000			nam a22 7a 4500
999			_c29480 _d29480
008			190425b xxu||||| |||| 00| 0 eng d
020			_a9781107057760 _c(hbk)
082			_a005.72 _bFOM
100			_aFomin, Fedor V.
245			_aKernelization : theory of parameterized preprocessing
260			_aUnited Kingdom : _bCambridge University Press, _c2019.
300			_axiv, 515 p. : _bill. ; _c24 cm.
365			_aGBP _b49.99 _d00
504			_aIncludes bibliographical references and index.
520			_aPreprocessing, 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			_aElectronic data processing
650			_aData preparation
650			_aData reduction
650			_aKernel functions
650			_aParameter estimation
700			_aLokshtanov, Daniel
700			_aSaurabh, Saket
700			_aZehavi, Meirav
942			_2ddc _cBK