Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/1082
Type: Artigo de periódico
Title: Structured minimal-memory inexact quasi-Newton method and secant preconditioners for augmented Lagrangian optimization
Author: BIRGIN, E. G.
MARTINEZ, J. M.
Abstract: Augmented Lagrangian methods for large-scale optimization usually require efficient algorithms for minimization with box constraints. On the other hand, active-set box-constraint methods employ unconstrained optimization algorithms for minimization inside the faces of the box. Several approaches may be employed for computing internal search directions in the large-scale case. In this paper a minimal-memory quasi-Newton approach with secant preconditioners is proposed, taking into account the structure of Augmented Lagrangians that come from the popular Powell-Hestenes-Rockafellar scheme. A combined algorithm, that uses the quasi-Newton formula or a truncated-Newton procedure, depending on the presence of active constraints in the penalty-Lagrangian function, is also suggested. Numerical experiments using the Cute collection are presented.
Subject: nonlinear programming
augmented Lagrangian methods
box constraints
quasi-Newton
truncated-Newton
Country: Estados Unidos
Editor: SPRINGER
Citation: COMPUTATIONAL OPTIMIZATION AND APPLICATIONS, v.39, n.1, p.1-16, 2008
Rights: fechado
Identifier DOI: 10.1007/s10589-007-9050-z
Address: http://dx.doi.org/10.1007/s10589-007-9050-z
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Date Issue: 2008
Appears in Collections:IMECC - Artigos e Materiais de Revistas Científicas

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