Eigenvectors of a matrix in Jordan Normal Form

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SUMMARY

The discussion centers on the eigenvectors of the matrix A = \begin{bmatrix} -14 & 5 & -2 & 1 \\ -38 & 13 & -4 & 5 \\ 25 & -10 & 7 & 5 \\ -17 & 5 & -2 & 4 \end{bmatrix} and its Jordan Normal Form J = \begin{bmatrix} 2 & 1 & 0 & 0 \\ 0 & 2 & 0 & 0 \\ 0 & 0 & 3 & 1 \\ 0 & 0 & 0 & 3 \end{bmatrix}. The eigenvectors for eigenvalues 2 and 3 are identified as \begin{bmatrix} 1 \\ 0 \\ 0 \\ 0 \end{bmatrix} and \begin{bmatrix} 0 \\ 0 \\ 1 \\ 0 \end{bmatrix}, respectively. However, these vectors do not belong to the kernels \text{Ker}(A-2I) and \text{Ker}(A-3I), indicating that they are eigenvectors of J, not A. The discussion highlights the importance of understanding the relationship between a matrix and its Jordan form.

PREREQUISITES
  • Understanding of Jordan Normal Form
  • Familiarity with eigenvalues and eigenvectors
  • Knowledge of kernel and null space concepts
  • Proficiency in linear algebra and matrix operations
NEXT STEPS
  • Study the properties of Jordan Normal Form in detail
  • Learn how to compute eigenvectors from Jordan blocks
  • Explore the relationship between a matrix and its Jordan form through change of basis
  • Investigate the implications of eigenvectors not belonging to the kernel of a matrix
USEFUL FOR

Mathematicians, students of linear algebra, and anyone studying matrix theory or eigenvalue problems will benefit from this discussion.

Ted123
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Let A =\begin{bmatrix} -14 & 5 & -2 & 1 \\ -38 & 13 & -4 & 5 \\ 25 & -10 & 7 & 5 \\ -17 & 5 & -2 & 4 \end{bmatrix} .

The Jordan Normal Form of A is J =\begin{bmatrix} 2 & 1 & 0 & 0 \\ 0 & 2 & 0 & 0 \\ 0 & 0 & 3 & 1 \\ 0 & 0 & 0 & 3 \end{bmatrix} .

Now, I've been told that eigenvectors can be read off as the columns with no 1's on the superdiagonal.

So for the eigenvalue 2, an eigenvector is \begin{bmatrix} 1 \\ 0 \\ 0 \\ 0 \end{bmatrix} and for the eigenvalue 3, an eigenvector is \begin{bmatrix} 0 \\ 0 \\ 1 \\ 0 \end{bmatrix} .

But \text{Ker} (A-2I) = \left\{ \begin{bmatrix} w \\ 3w \\ 0 \\ w \end{bmatrix} : w\in\mathbb{R} \right\} = \text{span} \left\{ \begin{bmatrix} 1 \\ 3 \\ 0 \\ 1 \end{bmatrix} \right\}

but clearly \begin{bmatrix} 1 \\ 0 \\ 0 \\ 0 \end{bmatrix} \notin \text{Ker} (A-2I)

All eigenvectors should be in the kernel so why isn't this - isn't it an eigenvector?

Similarly \text{Ker} (A-3I) = \left\{ \begin{bmatrix} 0 \\ \frac{2}{5}z \\ z \\ 0 \end{bmatrix} : z\in\mathbb{R} \right\} = \text{span} \left\{ \begin{bmatrix} 0 \\ 2 \\ 5 \\ 0 \end{bmatrix} \right\}

but clearly \begin{bmatrix} 0 \\ 0 \\ 1 \\ 0 \end{bmatrix} \notin \text{Ker} (A-3I)
 
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Ted123 said:
Let A =\begin{bmatrix} -14 & 5 & -2 & 1 \\ -38 & 13 & -4 & 5 \\ 25 & -10 & 7 & 5 \\ -17 & 5 & -2 & 4 \end{bmatrix} .

The Jordan Normal Form of A is J =\begin{bmatrix} 2 & 1 & 0 & 0 \\ 0 & 2 & 0 & 0 \\ 0 & 0 & 3 & 1 \\ 0 & 0 & 0 & 3 \end{bmatrix} .

Now, I've been told that eigenvectors can be read off as the columns with no 1's on the superdiagonal.

So for the eigenvalue 2, an eigenvector is \begin{bmatrix} 1 \\ 0 \\ 0 \\ 0 \end{bmatrix} and for the eigenvalue 3, an eigenvector is \begin{bmatrix} 0 \\ 0 \\ 1 \\ 0 \end{bmatrix} .

But \text{Ker} (A-2I) = \left\{ \begin{bmatrix} w \\ 3w \\ 0 \\ w \end{bmatrix} : w\in\mathbb{R} \right\} = \text{span} \left\{ \begin{bmatrix} 1 \\ 3 \\ 0 \\ 1 \end{bmatrix} \right\}

but clearly \begin{bmatrix} 1 \\ 0 \\ 0 \\ 0 \end{bmatrix} \notin \text{Ker} (A-2I)

All eigenvectors should be in the kernel so why isn't this - isn't it an eigenvector?

Similarly \text{Ker} (A-3I) = \left\{ \begin{bmatrix} 0 \\ \frac{2}{5}z \\ z \\ 0 \end{bmatrix} : z\in\mathbb{R} \right\} = \text{span} \left\{ \begin{bmatrix} 0 \\ 2 \\ 5 \\ 0 \end{bmatrix} \right\}

but clearly \begin{bmatrix} 0 \\ 0 \\ 1 \\ 0 \end{bmatrix} \notin \text{Ker} (A-3I)

The vectors (1,0,0,0)^T and (0,0,1,0)^T are eigenvectors of J, not of A.

RGV
 
Ray Vickson said:
The vectors (1,0,0,0)^T and (0,0,1,0)^T are eigenvectors of J, not of A.

RGV

Of course, J=P^{-1}AP \neq A for some change of basis matrix P.
 

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