作者简介 Michael Sipser 美国麻省理工学院应用数学系教授,计算机科学和人工智能实验室(CSAIL)成员。他从事理论计算机科学与其他数学课程的教学工作三十多年,目前为数学系主任。他痴迷于复杂性理论,喜欢复杂性理论的教学工作。
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目录 CONTENTS PrefacetotheFirstEdition.iv To the student.iv To the educatorv The frst editionvi Feedback to the authorvi Acknowledgments.vii Preface to the Second Edition.ix Preface to the Third Edition.xi 0 Introduction.1 0.1 Automata, Computability, and Complexity.1 Complexity theory.2 Computability theory.3 Automata theory3 0.2 Mathematical Notions and Terminology3 Sets.3 Sequences and tuples.6 Functions and relations7 Graphs.10 Strings and languages.13 Boolean logic14 Summary of mathematical terms.16 0.3 Defnitions, Theorems, and Proofs.17 Finding proofs.17 0.4 Typesof Proof21 Proof by construction.21 Proof by contradiction.21 Proof by induction.22 Exercises, Problems, and Solutions.25 PartOne: AutomataandLanguages.29 1 RegularLanguages.31 1.1 Finite Automata.31 Formal defnition of afnite automaton.35 Examples of fnite automata37 Formal defnition of computation40 Designing fnite automata.41 The regular operations44 1.2 Nondeterminism.47 Formal defnition of a nondeterministic fnite automaton53 Equivalence of NFAs and DFAs.54 Closure under the regular operations.58 1.3 Regular Expressions.63 Formal defnition of a regular expression64 Equivalence with fnite automata.66 1.4 Nonregular Languages77 The pumping lemma for regular languages.77 Exercises, Problems, and Solutions.83 2 Context-Free Languages.101 2.1 Context-Free Grammars.102 Formal defnition of acontext-free grammar104 Examples of context-free grammars.105 Designing context-free grammars106 Ambiguity.107 Chomsky normal form108 2.2 Pushdown Automata.111 Formal defnition of a pushdown automaton.113 Example of pushdow automata.114 Equivalence with context-free grammars.117 2.3Non-Context-Free Languages125 The pumping lemma for context-free languages.125 2.4 Deterministic Context-Free Languages.130 Properties of DCFLs.133 Deterministic context-free grammars135 Relationship of DPDAs and DCFGs.146 Parsing and LR(k) grammars.151 Exercises, Problems, and Solutions.154 PartTwo: Computability Theory.163 3 The Church–Turing Thesis.165 3.1 Turing Machines.165 Formal defnition of a Turing machine167 Examples of Turing machines.170 3.2 Variants of Turing Machines.176 Multitape Turing machines176 Nondeterministic Turing machines178 Enumerators180 Equivalence with other models181 3.3 The Defnition of Algorithm182 Hilbert’s problems.182 Terminology for describing Turing machines184 Exercises, Problems, and Solutions.187 4 Decidability.193 4.1 Decidable Languages.194 Decidable problems concerning regular languages.194 Decidable problems concerning context-free languages.198 4.2 Undecidability201 The diagonalization method.202 An undecidable language.207 A Turing-unrecognizable language209 Exercises, Problems, and Solutions.210 5 Reducibility.215 5.1 Undecidable Problems from Language Theory216 Reductions via computation histories.220 5.2 A Simple Undecidable Problem.227 5.3 Mapping Reducibility234 Computable functions.234 Formal defnition of mapping reducibility235 Exercises, Problems, and Solutions.239 6 Advanced Topicsin Computability Theory.245 6.1 The Recursion Theorem.245 Self-reference.246 Terminology for the recursion theorem.249 Applications250 6.2 Decidability of logical theories.252 A decidable theory.255 An undecidable theory.257 6.3 Turing Reducibility260 6.4 A Defnition of Information.261 Minimal length descriptions.262 Optimality of the defnition266 Incompressible strings and randomness.267 Exercises, Problems, and Solutions.270 Part Three: Complexity Theory.273 7 Time Complexity.275 7.1 Measuring Complexity275 Big-O and small-o notation276 Analyzing algorithms.279 Complexity relationships among models.282 7.2 The Class P284 Polynomial time284 Examples of problems in P286 7.3 The Class NP.292 Examples of problemsin NP.295 The Pversus NP question297 7.4 NP-completeness.299 Polynomial time reducibility.300 Defnition of NP-completeness304 The Cook–Levin Theorem304 7.5 Additional NP-complete Problems.311 The vertex cover problem.312 The Hamilto
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