Hubbry Logo
Binary relationBinary relationMain
Open search
Binary relation
Community hub
Binary relation
logo
8 pages, 0 posts
0 subscribers
Be the first to start a discussion here.
Be the first to start a discussion here.
Binary relation
Binary relation
from Wikipedia
Transitive binary relations
Symmetric Antisymmetric Connected Well-founded Has joins Has meets Reflexive Irreflexive Asymmetric
Total,
Semiconnex
Anti-
reflexive
Equivalence relation Green tickY Green tickY
Preorder (Quasiorder) Green tickY
Partial order Green tickY Green tickY
Total preorder Green tickY Green tickY
Total order Green tickY Green tickY Green tickY
Prewellordering Green tickY Green tickY Green tickY
Well-quasi-ordering Green tickY Green tickY
Well-ordering Green tickY Green tickY Green tickY Green tickY
Lattice Green tickY Green tickY Green tickY Green tickY
Join-semilattice Green tickY Green tickY Green tickY
Meet-semilattice Green tickY Green tickY Green tickY
Strict partial order Green tickY Green tickY Green tickY
Strict weak order Green tickY Green tickY Green tickY
Strict total order Green tickY Green tickY Green tickY Green tickY
Symmetric Antisymmetric Connected Well-founded Has joins Has meets Reflexive Irreflexive Asymmetric
Definitions,
for all and
Green tickY indicates that the column's property is always true for the row's term (at the very left), while indicates that the property is not guaranteed
in general (it might, or might not, hold). For example, that every equivalence relation is symmetric, but not necessarily antisymmetric,
is indicated by Green tickY in the "Symmetric" column and in the "Antisymmetric" column, respectively.

All definitions tacitly require the homogeneous relation be transitive: for all if and then
A term's definition may require additional properties that are not listed in this table.

An example of a binary relation R between two finite sets of natural numbers, A and B. Note that R is a subset of the Cartesian product, A × B. In this example, R = {(a, b) ∈ A × B: a < b}.

In mathematics, a binary relation associates some elements of one set called the domain with some elements of another set (possibly the same) called the codomain.[1] Precisely, a binary relation over sets and is a set of ordered pairs , where is an element of and is an element of .[2] It encodes the common concept of relation: an element is related to an element , if and only if the pair belongs to the set of ordered pairs that defines the binary relation.

An example of a binary relation is the "divides" relation over the set of prime numbers and the set of integers , in which each prime is related to each integer that is a multiple of , but not to an integer that is not a multiple of . In this relation, for instance, the prime number is related to numbers such as , , , , but not to or , just as the prime number is related to , , and , but not to or .

A binary relation is called a homogeneous relation when . A binary relation is also called a heterogeneous relation when it is not necessary that .

Binary relations, and especially homogeneous relations, are used in many branches of mathematics to model a wide variety of concepts. These include, among others:

A function may be defined as a binary relation that meets additional constraints.[3] Binary relations are also heavily used in computer science.

A binary relation over sets and can be identified with an element of the power set of the Cartesian product Since a powerset is a lattice for set inclusion (), relations can be manipulated using set operations (union, intersection, and complementation) and algebra of sets.

In some systems of axiomatic set theory, relations are extended to classes, which are generalizations of sets. This extension is needed for, among other things, modeling the concepts of "is an element of" or "is a subset of" in set theory, without running into logical inconsistencies such as Russell's paradox.

A binary relation is the most studied special case of an -ary relation over sets , which is a subset of the Cartesian product [2]

Definition

[edit]

Given sets and , the Cartesian product is defined as and its elements are called ordered pairs.

A binary relation over sets and is a subset of [2][4] The set is called the domain[2] or set of departure of , and the set the codomain or set of destination of . In order to specify the choices of the sets and , some authors define a binary relation or correspondence as an ordered triple , where is a subset of called the graph of the binary relation. The statement reads " is -related to " and is denoted by .[5][6][7][a] The domain of definition or active domain[2] of is the set of all such that for at least one . The codomain of definition, active codomain,[2] image or range of is the set of all such that for at least one . The field of is the union of its domain of definition and its codomain of definition.[9][10][11]

When a binary relation is called a homogeneous relation (or endorelation). To emphasize the fact that and are allowed to be different, a binary relation is also called a heterogeneous relation.[12][13][14] The prefix hetero is from the Greek ἕτερος (heteros, "other, another, different").

A heterogeneous relation has been called a rectangular relation,[14] suggesting that it does not have the square-like symmetry of a homogeneous relation on a set where Commenting on the development of binary relations beyond homogeneous relations, researchers wrote, "... a variant of the theory has evolved that treats relations from the very beginning as heterogeneous or rectangular, i.e. as relations where the normal case is that they are relations between different sets."[15]

The terms correspondence,[16] dyadic relation and two-place relation are synonyms for binary relation, though some authors use the term "binary relation" for any subset of a Cartesian product without reference to and , and reserve the term "correspondence" for a binary relation with reference to and .[citation needed]

In a binary relation, the order of the elements is important; if then can be true or false independently of . For example, divides , but does not divide .

Operations

[edit]

Union

[edit]

If and are binary relations over sets and then is the union relation of and over and .

The identity element is the empty relation. For example, is the union of < and =, and is the union of > and =.

Intersection

[edit]

If and are binary relations over sets and then is the intersection relation of and over and .

The identity element is the universal relation. For example, the relation "is divisible by 6" is the intersection of the relations "is divisible by 3" and "is divisible by 2".

Composition

[edit]

If is a binary relation over sets and , and is a binary relation over sets and then (also denoted by ) is the composition relation of and over and .

The identity element is the identity relation. The order of and in the notation used here agrees with the standard notational order for composition of functions. For example, the composition (is parent of)(is mother of) yields (is maternal grandparent of), while the composition (is mother of)(is parent of) yields (is grandmother of). For the former case, if is the parent of and is the mother of , then is the maternal grandparent of .

Converse

[edit]

If is a binary relation over sets and then is the converse relation,[17] also called inverse relation,[18] of over and .

For example, is the converse of itself, as is , and and are each other's converse, as are and A binary relation is equal to its converse if and only if it is symmetric.

Complement

[edit]

If is a binary relation over sets and then (also denoted by ) is the complementary relation of over and .

For example, and are each other's complement, as are and , and , and , and for total orders also and , and and .

The complement of the converse relation is the converse of the complement:

If the complement has the following properties:

  • If a relation is symmetric, then so is the complement.
  • The complement of a reflexive relation is irreflexive—and vice versa.
  • The complement of a strict weak order is a total preorder—and vice versa.

Restriction

[edit]

If is a binary homogeneous relation over a set and is a subset of then is the restriction relation of to over .

If is a binary relation over sets and and if is a subset of then is the left-restriction relation of to over and .[clarification needed]

If a relation is reflexive, irreflexive, symmetric, antisymmetric, asymmetric, transitive, total, trichotomous, a partial order, total order, strict weak order, total preorder (weak order), or an equivalence relation, then so too are its restrictions.

However, the transitive closure of a restriction is a subset of the restriction of the transitive closure, i.e., in general not equal. For example, restricting the relation " is parent of " to females yields the relation " is mother of the woman "; its transitive closure does not relate a woman with her paternal grandmother. On the other hand, the transitive closure of "is parent of" is "is ancestor of"; its restriction to females does relate a woman with her paternal grandmother.

Also, the various concepts of completeness (not to be confused with being "total") do not carry over to restrictions. For example, over the real numbers a property of the relation is that every non-empty subset with an upper bound in has a least upper bound (also called supremum) in However, for the rational numbers this supremum is not necessarily rational, so the same property does not hold on the restriction of the relation to the rational numbers.

A binary relation over sets and is said to be contained in a relation over and , written if is a subset of , that is, for all and if , then . If is contained in and is contained in , then and are called equal written . If is contained in but is not contained in , then is said to be smaller than , written For example, on the rational numbers, the relation is smaller than , and equal to the composition .

