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derivative: see calculus.

The Columbia Electronic Encyclopedia Copyright © 2004.

Licensed from Columbia University Press

Licensed from Columbia University Press

In mathematics, a fundamental concept of differential calculus representing the instantaneous rate of change of a function. The first derivative of a function is a function whose values can be interpreted as slopes of tangent lines to the graph of the original function at a given point. The derivative of a derivative (known as the second derivative) describes the rate of change of the rate of change, and can be thought of physically as acceleration. The process of finding a derivative is called differentiation.

Learn more about derivative with a free trial on Britannica.com.

Encyclopedia Britannica, 2008. Encyclopedia Britannica Online.

In calculus, a branch of mathematics, the derivative is a measurement of how a function changes when the values of its inputs change. Loosely speaking, a derivative can be thought of as how much a quantity is changing at some given point. For example, the derivative of the position or distance of a car at some point in time is the instantaneous velocity, or instantaneous speed (respectively), at which that car is traveling (conversely the integral of the velocity is the car's position).

A closely related notion is the differential of a function.

The derivative of a function at a chosen input value describes the best linear approximation of the function near that input value. For a real-valued function of a single real variable, the derivative at a point equals the slope of the tangent line to the graph of the function at that point. In higher dimensions, the derivative of a function at a point is a linear transformation called the linearization.

The process of finding a derivative is called differentiation. The fundamental theorem of calculus states that differentiation is the reverse process to integration.

Differentiation is a method to compute the rate at which a dependent output y, changes with respect to the change in the independent input x. This rate of change is called the derivative of y with respect to x. In more precise language, the dependency of y on x means that y is a function of x. If x and y are real numbers, and if the graph of y is plotted against x, the derivative measures the slope of this graph at each point. This functional relationship is often denoted y = ƒ(x), where ƒ denotes the function.

The simplest case is when y is a linear function of x, meaning that the graph of y against x is a straight line. In this case, y = ƒ(x) = m x + c, for real numbers m and c, and the slope m is given by

- $m=\{mbox\{change\; in\; \}\; y\; over\; mbox\{change\; in\; \}\; x\}\; =\; \{Delta\; y\; over\{Delta\; x\}\}$

- y + Δy = ƒ(x+ Δx) = m (x + Δx) + c = m x + c + m Δx = y + mΔx.

This gives an exact value for the slope of a straight line. If the function ƒ is not linear (i.e. its graph is not a straight line), however, then the change in y divided by the change in x varies: differentiation is a method to find an exact value for this rate of change at any given value of x.

The idea, illustrated by Figures 1-3, is to compute the rate of change as the limiting value of the ratio of the differences Δy / Δx as Δx becomes infinitely small.

In Leibniz's notation, such an infinitesimal change in x is denoted by dx, and the derivative of y with respect to x is written

- $frac\{dy\}\{dx\}\; ,!$

The most common approach to turn this intuitive idea into a precise definition uses limits, but there are other methods, such as non-standard analysis.

- $m\; =\; frac\{Delta\; f(x)\}\{Delta\; x\}\; =\; frac\{f(a+h)-f(a)\}\{h\}.$

- $f\text{'}(a)=lim\_\{hto\; 0\}\{f(a+h)-f(a)over\; h\}$

Equivalently, the derivative satisfies the property that

- $lim\_\{hto\; 0\}\{f(a+h)-f(a)\; -\; f\text{'}(a)cdot\; hover\; h\}\; =\; 0,$

- $f(a+h)\; approx\; f(a)\; +\; f\text{'}(a)h$

Substituting 0 for h in the difference quotient causes division by zero, so the slope of the tangent line cannot be found directly. Instead, define Q(h) to be the difference quotient as a function of h:

- $Q(h)\; =\; frac\{f(a\; +\; h)\; -\; f(a)\}\{h\}$.

In practice, the existence of a continuous extension of the difference quotient Q(h) to h = 0 is shown by modifying the numerator to cancel h in the denominator. This process can be long and tedious for complicated functions, and many short cuts are commonly used to simplify the process.

