Improving ultimate convergence of an augmented Lagrangian method
E. G. Birgin, J. M. Martínez
ARTIGO
Inglês
Optimization methods that employ the classical Powell-Hestenes-Rockafellar augmented Lagrangian are useful tools for solving nonlinear programming problems. Their reputation decreased in the last 10 years due to the comparative success of interior-point Newtonian algorithms, which are asymptotically...
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Optimization methods that employ the classical Powell-Hestenes-Rockafellar augmented Lagrangian are useful tools for solving nonlinear programming problems. Their reputation decreased in the last 10 years due to the comparative success of interior-point Newtonian algorithms, which are asymptotically faster. In this research, a combination of both approaches is evaluated. The idea is to produce a competitive method, being more robust and efficient than its ‘pure’ counterparts for critical problems. Moreover, an additional hybrid algorithm is defined, in which the interior-point method is replaced by the Newtonian resolution of a Karush-Kuhn-Tucker (KKT) system identified by the augmented Lagrangian algorithm. The software used in this work is freely available through the Tango Project web page:http://www.ime.usp.br/∼egbirgin/tango/
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FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP
06/53768-0
FUNDAÇÃO CARLOS CHAGAS FILHO DE AMPARO À PESQUISA DO ESTADO DO RIO DE JANEIRO - FAPERJ
E-26/171.164/2003-APQ1
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ
Fechado
Improving ultimate convergence of an augmented Lagrangian method
E. G. Birgin, J. M. Martínez
Improving ultimate convergence of an augmented Lagrangian method
E. G. Birgin, J. M. Martínez
Fontes
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Optimization methods and software Vol. 23, no. 2 (Apr., 2008), p. 177-195 |