Please use this identifier to cite or link to this item:
Type: Artigo
Title: A relaxed constant positive linear dependence constraint qualification and applications
Author: Andreani, Roberto
Haeser, Gabriel
Schuverdt, María Laura
Silva, Paulo J. S.
Abstract: In this work we introduce a relaxed version of the constant positive linear dependence constraint qualification (CPLD) that we call RCPLD. This development is inspired by a recent generalization of the constant rank constraint qualification by Minchenko and Stakhovski that was called RCRCQ. We show that RCPLD is enough to ensure the convergence of an augmented Lagrangian algorithm and that it asserts the validity of an error bound. We also provide proofs and counter-examples that show the relations of RCRCQ and RCPLD with other known constraint qualifications. In particular, RCPLD is strictly weaker than CPLD and RCRCQ, while still stronger than Abadie’s constraint qualification. We also verify that the second order necessary optimality condition holds under RCRCQ
Subject: Programação não-linear
Country: Alemanha
Editor: Springer
Rights: Fechado
Identifier DOI: 10.1007/s10107-011-0456-0
Date Issue: 2012
Appears in Collections:IMECC - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
000308647100009.pdf353.93 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.