ST and TS have the same eigenvalue

In summary: Since ##ST## is not invertible, there must be at least one of its eigenvalues equal to ##0##. But isn't it what we were starting from?
  • #1
maNoFchangE
116
4

Homework Statement


Prove that, if ##T,S\in \mathcal{L}(V)## then ##TS## and ##ST## have the same eigenvalues.

Homework Equations

The Attempt at a Solution


Suppose ##T## is written in a basis in which its matrix is upper triangular, and so is ##S## (these bases may be of different list of vectors in ##V##). Since both ##T## and ##S## are upper triangular, ##TS## and ##ST## are also upper triangular. Now, the diagonal element of ##TS## is
$$
\sum_i T_{pi}S_{ip}
$$
which is the same as the diagonal element of ##ST##
$$
\sum_i S_{pi}T_{ip}
$$
Therefore, ##ST## and ##TS## have the same eigenvalues.
What bothers me is my starting assumption; taking ##T## and ##S## in their own upper triangular bases. Since these bases can be a different list of vectors, is it alright then to multiply their matrices?
 
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  • #2
maNoFchangE said:

Homework Statement


Prove that, if ##T,S\in \mathcal{L}(V)## then ##TS## and ##ST## have the same eigenvalues.

Homework Equations

The Attempt at a Solution


Suppose ##T## is written in a basis in which its matrix is upper triangular, and so is ##S## (these bases may be of different list of vectors in ##V##). Since both ##T## and ##S## are upper triangular, ##TS## and ##ST## are also upper triangular. Now, the diagonal element of ##TS## is
$$
\sum_i T_{pi}S_{ip}
$$
which is the same as the diagonal element of ##ST##
$$
\sum_i S_{pi}T_{ip}
$$
Therefore, ##ST## and ##TS## have the same eigenvalues.
What bothers me is my starting assumption; taking ##T## and ##S## in their own upper triangular bases. Since these bases can be a different list of vectors, is it alright then to multiply their matrices?
Assume ##\lambda## is an eigenvalue of ##TS##.
Then, by definition of an eigenvalue, there exists an ##x\in V,\ x\neq \vec0## such that ##TSx=\lambda x##.
Now try to prove that ##\lambda## is an eigenvalue of ##ST##.

(You don't need a matrix representation of the operators.)
 
  • #3
Multiplying
$$
TSx=\lambda x
$$
with ##S##, I get
$$
ST(Sx) = \lambda (Sx)
$$
Alright, that looks good. But how do I prove that ##Sx## is not zero given ##x \neq 0##?
 
  • #4
maNoFchangE said:
Multiplying
$$
TSx=\lambda x
$$
with ##T##, I get
$$
ST(Tx) = \lambda (Tx)
$$
Alright, that looks good. But how do I prove that ##Tx## is not zero given ##x \neq 0##?
You can't. There is no reason why ##Tx## couldn't be the 0 vector.
Hint: multiply from the other side.
 
  • #5
I have edited my post.
Samy_A said:
You can't.
Why can't I have ##Sx=0##? What if ##x\in \textrm{null }S##?
 
  • #6
maNoFchangE said:
Multiplying
$$
TSx=\lambda x
$$
with ##S##, I get
$$
ST(Sx) = \lambda (Sx)
$$
Alright, that looks good. But how do I prove that ##Sx## is not zero given ##x \neq 0##?
Assume ##Sx=\vec 0##. What does that imply, given that ##TSx=\lambda x##?
 
  • #7
Samy_A said:
Assume ##Sx=\vec 0##. What does that imply, given that ##TSx=\lambda x##?
That means ##\lambda x=0##, since ##x \neq 0## then ##\lambda=0##. But what does it say about ##Sx## being not 0?
 
  • #8
maNoFchangE said:
That means ##\lambda x=0##, since ##x \neq 0## then ##\lambda=0##. But what does it say about ##Sx## being not 0?
Take the case ##\lambda \neq 0## first. What have you then proven?
 
  • #9
Samy_A said:
Take the case ##\lambda \neq 0## first. What have you then proven?
In that case ##x=0##, which disproves the starting hypothesis that ##x\neq 0##. Therefore in this case, ##Sx## cannot be zero.
But in the case ##\lambda=0##, what next?
It does not seem to violate any hypothesis we have assumed.
 
  • #10
maNoFchangE said:
In that case ##x=0##, which disproves the starting hypothesis that ##x\neq 0##. Therefore in this case, ##Sx=0##.
But in the case ##\lambda=0##, what next?
It does not seem to violate any hypothesis we have assumed.
Ok, so you have proven that ##TS## and ##ST## have the same non-zero eigenvalues.

Now for the tricky case ##\lambda =0##.
Still assuming ##x \neq \vec 0##, you have ##TSx=\vec0##, and thus ##ST(Sx)=\vec 0##.
If ##Sx \neq \vec 0##, then we are done.

