Distributed Global Optimization by Annealing

被引：0
|
作者：
Swenson, Brian [1 ]
Kar, Soummya [2 ]
Poor, H. Vincent [1 ]
Moura, Jose M. F. [2 ]
机构：
[1] Princeton Univ, Dept Elect Engn, Princeton, NJ 08540 USA
[2] Dept Elect & Comp Engn, Pittsburgh, PA 15213 USA
来源：
2019 IEEE 8TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING (CAMSAP 2019) | 2019年
基金：
美国国家科学基金会;
关键词：
Distributed optimization; nonconvex optimization; multiagent systems; consensus plus innovations; CONVERGENCE; ALGORITHM;
D O I：
10.1109/camsap45676.2019.9022513
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
The paper considers distributed global minimization of a nonconvex function. We study a first-order consensus + innovations type algorithm that incorporates decaying additive Gaussian noise for annealing to converge to the set of global minima under certain technical assumptions. The paper presents simple methods for verifying that the required technical assumptions hold and illustrates it with a distributed target-localization application.
引用
收藏
页码：181 / 185
页数：5
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