Complexity and performance of an Augmented Lagrangian algorithm. (2nd September 2020)

• Record Type: Journal Article
• Authors: Birgin, E. G.; Martínez, J. M.
• Is Part Of: Optimization methods and software. Volume 35:Number 5(2020)
• Page Start: 885
• Page End: 920
• Publication Date: 2020-09-02
• Subjects: Nonlinear programming -- Augmented Lagrangian methods -- complexity -- numerical experiments; 90C30 -- 65K05 -- 49M37 -- 90C60 -- 68Q25; Mathematical optimization -- Periodicals; Algorithms -- Periodicals; 519.7
• DOI: 10.1080/10556788.2020.1746962
• Languages: English
• ISSNs: 1055-6788

Abstract

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.