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简介：纸基于原来的条件生产得最高分的战略定理的新版本。而且，我们调查如此的条件的不仅充足，而且必要性。证明很简单并且预防任何拓扑的技术。

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简介：Thereporttothe17thCommunistPartyofChina(CPC)NationalCongresscalledforeortstoenableallpeopletohaveplacestolearnandputitintherstplaceofthevemajorissuesconcerningthelivelihoodofthepeople.Toenableeveryonetohaveplacesto

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简介：Inthispaper,atrustregionmethodforequalityconstrainedoptlmizationbasedonnondiferentiableexactpenaltyisproposed.Inthisalgorithin,thetrailstepischaracterizedbycomputationofitsnormalcomponentbeingseparatedfromcomputationofitstangentialcomponent,i.e.,onlythetangentialcomponentofthetrailstepisconstrainedbytrustradiuswhilethenormalcomponentandtrailstepitselfhavenoconstraints.Theothermaincharacteristicofthealgorithmisthedecisionoftrustregionradius.Here,thedecisionoftrustregionradiususestheinformationofthegradientofobjectivefunctionandreducedHessian.However,Maratoseffectwilloccurwhenweusethenondifferentiableexactpenaltyfunctionasthemeritfunction.Inordertoobtainthesuperlinearconvergenceofthealgorithm,weusethetwiceordercorrectiontechnique.Becauseofthespecialityoftheadaptivetrustregionmethod,weusetwiceordercorrectionwhenp=0(thedefinitionisasinSection2)andthisisdifferentfromthetraditionaltrustregionmethodsforequalityconstrainedopthnization.Sothecomputationofthealgorithminthispaperisreduced.Whatismore,wecanprovethatthealgorithmisgloballyandsuperlinearlyconvergent.

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简介：这份报纸建议为非线性的平等的一个缩小的过滤器行搜索算法抑制了优化。一个缩小的过滤器，是传统的过滤器的修正，在算法被采用。当步尺寸接近零，缩小的过滤器的信封变得越来越薄。这个新算法为试用步的接受有更多的灵活性并且与传统的过滤器算法相比要求不太计算的费用。建议算法的全球、本地的集中在一些合理条件下面被给。数字实验被报导显示出缩小的过滤器算法的有效性。

简介：Inacomposite-stepapproach,astepskiscomputedasthesumoftwocomponentsvkandhk.Thenormalcomponentvk,whichiscalledtheverticalstep,aimstoimprovethelinearizedfeasibility,whilethetangentialcomponenthk,whichisalsocalledhorizontalstep,concentratesonreducingamodelofthemeritfunctions.Asafiltermethod,itreducesboththeinfeasibilityandtheobjectivefunction.Thisisthesamepropertyofthesetwomethods.Inthispaper,oneconcernsthecomposite-steplikefilterapproach.Thatis,astepistangentialcomponenthkiftheinfeasibilityisreduced.Orelse,skisacompositestepcomposedofnormalcomponentVkandtangentialcomponenthk.

简介：SincethefoundingofthePeople’sRepublicofChina,theChinesegovernment,toensureequalityandunityamongethnicgroupsandachievetheircommondevelopment,hasformulatedaseriesofethnicandreligiouspoliciesonthebasisoftheactualsituationsofthevariousethnicgroupsandreligions,andthesepolicieshave

简介：Thispaperpresentsatrustregionalgorithmwithnullspacetechniquefornonlinearequalityconstrainedoptimization.Consideringinthenullspacemethodsthat,theconvergentrateofrangespacestepisfasterthanthenullspacestepforthemostcases,theproposedalgorithmcomputesnullstepsmoreoftenthanrangespacestep.Moreover,thenewalgorithmisbasedonthereducedHessianSQPmethod.Globalconvergenceoftheproposedalgorithmisproved.Theeffectivenessofthemethodisdemonstratedbysomenumericalexamples.

简介：Thispaperpresentsatrustregiontwo-phasemodelalgorithmforsolvingtheequalityandboundconstratinednonlinearoptimizationproblem.Aconceptofsubstationarypointisgiven.Undersutableassumptions.theglobalconvergenceofthisalgorithmisprovedwithoutassumingthelinearindependenceofthegradientofactiveconstraints.Anumericalexampleisalsopresented.

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简介：Thispaperstudiesafamilyofthelocalconvergenceoftheimprovedsecantmethodsforsolvingthenonlinearequalityconstrainedoptimizationsubjecttoboundsonvariables.TheHessianoftheLagrangianisapproximatedusingtheDFPortheBFGSsecantupdates.Theimprovedsecantmethodsareusedtogenerateasearchdirection.Combiningwithasuitablestepsize,eachiterateswitchestotrialstepofstrictinteriorfeasibility.WhentheHessianisonlypositivedefiniteinanaffinenullsubspace,oneshowsthatthealgorithmsgeneratethesequencesconvergingq-linearlyandtwo-stepq-superlinearly.Furthermore,undersomesuitableassumptions,somesequencesgeneratedbythealgorithmsconvergelocallyone-stepq-superlinearly.Finally,somenumericalresultsarepresentedtoillustratetheeffectivenessoftheproposedalgorithms.

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简介：在这份报纸，我们关于詹姆士常数考虑下列问题：什么时候做平等J(X*)为Banach空间X的=J(X)抓住？一个Banach空格的詹姆士常数不一般来说与它的双空间的与一致，这被知道。事实上，我们已经有与对称或绝对的标准被装备的二维的normed空格的反例。然而，我们看那二维的空间X上的标准是否对称、绝对，然后平等J(X*)=J(X)抓住。这在二维的盒子中提供一个全球答案给这个问题。

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