000			02183nam a22003017a 4500
003			IIITD
005			20241010155600.0
008			180625s2019 enk b 001 0 eng
020			_a9781107057760
040			_aIIITD
082	0	0	_a005.7 _bFOM-K
245	1	0	_aKernelization : _btheory of parameterized preprocessing _cby Fedor V. Fomin...[et al.].
260			_aCambridge : _bCambridge University Press, _c©2019
300			_axiii, 515 p. ; _c22 cm.
504			_aIncludes bibliographical references and index.
505			_t1.What Is a Kernel? _t2.Warm Up _t3.Inductive Priorities _t4.Crown Decomposition _t5.Expansion Lemma _t6.Linear Programming _t7.Hypertrees _t8.Sunflower Lemma _t9.Modules _t10.Matroids _t11.Representative Families _t12.Greedy Packing _t13.Euler's Formula _t14.Introduction to Treewidth
520			_a"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"--
650		0	_aElectronic data processing _xData preparation.
650		0	_aData reduction.
650		0	_aKernel functions.
650		0	_aParameter estimation.
700	1		_aFomin, Fedor V
700	1		_aLokshtanov, Daniel
700	1		_aSaurabh, Saket
700	1		_aZehavi, Meirav
942			_2ddc _cBK
999			_c189672 _d189672