In theoretical computer science and mathematics, the **theory of computation** is the branch that deals with what problems can be solved on a model of computation, using an algorithm, how efficiently they can be solved or to what degree (e.g., approximate solutions versus precise ones). The field is divided into three major branches: automata theory and formal languages, computability theory, and computational complexity theory, which are linked by the question: *"What are the fundamental capabilities and limitations of computers?".*^{ [1] }

- History
- Branches
- Automata theory
- Computability theory
- Computational complexity theory
- Models of computation
- References
- Further reading
- External links

In order to perform a rigorous study of computation, computer scientists work with a mathematical abstraction of computers called a model of computation. There are several models in use, but the most commonly examined is the Turing machine.^{ [2] } Computer scientists study the Turing machine because it is simple to formulate, can be analyzed and used to prove results, and because it represents what many consider the most powerful possible "reasonable" model of computation (see Church–Turing thesis).^{ [3] } It might seem that the potentially infinite memory capacity is an unrealizable attribute, but any decidable problem^{ [4] } solved by a Turing machine will always require only a finite amount of memory. So in principle, any problem that can be solved (decided) by a Turing machine can be solved by a computer that has a finite amount of memory.

The theory of computation can be considered the creation of models of all kinds in the field of computer science. Therefore, mathematics and logic are used. In the last century it became an independent academic discipline and was separated from mathematics.

Some pioneers of the theory of computation were Ramon Llull, Alonzo Church, Kurt Gödel, Alan Turing, Stephen Kleene, Rózsa Péter, John von Neumann and Claude Shannon.

Grammar | Languages | Automaton | Production rules (constraints) |
---|---|---|---|

Type-0 | Recursively enumerable | Turing machine | (no restrictions) |

Type-1 | Context-sensitive | Linear-bounded non-deterministic Turing machine | |

Type-2 | Context-free | Non-deterministic pushdown automaton | |

Type-3 | Regular | Finite state automaton | and |

Automata theory is the study of abstract machines (or more appropriately, abstract 'mathematical' machines or systems) and the computational problems that can be solved using these machines. These abstract machines are called automata. Automata comes from the Greek word (Αυτόματα) which means that something is doing something by itself. Automata theory is also closely related to formal language theory,^{ [5] } as the automata are often classified by the class of formal languages they are able to recognize. An automaton can be a finite representation of a formal language that may be an infinite set. Automata are used as theoretical models for computing machines, and are used for proofs about computability.

Language theory is a branch of mathematics concerned with describing languages as a set of operations over an alphabet. It is closely linked with automata theory, as automata are used to generate and recognize formal languages. There are several classes of formal languages, each allowing more complex language specification than the one before it, i.e. Chomsky hierarchy,^{ [6] } and each corresponding to a class of automata which recognizes it. Because automata are used as models for computation, formal languages are the preferred mode of specification for any problem that must be computed.

Computability theory deals primarily with the question of the extent to which a problem is solvable on a computer. The statement that the halting problem cannot be solved by a Turing machine^{ [7] } is one of the most important results in computability theory, as it is an example of a concrete problem that is both easy to formulate and impossible to solve using a Turing machine. Much of computability theory builds on the halting problem result.

Another important step in computability theory was Rice's theorem, which states that for all non-trivial properties of partial functions, it is undecidable whether a Turing machine computes a partial function with that property.^{ [8] }

Computability theory is closely related to the branch of mathematical logic called recursion theory, which removes the restriction of studying only models of computation which are reducible to the Turing model.^{ [9] } Many mathematicians and computational theorists who study recursion theory will refer to it as computability theory.

Complexity theory considers not only whether a problem can be solved at all on a computer, but also how efficiently the problem can be solved. Two major aspects are considered: time complexity and space complexity, which are respectively how many steps does it take to perform a computation, and how much memory is required to perform that computation.

In order to analyze how much time and space a given algorithm requires, computer scientists express the time or space required to solve the problem as a function of the size of the input problem. For example, finding a particular number in a long list of numbers becomes harder as the list of numbers grows larger. If we say there are *n* numbers in the list, then if the list is not sorted or indexed in any way we may have to look at every number in order to find the number we're seeking. We thus say that in order to solve this problem, the computer needs to perform a number of steps that grows linearly in the size of the problem.

To simplify this problem, computer scientists have adopted Big O notation, which allows functions to be compared in a way that ensures that particular aspects of a machine's construction do not need to be considered, but rather only the asymptotic behavior as problems become large. So in our previous example, we might say that the problem requires steps to solve.

