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.

<ASIN:1107057760>

 

Authors: Fedor V. Fomin, Daniel Lokshtanov, Saket Saurabh, and Meirav Zehavi
Publisher: Cambridge University Press
Date: February 2019
Pages: 528
ISBN: 978-1107057760
Print: 1107057760
Kindle: B07MBNBS2G
Audience: Computer Scientists and developers interested in theoretical computer science, optimization, combinatorics, and related fields
Level: Advanced

 

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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