Dimension of eigenspace, multiplicity of zero of char.pol.

In summary, the multiplicity of zero of the characteristic polynomial does not necessarily restrict the possible dimension of the corresponding eigenspace. For the eigenvalue \lambda=0, the eigenspace can be either one or two dimensional, depending on the matrix A. However, for the eigenvalue \lambda=1, it is likely that the eigenspace is one dimensional, although this cannot be assumed without considering the minimal polynomial.
  • #1
jostpuur
2,116
19
Does the multiplicity of zero of characteristic polynomial restrict from above the possible dimension of the corresponding eigenspace?

For example if we have a 3x3 matrix A, and a characteristic polynomial

[tex]
\textrm{det}(\lambda - A)=\lambda^2(\lambda - 1)
[/tex]

I can see that the eigenspace corresponding to the eigenvalue [tex]\lambda=0[/tex] can be either one or two dimensional. For example

[tex]
A=\left(\begin{array}{ccc}
0 & 0 & 0 \\
0 & 0 & 0 \\
0 & 0 & 1 \\
\end{array}\right)
[/tex]

or

[tex]
A=\left(\begin{array}{ccc}
0 & 1 & 0 \\
0 & 0 & 0 \\
0 & 0 & 1 \\
\end{array}\right)
[/tex]

What about the eigenspace corresponding to the eigenvalue [tex]\lambda=1[/tex]? Is it necessarily one dimensional, or could it be two dimensional?

I'm almost sure that it must be one dimensional, but its difficult to see certainly what's happening if the matrix is not diagonalizable.
 
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  • #2
Yes.
If the lambda=1 eigenspace was 2d, then you could choose a basis for which
[tex]
A=\left(\begin{array}{ccc}
1 & 0 & \cdot \\
0 & 1 & \cdot \\
0 & 0 & \cdot \\
\end{array}\right)
[/tex]
- just take the first two vectors of the basis in the eigenspace.
Then, it should be clear that the determinant of
[tex]
\lambda-A=\left(\begin{array}{ccc}
\lambda-1 & 0 & \cdot \\
0 & \lambda-1 & \cdot \\
0 & 0 & \cdot \\
\end{array}\right)
[/tex]
has a factor of [itex](\lambda-1)^2[/itex], which would contradict your assumption.
 
  • #3
I see.
 
  • #4
That is not correct: you cannot assume that the matrix is diagonalizable. What you get tells you about the dimension of the generalized eigenspaces. But really, you need the minimal polynomial, not just the characteristic polynomial, for full information.
 
  • #5
[tex]\frac{1}{2}\left(\begin{matrix}2 & 0 & 0 \\ 0 & 1 & 1 \\ 1 & 1 & 1\end{matrix}\right)[/tex]

nevermind this is a strange matrix, which doesn't quite work

Wow, i just made this way harder than it needs to be...

[tex]\left(\begin{matrix}1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 0\end{matrix}\right)[/tex]

should suffice
 
Last edited:
  • #6
Note, I misread Gel's post, but it's now too late to edit mine.
 

What is the dimension of eigenspace?

The dimension of eigenspace refers to the number of linearly independent eigenvectors associated with a particular eigenvalue. It represents the number of different directions in which a linear transformation stretches or shrinks a vector.

How is the dimension of eigenspace related to the characteristic polynomial?

The dimension of eigenspace is equal to the multiplicity of an eigenvalue in the characteristic polynomial. In other words, it is the number of times that eigenvalue appears as a root in the characteristic polynomial.

What is the multiplicity of a zero of the characteristic polynomial?

The multiplicity of a zero of the characteristic polynomial is the number of times that zero appears as a root in the polynomial. It is also equal to the dimension of the eigenspace associated with that eigenvalue.

How is the multiplicity of a zero of the characteristic polynomial related to the eigenvalues of a matrix?

The multiplicity of a zero of the characteristic polynomial is equal to the number of times that eigenvalue appears as a root in the polynomial. This means that the number of distinct eigenvalues of a matrix is equal to the number of distinct zeros of its characteristic polynomial.

Why is understanding the dimension of eigenspace and multiplicity of zeros important in linear algebra?

The dimension of eigenspace and multiplicity of zeros play a crucial role in understanding the behavior of linear transformations and the properties of matrices. They provide valuable information about how a matrix stretches, shrinks, or rotates vectors, and can be used to diagonalize matrices and solve systems of differential equations.

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