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computer science Computer science is the study of computation, automation, and information. Computer science spans theoretical disciplines (such as algorithms, theory of computation, information theory, and automation) to Applied science, practical discipli ...
, the time complexity is the
computational complexity In computer science, the computational complexity or simply complexity of an algorithm is the amount of resources required to run it. Particular focus is given to computation time (generally measured by the number of needed elementary operations) ...
that describes the amount of computer time it takes to run an
algorithm In mathematics and computer science, an algorithm () is a finite sequence of rigorous instructions, typically used to solve a class of specific problems or to perform a computation. Algorithms are used as specifications for performing ...
. Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, supposing that each elementary operation takes a fixed amount of time to perform. Thus, the amount of time taken and the number of elementary operations performed by the algorithm are taken to be related by a constant factor. Since an algorithm's running time may vary among different inputs of the same size, one commonly considers the worst-case time complexity, which is the maximum amount of time required for inputs of a given size. Less common, and usually specified explicitly, is the average-case complexity, which is the average of the time taken on inputs of a given size (this makes sense because there are only a finite number of possible inputs of a given size). In both cases, the time complexity is generally expressed as a function of the size of the input. Since this function is generally difficult to compute exactly, and the running time for small inputs is usually not consequential, one commonly focuses on the behavior of the complexity when the input size increases—that is, the asymptotic behavior of the complexity. Therefore, the time complexity is commonly expressed using
big O notation Big ''O'' notation is a mathematical notation that describes the limiting behavior of a function when the argument tends towards a particular value or infinity. Big O is a member of a family of notations invented by Paul Bachmann, Edmund L ...
, typically etc., where is the size in units of bits needed to represent the input. Algorithmic complexities are classified according to the type of function appearing in the big O notation. For example, an algorithm with time complexity O(n) is a ''linear time algorithm'' and an algorithm with time complexity O(n^\alpha) for some constant \alpha > 1 is a ''polynomial time algorithm''.


Table of common time complexities

The following table summarizes some classes of commonly encountered time complexities. In the table, , i.e., polynomial in ''x''.


Constant time

An algorithm is said to be constant time (also written as O(1) time) if the value of T(n) (the complexity of the algorithm) is bounded by a value that does not depend on the size of the input. For example, accessing any single element in an array takes constant time as only one
operation Operation or Operations may refer to: Arts, entertainment and media * ''Operation'' (game), a battery-operated board game that challenges dexterity * Operation (music), a term used in musical set theory * ''Operations'' (magazine), Multi-Man ...
has to be performed to locate it. In a similar manner, finding the minimal value in an array sorted in ascending order; it is the first element. However, finding the minimal value in an unordered array is not a constant time operation as scanning over each element in the array is needed in order to determine the minimal value. Hence it is a linear time operation, taking O(n) time. If the number of elements is known in advance and does not change, however, such an algorithm can still be said to run in constant time. Despite the name "constant time", the running time does not have to be independent of the problem size, but an upper bound for the running time has to be independent of the problem size. For example, the task "exchange the values of and if necessary so that a \le b" is called constant time even though the time may depend on whether or not it is already true that a \le b. However, there is some constant such that the time required is always ''at most'' . Here are some examples of code fragments that run in constant time: int index = 5; int item = list ndex if (condition true) then perform some operation that runs in constant time else perform some other operation that runs in constant time for i = 1 to 100 for j = 1 to 200 perform some operation that runs in constant time If T(n) is O(a), where is any constant value, this is equivalent to and stated in standard notation as T(n) being O(1).


Logarithmic time

An algorithm is said to take logarithmic time when T(n) = O(\log n). Since \log_a n and \log_b n are related by a constant multiplier, and such a multiplier is irrelevant to big O classification, the standard usage for logarithmic-time algorithms is O(\log n) regardless of the base of the logarithm appearing in the expression of . Algorithms taking logarithmic time are commonly found in operations on binary trees or when using binary search. An O(\log n) algorithm is considered highly efficient, as the ratio of the number of operations to the size of the input decreases and tends to zero when increases. An algorithm that must access all elements of its input cannot take logarithmic time, as the time taken for reading an input of size is of the order of . An example of logarithmic time is given by dictionary search. Consider a
dictionary A dictionary is a listing of lexemes from the lexicon of one or more specific languages, often arranged alphabetically (or by radical and stroke for ideographic languages), which may include information on definitions, usage, etymologie ...
which contains entries, sorted by alphabetical order. We suppose that, for 1 \le k \le n, one may access the th entry of the dictionary in a constant time. Let D(k) denote this th entry. Under these hypotheses, the test to see if a word is in the dictionary may be done in logarithmic time: consider D\left(\left\lfloor \frac \right\rfloor\right), where \lfloor\;\rfloor denotes the floor function. If w = D\left(\left\lfloor \frac \right\rfloor\right), then we are done. Else, if w < D\left(\left\lfloor \frac \right\rfloor\right), continue the search in the same way in the left half of the dictionary, otherwise continue similarly with the right half of the dictionary. This algorithm is similar to the method often used to find an entry in a paper dictionary.


