Invitation to Fixed Parameter Algorithms

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Format: Hardcover
Pub. Date: 2006-03-30
Publisher(s): Oxford University Press
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Summary

A fixed-parameter is an algorithm that provides an optimal solution to a combinatorial problem. This research-level text is an application-oriented introduction to the growing and highly topical area of the development and analysis of efficient fixed-parameter algorithms for hard problems. The book is divided into three parts: a broad introduction that provides the general philosophy and motivation; followed by coverage of algorithmic methods developed over the years in fixed-parameter algorithmics forming the core of the book; and a discussion of the essential from parameterized hardness theory with a focus on W [1]-hardness, which parallels NP-hardness, then stating some relations to polynomial-time approximation algorithms, and finishing up with a list of selected case studies to show the wide range of applicability of the presented methodology. Aimed at graduate and research mathematicians, programmers, algorithm designers and computer scientists, the book introduces the basic techniques and results and provides a fresh view on this highly innovative field of algorithmic research.

Author Biography

Rolf Niedermeier is Chair of Theoretical Computer Science/Computational Complexity at Universitat Jena, Germany.

Table of Contents

I FOUNDATIONS
1 Introduction to Fixed-Parameter Algorithms
3(14)
1.1 The satisfiability problem
4(3)
1.2 An example from railway optimization
7(3)
1.3 A communication problem in tree networks
10(2)
1.4 Summary
12(1)
1.5 Exercises
13(1)
1.6 Bibliographical remarks
14(3)
2 Preliminaries and Agreements
17(5)
2.1 Basic sets and problems
17(1)
2.2 Model of computation and running times
17(1)
2.3 Strings and graphs
18(2)
2.4 Complexity and approximation
20(1)
2.5 Bibliographical remarks
21(1)
3 Parameterized Complexity Theory—A Primer
22(9)
3.1 Basic theory
22(5)
3.2 Interpreting fixed-parameter tractability
27(2)
3.3 Exercises
29(1)
3.4 Bibliographical remarks
29(2)
4 Vertex Cover—An Illustrative Example
31(10)
4.1 Parameterizing '
32(1)
4.2 Specializing
33(1)
4.3 Generalizing
34(1)
4.4 Counting or enumerating
34(1)
4.5 Lower bounds
35(1)
4.6 Implementing and applying
35(1)
4.7 Using vertex cover structure for other problems
36(2)
4.8 Exercises
38(1)
4.9 Bibliographical remarks
38(3)
5 The Art of Problem Parameterization
41(8)
5.1 Parameter really small?
41(1)
5.2 Guaranteed parameter value?
42(1)
5.3 More than one obvious parameterization?
43(2)
5.4 Close to "trivial" problem instances?
45(2)
5.5 Exercises
47(1)
5.6 Bibliographical remarks
47(2)
6 Summary and Concluding Remarks
49(4)
II ALGORITHMIC METHODS
7 Data Reduction and Problem Kernels
53(35)
7.1 Basic definitions and facts
55(3)
7.2 Maximum Satisfiability
58(2)
7.3 Cluster Editing
60(4)
7.4 Vertex Cover
64(8)
7.4.1 Kernelization based on matching
64(4)
7.4.2 Kernelization based on linear programming
68(1)
7.4.3 Kernelization based on crown structures
69(3)
7.4.4 Comparison and discussion
72(1)
7.5 3-Hitting Set
72(2)
7.6 Dominating Set in Planar Graphs
74(6)
7.6.1 The neighborhood of a single vertex
74(3)
7.6.2 The neighborhood of a pair of vertices
77(2)
7.6.3 Reduced graphs and the problem kernel
79(1)
7.7 On lower bounds for problem kernels
80(2)
7.8 Summary and concluding remarks
82(1)
7.9 Exercises
83(2)
7.10 Bibliographical remarks
85(3)
8 Depth-Bounded Search Trees
88(36)
8.1 Basic definitions and facts
91(2)
8.2 Cluster Editing
93(5)
8.3 Vertex Cover
98(3)
8.4 Hitting Set
101(2)
8.5 Closest String
103(4)
8.6 Dominating Set in Planar Graphs
107(3)
8.6.1 Data reduction rules
108(1)
8.6.2 Main result and some remarks
109(1)
8.7 Interleaving search trees and kernelization
110(4)
8.7.1 Basic methodology
111(2)
8.7.2 Interleaving is necessary
113(1)
