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In computational complexity theory, the complexity class NP-complete, also known as NP-C or NPC, is a subset of NP ("non-deterministic polynomial time"); they are the most difficult problems in NP in the sense that a deterministic, polynomial-time solution to any NP-complete problem would provide a solution to every other problem in NP (and conversely, if any one of them provably lacks a deterministic polynomial-time solution, none of them has one). Problems in NP-complete are known as NP-complete problems. A more formal definition is given below.

One example of an NP-complete problem is the subset sum problem which is: given a finite set of integers, determine whether any non-empty subset of them sums to zero. A supposed answer is very easy to verify for correctness, but there is no known efficient algorithm to find an answer; that is, all known algorithms are impractically slow for large sets of integers.

Contents 1 Formal definition of NP-completeness 2 Background 3 NP-complete problems 4 Solving NP-complete problems 5 Completeness under different types of reduction 6 See also 7 References

A decision problem C is NP-complete if:

1. C is in NP, and
2. Every problem in NP is reducible to C.

C can be shown to be in NP by demonstrating that a candidate solution to C can be verified in polynomial time.

A problem L is reducible to C if there is a polynomial-time many-one reduction, a deterministic algorithm which transforms instances l ∈ L into instances c ∈ C, such that the answer to c is YES if and only if the answer to l is YES. To prove that an NP problem A is in fact an NP-complete problem it is sufficient to show that an already known NP-complete problem reduces to A.

A problem satisfying condition 2 but not necessarily condition 1 is said to be NP-hard.

A consequence of this definition is that if we had a polynomial time algorithm (on a UTM, or any other Turing-equivalent abstract machine) for C, we could solve all problems in NP in polynomial time.

The concept of "NP-complete" was introduced by Stephen Cook in a paper entitled 'The complexity of theorem-proving procedures' on pages 151-158 of the Proceedings of the 3rd Annual ACM Symposium on Theory of Computing in 1971, though the term "NP-complete" did not appear anywhere in his paper. At that computer science conference, there was a fierce debate among the computer scientists about whether NP-complete problems could be solved in polynomial time on a deterministic Turing machine. John Hopcroft brought everyone at the conference to a consensus that the question of whether NP-complete problems are solvable in polynomial time should be put off to be solved at some later date, since nobody had any formal proofs for their claims one way or the other. This is known as the question of whether P=NP.

Nobody has yet been able to determine conclusively whether NP-complete problems are in fact solvable in polynomial time, making this one of the great unsolved problems of mathematics. The Clay Mathematics Institute is offering a $1 million reward to anyone who has a formal proof that P=NP or that P≠NP.

In the celebrated Cook-Levin theorem (independently proved by Leonid Levin), Cook proved that the Boolean satisfiability problem is NP-complete (a simpler, but still highly technical proof of this is available). In 1972, Richard Karp proved that several other problems were also NP-complete (see Karp's 21 NP-complete problems); thus there is a class of NP-complete problems (besides the Boolean satisfiability problem). Since Cook's original results, thousands of other problems have been shown to be NP-complete by reductions from other problems previously shown to be NP-complete; many of these problems are collected in Garey and Johnson's 1979 book Computers and Intractability: A Guide to NP-Completeness.

Main article: List of NP-complete problems

An interesting example is the graph isomorphism problem, the graph theory problem of determining whether a graph isomorphism exists between two graphs. Two graphs are isomorphic if one can be transformed into the other simply by renaming vertices. Consider these two problems:

• Graph Isomorphism: Is graph G₁ isomorphic to graph G₂?
• Subgraph Isomorphism: Is graph G₁ isomorphic to a subgraph of graph G₂?

The Subgraph Isomorphism problem is NP-complete. The graph isomorphism problem is suspected to be neither in P nor NP-complete, though it is obviously in NP. This is an example of a problem that is thought to be hard, but isn't thought to be NP-complete.

The easiest way to prove that some new problem is NP-complete is first to prove that it is in NP, and then to reduce some known NP-complete problem to it. Therefore, it is useful to know a variety of NP-complete problems. The list below contains some well-known problems that are NP-complete when expressed as decision problems.

To the right is a diagram of some of the problems and the reductions typically used to prove their NP-completeness. In this diagram, an arrow from one problem to another indicates the direction of the reduction. Note that this diagram is misleading as a description of the mathematical relationship between these problems, as there exists a polynomial-time reduction between any two NP-complete problems; but it indicates where demonstrating this polynomial-time reduction has been easiest.

