Asynchronous Optimization Over Heterogeneous Networks Via Consensus ADMM

Kumar, Sandeep; Jain, Rahul; Rajawat, Ketan

doi:10.1109/tsipn.2016.2593896

Cited by 60 publications

(48 citation statements)

References 38 publications

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“…An application of the distributed ADMM algorithm to an online optimization scenario (i.e., with time-varying cost function) is analyzed in [113]. An asynchronous version of the distributed ADMM algorithm is proposed in [114]. Primal-dual algorithms for constrained optimization over networks are given in [115,116].…”

Section: Discussion and Referencesmentioning

confidence: 99%

Distributed Optimization for Smart Cyber-Physical Networks

Notarstefano¹,

Notarnicola²,

Camisa³

2019

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The presence of embedded electronics and communication capabilities as well as sensing and control in smart devices has given rise to the novel concept of cyber-physical networks, in which agents aim at cooperatively solving complex tasks by local computation and communication. Numerous estimation, learning, decision and control tasks in smart networks involve the solution of large-scale, structured optimization problems in which network agents have only a partial knowledge of the whole problem. Distributed optimization aims at designing local computation and communication rules for the network processors allowing them to cooperatively solve the global optimization problem without relying on any central unit. The purpose of this survey is to provide an introduction to distributed optimization methodologies. Principal approaches, namely (primal) consensus-based, duality-based and constraint exchange methods, are formalized. An analysis of the basic schemes is supplied, and state-of-the-art extensions are reviewed.

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Section: Discussion and Referencesmentioning

confidence: 99%

Distributed Optimization for Smart Cyber-Physical Networks

Notarstefano¹,

Notarnicola²,

Camisa³

2019

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“…Furthermore, collecting all the data in a fusion center creates a single point of failure. Therefore, it is imperative to develop algorithms that are capable of processing data spread across multiple agents [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Consensus-based Distributed Total Least-squares Estimation Using Parametric Semidefinite Programming

Gratton

Venkategowda

Arablouei

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We propose a new distributed algorithm to solve the total least-squares (TLS) problem when data are distributed over a multi-agent network. To develop the proposed algorithm, named distributed ADMM TLS (DA-TLS), we reformulate the TLS problem as a parametric semidefinite program and solve it using the alternating direction method of multipliers (ADMM). Unlike the existing consensus-based approaches to distributed TLS estimation, DA-TLS does not require careful tuning of any design parameter. Numerical experiments demonstrate that the DA-TLS converges to the centralized solution significantly faster than the existing consensus-based TLS algorithms.

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“…The convergence rate of conventional distributed algorithms can be accelerated theoretically if we enable the asynchronous information exchange mechanism in those algorithms. Although few studies have been published in this field, this topic has attracted the attention of researchers [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%