# Binary relation

Short description: Relationship between elements of two sets

In mathematics, a binary relation associates elements of one set, called the domain, with elements of another set, called the codomain.[1] A binary relation over sets X and Y is a new set of ordered pairs (x, y) consisting of elements x from X and y from Y.Cite error: Closing </ref> missing for <ref> tag Binary relations are also heavily used in computer science.

A binary relation over sets X and Y is an element of the power set of $\displaystyle{ X \times Y. }$ Since the latter set is ordered by inclusion (⊆), each relation has a place in the lattice of subsets of $\displaystyle{ X \times Y. }$ A binary relation is called a homogeneous relation when X = Y. A binary relation is also called a heterogeneous relation when it is not necessary that X = Y.

Since relations are sets, they can be manipulated using set operations, including union, intersection, and complementation, and satisfying the laws of an algebra of sets. Beyond that, operations like the converse of a relation and the composition of relations are available, satisfying the laws of a calculus of relations, for which there are textbooks by Ernst Schröder,[2] Clarence Lewis,[3] and Gunther Schmidt.[4] A deeper analysis of relations involves decomposing them into subsets called concepts, and placing them in a complete lattice.

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.

## Definition

Given sets X and Y, the Cartesian product $\displaystyle{ X \times Y }$ is defined as $\displaystyle{ \{ (x, y) : x \in X \text{ and } y \in Y \}, }$ and its elements are called ordered pairs.

A binary relation R over sets X and Y is a subset of $\displaystyle{ X \times Y. }$[5][6] The set X is called the domain[5] or set of departure of R, and the set Y the codomain or set of destination of R. In order to specify the choices of the sets X and Y, some authors define a binary relation or correspondence as an ordered triple (X, Y, G), where G is a subset of $\displaystyle{ X \times Y }$ called the graph of the binary relation. The statement $\displaystyle{ (x, y) \in R }$ reads "x is R-related to y" and is denoted by xRy.[2][3][4][note 1] The domain of definition or active domain[5] of R is the set of all x such that xRy for at least one y. The codomain of definition, active codomain,[5] image or range of R is the set of all y such that xRy for at least one x. The field of R is the union of its domain of definition and its codomain of definition.[8][9][10]

When $\displaystyle{ X = Y, }$ a binary relation is called a homogeneous relation (or endorelation). To emphasize the fact that X and Y are allowed to be different, a binary relation is also called a heterogeneous relation.[11][12][13]

In a binary relation, the order of the elements is important; if $\displaystyle{ x \neq y }$ then yRx can be true or false independently of xRy. For example, 3 divides 9, but 9 does not divide 3.

## Operations

### Union

If R and S are binary relations over sets X and Y then $\displaystyle{ R \cup S = \{ (x, y) : xRy \text{ or } xSy \} }$ is the union relation of R and S over X and Y.

The identity element is the empty relation. For example, $\displaystyle{ \,\leq\, }$ is the union of < and =, and $\displaystyle{ \,\geq\, }$ is the union of > and =.

### Intersection

If R and S are binary relations over sets X and Y then $\displaystyle{ R \cap S = \{ (x, y) : xRy \text{ and } xSy \} }$ is the intersection relation of R and S over X and Y.

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

Main page: Composition of relations

If R is a binary relation over sets X and Y, and S is a binary relation over sets Y and Z then $\displaystyle{ S \circ R = \{ (x, z) : \text{ there exists } y \in Y \text{ such that } xRy \text{ and } ySz \} }$ (also denoted by R; S) is the composition relation of R and S over X and Z.

The identity element is the identity relation. The order of R and S in the notation $\displaystyle{ S \circ R, }$ used here agrees with the standard notational order for composition of functions. For example, the composition (is parent of)$\displaystyle{ \,\circ\, }$(is mother of) yields (is maternal grandparent of), while the composition (is mother of)$\displaystyle{ \,\circ\, }$(is parent of) yields (is grandmother of). For the former case, if x is the parent of y and y is the mother of z, then x is the maternal grandparent of z.

### Converse

Main page: Converse relation

If R is a binary relation over sets X and Y then $\displaystyle{ R^\textsf{T} = \{ (y, x) : xRy \} }$ is the converse relation,[citation needed] also called inverse relation,[citation needed] of R over Y and X.

