Description: 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.
Brief description: Saket Saurabh is Professor of Theoretical Computer Science at the Institute of Mathematical Sciences, Chennai, and Professor of Computer Science at the Universitetet i Bergen, Norway. He has made important contributions to every aspect of parametrized complexity and kernelization, especially to general purpose results in kernelization and applications of extremal combinatorics in designing parameterized algorithms. He is a co-author of Parameterized Algorithms (2015).
Review Quotes: 'The book manages to present an incredible number of techniques, methods, and examples in its 528 pages. Each chapter ends with a bibliographic notes section, which often provides some small historical context for the material covered. It also points to more current results and papers although it does so very briefly. Together, this makes the textbook a valuable resource book to researchers.' Tim Jackman and Steve Homer, SIGACT News