This event will be in-person ONLY in Wu and Chen Auditorium.

ABSTRACT

This talk surveys recent advances on contraction theory for dynamical systems, as a robust, computationally-friendly and modular stability theory. Starting from basic notions, I will present novel theoretical properties and examples of contracting dynamics, including gradient systems, controlled Lure’ systems, constrained optimization solvers, and multiplayer games. As first application I will discuss online feedback optimization, where a dynamic plant is interconnected with a controller based on first-order optimization methods. Second, I will discuss the contractivity properties of recurrent neural networks and briefly review applications to unsupervised representation learning, implicit learning models, and reservoir computing.