Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/323385 
Erscheinungsjahr: 
2024
Quellenangabe: 
[Journal:] Journal of Global Optimization [ISSN:] 1573-2916 [Volume:] 92 [Issue:] 1 [Publisher:] Springer US [Place:] New York, NY [Year:] 2024 [Pages:] 159-186
Verlag: 
Springer US, New York, NY
Zusammenfassung: 
We present a branch-and-bound method for multiobjective mixed-integer convex quadratic programs that computes a superset of efficient integer assignments and a coverage of the nondominated set. The method relies on outer approximations of the upper image set of continuous relaxations. These outer approximations are obtained addressing the dual formulations of specific subproblems where the values of certain integer variables are fixed. The devised pruning conditions and a tailored preprocessing phase allow a fast enumeration of the nodes. Despite we do not require any boundedness of the feasible set, we are able to prove that the method stops after having explored a finite number of nodes. Numerical experiments on a broad set of instances with two, three, and four objectives are presented.
Schlagwörter: 
Multiobjective optimization
Convex quadratic optimization
Mixed-integer quadratic programming
Branch-and-bound algorithm
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