# Quadratic programming with linear inequality constraints

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 dc.degree.department Mathematics en_US dc.degree.discipline Mathematics en_US dc.degree.grantor Texas Tech University en_US dc.degree.level Masters en_US dc.degree.name M .S . en_US dc.rights.availability unrestricted en_US dc.creator Pore , Michael David en_US dc.date.accessioned 2014 -02 -19T18 :38 :24Z dc.date.available 2011 -02 -18T23 :05 :48Z en_US dc.date.available 2014 -02 -19T18 :38 :24Z dc.date.issued 1969 -08 en_US dc.identifier.uri http : / /hdl .handle .net /2346 /19405 en_US dc.description.abstract The least -squares method of optimization of quadratic functions is the most common and widely practiced . The exact procedure in matrix form , is described by Boot , p .25 [2] . Some of the merits of the least -squares method are discussed in [1] . This thesis discusses this least -squares method of optimization in several restricted cases . The matrix format is used throughout , and the less than full rank case (the matrix in the quadratic part of the objective function is of less than full rank ) is of particular interest . It is taken up in Chapter II along with the case of linear restrictions . en_US dc.language.iso en _US en_US dc.publisher Texas Tech University en_US dc.subject Programming en_US dc.subject Quadratic programming en_US dc.subject Linear programming en_US dc.title Quadratic programming with linear inequality constraints en_US dc.type Electronic Thesis en_US

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