Eigenvalues of A-adjoint and A

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In summary, the eigenvalues of A* are conjugates of the eigenvalues of A, as shown by taking the conjugate and transpose of the characteristic polynomial. This is because the characteristic polynomials of A and A* are complex conjugates of each other.
  • #1
Shackleford
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Eigenvalues of A* and A

Show that the eigenvalues of A* are conjugates of the eigenvalues of A.

I know this is an easy problem, but I've just been spinning my wheels manipulating the equations with the transpose, conjugate, and adjoint properties.

[itex]
\begin{align}

A^* = \bar{A}^T\\

A\vec{x} = \lambda\vec{x}\\

A^*\vec{x} = \bar\lambda\vec{x}\\
\end{align}

[/itex]
 
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  • #2
[itex]\lambda[/itex] is an eigenvalue of [itex]A[/itex] if [itex] det(A-\lambda I)=0[/itex]. Try taking conjugate and transpose on that.
 
  • #3
Dick said:
[itex]\lambda[/itex] is an eigenvalue of [itex]A[/itex] if [itex] det(A-\lambda I)=0[/itex]. Try taking conjugate and transpose on that.

You know, I thought about doing it like that but didn't because I figured the other way would be easier.

[itex]
\begin{align}
det(A-\lambda I) = 0\\\\
\overline{det(A-\lambda I)} = \bar0\\
det(\overline{A-\lambda I)} = 0\\
= det(\bar A-\bar\lambda I) = 0\\\\
= det(\bar A-\bar\lambda I)^T = 0^T\\
= det(\bar A^T-\bar\lambda I) = 0\\
= det(\bar A^T-\bar\lambda I) = 0\\
= det(A^*-\bar\lambda I)=0\\
\\

\end{align}
[/itex]
 
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  • #4
Just to make sure you are clear on this. [itex]A-\lambda I[/itex] is NOT EQUAL to [itex]A^* - \bar{\lambda} I[/itex]. What is equal is [itex]det(A-\lambda I)=det(A^* - \bar{\lambda} I)[/itex].
 
  • #5
Dick said:
Just to make sure you are clear on this. [itex]A-\lambda I[/itex] is NOT EQUAL to [itex]A^* - \bar{\lambda} I[/itex]. What is equal is [itex]det(A-\lambda I)=det(A^* - \bar{\lambda} I)[/itex].

Yes, I know that.

The characteristic polynomials for A and A* are equal?
 
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  • #6
Shackleford said:
Yes.

The characteristic polynomials are equal?

Well, no. That's not what they are asking you to show. If A=[[1,0],[0,i]], then how does the characteristic polynomial of A compare with the characteristic polynomial of A*?
 
  • #7
Dick said:
Well, no. That's not what they are asking you to show. If A=[[1,0],[0,i]], then how does the characteristic polynomial of A compare with the characteristic polynomial of A*?

I know what they're asking me. You're right. The characteristic polynomials aren't equal. I was overlooking a small detail.

The two characteristic polynomials are complex conjugates, right?

I'm not quite sure why this is true: [itex]det(A-\lambda I)=det(A^* - \bar{\lambda} I)[/itex].
 
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  • #8
Those two determinants are equal because they are both zero.
 
  • #9
A. Bahat said:
Those two determinants are equal because they are both zero.

Right. Since they are complex conjugates of each other, if one equals 0 then the other equals zero.
 
  • #10
Dick said:
Right. Since they are complex conjugates of each other, if one equals 0 then the other equals zero.

Oh, okay. I didn't know the characteristic polynomials were complex conjugates of each other.

I think I have it written out more correctly now in post #3.
 
  • #11
Shackleford said:
Oh, okay. I didn't know the characteristic polynomials were complex conjugates of each other.

I think I have it written out more correctly now in post #3.

Post 3 still looks a little funny. The properties of determinant that you need are [itex]det(\bar M)=\overline{det(M)}[/itex] and [itex]det(M^T)=det(M)[/itex].
 
  • #12
Dick said:
Post 3 still looks a little funny. The properties of determinant that you need are [itex]det(\bar M)=\overline{det(M)}[/itex] and [itex]det(M^T)=det(M)[/itex].

Is it okay now?
 
  • #13
Shackleford said:
Is it okay now?

Okay. I wouldn't bother with transposing a scalar. Notice if you start with [itex]det(A-\lambda I)=c[/itex] then you wind up with [itex]det(A^*-\bar{\lambda} I)=\bar c[/itex]
 
  • #14
Dick said:
Okay. I wouldn't bother with transposing a scalar. Notice if you start with [itex]det(A-\lambda I)=c[/itex] then you wind up with [itex]det(A^*-\bar{\lambda} I)=\bar c[/itex]

Oh, you're right. I mistakenly treated it as a vector.

Thanks for the help.
 

FAQ: Eigenvalues of A-adjoint and A

What is the definition of the adjoint of a matrix A?

The adjoint of a matrix A is the transpose of its complex conjugate. It is denoted as A*.

How is the adjoint of a matrix A calculated?

To calculate the adjoint of a matrix A, we first find the complex conjugate of A by changing the sign of the imaginary part of each element. Then, we take the transpose of this complex conjugate matrix to get the adjoint matrix A*.

What is the relationship between A and its adjoint matrix A*?

The adjoint of a matrix A is closely related to A. Specifically, A multiplied by its adjoint A* results in a diagonal matrix with the eigenvalues of A as its diagonal elements. This relationship is known as the Spectral Theorem.

How are eigenvalues of A-adjoint and A related?

The eigenvalues of A and A* are the same. This means that if we find the eigenvalues of A, we also automatically find the eigenvalues of A*.

Why are eigenvalues of A-adjoint and A important in linear algebra?

The eigenvalues of A and A* have important applications in linear algebra, such as in solving systems of linear equations and determining the stability of linear systems. They also provide valuable insights into the properties of a matrix and its transformations.

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