SUBGRADIENT PROJECTION OVER DIRECTED GRAPHS USING SURPLUS CONSENSUS

被引：0

作者：

Xin, Ran ^{[1
]}

Xi, Chenguang ^{[1
]}

Khan, Usman ^{[1
]}

机构：

[1] Tufts Univ, ECE Dept, Medford, MA 02155 USA

来源：

2018 IEEE DATA SCIENCE WORKSHOP (DSW) | 2018年

关键词：

optimization; distributed algorithms; directed graphs; sensor networks; CONVEX-OPTIMIZATION; ALGORITHM; LOCALIZATION; BIG;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we propose Directed-Distributed Projected Sub-gradient (D-DPS) to solve a distributed constrained optimization problem over a sensor network. Sensors collaboratively minimize a sum of convex functions, which are only locally known and constrained to some commonly known convex set. D-DPS is based on surplus consensus, which overcomes the information asymmetry caused by directed communication and has a convergence rate of O(lnk/root k).

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页码：105 / 109

页数：5

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