Matrix representation

[edit]

Binary relations over sets and can be represented algebraically by logical matrices indexed by and with entries in the Boolean semiring (addition corresponds to OR and multiplication to AND) where matrix addition corresponds to union of relations, matrix multiplication corresponds to composition of relations (of a relation over and and a relation over and ),[19] the Hadamard product corresponds to intersection of relations, the zero matrix corresponds to the empty relation, and the matrix of ones corresponds to the universal relation. Homogeneous relations (when ) form a matrix semiring (indeed, a matrix semialgebra over the Boolean semiring) where the identity matrix corresponds to the identity relation.[20]

Examples

[edit]
2nd example relation
ball car doll cup
John +
Mary +
Venus +
1st example relation
ball car doll cup
John +
Mary +
Ian
Venus +
  1. The following example shows that the choice of codomain is important. Suppose there are four objects and four people A possible relation on and is the relation "is owned by", given by That is, John owns the ball, Mary owns the doll, and Venus owns the car. Nobody owns the cup and Ian owns nothing; see the 1st example. As a set, does not involve Ian, and therefore could have been viewed as a subset of i.e. a relation over and see the 2nd example. But in that second example, contains no information about the ownership by Ian.

    While the 2nd example relation is surjective (see below), the 1st is not.

    Oceans and continents (islands omitted)
    Ocean borders continent
    NA SA AF EU AS AU AA
    Indian 0 0 1 0 1 1 1
    Arctic 1 0 0 1 1 0 0
    Atlantic 1 1 1 1 0 0 1
    Pacific 1 1 0 0 1 1 1
  2. Let , the oceans of the globe, and , the continents. Let represent that ocean borders continent . Then the logical matrix for this relation is:
    The connectivity of the planet Earth can be viewed through and , the former being a relation on , which is the universal relation ( or a logical matrix of all ones). This universal relation reflects the fact that every ocean is separated from the others by at most one continent. On the other hand, is a relation on which fails to be universal because at least two oceans must be traversed to voyage from Europe to Australia.
  3. Visualization of relations leans on graph theory: For relations on a set (homogeneous relations), a directed graph illustrates a relation and a graph a symmetric relation. For heterogeneous relations a hypergraph has edges possibly with more than two nodes, and can be illustrated by a bipartite graph. Just as the clique is integral to relations on a set, so bicliques are used to describe heterogeneous relations; indeed, they are the "concepts" that generate a lattice associated with a relation.
    The various axes represent time for observers in motion, the corresponding axes are their lines of simultaneity.
  4. Hyperbolic orthogonality: Time and space are different categories, and temporal properties are separate from spatial properties. The idea of simultaneous events is simple in absolute space and time since each time determines a simultaneous hyperplane in that cosmology. Hermann Minkowski changed that when he articulated the notion of relative simultaneity, which exists when spatial events are "normal" to a time characterized by a velocity. He used an indefinite inner product, and specified that a time vector is normal to a space vector when that product is zero. The indefinite inner product in a composition algebra is given by
    where the overbar denotes conjugation.
    As a relation between some temporal events and some spatial events, hyperbolic orthogonality (as found in split-complex numbers) is a heterogeneous relation.[21]
  5. A geometric configuration can be considered a relation between its points and its lines. The relation is expressed as incidence. Finite and infinite projective and affine planes are included. Jakob Steiner pioneered the cataloguing of configurations with the Steiner systems which have an n-element set and a set of k-element subsets called blocks, such that a subset with elements lies in just one block. These incidence structures have been generalized with block designs. The incidence matrix used in these geometrical contexts corresponds to the logical matrix used generally with binary relations.
    An incidence structure is a triple where and are any two disjoint sets and is a binary relation between and , i.e. The elements of will be called points, those of blocks, and those of flags.[22]

Types of binary relations

[edit]
Examples of four types of binary relations over the real numbers: one-to-one (in green), one-to-many (in blue), many-to-one (in red), many-to-many (in black).

Some important types of binary relations over sets and are listed below.

Uniqueness properties:

  • Injective[23] (also called left-unique[24]): for all and all if and then . In other words, every element of the codomain has at most one preimage element. For such a relation, is called a primary key of .[2] For example, the green and blue binary relations in the diagram are injective, but the red one is not (as it relates both and to ), nor the black one (as it relates both and to ).
  • Functional[23][25][26] (also called right-unique[24] or univalent[27]): for all and all if and then . In other words, every element of the domain has at most one image element. Such a binary relation is called a partial function or partial mapping.[28] For such a relation, is called a primary key of .[2] For example, the red and green binary relations in the diagram are functional, but the blue one is not (as it relates to both and ), nor the black one (as it relates to both and ).
  • One-to-one: injective and functional. For example, the green binary relation in the diagram is one-to-one, but the red, blue and black ones are not.
  • One-to-many: injective and not functional. For example, the blue binary relation in the diagram is one-to-many, but the red, green and black ones are not.
  • Many-to-one: functional and not injective. For example, the red binary relation in the diagram is many-to-one, but the green, blue and black ones are not.
  • Many-to-many: not injective nor functional. For example, the black binary relation in the diagram is many-to-many, but the red, green and blue ones are not.

Totality properties (only definable if the domain and codomain are specified):

  • Total[23] (also called left-total[24]): for all there exists a such that . In other words, every element of the domain has at least one image element. In other words, the domain of definition of is equal to . This property, is different from the definition of connected (also called total by some authors)[citation needed] in Properties. Such a binary relation is called a multivalued function. For example, the red and green binary relations in the diagram are total, but the blue one is not (as it does not relate to any real number), nor the black one (as it does not relate to any real number). As another example, is a total relation over the integers. But it is not a total relation over the positive integers, because there is no in the positive integers such that .[29] However, is a total relation over the positive integers, the rational numbers and the real numbers. Every reflexive relation is total: for a given , choose .
  • Surjective[23] (also called right-total[24]): for all , there exists an such that . In other words, every element of the codomain has at least one preimage element. In other words, the codomain of definition of is equal to . For example, the green and blue binary relations in the diagram are surjective, but the red one is not (as it does not relate any real number to ), nor the black one (as it does not relate any real number to ).

Uniqueness and totality properties (only definable if the domain and codomain are specified):

  • A function (also called mapping[24]): a binary relation that is functional and total. In other words, every element of the domain has exactly one image element. For example, the red and green binary relations in the diagram are functions, but the blue and black ones are not.
  • An injection: a function that is injective. For example, the green relation in the diagram is an injection, but the red one is not; the black and the blue relation is not even a function.
  • A surjection: a function that is surjective. For example, the green relation in the diagram is a surjection, but the red one is not.
  • A bijection: a function that is injective and surjective. In other words, every element of the domain has exactly one image element and every element of the codomain has exactly one preimage element. For example, the green binary relation in the diagram is a bijection, but the red one is not.

If relations over proper classes are allowed:

  • Set-like (also called local): for all , the class of all such that , i.e. , is a set. For example, the relation is set-like, and every relation on two sets is set-like.[30] The usual ordering < over the class of ordinal numbers is a set-like relation, while its inverse > is not.[citation needed]

Sets versus classes

[edit]

Certain mathematical "relations", such as "equal to", "subset of", and "member of", cannot be understood to be binary relations as defined above, because their domains and codomains cannot be taken to be sets in the usual systems of axiomatic set theory. For example, to model the general concept of "equality" as a binary relation , take the domain and codomain to be the "class of all sets", which is not a set in the usual set theory.

In most mathematical contexts, references to the relations of equality, membership and subset are harmless because they can be understood implicitly to be restricted to some set in the context. The usual work-around to this problem is to select a "large enough" set , that contains all the objects of interest, and work with the restriction instead of . Similarly, the "subset of" relation needs to be restricted to have domain and codomain (the power set of a specific set ): the resulting set relation can be denoted by Also, the "member of" relation needs to be restricted to have domain and codomain to obtain a binary relation that is a set. Bertrand Russell has shown that assuming to be defined over all sets leads to a contradiction in naive set theory, see Russell's paradox.

Another solution to this problem is to use a set theory with proper classes, such as NBG or Morse–Kelley set theory, and allow the domain and codomain (and so the graph) to be proper classes: in such a theory, equality, membership, and subset are binary relations without special comment. (A minor modification needs to be made to the concept of the ordered triple , as normally a proper class cannot be a member of an ordered tuple; or of course one can identify the binary relation with its graph in this context.)[31] With this definition one can for instance define a binary relation over every set and its power set.

Homogeneous relation

[edit]

A homogeneous relation over a set is a binary relation over and itself, i.e. it is a subset of the Cartesian product [14][32][33] It is also simply called a (binary) relation over .

