Eigenvalues of similar matrices

In summary, if the rotation matrix R is orthogonal and the diagonal matrix D is similar to A, then the eigenvalues of A are also the eigenvalues of D.
  • #1
Fernando Revilla
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I quote a question from Yahoo! Answers

If A=[R]*[diag(a1,a2,a3)]*[R]t can we conclude that a1,a2,a3 are the eigenvalues of A?(R is a rotation matrix)?
- We now that A is symmetric.
- R is a rotation matrix so it is orthogonal.
- [R]t is the transpose of R.

I have given a link to the topic there so the OP can see my response.
 
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  • #2
In general, if $A,B\in \mathbb{F}^{n\times n}$ are similar matrices then, $A$ and $B$ have the same characteristic polynomial, as a consequence the same eigenvalues. In our case we have:
$$A=R\text{ diag }(a_1,a_2,a_3)\;R^T=R\text{ diag }(a_1,a_2,a_3)\;R^{-1}$$
so, $A$ and $D=\text{diag }(a_1,a_2,a_3)$ are similar matrices. But the eigenvalues of $D$ are $a_1$, $a_2$ and $a_3$, hence the eigenvalues of $A$ are also $a_1$, $a_2$ and $a_3$.
 
  • #3
Fernando Revilla said:
In general, if $A,B\in \mathbb{F}^{n\times n}$ are similar matrices then, $A$ and $B$ have the same characteristic polynomial, as a consequence the same eigenvalues. In our case we have:
$$A=R\text{ diag }(a_1,a_2,a_3)\;R^T=R\text{ diag }(a_1,a_2,a_3)\;R^{-1}$$
so, $A$ and $D=\text{diag }(a_1,a_2,a_3)$ are similar matrices. But the eigenvalues of $D$ are $a_1$, $a_2$ and $a_3$, hence the eigenvalues of $A$ are also $a_1$, $a_2$ and $a_3$.

Thank You. So just the sequence of eigenvalues changes. The main Problem is:
Det( diag(a1,a2,a3) + R diag(a1,a2,a3)RT - xI )=0 I rewrote it in this form:
Det( R diag (a1,a2,a3)RT - x*I )=0
which means eigenvalues of R diag(a1,a2,a3)RT are the new parameter x*=x-diag(a1,a2,a3)
can we say x-diag(a1,a2,a3)= diag(a1,a2,a3) or x=2 diag(a1,a2,a3)
 

Related to Eigenvalues of similar matrices

What are eigenvalues of similar matrices?

Eigenvalues of similar matrices refer to the set of numbers that represent the scale factor of the corresponding eigenvectors when the matrices are multiplied together. They are also known as characteristic values.

How are eigenvalues of similar matrices related?

Eigenvalues of similar matrices are related in that they have the same number of eigenvalues, and their respective eigenvalues are equal when arranged in any order.

Why are eigenvalues of similar matrices important?

Eigenvalues of similar matrices are important because they provide valuable information about the properties of the matrices, such as their size, rotation, and scaling. They are also useful in solving systems of linear equations and in analyzing the stability of dynamic systems.

How do you calculate eigenvalues of similar matrices?

To calculate eigenvalues of similar matrices, you first need to find the characteristic polynomial of the matrix, which is the determinant of the matrix minus lambda multiplied by the identity matrix. Then, solve for the values of lambda that make the characteristic polynomial equal to zero. These values are the eigenvalues of the matrix.

Can similar matrices have different eigenvalues?

No, similar matrices cannot have different eigenvalues. As mentioned earlier, similar matrices have the same number of eigenvalues and their corresponding eigenvalues are equal when arranged in any order. Therefore, if two matrices are similar, they will have the same set of eigenvalues.

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