For example, $\displaystyle{ \,=\, }$ is the converse of itself, as is $\displaystyle{ \,\neq,\, }$ and $\displaystyle{ \,\lt \, }$ and $\displaystyle{ \,\gt \, }$ are each other's converse, as are $\displaystyle{ \,\leq\, }$ and $\displaystyle{ \,\geq.\, }$ A binary relation is equal to its converse if and only if it is symmetric.

### Complement

If R is a binary relation over sets X and Y then $\displaystyle{ \overline{R} = \{ (x, y) : \text{ not } xRy \} }$ (also denoted by R or ¬ R) is the complementary relation of R over X and Y.

For example, $\displaystyle{ \,=\, }$ and $\displaystyle{ \,\neq\, }$ are each other's complement, as are $\displaystyle{ \,\subseteq\, }$ and $\displaystyle{ \,\not\subseteq,\, }$ $\displaystyle{ \,\supseteq\, }$ and $\displaystyle{ \,\not\supseteq,\, }$ and $\displaystyle{ \,\in\, }$ and $\displaystyle{ \,\not\in,\, }$ and, for total orders, also $\displaystyle{ \,\lt \, }$ and $\displaystyle{ \,\geq,\, }$ and $\displaystyle{ \,\gt \, }$ and $\displaystyle{ \,\leq.\, }$

The complement of the converse relation $\displaystyle{ R^\textsf{T} }$ is the converse of the complement: $\displaystyle{ \overline{R^\mathsf{T}} = \bar{R}^\mathsf{T}. }$

If $\displaystyle{ X = Y, }$ 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

Main page: Restriction (mathematics)

If R is a binary homogeneous relation over a set X and S is a subset of X then $\displaystyle{ R_{\vert S} = \{ (x, y) \mid xRy \text{ and } x \in S \text{ and } y \in S \} }$ is the restriction relation of R to S over X.

If R is a binary relation over sets X and Y and if S is a subset of X then $\displaystyle{ R_{\vert S} = \{ (x, y) \mid xRy \text{ and } x \in S \} }$ is the left-restriction relation of R to S over X and Y.[clarification needed]

If R is a binary relation over sets X and Y and if S is a subset of Y then $\displaystyle{ R^{\vert S} = \{ (x, y) \mid xRy \text{ and } y \in S \} }$ is the right-restriction relation of R to S over X and Y.

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 "x is parent of y" to females yields the relation "x is mother of the woman y"; 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 $\displaystyle{ \,\leq\, }$ is that every non-empty subset $\displaystyle{ S \subseteq \R }$ with an upper bound in $\displaystyle{ \R }$ has a least upper bound (also called supremum) in $\displaystyle{ \R. }$ However, for the rational numbers this supremum is not necessarily rational, so the same property does not hold on the restriction of the relation $\displaystyle{ \,\leq\, }$ to the rational numbers.

A binary relation R over sets X and Y is said to be contained in a relation S over X and Y, written $\displaystyle{ R \subseteq S, }$ if R is a subset of S, that is, for all $\displaystyle{ x \in X }$ and $\displaystyle{ y \in Y, }$ if xRy, then xSy. If R is contained in S and S is contained in R, then R and S are called equal written R = S. If R is contained in S but S is not contained in R, then R is said to be smaller than S, written $\displaystyle{ R \subsetneq S. }$ For example, on the rational numbers, the relation $\displaystyle{ \,\gt \, }$ is smaller than $\displaystyle{ \,\geq,\, }$ and equal to the composition $\displaystyle{ \,\gt \,\circ\,\gt .\, }$

### Matrix representation

Binary relations over sets X and Y can be represented algebraically by logical matrices indexed by X and Y 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 X and Y and a relation over Y and Z),[14] 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 X = Y) form a matrix semiring (indeed, a matrix semialgebra over the Boolean semiring) where the identity matrix corresponds to the identity relation.[15]

