dc.contributor.author | Bof, Nicoletta | |
dc.contributor.author | Carli, Ruggero | |
dc.contributor.author | Notarstefano, Giuseppe | |
dc.contributor.author | Schenato, Luca | |
dc.contributor.author | Varagnolo, Damiano | |
dc.date.accessioned | 2020-03-24T14:34:45Z | |
dc.date.available | 2020-03-24T14:34:45Z | |
dc.date.created | 2020-01-19T15:26:46Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | IEEE Transactions on Automatic Control. 2019, 64 (7), 2983-2990. | en_US |
dc.identifier.issn | 0018-9286 | |
dc.identifier.uri | https://hdl.handle.net/11250/2648414 | |
dc.description.abstract | In this work, we study the problem of unconstrained convex optimization in a fully distributed multiagent setting, which includes asynchronous computation and lossy communication. In particular, we extend a recently proposed algorithm named Newton-Raphson consensus by integrating it with a broadcast-based average consensus algorithm, which is robust to packet losses. We show via the separation of time-scale principle that under mild conditions (i.e., persistency of the agents activation and bounded consecutive communication failures), the proposed algorithm is provably locally exponentially stable with respect to the optimal global solution. Finally, we complement the theoretical analysis with numerical simulations and comparisons based on real datasets. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.title | Multi-Agent Newton-Raphson Optimizaton Over Lossy Networks | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | acceptedVersion | en_US |
dc.source.pagenumber | 2983-2990 | en_US |
dc.source.volume | 64 | en_US |
dc.source.journal | IEEE Transactions on Automatic Control | en_US |
dc.source.issue | 7 | en_US |
dc.identifier.doi | 10.1109/TAC.2018.2874748 | |
dc.identifier.cristin | 1776759 | |
dc.description.localcode | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
cristin.unitcode | 194,63,25,0 | |
cristin.unitname | Institutt for teknisk kybernetikk | |
cristin.ispublished | true | |
cristin.fulltext | preprint | |
cristin.qualitycode | 2 | |