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A part of an infinite sequence of real numbers (in blue), indexed by a natural number . This sequence is neither increasing, decreasing, convergent, nor Cauchy. It is, however, bounded (by red dashed lines).

In mathematics, a sequence is an enumerated collection of objects in which repetitions are allowed and order matters. Like a set, it contains members (also called elements, or terms). The number of elements (possibly infinite) is called the length of the sequence. Unlike a set, the same elements can appear multiple times at different positions in a sequence, and unlike a set, the order does matter. Formally, a sequence can be defined as a function from natural numbers (the positions of elements in the sequence) to the elements at each position. The notion of a sequence can be generalized to an indexed family, defined as a function from an arbitrary index set.

For example, (M, A, R, Y) is a sequence of letters with the letter "M" first and "Y" last. This sequence differs from (A, R, M, Y). Also, the sequence (1, 1, 2, 3, 5, 8), which contains the number 1 at two different positions, is a valid sequence. Sequences can be finite, as in these examples, or infinite, such as the sequence of all even positive integers (2, 4, 6, ...).

The position of an element in a sequence is its rank or index; it is the natural number for which the element is the image. The first element has index 0 or 1, depending on the context or a specific convention. In mathematical analysis, a sequence is often denoted by letters in the form of , and , where the subscript n refers to the nth element of the sequence; for example, the nth element of the Fibonacci sequence is generally denoted as .

In computing and computer science, finite sequences are usually called strings, words or lists, with the specific technical term chosen depending on the type of object the sequence enumerates and the different ways to represent the sequence in computer memory. Infinite sequences are called streams.

The empty sequence ( ) is included in most notions of sequence. It may be excluded depending on the context.

Examples and notation

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A sequence can be thought of as a list of elements with a particular order.[1][2] Sequences are useful in a number of mathematical disciplines for studying functions, spaces, and other mathematical structures using the convergence properties of sequences. In particular, sequences are the basis for series, which are important in differential equations and analysis. Sequences are also of interest in their own right, and can be studied as patterns or puzzles, such as in the study of prime numbers.

There are a number of ways to denote a sequence, some of which are more useful for specific types of sequences. One way to specify a sequence is to list all its elements. For example, the first four odd numbers form the sequence (1, 3, 5, 7). This notation is used for infinite sequences as well. For instance, the infinite sequence of positive odd integers is written as (1, 3, 5, 7, ...). Because notating sequences with ellipsis leads to ambiguity, listing is most useful for customary infinite sequences which can be easily recognized from their first few elements. Other ways of denoting a sequence are discussed after the examples.

Examples

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A tiling with squares whose sides are successive Fibonacci numbers in length.

The prime numbers are the natural numbers greater than 1 that have no divisors but 1 and themselves. Taking these in their natural order gives the sequence (2, 3, 5, 7, 11, 13, 17, ...). The prime numbers are widely used in mathematics, particularly in number theory where many results related to them exist.

The Fibonacci numbers comprise the integer sequence in which each element is the sum of the previous two elements. The first two elements are either 0 and 1 or 1 and 1 so that the sequence is (0, 1, 1, 2, 3, 5, 8, 13, 21, 34, ...).[1]

Other examples of sequences include those made up of rational numbers, real numbers and complex numbers. The sequence (.9, .99, .999, .9999, ...), for instance, approaches the number 1. In fact, every real number can be written as the limit of a sequence of rational numbers (e.g. via its decimal expansion, also see completeness of the real numbers). As another example, π is the limit of the sequence (3, 3.1, 3.14, 3.141, 3.1415, ...), which is increasing. A related sequence is the sequence of decimal digits of π, that is, (3, 1, 4, 1, 5, 9, ...). Unlike the preceding sequence, this sequence does not have any pattern that is easily discernible by inspection.

Other examples are sequences of functions, whose elements are functions instead of numbers.

The On-Line Encyclopedia of Integer Sequences comprises a large list of examples of integer sequences.[3]

Indexing

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Other notations can be useful for sequences whose pattern cannot be easily guessed or for sequences that do not have a pattern such as the digits of π. One such notation is to write down a general formula for computing the nth term as a function of n, enclose it in parentheses, and include a subscript indicating the set of values that n can take. For example, in this notation the sequence of even numbers could be written as . The sequence of squares could be written as . The variable n is called an index, and the set of values that it can take is called the index set.

It is often useful to combine this notation with the technique of treating the elements of a sequence as individual variables. This yields expressions like , which denotes a sequence whose nth element is given by the variable . For example:

One can consider multiple sequences at the same time by using different variables; e.g. could be a different sequence than . One can even consider a sequence of sequences: denotes a sequence whose mth term is the sequence .

An alternative to writing the domain of a sequence in the subscript is to indicate the range of values that the index can take by listing its highest and lowest legal values. For example, the notation denotes the ten-term sequence of squares . The limits and are allowed, but they do not represent valid values for the index, only the supremum or infimum of such values, respectively. For example, the sequence is the same as the sequence , and does not contain an additional term "at infinity". The sequence is a bi-infinite sequence, and can also be written as .

