Complexity and performance of an Augmented Lagrangian algorithm

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...

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Published inOptimization methods & software Vol. 35; no. 5; pp. 885 - 920
Main Authors Birgin, E. G., Martínez, J. M.
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 02.09.2020
Taylor & Francis Ltd
Subjects
Algorithms
Augmented Lagrangian methods
Complexity
Constraints
Nonlinear programming
numerical experiments
Optimization
Online AccessGet full text
ISSN1055-6788
1026-7670
1029-4937
1029-4937
DOI10.1080/10556788.2020.1746962

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Summary: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.
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ISSN:1055-6788
1026-7670
1029-4937
1029-4937
DOI:10.1080/10556788.2020.1746962