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Function composition
Function composition
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In mathematics, the composition operator takes two functions, and , and returns a new function . Thus, the function g is applied after applying f to x. is pronounced "the composition of g and f".[1]

Reverse composition applies the operation in the opposite order, applying first and second. Intuitively, reverse composition is a chaining process in which the output of function f feeds the input of function g.

The composition of functions is a special case of the composition of relations, sometimes also denoted by . As a result, all properties of composition of relations are true of composition of functions,[2] such as associativity.

Examples

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Concrete example for the composition of two functions.
  • Composition of functions on a finite set: If f = {(1, 1), (2, 3), (3, 1), (4, 2)}, and g = {(1, 2), (2, 3), (3, 1), (4, 2)}, then gf = {(1, 2), (2, 1), (3, 2), (4, 3)}, as shown in the figure.
  • Composition of functions on an infinite set: If f: RR (where R is the set of all real numbers) is given by f(x) = 2x + 4 and g: RR is given by g(x) = x3, then:
    (fg)(x) = f(g(x)) = f(x3) = 2x3 + 4, and
    (gf)(x) = g(f(x)) = g(2x + 4) = (2x + 4)3.
  • If an airplane's altitude at time t is a(t), and the air pressure at altitude x is p(x), then (pa)(t) is the pressure around the plane at time t.
  • Function defined on finite sets which change the order of their elements such as permutations can be composed on the same set, this being composition of permutations.

Properties

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The composition of functions is always associative—a property inherited from the composition of relations.[2] That is, if f, g, and h are composable, then f ∘ (g ∘ h) = (f ∘ g) ∘ h.[3] Since the parentheses do not change the result, they are generally omitted.

In a strict sense, the composition g ∘ f is only meaningful if the codomain of f equals the domain of g; in a wider sense, it is sufficient that the former be an improper subset of the latter.[nb 1] Moreover, it is often convenient to tacitly restrict the domain of f, such that f produces only values in the domain of g. For example, the composition g ∘ f of the functions f : R(−∞,+9] defined by f(x) = 9 − x2 and g : [0,+∞)R defined by can be defined on the interval [−3,+3].

Compositions of two real functions, the absolute value and a cubic function, in different orders, show a non-commutativity of composition.

The functions g and f are said to commute with each other if g ∘ f = f ∘ g. Commutativity is a special property, attained only by particular functions, and often in special circumstances. For example, |x| + 3 = |x + 3| only when x ≥ 0. The picture shows another example.

The composition of one-to-one (injective) functions is always one-to-one. Similarly, the composition of onto (surjective) functions is always onto. It follows that the composition of two bijections is also a bijection. The inverse function of a composition (assumed invertible) has the property that (f ∘ g)−1 = g−1f−1.[4]

Derivatives of compositions involving differentiable functions can be found using the chain rule. Higher derivatives of such functions are given by Faà di Bruno's formula.[3]

Composition of functions is sometimes described as a kind of multiplication on a function space, but has very different properties from pointwise multiplication of functions (e.g. composition is not commutative).[5]

Composition monoids

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Suppose one has two (or more) functions f: XX, g: XX having the same domain and codomain; these are often called transformations. Then one can form chains of transformations composed together, such as ffgf. Such chains have the algebraic structure of a monoid, called a transformation monoid or (much more seldom) a composition monoid. In general, transformation monoids can have remarkably complicated structure. One particular notable example is the de Rham curve. The set of all functions f: XX is called the full transformation semigroup[6] or symmetric semigroup[7] on X. (One can actually define two semigroups depending how one defines the semigroup operation as the left or right composition of functions.[8])

Composition of a shear mapping (red) and a clockwise rotation by 45° (green). On the left is the original object. Above is shear, then rotate. Below is rotate, then shear.

If the given transformations are bijective (and thus invertible), then the set of all possible combinations of these functions forms a transformation group (also known as a permutation group); and one says that the group is generated by these functions.

The set of all bijective functions f: XX (called permutations) forms a group with respect to function composition. This is the symmetric group, also sometimes called the composition group. A fundamental result in group theory, Cayley's theorem, essentially says that any group is in fact just a subgroup of a symmetric group (up to isomorphism).[9]

In the symmetric semigroup (of all transformations) one also finds a weaker, non-unique notion of inverse (called a pseudoinverse) because the symmetric semigroup is a regular semigroup.[10]

Functional powers

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If Y X, then may compose with itself; this is sometimes denoted as . That is:

More generally, for any natural number n ≥ 2, the nth functional power can be defined inductively by fn = ffn−1 = fn−1f, a notation introduced by Hans Heinrich Bürmann[citation needed][11][12] and John Frederick William Herschel.[13][11][14][12] Repeated composition of such a function with itself is called function iteration.

