Abstract
AbstractThis chapter presents one of the most basic and most important techniques for developing fixed-parameter algorithms: data reduction and problem kernelization. It starts with some basic definitions and facts, in particular defining the notion of reduction to a problem kernel (kernelization for short). After having presented some simple examples, the chapter continues with several specific kernelization results, including problem kernels for problems such as Maximum Satisfiability, Cluster Editing, Vertex Cover, 3-Hitting Set, and Dominating Set in Planar Graphs. The chapter concludes with some initial results on lower bounds for problem kernel sizes and a general summary.
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