Repeating eigenvalues and diagonalizing

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Homework Help Overview

The discussion revolves around the diagonalization of a 2x2 matrix A = [0 -9; 1 -6], particularly focusing on the implications of having repeating eigenvalues. Participants explore whether the matrix can be diagonalized despite having equal eigenvalues.

Discussion Character

  • Conceptual clarification, Assumption checking, Mathematical reasoning

Approaches and Questions Raised

  • Participants examine the eigenvalues derived from the determinant equation det(A - λI) = 0, noting the presence of a repeated eigenvalue, λ = -3. They discuss the conditions under which a matrix with repeating eigenvalues can still be diagonalizable, emphasizing the need to investigate the corresponding eigenspaces.

Discussion Status

The conversation is ongoing, with participants questioning the original poster's reasoning regarding diagonalizability and rank. Some have provided clarifications about the relationship between eigenvalues, eigenvectors, and diagonalizability, while others are exploring the implications of the determinant and rank of the matrix.

Contextual Notes

There is a noted confusion regarding the relationship between rank, nullity, and diagonalizability, as well as the role of distinct versus repeated eigenvalues in determining the diagonalizability of the matrix.

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Homework Statement


A=[0 -9; 1 -6]
Can this matrix be diagonalized?

Homework Equations


det(A-[itex]\lambda[/itex]I)=0

The Attempt at a Solution


det(A-[itex]\lambda[/itex]I)=0 gives the eigenvalues of the matrix and yields two eigenvalues that are equal, [itex]\lambda[/itex]= -3

A matrix with repeating eigenvalues are defective and can therefore NOT be diagonalized.
I would further say that rank(A)=1 and nullity(A)=n-rank(A)=2-1=1... Matlab does not agree with me... what is wrong with my reasoning here?
 
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Liferider said:
A matrix with repeating eigenvalues are defective and can therefore NOT be diagonalized.

This is not true. A matrix with repeating eigenvalues may still be diagonalizable (or it may be that it can not be diagonalized). What you need to do is find the eigenspace belonging to the eigenvalue of -2. If this eigenspace has dimension 2 (that is: if there exist two linearly independent eigenvectors), then the matrix can be diagonalized.
 
micromass said:
This is not true. A matrix with repeating eigenvalues may still be diagonalizable (or it may be that it can not be diagonalized). What you need to do is find the eigenspace belonging to the eigenvalue of -2.
Don't you mean the eigenvalue λ = -3?

If so, the eigenspace of this eigenvalue is one-dimensional and consists of multiples of <3, 1>.
micromass said:
If this eigenspace has dimension 2 (that is: if there exist two linearly independent eigenvectors), then the matrix can be diagonalized.
 
Mark44 said:
Don't you mean the eigenvalue λ = -3?

If so, the eigenspace of this eigenvalue is one-dimensional and consists of multiples of <3, 1>.

Yes, I meant -3, thank you!

All I wanted to make clear is that being diagonalizable does not depend on there being repeated eigenvalues, but that we need to find the eigenspaces.

Anyway, let's continue with the rank and nullity. What is the determinant of the matrix? What does this imply about the rank?
 
Thanks, I think I went into a trap of reasoning here... If A then B, is not the same as B then A.

Can one still conclude that a matrix is diagonalizable if it has distinct eigenvalues, since distinct eigenvalues ensures linearly independence?
What is the determinant of the matrix? What does this imply about the rank?
... full rank when det(A)!=0, forgot about that one.
 
Liferider said:

Homework Statement


A=[0 -9; 1 -6]
Can this matrix be diagonalized?

Homework Equations


det(A-[itex]\lambda[/itex]I)=0

The Attempt at a Solution


det(A-[itex]\lambda[/itex]I)=0 gives the eigenvalues of the matrix and yields two eigenvalues that are equal, [itex]\lambda[/itex]= -3

A matrix with repeating eigenvalues are defective and can therefore NOT be diagonalized.
Caution! This is NOT true! For an obvious example, the matrix
[tex]\begin{bmatrix}2 & 0 \\ 0 & 2\end{bmatrix}[/tex]
has repeating eigenvalues but is already diagonalized.

What is true is that an n by n matrix with fewer than n independent eigenvectors cannot be diagonalized. If an n by n matrix has n distinct eigenvalues, the eigenvectors corresponding to each are independent so the matrix is diagonalizable. If there are repeating eigenvalues, you don't' know if there are n independent eigenvectors until you check the eigenvectors themselves.

I would further say that rank(A)=1 and nullity(A)=n-rank(A)=2-1=1... Matlab does not agree with me... what is wrong with my reasoning here?
In this particular case, saying that -3 is an eigenvalue means that
[tex]\begin{bmatrix}0 & -9 \\ 1 & -6\end{bmatrix}\begin{bmatrix}x \\ y\end{bmatrix}= \begin{bmatrix}-9y \\ x- 6y\end{bmatrix}= \begin{bmatrix}-3x \\ -3y\end{bmatrix}[/tex]
which is equivalent to -9y= -3x and x- 6y= -3y which are both equivalent to x= 3y. That is, every eigenvector corresponding to eigenvalue -3 is a multiple of <3, 1>. So, although your reasoning is wrong, your conlusion is true: this matrix has only one independent eigenvector and so is not diagonalizable.

But being diagonalizable has NOTHING to do with "rank". As long as a matrix is invertible, not diagonalizable, it has full rank. Because this matrix does not have 0 as an eigenvalue, there is NO vector, v, such that Av= 0, it is invertible and has rank 2.
 
Last edited by a moderator:
Liferider said:

Homework Statement


A=[0 -9; 1 -6]
Can this matrix be diagonalized?

Homework Equations


det(A-[itex]\lambda[/itex]I)=0

The Attempt at a Solution


det(A-[itex]\lambda[/itex]I)=0 gives the eigenvalues of the matrix and yields two eigenvalues that are equal, [itex]\lambda[/itex]= -3

A matrix with repeating eigenvalues are defective and can therefore NOT be diagonalized.
I would further say that rank(A)=1 and nullity(A)=n-rank(A)=2-1=1... Matlab does not agree with me... what is wrong with my reasoning here?

The determinant of A is not zero, so A is invertible, and hence has rank 2. However, you need to look instead at the matrix B = A - λI = A + 3*I (where I = 2x2 identity matrix) and determine its rank, etc.

RGV
 

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