Find the eigenvalue of a linear map

In summary, the conversation discusses a linear map with specific properties and the question of finding its eigenvalue. It is determined that the minimal polynomial of the map is $x^2 - 1$, leading to the conclusion that its eigenvalues are $\{-1,1\}$.
  • #1
Barioth
49
0
Hi everyone,
I have this linear map \(\displaystyle A:R^3 \rightarrow R^3\)

I have that \(\displaystyle A(v)=v-2(v\dot ô)ô); v,ô\in R^3 ;||ô||=1\)

I know that \(\displaystyle A(A(v))=v\) this telling me that A is it's own inverse.
From there, how can I find the eigenvalue of A?
Thanks
 
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  • #2
IF $A^2 = I$, then $A^2 - I = 0$, that is, $A$ satisfies the polynomial $x^2 - 1$.

This means the minimal polynomial of $A$ can only be:

$x^2 - 1, x - 1$ or $x + 1$.

Since $A \neq \pm\ I$, it must be that the minimal polynomial of $A$ is $x^2 - 1$, so the eigenvalues of $A$ are $\{-1,1\}$.
 
  • #3
Thanks!
 

1. What is an eigenvalue?

An eigenvalue is a scalar value that represents the scaling factor of a vector when it is transformed by a linear map. It is a characteristic of the linear map and is often denoted by the Greek letter lambda (λ).

2. How do you find the eigenvalues of a linear map?

To find the eigenvalues of a linear map, you need to find the values of λ that satisfy the equation (A-λI)v = 0, where A is the matrix representing the linear map, I is the identity matrix, and v is the eigenvector. This can be done by solving the characteristic equation det(A-λI) = 0.

3. What is the importance of eigenvalues in linear algebra?

Eigenvalues play a crucial role in linear algebra as they provide information about the behavior of linear maps. They help in understanding the stretching or shrinking of vectors, the stability of a system, and the behavior of solutions to differential equations. Eigenvalues also have applications in fields such as computer graphics, quantum mechanics, and data analysis.

4. Can a linear map have complex eigenvalues?

Yes, a linear map can have complex eigenvalues. In fact, if the matrix representing the linear map has complex entries, then the eigenvalues are likely to be complex as well. This is because complex eigenvalues and eigenvectors often arise in systems with oscillatory behavior.

5. Is it possible for a linear map to have no eigenvalues?

No, it is not possible for a linear map to have no eigenvalues. Every square matrix has at least one eigenvalue, even if it is a repeated or complex eigenvalue. However, a linear map can have a zero eigenvalue, which means that the map is singular and does not have a full rank.

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