In complexity theory, the NP-complete problems are the hardest problems in NP, in the sense that they are the ones most likely not to be in P. The reason is that if you could find a way to solve an NP-complete problem quickly, then you could use that algorithm to solve all NP problems quickly. (A more formal definition is given below. See also Complexity classes P and NP).
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 adds up to zero. A supposed answer is very easy to verify for correctness, but no-one knows a faster way to solve the problem than to try every single possible subset, which is very slow.
At present, all known algorithms for NP-complete problems require time which is exponential in the problem size. It is unknown whether there are any faster algorithms. Therefore, in order to solve an NP-complete problem for any non-trivial problem size, one of the following approaches is used:
- Approximation: An algorithm which quickly finds a suboptimal solution which is within a certain (known) range of the optimal one. Not all NP-complete problems have good approximation algorithms, and for some problems finding a good approximation algorithm is enough to solve the problem itself.
- Probabilistic: An algorithm which provably yields good average runtime behavior for a given distribution of the problem instances—ideally, one that assigns low probability to "hard" inputs.
- Special cases: An algorithm which is provably fast if the problem instances belong to a certain special case
- Heuristic: An algorithm which works "reasonably well" on many cases, but for which there is no proof that it is always fast.
Table of contents |
2 Example problems 3 Alternative approaches 4 References |
Formal definition of NP-completeness
A decision problem C is NP-complete if it is in NP and if every other problem in NP is reducible to it. "Reducible" here means that for every NP problem L, there is a polynomial-time algorithm which transforms instances of L into instances of C, such that the two instances have the same truth values. As a consequence, if we had a polynomial time algorithm for C, we could solve all NP problems in polynomial time.
In mathematical terms,
- L in NP
- L' ≤ L for every L' in NP
It isn't really correct to say that NP-complete problems are the hardest problems in NP. Assuming that P and NP are not equal, there are guaranteed to be an infinite number of problems that are in NP, but are neither NP-complete nor in P. Some of these problems may actually have higher complexity than some of the NP-complete problems.
Example problems
An interesting example is the problem, in graph theory, of graph isomorphism. Two graphs are isomorphic if one can be transformed into the other simply by renaming vertices. Consider these two problems:
Graph Isomorphism: Is graph G_{1} isomorphic to graph G_{2}? Subgraph Isomorphism: Is graph G_{1} isomorphic to a subgraph of graph G_{2}?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 to reduce some known NP-complete problem to it. Therefore, it is useful to know a variety of NP-complete problems. Here are a few:
- Boolean satisfiability problem (SAT)
- Fifteen puzzle
- Knapsack problem
- Minesweeper
- Tetris
- Hamiltonian cycle problem
- Traveling salesman problem
- Subgraph isomorphism problem
- Subset sum problem
- Clique problem
- Vertex cover problem
- Independent Set problem
Alternative approaches
In the definition of NP-complete given above, the term "reduction" was used in the technical meaning of 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, you 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 article on co-NP. Although the question of 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-completness 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.
References
- Garey, M. and D. Johnson, Computers and Intractability; A Guide to the Theory of NP-Completeness, 1979. ISBN 0716710455 (This book is a classic, developing the theory, then cataloging many NP-Complete problems)
- S. A. Cook, The complexity of theorem proving procedures, Proceedings, Third Annual ACM Symposium on the Theory of Computing, ACM, New York, 1971, 151-158
- Computational Complexity of Games and Puzzles
- Tetris is Hard, Even to Approximate
- Minesweeper is NP-complete!\n