Kernelization : theory of parameterized preprocessing

Contributor(s):

Material type: Text

9781107057760

Subject(s):

DDC classification:

005.7 FOM-K

Contents:

1.What Is a Kernel? 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 14.Introduction to Treewidth

Summary: "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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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds	Course reserves
Books	IIITD General Stacks	Computer Science and Engineering	REF 005.7 FOM-K (Browse shelf(Opens below))	Not for loan		013104		Paramaterized Algorithms (new) UG/PG MNS24

Total holds: 0

Includes bibliographical references and index.

"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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