Global Convergence of Control-Based Lagrangian Flows for Non-Convex Optimization
It provides the first global convergence guarantees for a class of non-convex constrained optimization problems using control-theoretic Lagrangian methods, addressing a key limitation of existing primal-dual gradient methods.
This paper proves global exponential convergence of control-based Lagrangian flows for non-convex equality-constrained optimization problems that satisfy a convexity property on the constraint manifold, without requiring strong convexity or boundedness assumptions.
This paper studies the flows of continuous-time dynamics for equality-constrained optimization based on control-theoretic Lagrangian methods. In particular, we consider dynamics induced by proportional-integral and feedback linearization controllers, which have been recently proposed as alternatives to primal-dual gradient methods. Unlike existing convergence results, which rely on strong convexity of the objective function or boundedness assumptions, we exploit the geometric structure induced by the constraints. Specifically, we show global exponential convergence for non-convex problems that satisfy a suitable convexity property when restricted to the constraint manifold.