Smart communicating devices with their sensing, computing and control capabilities promise to make our society more intelligent, energy-efficient, safe and secure. To achieve these goals the main challenge is to exploit these powerful, but unstructured, computational resources. The most natural, and widely implemented, centralized computation model is not applicable to this extremely complex system in which processors are spatially distributed and interact through asynchronous and unreliable communication. Thus, a novel peer-to-peer distributed computational model is emerging as a new opportunity in which a problem is solved cooperatively by peers, rather than by a unique provider that knows and owns all data. Scientists and analysts agree that today’s goal is to get full value from the available massive data. Optimization is a key part of this process, since it is a building block in several learning, decision making and control problems for complex cyber-physical networks. Very-large scale and dynamic optimization problems need to be solved in several domains as, e.g. big-data analytics, energy networks, smart mobility, and cooperative robotics.
The OPT4SMART project has a twofold ambitious goal: (i) to set-up a novel, comprehensive methodological framework to solve distributed optimization problems in peer-to-peer networks, and (ii) to provide numerical methods and toolboxes, based on this framework, to solve distributed estimation, learning, decision and control problems in complex cyber-physical networks. We aim at pursuing this twofold objective by developing interdisciplinary methodologies arising from a synergic combination of optimization, control-systems, and graph theories. The envisioned distributed methods should solve general classes of problems, as nonconvex and mixed-integer, and work under time-varying and possibly asynchronous communication models.