## Examples

2nd example relation
A
B
ball car doll cup
John +
Mary +
Venus +
1st example relation
A
B
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 $\displaystyle{ A = \{ \text{ball, car, doll, cup} \} }$ and four people $\displaystyle{ B = \{ \text{John, Mary, Ian, Venus} \}. }$ A possible relation on A and B is the relation "is owned by", given by $\displaystyle{ R = \{ (\text{ball, John}), (\text{doll, Mary}), (\text{car, Venus}) \}. }$ 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, R does not involve Ian, and therefore R could have been viewed as a subset of $\displaystyle{ A \times \{ \text{John, Mary, Venus} \}, }$ i.e. a relation over A and $\displaystyle{ \{ \text{John, Mary, Venus} \}; }$ see the 2nd example. While the 2nd example relation is surjective (see below), the 1st is not.
Oceans and continents (islands omitted)
Template:Pipe escape
2. Let A = {Indian, Arctic, Atlantic, Pacific}, the oceans of the globe, and B = { NA, SA, AF, EU, AS, AU, AA }, the continents. Let aRb represent that ocean a borders continent b. Then the logical matrix for this relation is:
$\displaystyle{ R = \begin{pmatrix} 0 & 0 & 1 & 0 & 1 & 1 & 1 \\ 1 & 0 & 0 & 1 & 1 & 0 & 0 \\ 1 & 1 & 1 & 1 & 0 & 0 & 1 \\ 1 & 1 & 0 & 0 & 1 & 1 & 1 \end{pmatrix} . }$
The connectivity of the planet Earth can be viewed through R RT and RT R, the former being a $\displaystyle{ 4 \times 4 }$ relation on A, which is the universal relation ($\displaystyle{ A \times A }$ 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, RT R is a relation on $\displaystyle{ B \times B }$ 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 t axes represent time for observers in motion, the corresponding x 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 time and space since each time t determines a simultaneous hyperplane in that cosmology. Herman 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
$\displaystyle{ \langle x, z\rangle \ =\ x \bar{z} + \bar{x}z\; }$ 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.[16]
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 $\displaystyle{ \text{S}(t, k, n) }$ which have an n-element set S and a set of k-element subsets called blocks, such that a subset with t 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 D = (V, B, I) where V and B are any two disjoint sets and I is a binary relation between V and B, i.e. $\displaystyle{ I \subseteq V \times \textbf{B}. }$ The elements of V will be called points, those of B blocks and those of I flags.[17]

## Specific types of binary relations

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 R over sets X and Y are listed below.

Uniqueness properties:

• Injective (also called left-unique):[18] for all $\displaystyle{ x, z \in X }$ and all $\displaystyle{ y \in Y, }$ if xRy and zRy then x = z. For such a relation, {Y} is called a primary key of R.[5] For example, the green and blue binary relations in the diagram are injective, but the red one is not (as it relates both −1 and 1 to 1), nor the black one (as it relates both −1 and 1 to 0).
• Univalent[4] (also called right-unique,[18] right-definite[19] or functional[20]): for all $\displaystyle{ x \in X }$ and all $\displaystyle{ y, z \in Y, }$ if xRy and xRz then y = z. Such a binary relation is called a partial function. For such a relation, $\displaystyle{ \{ X \} }$ is called a primary key of R.[5] For example, the red and green binary relations in the diagram are univalent, but the blue one is not (as it relates 1 to both −1 and 1), nor the black one (as it relates 0 to both −1 and 1).
• 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 X and codomain Y are specified):

• Total (also called left-total):[18] for all x in X there exists a y in Y such that xRy. In other words, the domain of definition of R is equal to X. 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 −1 to any real number), nor the black one (as it does not relate 2 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 y in the positive integers such that 1 > y.[21] However, < is a total relation over the positive integers, the rational numbers and the real numbers. Every reflexive relation is total: for a given x, choose y = x.
• Surjective (also called right-total[18] or onto): for all y in Y, there exists an x in X such that xRy. In other words, the codomain of definition of R is equal to Y. 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 −1), nor the black one (as it does not relate any real number to 2).

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

• A function: a binary relation that is functional and total. 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 binary relation in the diagram is an injection, but the red, blue and black ones are not.
• A surjection: a function that is surjective. For example, the green binary relation in the diagram is a surjection, but the red, blue and black ones are not.
• A bijection: a function that is injective and surjective. For example, the green binary relation in the diagram is a bijection, but the red, blue and black ones are not.

