In solving quadratic programming, analytical and dual decomposition based iterative method are 2 main approaches. However, the defects are evident: the former only works with small size problems and the latter only guarantees feasibility in the limit of iterations. In this paper, we propose a proactive method by combining these 2 methods to solve the optimal solution through dynamically identifying the active inequalities constraints. Further, to faster terminate the iterative process, we propose a suboptimal method based on cone programming to deliver feasible solutions with suboptimality guarantee. In addition to the mathematical proofs provided, various random simulations illustrate the effectiveness of the suboptimal method.