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:Analgorithmforsolvingaclassofsmoothconvexprogrammingisgiven.Usingsmoothexactmultiplierpenaltyfunction,asmoothconvexprogrammingisminimizedtoaminimizingstronglyconvexfunctiononthecompactsetwasreduced.ThenthestronglyconvexfunctionwithaNewtonmethodonthegivencompactsetwasminimized.
简介:Inthispaper,aswitchingmethodforunconstrainedminimizationisproposed.ThemethodisbasedonthemodifiedBFGSmethodandthemodifiedSR1method.Theeigenvaluesandconditionnumbersofboththemodifiedupdatesareevaluatedandusedintheswitchingrule.WhentheconditionnumberofthemodifiedSR1updateissuperiortothemodifiedBFGSupdate,thestepintheproposedquasi-NewtonmethodisthemodifiedSR1step.OtherwisethestepisthemodifiedBFGSstep.Theefficiencyoftheproposedmethodistestedbynumericalexperimentsonsmall,mediumandlargescaleoptimization.Thenumericalresultsarereportedandanalyzedtoshowthesuperiorityoftheproposedmethod.
简介:Thispaperconsiderstheexistenceandasymptoticestimatesofglobalsolutionsandfinitetimeblowupoflocalsolutionofnon-Newtonfiltrationequationwithspecialmediumvoidofthefollowingform:{ut/|x|^2-△pu=u^q,(x,t)∈Ω×(0,T),u(x,t)=0,(x,t)∈ЭΩ×(0,T),u(x,0)=u0(x),u0(x)≥0,u0(x)全不等于0,where△pu=div(|△↓u|^p-2△↓u),ΩisasmoothboundeddomaininR^N(N≥3),0∈Ω,2
简介:WeprovideconvergenceresultsanderrorestimatesforNewton-likemethodsingeneralizedBanachspaces.TheideaofageneralizednormisusedwhichisdefinedtobeamapfromalinearspaceintoapartiallyorderedBanachspace.Convergenceresultsanderrorestimatesareimprovedcomparedwiththerealnormtheory.
简介:牛顿的重复为单个Toeplitz矩阵的组逆的计算被修改。在每次重复,重复矩阵被一个矩阵与一个低排水量等级接近。因为重复矩阵的排水量结构,涉及牛顿的重复的thematrix向量增加能高效地被做。我们证明修改牛顿重复的集中仍然是很快的。数字结果被介绍表明建议方法的快集中。
简介:首先用微分中值定理推出了Newton-Leibniz公式,同时也用Newton-Leibniz公式推出了三个微分中值定理,从而证明了微分中值定理与Newton-Leibniz公式可互相证明.
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简介:Inthisstudy,weuseinexactnewtonmethodstofindsolutionsofnonlinear,nondifferenti-ableoperatorequationsonBanachspaceswithaconvergencestructure.ThistechniqueinvolvestheintroductionofageneralizednormasanoperatorfromalinearspaceintoapartiallyorderedBanachspace.Inthiswaythemetricpropertiesoftheexaminedproblemcanbeanalyzedmoreprecisely.Moreover,thisapproachallmvsustoderivefromthesametheorem,ontheonehand,semi-localresultsofKantorovich-type,andontheotherhand,globalresultsbasedonmono-tonicityconsiderations.Furthermore,iveshowthatspecialcasesofourresultsreducetothecorrespondingonesalreadyintheliterature.Finally>ourresultsareusedtosolveintegralequationsthatcannotbesolvedwithexistingmethods.
简介:Inthispaperweimprovethetwoversionsofthetwo-sidedprojectedquasi-Newtonmethod-onewasproposedbyNocedal&Overtonin[1]andtheotherwasdiscussedinourpreviouspaper,byintroducingthreedifferentmeritfunctionstomakeinexactone-dimensionalsearches.Itisshownthattheseimprovedquasi-Newtonalgorithmshavegainedglobalconvergencepropertywhichisnotpossessedbytheoriginaltwoalgorithms.
简介:InthispaperwereportasparsetruncatedNewtonalgorithmforhandlinglarge-scalesimpleboundnonlinearconstrainedminimixationproblem.ThetruncatedNewtonmethodisusedtoupdatethevariableswithindicesoutsideoftheactiveset,whiletheprojectedgradientmethodisusedtoupdatetheactivevariables.Ateachiterativelevel,thesearchdirectionconsistsofthreeparts,oneofwhichisasubspacetruncatedNewtondirection,theothertwoaresubspacegradientandmodifiedgradientdirections.ThesubspacetruncatedNewtondirectionisobtainedbysolvingasparsesystemoflinearequations.Theglobalconvergenceandquadraticconvergencerateofthealgorithmareprovedandsomenumericaltestsaregiven.