000			03919nam a2200217 a 4500
999			_c30352 _d30352
001			65348
020			_a9781447171645
082			_a005.1 DO FU
100			_aDowney, Rodney G. _937507
245	1	0	_aFundamentals of parameterized complexity _cRodney G. Downey, Michael R. Fellows
260			_aLondon : _bSpringer, _cc2013.
300			_axxx, 763 p. : _bill. ; _c24 cm.
490	1		_aTexts in computer science
505	0		_aParameterized Tractability. Preliminaries -- The Basic Definitions -- Elementary Positive Techniques. Bounded Search Trees -- Kernelization -- More on Kernelization -- Iterative Compression, and Measure and Conquer, for Minimization Problems -- Further Elementary Techniques -- Color Coding, Multilinear Detection, and Randomized Divide and Conquer -- Optimization Problems, Approximation Schemes, and Their Relation to FPT -- Techniques Based on Graph Structure. Treewidth and Dynamic Programming -- Heuristics for Treewidth -- Methods via Automata and Bounded Treewidth -- Courcelle's Theorem -- More on Width-Metrics: Applications and Local Treewidth -- Depth-First Search and the Plehn-Voigt Theorem -- Other Width Metrics -- Exotic Meta-techniques. Well-Quasi-Orderings and the Robertson-Seymour Theorems -- The Graph Minor Theorem -- Applications of the Obstruction Principle and WQOs -- Hardness Theory. Reductions -- The Basic Class W[1] and an Analog of Cook's Theorem -- Some Other W[1] Hardness Results -- The W-Hierarchy -- The Monotone and Antimonotone Collapse Theorems: Monotone W[2t+1]=W[2t] and Antimonotone W[2t+2]=W[2t+1] -- Beyond W[t]-Hardness -- Fixed Parameter Analogues of PSpace and k-Move Games -- Provable Intractability: The Class XP -- Another Basis for the W-Hierarchy and the Tradeoff Theorem -- Approximations, Connections, Lower Bounds. The M-Hierarchy, and XP-Optimality -- Kernelization Lower Bounds -- Further Topics. Parameterized Approximation -- Parameterized Counting and Randomization -- Research Horizons. Research Horizons -- Appendices. Appendix 1: Network Flows and Matchings / -- Appendix 2: Menger's Theorems.
520			_aThe field of parameterized complexity/multivariate complexity algorithmics is an exciting and vibrant part of theoretical computer science, responding to the vital need for efficient algorithms in modern society. This comprehensive and self-contained textbook presents an accessible overview of the state of the art of multivariate algorithmics and complexity. Increasingly, multivariate algorithmics is having significant practical impact in many application domains, with even more developments on the horizon. The text describes how the multivariate framework allows an extended dialog with a problem, enabling the reader who masters the complexity issues under discussion to use the positive and negative toolkits in their own research. Topics and features: Describes many of the standard algorithmic techniques available for establishing parametric tractability. Reviews the classical hardness classes. Explores the various limitations and relaxations of the methods. Showcases the powerful new lower bound techniques Examines various different algorithmic solutions to the same problems, highlighting the insights to be gained from each approach Demonstrates how complexity methods and ideas have evolved over the past 25 years This classroom-tested and easy-to-follow textbook/reference is essential reading for the beginning graduate student and advanced undergraduate student. The book will also serve as an invaluable resource for the general computer scientist and the mathematically-aware scientist seeking tools for their research.
650		7	_aComputer algorithms _9274
650		7	_aComputer science _xMathematics _927839
700			_aFellows, Michael R. _937508
856			_uhttps://uowd.box.com/s/awqkbckqx30dvedoyetvm3olrhf6uzdp _zLocation Map
942			_cREGULAR _2ddc