|Kernelization: Theory of Parameterized Preprocessing (Cambridge University Press)|
|Wednesday, 06 March 2019|
Written by a team of experts in the field, this book introduces a rapidly developing area of preprocessing analysis known as kernelization. The authors, Fedor V. Fomin, Daniel Lokshtanov, Saket Saurabh, and Meirav Zehavi, provide an overview of basic methods and important results, with accessible explanations of the most recent advances in the area, such as meta-kernelization, representative sets, polynomial lower bounds, and lossy kernelization.
Authors: Fedor V. Fomin, Daniel Lokshtanov, Saket Saurabh, and Meirav Zehavi
The text is divided into four parts, which cover the different theoretical aspects of the area: upper bounds, meta-theorems, lower bounds, and beyond kernelization. The methods are demonstrated through extensive examples using a single data set.
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