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In mathematics, a tuple is a finite sequence or ordered list of numbers or, more generally, mathematical objects, which are called the elements of the tuple. An n-tuple is a tuple of n elements, where n is a non-negative integer. There is only one 0-tuple, called the empty tuple. A 1-tuple and a 2-tuple are commonly called a singleton and an ordered pair, respectively. The term "infinite tuple" is occasionally used for "infinite sequences".

Tuples are usually written by listing the elements within parentheses "( )" and separated by commas; for example, (2, 7, 4, 1, 7) denotes a 5-tuple. Other types of brackets are sometimes used, although they may have a different meaning.[a]

An n-tuple can be formally defined as the image of a function that has the set of the n first natural numbers as its domain. Tuples may be also defined from ordered pairs by a recurrence starting from an ordered pair; indeed, an n-tuple can be identified with the ordered pair of its (n − 1) first elements and its nth element, for example, .

In computer science, tuples come in many forms. Most typed functional programming languages implement tuples directly as product types,[1] tightly associated with algebraic data types, pattern matching, and destructuring assignment.[2] Many programming languages offer an alternative to tuples, known as record types, featuring unordered elements accessed by label.[3] A few programming languages combine ordered tuple product types and unordered record types into a single construct, as in C structs and Haskell records. Relational databases may formally identify their rows (records) as tuples.

Tuples also occur in relational algebra; when programming the semantic web with the Resource Description Framework (RDF); in linguistics;[4] and in philosophy.[5]

Etymology

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The term originated as an abstraction of the sequence: single, couple/double, triple, quadruple, quintuple, sextuple, septuple, octuple, ..., n‑tuple, ..., where the prefixes are taken from the Latin names of the numerals. The unique 0-tuple is called the null tuple or empty tuple. A 1‑tuple is called a single (or singleton), a 2‑tuple is called an ordered pair or couple, and a 3‑tuple is called a triple (or triplet). The number n can be any nonnegative integer. For example, a complex number can be represented as a 2‑tuple of reals, a quaternion can be represented as a 4‑tuple, an octonion can be represented as an 8‑tuple, and a sedenion can be represented as a 16‑tuple.

Although these uses treat ‑tuple as the suffix, the original suffix was ‑ple as in "triple" (three-fold) or "decuple" (ten‑fold). This originates from medieval Latin plus (meaning "more") related to Greek ‑πλοῦς, which replaced the classical and late antique ‑plex (meaning "folded"), as in "duplex".[6][b]

Properties

[edit]

The general rule for the identity of two n-tuples is

if and only if .

Thus a tuple has properties that distinguish it from a set:

  1. A tuple may contain multiple instances of the same element, so
    tuple ; but set .
  2. Tuple elements are ordered: tuple , but set .
  3. A tuple has a finite number of elements, while a set or a multiset may have an infinite number of elements.

Definitions

[edit]

There are several definitions of tuples that give them the properties described in the previous section.

Tuples as functions

[edit]

The -tuple may be identified as the empty function. For the -tuple may be identified with the (surjective) function

with domain

and with codomain

that is defined at by

That is, is the function defined by

in which case the equality

necessarily holds.

Tuples as sets of ordered pairs

Functions are commonly identified with their graphs, which is a certain set of ordered pairs. Indeed, many authors use graphs as the definition of a function. Using this definition of "function", the above function can be defined as:

Tuples as nested ordered pairs

[edit]

Another way of modeling tuples in set theory is as nested ordered pairs. This approach assumes that the notion of ordered pair has already been defined.

  1. The 0-tuple (i.e. the empty tuple) is represented by the empty set .
  2. An n-tuple, with n > 0, can be defined as an ordered pair of its first entry and an (n − 1)-tuple (which contains the remaining entries when n > 1):

This definition can be applied recursively to the (n − 1)-tuple:

Thus, for example:

A variant of this definition starts "peeling off" elements from the other end:

  1. The 0-tuple is the empty set .
  2. For n > 0:

This definition can be applied recursively:

Thus, for example:

Tuples as nested sets

[edit]

Using Kuratowski's representation for an ordered pair, the second definition above can be reformulated in terms of pure set theory:

  1. The 0-tuple (i.e. the empty tuple) is represented by the empty set ;
  2. Let be an n-tuple , and let . Then, . (The right arrow, , could be read as "adjoined with".)