In cases where the set of indexing numbers is understood, the subscripts and superscripts are often left off. That is, one simply writes for an arbitrary sequence. Often, the index k is understood to run from 1 to ∞. However, sequences are frequently indexed starting from zero, as in

In some cases, the elements of the sequence are related naturally to a sequence of integers whose pattern can be easily inferred. In these cases, the index set may be implied by a listing of the first few abstract elements. For instance, the sequence of squares of odd numbers could be denoted in any of the following ways.

Moreover, the subscripts and superscripts could have been left off in the third, fourth, and fifth notations, if the indexing set was understood to be the natural numbers. In the second and third bullets, there is a well-defined sequence , but it is not the same as the sequence denoted by the expression.

Defining a sequence by recursion

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Sequences whose elements are related to the previous elements in a straightforward way are often defined using recursion. This is in contrast to the definition of sequences of elements as functions of their positions.

To define a sequence by recursion, one needs a rule, called recurrence relation to construct each element in terms of the ones before it. In addition, enough initial elements must be provided so that all subsequent elements of the sequence can be computed by successive applications of the recurrence relation.

The Fibonacci sequence is a simple classical example, defined by the recurrence relation

with initial terms and . From this, a simple computation shows that the first ten terms of this sequence are 0, 1, 1, 2, 3, 5, 8, 13, 21, and 34.

A complicated example of a sequence defined by a recurrence relation is Recamán's sequence,[4] defined by the recurrence relation

with initial term

A linear recurrence with constant coefficients is a recurrence relation of the form

where are constants. There is a general method for expressing the general term of such a sequence as a function of n; see Linear recurrence. In the case of the Fibonacci sequence, one has and the resulting function of n is given by Binet's formula.

A holonomic sequence is a sequence defined by a recurrence relation of the form

where are polynomials in n. For most holonomic sequences, there is no explicit formula for expressing as a function of n. Nevertheless, holonomic sequences play an important role in various areas of mathematics. For example, many special functions have a Taylor series whose sequence of coefficients is holonomic. The use of the recurrence relation allows a fast computation of values of such special functions.

Not all sequences can be specified by a recurrence relation. An example is the sequence of prime numbers in their natural order (2, 3, 5, 7, 11, 13, 17, ...).

Formal definition and basic properties

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There are many different notions of sequences in mathematics, some of which (e.g., exact sequence) are not covered by the definitions and notations introduced below.

Definition

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In this article, a sequence is formally defined as a function whose domain is an interval of integers. This definition covers several different uses of the word "sequence", including one-sided infinite sequences, bi-infinite sequences, and finite sequences (see below for definitions of these kinds of sequences). However, many authors use a narrower definition by requiring the domain of a sequence to be the set of natural numbers. This narrower definition has the disadvantage that it rules out finite sequences and bi-infinite sequences, both of which are usually called sequences in standard mathematical practice. Another disadvantage is that, if one removes the first terms of a sequence, one needs reindexing the remainder terms for fitting this definition. In some contexts, to shorten exposition, the codomain of the sequence is fixed by context, for example by requiring it to be the set of real numbers,[5] the set of complex numbers,[6] or a topological space.[7]

Although sequences are a type of function, they are usually distinguished notationally from functions in that the input is written as a subscript rather than in parentheses, that is, an rather than a(n). There are terminological differences as well: the value of a sequence at the lowest input (often 1) is called the "first element" of the sequence, the value at the second smallest input (often 2) is called the "second element", etc. Also, while a function abstracted from its input is usually denoted by a single letter, e.g. f, a sequence abstracted from its input is usually written by a notation such as , or just as Here A is the domain, or index set, of the sequence.

Sequences and their limits (see below) are important concepts for studying topological spaces. An important generalization of sequences is the concept of nets. A net is a function from a (possibly uncountable) directed set to a topological space. The notational conventions for sequences normally apply to nets as well.

Finite and infinite

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The length of a sequence is defined as the number of terms in the sequence.

A sequence of a finite length is a finite sequence. A finite sequence of length n is also called an n-tuple. Finite sequences include the empty sequence, denoted ( ), that has no elements.

Normally, the term infinite sequence refers to a sequence that is infinite in one direction, and finite in the other; such a sequence has a first element, but no final element, and are called singly infinite sequence or a one-sided infinite sequence when disambiguation is needed. In contrast, a sequence that is infinite in both directions—i.e. that has neither a first nor a final element—is called a bi-infinite sequence, two-way infinite sequence, or doubly infinite sequence. A function from the set of all integers, into a set, for example the sequence of all even integers ( ..., −4, −2, 0, 2, 4, 6, 8, ... ), is bi-infinite. This sequence could be denoted .

Increasing and decreasing

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A sequence is said to be monotonically increasing if each term is greater than or equal to the one before it. For example, the sequence is monotonically increasing if and only if for all If each consecutive term is strictly greater than (>) the previous term then the sequence is called strictly monotonically increasing. A sequence is monotonically decreasing if each consecutive term is less than or equal to the previous one, and is strictly monotonically decreasing if each is strictly less than the previous. If a sequence is either increasing or decreasing it is called a monotone sequence. This is a special case of the more general notion of a monotonic function.

