Proceedings Article10.1109/CDC.2012.6425866
Distributed subgradient projection algorithm for multi-agent optimization with nonidentical constraints and switching topologies
27
TL;DR: A distributed subgradient projection algorithm for multi-agent optimization with nonidentical constraints and switching topologies is studied and it is proved that distributed optimization can be achieved when the adjacency matrices are doubly stochastic and the union of the graphs is strongly connected.
read more
Abstract: In this paper, we study a distributed subgradient projection algorithm for multi-agent optimization with nonidentical constraints and switching topologies. We first show that distributed optimization might not be achieved on general strongly connected graphs. Instead, the agents optimize a weighted average of the local objective functions. Then we prove that distributed optimization can be achieved when the adjacency matrices are doubly stochastic and the union of the graphs is strongly connected among each time interval of a certain bounded length.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Distributed multi-agent optimization subject to nonidentical constraints and communication delays
Peng Lin,Wei Ren,Yongduan Song +2 more
TL;DR: The distributed optimization problem for multi-agent systems subject to nonidentical constraints and communication delays under local communication can be solved by introducing additional delays to the subgradient projection algorithm and the communication delays can be arbitrarily bounded.
252
Distributed Projection Subgradient Algorithm Over Time-Varying General Unbalanced Directed Graphs
TL;DR: A new distributed projection subgradient algorithm is proposed which is applicable to the time-varying general unbalanced directed graphs and does not need each agent to know its in-neighbors’ out-degree.
105
Continuous-Time Algorithms Based on Finite-Time Consensus for Distributed Constrained Convex Optimization
TL;DR: The optimality condition of the researched optimization problem is developed in terms of the saddle point theory, and the corresponding continuous-time primal-dual algorithm is constructed for the considered constrained convex optimization problem under time-varying undirected and connected graphs.
47
Distributed Constrained Optimization Over Unbalanced Directed Networks Using Asynchronous Broadcast-Based Algorithm
TL;DR: This article mainly focuses on an epigraph form of the original constrained optimization to overcome the unbalancedness of directed networks, and proposes a new distributed asynchronous broadcast-based optimization algorithm that allows that not only the updates of agents are asynchronous in a distributed fashion, but also the step-sizes of all agents are uncoordinated.
42
Analysis of Newton-Raphson consensus for multi-agent convex optimization under asynchronous and lossy communications
Ruggero Carli,Giuseppe Notarstefano,Luca Schenato,Damiano Varagnolo +3 more
- 01 Dec 2015
TL;DR: A multi-agent convex-optimization algorithm named Newton-Raphson consensus is extended to a network scenario that involves directed, asynchronous and lossy communications and sufficient conditions are provided that guarantee local exponential convergence of the node-states to the global centralized minimizer even in presence of packet losses.
References
On dual convergence of the distributed Newton method for Network Utility Maximization
Ermin Wei,Michael Zargham,Asuman Ozdaglar,Ali Jadbabaie +3 more
- 01 Dec 2011
TL;DR: A convergence rate analysis for the dual iterations that enables us to explicitly compute at each primal iteration the number of dual steps that can satisfy the error level is presented, yielding for the first time a fully distributed second order method for NUM problems with local quadratic convergence guarantee.
15
On Dual Convergence of the Distributed Newton Method for Network Utility
Ermin Wei,Michael Zargham,Asuman Ozdaglar,Ali Jadbabaie +3 more
- 01 Jan 2011
TL;DR: In this paper, the authors proposed a fully distributed second-order method for network utility maximization (NUM) problems with local quadratic convergence guarantee, where the error level in the Newton direction (resulting from finite termination of dual iterations) is defined.
13
Incremental Stochastic Subgradient Algorithms for Convex Optimization
TL;DR: Convergence results and error bounds for the Markov randomized method in the presence of stochastic errors for diminishing and constant step-sizes are obtained.
A distributed Newton method for Network Utility Maximization
Ermin Wei,Asuman Ozdaglar,Ali Jadbabaie +2 more
- 01 Dec 2010
TL;DR: This work develops an alternative distributed Newton-type fast converging algorithm for solving network utility maximization problems with self-concordant utility functions by using novel matrix splitting techniques and shows that even when the Newton direction and the stepsize in this method are computed within some error, the resulting objective function value still converges superlinearly to an explicitly characterized error neighborhood.
A simple peer-to-peer algorithm for distributed optimization in sensor networks
Björn Johansson,Maben Rabi,Mikael Johansson +2 more
- 01 Dec 2007
TL;DR: This work proposes a distributed algorithm that solves a special class of optimization problems using only peer-to- peer communication and illustrates the algorithm's performance, in terms of convergence rate and communication cost relative to alternative schemes, through several numerical examples.