In order to perform a rigorous study of computation, computer scientists work with a mathematical abstraction of computers called amodel of computation. There are several models in use, but the most commonly examined is theTuring 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 (seeChurch–Turing thesis).[3] It might seem that the potentially infinite memory capacity is an unrealizable attribute, but anydecidable 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 separated from mathematics and became an independent academic discipline with its own conferences such asFOCS in 1960 andSTOC in 1969, and its own awards such as theIMU Abacus Medal (established in 1981 as the Rolf Nevanlinna Prize), theGödel Prize, established in 1993, and theKnuth Prize, established in 1996.
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 toformal 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.
Formal language theory is a branch of mathematics concerned with describing languages as a set of operations over analphabet. 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 thehalting 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 wasRice's theorem, which states that for all non-trivial properties of partial functions, it isundecidable whether a Turing machine computes a partial function with that property.[8]
Computability theory is closely related to the branch ofmathematical logic calledrecursion 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.
A representation of the relation among complexity classes
Computational 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 andspace complexity, which are respectively how many steps it takes to perform a computation, and how much memory is required to perform that computation.
In order to analyze how much time and space a givenalgorithm 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 aren 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 grow linearly in the size of the problem.
To simplify this problem, computer scientists have adoptedbigO 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 theasymptotic behavior as problems become large. So in our previous example, we might say that the problem requires steps to solve.
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 ofBeta reduction.
is a concept which has many similarities to-calculus, but also important differences exist (e.g. fixed point combinatorY 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).
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 usingcomposition,primitive recursion orμ 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.
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 withGödel 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 appropriately huge natural number — unambiguity of both representation and interpretation can be established bynumber 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 toprogramming 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-deterministicpushdown 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 offormal languages that the model can generate; in such a way to theChomsky hierarchy of languages is obtained.
Hein, James L. (1996)Theory of Computation. Sudbury, MA: Jones & Bartlett.ISBN978-0-86720-497-1 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.ISBN978-0-19-510983-2 An unusually readable textbook, appropriate for upper-level undergraduates or beginning graduate students.
Jon Kleinberg, and Éva Tardos (2006):Algorithm Design, Pearson/Addison-Wesley, ISBN 978-0-32129535-4
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,ISBN0-12-206382-1. Covers a wider range of topics than most other introductory books, includingprogram semantics andquantification theory. Aimed at graduate students.
Books on computability theory from the (wider) mathematical perspective
Carl H. Smith,A recursive introduction to the theory of computation, Springer, 1994,ISBN0-387-94332-3. A shorter textbook suitable for graduate students in Computer Science.
Historical perspective
Richard L. Epstein andWalter A. Carnielli (2000).Computability: Computable Functions, Logic, and the Foundations of Mathematics, with Computability: A Timeline (2nd ed.). Wadsworth/Thomson Learning.ISBN0-534-54644-7..