In this paper,we present a successive quadratic programming(SQP)method for minimizing a class of nonsmooth functions,which are the sum of a convex function and a nonsmooth composite function.The method generates new i...In this paper,we present a successive quadratic programming(SQP)method for minimizing a class of nonsmooth functions,which are the sum of a convex function and a nonsmooth composite function.The method generates new iterations by using the Armijo-type line search technique after having found the search directions.Global convergence property is established under mild assumptions.Numerical results are also offered.展开更多
In this paper, a modified variation of the Limited SQP method is presented for constrained optimization. This method possesses not only the information of gradient but also the information of function value. Moreover,...In this paper, a modified variation of the Limited SQP method is presented for constrained optimization. This method possesses not only the information of gradient but also the information of function value. Moreover, the proposed method requires no more function or derivative evaluations and hardly more storage or arithmetic operations. Under suitable conditions, the global convergence is established.展开更多
A new SQP type feasible method for inequality constrained optimization is presented,it is a combination of a master algorithm and an auxiliary algorithm which is taken only in finite iterations.The directions of the m...A new SQP type feasible method for inequality constrained optimization is presented,it is a combination of a master algorithm and an auxiliary algorithm which is taken only in finite iterations.The directions of the master algorithm are generated by only one quadratic programming, and its step\|size is always one, the directions of the auxiliary algorithm are new “second\|order” feasible descent. Under suitable assumptions,the algorithm is proved to possess global and strong convergence, superlinear and quadratic convergence.展开更多
In this paper,the nonlinear complementarity problem is transformed into the least squares problem with nonnegative constraints,and a SQP algorithm for this reformulation based on a damped Gauss Newton type method is ...In this paper,the nonlinear complementarity problem is transformed into the least squares problem with nonnegative constraints,and a SQP algorithm for this reformulation based on a damped Gauss Newton type method is presented.It is shown that the algorithm is globally and locally superlinearly (quadratically) convergent without the assumption of monotonicity.展开更多
文摘In this paper,we present a successive quadratic programming(SQP)method for minimizing a class of nonsmooth functions,which are the sum of a convex function and a nonsmooth composite function.The method generates new iterations by using the Armijo-type line search technique after having found the search directions.Global convergence property is established under mild assumptions.Numerical results are also offered.
文摘In this paper, a modified variation of the Limited SQP method is presented for constrained optimization. This method possesses not only the information of gradient but also the information of function value. Moreover, the proposed method requires no more function or derivative evaluations and hardly more storage or arithmetic operations. Under suitable conditions, the global convergence is established.
基金Supported by the National Natural Science Foundation of China(1 980 1 0 0 9) and by the Natural Sci-ence Foundation of Guangxi
文摘A new SQP type feasible method for inequality constrained optimization is presented,it is a combination of a master algorithm and an auxiliary algorithm which is taken only in finite iterations.The directions of the master algorithm are generated by only one quadratic programming, and its step\|size is always one, the directions of the auxiliary algorithm are new “second\|order” feasible descent. Under suitable assumptions,the algorithm is proved to possess global and strong convergence, superlinear and quadratic convergence.
基金Supported by the National Natural Science Foundation of China(1 9971 0 0 2 )
文摘In this paper,the nonlinear complementarity problem is transformed into the least squares problem with nonnegative constraints,and a SQP algorithm for this reformulation based on a damped Gauss Newton type method is presented.It is shown that the algorithm is globally and locally superlinearly (quadratically) convergent without the assumption of monotonicity.
基金The National Natural Science Foundation of China(11271086)he Natural Science Foundation of Guangxi Province(2011GXNSFD018022)he Innovation Group of Talents Highland of Guangxi Higher School