The terms nondecreasing and nonincreasing are often used in place of increasing and decreasing in order to avoid any possible confusion with strictly increasing and strictly decreasing, respectively.

Bounded

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If the sequence of real numbers (an) is such that all the terms are less than some real number M, then the sequence is said to be bounded from above. In other words, this means that there exists M such that for all n, anM. Any such M is called an upper bound. Likewise, if, for some real m, anm for all n greater than some N, then the sequence is bounded from below and any such m is called a lower bound. If a sequence is both bounded from above and bounded from below, then the sequence is said to be bounded.

Subsequences

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A subsequence of a given sequence is a sequence formed from the given sequence by deleting some of the elements without disturbing the relative positions of the remaining elements. For instance, the sequence of positive even integers (2, 4, 6, ...) is a subsequence of the positive integers (1, 2, 3, ...). The positions of some elements change when other elements are deleted. However, the relative positions are preserved.

Formally, a subsequence of the sequence is any sequence of the form , where is a strictly increasing sequence of positive integers.

Other types of sequences

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Some other types of sequences that are easy to define include:

  • An integer sequence is a sequence whose terms are integers.
  • A polynomial sequence is a sequence whose terms are polynomials.
  • A positive integer sequence is sometimes called multiplicative, if anm = an am for all pairs n, m such that n and m are coprime.[8] In other instances, sequences are often called multiplicative, if an = na1 for all n. Moreover, a multiplicative Fibonacci sequence[9] satisfies the recursion relation an = an−1 an−2.
  • A binary sequence is a sequence whose terms have one of two discrete values, e.g. base 2 values (0,1,1,0, ...), a series of coin tosses (Heads/Tails) H,T,H,H,T, ..., the answers to a set of True or False questions (T, F, T, T, ...), and so on.

Limits and convergence

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The plot of a convergent sequence (an) is shown in blue. From the graph we can see that the sequence is converging to the limit zero as n increases.

An important property of a sequence is convergence. If a sequence converges, it converges to a particular value known as the limit. If a sequence converges to some limit, then it is convergent. A sequence that does not converge is divergent.

Informally, a sequence has a limit if the elements of the sequence become closer and closer to some value (called the limit of the sequence), and they become and remain arbitrarily close to , meaning that given a real number greater than zero, all but a finite number of the elements of the sequence have a distance from less than .

For example, the sequence shown to the right converges to the value 0. On the other hand, the sequences (which begins 1, 8, 27, ...) and (which begins −1, 1, −1, 1, ...) are both divergent.

If a sequence converges, then the value it converges to is unique. This value is called the limit of the sequence. The limit of a convergent sequence is normally denoted . If is a divergent sequence, then the expression is meaningless.

Formal definition of convergence

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A sequence of real numbers converges to a real number if, for all , there exists a natural number such that for all we have[5]

If is a sequence of complex numbers rather than a sequence of real numbers, this last formula can still be used to define convergence, with the provision that denotes the complex modulus, i.e. . If is a sequence of points in a metric space, then the formula can be used to define convergence, if the expression is replaced by the expression , which denotes the distance between and .

Applications and important results

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If and are convergent sequences, then the following limits exist, and can be computed as follows:[5][10]

  • for all real numbers
  • , provided that
  • for all and

Moreover:

  • If for all greater than some , then .[a]
  • (Squeeze theorem)
    If is a sequence such that for all and ,
    then is convergent, and .
  • If a sequence is bounded and monotonic then it is convergent.
  • A sequence is convergent if and only if all of its subsequences are convergent.

Cauchy sequences

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The plot of a Cauchy sequence (Xn), shown in blue, as Xn versus n. In the graph the sequence appears to be converging to a limit as the distance between consecutive terms in the sequence gets smaller as n increases. In the real numbers every Cauchy sequence converges to some limit.

A Cauchy sequence is a sequence whose terms become arbitrarily close together as n gets very large. The notion of a Cauchy sequence is important in the study of sequences in metric spaces, and, in particular, in real analysis. One particularly important result in real analysis is Cauchy characterization of convergence for sequences:

A sequence of real numbers is convergent (in the reals) if and only if it is Cauchy.

In contrast, there are Cauchy sequences of rational numbers that are not convergent in the rationals, e.g. the sequence defined by and is Cauchy, but has no rational limit (cf. Cauchy sequence § Non-example: rational numbers). More generally, any sequence of rational numbers that converges to an irrational number is Cauchy, but not convergent when interpreted as a sequence in the set of rational numbers.

Metric spaces that satisfy the Cauchy characterization of convergence for sequences are called complete metric spaces and are particularly nice for analysis.

Infinite limits

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In calculus, it is common to define notation for sequences which do not converge in the sense discussed above, but which instead become and remain arbitrarily large, or become and remain arbitrarily negative. If becomes arbitrarily large as , we write

In this case we say that the sequence diverges, or that it converges to infinity. An example of such a sequence is an = n.

If becomes arbitrarily negative (i.e. negative and large in magnitude) as , we write

and say that the sequence diverges or converges to negative infinity.

