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♯P-complete
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 Turing reduction or polynomial-time counting reduction to it. A counting reduction 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. A Turing reduction is an algorithm for the other problem that makes a polynomial number of calls to a subroutine for the given problem and, outside of those calls, uses polynomial t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Polynomial-time Counting Reduction
In the computational complexity theory of counting problems, a polynomial-time counting reduction is a type of reduction (a transformation from one problem to another) used to define the notion of completeness for the complexity class ♯P. These reductions may also be called polynomial many-one counting reductions or weakly parsimonious reductions; they are analogous to many-one reductions for decision problems and they generalize the parsimonious reductions. Definition A polynomial-time counting reduction is usually used to transform instances of a known-hard problem X into instances of another problem Y that is to be proven hard. It consists of two functions f and g, both of which must be computable in polynomial time. The function f transforms inputs for X into inputs for Y, and the function g transforms outputs for Y into outputs for X. These two functions must preserve the correctness of the output. That is, suppose that one transforms an input x for problem X to an input ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
♯P
In computational complexity theory, the complexity class #P (pronounced "sharp P" or, sometimes "number P" or "hash 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 ''f''(''x'')", where ''f'' 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. The most difficult, representative problems of this class are #P-complete. Relation to decision problems An NP decision 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 g ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Parsimonious Reduction
In computational complexity theory and game complexity, a parsimonious reduction is a transformation from one problem to another (a reduction) that preserves the number of solutions. Informally, it is a bijection between the respective sets of solutions of two problems. A general reduction from problem A to problem B is a transformation that guarantees that whenever A has a solution B also has ''at least one'' solution and vice versa. A parsimonious reduction guarantees that for every solution of A, there exists ''a unique solution'' of B and vice versa. Parsimonious reductions are commonly used in computational complexity for proving the hardness of counting problems, for counting complexity classes such as #P. Additionally, they are used in game complexity, as a way to design hard puzzles that have a unique solution, as many types of puzzles require. Formal definition Let x be an instance of problem X. A ''Parsimonious reduction'' R from problem X to problem Y is a reductio ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sharp-SAT
In computer science, the Sharp Satisfiability Problem (sometimes called Sharp-SAT or #SAT) is the problem of counting the number of interpretations that satisfies a given Boolean formula, introduced by Valiant in 1979. In other words, it asks in how many ways the variables of a given Boolean formula can be consistently replaced by the values TRUE or FALSE in such a way that the formula evaluates to TRUE. For example, the formula a\lor \neg b is satisfiable by three distinct boolean value assignments of the variables, namely, for any of the assignments (a = TRUE, b = FALSE), (a = FALSE, b = FALSE), (a = TRUE, b = TRUE), we have a\lor \neg b = TRUE. #SAT is different from Boolean satisfiability problem (SAT), which asks if there exists ''a solution'' of Boolean formula. Instead, #SAT asks to enumerate ''all'' ''the solutions'' to a Boolean Formula. #SAT is harder than SAT in the sense that, once the total number of solutions to a Boolean formula is known, SAT can be decided in ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |