Lagrangian ANN for convex programming with linear constraints
Gong, DJ; Gen, M; Yamazaki, G; Xu, WX
1997
Source PublicationCOMPUTERS & INDUSTRIAL ENGINEERING
ISSN0360-8352
Volume32Issue:2Pages:15,429-443
AbstractConvex programming with linear constraints represents a large class of optimization problems which have wide applications, such as linear programming, quadratic programming and some network flow programming problems. In this paper we discuss the artificial neural network approach based on the Lagrangian multiplier method (Lagrangian ANN) to it. The emphases of the paper are on analysing the defect of premature of the conventional Lagrangian ANN and giving a new modification to it in order to overcome its premature defect. We prove that this modified Lagrangian ANN can always give the optimal solution. Numerical simulations demonstrate the effectiveness of the proposed modification. (C) 1997 Elsevier Science Ltd.
Subject AreaComputer Science, Interdisciplinary Applications ; Engineering, Industrial
Indexed BySCI
Language英语
Document Type期刊论文
Identifierhttp://ir.casisd.cn/handle/190111/5431
Collection中国科学院科技政策与管理科学研究所(1985年6月-2015年12月)
Recommended Citation
GB/T 7714
Gong, DJ,Gen, M,Yamazaki, G,et al. Lagrangian ANN for convex programming with linear constraints[J]. COMPUTERS & INDUSTRIAL ENGINEERING,1997,32(2):15,429-443.
APA Gong, DJ,Gen, M,Yamazaki, G,&Xu, WX.(1997).Lagrangian ANN for convex programming with linear constraints.COMPUTERS & INDUSTRIAL ENGINEERING,32(2),15,429-443.
MLA Gong, DJ,et al."Lagrangian ANN for convex programming with linear constraints".COMPUTERS & INDUSTRIAL ENGINEERING 32.2(1997):15,429-443.
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