Is Positive Semidefinite matrix Same as Positive Number in Convex Optimisation?

Is Positive Semidefinite matrix Same as Positive Number in Convex
Optimisation?

Consider the optimisation problem expressed in a crude form
$\max_{\mathbf{Q}}\sum w_ir_i$
where $w_i$ are constants, $r_i$ are concave functions of positive
semidefinite matrix $\mathbf{Q}$ satisfying $\text{trace}[\mathbf{QA}]\leq
P$ for some other positive semidefinite $\mathbf{A}$.
Given the objective function and the feasible region, the problem is
obviously a convex problem. I studied about the concept of Lagrange and
KKT multiplier applied to constraints expressed in terms of real valued
functions. But for the positive definite constraint on $\mathbf{Q}$, is it
possible to attach a KKT multiplier with it, as if $\mathbf{Q}$ is a real
number? According to some articles, it's possible. But any explanation on
this concept of treating positive definite matrices as positive numbers
and why is this justified, which, I assume is part of a more generalised
KKT condition?
P. S. The problem is part of my research problem and the exact function
isn't important here. All I need is an explanation of using KKT condition
on $\mathbf{Q}$.