A family of minimum curvature variable-methods for unconstrained optimization. (c1998)

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dc.contributor.author Obeid, Samir Y.
dc.date.accessioned 2011-10-13T11:58:52Z
dc.date.available 2011-10-13T11:58:52Z
dc.date.copyright 1998 en_US
dc.date.issued 2011-10-13
dc.date.submitted 1998-05
dc.identifier.uri http://hdl.handle.net/10725/765
dc.description Includes bibliographical references. en_US
dc.description.abstract The major objective of the work in this thesis concentrates on deriving a Variable-Metric family of minimum curvature multi-step quasi-Newton methods for unconstrained optimization. The aim is to provide further gains in numerical performance to those obtained by the multi-step methods developed earlier, as well as to develop a general framework that encompasses all possible multi-step minimum curvature algorithms generated by appropriate parameter choices. The derivation will be based on a selected rational model with a free parameter, oriented towards securing a "smooth" interpolation of the points defining the multi-step curve. Different choices of the metric used in parameterizing the curves needed for updating the inverse Hessian approximation at each iteration, are tested and numerical results are then reported. en_US
dc.language.iso en en_US
dc.subject Mathematical optimization en_US
dc.subject Curvature en_US
dc.title A family of minimum curvature variable-methods for unconstrained optimization. (c1998) en_US
dc.type Thesis en_US
dc.term.submitted Spring en_US
dc.author.degree MS in Computer Science en_US
dc.author.school Arts and Sciences en_US
dc.author.idnumber 197929220 en_US
dc.author.commembers Dr. May Abboud
dc.author.commembers Dr. Samer Haber
dc.author.woa RA en_US
dc.description.physdesc 1 bound copy: 60 leaves; 30 cm. available at RNL. en_US
dc.author.division Computer Science en_US
dc.author.advisor Dr. Issam Moghrabi
dc.identifier.doi https://doi.org/10.26756/th.1998.11 en_US
dc.publisher.institution Lebanese American University en_US

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