Perhaps the most important open problem in all of computer science is the question of whether a certain broad class of problems denoted NP can be solved efficiently. This is discussed further at Complexity classes P and NP, and P versus NP problem is one of the seven Millennium Prize Problems stated by the Clay Mathematics Institute in 2000. The Official Problem Description was given by Turing Award winner Stephen Cook.

Aside from a Turing machine, other equivalent (See: Church–Turing thesis) models of computation are in use.

- Lambda calculus
- A computation consists of an initial lambda expression (or two if you want to separate the function and its input) plus a finite sequence of lambda terms, each deduced from the preceding term by one application of Beta reduction.
- Combinatory logic
- is a concept which has many similarities to -calculus, but also important differences exist (e.g. fixed point combinator
**Y**has normal form in combinatory logic but not in -calculus). Combinatory logic was developed with great ambitions: understanding the nature of paradoxes, making foundations of mathematics more economic (conceptually), eliminating the notion of variables (thus clarifying their role in mathematics).

- μ-recursive functions
- a computation consists of a mu-recursive function,
*i.e.*its defining sequence, any input value(s) and a sequence of recursive functions appearing in the defining sequence with inputs and outputs. Thus, if in the defining sequence of a recursive function the functions and appear, then terms of the form 'g(5)=7' or 'h(3,2)=10' might appear. Each entry in this sequence needs to be an application of a basic function or follow from the entries above by using composition, primitive recursion or μ recursion. For instance if , then for 'f(5)=3' to appear, terms like 'g(5)=6' and 'h(5,6)=3' must occur above. The computation terminates only if the final term gives the value of the recursive function applied to the inputs.

- Markov algorithm
- a string rewriting system that uses grammar-like rules to operate on strings of symbols.

- Register machine
- is a theoretically interesting idealization of a computer. There are several variants. In most of them, each register can hold a natural number (of unlimited size), and the instructions are simple (and few in number), e.g. only decrementation (combined with conditional jump) and incrementation exist (and halting). The lack of the infinite (or dynamically growing) external store (seen at Turing machines) can be understood by replacing its role with Gödel numbering techniques: the fact that each register holds a natural number allows the possibility of representing a complicated thing (e.g. a sequence, or a matrix etc.) by an appropriate huge natural number — unambiguity of both representation and interpretation can be established by number theoretical foundations of these techniques.

In addition to the general computational models, some simpler computational models are useful for special, restricted applications. Regular expressions, for example, specify string patterns in many contexts, from office productivity software to programming languages. Another formalism mathematically equivalent to regular expressions, Finite automata are used in circuit design and in some kinds of problem-solving. Context-free grammars specify programming language syntax. Non-deterministic pushdown automata are another formalism equivalent to context-free grammars. Primitive recursive functions are a defined subclass of the recursive functions.

Different models of computation have the ability to do different tasks. One way to measure the power of a computational model is to study the class of formal languages that the model can generate; in such a way to the Chomsky hierarchy of languages is obtained.

In computability theory, the **Church–Turing thesis** is a hypothesis about the nature of computable functions. It states that a function on the natural numbers can be calculated by an effective method if and only if it is computable by a Turing machine. The thesis is named after American mathematician Alonzo Church and the British mathematician Alan Turing. Before the precise definition of computable function, mathematicians often used the informal term effectively calculable to describe functions that are computable by paper-and-pencil methods. In the 1930s, several independent attempts were made to formalize the notion of computability:

In computability theory and computational complexity theory, a **decision problem** is a problem that can be posed as a yes-no question of the input values. An example of a decision problem is deciding whether a given natural number is prime. Another is the problem "given two numbers *x* and *y*, does *x* evenly divide *y*?". The answer is either 'yes' or 'no' depending upon the values of *x* and *y*. A method for solving a decision problem, given in the form of an algorithm, is called a **decision procedure** for that problem. A decision procedure for the decision problem "given two numbers *x* and *y*, does *x* evenly divide *y*?" would give the steps for determining whether *x* evenly divides *y*. One such algorithm is long division. If the remainder is zero the answer is 'yes', otherwise it is 'no'. A decision problem which can be solved by an algorithm is called *decidable*.

A **finite-state machine** (**FSM**) or **finite-state automaton**, **finite automaton**, or simply a **state machine**, is a mathematical model of computation. It is an abstract machine that can be in exactly one of a finite number of *states* at any given time. The FSM can change from one state to another in response to some inputs; the change from one state to another is called a *transition*. An FSM is defined by a list of its states, its initial state, and the inputs that trigger each transition. Finite-state machines are of two types—deterministic finite-state machines and non-deterministic finite-state machines. A deterministic finite-state machine can be constructed equivalent to any non-deterministic one.

