简介：SeveralsufficientconditionsforClassIaregiveninthispaper.

简介：MorseinequalitiesforMorsefunctionsundergeneralboundaryconditionswereinitiallystudiedbyMorseandVanSchaa(?)k.ItisourpurposetopresentanaturalextensionofthetheoremduetoMorseandVanSchaackwithaconsiderablyslmplifiedproof.

简介：ThepurposeofthispaperistoinvestigatethepricingEuropeancalloptionvaluationproblemsundertheexerciseprice,maturity,risk-freeinterestrate,andthevolatilityfunction.Anadvancemethodology,Chebyshevsimulatedannealingneuralnetwork(ChSANN),isenforcedfortheBlack-Scholes(B-S)modelwithboundaryconditions.OurschemeisstableandeasytoimplementonB-Sequation,forarbitraryvolatilityandarbitraryinterestratevalues.Also,thecomparativeresultsdemonstratethattheattainedapproximatesolutionsareconvergingtowardstheexactsolution.ThegraphicalresultsshowthattheincreasingflowoftheEuropeancalloptionastheexponentialincreasetakesplaceinassets.Thepresentedalgorithmcanbefurtherappliedtootherfinancialmodelswithcertainboundaryconditions.Thealgorithmofthemethodshowsthattheapproachcanalsobeeasilyemployedontime-fractionalB-Sequation.

简介：Fivekindsofconesareintroduced,whichareusedtoestablishtheconstraintsqualifications,underwhichthegeneralizedKuhn-Tuckernecessaryconditionsaredevelopedforaclassofgeneralized(h,φ)-differentiablesingle-objectiveandmultiobjectiveprogrammingproblemsbyusingMotzkin'salternativetheoremandBen-Talgeneralizedalgebraicoperations.

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简介：Thispaperdiscussestheneutrontransportequationinaslabwithgeneralizedreflectingboundaryconditions.BymeansofthepositiveC0-semigrouptheory,wehaveprovedthatthisproblemhasauniquenonnegativesolutionandfoundthespectralpropertyofthecorrespondingtransportoperator.Finallywegivetheasymptoticbehaviorofthesolutionforthisequation.

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简介：Inthispaper,aconstrainedgeneticalgorithm(CGA)isproposedtosolvethesinglemachinetotalweightedtardinessproblem.TheproposedCGAincorporatesdominancerulesfortheproblemunderconsiderationintotheGAoperators.ThisincorporationshouldenabletheproposedCGAtoobtainclosetooptimalsolutionswithmuchlessdeviationandmuchlesscomputationaleffortthantheconventionalGA(UGA).SeveralexperimentswereperformedtocomparethequalityofsolutionsobtainedbythethreeversionsofboththeCGAandtheUGAwiththeresultsobtainedbyadynamicprogrammingapproach.ThecomputationalresultsshowedthattheCGAwasbetterthantheUGAinbothqualityofsolutionsobtainedandtheCPUtimeneededtoobtaintheclosetooptimalsolutions.ThethreeversionsoftheCGAreducedthepercentagedeviationby15.6%,61.95%,and25%respectivelyandobtainedclosetooptimalsolutionswith59%lowerCPUtimethanwhatthethreeversionsoftheUGAdemanded.TheCGAperformedbetterthantheUGAintermsofqualityofsolutionsandcomputationaleffortwhenthepopulationsizeandthenumberofgenerationsaresmaller.

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