Functional equation

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In mathematics, a functional equation [1][2]Template:Irrelevant citation is, in the broadest meaning, an equation in which one or several functions appear as unknowns. So, differential equations and integral equations are functional equations. However, a more restricted meaning is often used, where a functional equation is an equation that relates several values of the same function. For example, the logarithm functions are essentially characterized by the logarithmic functional equation log(xy)=log(x)+log(y).

If the domain of the unknown function is supposed to be the natural numbers, the function is generally viewed as a sequence, and, in this case, a functional equation (in the narrower meaning) is called a recurrence relation. Thus the term functional equation is used mainly for real functions and complex functions. Moreover a smoothness condition is often assumed for the solutions, since without such a condition, most functional equations have very irregular solutions. For example, the gamma function is a function that satisfies the functional equation f(x+1)=xf(x) and the initial value f(1)=1. There are many functions that satisfy these conditions, but the gamma function is the unique one that is meromorphic in the whole complex plane, and logarithmically convex for Template:Mvar real and positive (Bohr–Mollerup theorem).

Examples

  • Recurrence relations can be seen as functional equations in functions over the integers or natural numbers, in which the differences between terms' indexes can be seen as an application of the shift operator. For example, the recurrence relation defining the Fibonacci numbers, Fn=Fn1+Fn2, where F0=0 and F1=1
  • The gamma function is the unique solution of the following system of three equations:Script error: No such module "Unsubst".
    • f(x)=f(x+1)x
    • f(y)f(y+12)=π22y1f(2y)
    • f(z)f(1z)=πsin(πz)Script error: No such module "String".(Euler's reflection formula)
  • The functional equation f(az+bcz+d)=(cz+d)kf(z) where Template:Math are integers satisfying adbc=1, i.e. |abcd| = 1, defines Template:Mvar to be a modular form of order Template:Mvar.

One feature that all of the examples listed above have in common is that, in each case, two or more known functions (sometimes multiplication by a constant, sometimes addition of two variables, sometimes the identity function) are inside the argument of the unknown functions to be solved for.

When it comes to asking for all solutions, it may be the case that conditions from mathematical analysis should be applied; for example, in the case of the Cauchy equation mentioned above, the solutions that are continuous functions are the 'reasonable' ones, while other solutions that are not likely to have practical application can be constructed (by using a Hamel basis for the real numbers as vector space over the rational numbers). The Bohr–Mollerup theorem is another well-known example.

Involutions

The involutions are characterized by the functional equation f(f(x))=x. These appear in Babbage's functional equation (1820),[3]

f(f(x))=1(1x)=x.

Other involutions, and solutions of the equation, include

  • f(x)=ax,
  • f(x)=ax, and
  • f(x)=bx1+cx,

which includes the previous three as special cases or limits.

Solution

One method of solving elementary functional equations is substitution.Script error: No such module "Unsubst".

Some solutions to functional equations have exploited surjectivity, injectivity, oddness, and evenness.Script error: No such module "Unsubst".

Some functional equations have been solved with the use of ansatzes, mathematical induction.Script error: No such module "Unsubst".

Some classes of functional equations can be solved by computer-assisted techniques.Template:Vague[4]

In dynamic programming a variety of successive approximation methods[5][6] are used to solve Bellman's functional equation, including methods based on fixed point iterations.

See also

Notes

  1. Script error: No such module "citation/CS1".
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  5. Bellman, R. (1957). Dynamic Programming, Princeton University Press.
  6. Sniedovich, M. (2010). Dynamic Programming: Foundations and Principles, Taylor & Francis.

References

External links

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