Complexity and performance of an Augmented Lagrangian algorithm

Abstract

Algencan is a well established safeguarded Augmented Lagrangian algorithm introduced in [R. Andreani, E. G. Birgin, J. M. Mart\'ınez and M. L. Schuverdt, On Augmented Lagrangian methods with general lower-level constraints, SIAM Journal on Optimization 18, pp. 1286-1309, 2008]. Complexity results that report its worst-case behavior 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, the computational performance of a new version of the method is presented, which shows that the updated software is a useful tool for solving large-scale constrained optimization problems.

References in corpus (2)

• On the Complexity of an Augmented Lagrangian Method for Nonconvex Optimization
• Adaptive Augmented Lagrangian Methods: Algorithms and Practical Numerical Experience

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