In this formulation:

n-tuples of m-sets

[edit]

In discrete mathematics, especially combinatorics and finite probability theory, n-tuples arise in the context of various counting problems and are treated more informally as ordered lists of length n.[7] n-tuples whose entries come from a set of m elements are also called arrangements with repetition, permutations of a multiset and, in some non-English literature, variations with repetition. The number of n-tuples of an m-set is mn. This follows from the combinatorial rule of product.[8] If S is a finite set of cardinality m, this number is the cardinality of the n-fold Cartesian power S × S × ⋯ × S. Tuples are elements of this product set.

Type theory

[edit]

In type theory, commonly used in programming languages, a tuple has a product type; this fixes not only the length, but also the underlying types of each component. Formally:

and the projections are term constructors:

The tuple with labeled elements used in the relational model has a record type. Both of these types can be defined as simple extensions of the simply typed lambda calculus.[9]

The notion of a tuple in type theory and that in set theory are related in the following way: If we consider the natural model of a type theory, and use the Scott brackets to indicate the semantic interpretation, then the model consists of some sets (note: the use of italics here that distinguishes sets from types) such that:

and the interpretation of the basic terms is:

.

The n-tuple of type theory has the natural interpretation as an n-tuple of set theory:[10]

The unit type has as semantic interpretation the 0-tuple.

For a list of tuple types in programming languages, see Product type#Product types in programming languages.

See also

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Notes

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References

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Sources

[edit]
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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
A tuple is a finite ordered sequence of elements, typically denoted in parentheses, that generalizes mathematical structures like the (x,y)(x, y) used in coordinate geometry. In , an nn-tuple consists of nn objects with a specified order, distinguishing it from unordered sets, and equality between tuples requires both matching lengths and corresponding elements. This concept extends to , where a tuple is implemented as an immutable that groups multiple values—often of heterogeneous types—into a single unit, similar to a list but unchangeable after creation to ensure . In relational databases, a tuple represents a single row in a relation (table), encapsulating values for a set of attributes that describe a specific instance or , forming the foundational unit of data storage and query operations. Tuples play a central role across disciplines due to their emphasis on order and immutability. In , they underpin s, functions, and vector spaces; for instance, the of sets AA and BB yields the set of all 2-tuples (a,b)(a, b) where aAa \in A and bBb \in B. Higher-dimensional tuples, such as 3-tuples for points in space, are essential in geometry, physics, and statistics. In programming languages like Python and , tuples facilitate efficient packing of data for return values, keys, and parallel assignments, with operations like unpacking allowing direct access to elements by index (starting from 0). Their immutability prevents accidental modifications, making them ideal for hashable structures in sets and mappings. Database tuples, by contrast, support operations like selection, projection, and joins, enabling declarative querying via languages such as SQL, where each tuple must adhere to the relation's to maintain consistency. Notable extensions include named tuples in Python, which add field labels for readability without sacrificing performance, and tuple calculus in for formal query specification. Overall, the tuple's versatility stems from its simple yet powerful abstraction of ordered collections, influencing fields from theoretical foundations to practical .

Fundamentals

Basic Concept

A tuple is defined as a finite ordered of elements drawn from a specified domain or set. It consists of a fixed number of components, denoted in parentheses with elements separated by commas, such as the 2-tuple (a,b)(a, b) or more generally the kk-tuple (a1,a2,,ak)(a_1, a_2, \dots, a_k). The order of the elements is essential, meaning that (a,b)(a, b) is distinct from (b,a)(b, a) unless a=ba = b, and repetitions of elements are permitted. This contrasts sharply with unordered collections like sets, where {1,2}={2,1}\{1, 2\} = \{2, 1\} and duplicates are ignored; for instance, the tuple (1,2)(1, 2) differs from (2,1)(2, 1), while the corresponding sets are identical. This intuitive concept positions tuples as basic sequences that underpin many mathematical structures, providing a prerequisite for understanding more advanced formalizations without requiring proofs or constructions at this stage. In practice, tuples appear in everyday mathematical reasoning, such as representing points or data points where sequence matters. The notion of tuples draws historical intuition from , where ordered coordinates specify locations in space—pioneered by in his 1637 , which used ordered pairs of real numbers to map geometric figures algebraically. Similarly, in logic, tuples capture ordered arguments supplied to functions or relations, ensuring the sequence aligns with the intended mapping or interpretation.

