Kernelization

Theory of Parameterized Preprocessing

Inbunden, Engelska, 2019

AvFedor V. Fomin,Daniel Lokshtanov,Saket Saurabh,Meirav Zehavi

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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.

Produktinformation

Utgivningsdatum2019-01-10
Mått157 x 235 x 31 mm
Vikt880 g
FormatInbunden
SpråkEngelska
Antal sidor528
FörlagCambridge University Press
ISBN9781107057760

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Fedor V. Fomin is Professor of Computer Science at the Universitetet i Bergen, Norway. He is known for his work in algorithms and graph theory. He has co-authored two books, Exact Exponential Algorithms (2010) and Parameterized Algorithms (2015), and received the EATCS Nerode prizes in 2015 and 2017 for his work on bidimensionality and Measure and Conquer. Daniel Lokshtanov is Professor of Informatics at the Universitetet i Bergen, Norway. His main research interests are in graph algorithms, parameterized algorithms, and complexity. He is a co-author of Parameterized Algorithms (2015) and is a recipient of the Meltzer prize, the Bergen Research Foundation young researcher grant, and an ERC starting grant on parameterized algorithms. 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). Meirav Zehavi is Assistant Professor of Computer Science at Ben-Gurion University. Her research interests lie primarily in the field of parameterized complexity. In her Ph.D. studies, she received three best student paper awards.

1. What is a kernel?; Part I. Upper Bounds: 2. Warm up; 3. Inductive priorities; 4. Crown decomposition; 5. Expansion lemma; 6. Linear programming; 7. Hypertrees; 8. Sunflower lemma; 9. Modules; 10. Matroids; 11. Representative families; 12. Greedy packing; 13. Euler's formula; Part II. Meta Theorems: 14. Introduction to treewidth; 15. Bidimensionality and protrusions; 16. Surgery on graphs; Part III. Lower Bounds: 17. Framework; 18. Instance selectors; 19. Polynomial parameter transformation; 20. Polynomial lower bounds; 21. Extending distillation; Part IV. Beyond Kernelization: 22. Turing kernelization; 23. Lossy kernelization.

'Kernelization is one of the most important and most practical techniques coming from parameterized complexity. In parameterized complexity, kernelization is the technique of data reduction with a performance guarantee. From humble beginnings in the 1990's it has now blossomed into a deep and broad subject with important applications, and a well-developed theory. Time is right for a monograph on this subject. The authors are some of the leading lights in this area. This is an excellent and well-designed monograph, fully suitable for both graduate students and practitioners to bring them to the state of the art. The authors are to be congratulated for this fine book.' Rod Downey, Victoria University of Wellington