In theoretical computer science and formal language theory, a **regular language** is a formal language that can be defined by a regular expression, in the strict sense in theoretical computer science.

A **Turing machine** is a mathematical model of computation that defines an abstract machine that manipulates symbols on a strip of tape according to a table of rules. Despite the model's simplicity, given any computer algorithm, a Turing machine capable of simulating that algorithm's logic can be constructed.

In computability theory, a system of data-manipulation rules is said to be **Turing-complete** or **computationally universal** if it can be used to simulate any Turing machine. This means that this system is able to recognize or decide other data-manipulation rule sets. Turing completeness is used as a way to express the power of such a data-manipulation rule set. Virtually all programming languages today are Turing-complete. The concept is named after English mathematician and computer scientist Alan Turing.

In mathematics, logic and computer science, a formal language is called **recursively enumerable** if it is a recursively enumerable subset in the set of all possible words over the alphabet of the language, i.e., if there exists a Turing machine which will enumerate all valid strings of the language.

**Automata theory** is the study of abstract machines and automata, as well as the computational problems that can be solved using them. It is a theory in theoretical computer science. The word *automata* comes from the Greek word αὐτόματα, which means "self-making". An automaton is an abstract self-propelled computing device which follows a predetermined sequence of operations automatically. An automaton with a finite number of states is called a Finite Automaton (FA) or Finite State Machine (FSM).

**Computability theory**, also known as **recursion theory**, is a branch of mathematical logic, computer science, and the theory of computation that originated in the 1930s with the study of computable functions and Turing degrees. The field has since expanded to include the study of generalized computability and definability. In these areas, recursion theory overlaps with proof theory and effective descriptive set theory.

**Hypercomputation** or **super-Turing computation** refers to models of computation that can provide outputs that are not Turing-computable. For example, a machine that could solve the halting problem would be a hypercomputer; so too would one that can correctly evaluate every statement in Peano arithmetic.

In computability theory, traditionally called recursion theory, a set *S* of natural numbers is called **recursively enumerable**, **computably enumerable**, **semidecidable**, **partially decidable**, **listable**, **provable** or **Turing-recognizable** if:

**Solomonoff's theory of inductive inference** is a mathematical theory of induction introduced by Ray Solomonoff, based on probability theory and theoretical computer science. In essence, Solomonoff's induction derives the posterior probability of any computable theory, given a sequence of observed data. This posterior probability is derived from Bayes rule and some *universal* prior, that is, a prior that assigns a positive probability to any computable theory.

**Computability** is the ability to solve a problem in an effective manner. It is a key topic of the field of computability theory within mathematical logic and the theory of computation within computer science. The computability of a problem is closely linked to the existence of an algorithm to solve the problem.

**Computable functions** are the basic objects of study in computability theory. Computable functions are the formalized analogue of the intuitive notion of algorithms, in the sense that a function is computable if there exists an algorithm that can do the job of the function, i.e. given an input of the function domain it can return the corresponding output. Computable functions are used to discuss computability without referring to any concrete model of computation such as Turing machines or register machines. Any definition, however, must make reference to some specific model of computation but all valid definitions yield the same class of functions. Particular models of computability that give rise to the set of computable functions are the Turing-computable functions and the general recursive functions.

In computability theory, a **machine that always halts**, also called a **decider** or a **total Turing machine**, is a Turing machine that eventually halts for every input.

In computer science, and more specifically in computability theory and computational complexity theory, a **model of computation** is a model which describes how an output of a mathematical function is computed given an input. A model describes how units of computations, memories, and communications are organized. The computational complexity of an algorithm can be measured given a model of computation. Using a model allows studying the performance of algorithms independently of the variations that are specific to particular implementations and specific technology.

In computability theory, **super-recursive algorithms** are a generalization of ordinary algorithms that are more powerful, that is, compute more than Turing machines. The term was introduced by Mark Burgin, whose book "Super-recursive algorithms" develops their theory and presents several mathematical models. Turing machines and other mathematical models of conventional algorithms allow researchers to find properties of recursive algorithms and their computations. In a similar way, mathematical models of super-recursive algorithms, such as inductive Turing machines, allow researchers to find properties of super-recursive algorithms and their computations.

In computability theory, the **halting problem** is the problem of determining, from a description of an arbitrary computer program and an input, whether the program will finish running, or continue to run forever. Alan Turing proved in 1936 that a general algorithm to solve the halting problem for all possible program-input pairs cannot exist.

In mathematics, logic and computer science, a formal language is called **recursive** if it is a recursive subset of the set of all possible finite sequences over the alphabet of the language. Equivalently, a formal language is recursive if there exists a total Turing machine that, when given a finite sequence of symbols as input, accepts it if it belongs to the language and rejects it otherwise. Recursive languages are also called **decidable**.

