Uittreksel
Taal  Engels 

Kwalificatie  Doctor in de Filosofie 
Begeleider(s)/adviseur 

Datum van toekenning  11 dec 2017 
Plaats van publicatie  Tilburg 
Uitgever  
Gedrukte ISBN's  9789056685348 
Status  Gepubliceerd  2017 
Extern gepubliceerd  Ja 
Vingerafdruk
Citeer dit
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Aspects of quadratic optimization : nonconvexity, uncertainty, and applications. / Marandi, A.
Tilburg : Tilburg University, 2017. 165 blz.Onderzoeksoutput: Scriptie › Dissertatie 4 (Onderzoek NIET TU/e / Promotie NIET TU/e) › Academic
TY  THES
T1  Aspects of quadratic optimization
T2  nonconvexity, uncertainty, and applications
AU  Marandi,A.
PY  2017
Y1  2017
N2  Quadratic Optimization (QO) has been studied extensively in the literature due to its application in reallife 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 reallife 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 reallife 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 reallife applications have been considered: pooling problem, portfolio problem, and norm approximation problem.
M3  Phd Thesis 4 Research NOT TU/e / Graduation NOT TU/e)
SN  9789056685348
PB  Tilburg University
CY  Tilburg
ER 