Kernelization: Theory of Parameterized Preprocessing (Hardcover) (ISBN-13: 9781107057760)

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.

Author: Fedor V. Fomin, Daniel Lokshtanov, Saket Saurabh
ISBN-10: 1107057760
ISBN-13: 9781107057760
Publisher: Cambridge University Press
Language: English
Published: 01/10/2019
Pages: 528
Format: Hardcover
Weight: 1.90lbs
Size: 9.00h x 6.10w x 1.40d

Review Citation(s):
Choice 10/01/2019