Etymology

The term "tuple" derives from the suffix "-uple" found in words denoting ordered collections of a specific cardinality, such as "double" (2-tuple), "triple" (3-tuple), "quadruple" (4-tuple), "quintuple" (5-tuple), and higher analogs like "sextuple" or "septuple," generalizing the pattern to arbitrary n without specifying the number explicitly. In mathematical literature, the term "n-tuple" first appeared in the 1940s to describe ordered structures in algebra and related fields. By the 1950s, it had become more common in abstract algebra and geometry texts for denoting ordered n-ary relations or sequences, as seen in John L. Kelley's "General Topology" (1955), a seminal work that employs the term for ordered collections in topological contexts, such as defining functions via tuples of points and neighborhoods. The adoption of "tuple" in emerged in the 1960s alongside the development of languages, where it standardized the representation of fixed-size ordered data aggregates. This shift is evident in the design of (finalized in 1968), which explicitly defined modes (data types) using n-tuples, such as trims as ordered sequences of integers for bounds, influencing subsequent languages in handling composite data. Similarly, early implementations and documentation from the late 1950s and 1960s referenced tuples for argument sequences and list-like structures, bridging mathematical notation to computational practice.

Formal Definitions

As Nested Ordered Pairs

Early set-theoretic definitions of ordered pairs include those proposed by Norbert Wiener in 1914 and Felix Hausdorff shortly thereafter. Wiener defined the ordered pair (x,y)(x, y) as {{{x},},{{y}}}\{\{\{x\}, \emptyset\}, \{\{y\}\}\}. This construction encodes order by using nested sets: the first component is distinguished through the set containing the singleton {x}\{x\} and the empty set \emptyset, while the second is in a singleton {{y}}\{\{y\}\}. Equality (x,y)=(a,b)(x, y) = (a, b) holds if and only if x=ax = a and y=by = b, as the structures ensure unique identification of each position. Hausdorff defined the ordered pair (x,y)(x, y) as {{x,},{y,{}}}\{\{x, \emptyset\}, \{y, \{\emptyset\}\}\}. Here, order is preserved by pairing the first element xx with the empty set \emptyset in one doubleton, and the second element yy with the singleton {}\{\emptyset\} in another, creating distinct structures that differentiate the components. Similarly, (x,y)=(a,b)(x, y) = (a, b) if and only if x=ax = a and y=by = b. These definitions, like the later Kuratowski definition, can be extended recursively to finite nn-tuples for n2n \geq 2 by right-nesting, where an nn-tuple (a1,a2,,an)(a_1, a_2, \dots, a_n) is constructed as a1,,an1,an\langle \langle a_1, \dots, a_{n-1} \rangle, a_n \rangle. The 1-tuple is simply the element itself, (a)=a(a) = a. This nesting preserves the sequential order through successive applications of the ordered pair construction. For example, using the Wiener definition, the 3-tuple (a,b,c)(a, b, c) is (a,b),c={{{{{{a},},{{b}}}},},{{c}}}\langle (a, b), c \rangle = \left\{ \left\{ \left\{ \left\{ \left\{ \left\{ a \right\}, \emptyset \right\}, \left\{ \left\{ b \right\} \right\} \right\} \right\}, \emptyset \right\}, \left\{ \left\{ c \right\} \right\} \right\}. Using the Hausdorff definition, the 3-tuple (a,b,c)(a, b, c) is (a,b),c={{{{a,},{b,{}}},},{c,{}}}\langle (a, b), c \rangle = \left\{ \left\{ \left\{ \{a, \emptyset \}, \{ b, \{\emptyset\} \} \right\}, \emptyset \right\}, \left\{ c, \{\emptyset\} \right\} \right\}. In set theory, the ordered pair (a,b)(a, b) is defined as the set {{a},{a,b}}\{\{a\}, \{a, b\}\}, known as the Kuratowski definition. This construction encodes order within the unordered nature of sets by distinguishing the first component through a singleton set {a}\{a\}, which is the unique singleton in the pair, while the second component bb is incorporated into the doubleton {a,b}\{a, b\} alongside aa. This definition extends recursively to finite nn-tuples for n2n \geq 2, where an nn-tuple (a1,a2,,an)(a_1, a_2, \dots, a_n) is constructed as the right-nested ordered pair a1,,an1,an\langle \langle a_1, \dots, a_{n-1} \rangle, a_n \rangle. For example, the 3-tuple (a,b,c)(a, b, c) is (a,b),c={{{(a)},{a,b}},{{{a},{a,b}},c}}\langle (a, b), c \rangle = \{\{\{(a)\}, \{a, b\}\}, \{\{\{a\}, \{a, b\}\}, c\}\}. The 1-tuple is simply the element itself, (a)=a(a) = a. This nesting preserves the sequential order of components through successive applications of the ordered pair construction. The order preservation follows from the structural distinction in the Kuratowski encoding: assuming aba \neq b, the pair (a,b)={{a},{a,b}}(a, b) = \{\{a\}, \{a, b\}\} differs from (b,a)={{b},{b,a}}(b, a) = \{\{b\}, \{b, a\}\} under set equality, as the singleton {a}\{a\} belongs to the first but not the second (since {a}{b}\{a\} \neq \{b\} and {a}{a,b}\{a\} \neq \{a, b\}, the latter having two elements by the axiom of pairing). For nn-tuples, the recursive nesting ensures that any permutation altering the sequence yields a distinct set, as the depth and composition of singletons and doubletons uniquely reflect the original order. Under the with the (ZFC), every finite tuple admits a unique set-theoretic representation via this nested construction, guaranteed by the (which equates sets with identical elements) and the explicit, deterministic that precludes alternative encodings for the same .

