I am new to the field of optimization and keep encountering objective functions of the form
$$f(x) = \frac 12 x^T A x - b^T x + c$$
Just wanted to know the reasoning behind some properties of the function above.
I know if $A$ is symmetric and positive semidefinite (PSD), then $f(x)$ is convex. Can $A$ be PSD and non-symmetric and $f(x)$ still be convex? How is being symmetric related to being convex? For example $$A = \begin{bmatrix} 1 & 1 \\-1 & 2\end{bmatrix}$$ is positive definite (PD) but not symmetric. Is $f(x)$ convex still? Most books list Hessian of the function being PSD to be a sufficient condition for $f$ to be a convex function.
If $A$ is not symmetric and PSD, then does $f(x)$ still have a global minimum?