  In this paper, we analyze in depth a simplicial decomposition like
algorithmic framework for large scale convex quadratic programming. In
particular, we first propose two tailored strategies for handling the master
problem. Then, we describe a few techniques for speeding up the solution of the
pricing problem. We report extensive numerical experiments on both real
portfolio optimization and general quadratic programming problems, showing the
efficiency and robustness of the method when compared to Cplex.
