Logarithmic Communication for Distributed Optimization in Multi-Agent Systems
Palma London, Shai Vardi, Adam Wierman · 2020
Classically, the design of multi-agent systems is approached using techniques from distributed optimization such as dual descent and consensus algorithms. Such algorithms depend on convergence to global consensus before any individual agent can determine its local action. This leads to challenges with respect to communication overhead and robustness, and improving algorithms with respect to these measures has been a focus of the community for decades.