GLOBAL CONVERGENCE AND IMPLEMENTATION OF NGTN METHOD FOR SOLVING LARGE-SCALE SMARSE NONLINEAR PROGRAMMING PROBLEMS

作     者：Qin Ni (Department of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China) 

作者机构：南京航空航天大学 江苏 南京 210016 

出 版 物：《Journal of Computational Mathematics》 (计算数学（英文）)

年 卷 期：2001年第19卷第4期

页      面：337-346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] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research was supported by Nationa Natural Science Foundation of China  LSEC of CAS in Beijingand Natural Science Foundati 

主　　题：Nonlinear programming Large-scale problem Sparse. 

摘      要：An NGTN method was proposed for solving large-scale sparse nonlinear programming (NLP) problems. This is a hybrid method of a truncated Newton direction and a modified negative gradient direction, which is suitable for handling sparse data structure and pos sesses Q-quadratic convergence rate. The global convergence of this new method is proved, the convergence rate is further analysed, and the detailed implementation is discussed in this paper. Some numerical tests for solving truss optimization and large sparse problems are reported. The theoretical and numerical results show that the new method is efficient for solving large-scale sparse NLP problems.