Meirav Zehavi Fedor V. FominCambridge University Press, 1/10/2019EAN 9781107057760, ISBN10: 1107057760Hardcover, 500 pages, 23.5 x 15.7 x 3.1 cmLanguage: EnglishPreprocessing, 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.1. What is a kernel?Part I. Upper Bounds2. Warm up3. Inductive priorities4. Crown decomposition5. Expansion lemma6. Linear programming7. Hypertrees8. Sunflower lemma9. Modules10. Matroids11. Representative families12. Greedy packing13. Euler's formulaPart II. Meta Theorems14. Introduction to treewidth15. Bidimensionality and protrusions16. Surgery on graphsPart III. Lower Bounds17. Framework18. Instance selectors19. Polynomial parameter transformation20. Polynomial lower bounds21. Extending distillationPart IV. Beyond Kernelization22. Turing kernelization23. Lossy kernelization.
