Automatic differentiation for reduced sequential quadratic programming

作     者：Liao Liangcai Li Jin Tan Yuejin 

作者机构：School of Information System & Management National Univ. of Defense Technology Changsha 410073 P. R. China The Fifth Inst. of the Missile Army Beijing 100085 P. R. China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2007年第18卷第1期

页      面：57-62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] 

主　　题：Automatic differentiation Reduced sequential quadratic programming Optimization algorithm 

摘      要：In order to slove the large-scale nonlinear programming （NLP） problems efficiently, an efficient optimization algorithm based on reduced sequential quadratic programming （rSQP） and automatic differentiation （AD） is presented in this paper. With the characteristics of sparseness, relatively low degrees of freedom and equality constraints utilized, the nonlinear programming problem is solved by improved rSQP solver. In the solving process, AD technology is used to obtain accurate gradient information. The numerical results show that the combined algorithm, which is suitable for large-scale process optimization problems, can calculate more efficiently than rSQP itself.