Distributed Multi-Agent Optimization via Dual Decomposition

In this master thesis, a new distributed multi-agent optimization algorithm is introduced. The algorithm is based upon the dual decomposition of the optimization problem, together with the subgradient method for finding the optimal dual solution. The convergence of the new optimization algorithm is proved for communication networks with bounded time-varying delays, and noisy communication. Further, an explicit bound on the convergence rate is given, that shows the dependency on the network parameters. Finally, the new optimization algorithm is also compared to an earlier known primal decomposition algorithm, with extensive numerical simulations