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A finite difference is a mathematical expression of the form f(x + b) − f(x + a). If a finite difference is divided by b − a, one gets a difference quotient. The approximation of derivatives by finite differences plays a central role in finite difference methods for the numerical solution of differential equations, especially boundary value problems.## Forward, backward, and central differences

## Relation with derivatives

## Higher-order differences

^{2}. This can be proven by expanding the above expression in Taylor series, or by using the calculus of finite differences, explained below.### Properties

## Finite difference methods

## Calculus of finite differences

## Generalizations

## Finite difference in several variables

## See also

## References

## External links

In mathematical analysis, operators involving finite differences are studied. A difference operator is an operator which maps a function f to a function whose values are the corresponding finite differences.

Only three forms are commonly considered: forward, backward, and central differences.

A forward difference is an expression of the form

- $Delta\_h[f](x)\; =\; f(x\; +\; h)\; -\; f(x).$

Depending on the application, the spacing h may be variable or held constant.

A backward difference uses the function values at x and x − h, instead of the values at x + h and x:

- $nabla\_h[f](x)\; =\; f(x)\; -\; f(x-h).$

Finally, the central difference is given by

- $delta\_h[f](x)\; =\; f(x+tfrac12h)-f(x-tfrac12h).$

The derivative of a function f at a point x is defined by the limit

- $f\text{'}(x)\; =\; lim\_\{hto0\}\; frac\{f(x+h)\; -\; f(x)\}\{h\}.$

If h has a fixed (non-zero) value, instead of approaching zero, then the right-hand side is

- $frac\{f(x\; +\; h)\; -\; f(x)\}\{h\}\; =\; frac\{Delta\_h[f](x)\}\{h\}.$

Hence, the forward difference divided by h approximates the derivative when h is small. The error in this approximation can be derived from Taylor's theorem. Assuming that f is continuously differentiable, the error is

- $frac\{Delta\_h[f](x)\}\{h\}\; -\; f\text{'}(x)\; =\; O(h)\; quad\; (h\; to\; 0).$

The same formula holds for the backward difference:

- $frac\{nabla\_h[f](x)\}\{h\}\; -\; f\text{'}(x)\; =\; O(h).$

However, the central difference yields a more accurate approximation. Its error is proportional to square of the spacing (if f is twice continuously differentiable):

- $frac\{delta\_h[f](x)\}\{h\}\; -\; f\text{'}(x)\; =\; O(h^\{2\})\; .\; !$

In an analogous way one can obtain finite difference approximations to higher order derivatives and differential operators. For example, by using the above central difference formula for $f\text{'}(x+h/2)$ and $f\text{'}(x-h/2)$ and applying a central difference formula for the derivative of $f\text{'}$ at x, we obtain the central difference approximation of the second derivative of f:

- $f\text{'}\text{'}(x)\; approx\; frac\{delta\_h^2[f](x)\}\{h^2\}\; =\; frac\{f(x+h)\; -\; 2\; f(x)\; +\; f(x-h)\}\{h^\{2\}\}\; .$

More generally, the n^{th}-order forward, backward, and central differences are respectively given by:

- $Delta^n\_h[f](x)\; =$

- $nabla^n\_h[f](x)\; =$

- $delta^n\_h[f](x)\; =$

Note that the central difference will, for odd $n$, have $h$ multiplied by non-integers. If this is a problem (usually it is), it may be remedied taking the average of $delta^n[f](x\; -\; h/2)$ and $delta^n[f](x\; +\; h/2)$.

The relationship of these higher-order differences with the respective derivatives is very straightforward:

- $frac\{d^n\; f\}\{d\; x^n\}(x)$ $=\; frac\{Delta\_h^n[f](x)\}\{h^n\}+O(h)$ $=\; frac\{nabla\_h^n[f](x)\}\{h^n\}+O(h)$ $=\; frac\{delta\_h^n[f](x)\}\{h^n\}\; +\; O(h^2).$

Higher-order differences can also be used to construct better approximations. As mentioned above, the first-order difference approximates the first-order derivative up to a term of order h. However, the combination

- $frac\{Delta\_h[f](x)\; -\; frac12\; Delta\_h^2[f](x)\}\{h\}\; =\; -\; frac\{f(x+2h)-4f(x+h)+3f(x)\}\{2h\}$

If necessary, the finite difference can be centered about any point by mixing forward, backward, and central differences.