Series

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A series is, informally speaking, the sum of the terms of a sequence. That is, it is an expression of the form or , where is a sequence of real or complex numbers. The partial sums of a series are the expressions resulting from replacing the infinity symbol with a finite number, i.e. the Nth partial sum of the series is the number

The partial sums themselves form a sequence , which is called the sequence of partial sums of the series . If the sequence of partial sums converges, then we say that the series is convergent, and the limit is called the value of the series. The same notation is used to denote a series and its value, i.e. we write .

Use in other fields of mathematics

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Topology

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Sequences play an important role in topology, especially in the study of metric spaces. For instance:

  • A metric space is compact exactly when it is sequentially compact.
  • A function from a metric space to another metric space is continuous exactly when it takes convergent sequences to convergent sequences.
  • A metric space is a connected space if and only if, whenever the space is partitioned into two sets, one of the two sets contains a sequence converging to a point in the other set.
  • A topological space is separable exactly when there is a dense sequence of points.

Sequences can be generalized to nets or filters. These generalizations allow one to extend some of the above theorems to spaces without metrics.

Product topology

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The topological product of a sequence of topological spaces is the cartesian product of those spaces, equipped with a natural topology called the product topology.

More formally, given a sequence of spaces , the product space

is defined as the set of all sequences such that for each i, is an element of . The canonical projections are the maps pi : XXi defined by the equation . Then the product topology on X is defined to be the coarsest topology (i.e. the topology with the fewest open sets) for which all the projections pi are continuous. The product topology is sometimes called the Tychonoff topology.

Analysis

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When discussing sequences in analysis, one will generally consider sequences of the form

which is to say, infinite sequences of elements indexed by natural numbers.

A sequence may start with an index different from 1 or 0. For example, the sequence defined by xn = 1/log(n) would be defined only for n ≥ 2. When talking about such infinite sequences, it is usually sufficient (and does not change much for most considerations) to assume that the members of the sequence are defined at least for all indices large enough, that is, greater than some given N.

The most elementary type of sequences are numerical ones, that is, sequences of real or complex numbers. This type can be generalized to sequences of elements of some vector space. In analysis, the vector spaces considered are often function spaces. Even more generally, one can study sequences with elements in some topological space.

Sequence spaces

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A sequence space is a vector space whose elements are infinite sequences of real or complex numbers. Equivalently, it is a function space whose elements are functions from the natural numbers to the field K, where K is either the field of real numbers or the field of complex numbers. The set of all such functions is naturally identified with the set of all possible infinite sequences with elements in K, and can be turned into a vector space under the operations of pointwise addition of functions and pointwise scalar multiplication. All sequence spaces are linear subspaces of this space. Sequence spaces are typically equipped with a norm, or at least the structure of a topological vector space.

The most important sequences spaces in analysis are the ℓp spaces, consisting of the p-power summable sequences, with the p-norm. These are special cases of Lp spaces for the counting measure on the set of natural numbers. Other important classes of sequences like convergent sequences or null sequences form sequence spaces, respectively denoted c and c0, with the sup norm. Any sequence space can also be equipped with the topology of pointwise convergence, under which it becomes a special kind of Fréchet space called an FK-space.

Linear algebra

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Sequences over a field may also be viewed as vectors in a vector space. Specifically, the set of F-valued sequences (where F is a field) is a function space (in fact, a product space) of F-valued functions over the set of natural numbers.

Abstract algebra

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Abstract algebra employs several types of sequences, including sequences of mathematical objects such as groups or rings.

Free monoid

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If A is a set, the free monoid over A (denoted A*, also called Kleene star of A) is a monoid containing all the finite sequences (or strings) of zero or more elements of A, with the binary operation of concatenation. The free semigroup A+ is the subsemigroup of A* containing all elements except the empty sequence.

Exact sequences

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In the context of group theory, a sequence

of groups and group homomorphisms is called exact, if the image (or range) of each homomorphism is equal to the kernel of the next:

The sequence of groups and homomorphisms may be either finite or infinite.

A similar definition can be made for certain other algebraic structures. For example, one could have an exact sequence of vector spaces and linear maps, or of modules and module homomorphisms.

Spectral sequences

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In homological algebra and algebraic topology, a spectral sequence is a means of computing homology groups by taking successive approximations. Spectral sequences are a generalization of exact sequences, and since their introduction by Jean Leray (1946), they have become an important research tool, particularly in homotopy theory.

Set theory

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An ordinal-indexed sequence is a generalization of a sequence. If α is a limit ordinal and X is a set, an α-indexed sequence of elements of X is a function from α to X. In this terminology an ω-indexed sequence is an ordinary sequence.

Computing

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In computer science, finite sequences are called lists. Potentially infinite sequences are called streams. Finite sequences of characters or digits are called strings.

Streams

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Infinite sequences of digits (or characters) drawn from a finite alphabet are of particular interest in theoretical computer science. They are often referred to simply as sequences or streams, as opposed to finite strings. Infinite binary sequences, for instance, are infinite sequences of bits (characters drawn from the alphabet {0, 1}). The set C = {0, 1} of all infinite binary sequences is sometimes called the Cantor space.

