Show that all the eigenvalues of a matrix but one (which is null) have negative real part I have a situation where a matrix $A=[a_{ij}]$ arises.
From the physics of the problem, I expect this matrix to have one null eigenvalue, while the remaining eigenvalues have negative real part.
However I have not been able to prove the second part of the statement.
I appreciate any help / hint /guidance on how to approach the problem.

The off-diagonal elements are given by
$$
a_{ij} =
\begin{cases}
n_{ji}, & \text{if } j<i \\
n_{ji}+1, & \text{if } j>i
\end{cases}
$$
Whereas the diagonal elements are given by
$ a_{ii} = -\sum_{k \neq i =1}^N a_{ki} $.
It is clear that the row vector of all-ones $\mathbb 1_N$ is always a left eigenvector with null eigenvalue.
Furthermore, the $n_{ij}$ are such that:
$\bullet$ $ n_{ij}>0 $
$\bullet$ $ n_{ij} $ increase with $i: n_{ij} < n_{(i+1)j}$
$\bullet$ $ n_{ij} $ decrease with $j: n_{ij} > n_{i(j+1)}$
Case N=2
$$
\begin{bmatrix}
-n_{12} & 1+n_{12} \\
n_{12} & -1-n_{12} \\
\end{bmatrix}
$$
The eigenvalues are $0$ and $-1-2 n_{12}$.
Case N=3
$$
\begin{bmatrix}
-n_{12}-n_{13} & 1+n_{12} & 1+n_{13} \\
n_{12} & -1-n_{12}-n_{23} & 1+n_{23} \\
n_{13} & n_{23} & -2-n_{13}-n_{23} \\
\end{bmatrix}
$$
The non-zero eigenvalues are given by (after some messy computations, or after asking Wolfram Mathematica):
$$
(-3 - 2 n_{12} - 2 n_{13} - 2 n_{23} \pm \sqrt{1 - 4 n_{12} + 4 n_{12}^2 - 4 n_{12} n_{13} + 4 n_{13}^2 + 4 n_{23} - 4 n_{12} n_{23} - 4 n_{13} n_{23} + 4 n_{23}^2})/2
$$
We can see that the real part must be negative by noting that $(3 + 2 n_{12} + 2 n_{13} + 2 n_{23})^2$ is strictly greater than $(1 - 4 n_{12} + 4 n_{12}^2 - 4 n_{12} n_{13} + 4 n_{13}^2 + 4 n_{23} - 4 n_{12} n_{23} - 4 n_{13} n_{23} + 4 n_{23}^2)$.
As $N$ increases, the eigenvalue computations becomes messier... Is there a simpler way to show that they will be negative?
 A: https://en.wikipedia.org/wiki/Gershgorin_circle_theorem may help you here.
Note that it implies that eigenvalues $\lambda$ satisfy
$$|\lambda - a_{ii}| \le \sum_{k\neq i} |a_{ki}| = \sum_{k\neq i}a_{ki} = - a_{ii}.$$
This proves that all eigenvalues are non-positive. Not sure at the moment the best way to prove that one and only one eigenvalue will be $0$.
EDIT:
However, it seems clear that at least one will be zero since the elements satisfy $\sum_{k=1}^N a_{ki} = 0$. The determinant is clearly going to be $0$ since an entire row can be zeroed out. So at least one eigenvalue is $0$.
Second EDIT:
As pointed out by user1551, the eigenvalues can be complex so the original simplification is incorrect. The result can be explicitly shown. I leave it to OP to verify that the real part of $\lambda$ is non-positive.
A: I think I found an answer:
First, use the Gershgorin circle theorem as in Gregory's answer to show that the eigenvalues of $A$ have non-positive real part.
Second, note that $A$ has at least one null eigenvalue since the rows of the matrix add up to $0$.
Third, show that $A$ has only one null eigenvalue:
Construct an auxiliary $N\times N$ matrix $B$, equal to $A$, but with the last row zeroed. Since row operations do not change the nullity of a matrix, $B$ has the same number of null eigenvalues as $A$.
$$ B = \begin{bmatrix}
a_{11} & 1+n_{12} & \cdots & 1+n_{1(N-1)} & 1+n_{1N} \\
n_{12} & a_{22} & \cdots & 1+n_{2(N-1)} & 1+n_{2N} \\
\vdots & \vdots & \ddots & \vdots & \vdots \\
n_{1(N-1)} & n_{2(N-1)} & \cdots & a_{(N-1)(N-1)} & 1+n_{(N-1)(N-1)} \\
0 & 0 & \cdots & 0 & 0
\end{bmatrix}
$$
Construct a second auxiliary $(N-1) \times (N-1)$ matrix $C$, equal to $B$ without the last row and column. Note that the eigenvalues of $C$ are also eigenvalues of $B$ since you can construct the eigenvectors of $B$ by padding the eigenvectors of $C$ with a $0$.
$$ \text{Let } v=[v_1 \cdots v_{N-1}] : Cv = \lambda v
$$
$$ \text{Note } w=[v_1 \cdots v_{N-1} \ 0] \Rightarrow Bw = \lambda w
$$
Finally, use once again the Gershgorin circle theorem to show that all the eigenvalues of $C$ are strictly negative.
$$ |\lambda - a_{ii}| \leq \sum_{k\neq i} c_{ki} = |a_{ii}| - n_{iN} < |a_{ii}|
$$
$C$ has no null eigenvalues $\Rightarrow$ $B$ has only one null eigenvalue $\Rightarrow$ $A$ has only one null eigenvalue $\blacksquare$