As Functions

In , an n-tuple (a1,,an)(a_1, \dots, a_n) can be formally defined as a function f:{1,,n}Df: \{1, \dots, n\} \to D, where DD is the domain of the components, such that f(i)=aif(i) = a_i for each i{1,,n}i \in \{1, \dots, n\}, and the {1,,n}\{1, \dots, n\} is equipped with the standard . This perspective treats the tuple as a mapping from ordered positions to values, providing a structured way to encode within . Within the context of Cartesian products, n-tuples arise naturally as elements of the product space i=1nDi\prod_{i=1}^n D_i, where each DiD_i may be the same or distinct domains, and the tuple corresponds to selecting one element from each factor via the function's values at successive indices. This functional representation unifies finite ordered collections under the broader theory of functions, facilitating operations like composition and restriction without relying on recursive set constructions. This view of tuples proves particularly advantageous in relational mathematics, where they model in as ordered assemblies of attribute values, with positions labeled by ordinal indices to ensure positional significance and enable efficient querying and joining. For instance, a 2-tuple can be expressed as a function f:{1,2}{a,b}f: \{1, 2\} \to \{a, b\} with f(1)=af(1) = a and f(2)=bf(2) = b, representing an that preserves the distinction between components based on their indexed locations.

As Nested Sets

One alternative set-theoretic construction of ordered pairs, proposed by Norbert Wiener in 1914, encodes the pair (a,b)(a, b) as the nested set {{{a},},{{b}}}\{\{\{a\}, \emptyset\}, \{\{b\}\}\}, where \emptyset serves as the base empty set and the nesting distinguishes the positions through membership levels without relying on explicit pairing primitives. This approach leverages the empty set as a foundational element, akin to the zero ordinal in von Neumann's construction, to impose order via hierarchical inclusion: the first component aa is embedded in a singleton within a singleton, while bb occupies a parallel but distinct nesting depth. For n-tuples, this Wiener-style encoding can be generalized recursively by treating an n-tuple as an ordered pair of an (n-1)-tuple and the nth element, yielding deeply nested sets that avoid primitive ordered pairs altogether. For instance, the 2-tuple (a,b)(a, b) becomes the above form, and a 3-tuple (a,b,c)(a, b, c) is then {{{(a,b)W},},{{c}}}\{\{\{(a, b)_W\}, \emptyset\}, \{\{c\}\}\}, where (a,b)W(a, b)_W denotes the Wiener encoding of the initial pair; this process embeds finite von Neumann ordinals (e.g., \emptyset for position 0, {}\{\emptyset\} for 1) as structural markers within the sets to track positions without external ordering mechanisms. A variant, akin to Kuratowski's 1921 refinement but emphasizing pure inclusion chains, represents the 2-tuple as {{a},{a,b}}\{\{a\}, \{a, b\}\}, where order arises from the proper subset relation {a}{a,b}\{a\} \subset \{a, b\}, and extends recursively to n-tuples through successive nestings that build membership hierarchies. The adequacy of these constructions is established by proving a between the class of n-tuples under the standard equational definition—where (a1,,an)=(b1,,bn)(a_1, \dots, a_n) = (b_1, \dots, b_n) ai=bia_i = b_i for all ii—and the class of these nested sets, with order preserved through unique recovery via membership chains. Specifically, projection functions can be defined set-theoretically: for a Wiener-encoded pair, the first element is the unique object at the innermost nesting excluding \emptyset, and the second is extracted from the singleton branch; recursively, this unpacks the hierarchy to match the tuple components exactly. Such encodings ensure foundational purity in , as all structures are built from the using union and operations. However, these nested set representations are less intuitive for practitioners outside pure set theory, as the resulting objects—highly irregular sets with complex membership relations—obscure the linear order of a tuple compared to the more direct recursive pairing in other formalisms.