Polylogarithmic time

An algorithm is said to run in polylogarithmic time if its time T(n) is O\bigl((\log n)^k\bigr) for some constant . Another way to write this is O(\log^kn). For example, matrix chain ordering can be solved in polylogarithmic time on a parallel random-access machine, and a graph can be determined to be planar in a fully dynamic way in O(\log^3n) time per insert/delete operation.


Sub-linear time

An algorithm is said to run in sub-linear time (often spelled sublinear time) if T(n)=o(n). In particular this includes algorithms with the time complexities defined above. Typical algorithms that are exact and yet run in sub-linear time use parallel processing (as the NC1 matrix determinant calculation does), or alternatively have guaranteed assumptions on the input structure (as the logarithmic time binary search and many tree maintenance algorithms do). However,
formal language In logic, mathematics, computer science, and linguistics, a formal language consists of words whose letters are taken from an alphabet and are well-formed according to a specific set of rules. The alphabet of a formal language consists of sym ...
s such as the set of all strings that have a 1-bit in the position indicated by the first \log n bits of the string may depend on every bit of the input and yet be computable in sub-linear time. The specific term ''sublinear time algorithm'' is usually reserved to algorithms that are unlike the above in that they are run over classical serial machine models and are not allowed prior assumptions on the input. They are however allowed to be
randomized In common usage, randomness is the apparent or actual lack of pattern or predictability in events. A random sequence of events, symbols or steps often has no order and does not follow an intelligible pattern or combination. Individual ran ...
, and indeed must be randomized for all but the most trivial of tasks. As such an algorithm must provide an answer without reading the entire input, its particulars heavily depend on the access allowed to the input. Usually for an input that is represented as a binary string b_1, ..., b_k it is assumed that the algorithm can in time O(1) request and obtain the value of b_i for any . Sub-linear time algorithms are typically randomized, and provide only approximate solutions. In fact, the property of a binary string having only zeros (and no ones) can be easily proved not to be decidable by a (non-approximate) sub-linear time algorithm. Sub-linear time algorithms arise naturally in the investigation of property testing.


Linear time

An algorithm is said to take linear time, or O(n) time, if its time complexity is O(n). Informally, this means that the running time increases at most linearly with the size of the input. More precisely, this means that there is a constant such that the running time is at most cn for every input of size . For example, a procedure that adds up all elements of a list requires time proportional to the length of the list, if the adding time is constant, or, at least, bounded by a constant. Linear time is the best possible time complexity in situations where the algorithm has to sequentially read its entire input. Therefore, much research has been invested into discovering algorithms exhibiting linear time or, at least, nearly linear time. This research includes both software and hardware methods. There are several hardware technologies which exploit parallelism to provide this. An example is content-addressable memory. This concept of linear time is used in string matching algorithms such as the Boyer–Moore algorithm and Ukkonen's algorithm.


