Algencan is a well established safeguarded Augmented Lagrangian algorithm introduced in [R. Andreani, E. G. Birgin, J. M. Martínez, and M. L. Schuverdt, On Augmented Lagrangian methods with general lower-level constraints, SIAM J. Optim. 18 (2008), pp. 1286–1309]. Complexity results that report its worst-case behaviour in terms of iterations and evaluations of functions and derivatives that are necessary to obtain suitable stopping criteria are presented in this work. In addition, its computational performance considering all problems from the CUTEst collection is presented, which shows that it is a useful tool for solving large-scale constrained optimization problems.

Algencan是一种完善的，有保障的增强拉格朗日算法，该算法在[R. Andreani，EG Birgin，JMMartínez和ML Schuverdt，关于具有一般下层约束的增强型Lagrangian方法，SIAM J. Optim。18（2008），pp。1286-1309]。在这项工作中，提出了复杂性结果，该结果以迭代的形式报告了其最坏情况的行为，并对获得合适的停止标准所必需的函数和导数进行了评估。此外，提出了考虑到CUTEst集合中所有问题的计算性能，这表明它是解决大规模约束优化问题的有用工具。