Theory of Computational Complexity

Praise for the First Edition
 
"...complete, up-to-date coverage of computational complexity theory...the book promises to become the standard reference on computational complexity." -Zentralblatt MATH
 
A thorough revision based on advances in the field of computational complexity and readers' feedback, the Second Edition of Theory of Computational Complexity presents updates to the principles and applications essential to understanding modern computational complexity theory. The new edition continues to serve as a comprehensive resource on the use of software and computational approaches for solving algorithmic problems and the related difficulties that can be encountered.
 
Maintaining extensive and detailed coverage, Theory of Computational Complexity, Second Edition, examines the theory and methods behind complexity theory, such as computational models, decision tree complexity, circuit complexity, and probabilistic complexity. The Second Edition also features recent developments on areas such as NP-completeness theory, as well as:
* A new combinatorial proof of the PCP theorem based on the notion of expander graphs, a research area in the field of computer science
* Additional exercises at varying levels of difficulty to further test comprehension of the presented material
* End-of-chapter literature reviews that summarize each topic and offer additional sources for further study
 
Theory of Computational Complexity, Second Edition, is an excellent textbook for courses on computational theory and complexity at the graduate level. The book is also a useful reference for practitioners in the fields of computer science, engineering, and mathematics who utilize state-of-the-art software and computational methods to conduct research. A thorough revision based on advances in the field of computational complexity and readers' feedback, the Second Edition of Theory of Computational Complexity presents updates to the principles and applications essential to understanding modern computational complexity theory. The new edition continues to serve as a comprehensive resource on the use of software and computational approaches for solving algorithmic problems and the related difficulties that can be encountered. Maintaining extensive and detailed coverage, Theory of Computational Complexity, Second Edition, examines the theory and methods behind complexity theory, such as computational models, decision tree complexity, circuit complexity, and probabilistic complexity. The Second Edition also features recent developments on areas such as NP-completeness theory, as well as: * A new combinatorial proof of the PCP theorem based on the notion of expander graphs, a research area in the field of computer science * Additional exercises at varying levels of difficulty to further test comprehension of the presented material * End-of-chapter literature reviews that summarize each topic and offer additional sources for further study Theory of Computational Complexity, Second Edition, is an excellent textbook for courses on computational theory and complexity at the graduate level. The book is also a useful reference for practitioners in the fields of computer science, engineering, and mathematics who utilize state-of-the-art software and computational methods to conduct research.