  • By convention, f0 is defined as the identity map on f's domain, idX.
  • If Y = X and f: XX admits an inverse function f−1, negative functional powers fn are defined for n > 0 as the negated power of the inverse function: fn = (f−1)n.[13][11][12]

Note: If f takes its values in a ring (in particular for real or complex-valued f), there is a risk of confusion, as fn could also stand for the n-fold product of f, e.g. f2(x) = f(x) · f(x).[12] For trigonometric functions, usually the latter is meant, at least for positive exponents.[12] For example, in trigonometry, this superscript notation represents standard exponentiation when used with trigonometric functions:

sin2(x) = sin(x) · sin(x).

However, for negative exponents (especially −1), it nevertheless usually refers to the inverse function, e.g., tan−1 = arctan ≠ 1/tan.

In some cases, when, for a given function f, the equation gg = f has a unique solution g, that function can be defined as the functional square root of f, then written as g = f1/2.

More generally, when gn = f has a unique solution for some natural number n > 0, then fm/n can be defined as gm.

Under additional restrictions, this idea can be generalized so that the iteration count becomes a continuous parameter; in this case, such a system is called a flow, specified through solutions of Schröder's equation. Iterated functions and flows occur naturally in the study of fractals and dynamical systems.

To avoid ambiguity, some mathematicians[citation needed] choose to use to denote the compositional meaning, writing fn(x) for the n-th iterate of the function f(x), as in, for example, f∘3(x) meaning f(f(f(x))). For the same purpose, f[n](x) was used by Benjamin Peirce[15][12] whereas Alfred Pringsheim and Jules Molk suggested nf(x) instead.[16][12][nb 2]

Alternative notations

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Many mathematicians, particularly in group theory, omit the composition symbol, writing gf for gf.[17]

During the mid-20th century, some mathematicians adopted postfix notation, writing xf for f(x) and (xf)g for g(f(x)).[18] This can be more natural than prefix notation in many cases, such as in linear algebra when x is a row vector and f and g denote matrices and the composition is by matrix multiplication. The order is important because function composition is not necessarily commutative. Having successive transformations applying and composing to the right agrees with the left-to-right reading sequence.

Mathematicians who use postfix notation may write "fg", meaning first apply f and then apply g, in keeping with the order the symbols occur in postfix notation, thus making the notation "fg" ambiguous. Computer scientists may write "f ; g" for this,[19] thereby disambiguating the order of composition. To distinguish the left composition operator from a text semicolon, in the Z notation the ⨾ character is used for left relation composition.[20] Since all functions are binary relations, it is correct to use the [fat] semicolon for function composition as well (see the article on composition of relations for further details on this notation).

Composition operator

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Given a function g, the composition operator Cg is defined as that operator which maps functions to functions as Composition operators are studied in the field of operator theory.

In programming languages

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Function composition appears in one form or another in numerous programming languages.

Multivariate functions

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Partial composition is possible for multivariate functions. The function resulting when some argument xi of the function f is replaced by the function g is called a composition of f and g in some computer engineering contexts, and is denoted f |xi = g

When g is a simple constant b, composition degenerates into a (partial) valuation, whose result is also known as restriction or co-factor.[21]

In general, the composition of multivariate functions may involve several other functions as arguments, as in the definition of primitive recursive function. Given f, a n-ary function, and n m-ary functions g1, ..., gn, the composition of f with g1, ..., gn, is the m-ary function

This is sometimes called the generalized composite or superposition of f with g1, ..., gn.[22] The partial composition in only one argument mentioned previously can be instantiated from this more general scheme by setting all argument functions except one to be suitably chosen projection functions. Here g1, ..., gn can be seen as a single vector/tuple-valued function in this generalized scheme, in which case this is precisely the standard definition of function composition.[23]

A set of finitary operations on some base set X is called a clone if it contains all projections and is closed under generalized composition. A clone generally contains operations of various arities.[22] The notion of commutation also finds an interesting generalization in the multivariate case; a function f of arity n is said to commute with a function g of arity m if f is a homomorphism preserving g, and vice versa, that is:[22]

A unary operation always commutes with itself, but this is not necessarily the case for a binary (or higher arity) operation. A binary (or higher arity) operation that commutes with itself is called medial or entropic.[22]

Generalizations

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Composition can be generalized to arbitrary binary relations. If RX × Y and SY × Z are two binary relations, then their composition amounts to

.

