Newton-Raphson Consensus under asynchronous and lossy communications for peer-to-peer networks
arXiv:1707.09178
Abstract
In this work we study the problem of unconstrained convex-optimization in a fully distributed multi-agent setting which includes asynchronous computation and lossy communication. In particular, we extend a recently proposed algorithm named Newton-Raphson Consensus by integrating it with a broadcast-based average consensus algorithm which is robust to packet losses. We show via the separation of time scales principle that under mild conditions (i.e., persistency of the agents activation and bounded consecutive communication failures) the proposed algorithm is proved to be locally exponentially stable with respect to the optimal global solution. Finally, we complement the theoretical analysis with numerical simulations that are based on real datasets.
References in corpus (6)
- Constrained Consensus
- Asynchronous Distributed ADMM for Large-Scale Optimization- Part I: Algorithm and Convergence Analysis
- A General Analysis of the Convergence of ADMM
- A Polyhedral Approximation Framework for Convex and Robust Distributed Optimization
- Lyapunov Theory for Discrete Time Systems
- On the O(1/k) Convergence of Asynchronous Distributed Alternating Direction Method of Multipliers
Cited by in corpus (4)
- Achieving Linear Convergence in Distributed Asynchronous Multi-agent Optimization
- Asynchronous Decentralized Successive Convex Approximation
- A Fast Distributed Asynchronous Newton-Based Optimization Algorithm
- A dual ascent algorithm for asynchronous distributed optimization with unreliable directed communications