Tools from control and dynamical systems have proven valuable for analyzing and developing optimization methods. In this paper, we establish rigorous theoretical foundations for using feedback linearization (FL) -- a well-established nonlinear control technique -- to solve constrained optimization problems. For equality-constrained optimization, we establish global convergence rates to first-order Karush-Kuhn-Tucker (KKT) points and uncover the close connection between the FL method and the Sequential Quadratic Programming (SQP) algorithm. Building on this relationship, we extend the FL approach to handle inequality-constrained problems. Furthermore, we introduce a momentum-accelerated feedback linearization algorithm and provide a rigorous convergence guarantee.
@article{arxiv.2503.12665,
title = {Constrained Optimization From a Control Perspective via Feedback Linearization},
author = {Runyu Zhang and Arvind Raghunathan and Jeff Shamma and Na Li},
journal= {arXiv preprint arXiv:2503.12665},
year = {2026}
}