An infinite binary sequence can represent a formal language (a set of strings) by setting the n th bit of the sequence to 1 if and only if the n th string (in shortlex order) is in the language. This representation is useful in the diagonalization method for proofs.[11]

See also

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Notes

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
In , a sequence is an ordered list of elements, typically numbers, arranged according to a specific rule or pattern, which may be finite or infinite in length. Formally, a sequence can be defined as a function from the natural numbers (or a subset thereof, such as the positive integers starting from 1 or 0) to a like the real numbers, where each term is denoted as ana_n for the nnth position. Sequences are fundamental in , serving as the building blocks for concepts like limits, convergence, and infinite series, which underpin differential equations, approximations, and many areas of advanced . Common types of sequences include arithmetic sequences, where each term after the first is obtained by adding a constant difference dd to the preceding term (e.g., 2, 5, 8, 11, ... with d=3d = 3), and geometric sequences, where each term is multiplied by a constant ratio rr (e.g., 3, 6, 12, 24, ... with r=2r = 2). Other notable sequences encompass the , defined recursively by F1=1F_1 = 1, F2=1F_2 = 1, and Fn=Fn1+Fn2F_n = F_{n-1} + F_{n-2} for n>2n > 2 (yielding 1, 1, 2, 3, 5, 8, ...), which appears in natural patterns like plant growth and is influential in and . Sequences can also be finite, terminating after a fixed number of terms, or infinite, continuing indefinitely, with the latter often studied for properties like monotonicity (always increasing or decreasing) or boundedness (confined within limits). A key aspect of infinite sequences is convergence: a sequence {an}\{a_n\} converges to a limit LL if, for every ϵ>0\epsilon > 0, there exists an NN such that for all n>Nn > N, anL<ϵ|a_n - L| < \epsilon, enabling the analysis of behavior as nn approaches infinity. This property is crucial for defining continuity in functions and sums of series, where the partial sums form a sequence themselves. Beyond pure mathematics, sequences model real-world phenomena, such as population growth in arithmetic or geometric progressions, financial compounding, and algorithmic iterations in computing.

Basic Concepts and Examples

Intuitive Examples

A sequence in mathematics can be intuitively understood as an ordered list of numbers, where each number is associated with a specific position, often indexed by positive integers starting from 1 or 0. This ordering distinguishes sequences from mere sets, as the position of each term matters; for example, rearranging the terms would yield a different sequence. One of the simplest examples is the sequence of natural numbers: 1,2,3,4,1, 2, 3, 4, \dots , which continues indefinitely and represents the basic counting process. Another straightforward infinite sequence is the even numbers: 2,4,6,8,2, 4, 6, 8, \dots , where each term is twice its position if indexed from 1 (an=2na_n = 2n). Finite sequences also arise naturally, such as the first four odd numbers: 1,3,5,71, 3, 5, 7, which terminates after a fixed number of terms. Arithmetic sequences provide an intuitive progression with a constant difference between terms, like 2,5,8,11,2, 5, 8, 11, \dots (common difference of 3, given by an=3n1a_n = 3n - 1), modeling steady increases such as daily temperature rises. Geometric sequences, in contrast, multiply by a constant ratio, as in 2,4,8,16,2, 4, 8, 16, \dots (ratio of 2), illustrating exponential growth like bacterial population doubling. The Fibonacci sequence offers a recursive intuitive example: starting with 1 and 1, each subsequent term is the sum of the previous two, yielding 1,1,2,3,5,8,13,1, 1, 2, 3, 5, 8, 13, \dots ; it models natural growth patterns, such as the idealized reproduction of rabbit pairs where each pair produces a new pair monthly after maturity. Alternating sequences, like 3,5,3,5,3, 5, 3, 5, \dots (given by an=4+(1)na_n = 4 + (-1)^n), highlight oscillatory behavior, akin to flipping between two states.

Standard Notation and Indexing

In mathematics, sequences are formally defined as functions from an ordered index set, typically the natural numbers, to a codomain such as the real numbers R\mathbb{R}. The standard notation employs subscripts to denote terms, with the general term written as ana_n, where nn serves as the index indicating the position. The full sequence is commonly expressed as (an)n=1(a_n)_{n=1}^\infty or {an}n=1\{a_n\}_{n=1}^\infty, signifying an infinite ordered list where the subscript nn ranges over the positive integers starting from 1. This convention aligns with the sequence's role as a mapping a:NRa: \mathbb{N} \to \mathbb{R}, emphasizing order and potential repetition. Indexing typically begins at n=1n=1 in pure mathematics to align with positive integers, though contexts like computer science or certain analytical treatments may start at n=0n=0. For instance, the sequence of squares can be an=n2a_n = n^2 for n1n \geq 1, yielding 1,4,9,1, 4, 9, \dots, or adjusted to include 00 if indexing from 0. Finite sequences use bounded indices, denoted as (an)n=1k(a_n)_{n=1}^k or listed explicitly as (a1,a2,,ak)(a_1, a_2, \dots, a_k), such as (2,3,5,7)(2, 3, 5, 7) for the first four primes. The subscript notation facilitates compact expressions for operations like limits, e.g., limnan=L\lim_{n \to \infty} a_n = L. While function notation a(n)a(n) is occasionally used for clarity in definitions, subscript ana_n is preferred for its brevity and tradition in analysis, avoiding confusion with general functions. Variations in starting index do not alter core properties like convergence, provided the domain is consistently specified; for example, shifting indices merely reindexes the terms without changing the tail behavior relevant to limits.

