Kernelization : Theory of Parameterized Preprocessing Hardback
by Fedor V. (Universitetet i Bergen, Norway) Fomin, Daniel (Universitetet i Bergen, Norway) Lokshtanov, Saket Saurabh, Meirav (Ben-Gurion University of the Negev, Israel) Zehavi
Hardback
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.
Information
-
Available to Order - This title is available to order, with delivery expected within 2 weeks
- Format:Hardback
- Pages:528 pages
- Publisher:Cambridge University Press
- Publication Date:10/01/2019
- Category:
- ISBN:9781107057760
Information
-
Available to Order - This title is available to order, with delivery expected within 2 weeks
- Format:Hardback
- Pages:528 pages
- Publisher:Cambridge University Press
- Publication Date:10/01/2019
- Category:
- ISBN:9781107057760