A homogeneous relation over a set may be identified with a directed simple graph permitting loops, where is the vertex set and is the edge set (there is an edge from a vertex to a vertex if and only if ). The set of all homogeneous relations over a set is the power set which is a Boolean algebra augmented with the involution of mapping of a relation to its converse relation. Considering composition of relations as a binary operation on , it forms a semigroup with involution.

Some important properties that a homogeneous relation over a set may have are:

  • Reflexive: for all . For example, is a reflexive relation but > is not.
  • Irreflexive: for all not . For example, is an irreflexive relation, but is not.
  • Symmetric: for all if then . For example, "is a blood relative of" is a symmetric relation.
  • Antisymmetric: for all if and then For example, is an antisymmetric relation.[34]
  • Asymmetric: for all if then not . A relation is asymmetric if and only if it is both antisymmetric and irreflexive.[35] For example, > is an asymmetric relation, but is not.
  • Transitive: for all if and then . A transitive relation is irreflexive if and only if it is asymmetric.[36] For example, "is ancestor of" is a transitive relation, while "is parent of" is not.
  • Connected: for all if then or .
  • Strongly connected: for all or .
  • Dense: for all if then some exists such that and .

A partial order is a relation that is reflexive, antisymmetric, and transitive. A strict partial order is a relation that is irreflexive, asymmetric, and transitive. A total order is a relation that is reflexive, antisymmetric, transitive and connected.[37] A strict total order is a relation that is irreflexive, asymmetric, transitive and connected. An equivalence relation is a relation that is reflexive, symmetric, and transitive. For example, " divides " is a partial, but not a total order on natural numbers "" is a strict total order on and " is parallel to " is an equivalence relation on the set of all lines in the Euclidean plane.

All operations defined in section § Operations also apply to homogeneous relations. Beyond that, a homogeneous relation over a set may be subjected to closure operations like:

Reflexive closure
the smallest reflexive relation over containing ,
Transitive closure
the smallest transitive relation over containing ,
Equivalence closure
the smallest equivalence relation over containing .

Calculus of relations

[edit]

Developments in algebraic logic have facilitated usage of binary relations. The calculus of relations includes the algebra of sets, extended by composition of relations and the use of converse relations. The inclusion meaning that implies , sets the scene in a lattice of relations. But since the inclusion symbol is superfluous. Nevertheless, composition of relations and manipulation of the operators according to Schröder rules, provides a calculus to work in the power set of

In contrast to homogeneous relations, the composition of relations operation is only a partial function. The necessity of matching target to source of composed relations has led to the suggestion that the study of heterogeneous relations is a chapter of category theory as in the category of sets, except that the morphisms of this category are relations. The objects of the category Rel are sets, and the relation-morphisms compose as required in a category.[citation needed]

Induced concept lattice

[edit]

Binary relations have been described through their induced concept lattices: A concept satisfies two properties:

  • The logical matrix of is the outer product of logical vectors logical vectors.[clarification needed]
  • is maximal, not contained in any other outer product. Thus is described as a non-enlargeable rectangle.

For a given relation the set of concepts, enlarged by their joins and meets, forms an "induced lattice of concepts", with inclusion forming a preorder.

The MacNeille completion theorem (1937) (that any partial order may be embedded in a complete lattice) is cited in a 2013 survey article "Decomposition of relations on concept lattices".[38] The decomposition is

, where and are functions, called mappings or left-total, functional relations in this context. The "induced concept lattice is isomorphic to the cut completion of the partial order that belongs to the minimal decomposition of the relation ."

Particular cases are considered below: total order corresponds to Ferrers type, and identity corresponds to difunctional, a generalization of equivalence relation on a set.

Relations may be ranked by the Schein rank which counts the number of concepts necessary to cover a relation.[39] Structural analysis of relations with concepts provides an approach for data mining.[40]

Particular relations

[edit]
  • Proposition: If is a surjective relation and is its transpose, then where is the identity relation.
  • Proposition: If is a serial relation, then where is the identity relation.

Difunctional

[edit]

The idea of a difunctional relation is to partition objects by distinguishing attributes, as a generalization of the concept of an equivalence relation. One way this can be done is with an intervening set of indicators. The partitioning relation is a composition of relations using functional relations Jacques Riguet named these relations difunctional since the composition involves functional relations, commonly called partial functions.

In 1950 Riguet showed that such relations satisfy the inclusion:[41]

In automata theory, the term rectangular relation has also been used to denote a difunctional relation. This terminology recalls the fact that, when represented as a logical matrix, the columns and rows of a difunctional relation can be arranged as a block matrix with rectangular blocks of ones on the (asymmetric) main diagonal.[42] More formally, a relation on is difunctional if and only if it can be written as the union of Cartesian products , where the are a partition of a subset of and the likewise a partition of a subset of .[43]

Using the notation , a difunctional relation can also be characterized as a relation such that wherever and have a non-empty intersection, then these two sets coincide; formally implies [44]

In 1997 researchers found "utility of binary decomposition based on difunctional dependencies in database management."[45] Furthermore, difunctional relations are fundamental in the study of bisimulations.[46]

In the context of homogeneous relations, a partial equivalence relation is difunctional.

Ferrers type

[edit]

A strict order on a set is a homogeneous relation arising in order theory. In 1951 Jacques Riguet adopted the ordering of an integer partition, called a Ferrers diagram, to extend ordering to binary relations in general.[47]

The corresponding logical matrix of a general binary relation has rows which finish with a sequence of ones. Thus the dots of a Ferrer's diagram are changed to ones and aligned on the right in the matrix.

An algebraic statement required for a Ferrers type relation R is

If any one of the relations is of Ferrers type, then all of them are. [48]

Contact

[edit]

Suppose is the power set of , the set of all subsets of . Then a relation is a contact relation if it satisfies three properties:

The set membership relation, "is an element of", satisfies these properties so is a contact relation. The notion of a general contact relation was introduced by Georg Aumann in 1970.[49][50]

In terms of the calculus of relations, sufficient conditions for a contact relation include where is the converse of set membership ().[51]: 280 

Preorder R\R

[edit]

Every relation generates a preorder which is the left residual.[52] In terms of converse and complements, Forming the diagonal of , the corresponding row of and column of will be of opposite logical values, so the diagonal is all zeros. Then

, so that is a reflexive relation.

To show transitivity, one requires that Recall that is the largest relation such that Then

(repeat)
(Schröder's rule)
(complementation)
(definition)

The inclusion relation Ω on the power set of can be obtained in this way from the membership relation on subsets of :

[51]: 283 

Fringe of a relation

[edit]

Given a relation , its fringe is the sub-relation defined as

When is a partial identity relation, difunctional, or a block diagonal relation, then . Otherwise the operator selects a boundary sub-relation described in terms of its logical matrix: is the side diagonal if is an upper right triangular linear order or strict order. is the block fringe if is irreflexive () or upper right block triangular. is a sequence of boundary rectangles when is of Ferrers type.

On the other hand, when is a dense, linear, strict order.[51]

Mathematical heaps

[edit]

Given two sets and , the set of binary relations between them can be equipped with a ternary operation where denotes the converse relation of . In 1953 Viktor Wagner used properties of this ternary operation to define semiheaps, heaps, and generalized heaps.[53][54] The contrast of heterogeneous and homogeneous relations is highlighted by these definitions:

There is a pleasant symmetry in Wagner's work between heaps, semiheaps, and generalised heaps on the one hand, and groups, semigroups, and generalised groups on the other. Essentially, the various types of semiheaps appear whenever we consider binary relations (and partial one-one mappings) between different sets and , while the various types of semigroups appear in the case where .

— Christopher Hollings, "Mathematics across the Iron Curtain: a history of the algebraic theory of semigroups"[55]

See also

[edit]

Notes

[edit]

References

[edit]

Bibliography

[edit]
[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
In mathematics, a binary relation (or simply relation) is a fundamental concept that establishes a correspondence between elements of two sets, capturing the idea of one element being connected to another in a specified way. Formally, given two sets A and B, a binary relation R from A to B is defined as a subset of the Cartesian product A × B, consisting of ordered pairs (a, b) such that aA, bB, and the pair satisfies the relation's condition. When A = B, the relation is said to be a binary relation on the set A. Binary relations can exhibit various structural properties that determine their behavior and utility in mathematical reasoning. Key properties include reflexivity (every element aA satisfies a R a), symmetry (if a R b then b R a), transitivity (if a R b and b R c then a R c), and antisymmetry (if a R b and b R a then a = b). Combinations of these properties classify relations into important types: for example, an is reflexive, symmetric, and transitive, partitioning a set into equivalence classes; a partial order is reflexive, antisymmetric, and transitive, modeling hierarchical structures like precedence. Binary relations form the basis for many core mathematical constructs and applications across disciplines. Functions, for instance, are special binary relations that are right-unique, meaning each element in the domain relates to exactly one element in the codomain. Classic examples include the "less than" relation (<) on the real numbers, which is irreflexive, asymmetric, and transitive (a strict partial order), and the equality relation (=), which is an equivalence relation. In set theory and logic, binary relations provide a common framework for describing structures like orders and equivalences, while in computer science, they underpin algorithms for graph traversal, database queries, and equivalence partitioning in programming.