Recursive Definitions

A recursive definition of a sequence specifies one or more initial terms and a recurrence relation that expresses each subsequent term as a function of the preceding terms. This approach contrasts with explicit definitions, which provide a direct formula for the nnth term without reference to prior terms. Recursive definitions are fundamental in discrete mathematics and are often used in conjunction with mathematical induction to prove properties of sequences. Formally, for a sequence (an)n=0(a_n)_{n=0}^\infty or (an)n=1(a_n)_{n=1}^\infty, the recursive definition consists of conditions, such as a0=c0a_0 = c_0 or a1=c1a_1 = c_1, and a recurrence relation an=f(an1,an2,,ank)a_n = f(a_{n-1}, a_{n-2}, \dots, a_{n-k}) for n>kn > k, where kk is the order of the recursion (the number of previous terms required). recursions depend on a single prior term, while higher-order ones involve multiple. The order determines the number of conditions needed; for example, a second-order recursion requires two starting values. Such definitions enable iterative computation of terms but may require unfolding the to find closed-form expressions for analysis. Arithmetic sequences provide a simple first-order example. Given an initial term a1a_1 and common difference dd, the recursive formula is an+1=an+da_{n+1} = a_n + d for n1n \geq 1. For instance, starting with a1=2a_1 = 2 and d=3d = 3, the sequence is 2, 5, 8, 11, ..., where each term adds 3 to the previous. This recursion directly reflects the constant difference property of arithmetic progressions. Geometric sequences are similarly defined recursively by a first-order relation. With initial term a1a_1 and common ratio rr, the formula is an+1=rana_{n+1} = r \cdot a_n for n1n \geq 1. For a1=1a_1 = 1 and r=2r = 2, the sequence is 1, 2, 4, 8, ..., doubling each term. This captures the multiplicative growth inherent to geometric progressions. Higher-order recursions appear in more complex sequences, such as the Fibonacci sequence, a second-order linear recurrence. Defined by f1=1f_1 = 1, f2=1f_2 = 1, and fn=fn1+fn2f_n = f_{n-1} + f_{n-2} for n3n \geq 3, it generates 1, 1, 2, 3, 5, 8, 13, .... The factorial sequence offers a non-linear example: 0!=10! = 1 and n!=n(n1)!n! = n \cdot (n-1)! for n1n \geq 1, yielding 1, 1, 2, 6, 24, .... These examples illustrate how recursive definitions model combinatorial growth and are solvable via methods like generating functions or characteristic equations for linear cases.

Formal Properties

Mathematical Definition

In , a sequence is formally defined as a function whose domain is the set of natural numbers (typically starting from 1) and whose is a specified set, such as the real numbers R\mathbb{R}. More precisely, given a function f:NXf: \mathbb{N} \to X, where N={1,2,3,}\mathbb{N} = \{1, 2, 3, \dots\} and XX is any set (often R\mathbb{R} or C\mathbb{C}), the sequence is the ordered list of values f(1),f(2),f(3),f(1), f(2), f(3), \dots, denoted as {an}n=1\{a_n\}_{n=1}^\infty where an=f(n)a_n = f(n) for each nNn \in \mathbb{N}. This definition emphasizes the ordered nature of the elements, distinguishing sequences from unordered sets. The range of the sequence, consisting of the terms ana_n, forms an infinite list that may or may not follow a discernible pattern, but the indexing by natural numbers ensures a well-defined order. For example, the sequence defined by f(n)=nf(n) = n yields {1,2,3,}\{1, 2, 3, \dots\}, while f(n)=1nf(n) = \frac{1}{n} gives {11,12,13,}\left\{\frac{1}{1}, \frac{1}{2}, \frac{1}{3}, \dots\right\}. In some contexts, particularly in computer science or when including an initial term, the domain may start from 0, so N0={0,1,2,}\mathbb{N}_0 = \{0, 1, 2, \dots\}, but in real analysis, indexing from 1 is conventional to align with limit definitions. This functional perspective allows sequences to be analyzed using tools from and , such as convergence, where the behavior of ana_n as nn \to \infty is studied. Sequences generalize to arbitrary index sets that are well-ordered, but the standard case uses countable indices for simplicity and applicability in .

