  This paper presents a distributed optimization scheme over a network of
agents in the presence of cost uncertainties and over switching communication
topologies. Inspired by recent advances in distributed convex optimization, we
propose a distributed algorithm based on a dual sub-gradient averaging. The
objective of this algorithm is to minimize a cost function cooperatively.
Furthermore, the algorithm changes the weights on the communication links in
the network to adapt to varying reliability of neighboring agents. A
convergence rate analysis as a function of the underlying network topology is
then presented, followed by simulation results for representative classes of
sensor networks.

Optimization and Control;Data Structures and Algorithms;Machine Learning;Multiagent Systems;Systems and Control