Negative determinants when calculating eigenvectors?

In summary: C^{-1} then, indeed your determinant will be -2 because you're also changing the order of the columns of C and you have to take into account the change of sign in the determinant of C^{-1} . In fact, this is the only way in which your claim can be made true.
  • #1
tamtam402
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Let M be a transformation matrix. C is the matrix which diagonalizes M.

I'm trying to use the formula D = C-1MC. I noticed that depending on how I arrange my vectors in C, I can change the sign of the determinant. If I calculate D using a configuration of C that gives me a negative value for the determinant, my matrix D will have a negative sign in front of the eigenvalues on it's diagonal. (Note: the determinant is needed when calculating the inverse of C, so a negative determinant will multiply the C-1MC equation by -1 )

However, I've read that the matrix D should always have the eigenvalues on it's diagonal, and I've also heard that it doesn't matter how you set-up the matrix C, as long as the eigenvectors are all there.

What's going on? Should I always make sure to use a configuration of C that will get me a positive determinant?
 
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  • #2
tamtam402 said:
Let M be a transformation matrix. C is the matrix which diagonalizes M.

I'm trying to use the formula D = C-1MC. I noticed that depending on how I arrange my vectors in C, I can change the sign of the determinant.

$${}$$
No, you can't. The determinant is an invariant of matrices (linear operators), and it equals the product of all the matrix's eigenvalues

(in some extension field, probably).

DonAntonio




If I calculate D using a configuration of C that gives me a negative value for the determinant, my matrix D will have a negative sign in front of the eigenvalues on it's diagonal. (Note: the determinant is needed when calculating the inverse of C, so a negative determinant will multiply the C-1MC equation by -1 )

However, I've read that the matrix D should always have the eigenvalues on it's diagonal, and I've also heard that it doesn't matter how you set-up the matrix C, as long as the eigenvectors are all there.

What's going on? Should I always make sure to use a configuration of C that will get me a positive determinant?
 
  • #3
You must be making a mistake somewhere, but without a specific example (preferably a small one that is easy to check!) we can't really say much more than post #2 already said.
 
  • #4
If you change the order of vectors, you change the matrix so it is NOT correct to say that you have changed the sign on an eigenvalue. You have an eigenvalue of a different matrix that has a different sign.

In particular, we can always represent an abstract linear operator on a vector space by a matrix by choosing a specific ordered basis for the vector space. But changing the basis or just changing the order of the vectors in the basis will represent the same linear operator by different matrix.
 
  • #5
Let's suppose I have 2 eigenvalues: 1 and 3.

The eigenvector for the first eigenvalue is (1,-1). The eigenvector for the second eigenvalue is (1,1).

I can write my matrix in the following form, which has a determinant equal to -2.

1 1
1 -1

I can also write my matrix in the following form, which has a determinant equal to 2.

1 1
-1 1
 
  • #6
I think I see what you're getting at here, you can write the eigenvector matrix in a different order which will make the determinant change sign
BUT
you have [itex]C^{-1}[/itex] there too
[itex]det(C^{-1} M C) = det(C^{-1}) det(M) det(C) = \frac{1}{det(C)} det(M) det(C) = \frac{det(C)}{det(C)} det(M) = det(M)[/itex]
Is this what was confusing you?
 
  • #7
tamtam402 said:
Let's suppose I have 2 eigenvalues: 1 and 3.

The eigenvector for the first eigenvalue is (1,-1). The eigenvector for the second eigenvalue is (1,1).

I can write my matrix in the following form, which has a determinant equal to -2.

1 1
1 -1

I can also write my matrix in the following form, which has a determinant equal to 2.

1 1
-1 1
This "example" is like the example of a (plane) triangle (in Euclidean geometry) with sides 3, 6, 9: impossible.

You cannot have a 2x2 matrix with eigenvalues 1,3 and its determinant different from 3: impossible. This is why, I

suppose, you were already asked to give a specific example , not only throwing numbers as eigenvalues

and "let's say an eigenvalue is..." things.

DonAntonio
 
  • #8
genericusrnme said:
I think I see what you're getting at here, you can write the eigenvector matrix in a different order which will make the determinant change sign
BUT
you have [itex]C^{-1}[/itex] there too
[itex]det(C^{-1} M C) = det(C^{-1}) det(M) det(C) = \frac{1}{det(C)} det(M) det(C) = \frac{det(C)}{det(C)} det(M) = det(M)[/itex]
Is this what was confusing you?

Yes, thank you. Somehow it was my first time getting a negative determinant, and I also messed up elsewhere in the process. That's what led me to believe that negative determinants were bad :)
 
  • #9
DonAntonio said:
This "example" is like the example of a (plane) triangle (in Euclidean geometry) with sides 3, 6, 9: impossible.

You cannot have a 2x2 matrix with eigenvalues 1,3 and its determinant different from 3: impossible. This is why, I

suppose, you were already asked to give a specific example , not only throwing numbers as eigenvalues

and "let's say an eigenvalue is..." things.

