Feedback control of Lagrange multipliers for non-smooth constrained optimization
This work addresses optimization problems with non-smooth constraints, which is incremental as it builds on existing proximal augmented Lagrangian methods.
The paper tackles constrained optimization with non-differentiable terms by developing a control-theoretic framework and two novel algorithms, achieving global exponential convergence under strong convexity and demonstrating effectiveness in numerical benchmarks against state-of-the-art methods.
In this work, we develop a control-theoretic framework for constrained optimization problems with composite objective functions including non-differentiable terms. Building on the proximal augmented Lagrangian formulation, we construct a plant whose equilibria correspond to the stationary points of the optimization problem. Within this framework, we propose two control strategies - a static controller and a dynamic controller - leading to two novel optimization algorithms. We provide a theoretical analysis, establishing global exponential convergence under strong convexity assumptions. Finally, we demonstrate the effectiveness of the proposed methods through numerical experiments, benchmarking their performance against state-of-the-art approaches.