An augmented Lagrangian method exploiting an active-set strategy and second-order information
| Autor: | Andrea Cristofari, Gianni Di Pillo, Giampaolo Liuzzi, Stefano Lucidi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Control and Optimization
Applied Mathematics 90C30 65K05 Settore MAT/09 Constrained optimization Augmented Lagrangian methods Nonlinear programming algorithms Large-scale optimization Management Science and Operations Research Augmented Lagrangian methods Constrained optimization Large-scale optimization Nonlinear programming algorithms Optimization and Control (math.OC) FOS: Mathematics Mathematics - Optimization and Control |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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