Properties

Equality and Comparison

In , two finite tuples of the same length are equal their corresponding components are equal; that is, (a1,,an)=(b1,,bn)(a_1, \dots, a_n) = (b_1, \dots, b_n) precisely when ai=bia_i = b_i for every i=1,,ni = 1, \dots, n. This component-wise equality follows from the set-theoretic construction of tuples, typically via recursive s, where the equality of an (a,b)=(c,d)(a, b) = (c, d) holds a=ca = c and b=db = d, ensuring the uniqueness of components through . In the functional representation of tuples as functions from {1,,n}\{1, \dots, n\} to a set, equality is likewise . Tuples over ordered domains admit natural comparison relations, including the product order and the . The product order on the X1××XnX_1 \times \cdots \times X_n, where each XiX_i carries a partial order i\leq_i, is defined component-wise: (x1,,xn)(y1,,yn)(x_1, \dots, x_n) \leq (y_1, \dots, y_n) if and only if xiiyix_i \leq_i y_i for all i=1,,ni = 1, \dots, n./01:_Foundations/1.04:_Partial_Orders) This induces a partial order on the , which becomes a if each i\leq_i is total. The coordinate projections πi:X1××XnXi\pi_i: X_1 \times \cdots \times X_n \to X_i, defined by πi(x1,,xn)=xi\pi_i(x_1, \dots, x_n) = x_i, are surjective and collectively injective in the sense that a tuple is uniquely determined by its images under all projections, underscoring the structure's fidelity to components. Alternatively, the provides a on tuples over totally ordered domains by comparing components sequentially from left to right: (a1,,an)<(b1,,bn)(a_1, \dots, a_n) < (b_1, \dots, b_n) if there exists knk \leq n such that ai=bia_i = b_i for all i<ki < k and ak<bka_k < b_k. For example, over the reals, (1,2)<(1,3)(1, 2) < (1, 3) in since the first components match and the second satisfies 2<32 < 3. In R2\mathbb{R}^2 under the product order, tuples are compared component-wise, so (1,2)<(1,3)(1, 2) < (1, 3) holds as well, but (1,3)(1, 3) and (2,2)(2, 2) are incomparable unless additional structure is imposed.

