An Augmented Lagrangian Method Exploiting an Active-Set Strategy and Second-Order Information
针对带等式和边界约束的非线性优化问题，修改ALGENCAN算法，融入二阶信息和主动集策略，在保持收敛性的同时实现渐近二次收敛，数值实验表明更稳健。
Abstract 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.