If ##Sx =\vec 0##, ##S## is not injective.
At this point, you need to take into account that ##V## is finite dimensional (you didn't specifically state this, but since you used matrices, I assume that it is the case).
If ##S## is not injective, it is not surjective. What does that tell you about ##ST##?
 
  • #11
If ##S## is not surjective, then ##ST## is not surjective either.
Sorry I just can't see where this is leading to. If ##ST## is not surjective, is it inconsistent with one of our assumptions?
 
  • #12
maNoFchangE said:
If ##S## is not surjective, then ##ST## is not surjective either.
Sorry I just can't see where this is leading to. If ##ST## is not surjective, is it inconsistent with one of our assumptions?
Correct, ##ST## is not surjective.
Can it be injective? If not, what does that imply as far as eigenvalues of ##ST## are concerned?
 
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  • #13
Samy_A said:
Correct, ##ST## is not surjective.
Can it be injective? If not, what does that imply as far as eigenvalues of ##ST## are concerned?
Since ##ST## is an operator in a complex vector space, if it's not surjective, it will not be injective nor it be invertible, am I right?
Since ##ST## is not invertible, there must be at least one of its eigenvalues equal to ##0##. But isn't it what we were starting from? In post #10, we have set ##\lambda=0##, aren't we just circulating here?
 
  • #14
maNoFchangE said:
Since ##ST## is an operator in a complex vector space, if it's not surjective, it will not be injective nor it be invertible, am I right?
Since ##ST## is not invertible, there must be at least one of its eigenvalues equal to ##0##. But isn't it what we were starting from? In post #10, we have set ##\lambda=0##, aren't we just circulating here?
No, in post #10 we started with an eigenvalue of ##TS##. You now have proved that if 0 is an eigenvalue of ##TS##, it also is an eigenvalue of ##ST##.
We are not circulating, we went from ##TS## to ##ST##.

Notice that for the case of eigenvalue 0, ##V## has to have finite dimension. If ##V## is an infinite dimensional vector space, it is possible for ##TS## to have 0 as eigenvalue, while ##ST## is injective.
 
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  • #15
I see! I was not aware that we have been switching to calculating the eigenvalue of ##ST##. So, ##\lambda=0## is also an eigevalue of ##ST##, and the corresponding eigenvector is zero, is that right?
 
  • #16
maNoFchangE said:
I see! I was not aware that we have been switching to calculating the eigenvalue of ##ST##.
Yes, that is the whole purpose of this exercise.
maNoFchangE said:
So, ##\lambda=0## is also an eigevalue of ##ST##, and the corresponding eigenvector is zero, is that right?
It is an eigenvalue, but we can't say anything about the eigenvector.
Certainly the eigenvector is not the zero vector. An eigenvector is, by definition, never the zero vector.
For a simple reason: ##T\vec 0=\lambda \vec 0## is true for any linear operator ##T## and any scalar ##\lambda##.
 
  • #17
Ok many thanks for the help.
Just one more thing, my original question was actually is it allowed to multiply two square matrices which are represented in different bases?
 
  • #18
maNoFchangE said:
Ok many thanks for the help.
Just one more thing, my original question was actually is it allowed to multiply two square matrices which are represented in different bases?
No, you can't just do that. You can of course multiply the matrices, but the resulting matrix is not necessarily the matrix representing the composition of the two operators (for what basis should that be?)
 
  • #19
Samy_A said:
No, you can't just do that. You can of course multiply the matrices, but the resulting matrix is not necessarily the matrix representing the composition of the two operators (for what basis should that be?)
Ah, that makes sense.
 

1. What does it mean for ST and TS to have the same eigenvalue?

When ST (a transformation of a vector space S to a vector space T) and TS (a transformation of a vector space T to a vector space S) have the same eigenvalue, it means that there exists a non-zero vector v in the intersection of S and T, such that both ST(v) and TS(v) are equal to the same scalar multiple of v.

2. Why is it significant for ST and TS to have the same eigenvalue?

This property is significant because it connects the eigenvalues of two different transformations, ST and TS, which are usually considered separately. It also provides insight into the relationship between S and T as vector spaces.

3. How can I determine if ST and TS have the same eigenvalue?

To determine if ST and TS have the same eigenvalue, you can find the eigenvalues and eigenvectors of both transformations and check if there are any common eigenvalues. Alternatively, you can use the commutative property of matrix multiplication to show that ST and TS have the same eigenvalues.

4. Is it possible for ST and TS to have the same eigenvalue but be different transformations?

Yes, it is possible for ST and TS to have the same eigenvalue but be different transformations. This can happen if the eigenvalue is repeated or if the eigenvectors are different for the two transformations.

5. What does it mean if ST and TS have the same eigenvalue for all eigenvalues?

If ST and TS have the same eigenvalue for all eigenvalues, it means that the two transformations have a very strong relationship and are likely to be similar in some way. It also suggests that S and T may have similar structures or properties as vector spaces.

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