Operations and Composition

Tuples support several algebraic operations that enable their formation, manipulation, and decomposition within mathematical structures. One fundamental operation is , which combines two tuples into a single longer tuple by appending the elements of the second to the end of the first. Formally, for tuples w=(w1,,wm)w = (w_1, \dots, w_m) and w=(w1,,wn)w' = (w'_1, \dots, w'_n) over a set SS, their www \Vert w' is the ordered tuple (w1,,wm,w1,,wn)(w_1, \dots, w_m, w'_1, \dots, w'_n) of length m+nm + n. This operation is commonly denoted by * or simple in algebraic contexts and extends the intuitive joining of finite sequences. Another key operation is projection, which extracts a specific component from a tuple. The ii-th projection πin:SnS\pi_i^n: S^n \to S applied to an nn-tuple (a1,,an)(a_1, \dots, a_n) yields aia_i, where 1in1 \leq i \leq n. For nested tuples, such as those constructed via recursive pairing (e.g., a 3-tuple as ((a1,a2),a3)((a_1, a_2), a_3)), projections compose naturally: πjmπin\pi_j^m \circ \pi_i^n retrieves subcomponents by first projecting to an inner tuple and then to the desired element. This composition preserves the ordered structure, allowing systematic access to elements in hierarchically defined tuples. Tuples are formed from relations through the , where the product S1××SnS_1 \times \cdots \times S_n consists of all possible nn-tuples with components from each SiS_i, and a relation is any thereof. Selecting from this product yields specific tuples or collections thereof, underpinning tuple construction in set-theoretic frameworks. These operations exhibit important algebraic properties. is associative: for tuples u,v,wu, v, w, (uv)w=u(vw)(u \Vert v) \Vert w = u \Vert (v \Vert w), as the resulting sequence matches element-wise regardless of grouping. Projections are injective in the sense that their joint application on the full family distinguishes distinct tuples, ensuring unique reconstruction from components via the universal property of the product.

Generalizations

Finite n-Tuples

A finite n-tuple, where n is a non-negative , is an ordered of n elements drawn from a domain set DD, typically denoted as (d1,d2,,dn)(d_1, d_2, \dots, d_n) with each diDd_i \in D. This structure generalizes the concept of ordered pairs to arbitrary finite lengths, emphasizing the positional order of components. A particular case of a 3-tuple is known as a triplet, an ordered collection of three elements from DD, constructed recursively via nested ordered pairs as described in the "As Nested Ordered Pairs" subsection of Formal Definitions. The collection of all such n-tuples forms the Cartesian power DnD^n, defined as the n-fold D×D××DD \times D \times \cdots \times D (n times). For n1n \geq 1, the length is fixed, providing a rigid suitable for representing multidimensional with uniform dimensionality. The case n=0n=0 corresponds to the empty tuple ()(), whose space D0D^0 is the , a singleton set containing only this unique empty element. If DD is a with D=k|D| = k, then the cardinality of the n-tuple space satisfies Dn=kn|D^n| = k^n, reflecting the in the number of possible ordered sequences. Furthermore, if DD is countable (finite or countably infinite), then DnD^n remains countable for any finite n, preserving countability under finite powering. In mathematical applications, finite n-tuples often model vectors in ; for instance, elements of Rn\mathbb{R}^n are n-tuples of real numbers, such as (x1,x2,,xn)(x_1, x_2, \dots, x_n) where each xiRx_i \in \mathbb{R}, forming the for n-dimensional vector spaces. This representation underpins linear algebra, where operations like and are defined componentwise on these tuples.

n-Tuples of m-Sets

In , an n-tuple of m-sets over a universe UU is defined as an ordered sequence (S1,S2,,Sn)(S_1, S_2, \dots, S_n), where each SiUS_i \subseteq U is a set with exactly Si=m|S_i| = m elements. Let v=Uv = |U|. The number of possible m-sets from UU is given by the (vm)\binom{v}{m}, which counts the ways to choose m elements from v without regard to order. By the multiplicative principle, since each of the n positions in the tuple independently selects one such m-set (with repetitions permitted), the total number of n-tuples of m-sets is ((vm))n\left( \binom{v}{m} \right)^n. Such enumerations arise in , where m-sets serve as elements in ordered collections for analyzing properties like unions and intersections. If repetitions among the SiS_i are allowed, the underlying collection corresponds to a of m-sets with possible multiplicities up to n. Conversely, requiring all SiS_i to be distinct yields the number of injections from an n-element set to the collection of all m-subsets of UU, which is the falling factorial (vm)((vm)1)((vm)n+1)\binom{v}{m} \left( \binom{v}{m} - 1 \right) \cdots \left( \binom{v}{m} - n + 1 \right). These structures appear in combinatorial constructions, where ordered selections of uniform subsets facilitate the study of configurations, such as in systems of distinct representatives.