The squaring function ƒ(x) = x² is differentiable at x = 3, and its derivative there is 6. This is proven by writing the difference quotient as follows:

- $\{f(3+h)-f(3)over\; h\}\; =\; \{(3+h)^2\; -\; 9over\{h\}\}\; =\; \{9\; +\; 6h\; +\; h^2\; -\; 9over\{h\}\}\; =\; \{6h\; +\; h^2over\{h\}\}\; =\; 6\; +\; h.$

Then we get the simplified function in the limit:

- $lim\_\{hto\; 0\}\; (6\; +\; h)\; =\; 6\; +\; 0\; =\; 6.$

The last expression shows that the difference quotient equals 6 + h when h is not zero and is undefined when h is zero. (Remember that because of the definition of the difference quotient, the difference quotient is always undefined when h is zero.) However, there is a natural way of filling in a value for the difference quotient at zero, namely 6. Hence the slope of the graph of the squaring function at the point (3, 9) is 6, and so its derivative at x = 3 is ƒ '(3) = 6.

More generally, a similar computation shows that the derivative of the squaring function at x = a is ƒ '(a) = 2a.

However, even if a function is continuous at a point, it may not be differentiable there. For example, the absolute value function y = |x| is continuous at x = 0, but it is not differentiable there. If h is positive, then the slope of the secant line from 0 to h is one, whereas if h is negative, then the slope of the secant line from 0 to h is negative one. This can be seen graphically as a "kink" in the graph at x = 0. Even a function with a smooth graph is not differentiable at a point where its tangent is vertical: For instance, the function y = ^{3}√x is not differentiable at x = 0.

In summary: in order for a function ƒ to have a derivative it is necessary for the function ƒ to be continuous, but continuity alone is not sufficient.

Most functions which occur in practice have derivatives at all points or at almost every point. However, a result of Stefan Banach states that the set of functions which have a derivative at some point is a meager set in the space of all continuous functions. Informally, this means that differentiable functions are very atypical among continuous functions. The first known example of a function that is continuous everywhere but differentiable nowhere is the Weierstrass function.

Let ƒ be a function that has a derivative at every point a in the domain of ƒ. Because every point a has a derivative, there is a function which sends the point a to the derivative of ƒ at a. This function is written f′(x) and is called the derivative function or the derivative of ƒ. The derivative of ƒ collects all the derivatives of ƒ at all the points in the domain of ƒ.

Sometimes ƒ has a derivative at most, but not all, points of its domain. The function whose value at a equals f′(a) whenever f′(a) is defined and is undefined elsewhere is also called the derivative of ƒ. It is still a function, but its domain is strictly smaller than the domain of ƒ.

Using this idea, differentiation becomes a function of functions: The derivative is an operator whose domain is the set of all functions which have derivatives at every point of their domain and whose range is a set of functions. If we denote this operator by D, then D(ƒ) is the function f′(x). Since D(ƒ) is a function, it can be evaluated at a point a. By the definition of the derivative function, D(ƒ)(a) = f′(a).

For comparison, consider the doubling function ƒ(x) =2x; ƒ is a real-valued function of a real number, meaning that it takes numbers as inputs and has numbers as outputs:

- $begin\{align\}$

- $begin\{align\}$

D(x mapsto 1) &= (x mapsto 0),

D(x mapsto x) &= (x mapsto 1),

D(x mapsto x^2) &= (x mapsto 2cdot x).end{align} Because the output of D is a function, the output of D can be evaluated at a point. For instance, when D is applied to the squaring function,

- $x\; mapsto\; x^2,$

- $x\; mapsto\; 2x\; ,$

Let ƒ be a differentiable function, and let f′(x) be its derivative. The derivative of f′(x) (if it has one) is written f′′(x) and is called the second derivative of ƒ. Similarly, the derivative of a second derivative, if it exists, is written f′′′(x) and is called the third derivative of ƒ. These repeated derivatives are called higher-order derivatives.

A function ƒ need not have a derivative, for example, if it is not continuous. Similarly, even if ƒ does have a derivative, it may not have a second derivative. For example, let

- $f(x)\; =\; begin\{cases\}\; x^2,\; \&\; mbox\{if\; \}xge\; 0\; -x^2,\; \&\; mbox\{if\; \}x\; le\; 0end\{cases\}$.

- $f\text{'}(x)\; =\; begin\{cases\}\; 2x,\; \&\; mbox\{if\; \}xge\; 0\; -2x,\; \&\; mbox\{if\; \}x\; le\; 0end\{cases\}$.

