Stochastic approximation for consensus seeking: Mean square and almost sure convergence

We consider stochastic consensus problems in strongly connected directed graph models where each agent has noisy measurements of its neighbors' states. For consensus seeking, we develop stochastic approximation type algorithms with a decreasing step size and establish mean square and almost sure convergence of the agents' states to the same limit.

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