Event Triggered Distributed Optimization Based on Dual Decomposition
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 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. However, most distributed optimization techniques require synchronous communication, where optimization results derived by individual computers are shared at the end of each iteration. This paper proposes a distributed optimization algorithm with event triggered communication where the iterations are synchronized but the communication does not happen in every iteration. Then both synchronous and event triggered algorithms are compared through numerical simulation to show that communication costs drop significantly.