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  2. Polynomial hierarchy - Wikipedia

    en.wikipedia.org/wiki/Polynomial_hierarchy

    In computational complexity theory, the polynomial hierarchy (sometimes called the polynomial-time hierarchy) is a hierarchy of complexity classes that generalize the classes NP and co-NP. [1] Each class in the hierarchy is contained within PSPACE. The hierarchy can be defined using oracle machines or alternating Turing machines.

  3. Toda's theorem - Wikipedia

    en.wikipedia.org/wiki/Toda's_theorem

    The class P #P consists of all the problems that can be solved in polynomial time if you have access to instantaneous answers to any counting problem in #P (polynomial time relative to a #P oracle). Thus Toda's theorem implies that for any problem in the polynomial hierarchy there is a deterministic polynomial-time Turing reduction to a ...

  4. List of complexity classes - Wikipedia

    en.wikipedia.org/wiki/List_of_complexity_classes

    The union of the classes in the polynomial hierarchy: P NP: Solvable in polynomial time with an oracle for a problem in NP; also known as Δ 2 P PP: Probabilistically Polynomial (answer is right with probability slightly more than 1/2) PPAD: Polynomial Parity Arguments on Directed graphs PR: Solvable by recursively building up arithmetic ...

  5. PH (complexity) - Wikipedia

    en.wikipedia.org/wiki/PH_(complexity)

    In computational complexity theory, the complexity class PH is the union of all complexity classes in the polynomial hierarchy: = PH was first defined by Larry Stockmeyer. [1] It is a special case of hierarchy of bounded alternating Turing machine.

  6. PSPACE - Wikipedia

    en.wikipedia.org/wiki/PSPACE

    An alternative characterization of PSPACE is the set of problems decidable by an alternating Turing machine in polynomial time, sometimes called APTIME or just AP. [4]A logical characterization of PSPACE from descriptive complexity theory is that it is the set of problems expressible in second-order logic with the addition of a transitive closure operator.

  7. PP (complexity) - Wikipedia

    en.wikipedia.org/wiki/PP_(complexity)

    M runs for polynomial time on all inputs; For all x in L, M outputs 1 with probability no less than 1/2; For all x not in L, M outputs 1 with probability strictly less than 1/2. Alternatively, PP can be defined using only deterministic Turing machines. A language L is in PP if and only if there exists a polynomial p and deterministic Turing ...

  8. P versus NP problem - Wikipedia

    en.wikipedia.org/wiki/P_versus_NP_problem

    It runs in polynomial time on inputs that are in SUBSET-SUM if and only if P = NP: // Algorithm that accepts the NP-complete language SUBSET-SUM. // // this is a polynomial-time algorithm if and only if P = NP. // // "Polynomial-time" means it returns "yes" in polynomial time when // the answer should be "yes", and runs forever when it is "no".

  9. PSPACE-complete - Wikipedia

    en.wikipedia.org/wiki/PSPACE-complete

    It is known that they lie outside of the class NC, a class of problems with highly efficient parallel algorithms, because problems in NC can be solved in an amount of space polynomial in the logarithm of the input size, and the class of problems solvable in such a small amount of space is strictly contained in PSPACE by the space hierarchy theorem.