Recent advances in wired and wireless technology necessitate the development of theory, models and tools to cope with new challenges posed by large-scale optimization problems over networks. In this talk, we consider distributed multi-agent network systems where each agent has its own convex objective function, which can be evaluated with stochastic errors. The problem consists of minimizing the sum of the agent functions, without a central coordinator and without agents sharing the explicit form of their objectives. However, the agents are willing to cooperate with each other locally to solve the problem, by exchanging their estimates of an optimal solution. We discuss such distributed algorithms for synchronous and asynchronous implementations. We present convergence results and convergence rate estimates, and provide some numerical results.
Reception immediately following outside of 202 Transportation Building.