Quasilinear time

An algorithm is said to run in quasilinear time (also referred to as log-linear time) if T(n)=O(n\log^kn) for some positive constant ; linearithmic time is the case k=1. Using soft O notation these algorithms are \tilde(n). Quasilinear time algorithms are also O(n^) for every constant \epsilon >0 and thus run faster than any polynomial time algorithm whose time bound includes a term n^c for any c>1. Algorithms which run in quasilinear time include: * In-place merge sort, O(n\log^2n) * Quicksort, O(n\log n), in its randomized version, has a running time that is O(n\log n) in expectation on the worst-case input. Its non-randomized version has an O(n\log n) running time only when considering average case complexity. *
Heapsort In computer science, heapsort is a comparison-based sorting algorithm. Heapsort can be thought of as an improved selection sort: like selection sort, heapsort divides its input into a sorted and an unsorted region, and it iteratively shrinks ...
, O(n\log n), merge sort,
introsort Introsort or introspective sort is a hybrid sorting algorithm that provides both fast average performance and (asymptotically) optimal worst-case performance. It begins with quicksort, it switches to heapsort when the recursion depth exceeds a ...
, binary tree sort,
smoothsort In computer science, smoothsort is a comparison-based sorting algorithm. A variant of heapsort, it was invented and published by Edsger Dijkstra in 1981. Like heapsort, smoothsort is an in-place algorithm with an upper bound of , but it is n ...
, patience sorting, etc. in the worst case *
Fast Fourier transform A fast Fourier transform (FFT) is an algorithm that computes the discrete Fourier transform (DFT) of a sequence, or its inverse (IDFT). Fourier analysis converts a signal from its original domain (often time or space) to a representation in ...
s, O(n\log n) * Monge array calculation, O(n\log n) In many cases, the O(n\log n) running time is simply the result of performing a \Theta (\log n) operation times (for the notation, see ). For example, binary tree sort creates a binary tree by inserting each element of the -sized array one by one. Since the insert operation on a self-balancing binary search tree takes O(\log n) time, the entire algorithm takes O(n\log n) time. Comparison sorts require at least \Omega (n\log n) comparisons in the worst case because \log (n!)=\Theta (n\log n), by Stirling's approximation. They also frequently arise from the recurrence relation T(n) = 2T\left(\frac\right)+O(n).


Sub-quadratic time

An
algorithm In mathematics and computer science, an algorithm () is a finite sequence of rigorous instructions, typically used to solve a class of specific problems or to perform a computation. Algorithms are used as specifications for performing ...
is said to be subquadratic time if T(n)=o(n^2). For example, simple, comparison-based sorting algorithms are quadratic (e.g. insertion sort), but more advanced algorithms can be found that are subquadratic (e.g. shell sort). No general-purpose sorts run in linear time, but the change from quadratic to sub-quadratic is of great practical importance.


Polynomial time

An algorithm is said to be of polynomial time if its running time is upper bounded by a
polynomial In mathematics, a polynomial is an expression consisting of indeterminates (also called variables) and coefficients, that involves only the operations of addition, subtraction, multiplication, and positive-integer powers of variables. An exampl ...
expression in the size of the input for the algorithm, that is, for some positive constant ''k''. Problems for which a deterministic polynomial-time algorithm exists belong to the
complexity class In computational complexity theory, a complexity class is a set of computational problems of related resource-based complexity. The two most commonly analyzed resources are time and memory. In general, a complexity class is defined in terms o ...
P, which is central in the field of
computational complexity theory In theoretical computer science and mathematics, computational complexity theory focuses on classifying computational problems according to their resource usage, and relating these classes to each other. A computational problem is a task solved ...
. Cobham's thesis states that polynomial time is a synonym for "tractable", "feasible", "efficient", or "fast". Some examples of polynomial-time algorithms: * The selection sort sorting algorithm on ''n'' integers performs An^2 operations for some constant ''A''. Thus it runs in time O(n^2) and is a polynomial-time algorithm. * All the basic arithmetic operations (addition, subtraction, multiplication, division, and comparison) can be done in polynomial time. * Maximum matchings in graphs can be found in polynomial time.


