Approximation Algorithms

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Format: Hardcover
Pub. Date: 2001-08-01
Publisher(s): Springer-Nature New York Inc
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Summary

This book covers the dominant theoretical approaches to the approximate solution of hard combinatorial optimization and enumeration problems. It contains elegant combinatorial theory, useful and interesting algorithms, and deep results about the intrinsic complexity of combinatorial problems. Its clarity of exposition and excellent selection of exercises will make it accessible and appealing to all those with a taste for mathematics and algorithms. Richard Karp,University Professor, University of California at Berkeley Following the development of basic combinatorial optimization techniques in the 1960s and 1970s, a main open question was to develop a theory of approximation algorithms. In the 1990s, parallel developments in techniques for designing approximation algorithms as well as methods for proving hardness of approximation results have led to a beautiful theory. The need to solve truly large instances of computationally hard problems, such as those arising from the Internet or the human genome project, has also increased interest in this theory. The field is currently very active, with the toolbox of approximation algorithm design techniques getting always richer. It is a pleasure to recommend Vijay Vazirani's well-written and comprehensive book on this important and timely topic. I am sure the reader will find it most useful both as an introduction to approximability as well as a reference to the many aspects of approximation algorithms. Làszlò Lovàsz, Senior Researcher, Microsoft Research

Table of Contents

Introduction
1(14)
Lower bounding OPT
2(3)
An approximation algorithm for cardinality vertex cover
3(1)
Can the approximation guarantee the improved?
3(2)
Well-characterized problems and min-max relations
5(2)
Exercises
7(3)
Notes
10(5)
Part I. Combinatorial Algorithms
Set Cover
15(12)
The greedy algorithm
16(1)
Layering
17(2)
Application to shortest superstring
19(3)
Exercises
22(4)
Notes
26(1)
Steiner Tree and TSP
27(11)
Metric Steiner tree
27(3)
MST-based algorithm
28(2)
Metric TSP
30(3)
A simple factor 2 algorithm
31(1)
Improving the factor to 3/2
32(1)
Exercises
33(4)
Notes
37(1)
Multiway Cut and k-Cut
38(9)
The multiway cut problem
38(2)
The minimum k-cut problem
40(4)
Exercises
44(2)
Notes
46(1)
k-Center
47(7)
Parametric pruning applied to metric k-center
47(3)
The weighted version
50(2)
Exercises
52(1)
Notes
53(1)
Feedback Vertex Set
54(7)
Cyclomatic weighted graphs
54(3)
Layering applied to feedback vertex set
57(3)
Exercises
60(1)
Notes
60(1)
Shortest Superstring
61(7)
A factor 4 algorithm
61(3)
Improving to factor 3
64(2)
Achieving half the optimal compression
66(1)
Exercises
66(1)
Notes
67(1)
Knapsack
68(6)
A pseudo-polynomial time algorithm for knapsack
69(1)
An FPTAS for knapsack
69(2)
Strong NP-hardness and the existence of FPTAS's
71(1)
Is an FPTAS the most desirable approximation algorithm?
72(1)
Exercises
72(1)
Notes
73(1)
Bin Packing
74(5)
An asymptotic PTAS
74(3)
Exercises
77(1)
Notes
78(1)
Minimum Makespan Scheduling
79(5)
Factor 2 algorithm
79(1)
A PTAS for minimum makespan
80(3)
Bin packing with fixed number of object sizes
81(1)
Reducing makespan to restricted bin packing
81(2)
Exercises
83(1)
Notes
83(1)
Euclidean TSP
84(9)
The algorithm
84(3)
Proof of correctness
87(2)
Exercises
89(1)
Notes
89(4)
Part II. LP-Based Algorithms
Introduction to LP-Duality
93(15)
The LP-duality theorem
93(4)
Min-max relations and LP-duality
97(3)
Two fundamental algorithm design techniques
100(3)
A comparison of the techniques and the notion of integrality gap
101(2)
Exercises
103(4)
Notes
107(1)
Set Cover via Dual Fitting
108(11)
Dual-fitting-based analysis for the greedy set cover algorithm
108(4)
Can the approximation guarantee be improved?
111(1)
Generalizations of set cover
112(4)
Dual fitting applied to constrained set multicover
112(4)
Exercises
116(2)
Notes
118(1)
Rounding Applied to Set Cover
119(6)
A simple rounding algorithm
119(1)
Randomized rounding
120(2)
Half-integrality of vertex cover
122(1)
Exercises
123(1)
Notes
124(1)
Set Cover via the Primal-Dual Schema
125(6)
Overview of the schema
125(2)
Primal-dual schema applied to set cover
127(2)
Exercises
129(1)
Notes
129(2)
Maximum Satisfiability
131(9)
Dealing with large clauses
132(1)
Derandomizing via the method of conditional expectation
