Convex Quadratic Mixed-Integer Problems with Quadratic Constraints

Simone Göttlich (), Kathinka Hameister and Michael Herty
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Simone Göttlich: Mannheim University
Kathinka Hameister: Mannheim University
Michael Herty: RWTH Aachen University

A chapter in Operations Research Proceedings 2019, 2020, pp 123-129 from Springer

Abstract: Abstract The efficient numerical treatment of convex quadratic mixed-integer optimization poses a challenging problem. Therefore, we introduce a method based on the duality principle for convex problems to derive suitable lower bounds that can used to select the next node to be solved within the branch-and-bound tree. Numerical results indicate that the new bounds allow the tree search to be evaluated quite efficiently compared to benchmark solvers.

Keywords: Mixed-integer nonlinear programming; Duality; Branch-and-bound (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-030-48439-2_15

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DOI: 10.1007/978-3-030-48439-2_15

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