Finite and Infinite Sequences

A finite sequence is defined as an ordered list of elements where the domain of the indexing function is the set of the first nn positive integers, {1,2,,n}\{1, 2, \dots, n\}, for some positive integer nn. This results in a sequence with exactly nn terms, denoted as (a1,a2,,an)(a_1, a_2, \dots, a_n) or {ak}k=1n\{a_k\}_{k=1}^n. For instance, the sequence defined by ak=k2a_k = k^2 for k=1k = 1 to 55 yields (1,4,9,16,25)(1, 4, 9, 16, 25). Finite sequences are straightforward to compute in their entirety, including operations like summation, which is simply k=1nak\sum_{k=1}^n a_k, without requiring limits. In contrast, an infinite sequence is an ordered list where the domain is the set of all positive integers, {1,2,3,}\{1, 2, 3, \dots\}, producing terms that continue indefinitely, denoted as (a1,a2,a3,)(a_1, a_2, a_3, \dots) or {an}n=1\{a_n\}_{n=1}^\infty. An example is the harmonic sequence an=1na_n = \frac{1}{n}, which begins (1,12,13,)(1, \frac{1}{2}, \frac{1}{3}, \dots). Infinite sequences form the foundation for studying convergence and limits, as their behavior is analyzed through the limit of terms as nn approaches infinity, rather than a fixed endpoint. Summation of infinite sequences leads to infinite series, where the total sum, if it exists, is the limit of partial sums limnk=1nak\lim_{n \to \infty} \sum_{k=1}^n a_k. The primary distinction between finite and infinite sequences lies in their length and analytical treatment: finite sequences terminate and allow complete enumeration, making them suitable for discrete computations, while infinite sequences require to determine properties like boundedness or monotonicity over unbounded indices. Both can be defined explicitly via a for the general term ana_n or recursively, but infinite sequences often exhibit patterns that persist indefinitely, such as arithmetic progressions where an+1=an+da_{n+1} = a_n + d for constant dd. The empty sequence, with n=0n=0, is sometimes considered a finite sequence of zero.

Monotonicity

In , a sequence {an}\{a_n\} is said to be monotonic if it is either monotonically increasing or monotonically decreasing. A sequence is monotonically increasing if anan+1a_n \leq a_{n+1} for all nNn \in \mathbb{N}, and strictly increasing if the inequality is strict, i.e., an<an+1a_n < a_{n+1}. Conversely, a sequence is monotonically decreasing if anan+1a_n \geq a_{n+1} for all nn, and strictly decreasing if an>an+1a_n > a_{n+1}. Monotonicity provides a fundamental for analyzing behavior, particularly in relation to convergence. The states that a monotonic converges it is bounded. Specifically, if {an}\{a_n\} is monotonically increasing and bounded above, it converges to its least upper bound (supremum); if monotonically decreasing and bounded below, it converges to its greatest lower bound (infimum). An unbounded monotonic diverges to ++\infty or -\infty, depending on the direction. For example, the sequence an=na_n = n is strictly increasing but unbounded above, hence diverges to ++\infty. In contrast, an=11na_n = 1 - \frac{1}{n} is strictly increasing and bounded above by 1, so it converges to 1. These properties are essential in , as they link order-preserving behavior to limit existence without requiring explicit computation.

Boundedness

In mathematics, a sequence {an}n=1\{a_n\}_{n=1}^\infty of real numbers is said to be bounded if there exist real numbers mm and MM such that manMm \leq a_n \leq M for all nNn \in \mathbb{N}. This condition ensures that all terms of the sequence lie within a finite interval [m,M][m, M]. Equivalently, a sequence is bounded if the set {an:nN}\{a_n : n \in \mathbb{N}\} is bounded as a subset of R\mathbb{R}. A sequence is bounded above if there exists a MM such that anMa_n \leq M for all nNn \in \mathbb{N}; this MM is an upper bound for the sequence. Similarly, it is bounded below if there exists a mm such that anma_n \geq m for all nNn \in \mathbb{N}, where mm serves as a lower bound. A sequence is bounded it is both bounded above and bounded below. If no such finite bounds exist, the sequence is unbounded. For example, the sequence an=1na_n = \frac{1}{n} is bounded, as 0<an10 < a_n \leq 1 for all n1n \geq 1. In contrast, the sequence an=na_n = n is unbounded above (and hence unbounded), since its terms grow without limit. Another illustrative case is an=(1)na_n = (-1)^n, which is bounded (with 1an1-1 \leq a_n \leq 1) but oscillates indefinitely. Boundedness is a fundamental property in the study of sequences, particularly in relation to convergence. Every convergent sequence is bounded: if {an}L\{a_n\} \to L, then for ϵ=1\epsilon = 1, there exists NN such that anL<1|a_n - L| < 1 for all n>Nn > N, implying anL+1|a_n| \leq |L| + 1 for all nn (and adjusting for the finite initial terms). However, the converse does not hold, as the bounded sequence an=(1)na_n = (-1)^n diverges by oscillation. Unbounded sequences necessarily diverge, as they cannot approach any finite limit. In more advanced contexts, the least upper bound (supremum) and greatest lower bound (infimum) of a bounded sequence provide tight bounds: a sequence is bounded above sup{an:nN}<\sup\{a_n : n \in \mathbb{N}\} < \infty, and similarly for the infimum below. For monotone sequences, boundedness is sufficient for convergence: a monotone increasing sequence that is bounded above converges to its supremum.