132(2)
Dealing with small clauses via LP-rounding
134(2)
A 3/4 factor algorithm
136(1)
Exercises
137(2)
Notes
139(1)
Scheduling on Unrelated Parallel Machines
140(6)
Parametric pruning in an LP setting
140(1)
Properties of extreme point solutions
141(1)
The algorithm
142(1)
Additional properties of extreme point solutions
143(1)
Exercises
144(1)
Notes
145(1)
Multicut and Integer Multicommodity Flow in Trees
146(9)
The problmes and their LP-relaxations
146(3)
Primal-dual schema based algorithm
149(3)
Exercises
152(2)
Notes
154(1)
Multiway Cut
155(13)
An interesting LP-relaxation
155(2)
Randomized rounding algorithm
157(3)
Half-integrality of node multiway cut
160(3)
Exercises
163(4)
Notes
167(1)
Multicut in General Graphs
168(12)
Sum multicommodity flow
168(2)
LP-rounding-based algorithm
170(5)
Growing a region: the continous process
171(1)
The discrete process
172(1)
Finding successive regions
173(2)
A tight example
175(1)
Some applications of multicut
176(1)
Exercises
177(2)
Notes
179(1)
Sparset Cut
180(18)
Demands multicommodity flow
180(1)
Linear programming formulation
181(2)
Metrics, cut packings, and l1-embeddability
183(3)
Cut packings for metrics
183(2)
l1-embeddability of metrics
185(1)
Low distortion l1-embeddings for metrics
186(5)
Ensuring that a single edge is not overshrunk
187(3)
Ensuring that no edge is overshrunk
190(1)
LP-rounding-based algorithm
191(1)
Applications
192(3)
Edge expansion
192(1)
Conductance
192(1)
Balanced cut
193(1)
Minimum cut linear arrangement
194(1)
Exercises
195(2)
Notes
197(1)
Steiner Forest
198(15)
LP-relaxation and dual
198(1)
Primal-dual schema with synchronization
199(5)
Analysis
204(3)
Exercises
207(5)
Notes
212(1)
Steiner Network
213(19)
The LP-relaxation and half-integrality
213(4)
The technique of iterated rounding
217(2)
Characterizing extreme point solutions
219(2)
A counting argument
221(3)
Exercises
224(7)
Notes
231(1)
Facility Location
232(11)
An intuitive understanding of the dual
233(1)
Relaxing primal complementary slackness conditions
234(1)
Primal-dual schema based algorithm
235(1)
Analysis
236(3)
Running time
238(1)
Tight example
238(1)
Exercises
239(3)
Notes
242(1)
k-Median
243(13)
LP-relaxation and dual
243(1)
The high-level idea
244(3)
Randomized rounding
247(3)
Derandomization
248(1)
Running time
249(1)
Tight example
249(1)
Integrality gap
250(1)
A Lagrangian relaxation technique for approximation algorithms
250(1)
Exercises
251(3)
Notes
254(2)
Semidefinite Programming
256(17)
Strict quadratic programs and vector programs
256(2)
Properties of positive semidefinite matrices
258(1)
The semidefinite programming problem
259(2)
Randomized rounding algorithm
261(3)
Improving the guarantee for MAX-2SAT
264(2)
Exercises
266(3)
Notes
269(4)
Part III. Other Topics
Shortest Vector
273(21)
Bases, determinants, and orthogonality defect
274(2)
The algorithms of Euclid and Gauss
276(2)
Lower bounding OPT using Gram-Schmidt orthogonalization
278(2)
Extension to n dimensions
280(4)
The dual lattice and its algorithmic use
284(4)
Exercises
288(4)
Notes
292(2)
Counting Problmes
294(12)
Counting DNF solutions
295(2)
Network reliability
297(5)
Upperbounding the number of near-minimum cuts
298(2)
Analysis
300(2)
Exercises
302(3)
Notes
305(1)
Hardness of Approximation
306(28)
Reductions, gaps, and hardness factors
306(3)
The PCP theorem
309(2)
Hardness of MAX-3SAT
311(2)
Hardness of MAX-3SAT with bounded occurence of variables
313(3)
Hardness of vertex cover and Steiner tree
316(2)
Hardness of clique
318(4)
Hardness of set cover
322(7)
The two-prover one-round characterization of NP
322(2)
The gadget
324(1)
Reducing error probability by parallel repetition
325(1)
The reduction
326(3)
Exercises
329(3)
Notes
332(2)
Open Problems
334(21)
Problems having constant factor algorithms
334(2)
Other optimization problems
336(2)
Counting problems
338(5)
Appendix
A An Overview of Complexity Theory for the Algorithm Designer
343(9)
A.1 Certificates and the class NP
343(1)
A.2 Reductions and NP-completeness
344(1)
A.3 NP-optimization problems and approximation algorithms
345(2)
A.3.1 Approximation factor preserving reductions
347(1)
A.4 Randomized complexity classes
347(1)
A.5 Self-reducibility
348(3)
A.6 Notes
351(1)
B Basic Facts from Probability Theory
352(3)
B.1 Expectation and moments
352(1)
B.2 Deviations from the mean
353(1)
B.3 Basic distributions
354(1)
B.4 Notes
354(1)
References 355(16)
Problem Index 371(4)
Subject Index 375

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