Linear Algebra – Matrices with Same Eigenvalues

eigenvalueslinear algebramatrices

This question is a more precise version of this question.

Let's assume we have the matrix

$$\left(
\begin{array}{ccccc}
0 & a & 0 & 0 & 0 \\
f & 0 & b & 0 & 0 \\
0 & e & 0 & c & 0 \\
0 & 0 & d & 0 & r \\
0 & 0 & 0 & g & 0 \\
\end{array}
\right)$$

If we square it, we get the matrix

$$\left(
\begin{array}{ccccc}
a f & 0 & a b & 0 & 0 \\
0 & a f+b e & 0 & b c & 0 \\
e f & 0 & b e+c d & 0 & c r \\
0 & d e & 0 & c d+g r & 0 \\
0 & 0 & d g & 0 & g r \\
\end{array}
\right)$$

We see that this matrix decomposes into two submatrices

$$C_1:=\left(\begin{array}{cccc}
a f & a b & 0 \\
e f & b e+c d & c r \\
0 & d g & g r \\
\end{array}\right)$$

and
$$C_2:=\left(\begin{array}{cc}
a f+b e & b c \\
d e & c d+g r \\
\end{array}\right)$$

Now, one can check explicitly that the two submatrices are isospectral apart from one eigenvalue zero. I wonder if there is an abstract argument why this is so?

It would for instance follow if we can write the matrices as $C_1 = AB$ and $C_2= BA$, but I don't see how such a decomposition could work. In particular, this does not seem to be restricted to 5×5 matrices but holds for arbitrary matrices of the above form.

Best Answer

To answer say a previous question, call such tridiagonal matrix $T$; the matrices $T$ and $-T$ (of dimension $n$) are unitarily congruent (they have the same spectra), that is there is an 'alternating' $\pm 1$ diagonal matrix $U$ such that $U^*TU=-T$.
As you noticed when you square $T$ and rearrange it by a permutation $P$ the diagonal blocks have the same eigenvalues. This is true as: for $P_i$ the permutation matrix exchanging $2i+1\leftrightarrow i+1$, set $P=\prod_{i=1}^{\lfloor \frac{n-1}{2}\rfloor}P_i=P_{\lfloor \frac{n-1}{2}\rfloor}\cdots P_1$; we get $PTP^*=\begin{pmatrix}0_{\lceil\frac{n}{2}\rceil}&A\\B&0_{\lfloor \frac{n}{2}\rfloor}\end{pmatrix}.$ The square block $0_m$ is a submatrix with all zeros of dimension $m$. Squaring the last identity gives your matrix $\begin{pmatrix}AB=C_1&0\\0&BA=C_2\end{pmatrix}.$

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