Strongly and weakly polynomial time

In some contexts, especially in optimization, one differentiates between strongly polynomial time and weakly polynomial time algorithms. These two concepts are only relevant if the inputs to the algorithms consist of integers. Strongly polynomial time is defined in the arithmetic model of computation. In this model of computation the basic arithmetic operations (addition, subtraction, multiplication, division, and comparison) take a unit time step to perform, regardless of the sizes of the operands. The algorithm runs in strongly polynomial time if: # the number of operations in the arithmetic model of computation is bounded by a polynomial in the number of integers in the input instance; and # the space used by the algorithm is bounded by a polynomial in the size of the input. Any algorithm with these two properties can be converted to a polynomial time algorithm by replacing the arithmetic operations by suitable algorithms for performing the arithmetic operations on a
Turing machine A Turing machine is a mathematical model of computation describing an abstract machine that manipulates symbols on a strip of tape according to a table of rules. Despite the model's simplicity, it is capable of implementing any computer alg ...
. The second condition is strictly necessary: given the integer 2^n (which takes up space proportional to ''n'' in the Turing machine model), it is possible to compute 2^ with ''n'' multiplications using repeated squaring. However, the space used to represent 2^ is proportional to 2^n, and thus exponential rather than polynomial in the space used to represent the input. Hence, it is not possible to carry out this computation in polynomial time on a Turing machine, but it is possible to compute it by polynomially many arithmetic operations. However, for the first condition, there are algorithms that run in a number of Turing machine steps bounded by a polynomial in the length of binary-encoded input, but do not take a number of arithmetic operations bounded by a polynomial in the number of input numbers. The
Euclidean algorithm In mathematics, the Euclidean algorithm,Some widely used textbooks, such as I. N. Herstein's ''Topics in Algebra'' and Serge Lang's ''Algebra'', use the term "Euclidean algorithm" to refer to Euclidean division or Euclid's algorithm, is an e ...
for computing the greatest common divisor of two integers is one example. Given two integers a and b, the algorithm performs O(\log a + \log b) arithmetic operations on numbers with at most O(\log a + \log b) bits. At the same time, the number of arithmetic operations cannot be bounded by the number of integers in the input (which is constant in this case, there are always only two integers in the input). Due to the latter observation, the algorithm does not run in strongly polynomial time. Its real running time depends ''logarithmically'' on the magnitudes of a and b (that is, on their length in bits) and not only on the number of integers in the input. An algorithm that runs in polynomial time but that is not strongly polynomial is said to run in weakly polynomial time. A well-known example of a problem for which a weakly polynomial-time algorithm is known, but is not known to admit a strongly polynomial-time algorithm, is
linear programming Linear programming (LP), also called linear optimization, is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented by linear relationships. Linear programming is ...
. Weakly polynomial time should not be confused with pseudo-polynomial time, which depends ''linearly'' on the magnitude of values in the problem and is not truly polynomial time.


Complexity classes

The concept of polynomial time leads to several complexity classes in computational complexity theory. Some important classes defined using polynomial time are the following. * P: The
complexity class In computational complexity theory, a complexity class is a set of computational problems of related resource-based complexity. The two most commonly analyzed resources are time and memory. In general, a complexity class is defined in terms o ...
of
decision problem In computability theory and computational complexity theory, a decision problem is a computational problem that can be posed as a yes–no question of the input values. An example of a decision problem is deciding by means of an algorithm whe ...
s that can be solved on a
deterministic Turing machine A Turing machine is a mathematical model of computation describing an abstract machine that manipulates symbols on a strip of tape according to a table of rules. Despite the model's simplicity, it is capable of implementing any computer alg ...
in polynomial time * NP: The complexity class of decision problems that can be solved on a non-deterministic Turing machine in polynomial time * ZPP: The complexity class of decision problems that can be solved with zero error on a probabilistic Turing machine in polynomial time * RP: The complexity class of decision problems that can be solved with 1-sided error on a probabilistic Turing machine in polynomial time. * BPP: The complexity class of decision problems that can be solved with 2-sided error on a probabilistic Turing machine in polynomial time * BQP: The complexity class of decision problems that can be solved with 2-sided error on a
quantum Turing machine A quantum Turing machine (QTM) or universal quantum computer is an abstract machine used to model the effects of a quantum computer. It provides a simple model that captures all of the power of quantum computation—that is, any quantum algori ...
in polynomial time P is the smallest time-complexity class on a deterministic machine which is robust in terms of machine model changes. (For example, a change from a single-tape Turing machine to a multi-tape machine can lead to a quadratic speedup, but any algorithm that runs in polynomial time under one model also does so on the other.) Any given abstract machine will have a complexity class corresponding to the problems which can be solved in polynomial time on that machine.


Superpolynomial time

An algorithm is defined to take superpolynomial time if ''T''(''n'') is not bounded above by any polynomial. Using little omega notation, it is ''ω''(''n''''c'') time for all constants ''c'', where ''n'' is the input parameter, typically the number of bits in the input. For example, an algorithm that runs for 2''n'' steps on an input of size ''n'' requires superpolynomial time (more specifically, exponential time). An algorithm that uses exponential resources is clearly superpolynomial, but some algorithms are only very weakly superpolynomial. For example, the
Adleman–Pomerance–Rumely primality test In computational number theory, the Adleman–Pomerance–Rumely primality test is an algorithm for determining whether a number is prime. Unlike other, more efficient algorithms for this purpose, it avoids the use of random numbers, so it is a dete ...
runs for time on ''n''-bit inputs; this grows faster than any polynomial for large enough ''n'', but the input size must become impractically large before it cannot be dominated by a polynomial with small degree. An algorithm that requires superpolynomial time lies outside the
complexity class In computational complexity theory, a complexity class is a set of computational problems of related resource-based complexity. The two most commonly analyzed resources are time and memory. In general, a complexity class is defined in terms o ...
P. Cobham's thesis posits that these algorithms are impractical, and in many cases they are. Since the P versus NP problem is unresolved, it is unknown whether NP-complete problems require superpolynomial time.


