#include <CGAL/QP_models.h>
$$
(QP)$$  minimize$$  x^{T}Dx+c^{T}x+c_{0} 
subject to$$  Ax ~ b,  
l x u 
in $$n real variables $$x=(x_{0},...,x_{n1}). Here,
This class is simply a wrapper for existing iterators, and it does not copy the program data.
It frequently happens that all values in one of the vectors from above are the same, for example if the system $$Ax ~ b is actually a system of equations $$Ax=b. To get an iterator over such a vector, it is not necessary to store multiple copies of the value in some container; an instance of the class Const_oneset_iterator<T>, constructed from the value in question, does the job more efficiently.
 
constructs qp from given randomaccess iterators and the constant c0. The passed iterators are merely stored, no copying of the program data takes place. How these iterators are supposed to encode the quadratic program is
described in QuadraticProgram.

QP_solver/first_qp_from_iterators.cpp
The following example for the simpler model Nonnegative_linear_program_from_iterators<A_it, B_it, R_it, C_it> should give you a flavor of the use of this model in practice.
QP_solver/solve_convex_hull_containment_lp.h
QP_solver/convex_hull_containment.cpp