- For all positive k and n

- $Delta^n\_\{kh\}\; (f,\; x)\; =\; sumlimits\_\{i\_1=0\}^\{k-1\}\; sumlimits\_\{i\_2=0\}^\{k-1\}\; ...\; sumlimits\_\{i\_n=0\}^\{k-1\}\; Delta^n\_h\; (f,\; x+i\_1h+i\_2h+...+i\_nh).$

- $Delta^n\_h\; (fg,\; x)\; =\; sumlimits\_\{k=0\}^n\; binom\{n\}\{k\}\; Delta^k\_h\; (f,\; x)\; Delta^\{n-k\}\_h(g,\; x+kh).$

An important application of finite differences is in numerical analysis, especially in numerical differential equations, which aim at the numerical solution of ordinary and partial differential equations respectively. The idea is to replace the derivatives appearing in the differential equation by finite differences that approximate them. The resulting methods are called finite difference methods.

Common applications of the finite difference method are in computational science and engineering disciplines, such as thermal engineering, fluid mechanics, etc.

The forward difference can be considered as a difference operator, which maps the function f to Δ_{h}[f]. This operator satisfies

- $Delta\_h\; =\; T\_h-I,\; ,$

Finite difference of higher orders can be defined in recursive manner as $Delta^n\_h(f,x):=Delta\_h(Delta^\{n-1\}\_h(f,x),\; x)$ or, in operators notation, $Delta^n\_h:=Delta\_h(Delta^\{n-1\}\_h).$ Another possible (and equivalent) definition is $Delta^n\_h\; =\; [T\_h-I]^n.$

The difference operator Δ_{h} is linear and satisfies Leibniz rule. Similar statements hold for the backward and central difference.

Taylor's theorem can now be expressed by the formula

- $Delta\_h\; =\; hD\; +\; frac12\; h^2D^2\; +\; frac1\{3!\}\; h^3D^3\; +\; cdots\; =\; mathrm\{e\}^\{hD\}\; -\; 1,$

where D denotes the derivative operator, mapping f to its derivative f'. Formally inverting the exponential suggests that

- $hD\; =\; log(1+Delta\_h)\; =\; Delta\_h\; -\; frac12\; Delta\_h^2\; +\; frac13\; Delta\_h^3\; +\; cdots.\; ,$

This formula holds in the sense that both operators give the same result when applied to a polynomial. Even for analytic functions, the series on the right is not guaranteed to converge; it may be an asymptotic series. However, it can be used to obtain more accurate approximations for the derivative. For instance, retaining the first two terms of the series yields the second-order approximation to $f\text{'}(x)$ mentioned at the end of the section Higher-order differences.

The analogous formulas for the backward and central difference operators are

- $hD\; =\; -log(1-nabla\_h)\; quadmbox\{and\}quad\; hD\; =\; 2\; ,\; operatorname\{arcsinh\}(tfrac12delta\_h).$

The calculus of finite differences is related to the umbral calculus in combinatorics.

A generalized finite difference is usually defined as

- $Delta\_h^mu[f](x)\; =\; sum\_\{k=0\}^N\; mu\_k\; f(x+kh),$

Finite differences can be considered in more than one variable. They are analogous to partial derivatives in several variables.

- Taylor series
- Numerical differentiation
- Five-point stencil
- Divided differences
- Modulus of continuity
- Time scale calculus

- William F. Ames, Numerical Methods for Partial Differential Equations, Section 1.6. Academic Press, New York, 1977. ISBN 0-12-056760-1.
- Francis B. Hildebrand, Finite-Difference Equations and Simulations, Section 2.2, Prentice-Hall, Englewood Cliffs, New Jersey, 1968.
- Boole, George, A Treatise On The Calculus of Finite Differences, 2
^{nd}ed., Macmillan and Company, 1872. [See also: Dover edition 1960]. - Robert D. Richtmyer and K. W. Morton, Difference Methods for Initial Value Problems, 2
^{nd}ed., Wiley, New York, 1967.

- Finite Difference Method
- Finite Difference Method for Boundary Value Problems
- Table of useful finite difference formula generated using [[Mathematica] ]

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Last updated on Thursday October 02, 2008 at 18:30:14 PDT (GMT -0700)

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