Advanced Sequence Structures

Subsequences

In mathematics, a subsequence of an infinite sequence (an)n=1(a_n)_{n=1}^\infty is obtained by selecting terms from the original sequence while preserving their relative order, specifically through a strictly increasing sequence of indices nkn_k such that 1n1<n2<n3<1 \leq n_1 < n_2 < n_3 < \cdots and the subsequence is given by (ank)k=1(a_{n_k})_{k=1}^\infty. This construction ensures that the subsequence is itself an infinite sequence derived from a subset of the indices of the parent sequence. For example, consider the sequence of rational numbers an=(1)n/na_n = (-1)^n / n, which alternates between positive and negative values approaching zero. The terms with even indices form the subsequence a2k=1/(2k)a_{2k} = 1/(2k), which is positive and decreasing to zero, while the odd-indexed terms yield a2k1=1/(2k1)a_{2k-1} = -1/(2k-1), approaching zero from the negative side. Subsequences can skip finitely or infinitely many terms, but the indices must remain strictly increasing to maintain the order. Subsequences inherit key properties from their parent sequences. If (an)(a_n) converges to a limit LL, then every subsequence (ank)(a_{n_k}) also converges to LL. Similarly, if (an)(a_n) is monotone (either non-decreasing or non-increasing), all its subsequences are monotone in the same direction. The relation of being a subsequence is reflexive (a sequence is a subsequence of itself via the identity indices) and transitive (a subsequence of a subsequence is a subsequence of the original), but not symmetric. A fundamental result is that every sequence of real numbers contains a monotone subsequence. This follows from the fact that, in any sequence, one can construct either a non-decreasing subsequence by greedily selecting terms that are at least as large as previous ones or a non-increasing one otherwise, ensuring the process continues indefinitely. The Bolzano–Weierstrass theorem provides another essential property: every bounded sequence of real numbers has at least one convergent subsequence. This theorem underpins much of real analysis, as it guarantees the existence of limit points for bounded sets derived from sequences, and its proof often relies on the monotone subsequence theorem combined with the completeness of the reals. For instance, in the bounded sequence an=sinna_n = \sin n, which does not converge, subsequences exist that converge to any value in [1,1][-1, 1] due to the density of the indices modulo 2π2\pi.

Constant and Arithmetic Sequences

A constant sequence is a sequence in which every term is identical, denoted as an=ca_n = c for all nNn \in \mathbb{N}, where cc is a fixed real number. This simplest form of sequence exhibits no variation across its terms, making it trivially convergent to cc and bounded by c|c|. Constant sequences serve as foundational examples in the study of sequence properties, illustrating uniform behavior without progression or oscillation. In contrast, an arithmetic sequence, also known as an arithmetic progression, is defined by a first term a1=aa_1 = a and a common difference dd, such that the nnth term is given by the explicit formula an=a+(n1)da_n = a + (n-1)d. This structure arises from successive additions of the fixed increment dd, resulting in a linear progression. Arithmetic sequences are monotonic—increasing if d>0d > 0, decreasing if d<0d < 0, or constant if d=0d = 0—and their partial sums form an arithmetic series with the formula sn=n2[2a+(n1)d]s_n = \frac{n}{2} [2a + (n-1)d]. They appear ubiquitously in applications like modeling evenly spaced data points or calculating cumulative totals in finance. To distinguish, while constant sequences represent stasis (d=0d = 0), arithmetic sequences generalize this to include directed change, enabling the representation of straight-line trends in discrete settings. For instance, the sequence of even numbers 2,4,6,2, 4, 6, \dots is arithmetic with a=2a = 2 and d=2d = 2, whereas the constant sequence 5,5,5,5, 5, 5, \dots has no such progression. Both types are finite or infinite, but their boundedness depends on dd: constant sequences are always bounded, while unbounded arithmetic sequences diverge linearly. These sequences underpin more complex structures, such as in the derivation of summation formulas for higher-order progressions.

Geometric and Other Special Sequences

A geometric sequence is a sequence of numbers where each term after the first is obtained by multiplying the preceding term by a fixed, non-zero constant known as the common ratio rr. The general term of such a sequence, starting with initial term a1a_1 (or aa), is given by an=a1rn1a_n = a_1 r^{n-1} for n1n \geq 1. This form arises directly from the recursive definition an+1=rana_{n+1} = r a_n, ensuring constant ratio r=an+1/anr = a_{n+1}/a_n for all nn. The sum of the first nn terms of a finite geometric sequence, denoted SnS_n, is Sn=a11rn1rS_n = a_1 \frac{1 - r^n}{1 - r} when r1r \neq 1. This formula derives from multiplying the sequence by rr and subtracting: SnrSn=a1(1rn)S_n - r S_n = a_1 (1 - r^n), then solving for SnS_n. For the infinite case, if r<1|r| < 1, the series converges to S=a11rS = \frac{a_1}{1 - r}, as the partial sums approach this limit due to rn0r^n \to 0. Among other special sequences, the Fibonacci sequence stands out for its recursive structure and widespread applications. Defined by F1=1F_1 = 1, F2=1F_2 = 1, and Fn=Fn1+Fn2F_n = F_{n-1} + F_{n-2} for n>2n > 2, it generates terms 1, 1, 2, 3, 5, 8, 13, and so on. This linear homogeneous recurrence with constant coefficients leads to Binet's closed-form expression Fn=ϕn(ϕ)n5F_n = \frac{\phi^n - (-\phi)^{-n}}{\sqrt{5}}
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