Distributed optimization has become a vital aspect of research for multi-agent systems. On the one hand, it is important to study and develop algorithms that solve global optimization problem at a local and distributed level. This has immediate applications in problems related to the development of “smart-grid” power systems, as well as problems related to team decision-making. On the other hand, tools from optimization can lead to deep insights into the behavior of highly complex and non-linear dynamical system. One research thread is using tools from saddle-point optimization to study and explain the manifestation of synchronization or clustering in multi-agent systems.