- Publisher: Cambridge University Press
- Online publication date: January 2019
- Print publication year: 2019
- Online ISBN: 9781107415157
- DOI: https://doi.org/10.1017/9781107415157
Preprocessing, or data reduction, is a standard technique for simplifying and speeding up computation. Written by a team of experts in the field, this book introduces a rapidly developing area of preprocessing analysis known as kernelization. The authors 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. 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. Written to be self-contained, the book only requires a basic background in algorithmics and will be of use to professionals, researchers and graduate students in theoretical computer science, optimization, combinatorics, and related fields.
Rod Downey - Victoria University of Wellington
Noga Alon - Princeton University, New Jersey and Tel Aviv University
Michael Fellows - Universitetet i Bergen, Norway
Hans L. Bodlaender - Universiteit Utrecht
D. Papamichail Source: Choice
Henning Fernau Source: MathSciNet
Efstratios Rappos Source: zbMATH
Tim Jackman and Steve Homer Source: SIGACT News
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