# Fixed point iteration

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Fixed point iteration

In numerical analysis, fixed point iteration is a method of computing fixed points of iterated functions.

More specifically, given a function $f$ defined on the real numbers with real values and given a point $x_0$ in the domain of $f$, the fixed point iteration is

:$x_\left\{n+1\right\}=f\left(x_n\right), , n=0, 1, 2, dots$

which gives rise to the sequence $x_0, x_1, x_2, dots$ which is hoped to converge to a point $x$. If $f$ is continuous, then one can prove that the obtained $x$ is a fixed point of $f$, i.e.,

:$f\left(x\right)=x$.

More generally, the function "f" can be defined on any metric space with values in that same space.

Examples

* A first simple and useful example is the Babylonian method for computing the square root of "a">0, which consists in taking $f\left(x\right)=frac 12left\left(frac ax + x ight\right)$, i.e. the mean value of "x" and "a/x", to approach the limit $x = sqrt a$ (from whatever starting point $x_0 gg 0$). This is a special case of Newton's method quoted below.

* The fixed point iteration $x_\left\{n+1\right\}=cos x_n,$ converges to the unique fixed point of the function $f\left(x\right)=cos x,$ for any starting point $x_0.$ This example does satisfy the hypotheses of the Banach fixed point theorem. Hence, the error after n steps satisfies $|x_n-x_0| leq \left\{ q^n over 1-q \right\} | x_1 - x_0 | = C q^n$ (where we can take $q = 0.85$, if we start from $x_0=1$.) When the error is less than a multiple of $q^n$ for some constant "q", we say that we have "linear convergence". The Banach fixed point theorem allows one to obtain fixed point iterations with linear convergence.

* The fixed point iteration $x_\left\{n+1\right\}=2x_n,$ will diverge unless $x_0=0$. We say that the fixed point of $f\left(x\right)=2x,$ is repelling.

* The requirement that "f" is continuous is important, as the following example shows. The iteration :converges to 0 for all values of $x_0$.However, 0 is "not" a fixed point of the function:this function is "not" continuous at $x=0$, and in fact has no fixed points.

Applications

* Newton's method for a given differentiable function $f\left(x\right)$ is $x_\left\{n+1\right\}=x_n-frac\left\{f\left(x_n\right)\right\}\left\{f\text{'}\left(x_n\right)\right\}$. If we write $g\left(x\right)=x-frac\left\{f\left(x\right)\right\}\left\{f\text{'}\left(x\right)\right\}$ we may rewrite the Newton iteration as the fixed point iteration $x_\left\{n+1\right\}=g\left(x_n\right)$. If this iteration converges to a fixed point $x$ of $g$ then $x=g\left(x\right)=x-frac\left\{f\left(x\right)\right\}\left\{f\text{'}\left(x\right)\right\}$ so $frac\left\{f\left(x\right)\right\}\left\{f\text{'}\left(x\right)\right\}=0$. The inverse of anything is nonzero, therefore $f\left(x\right)=0$: x is a "root" of f. Assuming the hypotheses of the Banach fixed point theorem are satisfied, we have that the Newton iteration converges linearly. However, a more detailed analysis shows that under certain circumstances,

* Halley's method is similar to Newton's method but, when it works correctly, its error is

* Runge-Kutta methods and numerical Ordinary Differential Equation solvers in general can be viewed as fixed point iterations. Indeed, the core idea when analyzing the A-stability of ODE solvers is to start with the special case $y\text{'}=ay$, where a is a complex number, and to check whether the ODE solver converges to the fixed point $y=0$ whenever the real part of a is negative. [One may also consider certain iterations A-stable if the iterates stay bounded for a long time, which is beyond the scope of this article.]

* The Picard–Lindelöf theorem, which shows that ordinary differential equations have solutions, is essentially an application of the Banach fixed point theorem to a special sequence of functions which forms a fixed point iteration.

Properties

If a function $f$ defined on the real line with real values is Lipschitz continuous with Lipschitz constant $L<1$, then this function has precisely one fixed point, and the fixed point iteration converges towards that fixed point for any initial guess $x_0.$ This theorem can be generalized to any metric space.

The speed of convergence of the iteration sequence can be increased by using a convergence acceleration method such as Aitken's delta-squared process. The application of Aitken's method to fixed point iteration is known as Steffensen's method, and it can be shown that Steffensen's method yields a rate of convergence that is at least quadratic.

Notes

ee also

* Banach fixed point theorem

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