A simplicial decomposition framework for large scale convex quadratic programming
نویسندگان
چکیده
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.
منابع مشابه
Global convergence of an inexact interior-point method for convex quadratic symmetric cone programming
In this paper, we propose a feasible interior-point method for convex quadratic programming over symmetric cones. The proposed algorithm relaxes the accuracy requirements in the solution of the Newton equation system, by using an inexact Newton direction. Furthermore, we obtain an acceptable level of error in the inexact algorithm on convex quadratic symmetric cone programmin...
متن کاملEfficient Discriminative Training Method for Structured Predictions
We propose an efficient discriminative training method for generative models under supervised learning. In our setting, fully observed instances are given as training examples, together with a specification of variables of interest for prediction. We formulate the training as a convex programming problem, incorporating the SVM-type large margin constraints to favor parameters under which the ma...
متن کاملAn Efficient Discriminative Training Method for Generative Models
We propose an efficient discriminative training method for generative models under supervised learning. In our setting, fully observed instances are given as training examples, together with a specification of variables of interest for prediction. We formulate the training as a convex programming problem, incorporating the SVM-type large margin constraints to favor parameters under which the ma...
متن کاملA Recurrent Neural Network for Solving Strictly Convex Quadratic Programming Problems
In this paper we present an improved neural network to solve strictly convex quadratic programming(QP) problem. The proposed model is derived based on a piecewise equation correspond to optimality condition of convex (QP) problem and has a lower structure complexity respect to the other existing neural network model for solving such problems. In theoretical aspect, stability and global converge...
متن کاملA Method for Solving Convex Quadratic Programming Problems Based on Differential-algebraic equations
In this paper, a new model based on differential-algebraic equations(DAEs) for solving convex quadratic programming(CQP) problems is proposed. It is proved that the new approach is guaranteed to generate optimal solutions for this class of optimization problems. This paper also shows that the conventional interior point methods for solving (CQP) problems can be viewed as a special case of the n...
متن کامل