Invitation to data reduction and problem kernelization

@article{Guo2007InvitationTD,
  title={Invitation to data reduction and problem kernelization},
  author={Jiong Guo and Rolf Niedermeier},
  journal={SIGACT News},
  year={2007},
  volume={38},
  pages={31-45}
}
To solve NP-hard problems, polynomial-time preprocessing is a natural and promising approach. Preprocessing is based on data reduction techniques that take a problem's input instance and try to perform a reduction to a smaller, equivalent problem kernel. Problem kernelization is a methodology that is rooted in parameterized computational complexity. In this brief survey, we present data reduction and problem kernelization as a promising research field for algorithm and complexity theory. 
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