Control approach to distributed optimization

By Jing Wang, Nicola Elia. Allerton Conference on Communication, Control, and Computing, 2010

352 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A control perspective for centralized and distributed convex optimization (2011, methodology): “In [10], the following dynamical system was proposed to solve Problem (18) instead.”
Distributed convex optimization via continuous-time coordination algorithms with discrete-time communication (2014, neutral): “,Duchi et al.,2012,Johansson et al.,2009,Nedi´c and Ozdaglar, 2009,Tsitsiklis et al.,1986,Zhu and Mart´ınez,2012]implementedindiscretetime.Recentwork[Gharesifard and Cort´es, 2014, Lu and Tang, 2012, Wang and Elia, 2010, 2011, Zanella et al., 2011] has introduced continuous-time dynamical solvers wh”
Linear Convergence Rate of a Class of Distributed Augmented Lagrangian Algorithms (2013, background): “Reference [20] establishes global convergence of a similar algorithm for τ = 1, ρ = 0, and a sufficiently small α and β.”
Primal-Dual Algorithm for Distributed Reinforcement Learning: Distributed GTD (2018, methodology): “The proposed algorithm was also motivated by the continuous-time consensus optimization algorithm from [14]–[16], where the consensus equality constraint was introduced.”
A survey of distributed optimization (2019, methodology): “These algorithms are discrete-time counterparts of the continuous-time distributed PI algorithms proposed in Gharesifard and Cortés (2014) ; Kia, Cortés, and Martínez (2015a) ; Wang and Elia (2010) and Xie and Lin (2017) , which will be reviewed in Section 3.2 .”