dc.creatorAndreani, Roberto
dc.creatorHaeser, Gabriel
dc.creatorLaura Schuverdt, Maria
dc.creatorSilva, Paulo J. S.
dc.date.accessioned2013-10-14T12:28:53Z
dc.date.accessioned2018-07-04T15:56:58Z
dc.date.available2013-10-14T12:28:53Z
dc.date.available2018-07-04T15:56:58Z
dc.date.created2013-10-14T12:28:53Z
dc.date.issued2012
dc.identifierMATHEMATICAL PROGRAMMING, NEW YORK, v. 135, n. 41306, supl., Part 3, pp. 255-273, OCT, 2012
dc.identifier0025-5610
dc.identifierhttp://www.producao.usp.br/handle/BDPI/34441
dc.identifier10.1007/s10107-011-0456-0
dc.identifierhttp://dx.doi.org/10.1007/s10107-011-0456-0
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1629580
dc.description.abstractIn 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.
dc.languageeng
dc.publisherSPRINGER
dc.publisherNEW YORK
dc.relationMATHEMATICAL PROGRAMMING
dc.rightsCopyright SPRINGER
dc.rightsrestrictedAccess
dc.subjectNONLINEAR PROGRAMMING
dc.subjectCONSTRAINT QUALIFICATIONS
dc.subjectAUGMENTED LAGRANGIAN
dc.subjectERROR BOUND PROPERTY
dc.titleA relaxed constant positive linear dependence constraint qualification and applications
dc.typeArtículos de revistas


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