Online Learning Over Dynamic Graphs via Distributed Proximal Gradient Algorithm

doi:10.1109/TAC.2020.3033712

Return

Online Learning Over Dynamic Graphs via Distributed Proximal Gradient Algorithm

2021-11-01

Rishabh Dixit

Amrit Singh Bedi

Ketan Rajawat *

DOI：10.1109/TAC.2020.3033712

Original

Save

View PDF

Abstract

En 中文

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 handling nondifferentiable regularization penalties. The proposed proximal online gradient descent algorithm is built to run in a fully decentralized manner and utilizes consensus updates over possibly disconnected graphs. The performance of the proposed algorithm is analyzed by developing bounds on its dynamic regret in terms of the cumulative path length of the timevarying optimum. It is shown that as compared to the centralized case, the dynamic regret incurred by the proposed algorithm over T time slots is worse by a factor of log(T) only, despite the disconnected and time-varying network topology. The empirical performance of the proposed algorithm is tested on the distributed dynamic sparse recovery problem, where it is shown to incur a dynamic regret that is close to that of the centralized algorithm.

Keywords:

Heuristic algorithms

Signal processing algorithms

Convex functions

Distributed algorithms

Network topology

Optimization

Robot sensing systems

Distributed optimization

dynamic regret

online convex optimization

sparse signal recovery

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.