List of contents

Preface ix
 
Notes on the Second Edition xv
 
Part I Uniform Complexity 1
 
1 Models of Computation and Complexity Classes 3
 
1.1 Strings, Coding, and Boolean Functions 3
 
1.2 Deterministic Turing Machines 7
 
1.3 Nondeterministic Turing Machines 14
 
1.4 Complexity Classes 17
 
1.5 Universal Turing Machine 23
 
1.6 Diagonalization 27
 
1.7 Simulation 31
 
Exercises 35
 
Historical Notes 41
 
2 NP-Completeness 43
 
2.1 NP 43
 
2.2 Cook's Theorem 47
 
2.3 More NP-Complete Problems 51
 
2.4 Polynomial-Time Turing Reducibility 58
 
2.5 NP-Complete Optimization Problems 64
 
Exercises 71
 
Historical Notes 75
 
3 The Polynomial-Time Hierarchy and Polynomial Space 77
 
3.1 Nondeterministic Oracle Turing Machines 77
 
3.2 Polynomial-Time Hierarchy 79
 
3.3 Complete Problems in PH 84
 
3.4 Alternating Turing Machines 90
 
3.5 PSPACE-Complete Problems 95
 
3.6 EXP-Complete Problems 102
 
Exercises 108
 
Historical Notes 111
 
4 Structure of NP 113
 
4.1 Incomplete Problems in NP 113
 
4.2 One-Way Functions and Cryptography 116
 
4.3 Relativization 122
 
4.4 Unrelativizable Proof Techniques 124
 
4.5 Independence Results 125
 
4.6 Positive Relativization 126
 
4.7 Random Oracles 128
 
4.8 Structure of Relativized NP 132
 
Exercises 137
 
Historical Notes 140
 
Part II Nonuniform Complexity 141
 
5 Decision Trees 143
 
5.1 Graphs and Decision Trees 143
 
5.2 Examples 149
 
5.3 Algebraic Criterion 153
 
5.4 Monotone Graph Properties 157
 
5.5 Topological Criterion 159
 
5.6 Applications of the Fixed Point Theorems 166
 
5.7 Applications of Permutation Groups 169
 
5.8 Randomized Decision Trees 172
 
5.9 Branching Programs 177
 
Exercises 184
 
Historical Notes 188
 
6 Circuit Complexity 191
 
6.1 Boolean Circuits 191
 
6.2 Polynomial-Size Circuits 195
 
6.3 Monotone Circuits 201
 
6.4 Circuits with Modulo Gates 208
 
6.5 NC 212
 
6.6 Parity Function 217
 
6.7 P-Completeness 224
 
6.8 Random Circuits and RNC 230
 
Exercises 234
 
Historical Notes 237
 
7 Polynomial-Time Isomorphism 241
 
7.1 Polynomial-Time Isomorphism 241
 
7.2 Paddability 245
 
7.3 Density of NP-Complete Sets 250
 
7.4 Density of EXP-Complete Sets 258
 
7.5 One-Way Functions and Isomorphism in EXP 262
 
7.6 Density of P-Complete Sets 272
 
Exercises 275
 
Historical Notes 278
 
Part III Probabilistic Complexity 281
 
8 Probabilistic Machines and Complexity Classes 283
 
8.1 Randomized Algorithms 283
 
8.2 Probabilistic Turing Machines 288
 
8.3 Time Complexity of Probabilistic Turing Machines 291
 
8.4 Probabilistic Machines with Bounded Errors 294
 
8.5 BPP and P 297
 
8.6 BPP and NP 300
 
8.7 BPP and the Polynomial-Time Hierarchy 302
 
8.8 Relativized Probabilistic Complexity Classes 306
 
Exercises 311
 
Historical Notes 314
 
9 Complexity of Counting 317
 
9.1 Counting Class #P 318
 
9.2 #P-Complete Problems 321
 
9.3 oplus P and the Polynomial-Time Hierarchy 330
 
9.4 #P and the Polynomial-Time Hierarchy 336
 
9.5 Circuit Complexity and Relativized oplus P and #P 338
 
9

About the author

DING-ZHU DU, PhD, is Professor in the Department of Computer Science at the University of Texas at Dallas. He has published over 180 journal articles in his areas of research interest, which include design and analysis of approximation algorithms for combinatorial optimization problems and communication networks. Dr. Du is also the coauthor of Problem Solving in Automata, Languages, and Complexity, also published by Wiley.
KER-I KO, PhD, is Professor in the Department of Computer Science at National Chiao Tung University, Taiwan. He has published extensively in his areas of research interest, which include computational complexity theory and its applications to numerical computation. Dr. Ko is also the coauthor of Problem Solving in Automata, Languages, and Complexity, also published by Wiley.

Summary

Providing the basic theory and methods that are essential for understanding complexity theory, this second edition of Theory of Computational Complexity emphasizes advances in the field of computational complexity, including newly developed algorithms and novel applications to quantum computing.

Product details

• Authors: Dingzhu Du, Ding-Zhu Du, Ding-Zhu (University of Minnesota) Ko Du, Ker-I Ko
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 11.07.2014
• EAN: 9781118306086
• ISBN: 978-1-118-30608-6
• No. of pages: 512
• Series: Wiley-Interscience Series in Discrete Mathematics and Optimization
• Subject: Natural sciences, medicine, IT, technology > Mathematics > Miscellaneous