Distributed augmented Lagrangian algorithms: Convergence rate

This paper presents explicit convergence rates for a class of deterministic distributed augmented Lagrangian methods. The expressions for the convergence rates show the dependence on the underlying network parameters. Simulations illustrate the analytical results.

[1]  Asuman E. Ozdaglar,et al.  On the O(1=k) convergence of asynchronous distributed alternating Direction Method of Multipliers , 2013, 2013 IEEE Global Conference on Signal and Information Processing.

[2]  José M. F. Moura,et al.  Cooperative Convex Optimization in Networked Systems: Augmented Lagrangian Algorithms With Directed Gossip Communication , 2010, IEEE Transactions on Signal Processing.

[3]  Gonzalo Mateos,et al.  Distributed Sparse Linear Regression , 2010, IEEE Transactions on Signal Processing.

[4]  João M. F. Xavier,et al.  Distributed Basis Pursuit , 2010, IEEE Transactions on Signal Processing.

[5]  Asuman E. Ozdaglar,et al.  Distributed Alternating Direction Method of Multipliers , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[6]  José M. F. Moura,et al.  Fast Distributed Gradient Methods , 2011, IEEE Transactions on Automatic Control.

[7]  Emiliano Dall'Anese,et al.  Fast Consensus by the Alternating Direction Multipliers Method , 2011, IEEE Transactions on Signal Processing.

[8]  José M. F. Moura,et al.  Linear Convergence Rate of a Class of Distributed Augmented Lagrangian Algorithms , 2013, IEEE Transactions on Automatic Control.

[9]  Qing Ling,et al.  On the Linear Convergence of the ADMM in Decentralized Consensus Optimization , 2013, IEEE Transactions on Signal Processing.

[10]  Alejandro Ribeiro,et al.  Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals , 2008, IEEE Transactions on Signal Processing.