Given two sets of natural numbers, we say is Turing reducible to and write
if and only if there is an oracle machine that computes the characteristic function of A when run with oracle B. In this case, we also say A is B-recursive and B-computable.
If there is an oracle machine that, when run with oracle B, computes a partial function with domain A, then A is said to be B-recursively enumerable and B-computably enumerable.
We say is Turing equivalent to and write if both and The equivalence classes of Turing equivalent sets are called Turing degrees. The Turing degree of a set is written .
Given a set , a set is called Turing hard for if for all . If additionally then is called Turing complete for .
Relation of Turing completeness to computational universality
Turing completeness, as just defined above, corresponds only partially to Turing completeness in the sense of computational universality. Specifically, a Turing machine is a universal Turing machine if its halting problem (i.e., the set of inputs for which it eventually halts) is many-one complete for the set of recursively enumerable sets. Thus, a necessary but insufficient condition for a machine to be computationally universal, is that the machine's halting problem be Turing-complete for . Insufficient because it may still be the case that, the language accepted by the machine is not itself recursively enumerable.
Let denote the set of input values for which the Turing machine with index e halts. Then the sets and are Turing equivalent (here denotes an effective pairing function). A reduction showing can be constructed using the fact that . Given a pair , a new index can be constructed using the smn theorem such that the program coded by ignores its input and merely simulates the computation of the machine with index e on input n. In particular, the machine with index either halts on every input or halts on no input. Thus holds for all e and n. Because the function i is computable, this shows . The reductions presented here are not only Turing reductions but many-one reductions, discussed below.
- Every set is Turing equivalent to its complement.
- Every computable set is Turing reducible to every other set. Because any computable set can be computed with no oracle, it can be computed by an oracle machine that ignores the given oracle.
- The relation is transitive: if and then . Moreover, holds for every set A, and thus the relation is a preorder (it is not a partial order because and does not necessarily imply ).
- There are pairs of sets such that A is not Turing reducible to B and B is not Turing reducible to A. Thus is not a total order.
- There are infinite decreasing sequences of sets under . Thus this relation is not well-founded.
- Every set is Turing reducible to its own Turing jump, but the Turing jump of a set is never Turing reducible to the original set.
There are two common ways of producing reductions stronger than Turing reducibility. The first way is to limit the number and manner of oracle queries.
- Set is many-one reducible to if there is a total computable function such that an element is in if and only if is in . Such a function can be used to generate a Turing reduction (by computing , querying the oracle, and then interpreting the result).
- A truth table reduction or a weak truth table reduction must present all of its oracle queries at the same time. In a truth table reduction, the reduction also gives a boolean function (a truth table) that, when given the answers to the queries, will produce the final answer of the reduction. In a weak truth table reduction, the reduction uses the oracle answers as a basis for further computation depending on the given answers (but not using the oracle). Equivalently, a weak truth table reduction is one for which the use of the reduction is bounded by a computable function. For this reason, weak truth table reductions are sometimes called "bounded Turing" reductions.
The second way to produce a stronger reducibility notion is to limit the computational resources that the program implementing the Turing reduction may use. These limits on the computational complexity of the reduction are important when studying subrecursive classes such as P. A set A is polynomial-time reducible to a set if there is a Turing reduction of to that runs in polynomial time. The concept of log-space reduction is similar.
These reductions are stronger in the sense that they provide a finer distinction into equivalence classes, and satisfy more restrictive requirements than Turing reductions. Consequently, such reductions are harder to find. There may be no way to build a many-one reduction from one set to another even when a Turing reduction for the same sets exists.
- M. Davis, ed., 1965. The Undecidable—Basic Papers on Undecidable Propositions, Unsolvable Problems and Computable Functions, Raven, New York. Reprint, Dover, 2004. ISBN 0-486-43228-9.
- S. C. Kleene, 1952. Introduction to Metamathematics. Amsterdam: North-Holland.
- S. C. Kleene and E. L. Post, 1954. "The upper semi-lattice of degrees of recursive unsolvability". Annals of Mathematics v. 2 n. 59, 379–407.
- Post, E. L. (1944). "Recursively enumerable sets of positive integers and their decision problems" (PDF). Bulletin of the American Mathematical Society. 50 (5): 284–316. doi:10.1090/s0002-9904-1944-08111-1. Retrieved 2015-12-17.
- A. Turing, 1939. "Systems of logic based on ordinals." Proceedings of the London Mathematical Society, ser. 2 v. 45, pp. 161–228. Reprinted in "The Undecidable", M. Davis ed., 1965.
- H. Rogers, 1967. Theory of recursive functions and effective computability. McGraw-Hill.
- R. Soare, 1987. Recursively enumerable sets and degrees, Springer.
- Davis, Martin (November 2006). "What is...Turing Reducibility?" (PDF). Notices of the American Mathematical Society. 53 (10): 1218–1219. Retrieved 2008-01-16.