Local and superlinear convergence of structured secant methods from the convex class

dc.contributor.advisorDennis, John E., Jr.en_US
dc.contributor.advisorTapia, Richard A.en_US
dc.creatorMartinez R., Hector Jairoen_US
dc.date.accessioned2009-06-04T00:00:10Zen_US
dc.date.available2009-06-04T00:00:10Zen_US
dc.date.issued1988en_US
dc.description.abstractIn this thesis we develop a unified theory for establishing the local and q-superlinear convergence of secant methods which use updates from Broyden's convex class and have been modified to take advantage of the structure present in the Hessian in constructing approximate Hessians. As an application of this theory, we show the local and q-superlinear convergence of any structured secant method which use updates from the convex class for the equality-constrained optimization problem and the nonlinear least-squares problem. Particular cases of these methods are the SQP augmented scale BFGS and DFP secant methods for constrained optimization problems introduced by Tapia. Another particular case, for which local and q-superlinear convergence is proved for the first time here, is the Al-Baali and Fletcher modification of the structured BFGS secant method considered by Dennis, Gay and Welsch for the nonlinear least-squares problem and implemented in the current version of the NL2SOL code.en_US
dc.format.extent58 p.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.callnoThesis Math. Sci. 1988 Martinez R.en_US
dc.identifier.citationMartinez R., Hector Jairo. "Local and superlinear convergence of structured secant methods from the convex class." (1988) Diss., Rice University. <a href="https://hdl.handle.net/1911/16167">https://hdl.handle.net/1911/16167</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/16167en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectMathematicsen_US
dc.subjectComputer scienceen_US
dc.subjectStatisticsen_US
dc.titleLocal and superlinear convergence of structured secant methods from the convex classen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentMathematical Sciencesen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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