The linearly constrained quadratic programming(QP) considered is : $$ min f(x) = c^T x + frac{1}{2}x^T Hx $$ $$ (1) subject to A^T x geq b,$$ where $c,x in R^n, b in R^m, H in R^{n imes n)}$, symmetric, and $A in R^{n imes n}$. If there are bounds on x, these are included in the matrix $A^T$. The Hessian matrix H may be positive definite or negative semi-difinite. For large problems H and the constraint matrix A are assumed to be sparse.