Eigenvalue proof. (2nd opinion if my proof is right please)

In summary: In other words, if a and b are different eigenvalues, then the eigenspaces corresponding to a and b are different.In summary, if two linear operators A and B commute and have non-degenerate eigenvalues, then they have common eigenfunctions. This can be proven by looking at [A,B]f, where Af=af, and showing that Bf is also an eigenfunction of A corresponding to the eigenvalue a. The fact that the eigenvalues are non-degenerate means that there is only one eigenvector (up to a constant multiple) corresponding to each eigenvalue, which allows us to conclude that A and B have common eigenfunctions.
  • #1
hoch449
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Homework Statement



Prove that if two linear operators A and B commute and have non-degenerate eigenvalues then the two operators have common eigenfunctions.

Homework Equations



[tex][A,B]= AB - BA= 0[/tex]

[tex] Af=af[/tex]

[tex] Bg=cg,\ let\ g=(f+1) --> B(f+1)=c(f+1)\ where\ a\neq c[/tex]



The Attempt at a Solution



[tex]Af[B(f+1)]-B(f+1)[Af]=0[/tex]
[tex]Af(Bf+B)-(Bf+B)Af=0[/tex]

I have stopped here because I feel that I am on the wrong track. I have not used the fact that the eigenvalues are non-degenerate in this proof. Although continuation of what I am doing should show that the two operators commute..
 
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  • #2
You don't want to show they commute. You already know that. You want to show they have common eigenfunctions. Look at [A,B]f where Af=af. That's ABf-BAf=A(Bf)-a(Bf)=0. That tells you Bf is also an eigenfunction of A corresponding to the eigenvalue a. Just like f. Now what does 'nondegenerate' tell you?
 
  • #3
Ah that totally makes sense! Non-degenerate eigenvalues means different ie. (not the same) eigenvalues does it not?
 
  • #4
hoch449 said:
Ah that totally makes sense! Non-degenerate eigenvalues means different ie. (not the same) eigenvalues does it not?

Sort of. It means there is only one eigenvector (up to a constant multiple) corresponding to every eigenvalue.
 

1. What is an eigenvalue?

An eigenvalue, denoted by λ (lambda), is a scalar value that represents a factor by which a linear transformation changes the length of a vector in the vector space.

2. What is an eigenvector?

An eigenvector, denoted by v, is a non-zero vector that when multiplied by a square matrix A, results in a scalar multiple of itself. In other words, when A is applied to v, the resulting vector is parallel to v.

3. How do you prove eigenvalues and eigenvectors?

To prove eigenvalues and eigenvectors, you can use the characteristic equation (det(A-λI) = 0) and solve for λ. The corresponding eigenvectors can be found by solving the system of linear equations (A-λI)v = 0. Alternatively, you can also use the power iteration method or the QR algorithm to find eigenvalues and eigenvectors.

4. What is the purpose of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are important concepts in linear algebra and have many applications in various fields such as physics, engineering, and computer science. They are used to understand the behavior of linear transformations, to solve systems of differential equations, and to perform data analysis and dimensionality reduction.

5. How do you know if your proof of eigenvalues is correct?

One way to check the correctness of your proof is to verify that the eigenvalues you have obtained satisfy the characteristic equation (det(A-λI) = 0) and that the corresponding eigenvectors satisfy the equation (A-λI)v = 0. You can also compare your results with known solutions or use numerical methods to validate your proof.

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