000135926 001__ 135926
000135926 005__ 20240627150548.0
000135926 0247_ $$2doi$$a10.1109/LCSYS.2024.3400699
000135926 0248_ $$2sideral$$a138878
000135926 037__ $$aART-2024-138878
000135926 041__ $$aeng
000135926 100__ $$0(orcid)0000-0001-9671-4056$$aSebastián, Eduardo$$uUniversidad de Zaragoza
000135926 245__ $$aAccelerated Alternating Direction Method of Multipliers Gradient Tracking for Distributed Optimization
000135926 260__ $$c2024
000135926 5060_ $$aAccess copy available to the general public$$fUnrestricted
000135926 5203_ $$aThis letter presents a novel accelerated distributed algorithm for unconstrained consensus optimization over static undirected networks. The proposed algorithm combines the benefits of acceleration from momentum, the robustness of the alternating direction method of multipliers, and the computational efficiency of gradient tracking to surpass existing state-of-the-art methods in convergence speed, while preserving their computational and communication cost. First, we prove that, by applying momentum on the average dynamic consensus protocol over the estimates and gradient, we can study the algorithm as an interconnection of two singularly perturbed systems: the outer system connects the consensus variables and the optimization variables, and the inner system connects the estimates of the optimum and the auxiliary optimization variables. Next, we prove that, by adding momentum to the auxiliary dynamics, our algorithm always achieves faster convergence than the achievable linear convergence rate for the non-accelerated alternating direction method of multipliers gradient tracking algorithm case. Through simulations, we numerically show that our accelerated algorithm surpasses the existing accelerated and non-accelerated distributed consensus first-order optimization protocols in convergence speed.
000135926 536__ $$9info:eu-repo/grantAgreement/EUR/AEI/TED2021-130224B-I00$$9info:eu-repo/grantAgreement/ES/DGA/T45-23R$$9info:eu-repo/grantAgreement/ES/MCIU/FPU19-05700$$9info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/PID2021-124137OB-I00$$9info:eu-repo/grantAgreement/ES/MCIN/AEI/10.13039/501100011033$$9info:eu-repo/grantAgreement/ES/MICINN/PID2021-125514NB-I00
000135926 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000135926 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000135926 700__ $$aFranceschelli, Mauro
000135926 700__ $$aGasparri, Andrea
000135926 700__ $$0(orcid)0000-0002-5176-3767$$aMontijano, Eduardo$$uUniversidad de Zaragoza
000135926 700__ $$0(orcid)0000-0002-3032-954X$$aSagüés, Carlos$$uUniversidad de Zaragoza
000135926 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000135926 773__ $$g8 (2024), 640-645$$tIEEE Control Systems Letters$$x2475-1456
000135926 8564_ $$s1015885$$uhttps://zaguan.unizar.es/record/135926/files/texto_completo.pdf$$yVersión publicada
000135926 8564_ $$s3425078$$uhttps://zaguan.unizar.es/record/135926/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000135926 909CO $$ooai:zaguan.unizar.es:135926$$particulos$$pdriver
000135926 951__ $$a2024-06-27-13:20:47
000135926 980__ $$aARTICLE