♯P-complete

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The ♯P-complete problems (pronounced "sharp P complete" or "number P complete") form a complexity class in computational complexity theory. The problems in this complexity class are defined by having the following two properties:

  • The problem is in ♯P, the class of problems that can be defined as counting the number of accepting paths of a polynomial-time non-deterministic Turing machine.
  • The problem is ♯P-hard, meaning that every other problem in ♯P has a polynomial-time counting reduction to it. This is a pair of polynomial-time transformations from inputs of the other problem to inputs of the given problem and from outputs of the given problem to outputs of the other problem, allowing the other problem to be solved using any subroutine for the given problem. In some cases parsimonious reductions, a more specific type of reduction that preserves the exact number of solutions, are used.

A polynomial-time algorithm for solving a ♯P-complete problem, if it existed, would solve the P versus NP problem by implying that P and NP are equal. No such algorithm is known, nor is a proof known that such an algorithm does not exist.

Examples[edit]

Examples of ♯P-complete problems include:

Easy problems with hard counting versions[edit]

Some ♯P-complete problems correspond to easy (polynomial time) problems. It is very easy to determine the satisfiability of a boolean formula in DNF: such a formula is satisfiable if and only if it contains a satisfiable conjunction (one that does not contain a variable and its negation), whereas counting the number of satisfying assignments is ♯P-complete. Also deciding 2-satisfiability is easy in contrast to counting the number of satisfying assignments. Topologically sorting is easy in contrast to counting the number of topological sortings. A single perfect matching can be found in polynomial time, but counting all perfect matchings is ♯P-complete. The perfect matching counting problem was the first counting problem corresponding to an easy P problem shown to be ♯P-complete, in a 1979 paper by Leslie Valiant which also defined the class ♯P and the ♯P-complete problems for the first time.[2]

Approximation[edit]

There are probabilistic algorithms that return good approximations to some ♯P-complete problems with high probability. This is one of the demonstrations of the power of probabilistic algorithms.

Many ♯P-complete problems have a fully polynomial-time randomized approximation scheme, or "FPRAS," which, informally, will produce with high probability an approximation to an arbitrary degree of accuracy, in time that is polynomial with respect to both the size of the problem and the degree of accuracy required. Jerrum, Valiant, and Vazirani showed that every ♯P-complete problem either has an FPRAS, or is essentially impossible to approximate; if there is any polynomial-time algorithm which consistently produces an approximation of a ♯P-complete problem which is within a polynomial ratio in the size of the input of the exact answer, then that algorithm can be used to construct an FPRAS.[3]

References[edit]

  1. ^ Brightwell, Graham R.; Winkler, Peter (1991). "Counting linear extensions". Order. 8 (3): 225–242. doi:10.1007/BF00383444..
  2. ^ Leslie G. Valiant (1979). "The Complexity of Computing the Permanent". Theoretical Computer Science. Elsevier. 8 (2): 189–201. doi:10.1016/0304-3975(79)90044-6.
  3. ^ Mark R. Jerrum; Leslie G. Valiant; Vijay V. Vazirani (1986). "Random Generation of Combinatorial Structures from a Uniform Distribution". Theoretical Computer Science. Elsevier. 43: 169–188. doi:10.1016/0304-3975(86)90174-x.

Further reading[edit]

  • Vazirani, Vijay V. (2003). Approximation Algorithms. Berlin: Springer. ISBN 3-540-65367-8.
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