Online Learning Over Dynamic Graphs via Distributed Proximal Gradient Algorithm

We consider the problem of tracking the minimum of a time-varying convex optimization problem over a dynamic graph. Motivated by target tracking and parameter estimation problems in intermittently connected robotic and sensor networks, the goal is to design a distributed algorithm capable of handlin...

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Bibliographic Details
Published in:	IEEE transactions on automatic control Vol. 66; no. 11; pp. 5065 - 5079
Main Authors:	Dixit, Rishabh, Bedi, Amrit Singh, Rajawat, Ketan
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.11.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Computational geometry Convexity Distributed algorithms Distributed optimization dynamic regret Empirical analysis Graphs Machine learning Network topologies Network topology online convex optimization Optimization Parameter estimation Regularization sparse signal recovery Time-varying systems Tracking
ISSN:	0018-9286, 1558-2523
Online Access:	Get full text
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