Theoretical Contexts

In

In , tuples are formalized as , which provide a means to combine multiple types into a single structured type. For pairs, the product type σ×τ\sigma \times \tau consists of elements that are ordered pairs (a,b)(a, b) where a:σa : \sigma and b:τb : \tau, allowing the simultaneous inhabitation of both component types. This construction generalizes to n-tuples via the n-fold product i=1nσi\prod_{i=1}^n \sigma_i, whose elements are sequences (a1,,an)(a_1, \dots, a_n) with ai:σia_i : \sigma_i for each ii. Associated with these products are projection functions πi:i=1nσiσi\pi_i : \prod_{i=1}^n \sigma_i \to \sigma_i, which extract the ii-th component from a tuple, ensuring that the structure supports decomposition. In the , tuples are encoded using pairing and projection constructs, often represented as closures or record-like terms to facilitate . The rule for formation states that if Γe1:σ1\Gamma \vdash e_1 : \sigma_1 and Γe2:σ2\Gamma \vdash e_2 : \sigma_2, then Γe1,e2:σ1×σ2\Gamma \vdash \langle e_1, e_2 \rangle : \sigma_1 \times \sigma_2, with to n-tuples following inductively. Elimination rules for projections, such as Γπ1e1,e2:σ1\Gamma \vdash \pi_1 \langle e_1, e_2 \rangle : \sigma_1 and Γπ2e1,e2:σ2\Gamma \vdash \pi_2 \langle e_1, e_2 \rangle : \sigma_2 provided the pair is well-typed, enable the recovery of components while preserving through and preservation properties. Under the Curry-Howard isomorphism, which equates proofs in with programs in , product types correspond directly to logical conjunctions, where a term of type σ×τ\sigma \times \tau serves as a proof of στ\sigma \land \tau. The pairing e1,e2\langle e_1, e_2 \rangle acts as an introduction rule for conjunction, while projections π1\pi_1 and π2\pi_2 correspond to the elimination rules (left and right projection), bridging computation and proof construction. Dependent variants of tuples arise in dependent type theory through Σ\Sigma-types, which generalize products to allow the second component's type to depend on the first. Formally, a Σ\Sigma-type Σx:AB(x)\Sigma_{x : A} B(x) consists of pairs (a,b)(a, b) where a:Aa : A and b:B(a)b : B(a), supporting indexed families of types. A canonical example is the type of vectors, defined as Σn:NVec(n)\Sigma_{n : \mathbb{N}} \mathsf{Vec}(n), where Vec(n)\mathsf{Vec}(n) is the type of lists of length nn, and the dependent pair includes both the length nn and a proof of that length via the list structure. Projections for Σ\Sigma-types, such as fst:Σx:AB(x)A\mathsf{fst} : \Sigma_{x : A} B(x) \to A and snd:Σx:AB(x)B(fst(p))\mathsf{snd} : \Sigma_{x : A} B(x) \to B(\mathsf{fst}(p)), ensure dependent elimination while maintaining the theory's consistency.

In Category Theory

In , an n-tuple of objects X1,,XnX_1, \dots, X_n in a category C\mathcal{C} is formalized as the product object i=1nXi\prod_{i=1}^n X_i, equipped with projection morphisms pi:i=1nXiXip_i: \prod_{i=1}^n X_i \to X_i for each ii, satisfying a : for any object QQ in C\mathcal{C} with morphisms qi:QXiq_i: Q \to X_i, there exists a unique q:Qi=1nXiq: Q \to \prod_{i=1}^n X_i such that piq=qip_i \circ q = q_i for all ii. This universal characterization ensures that the product, if it exists, is unique up to and captures the essence of simultaneous mapping into each component, abstracting the notion of tuples beyond specific structures like sets. In the Set\mathbf{Set}, the categorical product i=1nXi\prod_{i=1}^n X_i coincides with the classical , where elements are precisely the ordered n-tuples (x1,,xn)(x_1, \dots, x_n) with xiXix_i \in X_i, and the projections pip_i select the i-th component. This recovers the standard set-theoretic definition of tuples as functions from {1,,n}\{1, \dots, n\} to the of the XiX_i, with the universal property ensuring that any compatible family of functions factors uniquely through the product. Pullbacks provide a generalization of tuples to relational contexts, where the fiber product A×CBA \times_C B of morphisms f:ACf: A \to C and g:BCg: B \to C is the pullback object PP with projections pA:PAp_A: P \to A and pB:PBp_B: P \to B such that fpA=gpBf \circ p_A = g \circ p_B, satisfying the universal property that any commutative square factors uniquely through PP. In Set\mathbf{Set}, this yields the subset of A×BA \times B consisting of pairs (a,b)(a, b) where f(a)=g(b)f(a) = g(b), thus generalizing tuples to those constrained by a relation over CC. Fiber products extend this to fibered categories, such as bundles over a base, where tuples are taken fiberwise. Cartesian closed categories guarantee the existence of finite products as part of their structure, with the product monoidal structure being closed under exponentials. Moreover, the product functor Π:CnC\Pi: \mathcal{C}^n \to \mathcal{C}, which maps a tuple of objects to their product, is left to the diagonal functor Δ:CCn\Delta: \mathcal{C} \to \mathcal{C}^n that sends an object XX to the constant n-tuple (X,,X)(X, \dots, X), with the unit of the adjunction providing the projections. This adjunction underscores the universal role of products in preserving limits and structuring categorical compositions.

