Facet identification for the symmetric traveling salesman polytope
Mathematical Programming: Series A and B
The crown inequalities for the symmetric traveling salesman polytope
Mathematics of Operations Research
A faster algorithm for finding the minimum cut in a directed graph
SODA selected papers from the third annual ACM-SIAM symposium on Discrete algorithms
A new approach to the minimum cut problem
Journal of the ACM (JACM)
Experimental study of minimum cut algorithms
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
Separating a Superclass of Comb Inequalities in Planar Graphs
Mathematics of Operations Research
The vehicle routing problem
Separating Maximally Violated Comb Inequalities in Planar Graphs
Proceedings of the 5th International IPCO Conference on Integer Programming and Combinatorial Optimization
Finding Cuts in the TSP (A preliminary report)
Finding Cuts in the TSP (A preliminary report)
Cut structures and randomized algorithms in edge-connectivity problems
Cut structures and randomized algorithms in edge-connectivity problems
Journal of Computer and System Sciences
Privatized rural postman problems
Computers and Operations Research
Generating partitions of a graph into a fixed number of minimum weight cuts
Discrete Optimization
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We present a novel approach to the construction of the cactus representation of all minimum cuts of a graph. The representation is a supergraph that stores the set of all mincuts compactly and mirrors its structure. We focus on support graphs occurring in the branch-andcut approach to traveling salesman, vehicle routing and similar problems in a natural way. The ideas presented also apply to more general graphs. Unlike most previous construction approaches, we do not follow the Karzanov-Timofeev framework or a variation of it. Our deterministic algorithm is based on inclusion-minimal mincuts. We use Fleischer's approach [J. Algorithms, 33(1):51-72, 1999], one of the fastest to date, as benchmark. The new algorithm shows an average speed-up factor of 20 for TSP-related support graphs in practice. We report computational results. Compared to the benchmark, we reduce the space required during construction for n-vertex graphs with m edges from O(n2) to O(m).