 Wysłany: Czw 15:05, 31 Mar 2011 Temat postu: Constrained quadratic programming as a secondary m |
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| Constrained quadratic programming as a method of secondary
By {. ) According to formula (4) computing (], etc. (5)) {by equation (6) assembly [surface and {q}) '{by Lemke algorithm for solving equation (7) may') 【)) ) and {。),{) and {x. ) Are sufficiently close to you?) ) A (),{。 )()'' ENDLOOP constrained quadratic programming first order approximation as the initial solution. 2 first-order approximation with constrained sequential quadratic programming (hereinafter referred to as c1 a SQP) comparison. Equation (3) can be rewritten as ix the first one ([] + class H ]) I (ix a}]) J-lJ-I - ((A3 +;[,(, tl (CH x +1) _-v J-ll {。}==( O, ..., O], the corresponding c1 a SQP method. (c (+ cm )_{)) 』-I array in real terms is revised goal target Hsian gradient (ie, the essence of Newton-Raphsoa method) and the constraints of first order approximation a linear combination of the gradient. For c1 A SQP method, constrained Hess; an array as the first order approximation only provides information. and this method in addition to the constraint Hessian matrix play a role in first-order Taylor approximation, also amended the goals and objectives of Hessian matrix gradient (This ensures that the target decreased more Taiwan Science),belstaff outlet, amended the constraints of linear gradient group table (which is even more to ensure the feasibility of the design, which is a binding constraint region caused by a change order approximation of the compensation, because the constraints of the second order information through {.) for the right coefficient of the initiative to intervene in the direction). K-T conditions for the realization of optimal solutions to meet the necessary conditions; in the towel on its programming, it is necessary and sufficient conditions to ensure better convergence. The following numerical example shows that this law than the C1 - SQP method is more effective, Dian stability. AlgorithmforSolvingQuadraticProgrammingwithSecondOrder'ConstraintsSuiYunkang (ResearchInstituteofEngi ~ eerlngMe ~ Taa, aits.DUT) AbstractInordertosolvequadraticprogramming, withquadraticfunctionsofobecti4eandEonstraint ~. based0nKuhn-Tuckercoedition, akindofalgorithmofsequentialquadraticprogrammingwithsingleiteration, whichisinconsjderationoftheinf1aengeoftheconstraintSHessianmatrixupondirectionisadvancedinthispaper.AnumberofnumericalexperimentsshowthatthealgorithmhasmuchhigherSl ~ eedandmorestableCOOvergencythantheonethatisscquentia1quadraticprogrammingwithfirstorderapproximationoftheconstraints.KeyWords: quadraticprogrammingliterationmefhod / quadraticconstraintHeSSianmatrix Dalian University of Technology University ● pulp: _ the state seal state cancer plan horseleech Gu Xin Lin Rights stubble file Alice Lam laugh toad frog sounds of earthworms.. ∞ -'---- mad blood r × 1,4 Lei shoes summer summer clamor can offer banner Lu beast Chang Shu suddenly lack the amount of control the amount of mushroom Gu Zeng g sugar dish pan f beast = Lu Yu} din dragonfly photo miscellaneous plan -. I. - twenty-two .- familiar words of the!! Fortunately, I Gu State: 1,4: Fortunately, the main arms - I left foot in Gui Lu Qian 1,4 1,4 + left foot ten! -0 with l wang: 1,4:1,4 + + + suppressive debate Ji Zhi # ∞ I despair. 忸 turn pan Mount Cameroon v seven vertical stack plot |
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