A new iterative method,which is called positive interior-point algorithm,is presented for solving the nonlinear complementarity problems.This method is of the desirable feature of robustness.And the convergence theore...A new iterative method,which is called positive interior-point algorithm,is presented for solving the nonlinear complementarity problems.This method is of the desirable feature of robustness.And the convergence theorems of the algorithm is established.In addition,some numerical results are reported.展开更多
In this paper, we propose a primal-dual interior point method for solving general constrained nonlinear programming problems. To avoid the situation that the algorithm we use may converge to a saddle point or a local ...In this paper, we propose a primal-dual interior point method for solving general constrained nonlinear programming problems. To avoid the situation that the algorithm we use may converge to a saddle point or a local maximum, we utilize a merit function to guide the iterates toward a local minimum. Especially, we add the parameter ε to the Newton system when calculating the decrease directions. The global convergence is achieved by the decrease of a merit function. Furthermore, the numerical results confirm that the algorithm can solve this kind of problems in an efficient way.展开更多
In this paper we propose a class of new large-update primal-dual interior-point algorithms for P.（k） nonlinear complementarity problem （NCP）, which are based on a class of kernel functions investigated by Bai et a...In this paper we propose a class of new large-update primal-dual interior-point algorithms for P.（k） nonlinear complementarity problem （NCP）, which are based on a class of kernel functions investigated by Bai et al. in their recent work for linear optimization （LO）. The arguments for the algorithms are followed as Peng et al.＇s for P.（n） complementarity problem based on the self-regular functions [Peng, J., Roos, C., Terlaky, T.： Self-Regularity： A New Paradigm for Primal-Dual Interior- Point Algorithms, Princeton University Press, Princeton, 2002]. It is worth mentioning that since this class of kernel functions includes a class of non-self-regular functions as special case, so our algorithms are different from Peng et al.＇s and the corresponding analysis is simpler than theirs. The ultimate goal of the paper is to show that the algorithms based on these functions have favorable polynomial complexity.展开更多
文摘A new iterative method,which is called positive interior-point algorithm,is presented for solving the nonlinear complementarity problems.This method is of the desirable feature of robustness.And the convergence theorems of the algorithm is established.In addition,some numerical results are reported.
文摘In this paper, we propose a primal-dual interior point method for solving general constrained nonlinear programming problems. To avoid the situation that the algorithm we use may converge to a saddle point or a local maximum, we utilize a merit function to guide the iterates toward a local minimum. Especially, we add the parameter ε to the Newton system when calculating the decrease directions. The global convergence is achieved by the decrease of a merit function. Furthermore, the numerical results confirm that the algorithm can solve this kind of problems in an efficient way.
基金Supported by Natural Science Foundation of Hubei Province （Grant No. 2008CDZ047）Acknowledgements Thanks my supervisor Prof. M. W. Zhang for long-last guidance during the course of study.
文摘In this paper we propose a class of new large-update primal-dual interior-point algorithms for P.（k） nonlinear complementarity problem （NCP）, which are based on a class of kernel functions investigated by Bai et al. in their recent work for linear optimization （LO）. The arguments for the algorithms are followed as Peng et al.＇s for P.（n） complementarity problem based on the self-regular functions [Peng, J., Roos, C., Terlaky, T.： Self-Regularity： A New Paradigm for Primal-Dual Interior- Point Algorithms, Princeton University Press, Princeton, 2002]. It is worth mentioning that since this class of kernel functions includes a class of non-self-regular functions as special case, so our algorithms are different from Peng et al.＇s and the corresponding analysis is simpler than theirs. The ultimate goal of the paper is to show that the algorithms based on these functions have favorable polynomial complexity.
