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Journals ACM SIGACT News Volume 38 Issue 1

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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Invitation to data reduction and problem kernelization

Guo, Jiong; Niedermeier, Rolf
ACM SIGACT News , Volume 38 (1)
Association for Computing Machinery – Mar 1, 2007

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