NP-Completeness

本文探讨了NP-完全性的核心概念,详细介绍了CIRCUIT-SAT问题作为首个被证明为NP-完全的问题,以及它如何通过多项式大小的电路来表达任何多项式时间内可验证的算法。文章深入分析了从算法到电路转换的过程,强调了输入位数固定的重要性,以及这种转换对于理解NP-完全性问题的关键作用。

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NP-Completeness

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The “First” NP-Complete problem

Theorem. CIRCUIT-SAT is NP-complete. [Cook 1971, Levin 1973]

Pf.(sketch)

  • Any algorithm that takes a fixed number of bits n as input and produces a yes/no answer can be represented by such a circuit. Moreover, if algorithm takes poly-time, then circuit if of poly-size.
    sketchy part of proof; fixing the number of bits is important, and reflects basic distinction between algorithm and circuits.
  • Consider some problem X in NP. It has a poly-time certifier C(s,t). To determine whether s is in X, need to know if there exists a certificate t of length p(|s|) such that C(s,t) = yes.
  • View C(s,t) as an algorithm on |s| + p(|s|) bits(input s, certificate t) and convert it into a poly-size circuit.
    • first |s| bits are hard-coded with s
    • remaining p(|s|) bits are represent bits of t.
  • Circuit K is satisfiable iff C(s,t) = yes

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(为.djvu文件,可用WinDjView 打开) COMPUTERS AND INTRACTABILITY: A Guide to the Theory of NP-Completeness by Michael R. Garey & David S. Johnson Content 1 Computers, Complexity, and Intractability 1 1.1 Introduction 1 1.2 Problems, Algorithms, and Complexity 4 1.3 Polynomial Time Algorithms and Intractable Problems 6 1.4 Provably Intractable Problems 11 1.5 NP-Complete Problems 13 1.6 An Outline of the Book 14 2 The Theory of NP-Completeness 17 2.1 Decision Problems, Languages, and Encoding Schemes 18 2.2 Deterministic Turing Machines and the Class P 23 2.3 Nondeterministic Computation and the Class NP 27 2.4 The Relationship Between P and NP 32 2.5 Polynomial Transformations and NP-Completeness 34 2.6 Cook's Theorem 38 3 Proving NP-Completeness Results 45 3.1 Six Basic NP-Complete Problems 46 3.1.1 3-SATISF1ABIL1TY 48 3.1.2 3-DIMENS10NAL MATCHING 50 3.1.3 VERTEX COVER and CLIQUE 53 3.1.4 HAMILTONIAN CIRCUIT 56 3.1.5 PARTITION 60 3.2 Some Techniques for Proving NP-Completeness 63 3.2.1 Restriction 63 3.2.2 Local Replacement 66 3.2.3 Component Design 72 3.3 Some Suggested Exercises 74 4 Using NP-Completeness to Analyze Problems 77 4.1 Analyzing Subproblems 80 4.2 Number Problems and Strong NP-Completeness 90 4.2.1 Some Additional Definitions 92 4.2.2 Proving Strong NP-Completeness Results 95 4.3 Time Complexity as a Function of Natural Parameters .... 106 5 NP-Hardness 109 5.1 Turing Reducibility and NP-Hard Problems 109 5.2 A Terminological History 118 6 Coping with NP-Complete Problems 121 6.1 Performance Guarantees for Approximation Algorithms ...123 6.2 Applying NP-Completeness to Approximation Problems ...137 6.3 Performance Guarantees and Behavior "In Practice" 148 7 Beyond NP-Completeness 153 7.1 The Structure of NP 154 7.2 The Polynomial Hierarchy 161 7.3 The Complexity of Enumeration Problems 167 7.4 Polynomial Space Completeness 170 7.5 Logarithmic Space 177 7.6 Proofs of Intractability and P vs. NP 181
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