Decentralized Multitask Recursive Least Squares with Local Linear Constraints

Xuanyu Cao, Tamer Basar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we study decentralized multitask recursive least squares, where each node in a network has an unknown weight vector to estimate. The weight vectors of neigh-boring nodes are related through local linear equality constraints, e.g., the flow conservation constraints in network flow control. We propose a modified dual gradient ascent algorithm, which is both decentralized and online. The mean square convergence of the algorithm is established under standard assumptions. It is shown that both the mean square deviation and the excess mean square error converge to zero at geometric rates.

Original languageEnglish (US)
Title of host publicationConference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1410-1414
Number of pages5
ISBN (Electronic)9780738131269
DOIs
StatePublished - Nov 1 2020
Externally publishedYes
Event54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020 - Pacific Grove, United States
Duration: Nov 1 2020Nov 5 2020

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2020-November
ISSN (Print)1058-6393

Conference

Conference54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Country/TerritoryUnited States
CityPacific Grove
Period11/1/2011/5/20

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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