A Practical Analysis of Kernelization Techniques for the Maximum Cut Problem

@inproceedings{Ferizovi2019APA,
  title={A Practical Analysis of Kernelization Techniques for the Maximum Cut Problem},
  author={Damir Ferizovi{\'c}},
  year={2019}
}
We examine the application of existing and new kernelization techniques for a well-known NP-hard problem, Max-Cut. Given an undirected graph, the task is to nd a bipartition of the vertex set that maximizes the total weight of edges that have their endpoints in di erent partitions. We primarily focus on the unweighted case, but we also consider the signed and weighted versions to some extent. Reduction rules are e ective for solving many NP-hard problems in practice. They compute a smaller… 
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Engineering Kernelization for Maximum Cut

On social and biological networks in particular, kernelization enables us to solve four instances that were previously unsolved in a ten-hour time limit with state-of-the-art solvers, and three of these instances are now solved in less than two seconds.

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