Diffusion Based Stopping Criterion for Event-Triggered Distributed Optimization
Taylan Ayken, Jun‐ichi Imura · SICE Journal of Control Measurement and System Integration · 2015
As the size of systems to be controlled gets larger, distributed optimization with Event-Triggered messaging is becoming one of the significant topics, where each local optimization problem is solved by an individual computer in parallel and in a synchronize manner to derive a global optimal solution more quickly and robustly than centralized methods while passing messages when certain events are triggered to keep communication costs low. However, most distributed optimization techniques require a supervisor system which monitors the progress of the optimization algorithms and stops them when an optimum solution is reached. In this paper, the authors propose a diffusion based stopping criterion for distributed optimization algorithms with event triggered messaging. The authors then compare the standard supervised criterion and the proposed diffusion based criterion by numerical simulations to show that the latter method does not add any overhead.