Quasi-polynomial time

Quasi-polynomial time algorithms are algorithms that run longer than polynomial time, yet not so long as to be exponential time. The worst case running time of a quasi-polynomial time algorithm is 2^ for some fixed For c=1 we get a polynomial time algorithm, for c < 1 we get a sub-linear time algorithm. Quasi-polynomial time algorithms typically arise in reductions from an
NP-hard In computational complexity theory, NP-hardness ( non-deterministic polynomial-time hardness) is the defining property of a class of problems that are informally "at least as hard as the hardest problems in NP". A simple example of an NP-hard pr ...
problem to another problem. For example, one can take an instance of an NP hard problem, say 3SAT, and convert it to an instance of another problem B, but the size of the instance becomes 2^. In that case, this reduction does not prove that problem B is NP-hard; this reduction only shows that there is no polynomial time algorithm for B unless there is a quasi-polynomial time algorithm for 3SAT (and thus all of NP). Similarly, there are some problems for which we know quasi-polynomial time algorithms, but no polynomial time algorithm is known. Such problems arise in approximation algorithms; a famous example is the directed Steiner tree problem, for which there is a quasi-polynomial time approximation algorithm achieving an approximation factor of O(\log^3 n) (''n'' being the number of vertices), but showing the existence of such a polynomial time algorithm is an open problem. Other computational problems with quasi-polynomial time solutions but no known polynomial time solution include the planted clique problem in which the goal is to find a large clique in the union of a clique and a random graph. Although quasi-polynomially solvable, it has been conjectured that the planted clique problem has no polynomial time solution; this planted clique conjecture has been used as a
computational hardness assumption In computational complexity theory, a computational hardness assumption is the hypothesis that a particular problem cannot be solved efficiently (where ''efficiently'' typically means "in polynomial time"). It is not known how to prove (unconditio ...
to prove the difficulty of several other problems in computational
game theory Game theory is the study of mathematical models of strategic interactions among rational agents. Myerson, Roger B. (1991). ''Game Theory: Analysis of Conflict,'' Harvard University Press, p.&nbs1 Chapter-preview links, ppvii–xi It has appli ...
, property testing, and
machine learning Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine ...
. The complexity class QP consists of all problems that have quasi-polynomial time algorithms. It can be defined in terms of DTIME as follows. :\mbox = \bigcup_ \mbox \left(2^\right)


Relation to NP-complete problems

In complexity theory, the unsolved
P versus NP The P versus NP problem is a major unsolved problem in theoretical computer science. In informal terms, it asks whether every problem whose solution can be quickly verified can also be quickly solved. The informal term ''quickly'', used abov ...
problem asks if all problems in NP have polynomial-time algorithms. All the best-known algorithms for NP-complete problems like 3SAT etc. take exponential time. Indeed, it is conjectured for many natural NP-complete problems that they do not have sub-exponential time algorithms. Here "sub-exponential time" is taken to mean the second definition presented below. (On the other hand, many graph problems represented in the natural way by adjacency matrices are solvable in subexponential time simply because the size of the input is the square of the number of vertices.) This conjecture (for the k-SAT problem) is known as the exponential time hypothesis. Since it is conjectured that NP-complete problems do not have quasi-polynomial time algorithms, some inapproximability results in the field of
approximation algorithms In computer science and operations research, approximation algorithms are efficient algorithms that find approximate solutions to optimization problems (in particular NP-hard problems) with provable guarantees on the distance of the returned s ...
make the assumption that NP-complete problems do not have quasi-polynomial time algorithms. For example, see the known inapproximability results for the set cover problem.


Sub-exponential time

The term sub-exponential time is used to express that the running time of some algorithm may grow faster than any polynomial but is still significantly smaller than an exponential. In this sense, problems that have sub-exponential time algorithms are somewhat more tractable than those that only have exponential algorithms. The precise definition of "sub-exponential" is not generally agreed upon, and we list the two most widely used ones below.


First definition

A problem is said to be sub-exponential time solvable if it can be solved in running times whose logarithms grow smaller than any given polynomial. More precisely, a problem is in sub-exponential time if for every there exists an algorithm which solves the problem in time ''O''(2''n''''ε''). The set of all such problems is the complexity class SUBEXP which can be defined in terms of DTIME as follows. :\text=\bigcap_ \text\left(2^\right) This notion of sub-exponential is non-uniform in terms of ''ε'' in the sense that ''ε'' is not part of the input and each ε may have its own algorithm for the problem.


