A FAMILY OF THE LOCAL CONVERGENCE OF THE IMPROVED SECANT METHODS FOR NONLINEAR EQUALITY CONSTRAINED OPTIMIZATION SUBJECT TO BOUNDS ON VARIABLES-中国期刊网

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

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A FAMILY OF THE LOCAL CONVERGENCE OF THE IMPROVED SECANT METHODS FOR NONLINEAR EQUALITY CONSTRAINED OPTIMIZATION SUBJECT TO BOUNDS ON VARIABLES

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A FAMILY OF THE LOCAL CONVERGENCE OF THE IMPROVED SECANT METHODS FOR NONLINEAR EQUALITY CONSTRAINED OPTIMIZATION SUBJECT TO BOUNDS ON VARIABLES

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