I am wondering if there are any constraints which can be places on a matrix $A \in \mathbb{R}^{n \times n}$ to ensure that $A + A^\top$ is positive semidefinite (PSD) even though $A$ itself is not PSD.
It is easy to see that if $A$ is PSD, then...
$$ x^\top (A + A^\top)x = x^\top A x + x^\top A^\top x = 2 x^\top Ax \geq 0 $$
Can it ever be the case that $A$ is not PSD but $A + A^\top$ is? What would need to be satisfied?
EDIT:
I assume $A$ is not symmetric or triangular.
Let $x^T (A+A^T)x \geq 0$ and $x^TAx < 0$ (i.e. not positive semi-definite). We know that any matrix can be decomposed into symmetric and antisymmetric matrices that is $A=\frac{1}{2}(A+A^T)+\frac{1}{2}(A-A^T)$ and $x^T A^T x=x^TAx$ (i.e. scalars are symmetric). We have $$ x^T A x < 0 \implies \frac{1}{2} x^T(A+A^T)x < 0 $$
If $A$ negative definite, so is $(A+A^T)$ but this contradicts our assumption that $A+A^T$ is positive semi definite, therefore, $A$ must be positive semi definite.