Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Baermann, Andreas"'
Autor:
Aigner, Kevin-Martin, Bärmann, Andreas, Braun, Kristin, Liers, Frauke, Pokutta, Sebastian, Schneider, Oskar, Sharma, Kartikey, Tschuppik, Sebastian
Stochastic Optimization (SO) is a classical approach for optimization under uncertainty that typically requires knowledge about the probability distribution of uncertain parameters. As the latter is often unknown, Distributionally Robust Optimization
Externí odkaz:
http://arxiv.org/abs/2304.05377
We study mixed-integer programming (MIP) relaxation techniques for the solution of non convex mixed-integer quadratically constrained quadratic programs (MIQCQPs). We present MIP relaxation methods for non convex continuous variable products. In Part
Externí odkaz:
http://arxiv.org/abs/2211.00876
In this work we propose a high-quality decomposition approach for qubit routing by swap insertion. This optimization problem arises in the context of compiling quantum algorithms onto specific quantum hardware. Our approach decomposes the routing pro
Externí odkaz:
http://arxiv.org/abs/2206.01294
Autor:
Bärmann, Andreas, Schneider, Oskar
Publikováno v:
Math. Program. (2021)
In the present work, we consider Zuckerberg's method for geometric convex-hull proofs introduced in [Geometric proofs for convex hull defining formulations, Operations Research Letters 44(5), 625-629 (2016)]. It has only been scarcely adopted in the
Externí odkaz:
http://arxiv.org/abs/2101.09267
Publikováno v:
In EURO Journal on Transportation and Logistics 2024 13
We consider the bipartite boolean quadric polytope (BQP) with multiple-choice constraints and analyse its combinatorial properties. The well-studied BQP is defined as the convex hull of all quadric incidence vectors over a bipartite graph. In this wo
Externí odkaz:
http://arxiv.org/abs/2009.11674
Autor:
Bärmann, Andreas, Gemander, Patrick, Merkert, Maximilian, Wiertz, Ann-Kathrin, Zaragoza Martínez, Francisco Javier
Publikováno v:
In Discrete Applied Mathematics 15 January 2023 324:145-166
In this paper, we demonstrate how to learn the objective function of a decision-maker while only observing the problem input data and the decision-maker's corresponding decisions over multiple rounds. We present exact algorithms for this online versi
Externí odkaz:
http://arxiv.org/abs/1810.12997
Publikováno v:
In Discrete Applied Mathematics 15 September 2020 283:59-77
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