Turing reduction


In computability theory, a Turing reduction from a decision problem to a decision problem is an oracle machine which decides problem given an oracle for (Rogers 1967, Soare 1987). It can be understood as an algorithm that could be used to solve if it had available to it a subroutine for solving B. The concept can be analogously applied to function problems.

If a Turing reduction from to exists, then every algorithm for [a] can be used to produce an algorithm for , by inserting the algorithm for at each place where the oracle machine computing queries the oracle for . However, because the oracle machine may query the oracle a large number of times, the resulting algorithm may require more time asymptotically than either the algorithm for or the oracle machine computing . A Turing reduction in which the oracle machine runs in polynomial time is known as a Cook reduction.

The first formal definition of relative computability, then called relative reducibility, was given by Alan Turing in 1939 in terms of oracle machines. Later in 1943 and 1952 Stephen Kleene defined an equivalent concept in terms of recursive functions. In 1944 Emil Post used the term "Turing reducibility" to refer to the concept.


Given two sets   of natural numbers, we say   is Turing reducible to   and write


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 universalityEdit

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. Thus, a necessary but insufficient condition for a machine to be computationally universal, is that the machine's halting problem be Turing-complete for the set   of recursively enumerable sets. 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.

The use of a reductionEdit

Since every reduction from a set   to a set   has to determine whether a single element is in   in only finitely many steps, it can only make finitely many queries of membership in the set  . When the amount of information about the set   used to compute a single bit of   is discussed, this is made precise by the use function. Formally, the use of a reduction is the function that sends each natural number   to the largest natural number   whose membership in the set B was queried by the reduction while determining the membership of   in  .

Stronger reductionsEdit

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) which, 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.

Weaker reductionsEdit

According to the Church–Turing thesis, a Turing reduction is the most general form of an effectively calculable reduction. Nevertheless, weaker reductions are also considered. Set   is said to be arithmetical in   if   is definable by a formula of Peano arithmetic with   as a parameter. The set   is hyperarithmetical in   if there is a recursive ordinal   such that   is computable from  , the α-iterated Turing jump of  . The notion of relative constructibility is an important reducibility notion in set theory.

See alsoEdit


  1. ^ It is possible that B is an undecidable problem for which no algorithm 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: 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 Mathematics 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.

External linksEdit

  • NIST Dictionary of Algorithms and Data Structures: Turing reduction
  • University of Cambridge, Andrew Pitts, Tobias Kohn: Computational Theory
  • Prof. Jean Gallier’s Homepage