- ↑ Michael Sipser (2013).
*Introduction to the Theory of Computation 3rd*. Cengage Learning. ISBN 978-1-133-18779-0.central areas of the theory of computation: automata, computability, and complexity. (Page 1)

- ↑ Hodges, Andrew (2012).
*Alan Turing: The Enigma*(The Centenary ed.). Princeton University Press. ISBN 978-0-691-15564-7. - ↑ Rabin, Michael O. (June 2012).
*Turing, Church, Gödel, Computability, Complexity and Randomization: A Personal View*. - ↑ Donald Monk (1976).
*Mathematical Logic*. Springer-Verlag. ISBN 9780387901701. - ↑ Hopcroft, John E. and Jeffrey D. Ullman (2006).
*Introduction to Automata Theory, Languages, and Computation. 3rd ed*. Reading, MA: Addison-Wesley. ISBN 978-0-321-45536-9. - ↑ Chomsky hierarchy (1956). "Three models for the description of language".
*Information Theory, IRE Transactions on*. IEEE.**2**(3): 113–124. doi:10.1109/TIT.1956.1056813. - ↑ Alan Turing (1937). "On computable numbers, with an application to the Entscheidungsproblem".
*Proceedings of the London Mathematical Society*. IEEE.**2**(42): 230–265. doi:10.1112/plms/s2-42.1.230 . Retrieved 6 January 2015. - ↑ Henry Gordon Rice (1953). "Classes of Recursively Enumerable Sets and Their Decision Problems".
*Transactions of the American Mathematical Society*. American Mathematical Society.**74**(2): 358–366. doi: 10.2307/1990888 . JSTOR 1990888. - ↑ Martin Davis (2004).
*The undecidable: Basic papers on undecidable propositions, unsolvable problems and computable functions (Dover Ed)*. Dover Publications. ISBN 978-0486432281.

- Textbooks aimed at computer scientists

(There are many textbooks in this area; this list is by necessity incomplete.)

- Hopcroft, John E., and Jeffrey D. Ullman (2006).
*Introduction to Automata Theory, Languages, and Computation. 3rd ed*Reading, MA: Addison-Wesley. ISBN 978-0-321-45536-9 One of the standard references in the field. - Linz P.
*An introduction to formal language and automata*. Narosa Publishing. ISBN 9788173197819. - Michael Sipser (2013).
*Introduction to the Theory of Computation*(3rd ed.). Cengage Learning. ISBN 978-1-133-18779-0. - Eitan Gurari (1989).
*An Introduction to the Theory of Computation*. Computer Science Press. ISBN 0-7167-8182-4. Archived from the original on 2007-01-07. - Hein, James L. (1996)
*Theory of Computation.*Sudbury, MA: Jones & Bartlett. ISBN 978-0-86720-497-1 A gentle introduction to the field, appropriate for second-year undergraduate computer science students. - Taylor, R. Gregory (1998).
*Models of Computation and Formal Languages.*New York: Oxford University Press. ISBN 978-0-19-510983-2 An unusually readable textbook, appropriate for upper-level undergraduates or beginning graduate students. - Lewis, F. D. (2007).
*Essentials of theoretical computer science*A textbook covering the topics of formal languages, automata and grammars. The emphasis appears to be on presenting an overview of the results and their applications rather than providing proofs of the results. - Martin Davis, Ron Sigal, Elaine J. Weyuker,
*Computability, complexity, and languages: fundamentals of theoretical computer science*, 2nd ed., Academic Press, 1994, ISBN 0-12-206382-1. Covers a wider range of topics than most other introductory books, including program semantics and quantification theory. Aimed at graduate students.

- Books on computability theory from the (wider) mathematical perspective

- Hartley Rogers, Jr (1987).
*Theory of Recursive Functions and Effective Computability*, MIT Press. ISBN 0-262-68052-1 - S. Barry Cooper (2004).
*Computability Theory*. Chapman and Hall/CRC. ISBN 1-58488-237-9.. - Carl H. Smith,
*A recursive introduction to the theory of computation*, Springer, 1994, ISBN 0-387-94332-3. A shorter textbook suitable for graduate students in Computer Science.

- Historical perspective

- Richard L. Epstein and Walter A. Carnielli (2000).
*Computability: Computable Functions, Logic, and the Foundations of Mathematics, with Computability: A Timeline (2nd ed.)*. Wadsworth/Thomson Learning. ISBN 0-534-54644-7..

- Theory of Computation at MIT
- Theory of Computation at Harvard
- Computability Logic - A theory of interactive computation. The main web source on this subject.

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