Title | ||
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Distributed Optimization over Lossy Networks via Relaxed Peaceman-Rachford Splitting: a Robust ADMM Approach |
Abstract | ||
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In this work we address the problem of distributed optimization of the sum of convex cost functions in the context of multi-agent systems over lossy communication networks. Building upon operator theory, first, we derive an ADMMlike algorithm, referred to as relaxed ADMM (R-ADMM) via a generalized Peaceman-Rachford Splitting operator on the Lagrange dual formulation of the original optimization problem. This algorithm depends on two parameters, namely the averaging coefficient α and the augmented Lagrangian coefficient p and we show that by setting α = 1/2 we recover the standard ADMM algorithm as a special case. Moreover, first, we reformulate our R-ADMM algorithm into an implementation that presents reduced complexity in terms of memory, communication and computational requirements. Second, we propose a further reformulation which let us provide the first ADMM-like algorithm with guaranteed convergence properties even in the presence of lossy communication. Finally, this work is complemented with a set of compelling numerical simulations of the proposed algorithms over random geometric graphs subject to i.i.d. random packet losses. |
Year | DOI | Venue |
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2018 | 10.23919/ECC.2018.8550322 | 2018 European Control Conference (ECC) |
Keywords | Field | DocType |
distributed optimization,ADMM,operator theory,splitting methods,Peaceman-Rachford operator | Convergence (routing),Lossy compression,Computer science,Algorithm,Regular polygon,Convex function,Augmented Lagrangian method,Operator (computer programming),Operator theory,Optimization problem | Conference |
ISBN | Citations | PageRank |
978-1-5386-5303-6 | 0 | 0.34 |
References | Authors | |
10 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
N. Bastianello | 1 | 0 | 0.68 |
Marco Todescato | 2 | 27 | 6.63 |
Ruggero Carli | 3 | 894 | 69.17 |
L. Schenato | 4 | 839 | 72.18 |