简介:Inthispaper,wepresentasmoothingNewton-likemethodforsolvingnonlinearsystemsofequalitiesandinequalities.Byusingtheso-calledmaxfunction,wetransfertheinequalitiesintoasystemofsemismoothequalities.ThenasmoothingNewton-likemethodisproposedforsolvingthereformulatedsystem,whichonlyneedstosolveonesystemoflinearequationsandtoperformonelinesearchateachiteration.Theglobalandlocalquadraticconvergencearestudiedunderappropriateassumptions.Numericalexamplesshowthatthenewapproachiseffective.
简介:InthispaperwediscusstheconvergenceofamodifiedNewton’smethodpresentedbyA.Ostrowski[1]andJ.F.Traub[2],whichhasquadraticconvergenceorderbutreducesoneevaluationofthederivativeateverytwostepscomparedwithNewton’smethod.Aconvergencetheoremisestablishedbyusingaweakconditiona≤3-2(21/2)andasharperrorestimateisgivenabouttheiterativesequence.
简介:AconicNewtonmethodisattractivebecauseitconvergestoalocalminimizzerrapidlyfromanysufficientlygoodinitialguess.However,itmaybeexpensivetosolvetheconicNewtonequationateachiterate.InthispaperweconsideraninexactconicNewtonmethod,whichsolvesthecouicNewtonequationoldyapproximatelyandinsonmunspecifiedmanner.Furthermore,weshowthatsuchmethodislocallyconvergentandcharacterizestheorderofconvergenceintermsoftherateofconvergenceoftherelativeresiduals.
简介:Recentexperiencehasshownthatinterior-pointmethodsusingalogbarrierapproacharefarsuperiortoclassicalsimplexmethodsforcomputingsolutionstolargeparametricquantileregressionproblems.Inmanylargeempiricalapplications,thedesignmatrixhasaverysparsestructure.Atypicalexampleistheclassicalfixed-effectmodelforpaneldatawheretheparametricdimensionofthemodelcanbequitelarge,butthenumberofnon-zeroelementsisquitesmall.AdoptingrecentdevelopmentsinsparselinearalgebraweintroduceamodifiedversionoftheFrisch-NewtonalgorithmforquantileregressiondescribedinPortnoyandKoenker[28].Thenewalgorithmsubstantiallyreducesthestorage(memory)requirementsandincreasescomputationalspeed.Themodifiedalgorithmalsofacilitatesthedevelopmentofnonparametricquantileregressionmethods.Thepseudodesignmatricesemployedinnonparametricquantileregressionsmoothingareinherentlysparseinboththefidelityandroughnesspenaltycomponents.ExploitingthesparsestructureoftheseproblemsopensupawholerangeofnewpossibilitiesformultivariatesmoothingonlargedatasetsviaANOVA-typedecompositionandpartiallinearmodels.
简介:Thegeneralizedcomplementarityproblemincludesthewell-knownnonlinearcomplementarityproblemandlinearcomplementarityproblemasspecialcases.Inthispaper,basedonaclassofsmoothingfunctions,asmoothingNewton-typealgorithmisproposedforsolvingthegeneralizedcomplementarityproblem.Undersuitableassumptions,theproposedalgorithmiswell-definedandglobalconvergent.
简介:Analgorithmforsolvingaclassofsmoothconvexprogrammingisgiven.Usingsmoothexactmultiplierpenaltyfunction,asmoothconvexprogrammingisminimizedtoaminimizingstronglyconvexfunctiononthecompactsetwasreduced.ThenthestronglyconvexfunctionwithaNewtonmethodonthegivencompactsetwasminimized.
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