If relations over proper classes are allowed:

• Set-like (or local): for all x in X, the class of all y in Y such that yRx, i.e. $\displaystyle{ \{y\in Y : yRx\} }$, is a set. For example, the relation $\displaystyle{ \in }$ is set-like, and every relation on two sets is set-like.[22] The usual ordering < over the class of ordinal numbers is a set-like relation, while its inverse > is not.[citation needed]

## Sets versus classes

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 $\displaystyle{ \,=, }$ 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 A, that contains all the objects of interest, and work with the restriction =A instead of =. Similarly, the "subset of" relation $\displaystyle{ \,\subseteq\, }$ needs to be restricted to have domain and codomain P(A) (the power set of a specific set A): the resulting set relation can be denoted by $\displaystyle{ \,\subseteq_A.\, }$ Also, the "member of" relation needs to be restricted to have domain A and codomain P(A) to obtain a binary relation $\displaystyle{ \,\in_A\, }$ that is a set. Bertrand Russell has shown that assuming $\displaystyle{ \,\in\, }$ 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 (X, Y, G), 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.)[23] With this definition one can for instance define a binary relation over every set and its power set.

## Homogeneous relation

Main page: Homogeneous relation

A homogeneous relation over a set X is a binary relation over X and itself, i.e. it is a subset of the Cartesian product $\displaystyle{ X \times X. }$[13][24][25] It is also simply called a (binary) relation over X.

A homogeneous relation R over a set X may be identified with a directed simple graph permitting loops, where X is the vertex set and R is the edge set (there is an edge from a vertex x to a vertex y if and only if xRy). The set of all homogeneous relations $\displaystyle{ \mathcal{B}(X) }$ over a set X is the power set $\displaystyle{ 2^{X \times X} }$ 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 $\displaystyle{ \mathcal{B}(X) }$, it forms a semigroup with involution.

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

• Reflexive: for all $\displaystyle{ x \in X, }$ xRx. For example, $\displaystyle{ \,\geq\, }$ is a reflexive relation but > is not.
• Irreflexive: for all $\displaystyle{ x \in X, }$ not xRx. For example, $\displaystyle{ \,\gt \, }$ is an irreflexive relation, but $\displaystyle{ \,\geq\, }$ is not.
• Symmetric: for all $\displaystyle{ x, y \in X, }$ if xRy then yRx. For example, "is a blood relative of" is a symmetric relation.
• Antisymmetric: for all $\displaystyle{ x, y \in X, }$ if xRy and yRx then $\displaystyle{ x = y. }$ For example, $\displaystyle{ \,\geq\, }$ is an antisymmetric relation.[26]
• Asymmetric: for all $\displaystyle{ x, y \in X, }$ if xRy then not yRx. A relation is asymmetric if and only if it is both antisymmetric and irreflexive.[27] For example, > is an asymmetric relation, but $\displaystyle{ \,\geq\, }$ is not.
• Transitive: for all $\displaystyle{ x, y, z \in X, }$ if xRy and yRz then xRz. A transitive relation is irreflexive if and only if it is asymmetric.[28] For example, "is ancestor of" is a transitive relation, while "is parent of" is not.
• Connected: for all $\displaystyle{ x, y \in X, }$ if $\displaystyle{ x \neq y }$ then xRy or yRx.
• Strongly connected: for all $\displaystyle{ x, y \in X, }$ xRy or yRx.
• Dense: for all $\displaystyle{ x, y \in X, }$ if $\displaystyle{ xRy , }$ then some $\displaystyle{ z \in X }$ exists such that $\displaystyle{ xRz }$ and $\displaystyle{ zRy }$.

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.[29] 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, "x divides y" is a partial, but not a total order on natural numbers $\displaystyle{ \N, }$ "x < y" is a strict total order on $\displaystyle{ \N, }$ and "x is parallel to y" 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 X may be subjected to closure operations like:

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

## Heterogeneous relation

In mathematics, a heterogeneous relation is a binary relation, a subset of a Cartesian product $\displaystyle{ A \times B, }$ where A and B are possibly distinct sets.[30] The prefix hetero is from the Greek ἕτερος (heteros, "other, another, different").

