Turing completeness

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For the usage of this term in Turing reductions, see Turing complete set.

In computability theory, an abstract machine or programming language is called Turing complete, Turing equivalent, or (computationally) universal if it has a computational power equivalent to (i.e., capable of emulating) a simplified model of a programmable computer known as the universal Turing machine. Being equivalent to the universal Turing machine essentially means being able to perform any computational task – though it does not mean being able to perform such tasks efficiently, quickly, or easily.

The term derives from the name of mathematician Alan Turing who introduced the model of the universal Turing machine.

1 Overview
2 Related work
3 Examples
4 See also
5 References
6 External links

[edit] Overview

Turing completeness is significant in that every plausible design for a computing device so far advanced can be emulated by a universal Turing machine - an observation (it is not and cannot be mathematically proven) that has become known as the Church-Turing thesis. Thus, a machine that can act as a universal Turing machine can, in principle, perform any calculation that any other computer is capable of (that is, if it is programmable). Note, however, that this says nothing about the effort required to write a program for the machine, the time it may take for the machine to perform the calculation, or any abilities unrelated to computation (such as communication or randomness) which the machine may possess.

While truly Turing-complete machines are very likely physically impossible as they require unlimited storage, Turing completeness is often loosely attributed to physical machines or programming languages that would be universal if they had indefinitely enlargeable storage. All modern computers are Turing-complete in this sense.

Charles Babbage's analytical engine (1830s) would have been Turing-complete if it had ever been built, but the first actual implementation appeared in 1941: the program-controlled Z3 of Konrad Zuse. The universality of the Z3 was shown by Raúl Rojas in 1998. Prior to Rojas' 1998 paper, the first machine known to be Turing-complete was ENIAC (1946).

The gap from the 1830s to the 1940s was not a continuous "mechanical computer" development. A mathematical demonstration of the computational resolution of problems remains with the first formal programming languages (1930s), and a wide range of solutions was demonstrated in the 1930s and 1940s, justifying the "investment" on the modern Turing complete machines in the 1940s.

The hypothesis exists that the universe is computable on a universal Turing machine, which would imply that no computer more powerful than a universal Turing machine can be physically built (see philosophical implications in the Church-Turing thesis and digital physics).

See the article on computability theory for a long list of systems that are Turing-complete, as well as several systems that are less powerful, and several theoretical systems that are even more powerful than a universal Turing machine.

[edit] Related work

One important result from computability theory is that it is impossible in general to determine whether a program written in a Turing-complete language will continue executing forever or will stop within a finite period of time (see halting problem). One method of commonly getting around this is to cause programs to stop executing after a fixed period of time, or to limit the power of flow control instructions. Such systems are strictly not Turing-complete by design.

Another curious theorem from computability theory is that there are problems solvable by Turing-complete languages that cannot be solved by languages with finite looping capabilities (i.e. languages that guarantee any program will halt). This result is derived by, for example, Brainerd and Landweber using the PL and PL-{GOTO} languages.

[edit] Examples

The computational systems (algebras, calculi) that are discussed as Turing complete systems are those intended for studying theoretical computer science. They are intended to be as simple as possible, so that it would be easier to understand the limits of computation. Here are a few:

Automata theory the standard for teaching
Universal Turing machine the classic
Lambda calculus the original (Alonzo Church's paper predated Turing's, but Turing is credited for fuller explanation of the implications)
Formal grammar (language generators)
Formal language (language recognizers)
Rewrite system
Post-Turing machines

Most programming languages, conventional and unconventional, are Turing-complete. This includes:

All general-purpose languages in wide use.
- Procedural programming languages such as C.
- Object-oriented languages such as Java.
- Multi-paradigm languages such as Ada, C++ and Common Lisp.
Most languages using less common paradigms
- Functional languages such as LISP and Haskell.
- Logic programming languages such as Prolog.
- Declarative languages such as XSLT.

The specific language features used to achieve Turing-completeness can be quite different; FORTRAN systems would use loop constructs or possibly even GOTO statements to achieve repetition; Haskell and Prolog, lacking looping almost entirely, would use recursion. Turing-completeness is an abstract statement of capability, rather than a prescription of specific language features used to implement that capability.

It is difficult to find examples of non-Turing complete languages, as these languages are very limited (see, however, machines that always halt). Examples include some of the early versions of the pixel shader languages embedded in Direct3D and OpenGL extensions. Another example is a series of mathematical formulae in a spreadsheet with no cycles. While it is possible to perform many interesting operations in such a system, this fails to be Turing-complete as it is impossible to form loops; BASIC languages associated with common spreadsheet programs such as Excel and OpenOffice Calc are however Turing-complete. Another famous example is the category of regular expressions, which are generated by finite automata. A more powerful but still not Turing-complete extension of finite automata is the category of pushdown automata.

The untyped lambda calculus is Turing-complete, but many typed lambda calculi, including System F, are not. The value of typed systems is based in their ability to represent most "typical" computer programs while detecting more errors.