A Fast Finite-Time Consensus based Gradient Method for Distributed Optimization over Digraphs

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

Abstrakti

In this paper, we study the unconstrained optimization problem in a distributed way over directed strongly connected communication graphs. We propose an algorithm, which combines techniques of both gradient descent (GD) and finite-time exact ratio consensus (FTERC). Different from the techniques of average or dynamic average consensus with asymptotic convergence or techniques of finite-time “approximate” consensus with inexact accuracy in the literature, with the help of FTERC for gradient tracking, our proposed distributed FTERC based GD algorithm has a faster convergence rate related to the optimization iteration number and a larger step-size upper bound compared with other algorithms, as demonstrated in the simulations.
AlkuperäiskieliEnglanti
Otsikko2022 IEEE 61st Conference on Decision and Control (CDC)
KustantajaIEEE
Sivut6848-6854
Sivumäärä7
ISBN (elektroninen)978-1-6654-6761-2
DOI - pysyväislinkit
TilaJulkaistu - 2022
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE Conference on Decision and Control - Cancun, Meksiko
Kesto: 6 jouluk. 20229 jouluk. 2022
Konferenssinumero: 61

Julkaisusarja

NimiProceedings of the IEEE Conference on Decision & Control
ISSN (elektroninen)2576-2370

Conference

ConferenceIEEE Conference on Decision and Control
LyhennettäCDC
Maa/AlueMeksiko
KaupunkiCancun
Ajanjakso06/12/202209/12/2022

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