A ROBUST SQP METHOD BASED ON A SMOOTHING APPROXIMATE PENALTY FUNCTION FOR INEQUALITY CONSTRAINED OPTIMIZATION  

作　　者：ZHANGJuliang ZHANGXiangsun 

机构地区：[1]InstituteofAppliedMathematics,AcademyofMathematicsandSystemsScience,ChineseAcademyofSciences,Beijing100080,China [2]InstituteofAppliedMathematics,AcademyofMathematicsandSystemsScience,ChineseAcademyofSci

出　　处：《Journal of Systems Science & Complexity》2002年第1期102-112,共11页系统科学与复杂性学报（英文版）

基　　金：This research is supportedin part by the National Natural Science Foundation ofChina(Grant No. 39830070).

摘　　要：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.

关 键 词：SQP method global convergence inequality constrained optimization approximate differentiable exact penalty regularity condition. 

分 类 号：O224[理学—运筹学与控制论] N93[理学—数学]