In this paper,the new SQP feasible descent algorithm for nonlinear constrained optimization problems presented,and under weaker conditions of relative,we proofed the new method still possesses global convergence and i...In this paper,the new SQP feasible descent algorithm for nonlinear constrained optimization problems presented,and under weaker conditions of relative,we proofed the new method still possesses global convergence and its strong convergence.The numerical results illustrate that the new methods are valid.展开更多
A robust SQP method, which is analogous to Facchinei’s algorithm, is introduced. The algorithm is globally convergent. It uses automatic rules for choosing penalty parameter, and can efficiently cope with the possibl...A robust SQP method, which is analogous to Facchinei’s algorithm, is introduced. The algorithm is globally convergent. It uses automatic rules for choosing penalty parameter, and can efficiently cope with the possible inconsistency of the quadratic search subproblem. In addition, the algorithm employs a differentiable approximate exact penalty function as a merit function. Unlike the merit function in Facchinei’s algorithm, which is quite complicated and is not easy to be implemented in practice, this new merit function is very simple. As a result, we can use the Facchinei’s idea to construct an algorithm which is easy to be implemented in practice.展开更多
The authors propose a dwindling filter algorithm with Zhou's modified subproblem for nonlinear inequality constrained optimization.The feasibility restoration phase,which is always used in the traditional filter m...The authors propose a dwindling filter algorithm with Zhou's modified subproblem for nonlinear inequality constrained optimization.The feasibility restoration phase,which is always used in the traditional filter method,is not needed.Under mild conditions,global convergence and local superlinear convergence rates are obtained.Numerical results demonstrate that the new algorithm is effective.展开更多
基金Supported by the NNSF of China(10231060)Supported by the Soft Science Foundation of Henan Province(082400430820)
文摘In this paper,the new SQP feasible descent algorithm for nonlinear constrained optimization problems presented,and under weaker conditions of relative,we proofed the new method still possesses global convergence and its strong convergence.The numerical results illustrate that the new methods are valid.
基金This research is supportedin part by the National Natural Science Foundation ofChina(Grant No. 39830070).
文摘A robust SQP method, which is analogous to Facchinei’s algorithm, is introduced. The algorithm is globally convergent. It uses automatic rules for choosing penalty parameter, and can efficiently cope with the possible inconsistency of the quadratic search subproblem. In addition, the algorithm employs a differentiable approximate exact penalty function as a merit function. Unlike the merit function in Facchinei’s algorithm, which is quite complicated and is not easy to be implemented in practice, this new merit function is very simple. As a result, we can use the Facchinei’s idea to construct an algorithm which is easy to be implemented in practice.
基金supported by the National Natural Science Foundation of China(Nos.11201304,11371253)the Innovation Program of Shanghai Municipal Education Commission(No.12YZ174)the Group of Accounting and Governance Disciplines(No.10kq03)
文摘The authors propose a dwindling filter algorithm with Zhou's modified subproblem for nonlinear inequality constrained optimization.The feasibility restoration phase,which is always used in the traditional filter method,is not needed.Under mild conditions,global convergence and local superlinear convergence rates are obtained.Numerical results demonstrate that the new algorithm is effective.
