Jordan Forms Problem: Finding Det(A) and Eigenvalues

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In summary, the conversation is about efficiently finding the determinant of a matrix in order to find eigenvalues and use the Cayley-Hamilton theorem. The individual is having trouble finding the geometric multiplicities and is given advice on how to find them using eigenvectors. They are reminded that the dimension of the kernel of (A-λI) is equal to the geometric multiplicity.
  • #1
shaon0
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Homework Statement


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The Attempt at a Solution


I need an efficient way to find the det(A) so I can find the eigenvalues together with the trace or will use Cayley-Hamilton Thm. I can find the algebraic multiplicities but cannot find the geometric ones. If a matrix is diagonalisable; then AM=GM?
 

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  • #2
You do NOT need to find the determimant of that matrix. You are given one eigenvector and asked to show that it is an eigenector and find the corresponding eigenvalue. Solve the equation
[tex]\begin{bmatrix} 7 & -1 & 3 & 1 \\ -2 & 6 & -3 & -1 \\ -4 & 2 & -1 & -2 \\4 & -2 & 6 & 7 \end{bmatrix} \begin{bmatrix}1 \\ 2 \\ 0 \\ 0\end{bmatrix}= \lambda \begin{bmatrix}1 \\ 2 \\ 0 \\ 0\end{bmatrix}[/tex]
for [itex]\lambda[/itex].

You should then be able to reduce the matrix to 3 by 3 (or smaller) to find the remaining eigenvalues and eigenvectors. An n by n matrix is diagonalizable if and only if it has n independent eigenvectors.
 
  • #3
How would I find the geometric multiplicities? I can do the rest of the question
 
  • #4
Assuming you already found λ for the eigenvector.

Did you write out A-λI?

You should be able to find other eigenvector solutions easily.
That is, solutions of (A-λI)v=0.

This will give you the geometric multiplicity of this λ.

You should also find that the remaining eigenvalue is easy to deduce as well.
 
  • #5
I like Serena said:
Assuming you already found λ for the eigenvector.

Did you write out A-λI?

You should be able to find other eigenvector solutions easily.
That is, solutions of (A-λI)v=0.

This will give you the geometric multiplicity of this λ.

You should also find that the remaining eigenvalue is easy to deduce as well.

Ok, thanks. Really should have remembered that. So, the dim{Ker(A-λI)}=Geometric Multiplicity?
 
  • #6
Yes.
 
  • #7
I like Serena said:
Yes.

Thanks again.
 

1. How do you find the determinant of a Jordan form?

The determinant of a Jordan form matrix can be found by multiplying the diagonal elements of the matrix. This is because the Jordan form is a block diagonal matrix, with each block having the same eigenvalue. Therefore, the determinant of each block is equal to the eigenvalue, and the determinant of the entire matrix is the product of these eigenvalues.

2. Can a Jordan form have complex eigenvalues?

Yes, a Jordan form can have complex eigenvalues. This occurs when the original matrix has complex eigenvalues. In this case, the Jordan form will have complex blocks on the diagonal, with the eigenvalues being the same for each block.

3. How do you find the eigenvalues of a matrix given its Jordan form?

The eigenvalues of a matrix can be found by examining the diagonal elements of the Jordan form. Each block on the diagonal represents a set of eigenvalues with the same value. Therefore, the eigenvalues of the original matrix will be the same as the eigenvalues of each block on the diagonal.

4. What is the significance of the Jordan form in linear algebra?

The Jordan form is significant in linear algebra because it provides a way to simplify the analysis of a matrix by breaking it down into smaller blocks. This allows for easier computation of properties such as eigenvalues and determinants. It also reveals the structure of the matrix, particularly in terms of its eigenvalues and eigenvectors.

5. How does the Jordan form problem relate to diagonalization?

The Jordan form problem is related to diagonalization in that both involve breaking down a matrix into simpler forms. However, diagonalization is only possible for certain types of matrices (such as symmetric matrices), while the Jordan form is applicable to any square matrix. Additionally, the Jordan form allows for the presence of repeated eigenvalues, while diagonalization does not.

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