A generalized trust region SQP algorithm for equality constrained optimization

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dc.contributor.advisorHeinkenschloss, Matthiasen_US
dc.creatorWang, Zhenen_US
dc.date.accessioned2009-06-04T07:56:20Zen_US
dc.date.available2009-06-04T07:56:20Zen_US
dc.date.issued2004en_US
dc.description.abstractWe introduce and analyze a class of generalized trust region sequential quadratic programming (GTRSQP) algorithms for equality constrained optimization. Unlike in standard trust region SQP (TRSQP) algorithms, the optimization subproblems arising in our GTRSQP algorithm can be generated from models of the objective and constraint functions that are not necessarily based on Taylor approximations. The need for such generalizations is motivated by optimal control problems for which model problems can be generated using, e.g., different discretizations. Several existing TRSQP algorithms are special cases of our GTRSQP algorithm. Our first order global convergence result for the GTRSQP algorithm applied to TRSQP allows one to relax the condition that the so-called tangential step lies in the null-space of the linearized constraints. The application of the GTRSQP algorithm to an optimal control problem governed by Burgers equation is discussed.en_US
dc.format.extent114 p.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.callnoTHESIS MATH.SCI. 2004 WANGen_US
dc.identifier.citationWang, Zhen. "A generalized trust region SQP algorithm for equality constrained optimization." (2004) Diss., Rice University. <a href="https://hdl.handle.net/1911/18720">https://hdl.handle.net/1911/18720</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/18720en_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.subjectMechanical engineeringen_US
dc.titleA generalized trust region SQP algorithm for equality constrained optimizationen_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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