Positive Definiteness Constraint I want to add a constraint to a convex program, to guarantee some matrix $A$ to be positive semidefinite.
How should I do it?

The library I am working with can cope with linear/ quadratic inequalities only.

By definition, $A$ is positive semidefinite iff $\forall x \in \mathbb C^n : x^T A x \geq 0$, but this is a set of inifinitely many constraints. So, my question is: how can I formulate it using a finitely many set of contraints and using linear/ quadratic inequalities only.

Thanks in advance!
A matrix $A$ is positive semidefinite if and only if there exists a matrix $V$ such that $$A = V^\top V.$$
So, you can use the entries of $V$ as your unknowns, and express each entry of $A$ as a quadratic function of the unknowns.  Whenever you want to use $A$, instead rewrite that equation in terms of the entries of $V$.