Fundamentals

Definition

In set theory, the Cartesian product of two sets AA and BB, denoted A×BA \times B, is the set of all ordered pairs (a,b)(a, b) where aAa \in A and bBb \in B. A binary relation RR from a set AA to a set BB is a subset of the Cartesian product A×BA \times B. This formulation captures the idea that the relation specifies which elements of AA are connected to which elements of BB through selected ordered pairs. Equivalently, RR can be described as the collection of all ordered pairs (a,b)(a, b) with aAa \in A and bBb \in B such that aa is related to bb by RR. The standard notation RA×BR \subseteq A \times B emphasizes this set-theoretic foundation, while R:ABR: A \to B is often used to indicate the relation's direction from AA to BB.

Domain, Codomain, and Field

In the context of a binary relation RA×BR \subseteq A \times B, the codomain of RR is the set BB, which serves as the target set for the elements related by RR. The domain of RR, denoted \dom(R)\dom(R), is the subset of AA consisting of all elements aAa \in A such that there exists at least one bBb \in B with (a,b)R(a, b) \in R; formally, \dom(R)={aAbB such that (a,b)R}.\dom(R) = \{ a \in A \mid \exists b \in B \text{ such that } (a, b) \in R \}. This captures the elements from the source set that participate in the relation. The range or image of RR, denoted \im(R)\im(R), is the subset of the codomain BB consisting of all elements bBb \in B that are related to at least one aAa \in A; formally, \im(R)={bBaA such that (a,b)R}.\im(R) = \{ b \in B \mid \exists a \in A \text{ such that } (a, b) \in R \}. Unlike the codomain, which is fixed by the ambient , the range identifies the actual elements in BB that are "reached" by RR, and it is always a subset of BB. The field of RR, denoted \field(R)\field(R), is the union of the domain and the range: \field(R)=\dom(R)\im(R).\field(R) = \dom(R) \cup \im(R). This set encompasses all elements from either AA or BB that appear in at least one pair of RR. For the empty relation A×B\emptyset \subseteq A \times B, both the domain and range are empty sets, so \dom()=\dom(\emptyset) = \emptyset and \im()=\im(\emptyset) = \emptyset, with the field also empty. In contrast, the full relation R=A×BR = A \times B has domain equal to the entire source set AA and range equal to the entire codomain BB, so \dom(R)=A\dom(R) = A and \im(R)=B\im(R) = B, making the field ABA \cup B. The domain and range can be computed using existential projections from the pairs in RR. The domain is the first-coordinate projection π1(R)={xy (x,y)R}\pi_1(R) = \{ x \mid \exists y \ (x, y) \in R \}, while the range is the second-coordinate projection π2(R)={yx (x,y)R}\pi_2(R) = \{ y \mid \exists x \ (x, y) \in R \}. These projections provide a set-theoretic mechanism to extract the active elements without enumerating all pairs.

Visual Representations

Binary relations are often visualized using graphical and diagrammatic methods to provide intuitive insights into their structure and facilitate analysis. These representations emphasize the connections between elements without delving into computational operations. A primary visual tool is the arrow diagram, which depicts a binary relation RA×BR \subseteq A \times B by arranging elements of the domain AA on the left and the codomain BB on the right, with directed arrows from aAa \in A to bBb \in B if (a,b)R(a, b) \in R. This bipartite directed graph layout highlights the mapping from domain to codomain, making it straightforward to identify the range of related elements. For homogeneous binary relations where A=BA = B, the arrow diagram simplifies to a directed graph, or digraph, with vertices representing the elements of AA and directed edges indicating related pairs. In this representation, each vertex corresponds to an element, and an edge from xx to yy signifies xRyx R y, allowing for a compact view of intra-set connections. Another key representation is the incidence matrix, a rectangular binary matrix where rows index elements of AA, columns index elements of BB, and the entry mij=1m_{ij} = 1 if the corresponding pair is in RR, otherwise 0. For instance, consider the "less than" relation R={(1,2),(1,3),(2,3)}R = \{ (1,2), (1,3), (2,3) \} on the set {1,2,3}\{1, 2, 3\}; its incidence matrix is:
123
1011
2001
3000
This matrix form, along with arrow diagrams, serves as a precursor to more specialized visualizations like Hasse diagrams for partial orders, such as the less-than relation shown with directed edges from smaller to larger numbers. These visual methods offer advantages in revealing relational patterns, such as cycles (closed loops in digraphs indicating periodic dependencies) or chains (sequences of edges suggesting comparability), through direct inspection rather than algebraic computation. By converting abstract set memberships into tangible diagrams or matrices, they enhance conceptual understanding and aid in detecting structural features like transitivity or asymmetry.

Operations

Union and Intersection

Binary relations, being subsets of a Cartesian product A×BA \times B, inherit the standard set-theoretic operations of union and intersection. For two binary relations RA×BR \subseteq A \times B and SA×BS \subseteq A \times B, their union is defined as RS={(a,b)(a,b)R(a,b)S},R \cup S = \{ (a, b) \mid (a, b) \in R \lor (a, b) \in S \}, which consists of all ordered pairs related by at least one of the relations. Similarly, the intersection is RS={(a,b)(a,b)R(a,b)S},R \cap S = \{ (a, b) \mid (a, b) \in R \land (a, b) \in S \}, comprising only the ordered pairs common to both relations. These operations treat relations as sets of pairs, preserving the structure of the underlying . The union and intersection operations on binary relations exhibit key algebraic properties analogous to those in set theory. Both are commutative: RS=SRR \cup S = S \cup R and RS=SRR \cap S = S \cap R. They are also associative: (RS)T=R(ST)(R \cup S) \cup T = R \cup (S \cup T) and (RS)T=R(ST)(R \cap S) \cap T = R \cap (S \cap T). Moreover, they distribute over each other: R(ST)=(RS)(RT)R \cup (S \cap T) = (R \cup S) \cap (R \cup T) and R(ST)=(RS)(RT)R \cap (S \cup T) = (R \cap S) \cup (R \cap T). These properties hold for relations sharing the same Cartesian product and form the basis for the Boolean structure in relation algebras. As elements of the power set P(A×B)\mathcal{P}(A \times B), binary relations form a Boolean algebra under union, intersection, and complement, where union and intersection serve as the join and meet operations, respectively. This algebraic framework, developed in the calculus of relations, integrates with relational composition, enabling systematic manipulation of relations. For an illustrative example, consider the set of people and two binary relations: the parent relation PP, where (x,y)P(x, y) \in P if xx is a parent of yy, and the sibling relation SS, where (x,y)S(x, y) \in S if xx and yy share at least one parent. The union PSP \cup S then relates each person to both their parents and siblings, capturing a broader familial connection.

