A NONMONOTONE LINE SEARCH FILTER METHOD WITH REDUCED HESSIAN UPDATING FOR NONLINEAR OPTIMIZATION

作     者：GU Chao ZHU Detong 

作者机构：School of Mathematics and InformaticsShanghai Lixin University of Commerce Business CollegeShanghai Normal University 

出 版 物：《Journal of Systems Science & Complexity》 (系统科学与复杂性学报（英文版）)

年 卷 期：2013年第26卷第4期

页      面：534-555页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 070105[理学-运筹学与控制论] 0701[理学-数学] 

基　　金：supported by the National Science Foundation of China under Grant No.10871130 the Ph.D Foundation under Grant No.20093127110005 the Shanghai Leading Academic Discipline Project under Grant No.S30405 the Innovation Program of Shanghai Municipal Education Commission under Grant No.12YZ174 

主　　题：Convergence filter method lagrangian function line search maratos effect nomnono- tone. 

摘      要：This paper proposes a nonmonotone line search filter method with reduced Hessian updating for solving nonlinear equality constrained *** order to deal with large scale problems,a reduced Hessian matrix is approximated by BFGS *** new method assures global convergence without using a merit *** Lagrangian function in the filter and nonmonotone scheme,the authors prove that the method can overcome Maratos effect without using second order correction step so that the locally superlinear convergence is *** primary numerical experiments are reported to show effectiveness of the proposed algorithm.