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Type: Artigo
Title: Improving ultimate convergence of an augmented Lagrangian method
Author: Birgin, E. G.
Martínez, J. M.
Abstract: 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:∼egbirgin/tango/
Subject: Programação não-linear
Country: Reino Unido
Editor: Taylor & Francis
Rights: Fechado
Identifier DOI: 10.1080/10556780701577730
Date Issue: 2008
Appears in Collections:IMECC - Artigos e Outros Documentos

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