How do I find the change of basis matrix for the JCF of M?

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Discussion Overview

The discussion revolves around finding the change of basis matrix for the Jordan Canonical Form (JCF) of a given matrix M. Participants explore various methods to compute the JCF and the transition matrix, including the use of eigenvectors and generalized eigenvectors. The context includes theoretical aspects of linear algebra and matrix transformations.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant presents the matrix M and outlines steps to find the JCF, including calculating the characteristic polynomial and constructing an AMGM table.
  • Another participant suggests solving the equation ##SJ_M=MS## to find the transition matrix.
  • A later reply emphasizes the need to use eigenvectors and generalized eigenvectors for larger matrices, indicating a method to decompose the space into eigenspaces and generalized eigenspaces.
  • One participant provides a method to calculate the eigenspace by transforming ##(M + I)## into RREF and expresses uncertainty about whether the resulting vectors form the correct eigenspace.
  • Another participant challenges the correctness of the eigenvectors found, noting a contradiction with the characteristic polynomial and suggesting a different JCF structure.
  • Further discussion includes clarification on the row reduction process and the identification of eigenvalues and eigenvectors, with a request for assistance in computing the transition matrix.

Areas of Agreement / Disagreement

Participants express differing views on the structure of the JCF and the correctness of the eigenvectors identified. There is no consensus on the transition matrix or the eigenspace definitions, indicating ongoing debate and exploration of the topic.

Contextual Notes

Participants note limitations in their calculations, including potential errors in identifying eigenvectors and the implications of the characteristic polynomial. The discussion reflects the complexity of determining the JCF and transition matrices for matrices with repeated eigenvalues.

TMO
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Let

## \begin{align}M =\begin{pmatrix} 2& -3& 0 \\ 3& -4& 0 \\ -2& 2& 1 \end{pmatrix} \end{align}. ##

Here is how I think the JCF is found.

STEP 1: Find the characteristic polynomial

It's ## \chi(\lambda) = (\lambda + 1)^3 ##

STEP 2: Make an AMGM table and write an integer partition equation

The AM is given by looking at the power. The GM is found by finding the nullspace for each eigenvalue. For this matrix this is the table:

Code:
+-----+------+------+
|  λ  |  AM  |  GM  |
+-----+------+------+
| -1  |  3   |  2   |
+-----+------+------+

which gives the integer partition equation ## J_{1, \lambda_{-1}} + J_{2, \lambda_{-1}} = 3 ##. Because there's only one integer partition possible (up to permutation: remember that the JCF is unique only up to permutation not in general), we can guess the JCF is

## \begin{align}J_M =\begin{pmatrix} -1& 1& 0 \\ 0& -1& 0 \\ 0& 0& -1 \end{pmatrix} \end{align}. ##

But I don't know how to compute the transition matrix. I know it involves generalized eigenvectors. Can someone help me?
 
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You could solve for ##SJ_M=MS## or transform it from step to step.
 
fresh_42 said:
You could solve for ##SJ_M=MS## or transform it from step to step.

I need to find the change of basis matrix using eigenvectors and generalized eigenvectors. Why? Because for larger matrices I may not be able to get a nice number partition that allows me to guess the JCF. I know there's a way to do this. How do I do this?
 
O.k., another method is to calculate ##M.v=-v## which should give a decomposition ##\mathbb{R}^3= \mathbb{E}_{-1}^{(1)} \oplus \mathbb{E}_{-1}^{(2)}## with a one dimensional eigenspace ##\mathbb{E}_{-1}^{(1)}## and a two dimensional generalized eigenspace ##\mathbb{E}_{-1}^{(2)}=\{\,v\in \mathbb{R}^3\,|\,(M+I)^2.v=0\,\}##
 
Taking ## (M + I) = 0 ## and transforming it into RREF gives

##\begin{align}\begin{pmatrix} 1& -1& 0 \\ 0& 0& 0 \\ 0& 0& 0 \end{pmatrix} \end{align}##.

The non-pivot columns are two, so the eigenspace is given by

##\begin{align} v_1 - v_2 =& 0& \\ v_2 =& r \\ v_3 =& s \end{align}##

Rewriting this in terms of linear span gives

## \begin{align}\left\{\begin{pmatrix} 1 \\ 1 \\ 0\end{pmatrix}, \begin{pmatrix} 0 \\ 0 \\ 1\end{pmatrix}\right\}\end{align} ##.

Is this the eigenspace ## \mathbb{E}_{-1}^2 ##?
 
I haven't done your homework, but it is easy to check that ##(1,1,0)^\tau## is an eigenvector to ##-1## and ##(0,0,1)^\tau## an eigenvector to ##1##, which contradicts your characteristic polynomial. I also think that ##J_M=\operatorname{diag}(-1,-1,1)##.
 
TMO said:
Taking ## (M + I) = 0 ## and transforming it into RREF gives

##\begin{align}\begin{pmatrix} 1& -1& 0 \\ 0& 0& 0 \\ 0& 0& 0 \end{pmatrix} \end{align}##.
I got a 1 in the third column of the second row after row reduction.

I also got 1 and -1 as the eigenvalues, and you get only one eigenvector for ##\lambda = -1##.

TMO said:
But I don't know how to compute the transition matrix. I know it involves generalized eigenvectors. Can someone help me?
You just form a matrix where the columns are the generalized eigenvectors.
 

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