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