Kernelization : theory of parameterized preprocessing 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