There is often only a small difference between a problem in P and an NP-complete problem. For example, the 3SAT problem, a restriction of the boolean satisfiability problem, remains NP-complete, whereas the slightly more restricted 2SAT problem is in P (specifically, NL-complete), and the slightly more general MAX 2SAT problem is again NP-complete. Determining whether a graph can be colored with 2 colors is in P, but with 3 colors is NP-complete, even when restricted to planar graphs. Determining if a graph is a cycle or is bipartite is very easy (in L), but finding a maximum bipartite or a maximum cycle subgraph is NP-complete. A solution of the knapsack problem within any fixed percentage of the optimal solution can be computed in polynomial time, but finding the optimal solution is NP-complete.

At present, all known algorithms for NP-complete problems require time that is superpolynomial in the input size, and it is unknown whether there are any faster algorithms.

The following techniques can be applied to solve computational problems in general, and they often give rise to substantially faster algorithms:

• Approximation: Instead of searching for an optimal solution, search for an "almost" optimal one.
• Randomization: Use randomness to get a faster average running time, and allow the algorithm to fail with some small probability.
• Restriction: By restricting the structure of the input (e.g., to planar graphs), faster algorithms are usually possible.
• Parameterization: Often there are fast algorithms if certain parameters of the input are fixed.
• Heuristic: An algorithm that works "reasonably well" on many cases, but for which there is no proof that it is both always fast and always produces a good result. Metaheuristic approaches are often used.

One example of a heuristic algorithm is a suboptimal O(n log n) greedy algorithm used for graph coloring during the register allocation phase of some compilers, a technique called graph-coloring global register allocation. Each vertex is a variable, edges are drawn between variables which are being used at the same time, and colors indicate the register assigned to each variable. Because most RISC machines have a fairly large number of general-purpose registers, even a heuristic approach is effective for this application.

In the definition of NP-complete given above, the term "reduction" was used in the technical meaning of a polynomial-time many-one reduction.

Another type of reduction is polynomial-time Turing reduction. A problem X is polynomial-time Turing-reducible to a problem Y if, given a subroutine that solves Y in polynomial time, one could write a program that calls this subroutine and solves X in polynomial time. This contrasts with many-one reducibility, which has the restriction that the program can only call the subroutine once, and the return value of the subroutine must be the return value of the program.

If one defines the analogue to NP-complete with Turing reductions instead of many-one reductions, the resulting set of problems won't be smaller than NP-complete; it is an open question whether it will be any larger. If the two concepts were the same, then it would follow that NP = co-NP. This holds because by their definition the classes of NP-complete and co-NP-complete problems under Turing reductions are the same and because these classes are both supersets of the same classes defined with many-one reductions. So if both definitions of NP-completeness are equal then there is a co-NP-complete problem (under both definitions) such as for example the complement of the boolean satisfiability problem that is also NP-complete (under both definitions). This implies that NP = co-NP as is shown in the proof in the co-NP article. Although whether NP = co-NP is an open question it is considered unlikely and therefore it is also unlikely that the two definitions of NP-completeness are equivalent.

Another type of reduction that is also often used to define NP-completeness is the logarithmic-space many-one reduction which is a many-one reduction that can be computed with only a logarithmic amount of space. Since every computation that can be done in logarithmic space can also be done in polynomial time it follows that if there is a logarithmic-space many-one reduction then there is also a polynomial-time many-one reduction. This type of reduction is more refined than the more usual polynomial-time many-one reductions and it allows us to distinguish more classes such as P-complete. Whether under these types of reductions the definition of NP-complete changes is still an open problem.

• List of NP-complete problems
• Almost complete
• Ladner's theorem
• Strongly NP-complete
• P = NP problem

• Garey, M. and D. Johnson, Computers and Intractability; A Guide to the Theory of NP-Completeness, 1979. ISBN 0-7167-1045-5 (This book is a classic, developing the theory, then cataloguing many NP-Complete problems)
• S. A. Cook (1971). "The complexity of theorem proving procedures". Proceedings, Third Annual ACM Symposium on the Theory of Computing, ACM, New York: 151-158. 
• Paul E. Dunne. An Annotated List of Selected NP-complete Problems. The University of Liverpool, Dept of Computer Science, COMP202.
• Pierluigi Crescenzi, Viggo Kann, Magnús Halldórsson, Marek Karpinski, and Gerhard Woeginger. A compendium of NP optimization problems. KTH NADA. Stockholm.
• Computational Complexity of Games and Puzzles
• Tetris is Hard, Even to Approximate
• Minesweeper is NP-complete!
• Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms, Second Edition. MIT Press and McGraw-Hill, 2001. ISBN 0-262-03293-7. Chapter 34: NP-Completeness, pp.966–1021.
• Michael Sipser (1997). Introduction to the Theory of Computation. PWS Publishing. ISBN 0-534-94728-X.  Sections 7.4–7.5 (NP-completeness, Additional NP-complete Problems), pp.248–271.
• Christos Papadimitriou (1994). Computational Complexity, 1st edition, Addison Wesley. ISBN 0-201-53082-1.  Chapter 9: NP-complete problems, pp.181–218.

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