Invitation to data reduction and problem kernelization

Authors:
Jiong Guo;Rolf Niedermeier
Affiliations:
Friedrich-Schiller-Universitat Jena, Jena, Germany;Friedrich-Schiller-Universitat Jena, Jena, Germany
Venue:
ACM SIGACT News
Year:
2007

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Abstract

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.