Bases of Generalized Eigenvectors

In summary, the conversation discusses finding a basis of generalized eigenvectors for a non-diagonalizable operator. Steps for finding the eigenvalues and corresponding eigenspaces are mentioned, as well as the confusion surrounding calculating the generalized eigenspace and the use of (T-λI)^p and (T-λI)^(p-1). The participants also suggest looking at a specific example and finding an online resource for computing generalized eigenvectors.
  • #1
quasar_4
290
0
Hi all.

So I'm a bit confused about finding a basis of generalized eigenvectors for an operator that is not diagonalizable. I have some "steps" in mind, but maybe someone can help me out here:

1) Find the eigenvalues of the matrix/operator
2) Find the eigenspaces corresponding to each eigenvector; for those which do not have a "big enough" basis, we must compute the generalized eigenspace for that eigenvalue
3) Here's where I'm confused! I know that the generalized eigenspace is given by kernel((T-λI)^p) for some positive integer p, and that (T-λI)^(p-1) is an eigenvector of T. I guess I'm lost with calculating this nullspace.

Are we literally taking the matrix (T-λI) and raising it to the power p? Because it doesn't seem to work on any problems I've attempted so far... anyone know? :eek:
 
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  • #2
Could you show us first a small example of an operator that is not diagonalizable.
Take then the opportunity to illustrate the steps 1 and 2.
Then we can discuss maybe step 3.
 
  • #3
I recommend you do what lalbatross suggests- looking at a specific simple example might help you see what is happening.

You first find the lowest integer p such that (T-λI)^p has the entire eigenspace of [itex]\lamba[/itex] as kernel. But I don't understand what you mean by "(T-λI)^(p-1) is an eigenvector of T". (T-λI)^(p-1) isn't a vector, it is a linear transformation. Did you mean to apply it to something?
 
  • #4
I wonder if there is any online document available to show us how to compute all the generalized eigenvectors?
 

What are generalized eigenvectors?

Generalized eigenvectors are a type of vector that are associated with a given matrix and eigenvalue. They are used to solve systems of linear equations and have applications in areas such as differential equations and quantum mechanics.

How are generalized eigenvectors related to regular eigenvectors?

Generalized eigenvectors are an extension of regular eigenvectors. While regular eigenvectors are associated with a specific eigenvalue, generalized eigenvectors can be associated with multiple eigenvalues. They are used when the matrix is not diagonalizable, meaning it does not have enough regular eigenvectors.

What is the difference between a right and left generalized eigenvector?

A right generalized eigenvector is associated with the right side of the matrix in the equation Ax = λx, while a left generalized eigenvector is associated with the left side of the matrix in the equation xA = λx. Right generalized eigenvectors are used in solving systems of linear equations, while left generalized eigenvectors are used in solving differential equations.

How are generalized eigenvectors calculated?

To calculate generalized eigenvectors, one must first find the regular eigenvectors and eigenvalues of a matrix. Then, using these eigenvectors and eigenvalues, the generalized eigenvectors can be found by solving a system of linear equations. The number of equations needed to solve depends on the number of repeated eigenvalues and the size of the matrix.

What are the applications of generalized eigenvectors?

Generalized eigenvectors have various applications in mathematics and science. They are used in solving systems of linear equations, finding the solutions to differential equations, and studying quantum mechanics. They also have applications in areas such as control theory, signal processing, and image processing.

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