Showing geometric multiplicity is the same for two similar Matrices

In summary: So, if we have an eigenvalue of A and we know that (B- \mu I)^n= 0, then we can say that (B- \mu I) is an eigenvalue of A too. In summary, we showed that if A and B are similar matrices, and that (mu) is an eigenvalue of A, then (mu) is also an eigenvalue of B, with the same geometric multiplicity (g). We showed that (mu) has geometric multiplicity g as an eigenvalue of A.
  • #1
Muffins
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Homework Statement


Suppose A and B are similar matrices, and that (mu) is an eigenvalue of A. We know that (mu) is also an eigenvalue of B, with the same algebraic multiplicity(proved in class) Suppose that g is the geometric multiplicity of (mu), as an eigenvalue of B. Show that (mu) has geometric multiplicity g as an eigenvalue of A.

Homework Equations



A=S*B*S^-1
B=S^-1 *A*S

The Attempt at a Solution



We know that g = dim(EigenSpace of (mu) for (B)), so this tells us that the space has a basis
(v1, v2, ... , vg)
From our previously worked problem, we know that if A and B are similar, an v is an eigenvector for B, then S*v is an eigenvector for B, so it follows that
(Sv1, Sv2, ... , Svg) are eigenvectors for A. Now to verify that this is a basis of A, we need to show that they are linearly independent and span (EgienSpace of (mu) for A).
So, (c1*S*v1 + c2*S*v2 + ... + cg*S*vg) = 0 vector, factor out an S and we get
S*(c1*v1 + c2*v2 + ... + cg*vg) = 0 vector, and because the v's form a basis they're linearly independent, forcing the c's to be 0.
Here's where my troubles hit. I'm entirely confused as to how to show (S*v1, ... S*vg) span the eigenspace(mu) for A. My professor suggested to take an arbitrary vector w, which is an element of the eigenspace(mu) for A, and show that the w is a linear combination of the S*v's.. but I'm at a loss on how to logically show that.

Any help will be greatly appreciated guys!
Thank you mucho!
 
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  • #2
Hint: S is invertible.
 
  • #3
So something like this?

We want to show that w is a linear combination of (Sv1, Sv2, ... ,Svg)
which says we have

c1*S*v1 + c2*S*v2 + ... + cg*S*vg = w

we can pull out the S...

S(c1v1 + c2v2 + ... + cgvg) = w

S^-1(S(c1v1 + c2v2 + ... + cgvg) = S^-1*w
(c1v1 + c2v2 + ... + cgvg) = S^-1 * w?

I tried going on a path similar to this last night but I wasn't seeing how this could show w is a linear combination of the S*v's... What am I still missing...?
 
  • #4
I don't think you need to be that complicated. Saying that [itex]\mu[/itex] is an eigenvalue of A of multiplicity n basically means that [itex](A- \mu I)^n= 0[/itex], where I and 0 are, of course, the identity and zero matrices of the same size as A. Now, if B is similar to A, B= SAS-1 and A= S-1BS for some invertible matrix, just as you say! Replace A in the previous equation by that and show that [itex](B- \mu I)^n= 0[/itex] also.

Conceptually, of course, if A and B are similar matrices then they represent the same linear transformation written in different bases. "Eigenvalues" and "multiplicity of eigenvalues" are properties of the linear transformations not just specific representations so are the same for similar matrices.
 

Related to Showing geometric multiplicity is the same for two similar Matrices

1. How do you define geometric multiplicity for a matrix?

The geometric multiplicity of an eigenvalue for a matrix is the dimension of the corresponding eigenspace. This can be thought of as the number of linearly independent eigenvectors associated with the eigenvalue.

2. What does it mean for two matrices to be similar?

Two matrices are considered similar if there exists an invertible matrix P such that P-1AP = B, where A and B are the two matrices in question. This essentially means that the matrices have the same eigenvalues and their corresponding eigenvectors are related by a linear transformation.

3. How can you show that two matrices have the same geometric multiplicity?

To show that two matrices have the same geometric multiplicity, you can first find the eigenvalues of both matrices. Then, for each eigenvalue, calculate the dimension of its associated eigenspace for both matrices. If the dimensions are the same, then the geometric multiplicities are also the same.

4. What is the significance of having the same geometric multiplicity for two similar matrices?

If two matrices are similar and have the same geometric multiplicity, it means that they share the same eigenvectors and therefore, can be transformed into each other using a change of basis. This is useful in solving systems of linear equations and understanding the behavior of certain systems.

5. Are there any other ways to show that two matrices are similar besides having the same geometric multiplicity?

Yes, there are other ways to show that two matrices are similar. For example, if the matrices have the same characteristic polynomial, they are similar. Additionally, if they have the same Jordan canonical form, they are also similar. However, showing that two matrices have the same geometric multiplicity is a straightforward and commonly used method for proving similarity.

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