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{{Correct title|title=#P|reason=hash}} | |||
In [[computational complexity theory]], the complexity class '''#P''' (pronounced "number P" or, sometimes "sharp P" or "hashtag P") is the set of the counting problems associated with the [[decision problem]]s in the set '''[[NP (complexity)|NP]]'''. More formally, '''#P''' is the class of function problems of the form "compute ''ƒ''(''x'')", where ''ƒ'' is the number of accepting paths of a [[nondeterministic Turing machine]] running in polynomial time. Unlike most well-known complexity classes, it is not a class of [[decision problem]]s but a class of [[function problem]]s. | |||
An '''NP''' problem is often of the form "Are there any solutions that satisfy certain constraints?" For example: | |||
* Are there any subsets of a list of integers that add up to zero? ([[subset sum problem]]) | |||
* Are there any [[Hamiltonian cycle]]s in a given [[graph theory|graph]] with cost less than 100? ([[traveling salesman problem]]) | |||
* Are there any variable assignments that satisfy a given [[conjunctive normal form|CNF]] formula? ([[Boolean satisfiability problem]]) | |||
The corresponding '''#P''' problems ask "how many" rather than "are there any". For example: | |||
* How many subsets of a list of integers add up to zero? | |||
* How many Hamiltonian cycles in a given graph have cost less than 100? | |||
* How many variable assignments satisfy a given CNF formula? | |||
Clearly, a '''#P''' problem must be at least as hard as the corresponding '''NP''' problem. If it's easy to count answers, then it must be easy to tell whether there are any answers – just count them and see whether the count is greater than zero. | |||
One consequence of [[Toda's theorem]] is that a polynomial-time machine with a '''#P''' [[oracle machine|oracle]] ('''P'''<sup>'''#P'''</sup>) can solve all problems in '''[[PH (complexity)|PH]]''', the entire [[polynomial hierarchy]]. In fact, the polynomial-time machine only needs to make one '''#P''' query to solve any problem in '''PH'''. This is an indication of the extreme difficulty of solving '''#P'''-complete problems exactly. | |||
Surprisingly, some '''#P''' problems that are believed to be difficult correspond to easy '''[[P (complexity)|P]]''' problems. For more information on this, see [[sharp-P-complete|#P-complete]]. | |||
The closest decision problem class to '''#P''' is '''[[PP (complexity)|PP]]''', which asks whether a majority (more than half) of the computation paths accept. This finds the most significant bit in the '''#P''' problem answer. The decision problem class '''[[Parity P|⊕P]]''' instead asks for the least significant bit of the '''#P''' answer. | |||
The complexity class '''#P''' was first defined by [[Leslie Valiant]] in a 1979 article on the computation of the [[permanent]], in which he proved that [[permanent is sharp-P-complete|permanent is #P-complete]].<ref>{{cite journal | |||
| author = Leslie G. Valiant | |||
| title = The Complexity of Computing the Permanent | |||
| journal = Theoretical Computer Science | |||
| volume = 8 | |||
| pages = 189–201 | |||
| publisher = [[Elsevier]] | |||
| location = | |||
| date = 1979 | |||
| doi = 10.1016/0304-3975(79)90044-6 | |||
| issue = 2}}</ref> | |||
[[Larry Stockmeyer]] has proved that for every #P problem ''P'' there exists a randomized algorithm using oracle for SAT, which given an instance ''a'' of ''P'' and ''ε'' > 0 returns with high probability a number ''x'' such that <math>(1-\epsilon) P(a) \leq x \leq (1+\epsilon) P(a)</math>. The runtime of the algorithm is polynomial in ''a'' and 1/''ε''. The algorithm is based on [[leftover hash lemma]]. | |||
== References == | |||
<references/> | |||
== External links == | |||
* {{CZoo|Class #P|Symbols#sharpp}} | |||
{{ComplexityClasses}} | |||
[[Category:Complexity classes]] |
Revision as of 22:49, 3 February 2014
In computational complexity theory, the complexity class #P (pronounced "number P" or, sometimes "sharp P" or "hashtag P") is the set of the counting problems associated with the decision problems in the set NP. More formally, #P is the class of function problems of the form "compute ƒ(x)", where ƒ is the number of accepting paths of a nondeterministic Turing machine running in polynomial time. Unlike most well-known complexity classes, it is not a class of decision problems but a class of function problems.
An NP problem is often of the form "Are there any solutions that satisfy certain constraints?" For example:
- Are there any subsets of a list of integers that add up to zero? (subset sum problem)
- Are there any Hamiltonian cycles in a given graph with cost less than 100? (traveling salesman problem)
- Are there any variable assignments that satisfy a given CNF formula? (Boolean satisfiability problem)
The corresponding #P problems ask "how many" rather than "are there any". For example:
- How many subsets of a list of integers add up to zero?
- How many Hamiltonian cycles in a given graph have cost less than 100?
- How many variable assignments satisfy a given CNF formula?
Clearly, a #P problem must be at least as hard as the corresponding NP problem. If it's easy to count answers, then it must be easy to tell whether there are any answers – just count them and see whether the count is greater than zero.
One consequence of Toda's theorem is that a polynomial-time machine with a #P oracle (P#P) can solve all problems in PH, the entire polynomial hierarchy. In fact, the polynomial-time machine only needs to make one #P query to solve any problem in PH. This is an indication of the extreme difficulty of solving #P-complete problems exactly.
Surprisingly, some #P problems that are believed to be difficult correspond to easy P problems. For more information on this, see #P-complete.
The closest decision problem class to #P is PP, which asks whether a majority (more than half) of the computation paths accept. This finds the most significant bit in the #P problem answer. The decision problem class ⊕P instead asks for the least significant bit of the #P answer.
The complexity class #P was first defined by Leslie Valiant in a 1979 article on the computation of the permanent, in which he proved that permanent is #P-complete.[1]
Larry Stockmeyer has proved that for every #P problem P there exists a randomized algorithm using oracle for SAT, which given an instance a of P and ε > 0 returns with high probability a number x such that . The runtime of the algorithm is polynomial in a and 1/ε. The algorithm is based on leftover hash lemma.
References
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