Similar Matrices: Are Eigenvalues the Same?

In summary, if two matrices are similar, their eigenvalues are not necessarily the same. The converse is not true in general, as there can be matrices with the same eigenvalues that are not similar. An example is \(A=\begin{pmatrix}1 & 0\\0 & 1\end{pmatrix}\) and \(B=\begin{pmatrix}0 & 1\\1 & 0\end{pmatrix}\). However, if \(C=\begin{pmatrix}1 & 1\\0 & 1\end{pmatrix}\), then \(A\) and \(C\) have the same eigenvalues but are not similar.
  • #1
Sudharaka
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saravananbs's question from Math Help Forum,

if A and B are similar matrices then the eigenvalues are same. is the converse is true? why?
thank u

Hi saravananbs,

No. The converse is not true in general. Take the two matrices, \(A=\begin{pmatrix}1 & 0\\0 & 1\end{pmatrix}\mbox{ and }B=\begin{pmatrix}0 & 1\\1 & 0\end{pmatrix}\).

\[\begin{pmatrix}1 & 0\\0 & 1\end{pmatrix}\begin{pmatrix}1 \\ 1\end{pmatrix}=1.\begin{pmatrix}1 \\ 1\end{pmatrix}\]

\[\begin{pmatrix}0 & 1\\1 & 0\end{pmatrix}\begin{pmatrix}1 \\ 1\end{pmatrix}=1.\begin{pmatrix}1 \\ 1\end{pmatrix}\]

Therefore both matrices have the same eigenvalue 1. However it can be easily shown that \(A\mbox{ and }B\) are not similar matrices.
 
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  • #2
Sudharaka said:
saravananbs's question from Math Help Forum,
Hi saravananbs,

No. The converse is not true in general. Take the two matrices, \(A=\begin{pmatrix}1 & 0\\0 & 1\end{pmatrix}\mbox{ and }B=\begin{pmatrix}0 & 1\\1 & 0\end{pmatrix}\).

\[\begin{pmatrix}1 & 0\\0 & 1\end{pmatrix}\begin{pmatrix}1 \\ 1\end{pmatrix}=1.\begin{pmatrix}1 \\ 1\end{pmatrix}\]

\[\begin{pmatrix}0 & 1\\1 & 0\end{pmatrix}\begin{pmatrix}1 \\ 1\end{pmatrix}=1.\begin{pmatrix}1 \\ 1\end{pmatrix}\]

Therefore both matrices have the same eigenvalue 1. However it can be easily shown that \(A\mbox{ and }B\) are not similar matrices.
That example does not really work, because $A$ and $B$ do not have the same eigenvalues: $A$ has a (repeated) eigenvalue 1, whereas the eigenvalues of $B$ are 1 and –1.

However, if $C = \begin{pmatrix}1 & 1 \\0 & 1\end{pmatrix}$, then $A$ and $C$ have the same eigenvalues (namely 1, repeated), but are not similar.
 
  • #3
Opalg said:
That example does not really work, because $A$ and $B$ do not have the same eigenvalues: $A$ has a (repeated) eigenvalue 1, whereas the eigenvalues of $B$ are 1 and –1.

However, if $C = \begin{pmatrix}1 & 1 \\0 & 1\end{pmatrix}$, then $A$ and $C$ have the same eigenvalues (namely 1, repeated), but are not similar.

Thanks for correcting that. Of course I now see that only the eigenvalue 1 is common to both \(A\) and \(B\).
 

FAQ: Similar Matrices: Are Eigenvalues the Same?

What are similar matrices?

Similar matrices are matrices that have the same size and shape, and can be transformed into each other through a change of basis. This means that they represent the same linear transformation, but with respect to different bases.

How do you determine if two matrices are similar?

To determine if two matrices are similar, you can check if they have the same size and shape, and if their corresponding entries are related by a similarity transformation. This means that if A and B are similar matrices, there exists an invertible matrix P such that A = PBP^-1.

Are the eigenvalues the same for similar matrices?

Yes, the eigenvalues of similar matrices are always the same. This is because the eigenvalues represent the characteristics of the linear transformation, and since similar matrices represent the same transformation, their eigenvalues must also be the same.

Can similar matrices have different eigenvectors?

Yes, similar matrices can have different eigenvectors. Although the eigenvalues are the same, the corresponding eigenvectors may differ due to the change of basis. However, the eigenvectors of similar matrices will still span the same vector space.

How can similar matrices be useful in applications?

Similar matrices are useful in applications where different representations of the same linear transformation are needed. For example, in computer graphics, similar matrices can be used to transform objects in 3D space without changing their shape or orientation. They are also used in solving systems of linear equations and in analyzing the behavior of dynamical systems.

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