A dual method for solving general convex quadratic programs
نویسندگان
چکیده
In this paper, we present a new method for solving quadratic programming problems, not strictly convex. Constraints of the problem are linear equalities and inequalities, with bounded variables. The suggested method combines the active-set strategies and support methods. The algorithm of the method and numerical experiments are presented, while comparing our approach with the active set method on randomly generated problems. Keywords—Convex quadratic programming, dual support methods, active set methods.
منابع مشابه
An Interior Point Algorithm for Solving Convex Quadratic Semidefinite Optimization Problems Using a New Kernel Function
In this paper, we consider convex quadratic semidefinite optimization problems and provide a primal-dual Interior Point Method (IPM) based on a new kernel function with a trigonometric barrier term. Iteration complexity of the algorithm is analyzed using some easy to check and mild conditions. Although our proposed kernel function is neither a Self-Regular (SR) fun...
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 کاملBenchmarking large-scale distributed convex quadratic programming algorithms
This paper aims to collect, benchmark and implement state-of-the-art decomposable convex quadratic programming methods employing duality. In order to decouple the original problem, these methods relax some constraints by introducing dual variables and apply a hierarchical optimization scheme. In the lower level of this scheme, a sequence of parametric quadratic programs is solved in parallel, w...
متن کاملSolving General Convex Qp Problems via an Exact Quadratic Augmented Lagrangian with Bound Constraints
Large convex quadratic programs, where constraints are of box type only, can be solved quite eeciently 1], 2], 12], 13], 16]. In this paper an exact quadratic augmented Lagrangian with bound constraints is constructed which allows one to use these methods for general constrained convex quadratic programming. This is in contrast to well known exact diierentiable penalty functions for this type o...
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 کامل