Find the canonical representative of the matrix-equivalence class of
each matrix.
Answer
We need only decide what the rank of each is.
Problem 3
Suppose that, with respect to
the transformation is represented by
this matrix.
Use change of basis matrices to represent with respect
to each pair.
,
,
Answer
Recall the diagram
and the formula.
These two
show that
and similarly these two
give the other nonsingular matrix.
Then the answer is this.
Although not strictly necessary, a check is reassuring.
Arbitrarily fixing
we have that
and so is this.
Doing the calculation with respect to starts with
and then checks that this is the same result.
These two
show that
and these two
show this.
With those, the conversion goes in this way.
As in the prior item, a check provides some confidence that this
calculation was performed without mistakes.
We can for instance, fix the vector
(this is selected for no reason, out of thin air).
Now we have
and so is this vector.
With respect to we first calculate
and, sure enough, that is the same result for .
This exercise is recommended for all readers.
Problem 4
What sizes are and in the equation ?
Answer
Where and are , the matrix is while is .
This exercise is recommended for all readers.
Problem 5
Use Theorem 2.6 to show that a square matrix is nonsingular if and only if it is equivalent to an identity matrix.
Answer
Any matrix is nonsingular if and only if it has
rank , that is, by Theorem 2.6,
if and only if it is matrix equivalent to
the matrix whose diagonal is all ones.
This exercise is recommended for all readers.
Problem 6
Show that, where is a nonsingular square matrix, if
and are nonsingular square matrices such that
then .
By the definition following Example 2.2, a matrix is diagonalizable if it represents a transformation with the property that there is some basis such that is a diagonal matrix— the starting and ending bases must be equal. But Theorem 2.6 says only that there are and such that we can change to a representation and get a diagonal matrix. We have no reason to suspect that we could pick the two and so that they are equal.
Problem 8
Must matrix equivalent matrices have matrix equivalent transposes?
Answer
Yes. Row rank equals column rank, so the rank of the transpose equals the rank of the matrix. Same-sized matrices with equal ranks are matrix equivalent.
Show that matrix-equivalence is an equivalence relation.
Answer
For reflexivity, to show that any matrix is matrix equivalent to itself, take and to be identity matrices. For symmetry, if then (inverses exist because and are nonsingular). Finally, for transitivity, assume that and that . Then substitution gives . A product of nonsingular matrices is nonsingular (we've shown that the product of invertible matrices is invertible; in fact, we've shown how to calculate the inverse) and so is therefore matrix equivalent to .
This exercise is recommended for all readers.
Problem 11
Show that a zero matrix is alone in its matrix equivalence
class.
Are there other matrices like that?
Answer
By Theorem 2.6, a zero matrix is alone in its class because it is the only of rank zero. No other matrix is alone in its class; any nonzero scalar product of a matrix has the same rank as that matrix.
Problem 12
What are the matrix equivalence classes of matrices of
transformations on ?
?
Answer
There are two matrix-equivalence classes of matrices— those of rank zero and those of rank one. The matrices fall into four matrix equivalence classes.
Problem 13
How many matrix equivalence classes are there?
Answer
For matrices there are classes for each possible rank: where is the minimum of and there are classes for the matrices of rank , , ..., . That's classes. (Of course, totaling over all sizes of matrices we get infinitely many classes.)
Problem 14
Are matrix equivalence classes closed under scalar
multiplication?
Addition?
Answer
They are closed under nonzero scalar multiplication, since a nonzero scalar multiple of a matrix has the same rank as does the matrix. They are not closed under addition, for instance, has rank zero.
Problem 15
Let represented by
with respect to .
Find in this specific case.
Describe in the general case where
.
Answer
We have
and thus the answer is this.
As a quick check, we can take a vector at random
giving
while the calculation with respect to
yields the same result.
We have
and, as in the first item of this question
so, writing for the matrix whose columns are the basis vectors,
we have that .
Problem 16
Let have bases and and
suppose that has the basis .
Where , find the formula that computes
from .
Repeat the prior question with one basis
for and two bases for .
Answer
The adapted form of the arrow diagram is this.
Since there is no need to change bases in
(or we can
say that the change of basis matrix is the identity), we have
where
.
Here, this is the arrow diagram.
We have that where
.
Problem 17
If two matrices are matrix-equivalent and invertible,
must their
inverses be matrix-equivalent?
If two matrices have matrix-equivalent inverses, must the two
be matrix-equivalent?
If two matrices are square and matrix-equivalent, must their
squares be matrix-equivalent?
If two matrices are square and have matrix-equivalent squares,
must they be matrix-equivalent?
Answer
Here is the arrow diagram, and a version of that diagram
for inverse functions.
Yes, the inverses of the matrices represent the
inverses of the maps.
That is, we can move from the lower right to the lower left by
moving up, then left, then down.
In other words, where (and invertible)
and are invertible then
.
Yes; this is the prior part repeated in different terms.
No, we need another assumption: if represents
with respect to the same starting as ending bases ,
for some then represents
.
As a specific example,
these two matrices are both rank one and so they are
matrix equivalent
but the squares are not matrix equivalent— the square of the
first has rank one while the square of the second has rank zero.
No.
These two are not matrix equivalent but have matrix equivalent
squares.
This exercise is recommended for all readers.
Problem 18
Square matrices are similar if they represent the same
transformation, but each with respect to the same ending as starting
basis.
That is, is similar to .
Give a definition of matrix similarity like that of
Definition 2.3.
Prove that similar matrices are matrix equivalent.
Show that similarity is an equivalence relation.
Show that if is similar to then
is similar to , the cubes are similar, etc.
Contrast with the prior exercise.
Prove that there are matrix equivalent matrices
that are not similar.
Answer
The definition is suggested by the appropriate
arrow diagram.
Call matrices similar if there
is a nonsingular matrix such that
.
Take to be and take to be .
This is as in Problem 10.
Reflexivity is obvious: .
Symmetry is also easy: implies that
(multiply the first equation from the right
by and from the left by ).
For transitivity, assume that and that
.
Then and we are finished
on noting that is an invertible matrix with inverse
.
Assume that .
For the squares:
.
Higher powers follow by induction.
These two are matrix equivalent but their squares are not
matrix equivalent.
By the prior item, matrix similarity and matrix equivalence are thus
different.