Considering a function as a special case of a binary relation (namely functional relations), function composition satisfies the definition for relation composition. A small circle RS has been used for the infix notation of composition of relations, as well as functions. When used to represent composition of functions however, the text sequence is reversed to illustrate the different operation sequences accordingly.

The composition is defined in the same way for partial functions and Cayley's theorem has its analogue called the Wagner–Preston theorem.[24]

The category of sets with functions as morphisms is the prototypical category. The axioms of a category are in fact inspired from the properties (and also the definition) of function composition.[25] The structures given by composition are axiomatized and generalized in category theory with the concept of morphism as the category-theoretical replacement of functions. The reversed order of composition in the formula (f ∘ g)−1 = (g−1f−1) applies for composition of relations using converse relations, and thus in group theory. These structures form dagger categories.

The standard "foundation" for mathematics starts with sets and their elements. It is possible to start differently, by axiomatising not elements of sets but functions between sets. This can be done by using the language of categories and universal constructions.


. . . the membership relation for sets can often be replaced by the composition operation for functions. This leads to an alternative foundation for Mathematics upon categories -- specifically, on the category of all functions. Now much of Mathematics is dynamic, in that it deals with morphisms of an object into another object of the same kind. Such morphisms (like functions) form categories, and so the approach via categories fits well with the objective of organizing and understanding Mathematics. That, in truth, should be the goal of a proper philosophy of Mathematics.

- Saunders Mac Lane, Mathematics: Form and Function[26]

Typography

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The composition symbol is encoded as U+2218 RING OPERATOR (∘, ∘); see the Degree symbol article for similar-appearing Unicode characters. In TeX, it is written \circ.

See also

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Notes

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
In , function composition is an operation that combines two functions f:XYf: X \to Y and g:YZg: Y \to Z to form a new function gf:XZg \circ f: X \to Z, defined by (gf)(x)=g(f(x))(g \circ f)(x) = g(f(x)) for all xXx \in X, where the output of the inner function ff serves as the input to the outer function gg. This process chains the functions, enabling the construction of complex mappings from simpler ones, provided the codomain of ff matches or is a of the domain of gg. Function composition exhibits key properties that underpin its utility across mathematical structures. It is associative, meaning (hg)f=h(gf)(h \circ g) \circ f = h \circ (g \circ f) for compatible functions ff, gg, and hh, allowing parentheses to be omitted in chains without ambiguity. However, it is generally non-commutative, as gffgg \circ f \neq f \circ g unless the functions satisfy specific conditions, such as when one is the identity. Each set admits identity functions that act as neutral elements under composition, preserving the original function when composed. Beyond basic , function composition plays a central role in diverse fields. In , it motivates the chain rule for differentiation: if y=f(g(x))y = f(g(x)), then dydx=f(g(x))g(x)\frac{dy}{dx} = f'(g(x)) \cdot g'(x), facilitating derivatives of composite functions. In , composition of morphisms generalizes the concept across abstract structures, with sets and functions forming the category Set where arrows are functions and composition follows the standard rule. In , it enables modular code construction by piping outputs of pure functions as inputs to others, as seen in languages like with the . operator for (f.g)x=f(gx)(f . g) x = f (g x).

Fundamentals

Definition

In mathematics, a function is a mapping that assigns to each element in a set (the domain) a unique element in another set (the codomain). For the composition of two functions to be well-defined, the codomain of the first function must match the domain of the second, ensuring that the output of the first lies within the input set of the second. This prerequisite guarantees that the composition operates consistently across the entire domain of the initial function. Given functions f:YZf: Y \to Z and g:XYg: X \to Y, their composition, denoted fg:XZf \circ g: X \to Z, is the function defined by (fg)(x)=f(g(x))(f \circ g)(x) = f(g(x)) for all xXx \in X. This operation chains the mappings, where the result of applying gg to xx serves as the input to ff. In the case of partial functions, where the domain of definition is a proper of the intended domain, the composition fgf \circ g is defined only on the subset of XX such that g(x)g(x) falls within the domain of ff, resulting in a potentially non-total function. This restriction ensures the operation remains meaningful, though it may exclude some elements from the original domain. The concept of function composition originated in 18th-century , with providing an early description in 1718 as a quantity formed by combining a variable with constants in any manner. Leonhard Euler further developed it in his 1748 work , solidifying its role in analytic expressions by defining functions in terms of analytic expressions composed from variables and constants.

