A projected-based neural network method for second-order cone programming

被引：9
|
作者：
Zhang, Yaling [1 ,2 ]
机构：
[1] Xian Sci & Technol Univ, Sch Comp Sci, Xian 710054, Shaanxi, Peoples R China
[2] Xidian Univ, Sch Math & Stat, Xian 710071, Peoples R China
来源：
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | 2017年 / 8卷 / 06期
关键词：
Second-order cone programming; Projection equation; Neural network method; Primal-dual interior point method; INTERIOR-POINT METHODS; VARIATIONAL-INEQUALITIES; NEWTON METHODS; OPTIMIZATION; DESIGN;
D O I：
10.1007/s13042-016-0569-0
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A projected-based neural network method for second-order cone programming is proposed. The second-order cone programming is transformed into an equivalent projection equation. The projection on the second-order cone is simple and costs less computation time. We prove that the proposed neural network is stable in the sense of Lyapunov and converges to an exact solution of the second-order cone programming problem. The simulation experiments show our method is an efficient method for second-order cone programming problems.
引用
收藏
页码：1907 / 1914
页数：8
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