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 in:Optimization methods & software Vol. 35; no. 5; pp. 885 - 920
Main Authors: Birgin, E. G., Martínez, J. M.
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis 02.09.2020
Taylor & Francis Ltd
Subjects:
Algorithms
Augmented Lagrangian methods
Complexity
Constraints
Nonlinear programming
numerical experiments
Optimization
ISSN:1055-6788, 1029-4937
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Abstract 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.
AbstractList 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.
Author Martínez, J. M.
Birgin, E. G.
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  surname: Martínez
  fullname: Martínez, J. M.
  organization: Dept. of Applied Mathematics, Institute of Mathematics, Statistics and Scientific Computing, State University of Campinas
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Snippet Algencan is a well established safeguarded Augmented Lagrangian algorithm introduced in [R. Andreani, E. G. Birgin, J. M. Martínez, and M. L. Schuverdt, On...
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SubjectTerms Algorithms
Augmented Lagrangian methods
Complexity
Constraints
Nonlinear programming
numerical experiments
Optimization
Title Complexity and performance of an Augmented Lagrangian algorithm
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