DonAntonio

Please see the post above yours, it seems like my example was specific enough.
 
  • #10
tamtam402 said:
Please see the post above yours, it seems like my example was specific enough.

Not really. We still don't know what you did wrong.
 
  • #11
tamtam402 said:
Please see the post above yours, it seems like my example was specific enough.


No, it isn't, and I find it odd that you stubbornly insist on. You must give us one specific matrix where you have

specifically calculated its eigenvalues and its eigenvectors and and where you've specifically changed the order of the eigenvectors and

get a DIFFERENT determinant. Of course, you can't do this but if you send what the above we'll be able, perhaps, to specifically

point where your mistake is.

BTW, my last post has a typo: of course that the determinant of any 2x2 matrix with eigenvalues 1,3 will ALWAYS be 3. I meant

to write -3 referring to your claim that you can get the opposite sign.

DonAntonio

Pd If you refer, as "specific example, to the matrices
[tex]\begin{pmatrix}1&1\\1&-1\end{pmatrix}\,\,,\,\,\begin{pmatrix}1&1\\-1&1\end{pmatrix}[/tex]
then this a non-example as these matrices have neither the same determinant nor the same trace so they can't

be similar. Please do present us the ORIGINAL matrix before you try to change it.
 
  • #12
micromass, donantonio, the problem the guy was having is this;

Let A be some matrix then we can decompose it as [itex]A = S D S^{-1}[/itex]. But this isn't dependant on the order we write the eigenvalues and eigen vectors, so we can change the rows of S and alter D whilst leaving A unchanged. He knew that permuting the rows of a matrix changes the sign of its determinant which lead him to be confused since he knows the determinant of the RHS should be equal to the determinant of the LHS. He was forgetting about the fact that there is an [itex]S[/itex] and an [/itex]S^{-1}[/itex] which will cancel any changes caused by changing rows of S. He's not trying to claim that two matrices with permuted rows are the same or have the same eigen values or anything along those lines.
 
  • #13
genericusrnme said:
micromass, donantonio, the problem the guy was having is this;

Let A be some matrix then we can decompose it as [itex]A = S D S^{-1}[/itex]. But this isn't dependant on the order we write the eigenvalues and eigen vectors, so we can change the rows of S and alter D whilst leaving A unchanged. He knew that permuting the rows of a matrix changes the sign of its determinant which lead him to be confused since he knows the determinant of the RHS should be equal to the determinant of the LHS. He was forgetting about the fact that there is an [itex]S[/itex] and an [/itex]S^{-1}[/itex] which will cancel any changes caused by changing rows of S. He's not trying to claim that two matrices with permuted rows are the same or have the same eigen values or anything along those lines.



Wonderful. Why then didn't he send any specific example of a matrix with that decomposition? That could have cleared things out way before.

DonAntonio
 
  • #14
DonAntonio said:
Wonderful. Why then didn't he send any specific example of a matrix with that decomposition? That could have cleared things out way before.

DonAntonio

Sorry, I didn't check back on the thread after genericusrnme replied. I didn't post a specific example because I had trashed my work and I couldn't find out what exercise I had failed. When I came back to the thread I was ready to do the exercise from scratch to post my solution, but I saw genericusrnme's post and he proved that the sign of the determinant should have no impact on the solution, so I had surely screwed up a calculation somewhere.
 

FAQ: Negative determinants when calculating eigenvectors?

1. What are negative determinants?

Negative determinants are values that result from the calculation of a matrix's determinant. The determinant is a mathematical value that is used to determine properties of a matrix, such as whether it is invertible or singular. A negative determinant means that the matrix is not invertible and has no inverse.

2. How do negative determinants affect the calculation of eigenvectors?

Negative determinants can affect the calculation of eigenvectors in that they can indicate that the matrix does not have any eigenvalues or eigenvectors. This means that the matrix cannot be diagonalized, and the eigenvalues and eigenvectors cannot be calculated using traditional methods.

3. Can a matrix have negative determinants but still have eigenvalues and eigenvectors?

No, a matrix with negative determinants cannot have eigenvalues or eigenvectors. This is because a negative determinant indicates that the matrix is not invertible, and without an inverse, the matrix cannot have eigenvalues or eigenvectors.

4. What are some real-world applications of negative determinants in the calculation of eigenvectors?

Negative determinants can be used in various fields of science and engineering, such as physics, chemistry, and economics. For example, in physics, negative determinants can indicate the stability of a system, while in economics, they can represent the profitability of a business.

5. How can one deal with negative determinants when calculating eigenvectors?

If a matrix has negative determinants, it means that the matrix cannot be diagonalized using traditional methods. In this case, alternative methods such as the Jordan decomposition or the singular value decomposition can be used to calculate the eigenvalues and eigenvectors of the matrix.

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