]> 2026-06-28T16:46:08+00:00 Decentralized Multi-Agent Optimization via Dual Decomposition 0 en We study a distributed multi-agent optimization problem of minimizing the sum of convex objective functions. A new decentralized optimization algorithm is introduced, based on dual decomposition, together with the subgradient method for finding the optimal solution. The iterative algorithm is implemented on a multi-hop network and is designed to handle communication delays. The convergence of the algorithm is proved for communication networks with bounded delays. An explicit bound, which depends on the communication delays, on the convergence rate is given. A numerical comparison with a decentralized primal algorithm shows that the dual algorithm converges faster, with less communication. Håkan Terelius, Ufuk Topcu, Richard M Murray 2010k IFAC World Congress, 2011 (Submitted) ttm11-ifac Preprint 2016-05-20T08:17:38Z 2457528.8455787 Decentralized Multi-Agent Optimization via Dual Decomposition Title 102 en Title