A heterogeneous relation has been called a rectangular relation,[13] suggesting that it does not have the square-like symmetry of a homogeneous relation on a set where $\displaystyle{ A = B. }$ 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."[31]

## Calculus of relations

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 $\displaystyle{ R \subseteq S, }$ meaning that aRb implies aSb, sets the scene in a lattice of relations. But since $\displaystyle{ P \subseteq Q \equiv (P \cap \bar{Q} = \varnothing ) \equiv (P \cap Q = P), }$ 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 $\displaystyle{ A \times B. }$

In contrast to homogeneous relations, the composition of relations operation is only a partial function. The necessity of matching range to domain 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

Binary relations have been described through their induced concept lattices: A concept CR satisfies two properties: (1) The logical matrix of C is the outer product of logical vectors

$\displaystyle{ C_{i j} \ = \ u_i v_j , \quad u, v }$ logical vectors.[clarification needed] (2) C is maximal, not contained in any other outer product. Thus C is described as a non-enlargeable rectangle.

For a given relation $\displaystyle{ R \subseteq X \times Y, }$ the set of concepts, enlarged by their joins and meets, forms an "induced lattice of concepts", with inclusion $\displaystyle{ \sqsubseteq }$ 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".[32] The decomposition is

$\displaystyle{ R \ = \ f \ E \ g^\textsf{T} , }$ where f and g are functions, called mappings or left-total, univalent relations in this context. The "induced concept lattice is isomorphic to the cut completion of the partial order E that belongs to the minimal decomposition (f, g, E) of the relation R."

Particular cases are considered below: E total order corresponds to Ferrers type, and E 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.[33] Structural analysis of relations with concepts provides an approach for data mining.[34]

## Particular relations

• Proposition: If R is a serial relation and RT is its transpose, then $\displaystyle{ I \subseteq R^\textsf{T} R }$ where I is the m × m identity relation.
• Proposition: If R is a surjective relation, then $\displaystyle{ I \subseteq R R^\textsf{T} }$ where I is the $\displaystyle{ n \times n }$ identity relation.

### Difunctional

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 $\displaystyle{ Z = \{ x, y, z, \ldots \} }$ of indicators. The partitioning relation $\displaystyle{ R = F G^\textsf{T} }$ is a composition of relations using univalent relations $\displaystyle{ F \subseteq A \times Z \text{ and } G \subseteq B \times Z. }$ Jacques Riguet named these relations difunctional since the composition F GT involves univalent relations, commonly called partial functions.

In 1950 Rigeut showed that such relations satisfy the inclusion:[35]

$\displaystyle{ R \ R^\textsf{T} \ R \ \subseteq \ R }$

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.[36] More formally, a relation R on $\displaystyle{ X \times Y }$ is difunctional if and only if it can be written as the union of Cartesian products $\displaystyle{ A_i \times B_i }$, where the $\displaystyle{ A_i }$ are a partition of a subset of X and the $\displaystyle{ B_i }$ likewise a partition of a subset of Y.[37]

Using the notation {y: xRy} = xR, a difunctional relation can also be characterized as a relation R such that wherever x1R and x2R have a non-empty intersection, then these two sets coincide; formally $\displaystyle{ x_1 \cap x_2 \neq \varnothing }$ implies $\displaystyle{ x_1 R = x_2 R. }$[38]

In 1997 researchers found "utility of binary decomposition based on difunctional dependencies in database management."[39] Furthermore, difunctional relations are fundamental in the study of bisimulations.[40]

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

### Ferrers type

A strict order on a set is a homogeneous relation arising in order theory. In 1951 Jacques Riguet adopted the ordering of a partition of an integer, called a Ferrers diagram, to extend ordering to binary relations in general.[41]

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 $\displaystyle{ R \bar{R}^\textsf{T} R \subseteq R. }$

If any one of the relations $\displaystyle{ R, \ \bar{R}, \ R^\textsf{T} }$ is of Ferrers type, then all of them are. [30]

