Distributed Optimization with Binary Relative Information over Deterministically Time-varying Graphs
Jiaqi Zhang, Keyou You · 2018
This paper extends our recently proposed distributed optimization algorithm to the time-varying graphs. The striking feature of the algorithm is that each node only uses binary relative state information from its neighbors. Different from the stochastically time-varying case, the powerful tool of the stochastic approximation theory is no longer applicable here. We show that if the time-varying graphs are uniformly jointly connected, each node of the algorithm asymptotically converges to some common optimal solution of the optimization problem. We also include simulation examples to validate our results.