Eigenvalues of a linear map over a finite field

In summary, the conversation discusses the application of Fermat's little theorem to prove that the Frobenius morphism in a finite field is a linear map, and that the geometric multiplicity of 1 as an eigenvalue of this map is 1. The conversation also explores how to show that 2 is not an eigenvalue in a finite field with dimension 2 over Z_7, by using the fact that the field has p^n elements and the application of Lagrange's theorem.
  • #1
snipez90
1,101
5

Homework Statement


Let F be a finite field of characteristic p. As such, it is a finite
dimensional vector space over Z_p.
(a) Prove that the Frobenius morphism T : F -> F, T(a) = a^p is a
linear map over Z_p.
(b) Prove that the geometric multiplicity of 1 as an eigenvalue of T
is 1.
(c) Let F have dimension 2 over Z_7. Prove that 2 is not an eigenvalue
of T.

Homework Equations


Fermat's little theorem (lagrange's theorem applied to multiplicative group)

The Attempt at a Solution


I got a) and b), which are essentially straightforward applications of Fermat's little theorem. For c), I'm trying to show that T has 2 eigenvalues (neither of which is 2 of course) since it's a well-known theorem that a linear map cannot have more than dim(F) eigenvalues. Again 1 is an eigenvalue by Fermat's theorem as before, but I can't find another eigenvalue. Is there a better approach? Thanks in advance.
 
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  • #2
What's T^7(a)?
 
  • #3
Hmm, I think I made this way harder than it should be. We already have a^7 = a. If a^7 = 2a, we would have a = 0, which we could have forbidden since 0 is never considered an eigenvector.

But supposing a =/= 0 is an eigenvector with eigenvalue x, calculating T^7(a) gives a(x^7 - 1) = 0 which implies x^7 - 1 = 0, and x cannot be 2. Is this what you were getting at Dick? Thanks.
 
  • #4
snipez90 said:
Hmm, I think I made this way harder than it should be. We already have a^7 = a. If a^7 = 2a, we would have a = 0, which we could have forbidden since 0 is never considered an eigenvector.

But supposing a =/= 0 is an eigenvector with eigenvalue x, calculating T^7(a) gives a(x^7 - 1) = 0 which implies x^7 - 1 = 0, and x cannot be 2. Is this what you were getting at Dick? Thanks.

Almost, but not quite. a^7 is not necessarily equal to a. And T^7(a) isn't a^7. It's a^49. Since T(a)=a^7. Can you fix that up? You need to use that F has dimension 2. Can you use that to show a^49=a?
 
Last edited:
  • #5
Bah, I wondered why the hypothesis that F has dim 2 over Z_7 seemed confusing to me. Of course an n-dimensional vector space over Z_p has p^n elements. Here V has 49 elements. V\{0} is multiplicative, so by Lagrange, a^48 = 1, whence a^49 = a, and the original proof carries out correctly when modified. Thanks.
 
  • #6
snipez90 said:
Bah, I wondered why the hypothesis that F has dim 2 over Z_7 seemed confusing to me. Of course an n-dimensional vector space over Z_p has p^n elements. Here V has 49 elements. V\{0} is multiplicative, so by Lagrange, a^48 = 1, whence a^49 = a, and the original proof carries out correctly when modified. Thanks.

Bingo.
 
  • #7
Okay my friend pointed out that part b) might not be as simple as I thought it to be. I assumed that F had dimension 1 over Z_p in part b), which implies a^p = a for any a in V = F/Z_p which means that the eigenspace has the same dimension as V.

But if F is n-dimensional, then F has p^n elements. Then you deduce a^(p^n) = a, but how do you go from that to a^p = a?
 
  • #8
snipez90 said:
Okay my friend pointed out that part b) might not be as simple as I thought it to be. I assumed that F had dimension 1 over Z_p in part b), which implies a^p = a for any a in V = F/Z_p which means that the eigenspace has the same dimension as V.

But if F is n-dimensional, then F has p^n elements. Then you deduce a^(p^n) = a, but how do you go from that to a^p = a?

You don't. You know p roots of a^p-a=0. So it factors completely. How many roots can it have?
 
  • #9
You were very much right in using Fermat's little theorem. By using it, you should be able to prove that

[tex]
\mathbb{F}_p \subseteq \text{ker}(T-I)
[/tex]

and using Dick's suggestion, you can then show equality.
 

Related to Eigenvalues of a linear map over a finite field

What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are concepts in linear algebra that are used to characterize the behavior of a linear map or transformation. Eigenvalues represent the scalar values that are associated with the eigenvectors, which are the non-zero vectors that remain unchanged (up to a scalar multiple) when the linear map is applied to them.

How are eigenvalues and eigenvectors calculated for a linear map over a finite field?

To calculate the eigenvalues and eigenvectors of a linear map over a finite field, you can use the characteristic polynomial method. This involves finding the characteristic polynomial of the linear map, which is a polynomial equation in the variable lambda. The eigenvalues are then the roots of this polynomial, and the corresponding eigenvectors can be found by solving the system of linear equations represented by the linear map.

Why are eigenvalues and eigenvectors important in linear algebra?

Eigenvalues and eigenvectors are important because they provide a way to understand and analyze the behavior of a linear map. They can be used to determine whether a linear map has a unique solution, to find a basis for the vector space on which the linear map is defined, and to simplify calculations involving the linear map.

Can a linear map over a finite field have complex eigenvalues?

No, a linear map over a finite field can only have real eigenvalues. This is because the field of scalars used in linear algebra over a finite field is always a subset of the real numbers, and therefore the eigenvalues must also be real numbers.

How do eigenvalues and eigenvectors change if the linear map is scaled by a constant factor?

If a linear map is scaled by a constant factor, the eigenvalues will also be scaled by the same factor. However, the eigenvectors will remain unchanged. This is because eigenvectors are only defined up to a scalar multiple, so they are not affected by scaling the linear map.

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