04507nam 22006735 450 991029922630332120200630083304.03-319-21275-310.1007/978-3-319-21275-3(CKB)3710000000454186(SSID)ssj0001584297(PQKBManifestationID)16265333(PQKBTitleCode)TC0001584297(PQKBWorkID)14865678(PQKB)11718233(DE-He213)978-3-319-21275-3(MiAaPQ)EBC6313185(MiAaPQ)EBC5589001(Au-PeEL)EBL5589001(OCoLC)915052438(PPN)190518855(EXLCZ)99371000000045418620150720d2015 u| 0engurnn|008mamaatxtccrParameterized Algorithms /by Marek Cygan, Fedor V. Fomin, Łukasz Kowalik, Daniel Lokshtanov, Dániel Marx, Marcin Pilipczuk, Michał Pilipczuk, Saket Saurabh1st ed. 2015.Cham :Springer International Publishing :Imprint: Springer,2015.1 online resource (XVII, 613 p. 84 illus., 25 illus. in color.) Bibliographic Level Mode of Issuance: Monograph3-319-21274-5 Includes bibliographical references and index.Introduction -- 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.This 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.AlgorithmsAlgorithm Analysis and Problem Complexityhttps://scigraph.springernature.com/ontologies/product-market-codes/I16021Algorithmshttps://scigraph.springernature.com/ontologies/product-market-codes/M14018Algorithms.Algorithm Analysis and Problem Complexity.Algorithms.519.544Cygan Marekauthttp://id.loc.gov/vocabulary/relators/aut718193Fomin Fedor Vauthttp://id.loc.gov/vocabulary/relators/autKowalik Łukaszauthttp://id.loc.gov/vocabulary/relators/autLokshtanov Danielauthttp://id.loc.gov/vocabulary/relators/autMarx Dánielauthttp://id.loc.gov/vocabulary/relators/autPilipczuk Marcinauthttp://id.loc.gov/vocabulary/relators/autPilipczuk Michałauthttp://id.loc.gov/vocabulary/relators/autSaurabh Saketauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910299226303321Parameterized algorithms1392410UNINA