Stochastic and graphical comparisons of the convergence property for multiple runs of non-linear programming algorithms

作     者：Rahardja, Dewi Zhao, Yan D. Qu, Yongming 

作者机构：Department of Mathematics and Computer Science University of Indianapolis 1400 East Hanna Avenue Indianapolis IN 46227 United States Eli Lilly and Company Indianapolis IN 46285 United States 

出 版 物：《International Journal of Industrial and Systems Engineering》 (Int. J. Ind. Syst. Eng.)

年 卷 期：2007年第2卷第2期

页      面：159-165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)] 081203[工学-计算机应用技术] 08[工学] 0816[工学-测绘科学与技术] 0835[工学-软件工程] 0813[工学-建筑学] 0714[理学-统计学（可授理学、经济学学位）] 0814[工学-土木工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Nonlinear programming 

摘      要：Non-Linear Programming (NLP) problems are often encountered in real world applications. For such problems numerous algorithms have been proposed and the convergence properties of single run of these algorithms have typically been assessed. In this article, we focus on computationally unintensive NLP algorithms with random starting values. Such algorithms enable multiple runs for a particular application and the optimum from the multiple runs is taken as the final solution. In such a scenario, the convergence property of multiple runs of an algorithm is of more interest than that of a single run. Therefore, we propose a stochastic and graphical method to assess the convergence property for multiple runs of NLP algorithms. We plot the mean best objective function values found in multiple runs versus the number of runs for several algorithms to examine how each algorithm converges and to compare among algorithms. Copyright © 2007 Inderscience Enterprises Ltd.