It is known that a 3 by 3 real symmetric matrix $A$ has an eigendecomposition
$$ A = Q E Q^T $$
where $Q$ is an orthogonal matrix and $E$ is a diagonal matrix whose elements, $E_{11}$, $E_{22}$ and $E_{33}$, are the eigenvalues of $A$.
Moreover, if those eigenvalues are non-negative then $A$ is positive-semidefinite.
The question is: if those eigenvalues are not only non-negative but also verify the triangle inequalities $$ \begin{aligned} E_{11} + E_{22} &\geq E_{33}\\ E_{11} + E_{33} &\geq E_{22}\\ E_{22} + E_{33} &\geq E_{11} \end{aligned} $$ is there anything special about the structure of $A$ besides the fact that it is positive-semidefinite?
Can those extra triangle inequalities constraints be written as functions of the elements of $A$, just like the positive-semidefinite constraints can be written down using the Sylvester's criterion?
Can those extra constraints be somehow represented in a semidefinite programming / linear matrix inequalities framework?