000			03463nmm a22003015i 4500
005			20230705150629.0
008			150720s2015 sz | s |||| 0|eng d
020			_a9783319212753 _9978-3-319-21275-3
082			_a518.1 _223
100			_aCygan, Marek. _920029
245			_aParameterized Algorithms _h[electronic resource] / _cby Marek Cygan, Fedor V. Fomin, Łukasz Kowalik, Daniel Lokshtanov, Dániel Marx, Marcin Pilipczuk, Michał Pilipczuk, Saket Saurabh.
250			_a1st ed. 2015.
260			_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2015.
300			_aXVII, 613 p. 84 illus., 25 illus. in color. _bonline resource.
505			_aIntroduction -- Kernelization -- Bounded Search Trees -- Iterative Compression -- Randomized Methods in Parameterized Algorithms -- Miscellaneous -- Treewidth -- Finding Cuts and Separators -- Advanced Kernelization Algorithms -- Algebraic Techniques: Sieves, Convolutions, and Polynomials -- Improving Dynamic Programming on Tree Decompositions -- Matroids -- Fixed-Parameter Intractability -- Lower Bounds Based on the Exponential-Time Hypothesis -- Lower Bounds for Kernelization.
520			_aThis comprehensive textbook presents a clean and coherent account of most fundamental tools and techniques in Parameterized Algorithms and is a self-contained guide to the area. The book covers many of the recent developments of the field, including application of important separators, branching based on linear programming, Cut & Count to obtain faster algorithms on tree decompositions, algorithms based on representative families of matroids, and use of the Strong Exponential Time Hypothesis. A number of older results are revisited and explained in a modern and didactic way. The book provides a toolbox of algorithmic techniques. Part I is an overview of basic techniques, each chapter discussing a certain algorithmic paradigm. The material covered in this part can be used for an introductory course on fixed-parameter tractability. Part II discusses more advanced and specialized algorithmic ideas, bringing the reader to the cutting edge of current research. Part III presents complexity results and lower bounds, giving negative evidence by way of W[1]-hardness, the Exponential Time Hypothesis, and kernelization lower bounds. All the results and concepts are introduced at a level accessible to graduate students and advanced undergraduate students. Every chapter is accompanied by exercises, many with hints, while the bibliographic notes point to original publications and related work.
650			_aAlgorithms. _920030
650			_aAlgorithms. _920030
700			_aFomin, Fedor V. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _920031
700			_aKowalik, Łukasz. _eauthor. _0(orcid)0000-0002-7546-2969 _1https://orcid.org/0000-0002-7546-2969 _4aut _4http://id.loc.gov/vocabulary/relators/aut _920032
700			_aLokshtanov, Daniel. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _920033
700			_aMarx, Dániel. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _920034
700			_aPilipczuk, Marcin. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _920035
700			_aPilipczuk, Michał. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _920036
700			_aSaurabh, Saket. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _920037
856			_uhttps://doi.org/10.1007/978-3-319-21275-3
942			_cEBK
999			_c13595 _d13595