Composition and Converse

In the theory of binary relations, composition provides a means to chain relations across sets, forming a new relation that connects elements through an intermediate set. Given a binary relation RA×BR \subseteq A \times B and another binary relation SB×CS \subseteq B \times C, their composition, denoted SRS \circ R, is the binary relation SRA×CS \circ R \subseteq A \times C defined by SR={(a,c)A×CbB such that (a,b)R and (b,c)S}.S \circ R = \{ (a, c) \in A \times C \mid \exists b \in B \text{ such that } (a, b) \in R \text{ and } (b, c) \in S \}. The domain of SRS \circ R is a subset of AA, and its codomain is a subset of CC. This operation captures the idea of sequential application, where elements in AA are related to elements in CC via some intermediary in BB. Composition is associative: for relations RA×BR \subseteq A \times B, SB×CS \subseteq B \times C, and TC×DT \subseteq C \times D, it holds that (TS)R=T(SR)(T \circ S) \circ R = T \circ (S \circ R). This property allows for unambiguous chaining of multiple relations without parentheses, mirroring the associativity in function composition. The converse (or inverse) of a binary relation RA×BR \subseteq A \times B, denoted R1R^{-1}, is the relation R1B×AR^{-1} \subseteq B \times A given by R1={(b,a)B×A(a,b)R}.R^{-1} = \{ (b, a) \in B \times A \mid (a, b) \in R \}. This operation reverses the direction of the relation, effectively swapping the roles of the domain and codomain. Every binary relation has a unique converse, and applying the converse twice yields the original relation: (R1)1=R(R^{-1})^{-1} = R. The converse interacts naturally with composition; specifically, for relations RR and SS as above, the converse of their composition satisfies (SR)1=R1S1(S \circ R)^{-1} = R^{-1} \circ S^{-1}. This reversal property ensures that the order of relations is inverted when taking converses, preserving the chaining structure in the opposite direction. To illustrate, consider the successor relation SS on the natural numbers N\mathbb{N}, where S={(n,n+1)nN}S = \{ (n, n+1) \mid n \in \mathbb{N} \}, relating each number to its successor. Its converse is S1={(n+1,n)nN}S^{-1} = \{ (n+1, n) \mid n \in \mathbb{N} \}, relating each number to its predecessor. The composition S1SS^{-1} \circ S consists of pairs (n,m)(n, m) where there exists kk such that (n,k)S(n, k) \in S (so k=n+1k = n+1) and (k,m)S1(k, m) \in S^{-1} (so m=k1=nm = k-1 = n), yielding pairs (n,n)(n, n) for all nNn \in \mathbb{N}, which is the identity relation on N\mathbb{N}. Such examples highlight how composition and converse enable modeling of sequential or hierarchical connections in relational structures.

Complement and Restriction

The complement of a binary relation RA×BR \subseteq A \times B is the relation Rc={(a,b)A×B(a,b)R}R^c = \{ (a, b) \in A \times B \mid (a, b) \notin R \}, consisting of all ordered pairs in the ambient product set that are not in RR. This operation is taken relative to the full A×BA \times B, treating RR as a subset thereof. The complement satisfies key properties analogous to those in set theory, including De Morgan's laws: for binary relations R,SA×BR, S \subseteq A \times B, the complement of their union is the intersection of the complements, (RS)c=RcSc(R \cup S)^c = R^c \cap S^c, and the complement of their intersection is the union of the complements, (RS)c=RcSc(R \cap S)^c = R^c \cup S^c. These laws follow from the set-theoretic nature of relations and hold relative to the same ambient product. Additionally, the complement operation is involutive, meaning (Rc)c=R(R^c)^c = R. The domain restriction of RA×BR \subseteq A \times B to a subset AAA' \subseteq A is the relation RA={(a,b)RaA}=R(A×B)R|_{A'} = \{ (a, b) \in R \mid a \in A' \} = R \cap (A' \times B), retaining only those pairs whose first component lies in AA'. Similarly, the codomain restriction to BBB' \subseteq B is RB={(a,b)RbB}=R(A×B)R|_{B'} = \{ (a, b) \in R \mid b \in B' \} = R \cap (A \times B'), retaining pairs whose second component lies in BB'. These operations limit the relation to a subproduct while preserving the original pairs within that subproduct. The cylindrical extension serves as the inverse of restriction, embedding a relation defined on a subproduct into a larger ambient product without adding new pairs; for instance, if SA×BS \subseteq A' \times B with AAA' \subseteq A and BCB \subseteq C, the extension to A×CA \times C is simply SS viewed as a subset of A×CA \times C. This preserves the relational structure while expanding the domain and codomain sets. For example, consider the equality relation == on the set of real numbers, where x=yx = y for x,yRx, y \in \mathbb{R}; its complement is the "not equals" relation \neq, consisting of all pairs (x,y)(x, y) where xyx \neq y.

Algebraic Aspects

Matrix Representation

A binary relation RA×BR \subseteq A \times B on finite sets A={a1,,an}A = \{a_1, \dots, a_n\} and B={b1,,bm}B = \{b_1, \dots, b_m\} can be represented by an n×mn \times m adjacency matrix MRM_R, where the entry MR(i,j)=1M_R(i,j) = 1 if (ai,bj)R(a_i, b_j) \in R and 00 otherwise. This zero-one matrix provides a compact algebraic structure for encoding the relation, facilitating computational analysis. Matrix representations enable direct translation of relational operations into matrix algebra over the Boolean semiring, where addition is logical OR (\lor) and multiplication is logical AND (\land). The union RSR \cup S corresponds to the entry-wise Boolean OR of MRM_R and MSM_S, i.e., MRS=MRMSM_{R \cup S} = M_R \lor M_S. Similarly, the intersection RSR \cap S is given by the entry-wise Boolean AND, MRS=MRMSM_{R \cap S} = M_R \land M_S. For composition, if SB×CS \subseteq B \times C, then MSR=MSMRM_{S \circ R} = M_S \cdot M_R, where the product uses Boolean multiplication: the (i,k)(i,k)-entry is j=1m(MR(i,j)MS(j,k))\bigvee_{j=1}^m (M_R(i,j) \land M_S(j,k)). The converse relation R1R^{-1} is represented by the transpose matrix MR1=MRTM_{R^{-1}} = M_R^T. For a homogeneous relation RA×AR \subseteq A \times A, the matrix powers capture iterated compositions: MRk=MRkM_{R^k} = M_R^k, where the kk-th power is computed via repeated Boolean matrix multiplication, corresponding to paths of length kk in the associated directed graph. This is particularly useful for analyzing reachability, as the transitive closure R+=k=1nRkR^+ = \bigcup_{k=1}^n R^k has matrix MR+=k=1nMRkM_{R^+} = \bigvee_{k=1}^n M_R^k. Matrix representations offer computational advantages, notably in algorithms for deriving transitive closures efficiently. Warshall's algorithm computes the transitive closure of an n×nn \times n relation matrix in O(n3)O(n^3) time by iteratively updating entries: for each intermediate vertex k=1k = 1 to nn, set M(i,j)M(i,j)(M(i,k)M(k,j))M(i,j) \leftarrow M(i,j) \lor (M(i,k) \land M(k,j)) for all i,ji,j. This dynamic programming approach leverages the matrix structure to identify all pairs connected by paths, enabling applications in graph analysis and relational databases.

Relation Algebras

Relation algebras provide an abstract algebraic framework for modeling , extending to capture relational operations and their properties. Formally, a relation algebra is a structure consisting of a equipped with additional operations: relative composition (denoted ∘), converse (denoted ^−1), and a distinguished constant element representing the identity relation (Id). The Boolean operations include meet (∧), join (∨), complement (∼), and constants for the zero (empty) relation (0) and the universal relation (1). These structures allow for the algebraic manipulation of relations in a way that abstracts from specific sets, enabling proofs of general properties that hold for all . The axioms defining relation algebras were systematized by Alfred Tarski, comprising the standard axioms of Boolean algebras (typically around 10 equations) augmented by additional relational axioms (about 10–12, depending on the formulation), totaling over 20 when fully enumerated. Key among the relational axioms are those governing composition and converse, such as the associativity of composition (R ∘ (S ∘ T) = (R ∘ S) ∘ T), the interaction with Boolean operations (e.g., (R ∨ S) ∘ T = (R ∘ T) ∨ (S ∘ T)), and the crucial converse property (R ∘ S)^−1 = S^−1 ∘ R^−1, which ensures that the converse reverses the order of composition. Additional axioms define the identity relation as its own converse (Id^−1 = Id) and specify its role in composition (R ∘ Id = Id ∘ R = R). These axioms form an equational theory, making relation algebras a variety in the sense of universal algebra, and they ensure that the algebra behaves consistently with the semantics of concrete binary relations. A subclass of relation algebras, known as representable relation algebras, consists of those isomorphic to subalgebras of the full relation algebra on some set U—that is, algebras of binary relations over U closed under the standard set-theoretic operations of union, intersection, complement, composition, and converse. Not all relation algebras satisfying Tarski's axioms are representable; the representability problem, which asks whether an abstract relation algebra can be embedded into a concrete one, remains undecidable in general. However, finite representable relation algebras are well-understood and play a key role in computational applications. The development of relation algebras traces back to the 1940s, when Alfred Tarski and his collaborators, including Roger Lyndon and , formalized them as part of efforts to axiomatize the calculus of relations, building on 19th-century foundations by , , and Ernst Schröder. Tarski's seminal 1941 paper introduced the core axiomatic system, aiming to provide a rigorous algebraic foundation for relational reasoning in logic and set theory. Relation algebras find applications in decision procedures for verifying properties of binary relations, such as transitivity or reflexivity, through algebraic manipulation and equational reasoning, which can be mechanized using tools like RelView for computing normal forms or checking satisfiability. They also support qualitative reasoning in temporal and spatial domains by modeling interval relations (e.g., Allen's algebra) or spatial configurations, enabling automated inference in artificial intelligence and database query optimization.

