A New Inexact PVD Algorithm for General Programming Problem

被引：0
|
作者：
Xu Fangfang [1 ]
Liu Weihui [1 ]
Zhu Gengfeng [1 ]
机构：
[1] Shandong Univ Sci & Technol, Coll Informat Sci & Technol, Qingdao 266510, Peoples R China
来源：
PROCEEDINGS OF 2009 CONFERENCE ON COMMUNICATION FACULTY | 2009年
关键词：
parallel variable distribution; projected gradient residual function; sufficient descent condition; general convex constraints; VARIABLE DISTRIBUTION ALGORITHMS;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper consider the parallel variable distribution (PVD) approach proposed by Ferris and Mangasarian for solving optimization problem. This paper propose to apply the PVD approach to problems with general convex constraints and show that the algorithm converges, provided certain conditions are imposed on the change of secondary variables. In this paper, we choose to use the projected gradient direction for secondary variables and replace the minimization problem with a sufficient descent condition in the parallelization stage.
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页码：117 / 119
页数：3
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