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dc.contributor.authorColeman, Thomas F.en_US
dc.contributor.authorLi, Yuyingen_US
dc.date.accessioned2007-04-23T17:37:44Z
dc.date.available2007-04-23T17:37:44Z
dc.date.issued1989-07en_US
dc.identifier.citationhttp://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR89-1026en_US
dc.identifier.urihttp://hdl.handle.net/1813/6826
dc.description.abstractRecently, various interior point algorithms - related to the Karmarkar algorithm - have been developed for linear programming. In this paper, we first show how this "interior point" philosophy can be adapted to the linear $l_{1}$ problem (in which there are no feasibility constraints) to yield a globally convergent algorithm. We then show that the linear algorithm can be modified to provide a globally and ultimately quadratically convergent algorithm. This modified algorithm is significantly more efficient in practice: we present numerical results to support this claim.en_US
dc.format.extent2249166 bytes
dc.format.extent531866 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.language.isoen_USen_US
dc.publisherCornell Universityen_US
dc.subjectcomputer scienceen_US
dc.subjecttechnical reporten_US
dc.titleA Global and Quadratic Affine Scaling Method for Linear $L_{1}$ Problems.en_US
dc.typetechnical reporten_US


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