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(Reference retrieved automatically from Google Scholar through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Improving ultimate convergence of an Augmented Lagrangian method

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Author(s):
Birgin‚ EG ; Martinez‚ JM
Total Authors: 2
Document type: Journal article
Source: OPTIMIZATION METHODS & SOFTWARE; v. 23, n. 2, p. 177-195, 2008.
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:http://www.ime.usp.br/similar to egbirgin/tango/. (AU)

FAPESP's process: 06/53768-0 - Computational methods of optimization
Grantee:José Mário Martinez Perez
Support Opportunities: Research Projects - Thematic Grants