### Contact

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

1. $\displaystyle{ \text{for all } x \in A, Y = \{ x \} \text{ implies } xgY. }$
2. $\displaystyle{ Y \subseteq Z \text{ and } xgY \text{ implies } xgZ. }$
3. $\displaystyle{ \text{for all } y \in Y, ygZ \text{ and } xgY \text{ implies } xgZ. }$

The set membership relation, ε = "is an element of", satisfies these properties so ε is a contact relation. The notion of a general contact relation was introduced by Georg Aumann in 1970.[42][43]

In terms of the calculus of relations, sufficient conditions for a contact relation include $\displaystyle{ C^\textsf{T} \bar{C} \ \subseteq \ \ni \bar{C} \ \ \equiv \ C \ \overline{\ni \bar{C}} \ \subseteq \ C, }$ where $\displaystyle{ \ni }$ is the converse of set membership ().[44]:280

## Preorder R\R

Every relation R generates a preorder $\displaystyle{ R \backslash R }$ which is the left residual.[45] In terms of converse and complements, $\displaystyle{ R \backslash R \ \equiv \ \overline{R^\textsf{T} \bar{R}}. }$ Forming the diagonal of $\displaystyle{ R^\textsf{T} \bar{R} }$, the corresponding row of $\displaystyle{ R^{\text{T}} }$ and column of $\displaystyle{ \bar{R} }$ will be of opposite logical values, so the diagonal is all zeros. Then

$\displaystyle{ R^\textsf{T} \bar{R} \subseteq \bar{I} \ \implies \ I \subseteq \overline{R^\textsf{T} \bar{R}} \ = \ R \backslash R , }$ so that $\displaystyle{ R \backslash R }$ is a reflexive relation.

To show transitivity, one requires that $\displaystyle{ (R\backslash R)(R\backslash R) \subseteq R \backslash R. }$ Recall that $\displaystyle{ X = R \backslash R }$ is the largest relation such that $\displaystyle{ R X \subseteq R. }$ Then

$\displaystyle{ R(R\backslash R) \subseteq R }$
$\displaystyle{ R(R\backslash R) (R\backslash R )\subseteq R }$ (repeat)
$\displaystyle{ \equiv R^\textsf{T} \bar{R} \subseteq \overline{(R \backslash R)(R \backslash R)} }$ (Schröder's rule)
$\displaystyle{ \equiv (R \backslash R)(R \backslash R) \subseteq \overline{R^\textsf{T} \bar{R}} }$ (complementation)
$\displaystyle{ \equiv (R \backslash R)(R \backslash R) \subseteq R \backslash R. }$ (definition)

The inclusion relation Ω on the power set of U can be obtained in this way from the membership relation $\displaystyle{ \,\in\, }$ on subsets of U:

$\displaystyle{ \Omega \ = \ \overline{\ni \bar{\in}} \ = \ \in \backslash \in . }$[44]:283

## Fringe of a relation

Given a relation R, a sub-relation called its fringe is defined as $\displaystyle{ \operatorname{fringe}(R) = R \cap \overline{R \bar{R}^\textsf{T} R}. }$

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

On the other hand, Fringe(R) = ∅ when R is a dense, linear, strict order.[44]

## Mathematical heaps

Main page: Heap (mathematics)

Given two sets A and B, the set of binary relations between them $\displaystyle{ \mathcal{B}(A,B) }$ can be equipped with a ternary operation $\displaystyle{ [a, \ b,\ c] \ = \ a b^\textsf{T} c }$ where bT denotes the converse relation of b. In 1953 Viktor Wagner used properties of this ternary operation to define semiheaps, heaps, and generalized heaps.[46][47] 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 A and B, while the various types of semigroups appear in the case where A = B.

