Proof that eigenvalues are constants

In summary, the conversation discusses the construction and properties of matrices of the form $$A_i=\left(\begin{array}[cc] ccos(x_i)&sin(x_i)\\sin(x_i)&-cos(x_i)\end{array}\right)$$ and the matrix $$C=A1\otimes A2\otimes 1_4-A1\otimes 1_4\otimes A2$$. The goal is to prove that the eigenvalues of C are independent of the variable x. It is shown that the spectrum of ##A_2\otimes I_4 - I_4\otimes A_2## does not depend on x by using unitary equivalence.
  • #1
jk22
729
24
i suppose matrices of the form $$A_i=\left(\begin{array}[cc] ccos(x_i)&sin(x_i)\\sin(x_i)&-cos(x_i)\end{array}\right)$$

And i consider the matrix

$$C=A1\otimes A2\otimes 1_4-A1\otimes 1_4\otimes A2$$

I would like to show that the eigenvalues of C are independent of the x
i tried with mathematica but i have no proof.
 
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  • #2
What is ##1_4##?
 
  • #3
The identity matrix of dimension 4 $$\left(\begin{array}[cccc]a1&0&0&0\\0&1&0&0\\0&0&1&0\\0&0&0&1\end{array}\right)$$
 
  • #4
What do you mean by ##\otimes##?
 
  • #5
Hawkeye18 said:
What do you mean by ##\otimes##?
The usual Kronecker product or tensor product
 
  • #6
First, your matrices ##A_i## are the reflection matrices: in particular that means that the spectrum of ##A_i## is ##\{-1, 1\}## and that ##A_i## are unitarily equivalent to $$A_0= \left(\begin{array}{cc} 1 & 0 \\ 0 & -1 \end{array} \right),$$ meaning that ##A=U_i A_0 U_i^{-1}##, where ##U_i## are unitary operators (with real entries). Matrices ##U_i## can be easily computed, but it does not matter here.

Since the spectrum ##\sigma(A\otimes B)## is the product of ##\sigma(A)## and ##\sigma (B)## you just need to prove that the spectrum of ##A_2\otimes I_4 - I_4\otimes A_2## does not depend on ##x_2##. To see that you can notice that ##I_4 = I_2\otimes I_2##, so $$A_2\otimes I_4 - I_4\otimes A_2 = A_2\otimes I_2\otimes I_2 - I_2\otimes I_2\otimes A_2, $$ and the latter operator is unitarily equivalent to $$A_0\otimes I_2\otimes I_2 - I_2\otimes I_2\otimes A_0$$ (can you see why?)

So the spectrum does not depend on ##x_2##.
 
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Likes jk22
  • #7
To show this I think we multiply left by $$U\otimes U\otimes U$$ and right by the inverses of U ?

Thanks a lot.
 
  • #8
If by ##U## you mean ##U_2##, then yes, you are correct. You can also use ##U_2\otimes I_2\otimes U_2##.
 

1. What are eigenvalues?

Eigenvalues are a set of numbers associated with a square matrix that represent the scaling factor of the corresponding eigenvector. They are important in linear algebra and are used to solve systems of differential equations, among other applications.

2. How do you prove that eigenvalues are constants?

The proof that eigenvalues are constants involves using the properties of matrix multiplication and linear transformations. It is shown that the eigenvalues remain the same regardless of the basis chosen for the vector space.

3. Why is it important to prove that eigenvalues are constants?

Proving that eigenvalues are constants is important because it allows for the use of eigenvalues and eigenvectors in various applications, such as solving differential equations and finding the optimal solutions to optimization problems.

4. Can you provide an example of a proof that eigenvalues are constants?

One example of a proof that eigenvalues are constants involves using the fact that the eigenvalues of a diagonal matrix are the diagonal entries themselves. By showing that the eigenvalues of a diagonal matrix remain the same regardless of the basis chosen, the proof is complete.

5. Are there any practical implications of the fact that eigenvalues are constants?

Yes, there are many practical implications of the fact that eigenvalues are constants. For example, this property allows for the use of eigenvalues and eigenvectors in various applications, such as data analysis, image processing, and signal processing. It also simplifies the process of solving differential equations, making it more efficient and accurate.

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