Applications

In Mathematics

In algebra, tuples play a central role in the study of multilinear forms and tensor components. A multilinear form on vector spaces V1,,VkV_1, \dots, V_k is a function T:V1××VkFT: V_1 \times \cdots \times V_k \to \mathbb{F} that is linear in each argument separately, where the domain V1××VkV_1 \times \cdots \times V_k consists of kk-tuples (v1,,vk)(v_1, \dots, v_k) with viViv_i \in V_i. These forms generalize linear functionals to multiple variables, enabling the analysis of structures like determinants and inner products on products of spaces. In tensor algebra, the components of a tensor TV1VkT \in V_1^* \otimes \cdots \otimes V_k^* are specified by a multi-index (i1,,ik)(i_1, \dots, i_k), forming a tuple of indices that indexes the entries in a multi-dimensional array, with the tensor product space having dimension dimVi\prod \dim V_i. This representation allows tensors to encode multilinear relationships, such as contractions and symmetries, essential for algebraic manipulations in representation theory and invariant theory. In , tuples represent coordinates of points in nn-dimensional Rn\mathbb{R}^n, where each point is identified with an ordered nn-tuple (x1,,xn)(x_1, \dots, x_n) of real numbers, generalizing the Cartesian plane (n=2n=2) and (n=3n=3), particularly as triplets (x,y,z)(x, y, z) for coordinates of points and vectors in physics and engineering. This coordinate system facilitates the description of geometric objects, such as lines as parametric tuples and subspaces as spans of basis tuples. Affine transformations, defined by F(p)=Ap+bF(\mathbf{p}) = A\mathbf{p} + \mathbf{b} where AA is an invertible and b\mathbf{b} a fixed tuple ( vector), preserve the affine structure of Rn\mathbb{R}^n by maintaining and ratios along lines, though not necessarily distances or angles. For instance, rotations and shears map coordinate tuples to new tuples while keeping parallel lines parallel, underpinning applications in and . In , tuples model ordered selections, with of a often represented as tuples that rearrange elements, such as the six permutations of {1,2,3}\{1,2,3\} as (1,2,3)(1,2,3), (1,3,2)(1,3,2), etc. A kk- of an nn-element set is precisely a tuple of kk distinct elements from the set, counted by P(n,k)=n!/(nk)!P(n,k) = n! / (n-k)!, distinguishing order from combinations. provide a powerful tool for enumerating such tuples; the exponential generating function for the number of of nn elements is n0n!xnn!=11x\sum_{n \geq 0} n! \frac{x^n}{n!} = \frac{1}{1-x}, capturing the growth, while ordinary generating functions like 2nxn=1/(12x)\sum 2^n x^n = 1/(1-2x) count binary tuples (sequences) of length nn. These functions extend to derangements and cycle structures, aiding in the of enumerations. In , sequences of tuples in Rn\mathbb{R}^n are studied for convergence properties, where a sequence {xk=(xk1,,xkn)}\{ \mathbf{x}_k = (x_k^1, \dots, x_k^n) \} converges to L=(L1,,Ln)\mathbf{L} = (L_1, \dots, L_n) it converges componentwise, meaning each scalar {xki}\{x_k^i\} converges to LiL_i for i=1,,ni=1,\dots,n. This equivalence holds because the Euclidean norm xkL=(xkiLi)2\| \mathbf{x}_k - \mathbf{L} \| = \sqrt{\sum (x_k^i - L_i)^2 }
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