In this paper, we consider nonlinear optimization problems with nonlinear equality constraints and bound constraints on the variables. For the solution of such problems, many augmented Lagrangian methods have been defined in the literature. Here, we propose to modify one of these algorithms, namely ALGENCAN by Andreani et al., in such a way to incorporate second-order information into the augmented Lagrangian framework, using an active-set strategy. We show that the overall algorithm has the same convergence properties as ALGENCAN and an asymptotic quadratic convergence rate under suitable assumptions. The numerical results confirm that the proposed algorithm is a viable alternative to ALGENCAN with greater robustness.
2022, JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS, Pages -
An Augmented Lagrangian Method Exploiting an Active-Set Strategy and Second-Order Information (01a Articolo in rivista)
Cristofari Andrea, Di Pillo Gianni, Liuzzi Giampaolo, Lucidi Stefano
Gruppo di ricerca: Continuous Optimization