Notation

The standard notation for the composition of two functions ff and gg is the circle operator \circ (Unicode U+2218), defined such that (fg)(x)=f(g(x))(f \circ g)(x) = f(g(x)). This symbol, denoting the application of gg followed by ff, was introduced by the collective in their seminal series , with early documented usage appearing in a 1944 seminar report and formalized in the 1949 volume Fonctions d'une variable réelle. In certain mathematical contexts, particularly within and , function composition is instead denoted by simple , where fg(x)=f(g(x))fg(x) = f(g(x)). This convention treats functions as elements of a under composition and is widely adopted in treatments of transformation groups and morphisms, emphasizing the operator-like nature of functions without an explicit symbol. The circle operator \circ has higher precedence than in standard mathematical conventions, meaning expressions like fgxf \circ g \, x are parsed as (fg)(x)=f(g(x))(f \circ g)(x) = f(g(x)) rather than f(g(x))f(g(x)), avoiding the need for additional parentheses in chained compositions. Historically, the notation for function composition evolved from verbal descriptions in the —such as Euler's explicit writings of composite expressions like f(ϕ(x))f(\phi(x))—to more symbolic representations in the , as the concept of functions became increasingly abstract and algebraic. This shift, driven by the rigorous development of by figures like Dirichlet and Weierstrass, paved the way for modern operator symbols, though no unified notation like \circ emerged until the structuralist approach of Bourbaki in the .

Examples

Unary Function Examples

A fundamental illustration of function composition involves basic arithmetic operations. Consider the unary functions g(x)=x+1g(x) = x + 1 and f(x)=x2f(x) = x^2, both defined on the real numbers. The composition (fg)(x)(f \circ g)(x) applies gg first, yielding x+1x + 1, and then ff to that result, giving (x+1)2=x2+2x+1(x + 1)^2 = x^2 + 2x + 1. Another example combines transcendental functions: let g(x)=exg(x) = e^x and f(x)=sinxf(x) = \sin x, with domains and ranges ensuring the composition is well-defined for real xx. Then (fg)(x)=sin(ex)(f \circ g)(x) = \sin(e^x). To evaluate step-by-step at x=0x = 0, first compute g(0)=e0=1g(0) = e^0 = 1, then f(1)=sin10.8415f(1) = \sin 1 \approx 0.8415. Composition also demonstrates the effect of inverse functions, which each other to produce the identity. For instance, with g(x)=exg(x) = e^x (defined for all real xx, range positive reals) and f(x)=logxf(x) = \log x (, domain positive reals), the composition (fg)(x)=log(ex)=x(f \circ g)(x) = \log(e^x) = x for all real xx, recovering the input unchanged. In geometric contexts, function composition chains linear transformations on the plane, such as scaling followed by shifting. For example, a scaling function g(x)=2xg(x) = 2x stretches distances from the origin by a factor of 2, while a shifting function f(x)=x+3f(x) = x + 3 translates horizontally by 3 units; their composition (fg)(x)=2x+3(f \circ g)(x) = 2x + 3 first doubles the input and then shifts it, resulting in a combined that alters both scale and position.

