Algorithms for asymptotically exact minimizations in Karush-Kuhn-Tucker methods.

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dc.contributor.authorDEGLA, Guy
dc.date.accessioned2026-06-02T16:06:57Z
dc.date.available2026-06-02T16:06:57Z
dc.date.issued2018
dc.description.abstractWe provide two new algorithms with applications to asymptotically exact minimizations with inequalities constraints. These results generalize and improve the works of Andreani, Birgin, Martinez and Schuverdt on minimization with equality constraints. Numerical examples show that our proposed analysis gives convergence results.
dc.identifier.doi10.5539/jmr.v10n2p36
dc.identifier.otherBECDB-6748
dc.identifier.urihttps://dspace.uac.bj/handle/123456789/6115
dc.language.isofr
dc.relation.ispartofJournal of Mathematics Research
dc.subjectnonlinear programming
dc.subjectaugmented lagrangian methods
dc.subjectnumerical experiments
dc.subjectapproximate KKT point.
dc.titleAlgorithms for asymptotically exact minimizations in Karush-Kuhn-Tucker methods.
dc.typeArticle

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