Advances in contraction theory for robust optimization, control, and neural computation
F. Bullo, S. Coogan, E. Dall'Anese, I. Manchester, G. Russo
IEEE Conference on Decision and Control, 2025
Abstract
This tutorial provides an overview of recent developments in contraction theory, highlighting theoretical advances, practical applications, and emerging extensions. We explore topics including time-varying convex optimization through equilibrium tracking, biologically plausible optimization in neural networks, and the analysis of interconnected and sampled-data systems. Additional focus is given to linear differential inclusions, reachability analysis, and the integration of contraction theory with robust, control-oriented machine learning.