I am reading on Convex Optimization by Stephen Boyd.
I want to show that the symmetric positive semidefinite cone $K = S_+^n$, where $S_+^n$ is a set of symmetric $ n \times n$ matrices, is a proper cone
i am using the definition that a cone $K \subseteq \mathbb{R}^{n}$ is called a proper cone if K is convex, closed, pointed (contains no line) and nonempty.
Looking through, $S_+^n$ is indeed a convex cone, but I can't seem to prove to myself that it is also indeed a proper cone.
This is what I know so far:
To prove $S_+^n$ is convex, we can prove it by using:
$X \in S_+^n \iff Z^TXZ \geq 0, \forall Z$
$X \geq 0$, $Y \geq 0$
$Z^T(\theta_1 X + (\theta_2 Y)Z$
$ = \theta_1Z^TXZ + \theta_2Z^TYZ$
Hence, since $X$ and $Y$ are affine, they are convex and hence, $K$ is convex.
The following fact might help in proving that the cone is pointed: A symmetric matrix $A$ belongs to $S_{+}^{n}$ if and only if all the eigenvalues of $A$ is positive. Now write $A = U \Lambda U^{T}$, where $\Lambda$ is a diagonal matrix containing all the eigenvalues of $A$. What can you say about the eigenvalues of $A$ if $A \in -S_{+}^{n}$, i.e., $-A\in S_{+}^{n}$?