Distributed Control and Optimization. 3 credits

Catalog description

This course introduces the state-of-the-art design of distributed algorithms, which provide efficient and scalable tools for multi-agent systems to perform complex tasks such as cooperative control, optimization, and machine learning. To this end, the course will familiarize students with fundamental concepts and tools in dynamics and control (differential equations), matrix theory (spectrum), graph theory (topology and connectivity), and game theory (Nash equilibrium). Building on these topics, applications to be covered include multi-robot formation control (by distributed gradient flow), sensor network information fusion (by distributed averaging), resilient multi-agent decision-making (by resilient consensus), multi-robot task allocation (by distributed auction), large-scale network reconstruction (by distributed data-driven identification), and learning-based distributed coordination (by learning differentiable games). Offered by Electrical & Comp. Engineering . May not be repeated for credit.