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Kernelization

Theory of Parameterized Preprocessing

By: Fedor V. Fomin,Daniel Lokshtanov,Saket Saurabh,Meirav Zehavi

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Kernelization

Theory of Parameterized Preprocessing

By: Fedor V. Fomin,Daniel Lokshtanov,Saket Saurabh,Meirav Zehavi

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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Page Count

531

Publisher

Cambridge University Press

Published Date

ISBN 10

1108577334

ISBN 13

9781108577335

Kernelization

Theory of Parameterized Preprocessing

By: Fedor V. Fomin,Daniel Lokshtanov,Saket Saurabh,Meirav Zehavi

Write a review Read reviews Take a Quiz Solve Book Puzzle

Kernelization

Theory of Parameterized Preprocessing

By: Fedor V. Fomin,Daniel Lokshtanov,Saket Saurabh,Meirav Zehavi

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.

Average ratings

N/A

5

0%

4

0%

3

0%

2

0%

1

0%

Solve jigsaw puzzle

Age

Page Count

531

Publisher

Cambridge University Press

Published Date

ISBN 10

1108577334

ISBN 13

9781108577335