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.
Alkuperäiskieli | Englanti |
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Otsikko | 2022 IEEE 61st Conference on Decision and Control (CDC) |
Kustantaja | IEEE |
Sivut | 6848-6854 |
Sivumäärä | 7 |
ISBN (elektroninen) | 978-1-6654-6761-2 |
DOI - pysyväislinkit | |
Tila | Julkaistu - 2022 |
OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisuussa |
Tapahtuma | IEEE Conference on Decision and Control - Cancun, Meksiko Kesto: 6 jouluk. 2022 → 9 jouluk. 2022 Konferenssinumero: 61 |
Julkaisusarja
Nimi | Proceedings of the IEEE Conference on Decision & Control |
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ISSN (elektroninen) | 2576-2370 |
Conference
Conference | IEEE Conference on Decision and Control |
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Lyhennettä | CDC |
Maa/Alue | Meksiko |
Kaupunki | Cancun |
Ajanjakso | 06/12/2022 → 09/12/2022 |