# Show that eigenvalues are symmetric with respect to the origin

The matrices that I am considering are

$$M = \begin{bmatrix} A & B \\ C & -A^\top \end{bmatrix},$$

with $$A,B,C\in\mathbb{C}^{n\times n}$$, $$C = C^\top$$ and $$B = B^\top$$. I noticed from numerical calculations that the eigenvalues are symmetric with respect to the origin, i.e., if $$\lambda$$ is an eigenvalue of $$M$$ then $$-\lambda$$ is as well.

I have not been able to shown that this has to be the case. Initially I tried to see if I could come up with a similarity transformation which would make it block diagonal, with one block the negative of the other, but with no success. Next I tried to use some tricks to manipulate the determinant, namely I showed that

\begin{align} \det(M - \lambda\,I) &= \det(B) \det((-A^\top - \lambda\,I) B^{-1} (A - \lambda\,I) - C) \\ &= \det(C) \det((A - \lambda\,I) C^{-1} (-A^\top - \lambda\,I) - B) \\ \end{align}

assuming that either $$\det(B)\neq0$$ or $$\det(C)\neq0$$. However, this did not seem to bring me any closer to showing that all eigenvalues are mirrored around the imaginary axis.

• Are you sure you want to take transpose and not conjugated transpose? Also, the reflection of $\lambda$ to the imaginary axis is not $-\lambda$ but $-\bar\lambda$. Feb 8 '19 at 1:09
• I have constructed a $8\times8$ matrix with the form of your matrix $M$ in Octave, and the eigenvalues are not mirrored around the imaginary axis, but if $\lambda$ is an eigenvalue, then $-\lambda$ is an eigenvalue as well.
– jobe
Feb 8 '19 at 1:15
• @Berci I did mean the normal transpose, but initially I did some test calculations using real matrices, which also have complex conjugate eigenvalues, so did not notice that it was actually a origin reflection instead of axis reflection. I also just figured out that my definition of $M$ is essentially the definition of an Hamiltonian matrix. Feb 8 '19 at 1:15
• @KwinvanderVeen: I'd like to see how that works! Feb 8 '19 at 1:25

The definition of $$M$$ is the same as that of an Hamiltonian matrix. In order to show that if $$\lambda$$ is an eigenvalue then $$-\lambda$$ is an eigenvalue as well, the following identity can be used

$$\det(M - \lambda\,I) = \det(M^\top - \lambda\,I)$$

Defining a new matrix as

$$J = \begin{bmatrix} 0 & -I \\ I & 0 \end{bmatrix},$$

where it can be noted that $$\det(J)=-1$$ and $$J^{-1} = J^\top = -J$$. Using this matrix it can be shown that $$J\,M^\top J = M$$, namely

\begin{align} J\,M^\top J &= \begin{bmatrix} 0 & -I \\ I & 0 \end{bmatrix} \begin{bmatrix} A & B \\ C & -A^\top \end{bmatrix}^\top \begin{bmatrix} 0 & -I \\ I & 0 \end{bmatrix} \\ &= \begin{bmatrix} 0 & -I \\ I & 0 \end{bmatrix} \begin{bmatrix} A^\top & C \\ B & -A \end{bmatrix} \begin{bmatrix} 0 & -I \\ I & 0 \end{bmatrix} \\ &= \begin{bmatrix} 0 & -I \\ I & 0 \end{bmatrix} \begin{bmatrix} C & -A^\top \\ -A & -B \end{bmatrix} \\ &= \begin{bmatrix} A & B \\ C & -A^\top \end{bmatrix} \end{align}

Incorporating $$J$$ into the determinant from earlier allows use to show

\begin{align} \det\left(J(M^\top - \lambda\,I)J\right) &= \det(J)^2\det\left(M^\top - \lambda\,I\right) = \det\left(M^\top - \lambda\,I\right) \\ &= \det\left(J\,M^\top J - \lambda\,J^2\right) \\ &= \det\left(M + \lambda\,I\right) \end{align}

Therefore $$\det\left(M - \lambda\,I\right) = \det\left(M + \lambda\,I\right)$$. So if $$\lambda$$ is an eigenvalue $$\det\left(M - \lambda\,I\right)$$ is zero, but then $$\det\left(M + \lambda\,I\right)$$ would be zero as well and thus $$-\lambda$$ has to be an eigenvalue as well.