Aspects of quadratic optimization: nonconvexity, uncertainty, and applications

Onderzoeksoutput: ScriptieDissertatie 4 (Onderzoek NIET TU/e / Promotie NIET TU/e)Academic

Uittreksel

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.
TaalEngels
KwalificatieDoctor in de Filosofie
Begeleider(s)/adviseur
  • den Hertog, Dick, Promotor, Externe Persoon
  • de Klerk, Etienne, Promotor, Externe Persoon
Datum van toekenning11 dec 2017
Plaats van publicatieTilburg
Uitgever
Gedrukte ISBN's978-90-5668-534-8
StatusGepubliceerd - 2017
Extern gepubliceerdJa

Vingerafdruk

Non-convexity
Quadratic Optimization
Optimization Problem
Uncertainty
Nonconvex Optimization
Robust Optimization
Pooling
Approximation Problem
Convex Optimization
Norm

Citeer dit

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title = "Aspects of quadratic optimization: nonconvexity, uncertainty, and applications",
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Aspects of quadratic optimization : nonconvexity, uncertainty, and applications. / Marandi, A.

Tilburg : Tilburg University, 2017. 165 blz.

Onderzoeksoutput: ScriptieDissertatie 4 (Onderzoek NIET TU/e / Promotie NIET TU/e)Academic

TY - THES

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N2 - 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.

AB - 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.

M3 - Phd Thesis 4 Research NOT TU/e / Graduation NOT TU/e)

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