# Iterated function

In mathematics, an iterated function is a function which is composed with itself, possibly ad infinitum, in a process called iteration. In this process, starting from some initial number, the result of applying a given function is fed again in the function as input, and this process is repeated. The sequence of functions that is obtained from this process is called the splinter or the discrete part of the iteration orbit.

Iterated functions are objects of study in computer science, fractals, dynamical systems, mathematics and renormalization group physics.

## Definition

The formal definition of an iterated function on a set X follows.

Let Template:Mvar be a set and f: X → X be a function.

Define f n as the n-th iterate of Template:Mvar, where n is a non-negative integer, by:

$f^{0}~{\stackrel {\mathrm {def} }{=}}~\operatorname {id} _{X}\,$ and

$f^{n+1}~{\stackrel {\mathrm {def} }{=}}~f\circ f^{n},\,$ where idX is the identity function on Template:Mvar and fg denotes function composition. That is,

(fg)(x) = f (g(x)),

always associative.

Because the notation f n may refer to both iteration (composition) of the function Template:Mvar or exponentiation of the function Template:Mvar (the latter is used in trigonometry), some mathematicians choose to write f °n for the n-th iterate of the function Template:Mvar.

## Abelian property and Iteration sequences

In general, the following identity holds for all non-negative integers Template:Mvar and Template:Mvar,

$f^{m}\circ f^{n}=f^{n}\circ f^{m}=f^{m+n}~.\,$ This is structurally identical to the property of exponentiation that aman = am+n, i.e. the special case f(x)=ax.

In general, for arbitrary general (negative, non-integer, etc.) indices Template:Mvar and Template:Mvar, this relation is called the translation functional equation, cf. Schröder's equation. On a logarithmic scale, this reduces to the nesting property of Chebyshev polynomials, Tm(Tn(x))=Tm n(x), since Tn(x) = cos(n arcos(x )).

The relation (f m )n(x) = (f n )m(x) = f mn(x) also holds, analogous to the property of exponentiation that (am )n =(an )m = amn.

The sequence of functions f n is called a Picard sequence, named after Charles Émile Picard.

For a given Template:Mvar in Template:Mvar, the sequence of values f n(x) is called the orbit of Template:Mvar.

If f n (x) = f n+m (x) for some integer Template:Mvar, the orbit is called a periodic orbit. The smallest such value of Template:Mvar for a given Template:Mvar is called the period of the orbit. The point Template:Mvar itself is called a periodic point. The cycle detection problem in computer science is the algorithmic problem of finding the first periodic point in an orbit, and the period of the orbit.

## Fixed points

If f(x) = x for some x in X, then x is called a fixed point of the iterated sequence. The set of fixed points is often denoted as Fix(f ). There exist a number of fixed-point theorems that guarantee the existence of fixed points in various situations, including the Banach fixed point theorem and the Brouwer fixed point theorem.

There are several techniques for convergence acceleration of the sequences produced by fixed point iteration. For example, the Aitken method applied to an iterated fixed point is known as Steffensen's method, and produces quadratic convergence.

## Limiting behaviour

Upon iteration, one may find that there are sets that shrink and converge towards a single point. In such a case, the point that is converged to is known as an attractive fixed point. Conversely, iteration may give the appearance of points diverging away from a single point; this would be the case for an unstable fixed point. When the points of the orbit converge to one or more limits, the set of accumulation points of the orbit is known as the limit set or the ω-limit set.

The ideas of attraction and repulsion generalize similarly; one may categorize iterates into stable sets and unstable sets, according to the behaviour of small neighborhoods under iteration. (Also see Infinite compositions of analytic functions.)

Other limiting behaviours are possible; for example, wandering points are points that move away, and never come back even close to where they started.

## Fractional iterates and flows, and negative iterates

In some instances, fractional iteration of a function can be defined: for instance, a half iterate of a function Template:Mvar is a function Template:Mvar such that g(g(x)) = f(x). This function g(x) can be written using the index notation as f ½(x) . Similarly, f(x) is the function defined such that f(f(f(x))) = f(x), while f(x) may be defined equal to f(f(x)), and so forth, all based on the principle, mentioned earlier, that f mf n = f m + n. This idea can be generalized so that the iteration count Template:Mvar becomes a continuous parameter, a sort of continuous "time" of a continuous orbit.

In such cases, one refers to the system as a flow, specified by Schröder's equation. (cf. Section on conjugacy below.)

