Convergence Analysis of Generalized Primal-Dual Interior-Point Algorithms for Linear Optimization

dc.contributor.authorWei, Huaen
dc.date.accessioned2006-08-22T14:25:56Z
dc.date.available2006-08-22T14:25:56Z
dc.date.issued2002en
dc.date.submitted2002en
dc.description.abstractWe study the zeroth-, first-, and second-order algorithms proposed by Tuncel. The zeroth-order algorithms are the generalization of the classic primal-dual affine-scaling methods, and have a strong connection with the quasi-Newton method. Although the zeroth-order algorithms have the property of strict monotone decrease in both primal and dual objective values, they may not converge. We give an illustrative example as well as an algebraic proof to show that the zeroth-order algorithms do not converge to an optimal solution in some cases. The second-order algorithms use the gradients and Hessians of the barrier functions. Tuncel has shown that all second-order algorithms have a polynomial iteration bound. The second-order algorithms have a range of primal-dual scaling matrices to be chosen. We give a method to construct such a primal-dual scaling matrix. We then analyze a new centrality measure. This centrality measure appeared in both first- and second-order algorithms. We compare the neighbourhood defined by this centrality measure with other known neighbourhoods. We then analyze how this centrality measure changes in the next iteration in terms of the step length and some other information of the current iteration.en
dc.formatapplication/pdfen
dc.format.extent456855 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10012/1083
dc.language.isoenen
dc.pendingfalseen
dc.publisherUniversity of Waterlooen
dc.rightsCopyright: 2002, Wei, Hua. All rights reserved.en
dc.subjectMathematicsen
dc.subjectPrimal-dual interior-point methodsen
dc.subjectLinear Optimizationen
dc.subjectConvergenceen
dc.subjectPolynomial algorithmen
dc.titleConvergence Analysis of Generalized Primal-Dual Interior-Point Algorithms for Linear Optimizationen
dc.typeMaster Thesisen
uws-etd.degreeMaster of Mathematicsen
uws-etd.degree.departmentCombinatorics and Optimizationen
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen
uws.typeOfResourceTexten

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