Calculus of Relations

The calculus of relations provides a set of equational laws and deductive rules for manipulating binary relations through operations such as union, intersection, composition, and converse, enabling rigorous proofs of relational properties without reference to underlying sets. This framework emerged in the late 19th century as part of , allowing computations analogous to but extended to relative terms. Historically, the foundations were laid by Charles Sanders Peirce in his 1870 paper "Description of a Notation for the Logic of Relatives," where he introduced composition and converse as core operations for binary relations, distinguishing them from class-based logic. Ernst Schröder significantly expanded this in the 1890s, particularly in Volume III of his Vorlesungen über die Algebra der Logik (1895), developing a comprehensive deductive system known as the Schröder calculus. Schröder's work formalized rules for equational reasoning, including substitution of equals, detachment (modus ponens for equations), and specific axioms governing relational operations like composition (denoted ∘) and converse (denoted ⁻¹). The Schröder calculus serves as a deductive system for deriving equations between relational terms, treating binary relations as abstract objects manipulated via Boolean combinations and relative products. Key rules include the associative law for composition (R ∘ (S ∘ T) = (R ∘ S) ∘ T) and the converse properties ( (R ∘ S)^−¹ = S^−¹ ∘ R^−¹ ; R^{−¹^{−¹}} = R ). Modular laws form a cornerstone, such as the right modular law: (R ∩ S) ∘ T ⊆ (R ∘ T) ∩ (S ∘ T), and its dual for left composition, which facilitate distribution of intersection over composition under certain conditions. A pivotal identity is Dedekind's rule, named after Bernhard Dedekind's contributions to related algebraic structures: R ∘ (R^{−¹} ∘ S) ∩ S = R ∘ (R^{−¹} ∘ S), which captures a form of absorption and modularity in relational composition. These laws, derivable within the system, enable simplification of complex relational expressions. The Peirce-Schröder calculus extends the original framework by incorporating additional unary operations, such as the transitive closure (R⁺, the smallest transitive relation containing R) and its reflexive variant (R^*, including the identity relation), along with De Morgan duals like the "inaccessible" and "unapproachable" relations. Peirce anticipated these in his 1870 and 1880 works, while Schröder systematized them in the 1890s, providing equational characterizations like R⁺ = ⋃_{n=1}^∞ R^n, where R^n denotes iterated composition. These extensions support iterative computations and closures essential for dynamic logics. Applications of the calculus include proving relational properties from axioms, such as establishing transitivity of the transitive closure: if R is a binary relation, then R⁺ ∘ R⁺ = R⁺, derived using modular laws and Dedekind's rule to handle intersections in iterative definitions. This calculational approach underpins relation algebras, which axiomatize the calculus abstractly for broader algebraic study.

Properties and Classifications

Basic Properties

A binary relation RR on a set AA (where RA×AR \subseteq A \times A) exhibits several fundamental properties that characterize its structure and behavior. These properties are defined in terms of the elements of AA and the pairs in RR. Reflexivity requires that every element relates to itself: for all aAa \in A, (a,a)R(a, a) \in R. This property holds when the relation includes all diagonal pairs in the Cartesian product A×AA \times A. For instance, the equality relation on AA, defined as {(a,a)aA}\{(a, a) \mid a \in A\}, is reflexive because each element equals itself. Symmetry means that the relation is bidirectional: for all a,bAa, b \in A, if (a,b)R(a, b) \in R, then (b,a)R(b, a) \in R. This ensures that whenever one element relates to another, the reverse also holds. The equality relation is symmetric, as a=ba = b implies b=ab = a. Transitivity captures chaining: for all a,b,cAa, b, c \in A, if (a,b)R(a, b) \in R and (b,c)R(b, c) \in R, then (a,c)R(a, c) \in R. This property allows relations to extend across multiple steps. The equality relation is transitive, since if a=ba = b and b=cb = c, then a=ca = c. Antisymmetry prevents mutual relations except for equality: for all a,bAa, b \in A, if (a,b)R(a, b) \in R and (b,a)R(b, a) \in R, then a=ba = b. This distinguishes relations that impose a direction without cycles between distinct elements. The equality relation is antisymmetric, as mutual equality forces identity. Another example is the "divides" relation on the positive integers, where aa relates to bb if aa divides bb; it is reflexive (every integer divides itself), transitive (if aa divides bb and bb divides cc, then aa divides cc), and antisymmetric (if aa divides bb and bb divides aa, then a=ba = b). Combinations of these properties yield important subclasses. A preorder is a relation that is both reflexive and transitive, providing a weak ordering without requiring symmetry or antisymmetry. The "divides" relation exemplifies a preorder on the positive integers.

Equivalence and Order Relations

An equivalence relation on a set XX is a binary relation \sim that is reflexive, symmetric, and transitive. Reflexivity means that for every xXx \in X, xxx \sim x; symmetry ensures that if xyx \sim y, then yxy \sim x; and transitivity guarantees that if xyx \sim y and yzy \sim z, then xzx \sim z. These properties imply that an equivalence relation partitions XX into disjoint equivalence classes, where each class consists of all elements related to a fixed element, and every element belongs to exactly one such class. Classic examples include congruence modulo nn on the integers Z\mathbb{Z}, defined by ab(modn)a \equiv b \pmod{n} if nn divides aba - b, which partitions Z\mathbb{Z} into nn classes represented by residues 0,1,,n10, 1, \dots, n-1. Another is the kernel of a function f:XYf: X \to Y, where xyx \sim y if f(x)=f(y)f(x) = f(y), grouping elements that map to the same output and thus forming equivalence classes as the preimages of points in the codomain. A partial order on a set XX is a binary relation \leq that is reflexive, antisymmetric, and transitive. Antisymmetry means that if xyx \leq y and yxy \leq x, then x=yx = y. A set equipped with a partial order is called a partially ordered set, or poset. In a poset, not all pairs of elements need to be comparable, allowing for complex hierarchical structures. A total order is a partial order where every pair of distinct elements is comparable, meaning for any x,yXx, y \in X, either xyx \leq y or yxy \leq x. The standard ordering on the real numbers R\mathbb{R} exemplifies a total order. Strict orders, such as strict partial orders, are irreflexive and transitive relations derived from partial orders by excluding equality; for instance, the strict less-than relation << on R\mathbb{R} is the strict version of \leq. Partial orders are often visualized using Hasse diagrams, which represent the poset by drawing elements as points and connecting them with lines only for covering relations—where xx covers yy if x>yx > y and no zz satisfies y<z<xy < z < x—omitting reflexive loops and transitive edges for clarity. This graphical tool highlights the structure of the poset without redundant information.

