similarity transformation into symmetric matrices I want to determine some structures of matrices that can be transformed into a symmetric matrices using similarity transformation, i.e.,
$B=T^{-1}AT$
where $T$ is the similarity transformation matrix. 
Here, $A$ is a non-negative matrix and the diagonal elements of $A$ is supposed to be the same $(diag(A)=[a, a, \ldots , a])$.
Any ideas?
 A: We can say something on such matrices $B$ by characterizing its eigenvalues, which coincide with the eigenvalues of $A$. 
Since $A$ is a real symmetric matrix, it has real eigenvalues. Hence a necessary condition on $B$ is that it has real eigenvalues. But one can say more. There are necessary and sufficient conditions known for real numbers $\lambda_1,\ldots ,\lambda_r$ to be the eigenvalues of a non-negative symmetric matrix. See for example the paper of M. Fiedler, Eigenvalues of nonnegative symmetric matrices here: http://www.sciencedirect.com/science/article/pii/0024379574900317. 
Update: We still have the additional condition that the diagonal of $A$ is $(a,a,\ldots ,a)$, but this is also discussed in the paper of Fiedler. Given $2n$ real numbers $\lambda_i$, one can say whether there is a nonnegative symmetric matrix with eigenvalues  $\lambda_1,\ldots ,\lambda_n$ and diagonal $\lambda_{n+1},\ldots ,\lambda_{2n}$.
A: An addition to Dietrich Burde's answer: Since symmetric matrices are diagonizable, they are semisimple (each invariant subspace has an invariant complement). Thus $B$ has to be semisimple ($\iff$ the minimal polynomial is square free).