Multivariate Examples

In multivariate function composition, functions map from and to spaces of higher dimensions, such as Rn\mathbb{R}^n to Rm\mathbb{R}^m, requiring the output of the inner function to match the input domain of the outer function for the composition to be well-defined. This extends the univariate case by handling vector inputs and outputs, often arising in and linear algebra applications. Consider the functions g:R2Rg: \mathbb{R}^2 \to \mathbb{R} defined by g(x,y)=x+yg(x, y) = x + y and f:RR2f: \mathbb{R} \to \mathbb{R}^2 defined by f(z)=(z,z)f(z) = (z, z). The composition fg:R2R2f \circ g: \mathbb{R}^2 \to \mathbb{R}^2 is given by (fg)(x,y)=f(g(x,y))=f(x+y)=(x+y,x+y),(f \circ g)(x, y) = f(g(x, y)) = f(x + y) = (x + y, x + y), illustrating how the scalar output of gg feeds into the vector-producing ff. Linear transformations, represented by matrices, provide another key example of multivariate composition. For instance, let RR be the rotation matrix by θ\theta degrees in R2\mathbb{R}^2, R=(cosθsinθsinθcosθ),R = \begin{pmatrix} \cos \theta & -\sin \theta \\ \sin \theta & \cos \theta \end{pmatrix}, and SS be the scaling matrix by factors aa and bb, S=(a00b).S = \begin{pmatrix} a & 0 \\ 0 & b \end{pmatrix}. The composition SRS \circ R first rotates a vector and then scales it, with matrix SRS R, yielding SR=(acosθasinθbsinθbcosθ).S R = \begin{pmatrix} a \cos \theta & -a \sin \theta \\ b \sin \theta & b \cos \theta \end{pmatrix}. This matrix product corresponds to applying the transformations sequentially. In the context of differentiable multivariate functions, composition appears in the chain rule for partial derivatives. Suppose h:R2Rh: \mathbb{R}^2 \to \mathbb{R} is the outer function h(u,v)=u2+vh(u, v) = u^2 + v, and g:R2R2g: \mathbb{R}^2 \to \mathbb{R}^2 is the inner function g(x,y)=(xy,y)g(x, y) = (x y, y). The composite f=hg:R2Rf = h \circ g: \mathbb{R}^2 \to \mathbb{R} is f(x,y)=(xy)2+yf(x, y) = (x y)^2 + y. The partial derivatives satisfy fx=hug1x+hvg2x=2(xy)(y)+10=2xy2,\frac{\partial f}{\partial x} = \frac{\partial h}{\partial u} \frac{\partial g_1}{\partial x} + \frac{\partial h}{\partial v} \frac{\partial g_2}{\partial x} = 2 (x y) (y) + 1 \cdot 0 = 2 x y^2, demonstrating how gradients of the outer function apply to the of the inner. A real-world application occurs in pipelines, where vector-valued signals undergo sequential transformations. For example, consider a filtering function f:RnRnf: \mathbb{R}^n \to \mathbb{R}^n that applies a to an nn-channel x\mathbf{x}, producing y=f(x)\mathbf{y} = f(\mathbf{x}), followed by an amplification g:RnRng: \mathbb{R}^n \to \mathbb{R}^n with g(y)=kyg(\mathbf{y}) = k \mathbf{y} for gain k>1k > 1. The composition gfg \circ f processes the raw signal through denoising before boosting, common in modular filter designs for audio or image data.

Properties

Associativity

Function composition exhibits the property of associativity. For functions f:CDf: C \to D, g:BCg: B \to C, and h:ABh: A \to B with compatible domains and codomains, the composition satisfies (fg)h=f(gh)(f \circ g) \circ h = f \circ (g \circ h). This equality holds because both sides define the same mapping from the domain of hh to the codomain of ff. To verify, let xx be in the domain of hh. Then, ((fg)h)(x)=(fg)(h(x))=f(g(h(x))),((f \circ g) \circ h)(x) = (f \circ g)(h(x)) = f(g(h(x))), and (f(gh))(x)=f((gh)(x))=f(g(h(x))).(f \circ (g \circ h))(x) = f((g \circ h)(x)) = f(g(h(x))). Since the outputs match for every xx, the composed functions are identical. The associativity of function composition implies that multiple compositions can be written without parentheses, such as fghf \circ g \circ h, as the result is independent of grouping. This property is inherited from the more general composition of binary relations in set theory, which is also associative but differs slightly in that relations permit multiple outputs while functions do not. For a concrete verification, consider the arithmetic functions f(x)=x2f(x) = x^2, g(x)=x+1g(x) = x + 1, and h(x)=2xh(x) = 2x over the reals. At x=1x = 1, ((fg)h)(1)=(fg)(2)=f(3)=9,((f \circ g) \circ h)(1) = (f \circ g)(2) = f(3) = 9, and (f(gh))(1)=f((gh)(1))=f(g(2))=f(3)=9.(f \circ (g \circ h))(1) = f((g \circ h)(1)) = f(g(2)) = f(3) = 9. The equality confirms associativity at this point, and it holds generally by the proof above.

