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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 ''&fnof;''(''x'')", where ''&fnof;'' 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|&oplus;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

Template:Correct title

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:

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 (1ϵ)P(a)x(1+ϵ)P(a). 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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External links

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