Abstract
This paper considers a distributed unconstrained optimization problem where each agent has a local convex cost function and the sum of these functions is defined as a global cost function. We propose an event-driven subgradient algorithm based on consensus control to minimize the global cost function. Each agent has an estimate of the optimal solution as a state. In the proposed algorithm, each agent sends its state to the neighbor agents only at trigger-times when the error of its state exceeds a threshold. We show that the error between the estimate of the global cost function of the agents given by the proposed event-driven algorithm and the optimal cost is upper bounded. The simulation results show that the convergence speed by the proposed event-driven algorithm improves and the number of trigger-times can be reduced compared with the existing subgradient methods.
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