Invertibility and Other Properties

A function composition fgf \circ g is invertible if and only if both ff and gg are invertible, provided the range of gg lies within the domain of ff. In this case, the inverse is given by (fg)1=g1f1(f \circ g)^{-1} = g^{-1} \circ f^{-1}. To verify this, substitute into the definition of the inverse: compute (fg)(g1f1)(f \circ g) \circ (g^{-1} \circ f^{-1}). First, the inner composition yields g(g1f1)=(gg1)f1=idBf1=f1g \circ (g^{-1} \circ f^{-1}) = (g \circ g^{-1}) \circ f^{-1} = \mathrm{id}_B \circ f^{-1} = f^{-1}, where idB\mathrm{id}_B is the identity on the codomain of gg. Then, ff1=idCf \circ f^{-1} = \mathrm{id}_C, the identity on the codomain of ff. Similarly, (g1f1)(fg)=idA(g^{-1} \circ f^{-1}) \circ (f \circ g) = \mathrm{id}_A. Thus, g1f1g^{-1} \circ f^{-1} serves as the two-sided inverse. Function composition is generally non-commutative, meaning fggff \circ g \neq g \circ f in most cases. For example, consider f(x)=x+1f(x) = x + 1 and g(x)=2xg(x) = 2x over the real numbers. Then (fg)(x)=2x+1(f \circ g)(x) = 2x + 1, while (gf)(x)=2(x+1)=2x+2(g \circ f)(x) = 2(x + 1) = 2x + 2, which differ for all xx. A function ff is idempotent under composition if ff=ff \circ f = f. Projection functions provide classic examples; for instance, in linear algebra, an orthogonal projection operator PP onto a subspace satisfies P2=PP^2 = P, as applying the projection twice yields the same result as once. Composition preserves continuity: if gg is continuous at a point and the range of gg lies in the domain of ff, where ff is continuous at g(a)g(a), then fgf \circ g is continuous at aa. Similarly, composition preserves differentiability: if gg is differentiable at aa and ff is differentiable at g(a)g(a), with the range of gg in the domain of ff, then fgf \circ g is differentiable at aa.

Iteration and Powers

Functional Iteration

Functional iteration refers to the repeated composition of a single function with itself. For a function f:XXf: X \to X where XX is a set, the nn-th iterate fnf^n is defined recursively as f0(x)=xf^0(x) = x (the ) and fn+1(x)=f(fn(x))f^{n+1}(x) = f(f^n(x)) for nonnegative integers nn, with f1=ff^1 = f. This notation captures the process of applying ff exactly nn times to an input xXx \in X. A fixed point of ff is an element xXx \in X satisfying f(x)=xf(x) = x, meaning the iterate fn(x)=xf^n(x) = x for all n1n \geq 1. More generally, a of period nn is a point xXx \in X such that fn(x)=xf^n(x) = x but fk(x)xf^k(x) \neq x for all 1k<n1 \leq k < n, forming a cycle under iteration. These points are fundamental in analyzing the long-term behavior of iterates, as orbits under fnf^n may converge to fixed points, enter periodic cycles, or exhibit other dynamics. A classic example arises in chaos theory with the logistic map f(x)=rx(1x)f(x) = r x (1 - x) for x[0,1]x \in [0,1] and parameter r(0,4]r \in (0,4]. For 1<r<31 < r < 3, iterations from most initial xx converge to the stable fixed point x=11/rx^* = 1 - 1/r. As rr increases beyond 3, the fixed point destabilizes, leading to period-doubling bifurcations where stable cycles of period 2, 4, 8, and higher emerge; at an accumulation point r3.57r_\infty \approx 3.57, chaotic behavior begins, with dense orbits and sensitive dependence on initial conditions, though periodic points persist densely in the interval. Convergence of the sequence of iterates {fn(x)}\{f^n(x)\} to a fixed point occurs under certain conditions, such as when ff is a contraction mapping on a complete metric space. The states that if there exists k(0,1)k \in (0,1) such that d(f(y),f(z))kd(y,z)d(f(y), f(z)) \leq k \, d(y, z) for all y,zy, z in the space (where dd is the metric), then ff has a unique fixed point pp, and iterations from any initial xx converge to pp with rate fn(x)pkn1kxf(x)|f^n(x) - p| \leq \frac{k^n}{1-k} |x - f(x)|. This guarantees global convergence for contractions, providing a foundational result for iterative methods in analysis.