## Notes

1. Authors who deal with binary relations only as a special case of n-ary relations for arbitrary n usually write Rxy as a special case of Rx1...xn (prefix notation).[7]

## References

1. Ernst Schröder (1895) Algebra und Logic der Relative, via Internet Archive
2. C. I. Lewis (1918) A Survey of Symbolic Logic, pages 269–279, via internet Archive
3. Gunther Schmidt, 2010. Relational Mathematics. Cambridge University Press, ISBN:978-0-521-76268-7, Chapt. 5
4. Cite error: Invalid <ref> tag; no text was provided for refs named Codd1970
5. Enderton 1977, Ch 3. pg. 40
6. Hans Hermes (1973). Introduction to Mathematical Logic. Hochschultext (Springer-Verlag). London: Springer. ISBN 3540058192.  Sect.II.§1.1.4
7. Set Theory and the Continuum Problem. Dover. 2010. ISBN 978-0-486-47484-7.
8. Basic Set Theory. Dover. 2002. ISBN 0-486-42079-5.
9. Schmidt, Gunther; Ströhlein, Thomas (2012). Relations and Graphs: Discrete Mathematics for Computer Scientists. Springer Science & Business Media. Definition 4.1.1.. ISBN 978-3-642-77968-8.
10. Christodoulos A. Floudas; Panos M. Pardalos (2008). Encyclopedia of Optimization (2nd ed.). Springer Science & Business Media. pp. 299–300. ISBN 978-0-387-74758-3.
11. Michael Winter (2007). Goguen Categories: A Categorical Approach to L-fuzzy Relations. Springer. pp. x-xi. ISBN 978-1-4020-6164-6.
12. John C. Baez (6 Nov 2001). "quantum mechanics over a commutative rig". Newsgroupsci.physics.research. Usenet: 9s87n0\$iv5@gap.cco.caltech.edu. Retrieved November 25, 2018.
13. Droste, M., & Kuich, W. (2009). Semirings and Formal Power Series. Handbook of Weighted Automata, 3–28. doi:10.1007/978-3-642-01492-5_1, pp. 7-10
14. Relative simultaneity at Wikibooks
15. Beth, Thomas; Jungnickel, Dieter; Lenz, Hanfried (1986). Design Theory. Cambridge University Press. p. 15. . 2nd ed. (1999) ISBN:978-0-521-44432-3
16. Kilp, Knauer and Mikhalev: p. 3. The same four definitions appear in the following:
• Peter J. Pahl; Rudolf Damrath (2001). Mathematical Foundations of Computational Engineering: A Handbook. Springer Science & Business Media. p. 506. ISBN 978-3-540-67995-0.
• Eike Best (1996). Semantics of Sequential and Parallel Programs. Prentice Hall. pp. 19–21. ISBN 978-0-13-460643-9.
• Robert-Christoph Riemann (1999). Modelling of Concurrent Systems: Structural and Semantical Methods in the High Level Petri Net Calculus. Herbert Utz Verlag. pp. 21–22. ISBN 978-3-89675-629-9.
17. Mäs, Stephan (2007), "Reasoning on Spatial Semantic Integrity Constraints", Spatial Information Theory: 8th International Conference, COSIT 2007, Melbourne, Australia, September 19–23, 2007, Proceedings, Lecture Notes in Computer Science, 4736, Springer, pp. 285–302, doi:10.1007/978-3-540-74788-8_18
18. A. Sengupta (2003). "Toward a Theory of Chaos". International Journal of Bifurcation and Chaos 13 (11): 3147-3233. doi:10.1142/S021812740300851X.
19. Yao, Y.Y.; Wong, S.K.M. (1995). "Generalization of rough sets using relationships between attribute values". Proceedings of the 2nd Annual Joint Conference on Information Sciences: 30–33. .
20. Kunen, Kenneth (1980). Set theory: an introduction to independence proofs. North-Holland. p. 102. ISBN 0-444-85401-0.
21. Tarski, Alfred; Givant, Steven (1987). A formalization of set theory without variables. American Mathematical Society. p. 3. ISBN 0-8218-1041-3.
22. M. E. Müller (2012). Relational Knowledge Discovery. Cambridge University Press. p. 22. ISBN 978-0-521-19021-3.
23. Peter J. Pahl; Rudolf Damrath (2001). Mathematical Foundations of Computational Engineering: A Handbook. Springer Science & Business Media. p. 496. ISBN 978-3-540-67995-0.
