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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Veröffentlicht in:Optimization methods & software Jg. 35; H. 5; S. 885 - 920
Hauptverfasser: Birgin, E. G., Martínez, J. M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Abingdon Taylor & Francis 02.09.2020
Taylor & Francis Ltd
Schlagworte:
Algorithms
Augmented Lagrangian methods
Complexity
Constraints
Nonlinear programming
numerical experiments
Optimization
ISSN:1055-6788, 1029-4937
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1055-6788
1029-4937
DOI:10.1080/10556788.2020.1746962