Functional Powers

In the context of function composition, the integer powers of a function, known as functional powers, are defined through repeated self-composition. For a function f:XYf: X \to Y where YXY \subseteq X, the nn-th functional power fnf^n for a positive integer nn is the nn-fold composition ffff \circ f \circ \cdots \circ f (applied nn times), so fn(x)=f(f(f(x)))f^n(x) = f(f(\cdots f(x) \cdots)). This extends the base case of functional iteration to exponentiation within the semigroup of compositions, with f1=ff^1 = f and f0f^0 as the identity function. For functions that commute under composition (fg=gff \circ g = g \circ f), more general exponentiation can be approached using exponential or logarithmic mappings, though integer powers remain the primary focus as they directly correspond to iterated application. A significant extension of functional powers arises in the algebra of formal power series, where composition defines a ring structure under suitable conditions. Consider formal power series over a ring RR, such as f(x)=k=1akxkf(x) = \sum_{k=1}^\infty a_k x^k (with no constant term) and g(x)=k=0bkxkg(x) = \sum_{k=0}^\infty b_k x^k; their composition gfg \circ f yields another formal power series k=0ckxk\sum_{k=0}^\infty c_k x^k, with coefficients determined recursively. When ff includes a nonzero constant term, composition may not always exist as a formal power series, but necessary and sufficient conditions ensure it does, such as the series being invertible or satisfying specific valuation properties. A classic example is the composition exp(x)sin(x)=n=01n!(sin(x))n\exp(x) \circ \sin(x) = \sum_{n=0}^\infty \frac{1}{n!} \left( \sin(x) \right)^n, which converges formally and illustrates how transcendental functions generate new series via powering and composition. This framework is foundational in algebraic analysis and generating function theory. Fractional functional powers generalize integer powers to non-integer exponents by solving associated functional equations. For a square root, one seeks a function hh such that hh=fh \circ h = f, denoted h=f1/2h = f^{1/2}; more broadly, for rational p/qp/q, (fp/q)q=fp(f^{p/q})^q = f^p. Real or fractional iterates fαf^\alpha satisfy fαfβ=fα+βf^\alpha \circ f^\beta = f^{\alpha + \beta} and f1=ff^1 = f, often constructed via Abel's functional equation α(f(x))=α(x)+1\alpha(f(x)) = \alpha(x) + 1 or Schröder's equation for linearization near fixed points. These solutions, typically real-analytic for suitable ff, enable exponentiation in continuous iteration settings, as explored for exponentially growing functions like exe^x. An application of integer functional powers appears in quantum mechanics through the time evolution operator. For a time-independent Hamiltonian H^\hat{H}, the unitary operator U(t)=eiH^t/U(t) = e^{-i \hat{H} t / \hbar} evolves states via ψ(t)=U(t)ψ(0)|\psi(t)\rangle = U(t) |\psi(0)\rangle. For integer nn, this decomposes as U(t)=[U(t/n)]nU(t) = [U(t/n)]^n, where the power signifies nn-fold composition of the operator, analogous to functional powering in the group of unitary transformations on Hilbert space. This structure underpins Trotter-Suzuki approximations for simulating evolution.

