Proving Eigenvalues and Eigenvectors for T and T*: A Comprehensive Guide

In summary, the conversation discusses the relationship between eigenvalues and eigenvectors of T and T*, and whether they are always shared between the two operators. It is stated that if T is a normal operator, then this is true, but there is no proof because it is not always the case. The question is raised about the converse, but it is not mentioned in the textbook.
  • #1
mathboy
182
0
I know that if T has eigenvalue k, then T* has eigenvalue k bar. But if T has eigenvector x, does T* also have eigenvector x? If so, how do you prove it? I don't see that in my textbook.
 
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  • #2
There is no proof because it is not true. For example, if
[tex]T= \left[\begin{array}{cc}0 & i \\i & 0\end{array}right][/tex]
Then T has eigenvalue i, with eigenvecor [a, a] and eigenvalue -i with eigenvector [1, -1].
[tex]T*= \left[\begin{array}{cc}0 & -i \\ -i & 0\end{array}\right][/tex]
which also eigenvalue i but now with eigenvectors [a, -a] and eigenvalue -i with eigenvectors [a, a].
 
  • #3
Ok, I just read that it is true if T is a normal operator. Thanks.
 
  • #4
What about the converse? Namely, if T and T* share their eigenvectors, is T necessarily normal?
 
  • #5
morphism said:
What about the converse? Namely, if T and T* share their eigenvectors, is T necessarily normal?

I don't know if you are asking rhetorically or not. But my textbook doesn't state the converse.
 
  • #6
I'm just throwing it out there. It may be a good exercise to think about this.
 

1. What is the concept of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are concepts in linear algebra that are used to analyze the properties of a linear transformation or a matrix. Eigenvalues represent the scaling factor of the eigenvectors when the transformation is applied, and they can reveal important information about the transformation, such as its stability or invertibility.

2. Why is proving eigenvalues and eigenvectors important?

Proving the existence of eigenvalues and eigenvectors is important because it allows us to understand the behavior of a linear transformation or a matrix. It can help us determine the stability of a system, find the optimal solution to a problem, or analyze the properties of a matrix. Additionally, eigenvalues and eigenvectors are used in various fields of science, including physics, engineering, and computer science.

3. How do you prove eigenvalues and eigenvectors for T and T*?

To prove eigenvalues and eigenvectors for T and T*, you can use various methods such as the characteristic polynomial method, the diagonalization method, or the power method. These methods involve finding the characteristic polynomial of the matrix, solving for its roots, and then finding the corresponding eigenvectors.

4. What are some applications of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors have many applications in various fields. In physics, they are used to represent the energy levels of quantum systems. In engineering, they are used to analyze the stability of structures. In computer science, they are used in data compression and image processing. They are also used in statistics, finance, and many other areas of science and technology.

5. Are there any limitations to the methods used for proving eigenvalues and eigenvectors?

Yes, there are some limitations to the methods used for proving eigenvalues and eigenvectors. For example, the characteristic polynomial method may not always work for matrices with complex eigenvalues. Additionally, the power method may not converge or may converge to a wrong eigenvector if the matrix is not diagonalizable. It is important to choose the appropriate method depending on the properties of the matrix and the desired results.

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