EECI International Graduate School in Control

Multi-Agent Optimization and Learning: Resilient and Adaptive Solutions

February 13-17 2023, University of Paris-Saclay, Paris, France

Abstract of the Course:

Recent technological advances have spawned a number of applications – ranging from decentralized learning to smart grids and IoT – in which the control of multi-agent decision-making systems is of central importance. In this context, many engineering problems can be cast as optimization and learning problems over networks of cooperating agents. The course will provide a thorough introduction to the solution methods that have been developed to tackle these challenging scenarios, as well as an overview of current trends and advanced topics. During the first part of the course, specific emphasis will be given to the challenging set-up of networks with asynchronous operations and faulty communications, leveraging both gradient- and non-expansive operator-based approaches. The second part will then discuss the application of these methods to learning in decentralized scenarios, and their translation to online problems, which are characterized by time-varying objectives and constraints.

Outline:

The Workshop in intended to provide to a wide and diverse audience interested in distributed optimization in large scale networks with an overview of the state-of-the-art from a control point of view. In particular, being the fist part of the workshop devoted to tutorial seminars, it is particularly suitable for Ph.D. students and young researchers who are willing to enter this new area of research and are not necessarily experts, since most relevant mathematical tools are references will be provided. However, it is also relevant for practitioners and researchers in distributed optimization, since the second part of the workshop will present some recent advances in this area and some industrial application of these tools.

Organizers:

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