Let $A\in \mathbb{n\times n}$ be a symmetric matrix. Let $x\in \mathbb{R}^{n\times 1}$ be an unknown vector.
The problem is $$\min \limits_x \{E(x)=x^TAx\}$$
where $x\in C$, $C$ is a convex set. $C=\{x|\sum\limits_i^n x_i=0\}$.
Is there any way to find $x^*=\arg\min\limits_{x\in C} x^TAx$?
Since $A$ is an input, I am not sure
1 it is positive semidefinite (the objective is convex);
2 or it is negative semidefinite (the objective is concave);
3 or indefinite (the objective is neither concave nor convex. )
Case 1 is simple to calculate the global solution.
In case 2, 3, is there any way to calculate $x^*$?
The convex conjugate of the convex conjugate of $E(x)$ is convex.
The convex conjugate of $E(x)$ is, $$E^*(x)=\max\limits_y<x,y>-y^TAy.$$
Since $A $ may be indefinite, it is still difficult for me. Is there any available literature for this problem? Thank you in advance.