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dc.contributor.authorColeman, Thomas F.en_US
dc.contributor.authorYuan, Weien_US
dc.date.accessioned2007-04-23T18:00:20Z
dc.date.available2007-04-23T18:00:20Z
dc.date.issued1995-03en_US
dc.identifier.citationhttp://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR95-1481en_US
dc.identifier.urihttps://hdl.handle.net/1813/7140
dc.description.abstractWe present a modified $L_{2}$ penalty function method for equality constrained optimization problems. The pivotal feature of our algorithm is that at every iterate we invoke a special change of variables to improve the ability of the algorithm to follow the constraint level sets. This change of variables gives rise to a suitable block diagonal approximation to the Hessian which is then used to construct a quasi-Newton method. We show that the complete algorithm is globally convergent with a local Q-superlinearly convergence rate. Preliminary computational results are given for a few problems.en_US
dc.format.extent340118 bytes
dc.format.extent471996 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 Quasi-Newton $L_{2}$-Penalty Method for Minimization Subject toNonlinear Equality Constraintsen_US
dc.typetechnical reporten_US


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