Constrained Consensus and Optimization in Multi-Agent Networks
A. Nedić, Asuman Ozdaglar, Pablo A. Parrilo · IEEE Transactions on Automatic Control · 2010
We present distributed algorithms that can be used by multiple agents to align their estimates with a particular value over a network with time-varying connectivity. Our framework is general in that this value can represent a consensus value among multiple agents or an optimal solution of an optimization problem, where the global objective function is a combination of local agent objective functions. Our main focus is on constrained problems where the estimates of each agent are restricted to lie in different convex sets.