Negative iterates correspond to function inverses and their compositions. For example, f −1(x) is the normal inverse of Template:Mvar, while f −2(x) is the inverse composed with itself, i.e. f −2(x) = f −1(f −1(x)). Fractional negative iterates are defined analogously to fractional positive ones; for example, f −½(x) is defined such that f − ½(f −½(x)) = f −1(x), or, equivalently, such that f −½(f ½(x)) = f 0(x) = x.

## Some formulas for fractional iteration

One of several methods of finding a series formula for fractional iteration, making use of a fixed point, is as follows.

(1) First determine a fixed point for the function such that f(a)=a .

(2) Define f n(a)=a for all n belonging to the reals. This, in some ways, is the most natural extra condition to place upon the fractional iterates.

(3) Expand f n(x) around the fixed point a as a Taylor series,

$f^{n}(x)=f^{n}(a)+(x-a){\frac {d}{dx}}f^{n}(x)|_{x=a}+{\frac {(x-a)^{2}}{2!}}{\frac {d^{2}}{dx^{2}}}f^{n}(x)|_{x=a}+\cdots$ (4) Expand out

$f^{n}\left(x\right)=f^{n}(a)+(x-a)f'(a)f'(f(a))f'(f^{2}(a))\cdots f'(f^{n-1}(a))+\cdots$ (5) Substitute in for f k(a)= a, for any k,

$f^{n}\left(x\right)=a+(x-a)f'(a)^{n}+{\frac {(x-a)^{2}}{2!}}(f''(a)f'(a)^{n-1})\left(1+f'(a)+\cdots +f'(a)^{n-1}\right)+\cdots$ (6) Make use of the geometric progression to simplify terms,

$f^{n}\left(x\right)=a+(x-a)f'(a)^{n}+{\frac {(x-a)^{2}}{2!}}(f''(a)f'(a)^{n-1}){\frac {f'(a)^{n}-1}{f'(a)-1}}+\cdots$ (6b) There is a special case when f '(a)=1,

$f^{n}\left(x\right)=x+{\frac {(x-a)^{2}}{2!}}(nf''(a))+{\frac {(x-a)^{3}}{3!}}\left({\frac {3}{2}}n(n-1)f''(a)^{2}+nf'''(a)\right)+\cdots$ (7) When n is not an integer, make use of the power formula y n = exp(n ln(y)).

This can be carried on indefinitely, although inefficiently, as the latter terms become increasingly complicated.

A more systematic procedure is outlined in the following section on Conjugacy.

### Example 1

For example, setting f(x) = Cx+D gives the fixed point a = D/(1-C), so the above formula terminates to just

$f^{n}(x)={\frac {D}{1-C}}+(x-{\frac {D}{1-C}})C^{n}=C^{n}x+{\frac {1-C^{n}}{1-C}}D~,$ which is trivial to check.

### Example 2

Find the value of ${\sqrt {2}}^{{\sqrt {2}}^{{\sqrt {2}}^{\cdots }}}$ where this is done n times (and possibly the interpolated values when n is not an integer). We have f(x)=Template:Sqrtx. A fixed point is a=f(2)=2.

So set x=1 and f n (1) expanded around the fixed point value of 2 is then an infinite series,

${\sqrt {2}}^{{\sqrt {2}}^{{\sqrt {2}}^{\cdots }}}=f^{n}(1)=2-(\ln 2)^{n}+{\frac {(\ln 2)^{n+1}((\ln 2)^{n}-1)}{4(\ln 2-1)}}-\cdots$ which, taking just the first three terms, is correct to the first decimal place when n is positive—cf. Tetration: f n(1) = nTemplate:Sqrt . (Using the other fixed point a = f(4) = 4 causes the series to diverge.)

### Example 3

For n = −1, the series computes the inverse function, 2 lnx/ln2.

### Example 4

With the function f(x) = xb, expand around the fixed point 1 to get the series

$f^{n}(x)=1+b^{n}(x-1)+{\frac {1}{2!}}b^{n}(b^{n}-1)(x-1)^{2}+{\frac {1}{3!}}b^{n}(b^{n}-1)(b^{n}-2)(x-1)^{3}+\cdots ~,$ which is simply the Taylor series of x(bn ) expanded around 1.

## Conjugacy

If Template:Mvar and Template:Mvar are two iterated functions, and there exists a homeomorphism Template:Mvar such that g = h−1fh , then Template:Mvar and Template:Mvar are said to be topologically conjugate.

