000			05330nam a22004218i 4500
001			CR9781139177801
003			UkCbUP
005			20200124160317.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			111101s2014||||enk o ||1 0|eng|d
020			_a9781139177801 (ebook)
020			_z9781107025196 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA76.9.A43 _bT733 2014
082	0	4	_a005.1 _223
245	0	0	_aTractability / _cedited by Lucas Bordeaux (Microsoft Research), Youssef Hamadi (Microsoft Research), Pushmeet Kohli (Microsoft Research).
264		1	_aCambridge : _bCambridge University Press, _c2014.
300			_a1 online resource (xxi, 377 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
505	8		_aCover; Tractability; Title Page; Copyright Page; Contents; Contributors; Introduction; Part 1: Graphical Structure; 1 Treewidth and Hypertree Width; 1.1 Treewidth; 1.2 Hypertree width; 1.3 Applications of hypertree width; 1.4 Beyond (hyper)tree decompositions; 1.5 Tractability frontiers (for CSPs); 1.6 Conclusion; References; 2 Perfect Graphs and Graphical Modeling; 2.1 Berge Graphs and Perfect Graphs; 2.2 Computational Properties of Perfect Graphs; 2.3 Graphical Models; 2.4 Nand Markov Random Fields; 2.5 Maximum Weight Stable Set; 2.6 Tractable Graphical Models; 2.7 Discussion.
505	8		_a2.8 Acknowledgments; 2.9 Appendix; References; Part 2: Language Restrictions; 3 Submodular Function Maximization; 3.1 Submodular Functions; 3.2 Greedy Maximization of Submodular Functions; 3.3 Beyond the Greedy Algorithm: Handling More Complex Constraints; 3.4 Online Maximization of Submodular Functions; 3.5 Adaptive Submodularity; 3.6 Conclusions; References; 4 Tractable Valued Constraints; 4.1 Introduction; 4.2 Constraint Satisfaction Problems; 4.3 Valued Constraint Satisfaction Problems; 4.4 Examples of Valued Constraint Languages; 4.5 Expressive Power.
505	8		_a4.6 Submodular Functions and Multimorphisms; 4.7 Conservative Valued Constraint Languages; 4.8 A General Algebraic Theory of Complexity; 4.9 Conclusions and Open Problems; References; 5 Tractable Knowledge Representation Formalisms; 5.1 Introduction; 5.2 A Motivating Example; 5.3 Negation Normal Form; 5.4 Structured Decomposability; 5.5 (X, Y)-Decompositions of Boolean Functions; 5.6 Sentential Decision Diagrams; 5.7 The Process of Compilation; 5.8 Knowledge Compilation in Probabilistic Reasoning; 5.9 Conclusion; References; Part 3: Algorithms and their Analysis.
505	8		_a6 Tree-Reweighted Message Passing; 6.1 Introduction; 6.2 Preliminaries; 6.3 Sequential Tree-Reweighted Message Passing (TRW-S); 6.4 Analysis of the Algorithm; 6.5 TRW-S with Monotonic Chains; 6.6 Summary of the TRW-S Algorithm; 6.7 Related Approaches; 6.8 Conclusions and Discussion; References; 7 Tractable Optimization in Machine Learning; 7.1 Introduction; 7.2 Background; 7.3 Smooth Convex Optimization; 7.4 Nonsmooth Convex Optimization; 7.5 Stochastic Optimization; 7.6 Summary; References; 8 Approximation Algorithms; 8.1 Introduction; 8.2 Combinatorial Algorithms.
505	8		_a8.3 Linear Programming Based Algorithms; 8.4 Semi-Definite Programming Based Algorithms; 8.5 Algorithms for Special Instances; 8.6 Metric Embeddings; 8.7 Hardness of Approximation; References; 9 Kernelization Methods for Fixed-Parameter Tractability; 9.1 Introduction; 9.2 Basic Definitions; 9.3 Classical Techniques; 9.4 Recent Upper Bound Machinery; 9.5 Conclusion; References; Part 4: Tractability in Some Specific Areas; 10 Efficient Submodular Function Minimization for Computer Vision; 10.1 Labeling Problems in Computer Vision; 10.2 Markov and Conditional Random Fields; 10.3 Minimizing Energy Functions for MAP Inference.
520			_aClassical computer science textbooks tell us that some problems are 'hard'. Yet many areas, from machine learning and computer vision to theorem proving and software verification, have defined their own set of tools for effectively solving complex problems. Tractability provides an overview of these different techniques, and of the fundamental concepts and properties used to tame intractability. This book will help you understand what to do when facing a hard computational problem. Can the problem be modelled by convex, or submodular functions? Will the instances arising in practice be of low treewidth, or exhibit another specific graph structure that makes them easy? Is it acceptable to use scalable, but approximate algorithms? A wide range of approaches is presented through self-contained chapters written by authoritative researchers on each topic. As a reference on a core problem in computer science, this book will appeal to theoreticians and practitioners alike.
650		0	_aComputer algorithms.
650		0	_aCombinatorial optimization _xData processing.
650		0	_aComputational complexity.
700	1		_aBordeaux, Lucas, _eeditor.
700	1		_aHamadi, Youssef _c(Computer science researcher), _eeditor.
700	1		_aKohli, Pushmeet, _eeditor.
776	0	8	_iPrint version: _z9781107025196
856	4	0	_uhttps://doi.org/10.1017/CBO9781139177801
999			_c521562 _d521560