Kernelization

Theory of Parameterized Preprocessing

Nonfiction, Science & Nature, Mathematics, Computers, General Computing, Programming
Cover of the book Kernelization by Fedor V. Fomin, Daniel Lokshtanov, Saket Saurabh, Meirav Zehavi, Cambridge University Press
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Author: Fedor V. Fomin, Daniel Lokshtanov, Saket Saurabh, Meirav Zehavi ISBN: 9781108577335
Publisher: Cambridge University Press Publication: January 10, 2019
Imprint: Cambridge University Press Language: English
Author: Fedor V. Fomin, Daniel Lokshtanov, Saket Saurabh, Meirav Zehavi
ISBN: 9781108577335
Publisher: Cambridge University Press
Publication: January 10, 2019
Imprint: Cambridge University Press
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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