34.NP Completeness Hsu, Lih-Hsing

Computer Theory Lab. Chapter 34P.2 為何學 NP-Complete 50 年前的電腦 ( 一間房間大 ) 可解的問題的極限 與現在電腦 ( 個人電腦 ) 可解的極限理論上是相 同的, 除非電腦的製程有重大突破 ( 即不是用目 前半導體技術 ) 。 請問 C ， C++ ， java, Which one is best? 學習的態度是如果你發現你的問題是屬於 NP- C 則大部份的努力的方向是要放在找 Approximate algorithm 而不是 fast algorithm.

Computer Theory Lab. Chapter 34P.3 P and NP Non-formal description P: solvable polynomial time NP: nondeterministic polynomial time Verifiable in polynomial time by deterministic Turing machine.

Computer Theory Lab. Chapter 34P.4 NP-complete NP-Complete: No polynomial-time algorithm has yet been discovered for an NP-computer problem, nor has anyone yet been able to prove that no polynomial-time algorithm can exist for any one of them.

Computer Theory Lab. Chapter 34P.5 千禧年大獎問題 2000 年 5 月 7 大問題懸賞求解 100 萬美元。由美國克 萊數學院提供 P 與 NP

Computer Theory Lab. Chapter 34P.6 Given a description of a program and a finite input, decide whether the program Finishes running or will run forever.

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Computer Theory Lab. Chapter 34P.8 The subject of this chapter, however, is an interesting class of problems, called the “ NP-complete” problems, whose status is unknown. No polynomial-time algorithm has yet been discovered for an NP-computer problem, nor has anyone yet been able to prove that no polynomial-time algorithm can exist for any one of them. This so-called P ≠ NP question has been one of the deepest, most perplexing open research problems in theoretical computer science since it was first posed in 1971.

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Computer Theory Lab. Chapter 34P.10 The difference between these problems Shortest vs. longest simple paths: Euler tour vs. hamiltonian cycle: 2-CNF satisfiability vs. 3 CNF satisfiablility NP-completeness and the classes P and NP Overview of showing problems to be NP- complete Decision problems vs. optimization problems

Computer Theory Lab. Chapter 34P.11 Reductions Suppose that there is a different decision problem, say B, that we already know how to solve in polynomial time. Finally, suppose that we have a procedure that transforms any instance  of A into some instance  of B with the following characteristics: 1.The transformation takes polynomial time. 2.The answer are the same. That is, the answer for  is “yes” if and only if the answer for  is also “yes.”

Computer Theory Lab. Chapter 34P.12 We can call such a procedure a polynomial-time reduction algorithm and, it provides us a way to solve problem A in polynomial time: 1.Given an instance  of problem A, use a polynomial- time reduction algorithm to transform it to an instance  of problem B. 2.Run the polynomial-time decision algorithm for B on the instance . 3.Use the answer for  as the answer for . Polynomial-time reduction algorithm Polynomial-time algorithm to decide A Polynomial-time Algorithm to decide B   yes no

Computer Theory Lab. Chapter 34P.13 A First NP-complete problem Because the technique of reduction relies on having a problem already known to be NP-complete in order to prove a different problem NP-complete, we need a “ first ” NPC problem. Circuit-satisfiability problem

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Computer Theory Lab. Chapter 34P NP-Complete Problems

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Computer Theory Lab. Chapter 34P.77 Theorem The hamiltonian cycle problem is NP- complete.

Computer Theory Lab. Chapter 34P.78 Proof. First, show that HAM-CYCLE belongs to NP. We now prove that VERTEX-COVER  p HAM- CYCLE, which shows that HAM-CYCLE is NP- complete. Given an undirected graph G=(V,E) and an integer k, we construct an undirected graph G ’ =(V ’,E ’ ) that has a hamiltonian cycle iff G has a vertex cover of size k.

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Computer Theory Lab. Chapter 34P.80 The reduction of an instance of the vertex-cover problem to an instance of the hamiltonian-cycle problem. (a) An undirected graph G with a vertex of size 2, consisting if the lightly shaded vertices w and y. (b) the undirected graph G ’ produced by the reduction, with the hamiltonian path corresponding to the vertex cover shaded. The vertex cover {w,y} corresponds to edges (s 1,[w,x,1]) and (s 2,[y,x,1]) appearing in the hamiltonian cycle.

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Computer Theory Lab. Chapter 34P.82 Three types of edges in E ’ 1. Edges in widget. 2. {([u,u (i),6],[u,u (i+1),1]) : 1  i  degree(u)-1} 3. {(s j,[u,u (1),1]) : u  V and 1  j  k}  {(s j,[u,u (degree(u)),6]) : u  V and 1  j  k}

Computer Theory Lab. Chapter 34P.83 The reduction performed in polynomial time |V ’ | = 12|E| + k  12|E| + |V| |E ’ | = (14|E|) + (2|E| -|V|) + (2k|V|) = 16|E| + (2k-1)|V|  16|E| + (2|V|-1)|V|

Computer Theory Lab. Chapter 34P.84 The transformation from graph G to G ’ is a reduction. That is, G has a vertex cover of size k iff G ’ has a hamiltonian cycle.

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Computer Theory Lab. Chapter 34P.87 Theorem The subset-sum problem is NP- complete.

Computer Theory Lab. Chapter 34P.88 Proof. First, show that SUBSET-SUM is in NP. We now show that 3-CNF-SAT  p SUBSET- SUM. Given a 3-CNF formula  over variables x 1, x 2, …, x n with clauses C 1, C 2, …, C k, each containing exactly three distinct literals. The reduction algorithm constructs an instance of the subset-sum problem such that  is satisfiable iff there is a subset of S whose sum is exactly t.

Computer Theory Lab. Chapter 34P.89 Example The formula in 3-CNF is  = C 1  C 2  C 3  C 4, where C 1 = (x 1  ¬ x 2  ¬ x 3 ), C 2 = ( ¬ x 1  ¬ x 2  ¬ x 3 ), C 3 = ( ¬ x 1  ¬ x 2  x 3 ), and C 4 = (x 1  x 2  x 3 ). A satisfying assignment of  is.

Computer Theory Lab. Chapter 34P.90 The reduction of 3-CNF-SAT to SUBSET-SUM C 4 has no ¬x 1 C 4 has x 2

Computer Theory Lab. Chapter 34P.91 The reduction performed in polynomial time The set S contains 2n+2k values, each of which has n+k digits, and the time to produce each digit is polynomial in n+k. The target t has n+k digits, and the reduction produces each in constant time.

Computer Theory Lab. Chapter 34P.92 3-CNF formula  is satisfiable iff there is a subset S ’  S whose sum is t.