Algebraic Structures

Composition Monoids

In abstract algebra, the set of all functions from a nonempty set XX to itself, denoted XXX^X, forms a monoid under the operation of function composition. The binary operation is defined by (fg)(x)=f(g(x))(f \circ g)(x) = f(g(x)) for all xXx \in X, which is associative, and the identity function idX\mathrm{id}_X, where idX(x)=x\mathrm{id}_X(x) = x for all xXx \in X, serves as the unit element satisfying fidX=idXf=ff \circ \mathrm{id}_X = \mathrm{id}_X \circ f = f for any fXXf \in X^X. This structure is known as the full transformation monoid on XX, and when XX is finite with X=n|X| = n, it contains exactly nnn^n elements. Within this monoid, the subset of all bijective functions from XX to itself forms a submonoid consisting solely of invertible elements; this is the symmetric group on XX, often denoted SXS_X or SnS_n when X=n|X| = n. For instance, when X={1,2,3}X = \{1, 2, 3\}, the symmetric group S3S_3 comprises the six permutations of these elements and is itself a monoid under composition, with the identity permutation as the unit. More generally, any submonoid of the full transformation monoid arises from a subset of transformations closed under composition and containing the identity. Transformation monoids find significant applications in automata theory, where the set XX represents the state space of a finite automaton, and elements of the monoid model transitions between states induced by input symbols, with composition corresponding to the sequential application of inputs. This perspective allows algebraic techniques to analyze automaton behavior. In abstract algebra, a semigroup is defined as a nonempty set equipped with an associative binary operation, forming an associative magma without necessarily requiring an identity element. Under function composition, the set of all functions from a finite set XX to itself constitutes the full transformation semigroup TXT_X, where composition serves as the operation; this structure is associative by the properties of function composition. Similarly, the set of all endomorphisms of a vector space VV over a field forms the endomorphism semigroup End(V)\mathrm{End}(V), consisting of linear maps VVV \to V closed under composition, which is associative. In transformation semigroups like TXT_X, Green's relations provide a classification of elements based on the principal ideals they generate. These include the left relation L\mathcal{L}, where aLba \mathcal{L} b if S1a=S1bS^1 a = S^1 b for the semigroup SS with adjoined identity S1S^1; the right relation R\mathcal{R}, where aRba \mathcal{R} b if aS1=bS1a S^1 = b S^1; the two-sided relation J\mathcal{J}, where aJba \mathcal{J} b if S1aS1=S1bS1S^1 a S^1 = S^1 b S^1; the intersection H=LR\mathcal{H} = \mathcal{L} \cap \mathcal{R}; and the relation D=LR\mathcal{D} = \mathcal{L} \circ \mathcal{R}. In the context of finite transformation semigroups, the D\mathcal{D}-relation particularly distinguishes elements by the size of their images (rank), linking transformations with equivalent domain-codomain behaviors through shared principal ideals generated by compositions. For instance, two transformations are D\mathcal{D}-related if they produce isomorphic structures in terms of image sets and relational restrictions. Semigroup theory under function composition connects to combinatorics on words through functional graphs, where a function f:XXf: X \to X induces a directed graph with out-degree one at each vertex, and iterated compositions correspond to paths in this graph. Words over an alphabet can encode such paths, while the Cayley graph of a semigroup encodes words as paths, facilitating the study of free semigroups generated by alphabets under concatenation, akin to composition in transformation settings. This interplay appears in analyses of word avoidability and growth rates in subsemigroups of transformations.

Multivariate and Higher-Dimensional Composition

Composition of Multivariate Functions

In multivariable calculus, the composition of functions extends the unary case to mappings between Euclidean spaces of arbitrary dimensions. Consider functions g:XYg: X \to Y where XRmX \subset \mathbb{R}^m and YRnY \subset \mathbb{R}^n, and f:YZf: Y \to Z where ZRpZ \subset \mathbb{R}^p. The composite function fg:XZf \circ g: X \to Z is defined by (fg)(x)=f(g(x))(f \circ g)(\mathbf{x}) = f(g(\mathbf{x})) for xX\mathbf{x} \in X, provided the composition is well-defined on the relevant domains. For the composition to be defined pointwise, the image of gg must be contained in the domain of ff, ensuring that g(x)g(\mathbf{x}) lies within the domain of ff for all xX\mathbf{x} \in X; however, gg need not be surjective onto the domain of ff, as the image of gg only requires inclusion in that domain. This compatibility condition generalizes the domain restrictions in single-variable composition while accommodating vector inputs and outputs. The differentiability of composite multivariate functions is governed by an adaptation of the chain rule, expressed via Jacobian matrices. If gg is differentiable at aX\mathbf{a} \in X and ff is differentiable at g(a)g(\mathbf{a}), then fgf \circ g is differentiable at a\mathbf{a}, with the Jacobian matrix satisfying D(fg)(a)=Df(g(a))Dg(a),D(f \circ g)(\mathbf{a}) = Df(g(\mathbf{a})) \cdot Dg(\mathbf{a}), where Dg(a)Dg(\mathbf{a}) is the n×mn \times m Jacobian of gg and Df(g(a))Df(g(\mathbf{a})) is the p×np \times n Jacobian of ff, and the product is matrix multiplication. A representative example arises in parametric representations, such as composing a map g:R2R2g: \mathbb{R}^2 \to \mathbb{R}^2 that distorts coordinates with an embedding f:R2R3f: \mathbb{R}^2 \to \mathbb{R}^3 that lifts to a surface; for instance, let g(x,y)=(x2y2,2xy)g(x,y) = (x^2 - y^2, 2xy) and f(u,v)=(u,v,u2+v2)f(u,v) = (u, v, \sqrt{u^2 + v^2})
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