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The Bottleneck Shortest Path Problem is a basic problem in network optimization. The goal is to determine the limiting capacity of any path between two specified vertices of the network. This is equivalent to determining the unsplittable maximum flow between the two vertices. In this note we analyze the complexity of the problem, its relation to the… (More)

The topic of this paper are integer programming models in which a subset of 0/1-variables encode a partitioning of a set of objects into disjoint subsets. Such models can be surprisingly hard to solve by branch-and-cut algorithms if the permutation of the subsets of the partition is irrelevant. This kind of symmetry unnecessarily blows up the branch-and-cut… (More)

- STEFAN HEINZ, VOLKER KAIBEL, MATTHIAS PEINHARDT, JÖRG RAMBAU, ANDREAS TUCHSCHERER, Stefan Heinz +4 others
- 2006

The standard computational methods for computing the optimal value functions of Markov Decision Problems (MDP) require the exploration of the entire state space. This is practically infeasible for applications with huge numbers of states as they arise, e. g., from modeling the decisions in online optimization problems by MDPs. Exploiting column generation… (More)

- VOLKER KAIBEL, MATTHIAS PEINHARDT, MARC E. PFETSCH, ORBITOPAL FIXING
- 2006

The topic of this paper are integer programming models in which a subset of 0/1-variables encode a partitioning of a set of objects into disjoint subsets. Such models can be surprisingly hard to solve by branch-and-cut algorithms if the permutation of the subsets of the partition is irrelevant. This kind of symmetry unnecessarily blows up the branch-and-cut… (More)

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