Heterogeneous and Homogeneous Relations

A binary relation RR is defined as a subset of the Cartesian product A×BA \times B, where AA and BB are sets. When ABA \neq B, the relation is termed heterogeneous, allowing connections between elements of distinct sets. In contrast, a homogeneous relation occurs when A=BA = B, restricting the relation to a single set and enabling interpretations such as directed graphs, where vertices represent elements of AA and arcs represent pairs in RR. Homogeneous relations are fundamental in modeling intra-set connections, such as the "less than" relation on the real numbers R\mathbb{R}, where R={(x,y)R×Rx<y}R = \{ (x, y) \in \mathbb{R} \times \mathbb{R} \mid x < y \}. This setup supports directed graph representations, with reflexivity defined as aA,(a,a)R\forall a \in A, (a, a) \in R, a property meaningful only for homogeneous relations since it requires elements to pair with themselves within the same set. Other properties like symmetry ((a,b)R(a, b) \in R implies (b,a)R(b, a) \in R) and transitivity ((a,b)R(a, b) \in R and (b,c)R(b, c) \in R imply (a,c)R(a, c) \in R) also presuppose A=BA = B, as they involve swapping or chaining elements across identical domains and codomains. For heterogeneous relations, these properties are not standardly applicable without additional structure, such as embeddings into a common set. Heterogeneous relations generalize binary connections across different sets, exemplified by the successor function on natural numbers viewed as a relation from N\mathbb{N} to N+1\mathbb{N} + 1 (if considering extended codomain), or more distinctly, "is a root of" where RR×(RR)R \subseteq \mathbb{R} \times (\mathbb{R} \to \mathbb{R}) pairs real numbers with polynomial functions they satisfy. Functions themselves are special heterogeneous relations: a function f:ABf: A \to B corresponds to a left-total, right-unique relation R={(a,b)A×Bb=f(a)}R = \{ (a, b) \in A \times B \mid b = f(a) \}, with ABA \neq B possible, such as mapping integers to their string representations. In graph theory, heterogeneous relations model , with edges linking vertices from two disjoint sets. In database applications, heterogeneous relations are crucial for modeling inter-table links via foreign keys, where a foreign key in one relation references the primary key of another, establishing a binary connection between distinct entity sets, as formalized in the relational model. For instance, a "capital of" relation might link a countries table to a cities table, enforcing referential integrity across heterogeneous schemas. This approach supports data integration in federated systems, where implied binary relationships merge entities from disparate sources.

Special Relations

Difunctional and Ferrers Relations

A binary relation RA×BR \subseteq A \times B is difunctional if it satisfies the inclusion RR1RRR \circ R^{-1} \circ R \subseteq R, where \circ denotes relational composition and R1R^{-1} the converse relation. This defining property, known as the multiplication property, ensures that the relation behaves multiplicatively under composition with its converse. The concept was introduced by Jacques Riguet in his foundational work on . Difunctional relations admit several equivalent characterizations that highlight their structural simplicity. One such characterization is that RR can be expressed as the composition R=f1gR = f^{-1} \circ g, where f:ACf: A \to C and g:BCg: B \to C are partial functions for some set CC, satisfying ff1=ff \circ f^{-1} = f and gg1=gg \circ g^{-1} = g. Alternatively, RR is the union of a family of pairwise disjoint rectangles, where each rectangle is a full bipartite relation Ci×DiC_i \times D_i with the CiAC_i \subseteq A and DiBD_i \subseteq B forming disjoint partitions. This block structure corresponds to partial functions between quotient sets induced by equivalence relations on AA and BB, or more generally to block designs where blocks are fully matched. In particular, difunctional relations include partial functions as a special case, where each block is a singleton, implying that they are thin in the sense that each element in the domain relates to elements within at most one block, though not necessarily to a single codomain element. A representative example of a difunctional relation is a matching in a with parts AA and BB, which consists of disjoint 1×1 rectangles and thus satisfies the defining inclusion. More broadly, any partial permutation between subsets of AA and BB (arising from disjoint cycles or fixed points in permutation representations) is difunctional, linking the concept to applications in and . A Ferrers relation is a binary relation FX×YF \subseteq X \times Y satisfying the condition that whenever (g,m)F(g, m) \in F and (h,n)F(h, n) \in F but (g,n)F(g, n) \notin F, then (h,m)F(h, m) \in F. This property, analogous to the staircase shape of Ferrers diagrams in partition theory, ensures a form of "consecutive ones" structure in the relation's matrix representation. The notion was originated by Jacques Riguet as a dual to certain closure properties in binary relations. For homogeneous Ferrers relations on a single set ZZ (i.e., FZ×ZF \subseteq Z \times Z), assuming a fixed linear order on ZZ, the relation corresponds to a matrix where the positions of the elements satisfy the Ferrers property: if (i,j)F(i, j) \in F with i<ki < k and j>lj > l, then (k,l)F(k, l) \in F. Such relations are closely tied to semiorders and interval orders in order theory, where the complement of a Ferrers relation is again Ferrers, providing a symmetric structure for analyzing incomparabilities. In this homogeneous case, Ferrers relations generalize the complement of a strict linear order by allowing "staircase" incomparabilities rather than total comparability, facilitating decompositions in poset dimension theory. Difunctional and Ferrers relations are connected through duality in relation algebras: the converse of a difunctional relation is Ferrers, and vice versa, reflecting their roles in multiplicative and additive properties of relations. This interplay underscores their importance in classifying binary relations beyond basic reflexive, symmetric, or transitive types.

Contact and Preorder Relations

A contact relation is a binary relation used in mereotopology to model the notion of two spatial regions touching or being connected without one being a part of the other, characterized by symmetry and non-transitivity. In the Region Connection Calculus (RCC), the external contact relation EC(x, y) holds when regions x and y share at least one point on their boundaries but their interiors do not overlap, making it symmetric—EC(x, y) iff EC(y, x)—and non-transitive, as a chain of touching regions may not result in contact between the endpoints. This relation extends mereological concepts by incorporating topological connection, where contact often aligns with overlap in mereology but emphasizes boundary sharing in geometric models. An example of a contact relation is the "shares a " relation among , where two countries are related if they have a common boundary but neither encompasses the other; this is symmetric, as bordering is mutual, and non-transitive, since Country A bordering Country B and Country B bordering Country C does not imply Country A borders Country C. In modern mereotopology, such relations support qualitative spatial reasoning in geographic information systems, enabling queries about adjacency without precise measurements. In contrast, a R\RR \backslash R arises from a binary relation R as its left residual, defined in relational as R\R=RTRR \backslash R = \overline{R^T \cdot \overline{R}}, where RTR^T is the converse of R and the overline denotes complement; this yields a reflexive and , hence a preorder. Specifically, reflexivity follows from RTRIR^T \cdot \overline{R} \subseteq \overline{I}, implying the identity relation I is contained in R\RR \backslash R, and transitivity is established via Schröder's rule and the property R(R\R)RR \cdot (R \backslash R) \subseteq R. This preorder captures a "generated ordering" from R, often used in heterogeneous relations to induce partial orders on sets with differing structures. Unlike strict preorders, which are irreflexive and transitive (e.g., the strict order < on real , the preorder R\RR \backslash R includes reflexivity, allowing self-relations and modeling inclusive orderings such as "less than or equal to." In mereotopological applications, such preorders can represent cumulative connections, like the of contact relations to model in spatial networks.

Other Particular Relations

The fringe of a binary relation RR on sets XX and YY is the largest difunctional subrelation embedded within RR, consisting of those pairs that belong to exactly one maximal in the relation's graphical representation. Formally, it is computed as fringe(R)=R(R;RT;R)\operatorname{fringe}(R) = R \cap (R ; R^T ; R), where ;; denotes relational composition and RTR^T is the (converse) of RR. This , introduced by Riguet in his foundational work on difunctional relations, identifies "isolated points" or boundary elements that cannot be extended into larger rectangular blocks without leaving RR. The fringe is itself difunctional and idempotent, meaning fringe(fringe(R))=fringe(R)\operatorname{fringe}(\operatorname{fringe}(R)) = \operatorname{fringe}(R), and it remains invariant under reduction to the block-transitive kernel of RR. In partially ordered sets (posets), where RR represents a strict order <<, the fringe is contained within the Hasse relation—the relations forming the poset's —capturing extremal pairs such as those connecting minimal to immediate successor elements without intermediates. For instance, in a dense linear order like the reals under <<, the fringe is empty due to the absence of such isolated . This property makes the fringe useful in for extracting minimal conceptual decompositions and invariant structures from relational data. Mathematical heaps provide another specialized perspective on relations, bridging algebraic structures and combinatorial models. An algebraic heap is a set HH equipped with a ternary operation [x,y,z][x, y, z] satisfying [x,y,[u,v,w]]=[[x,y,u],v,w]=[x,[y,u,v],w][x, y, [u, v, w]] = [[x, y, u], v, w] = [x, [y, u, v], w] and [x,y,z]=[u,y,v][x, y, z] = [u, y, v] implying x=ux = u and z=vz = v, generalizing groups by omitting a specified identity while allowing recovery of one. Such heaps induce binary relations via projections, such as the derived from fixing an element, but fundamentally rely on ternary relations for their non-associative structure. In combinatorial contexts, a heap is a poset (P,)(P, \leq) labeled by elements from a set BB under a symmetric, reflexive binary relation RB×BR \subseteq B \times B, where RR enforces ordering constraints: if the labels a=(x)a = \ell(x) and b=(y)b = \ell(y) satisfy aRba R b, then xyx \leq y or yxy \leq x in the poset, with \leq as the incorporating RR-based interchanges. This binary RR models "blocking" between labels, as in heaps of monomers and dimers where RR distinguishes compatible placements, enabling enumeration of polyominoes via the Cartier–Foata monoid. Heaps thus project ternary interactions onto binary constraints for partial orders, with applications in rewriting systems and trace s. Computationally, binary heaps extend this to data structures, realized as complete binary trees where the parent-child relation RR satisfies the heap property: for a min-heap, each is less than or equal to its children, forming a binary relation on array indices that ensures efficient operations like extract-min in O(logn)O(\log n) time. This relational view underpins algorithms for sorting and graph processing, with the tree's fringe (leaf level) often holding extremal elements analogous to relational boundaries.