24. Smith, Douglas; Eggen, Maurice; St. Andre, Richard (2006), A Transition to Advanced Mathematics (6th ed.), Brooks/Cole, p. 160, ISBN 0-534-39900-2
25. Nievergelt, Yves (2002), Foundations of Logic and Mathematics: Applications to Computer Science and Cryptography, Springer-Verlag, p. 158 .
26. Flaška, V.; Ježek, J.; Kepka, T.; Kortelainen, J. (2007). Transitive Closures of Binary Relations I. Prague: School of Mathematics – Physics Charles University. p. 1.  Lemma 1.1 (iv). This source refers to asymmetric relations as "strictly antisymmetric".
27. Joseph G. Rosenstein, Linear orderings, Academic Press, 1982, ISBN:0-12-597680-1, p. 4
28. Schmidt, Gunther; Ströhlein, Thomas (2012). Relations and Graphs: Discrete Mathematics for Computer Scientists. Springer Science & Business Media. p. 77. ISBN 978-3-642-77968-8.
29. G. Schmidt, Claudia Haltensperger, and Michael Winter (1997) "Heterogeneous relation algebra", chapter 3 (pages 37 to 53) in Relational Methods in Computer Science, Advances in Computer Science, Springer books ISBN:3-211-82971-7
30. R. Berghammer & M. Winter (2013) "Decomposition of relations on concept lattices", Fundamenta Informaticae 126(1): 37–82 doi:10.3233/FI-2013-871
31. Ki Hang Kim (1982) Boolean Matrix Theory and Applications, page 37, Marcel Dekker ISBN:0-8247-1788-0
32. Ali Jaoua, Rehab Duwairi, Samir Elloumi, and Sadok Ben Yahia (2009) "Data mining, reasoning and incremental information retrieval through non enlargeable rectangular relation coverage", pages 199 to 210 in Relations and Kleene algebras in computer science, Lecture Notes in Computer Science 5827, Springer MR2781235
33. Riguet, Jacques (January 1950). "Quelques proprietes des relations difonctionelles" (in fr). Comptes rendus 230: 1999–2000.
34. Julius Richard Büchi (1989). Finite Automata, Their Algebras and Grammars: Towards a Theory of Formal Expressions. Springer Science & Business Media. pp. 35–37. ISBN 978-1-4613-8853-1.
35. East, James; Vernitski, Alexei (February 2018). "Ranks of ideals in inverse semigroups of difunctional binary relations". Semigroup Forum 96 (1): 21–30. doi:10.1007/s00233-017-9846-9.
36. Chris Brink; Wolfram Kahl; Gunther Schmidt (1997). Relational Methods in Computer Science. Springer Science & Business Media. p. 200. ISBN 978-3-211-82971-4.
37. Ali Jaoua, Nadin Belkhiter, Habib Ounalli, and Theodore Moukam (1997) "Databases", pages 197–210 in Relational Methods in Computer Science, edited by Chris Brink, Wolfram Kahl, and Gunther Schmidt, Springer Science & Business Media ISBN 978-3-211-82971-4
38. Gumm, H. P.; Zarrad, M. (2014). "Coalgebraic Simulations and Congruences". Coalgebraic Methods in Computer Science. Lecture Notes in Computer Science. 8446. pp. 118. doi:10.1007/978-3-662-44124-4_7. ISBN 978-3-662-44123-7.
39. J. Riguet (1951) "Les relations de Ferrers", Comptes Rendus 232: 1729,30
40. Georg Aumann (1971). "Kontakt-Relationen". Sitzungsberichte der mathematisch-physikalischen Klasse der Bayerischen Akademie der Wissenschaften München 1970 (II): 67–77.
41. Anne K. Steiner (1970) Review:Kontakt-Relationen from Mathematical Reviews
42. Gunther Schmidt (2011) Relational Mathematics, pages 211−15, Cambridge University Press ISBN:978-0-521-76268-7
43. In this context, the symbol $\displaystyle{ \,\backslash\, }$ does not mean "set difference".
44. Viktor Wagner (1953) "The theory of generalised heaps and generalised groups", Matematicheskii Sbornik 32(74): 545 to 632 MR0059267
45. C.D. Hollings & M.V. Lawson (2017) Wagner's Theory of Generalised Heaps, Springer books ISBN:978-3-319-63620-7 MR3729305