Solving linear program with 1 quadratic constraint complexity Consider the following linear program,

$$\min y \\
xc_1 \leq c_2 + yz,\\
x = x_1 + \dots + x_n,\\
z \leq x_1 + x_2, \\
z \leq x_2 + x_3, \\
\vdots\\
z \leq x_{n-1} + x_n, \\
x,x_1, \dots, x_n,y,z \geq 0
$$ 
where $c_1, c_2$ are constants. This is an example of quadratically constrained linear program where I have 1 quadratic constraint. I wish to find out if this problem is NP-Hard or not. The quadratic constraint can be expressed in the form $\vec{y}M\vec{y}^T$ where $M$ for my problem is not positive semidefinite (and thus, non-convex) which is perhaps evidence of hardness

Listing specific questions below:


Can this problem be transformed into a linear program by taking logarithms?
Is there any literature reference or reduction showing that linear programs with non-convex quadratic constraints is an NP-Hard problem? 


Edit : Cross posted question at math.stackexchange
Based on posts from or-exchange and some internet reading, the following algorithm works. We adapt the cutting plane method to binary search for variable $y$. For my problem, the upper and lower bounds for all variables are known (but if these are not known, one can find a bound on $y$ by solving the LP by removing the first constraint). Let $l \leq y \leq u$.

Fix $y = (l+u)/2$. This converts the program to a linear program. If the resulting constraints are feasible, update $u(l) = (l+u)/2$. Keep performing the binary search until we reach the optimal solution with an additive $\epsilon_0$. The running time of the algorithm is $O(PTIME)\log\frac{u-l}{\epsilon_0}$. 

While this algorithm can find the optimal solution within a small additive constant, I am still not sure about the hardness of the problem for solving exactly. Any comments regarding complexity are welcome!