Aspects of quadratic optimization: nonconvexity, uncertainty, and applications

Research output: ThesisPhd Thesis 4 Research NOT TU/e / Graduation NOT TU/e)

Abstract

Quadratic Optimization (QO) has been studied extensively in the literature due to its application in real-life problems. This thesis deals with two complicated aspects of QO problems, namely nonconvexity and uncertainty. A nonconvex QO problem is intractable in general. The first part of this thesis presents methods to approximate a nonconvex QP problem. Another important aspect of a QO problem is taking into account uncertainties in the parameters since they are mostly approximated/estimated from data. The second part of the thesis contains analyses of two methods that deal with uncertainties in a convex QO problem, namely Static and Adjustable Robust Optimization problems. To test the methods proposed in this thesis, the following three real-life applications have been considered: pooling problem, portfolio problem, and norm approximation problem.
Original languageEnglish
QualificationDoctor of Philosophy
Supervisors/Advisors
  • den Hertog, Dick, Promotor, External person
  • de Klerk, Etienne, Promotor, External person
Award date11 Dec 2017
Place of PublicationTilburg
Publisher
Print ISBNs978-90-5668-534-8
Publication statusPublished - 2017
Externally publishedYes

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