Pareto front approximation through a multi-objective augmented Lagrangian method

In this manuscript, we consider smooth multi-objective optimization problems with convex constraints. We propose an extension of a augmented Lagrangian Method from recent literature. The new algorithm is specifically designed to handle sets points and produce good approximations the whole Pareto front, as opposed original one which converges single solution. prove properties global convergence stationarity for sequences generated by our procedure. then compare performance proposed method those main state-of-the-art algorithms available considered class problems. results experiments show effectiveness general superiority w.r.t. competitors approach.