Clearly, topological conjugacy is preserved under iteration, as gn=h−1f nh. Thus, if one can solve for one iterated function system, one also has solutions for all topologically conjugate systems. For example, the tent map is topologically conjugate to the logistic map. As a special case, taking f(x) = x+1, one has the iteration of g(x) = h−1(h(x)+1) as

gn(x) = h−1(h(x) + n),   for any function Template:Mvar.

Making the substitution x = h−1(y) = ϕ(y) yields

g(ϕ(y)) = ϕ(y+1),   a form known as the Abel equation.

Even in the absence of a strict homeomorphism, near a fixed point, here taken to be at Template:Mvar = 0, Template:Mvar(0) = 0, one may often solve Schröder's equation for a function Template:Mvar, which makes f(x) locally conjugate to a mere dilation, g(x) = f '(0) x, that is

f(x) = Ψ−1(f '(0) Ψ(x)).

Thus, its iteration orbit, or flow, under suitable provisions (e.g., f '(0) ≠ 1), amounts to the conjugate of the orbit of the monomial,

Ψ−1(f '(0)n Ψ(x)),

where Template:Mvar in this expression serves as a plain exponent: functional iteration has been reduced to multiplication! Here, however, the exponent Template:Mvar no longer needs be integer or positive, and is a continuous "time" of evolution for the full orbit: the monoid of the Picard sequence (cf. transformation semigroup) has generalized to a full continuous group.

This method (perturbative determination of the principal eigenfunction Template:Mvar, cf. Carleman matrix) is equivalent to the algorithm of the preceding section, albeit, in practice, more powerful and systematic.

## Markov chains

If the function is linear and can be described by a stochastic matrix, that is, a matrix whose rows or columns sum to one, then the iterated system is known as a Markov chain.

## Examples

There are many chaotic maps. Well-known iterated functions include the Mandelbrot set and iterated function systems.

Ernst Schröder, in 1870, worked out special cases of the logistic map, such as the chaotic case f(x) = 4x(1−x), so that Ψ(x) = arcsin2(√x), hence f n(x) = sin2(2n arcsin(√x)).

A nonchaotic case Schröder also illustrated with his method, f(x) = 2x(1 − x), yielded Ψ(x) = −½ ln(1−2x), and hence f n(x) = −½((1−2x)2n−1).

If Template:Mvar is the action of a group element on a set, then the iterated function corresponds to a free group.

Most functions do not have explicit general closed-form expressions for the n-th iterate. The table below lists some that do. Note that all these expressions are valid even for non-integer and negative n, as well as positive integer n.

Note: these two special cases of ax2 + bx + c are the only cases that have a closed-form solution. Choosing b = 2 = –a and b = 4 = –a, respectively, further reduces them to the nonchaotic and chaotic logistic cases discussed prior to the table.

Some of these examples are related among themselves by simple conjugacies. A few further examples, essentially amounting to simple conjugacies of Schröder's examples can be found in ref.

## Means of study

Iterated functions can be studied with the Artin–Mazur zeta function and with transfer operators.

## In computer science

In computer science, iterated functions occur as a special case of recursive functions, which in turn anchor the study of such broad topics as lambda calculus, or narrower ones, such as the denotational semantics of computer programs.

## Definitions in terms of Iterated Functions

Two important functionals can be defined in terms of iterated functions. These are Summation:

$\left\{b+1,\sum _{i=a}^{b}g(i)\right\}\equiv \left(\{i,x\}\rightarrow \{i+1,x+g(i)\}\right)^{b-a+1}\{a,0\}$ and the equivalent product:

$\left\{b+1,\prod _{i=a}^{b}g(i)\right\}\equiv \left(\{i,x\}\rightarrow \{i+1,xg(i)\}\right)^{b-a+1}\{a,1\}$ ## Lie's data transport equation

Iterated functions crop up in the series expansion of the combined functions, such as g(f(x)).

$g(f(x))=\exp \left[v(x){\dfrac {\partial }{\partial x}}\right]g(x).$ For example, for rigid advection, if f(x) = x + a, then v(x) = a. Consequently g(x + a) = exp(a ∂/∂x) g(x), a plain shift operator.

One may further find f(x) given v(x), through the Abel equation discussed above,

$f(x)=h^{-1}(h(x)+1),$ where

$h(x)=\int {{\frac {1}{v(x)}}dx}.$ This is evident by noting that

$f^{n}(x)=h^{-1}(h(x)+n)~.$ 