In computational complexity theory, the complexity class # P ( pronounced " number P " or, sometimes " sharp P " or " hash P ") is the set of the counting problems associated with the decision problems in the set NP.

In computational complexity theory, the complexity class NP-equivalent is the set of function problems that are both NP-easy and NP-hard.

In computational complexity theory, the complexity class EXPTIME ( sometimes called EXP ) is the set of all decision problems solvable by a deterministic Turing machine in O ( 2 < sup > p ( n )</ sup >) time, where p ( n ) is a polynomial function of n.

In computational complexity theory, the complexity class containing all recursively enumerable sets is RE.

In computational complexity theory, the complexity class NSPACE ( f ( n )) is the set of decision problems that can be solved by a non-deterministic Turing machine, M, using space O ( f ( n )), where f ( n ) is the maximum number of tape cells that M scans on any input of length n. It is the non-deterministic counterpart of DSPACE.

In computational complexity theory, the complexity class NTIME ( f ( n )) is the set of decision problems that can be solved by a non-deterministic Turing machine which runs in time O ( f ( n )).

In computational complexity theory, the complexity class PH is the union of all complexity classes in the polynomial hierarchy:

In computational complexity theory, the complexity class FP is the set of function problems which can be solved by a deterministic Turing machine in polynomial time ; it is the function problem version of the decision problem class P. Roughly speaking, it is the class of functions that can be efficiently computed on classical computers without randomization.

In computational complexity theory, the complexity class FNP is the function problem extension of the decision problem class NP.

In computational complexity theory, the complexity class ESPACE is the set of decision problems that can be solved by a deterministic Turing machine in space 2 < sup > O ( n )</ sup >.

In computational complexity theory, the complexity class NEXPTIME ( sometimes called NEXP ) is the set of decision problems that can be solved by a non-deterministic Turing machine using time O ( 2 < sup > p ( n )</ sup >) for some polynomial p ( n ), and unlimited space.

In computational complexity theory, the complexity class NE is the set of decision problems that can be solved by a non-deterministic Turing machine in time O ( k < sup > n </ sup >) for some k.

In computational complexity theory, the complexity class E is the set of decision problems that can be solved by a deterministic Turing machine in time 2 < sup > O ( n )</ sup > and is therefore equal to the complexity class DTIME ( 2 < sup > O ( n )</ sup >).

In computational complexity theory, the complexity class ELEMENTARY of elementary recursive functions is the union of the classes in the exponential hierarchy.

In computational complexity theory, the complexity class FL is the set of function problems which can be solved by a deterministic Turing machine in a logarithmic amount of memory space.

In computational complexity theory, the complexity class ( pronounced " parity P ") is the class of decision problems solvable by a nondeterministic Turing machine in polynomial time, where the acceptance condition is that the number of accepting computation paths is odd.

In computational complexity theory, the complexity class NONELEMENTARY is the complement of the class ELEMENTARY.

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