Advanced Topics

Sets Versus Classes

In ZFC , a binary relation on sets AA and BB is defined as a subset of the A×BA \times B, which itself is a set constructed via the axioms of pairing, union, and ; thus, all such relations are sets and inherit the well-foundedness of the cumulative hierarchy VV. This framework ensures that relations remain within the bounds of sets, avoiding the formation of overly large collections that could lead to inconsistencies. However, ZFC's restriction to sets imposes limitations: for instance, there is no containing all sets, so no binary relation can encompass the entire universe VV as a set, precluding direct set-theoretic operations on "global" relations like membership across all sets. To address these limitations and formalize "large" collections without , class theories such as von Neumann–Bernays–Gödel (NBG) and Morse-Kelley (MK) introduce proper classes alongside sets. In NBG, a binary relation is a class comprising ordered pairs, where classes are defined by formulas via the of comprehension (restricted to set quantifiers), allowing relations to be proper classes if they are not elements of any set; the membership relation \in, for example, forms such a proper class, relating every set to its elements without being a set itself. MK extends this by permitting class quantifiers in comprehension, enabling more expressive definitions of class relations while remaining consistent with ZFC for set-level assertions. These theories distinguish the class VV of all sets (a proper class) from any set, ensuring that relations on VV can be handled as classes without violating or foundation. Historically, this distinction arose in response to , which demonstrated that unrestricted comprehension—allowing any definable collection to be a set—leads to contradictions, such as the paradoxical set R={xxx}R = \{x \mid x \notin x\}; in class theories, the corresponding collection is a proper class, not a set, thereby resolving the issue without abandoning comprehension entirely. Bernays' axiomatization built on von Neumann's class-based approach to avoid such paradoxes, while Gödel streamlined it for consistency proofs relative to ZFC. In ZFC, these class-theoretic insights manifest indirectly through definable classes (e.g., via replacement and separation), but the lack of formal classes limits proofs about the as a whole, such as global well-orderings or reflections, which NBG and MK handle more robustly.

Induced Concept Lattice

In (FCA), a binary relation serves as the foundational structure for modeling associations between a set of objects and a set of attributes, forming what is known as a formal context. Specifically, a formal context is defined as a triple (G,M,I)(G, M, I), where GG is the set of objects, MM is the set of attributes, and IG×MI \subseteq G \times M is the binary incidence relation specifying which objects possess which attributes. This setup captures the inherent relational structure of data, enabling the derivation of hierarchical knowledge representations from binary relations. The concept lattice induced by this binary relation arises through the derivation operators defined by the relation II. For a subset AGA \subseteq G of objects, the intent A={mMgA:(g,m)I}A' = \{ m \in M \mid \forall g \in A: (g, m) \in I \} consists of all attributes common to those objects; dually, for BMB \subseteq M, the extent B={gGmB:(g,m)I}B' = \{ g \in G \mid \forall m \in B: (g, m) \in I \} includes all objects sharing those attributes. These operators form a Galois connection between the power sets P(G)\mathcal{P}(G) and P(M)\mathcal{P}(M), which induces closure operators AAA \mapsto A'' on P(G)\mathcal{P}(G) and BBB \mapsto B'' on P(M)\mathcal{P}(M). A formal concept is then a pair (A,B)(A, B) where A=BA = B' and B=AB = A', with AA as the extent and BB as the intent. The collection of all such formal concepts, partially ordered by (A1,B1)(A2,B2)(A_1, B_1) \leq (A_2, B_2) if and only if A1A2A_1 \subseteq A_2 (equivalently, B2B1B_2 \subseteq B_1), constitutes the concept lattice B(G,M,I)\mathfrak{B}(G, M, I), a complete lattice structure that hierarchically organizes the concepts by inclusion. This lattice directly emerges from the binary relation, as the closure operators identify the maximal consistent groupings of objects and attributes. Key properties of the induced concept lattice depend on characteristics of the underlying binary relation. The lattice is always complete, with meets and joins computed via intersections of extents or unions of intents, respectively. It is distributive precisely when the binary relation defines a Horn context, where attribute implications can be expressed as Horn clauses (conjunctions implying a single positive literal), ensuring the lattice satisfies the distributive laws x(yz)=(xy)(xz)x \wedge (y \vee z) = (x \wedge y) \vee (x \wedge z) and its dual. This distributivity facilitates efficient computation and interpretation in applications requiring modular knowledge structures. The induced concept lattice has significant applications in knowledge representation, where it provides a lattice-theoretic framework for organizing conceptual hierarchies derived from binary relations, enabling transparent reasoning about object-attribute dependencies. In the 2020s, FCA and its concept lattices have gained traction in AI-driven , particularly for tasks such as pattern discovery, data cleaning, and visualization in large-scale datasets like data lakes, where binary relations model feature-object interactions to uncover hidden structures without assuming completeness in the data. For instance, recent work leverages concept lattices to systematically organize and analyze heterogeneous data sources, improving scalability in pipelines.

Fringe and Mathematical Heaps

In the context of binary relations, the fringe of a relation RX×YR \subseteq X \times Y is a difunctional subrelation that captures the boundary or edge elements of RR, specifically those pairs belonging to exactly one maximal in the matrix representation of RR. Introduced by Riguet, this construct serves as an approximation tool in , where it embeds the core regular structure of RR while excluding overlapping or interior points. The fringe is computed via as fringe(R)=RRRR\mathrm{fringe}(R) = R \cap \overline{R} \circ R^\top \circ \overline{R}, where \circ denotes composition, ^\top the converse (transpose), and R\overline{R} the complement of RR; this yields a subrelation that is always difunctional, satisfying F=FFFF = F \circ F^\top \circ F, and equals RR precisely when RR itself is difunctional. In approximation theory, the fringe facilitates minimal coverage of RR by rectangles, aiding in tasks like from binary data by isolating invariant boundary structures without full enumeration of all bicliques. Mathematical heaps extend binary relations into algebraic structures that generalize groups by forgoing a fixed identity element, instead relying on relational definitions for operations. A heap is formalized as a triple (P,,)(P, \leq, \ell), where (P,)(P, \leq) is a poset induced by a reflexive, antisymmetric, and transitive binary relation on the set PP of positions, and :PB\ell: P \to B is a labeling function to a set BB of pieces equipped with a symmetric and reflexive binary relation RB×BR \subseteq B \times B that enforces compatibility constraints (e.g., pieces related by RR cannot cross in rearrangements). The heap operation, derived relationally through transitive closure under \leq and RR-constrained label swaps, produces a ternary structure [x,y,z][x, y, z] that is para-associative: [w,[x,y,z],u]=[[w,x,y],z,u][w, [x, y, z], u] = [[w, x, y], z, u], mirroring group multiplication without identity. Key properties of heaps include associativity in their of compositions, where the empty heap \emptyset acts as a neutral element under relational , and the ability to derive inverses relationally by fixing an arbitrary element eHe \in H to define a xy=[x,e,y]x \cdot y = [x, e, y], transforming the heap into a group isomorphic to its . Post-2000 developments connect heaps to via representations in Coxeter groups and affine Kac-Moody algebras, where the relational labeling models reduced expressions and braid relations in Weyl groups. For instance, heaps relationalize operations by treating element priorities as labels under an ordering relation \leq, enabling associative insertions and extractions through constrained poset manipulations that simulate heap-order maintenance without explicit structures.

References

Add your contribution
Related Hubs
User Avatar
No comments yet.