8.8 Automated search tree generation and analysis
114(5)
8.9 Summary and concluding remarks
119(1)
8.10 Exercises
120(1)
8.11 Bibliographical remarks
121(3)
9 Dynamic Programming
124(26)
9.1 Basic definitions and facts
125(1)
9.2 Knapsack
126(2)
9.3 Steiner Problem in Graphs
128(3)
9.4 Multicommodity Demand Flow in Trees
131(5)
9.5 Tree-structured variants of Set Cover
136(9)
9.5.1 Basic definitions and facts
136(3)
9.5.2 Algorithm for Path-like Weighted Set Cover
139(1)
9.5.3 Algorithm for Tree-like Weighted Set Cover
140(5)
9.6 Shrinking search trees
145(1)
9.7 Summary and concluding remarks
146(1)
9.8 Exercises
147(1)
9.9 Bibliographical remarks
148(2)
10 Tree Decompositions of Graphs
150(27)
10.1 Basic definitions and facts
151(2)
10.2 On the construction of tree decompositions
153(2)
10.3 Planar graphs
155(5)
10.4 Dynamic programming for Vertex Cover
160(4)
10.5 Dynamic programming for Dominating Set
164(5)
10.6 Monadic second-order logic (MSO)
169(3)
10.7 Related graph width parameters
172(2)
10.8 Summary and concluding remarks
174(1)
10.9 Exercises
175(1)
10.10 Bibliographical remarks
176(1)
11 Further Advanced Techniques
177(24)
11.1 Color-coding
178(3)
11.2 Integer linear programming
181(3)
11.3 Iterative compression
184(6)
11.3.1 Vertex Cover
185(2)
11.3.2 Feedback Vertex Set
187(3)
11.4 Greedy localization
190(5)
11.4.1 Set Splitting
191(2)
11.4.2 Set Packing
193(2)
11.5 Graph minor theory
195(2)
11.6 Summary and concluding remarks
197(1)
11.7 Exercises
198(1)
11.8 Bibliographical remarks
199(2)
12 Summary and Concluding Remarks
201(4)
III SOME THEORY, SOME CASE STUDIES
13 Parameterized Complexity Theory
205(32)
13.1 Basic definitions and concepts
206(6)
13.1.1 Parameterized reducibility
207(2)
13.1.2 Parameterized complexity classes
209(3)
13.2 The complexity class W[1]
212(4)
13.3 Concrete parameterized reductions
216(14)
13.3.1 W[1]-hardness proofs
218(8)
13.3.2 Further reductions and W[2]-hardness
226(4)
13.4 Some recent developments
230(4)
13.4.1 Lower bounds and the complexity class M[1]
230(2)
13.4.2 Lower bounds and linear FPT reductions
232(1)
13.4.3 Machine models, limited nondeterminism, and bounded FPT
233(1)
13.5 Summary and concluding remarks
234(1)
13.6 Exercises
235(1)
13.7 Bibliographical remarks
235(2)
14 Connections to Approximation Algorithms
237(6)
14.1 Approximation helping parameterization
238(1)
14.2 Parameterization helping approximation
239(2)
14.3 Further (non-)relations
241(1)
14.4 Discussion and concluding remarks
241(1)
14.5 Bibliographical remarks
242(1)
15 Selected Case Studies
243(34)
15.1 Planar and more general graphs
243(2)
15.1.1 Planar graphs
243(2)
15.1.2 More general graphs
245(1)
15.2 Graph modification problems
245(6)
15.2.1 Graph modification and hereditary properties
246(1)
15.2.2 Feedback Vertex Set revisited
247(1)
15.2.3 Graph Bipartization
248(1)
15.2.4 Minimum Fill-In
249(1)
15.2.5 Closest 3-Leaf Power
250(1)
15.3 Miscellaneous graph problems
251(7)
15.3.1 Capacitated Vertex Cover
251(2)
15.3.2 Constraint Bipartite Vertex Cover
253(2)
15.3.3 Graph Coloring
255(1)
15.3.4 Crossing Number
256(1)
15.3.5 Power Dominating Set
257(1)
15.4 Computational biology problems
258(8)
15.4.1 Minimum Quartet Inconsistency
259(2)
15.4.2 Compatibility of Unrooted Phylogenetic Trees
261(1)
15.4.3 Longest Arc-Preserving Common Subsequences
262(2)
15.4.4 Incomplete Perfect Path Phylogeny Haplotyping
264(2)
15.5 Logic and related problems
266(5)
15.5.1 Satisfiability
266(2)
15.5.2 Maximum Satisfiability
268(1)
15.5.3 Constraint satisfaction problems
269(1)
15.5.4 Database queries
270(1)
15.6 Miscellaneous problems
271(4)
15.6.1 Two-dimensional Euclidean TSP
272(1)
15.6.2 Multidimensional matching
273(1)
15.6.3 Matrix Domination
273(1)
15.6.4 Vapnik-Chervonenkis Dimension
274(1)
15.7 Summary and concluding remarks
275(2)
16 Zukunftsmusik
277(2)
References 279(15)
Index 294

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