2 papers
cs.RO2026
Infeasible optimization problems and the hierarchical augmented Lagrangian method in imitation learning
Roland Andrews, Justin Carpentier, Ajay Sathya
Imitation learning (IL) is an effective approach to train complex robotics policies. Recent works have introduced hard constraints into imitation-learning optimization problems to…
math.OC2026
Augmented Lagrangian methods for infeasible convex optimization problems and diverging proximal-point algorithms
Roland Andrews, Justin Carpentier, Adrien Taylor
This work investigates the convergence behavior of augmented Lagrangian methods (ALMs) when applied to convex optimization problems that may be infeasible. ALMs are a popular class…