Second definition

Some authors define sub-exponential time as running times in 2^. This definition allows larger running times than the first definition of sub-exponential time. An example of such a sub-exponential time algorithm is the best-known classical algorithm for integer factorization, the
general number field sieve In number theory, the general number field sieve (GNFS) is the most efficient classical algorithm known for factoring integers larger than . Heuristically, its complexity for factoring an integer (consisting of bits) is of the form :\exp\lef ...
, which runs in time about where the length of the input is . Another example was the graph isomorphism problem, which the best known algorithm from 1982 to 2016 solved in However, at STOC 2016 a quasi-polynomial time algorithm was presented. It makes a difference whether the algorithm is allowed to be sub-exponential in the size of the instance, the number of vertices, or the number of edges. In parameterized complexity, this difference is made explicit by considering pairs (L,k) of
decision problem In computability theory and computational complexity theory, a decision problem is a computational problem that can be posed as a yes–no question of the input values. An example of a decision problem is deciding by means of an algorithm whe ...
s and parameters ''k''. SUBEPT is the class of all parameterized problems that run in time sub-exponential in ''k'' and polynomial in the input size ''n'': :\text=\text\left(2^ \cdot \text(n)\right). More precisely, SUBEPT is the class of all parameterized problems (L,k) for which there is a computable function f : \N \to \N with f \in o(k) and an algorithm that decides ''L'' in time 2^ \cdot \text(n).


Exponential time hypothesis

The exponential time hypothesis (ETH) is that 3SAT, the satisfiability problem of Boolean formulas in conjunctive normal form with at most three literals per clause and with ''n'' variables, cannot be solved in time 2''o''(''n''). More precisely, the hypothesis is that there is some absolute constant such that 3SAT cannot be decided in time 2''cn'' by any deterministic Turing machine. With ''m'' denoting the number of clauses, ETH is equivalent to the hypothesis that ''k''SAT cannot be solved in time 2''o''(''m'') for any integer . The exponential time hypothesis implies P ≠ NP.


Exponential time

An algorithm is said to be exponential time, if ''T''(''n'') is upper bounded by 2poly(''n''), where poly(''n'') is some polynomial in ''n''. More formally, an algorithm is exponential time if ''T''(''n'') is bounded by ''O''(2''n''''k'') for some constant ''k''. Problems which admit exponential time algorithms on a deterministic Turing machine form the complexity class known as EXP. :\text = \bigcup_ \text\left(2^\right) Sometimes, exponential time is used to refer to algorithms that have ''T''(''n'') = 2''O''(''n''), where the exponent is at most a linear function of ''n''. This gives rise to the complexity class E. :\text = \bigcup_ \text\left(2^\right)


Factorial time

An algorithm is said to be factorial time if ''T(n)'' is upper bounded by the factorial function ''n!''. Factorial time is a subset of exponential time (EXP) because n! = O \left(2^ \right) for all \epsilon > 0. However, it is not a subset of E. An example of an algorithm that runs in factorial time is bogosort, a notoriously inefficient sorting algorithm based on trial and error. Bogosort sorts a list of ''n'' items by repeatedly shuffling the list until it is found to be sorted. In the average case, each pass through the bogosort algorithm will examine one of the ''n''! orderings of the ''n'' items. If the items are distinct, only one such ordering is sorted. Bogosort shares patrimony with the
infinite monkey theorem The infinite monkey theorem states that a monkey hitting keys at random on a typewriter keyboard for an infinite amount of time will almost surely type any given text, such as the complete works of William Shakespeare. In fact, the monkey would ...
.


Double exponential time

An algorithm is said to be double exponential time if ''T''(''n'') is upper bounded by 22poly(''n''), where poly(''n'') is some polynomial in ''n''. Such algorithms belong to the complexity class 2-EXPTIME. :\mbox = \bigcup_ \mbox\left( 2^\right) Well-known double exponential time algorithms include: * Decision procedures for Presburger arithmetic * Computing a Gröbner basis (in the worst case) * Quantifier elimination on real closed fields takes at least double exponential time, and can be done in this time.


See also

* L-notation * Space complexity


References

{{Use dmy dates, date=September 2019 Analysis of algorithms Computational complexity theory Computational resources Time