no code implementations • 28 Mar 2022 • Yuanyuan Dong, Andrew V. Goldberg, Alexander Noe, Nikos Parotsidis, Mauricio G. C. Resende, Quico Spaen
To solve instances of this size, we develop a new local search algorithm, which is a metaheuristic in the greedy randomized adaptive search (GRASP) framework.
1 code implementation • 13 Jan 2021 • Monika Henzinger, Alexander Noe, Christian Schulz
We present a practically efficient algorithm for maintaining a global minimum cut in large dynamic graphs under both edge insertions and deletions.
Data Structures and Algorithms
1 code implementation • 24 Apr 2020 • Monika Henzinger, Alexander Noe, Christian Schulz
We give an improved branch-and-bound solver for the multiterminal cut problem, based on the recent work of Henzinger et al.. We contribute new, highly effective data reduction rules to transform the graph into a smaller equivalent instance.
Data Structures and Algorithms Combinatorics
1 code implementation • 17 Feb 2020 • Monika Henzinger, Alexander Noe, Christian Schulz, Darren Strash
We present a practically efficient algorithm that finds all global minimum cuts in huge undirected graphs.
Data Structures and Algorithms
no code implementations • 12 Aug 2019 • Monika Henzinger, Alexander Noe, Christian Schulz
We introduce the fastest known exact algorithm~for~the multiterminal cut problem with k terminals.
Data Structures and Algorithms Distributed, Parallel, and Cluster Computing
2 code implementations • 16 Aug 2018 • Monika Henzinger, Alexander Noe, Christian Schulz
State-of-the-art algorithms like the algorithm of Padberg and Rinaldi or the algorithm of Nagamochi, Ono and Ibaraki identify edges that can be contracted to reduce the graph size such that at least one minimum cut is maintained in the contracted graph.
Data Structures and Algorithms
2 code implementations • 21 Aug 2017 • Monika Henzinger, Alexander Noe, Christian Schulz, Darren Strash
The minimum cut problem for an undirected edge-weighted graph asks us to divide its set of nodes into two blocks while minimizing the weight sum of the cut edges.
Data Structures and Algorithms Distributed, Parallel, and Cluster Computing
