞<br />
⎛<br />
⎞<br />
(<br />
0 0<br />
0 0