On the real line, every polynomial function is infinitely differentiable. By standard differentiation rules, if a polynomial of degree n is differentiated n times, then it becomes a constant function. All of its subsequent derivatives are identically zero. In particular, they exist, so polynomials are smooth functions.

The derivatives of a function ƒ at a point x provide polynomial approximations to that function near x. For example, if ƒ is twice differentiable, then

- $f(x+h)\; approx\; f(x)\; +\; f\text{'}(x)h\; +\; tfrac12\; f(x)\; h^2$

- $lim\_\{hto\; 0\}frac\{f(x+h)\; -\; f(x)\; -\; f\text{'}(x)h\; -\; frac12\; f(x)\; h^2\}\{h^2\}=0.$

The notation for derivatives introduced by Gottfried Leibniz is one of the earliest. It is still commonly used when the equation y = ƒ(x) is viewed as a functional relationship between dependent and independent variables. Then the first derivative is denoted by

- $frac\{dy\}\{dx\},quadfrac\{d\; f\}\{dx\}(x),;;mathrm\{or\};;\; frac\{d\}\{dx\}f(x).$

Higher derivatives are expressed using the notation

- $frac\{d^ny\}\{dx^n\},$

for the nth derivative of y = ƒ(x) (with respect to x). These are abbreviations for multiple applications of the derivative operator. For example,

- $frac\{d^2y\}\{dx^2\}\; =\; frac\{d\}\{dx\}left(frac\{dy\}\{dx\}right).$

With Leibniz's notation, we can write the derivative of y at the point x = a in two different ways:

- $frac\{dy\}\{dx\}left.\{!!frac\{\}\{\}\}right|\_\{x=a\}\; =\; frac\{dy\}\{dx\}(a).$

Leibniz's notation allows one to specify the variable for differentiation (in the denominator). This is especially relevant for partial differentiation. It also makes the chain rule easy to remember:

- $frac\{dy\}\{dx\}\; =\; frac\{dy\}\{du\}\; cdot\; frac\{du\}\{dx\}.$

- $(f\text{'})\text{'}=f$, and $(f$)'=f''',.

- $f^\{mathrm\{iv\}\},$

- $f^\{(4)\},$

Newton's notation for differentiation, also called the dot notation, places a dot over the function name to represent a derivative. If y = ƒ(t), then

- $dot\{y\}$ and $ddot\{y\}$

Euler's notation uses a differential operator D, which is applied to a function ƒ to give the first derivative Df. The second derivative is denoted D^{2}ƒ, and the nth derivative is denoted D^{n}ƒ.

If y = ƒ(x) is a dependent variable, then often the subscript x is attached to the D to clarify the independent variable x. Euler's notation is then written

- $D\_x\; y,$ or $D\_x\; f(x),$,

Euler's notation is useful for stating and solving linear differential equations.

The derivative of a function can, in principle, be computed from the definition by considering the difference quotient, and computing its limit. For some examples, see Derivative (examples). In practice, once the derivatives of a few simple functions are known, the derivatives of other functions are more easily computed using rules for obtaining derivatives of more complicated functions from simpler ones.

Most derivative computations eventually require taking the derivative of some common functions. The following incomplete list gives some of the most frequently used functions and their derivatives. For a complete list, see Table of derivatives.

- $f(x)\; =\; x^r,$,

where r is any real number, then

- $f\text{'}(x)\; =\; rx^\{r-1\},$,

wherever this function is defined. For example, if r = 1/2, then

- $f\text{'}(x)\; =\; frac\{1\}\{2\}x^\{-tfrac12\},$.

and the function is defined only for non-negative x. When r = 0, this rule recovers the constant rule.

- Exponential and logarithmic functions:

- $frac\{d\}\{dx\}e^x\; =\; e^x$

- $frac\{d\}\{dx\}a^x\; =\; ln(a)a^x$

- $frac\{d\}\{dx\}ln(x)\; =\; 1/x,qquad\; x\; >\; 0$

- $frac\{d\}\{dx\}log\_a(x)\; =\; frac\{1\}\{xln(a)\}$

- $frac\{d\}\{dx\}sin(x)\; =\; cos(x).$

- $frac\{d\}\{dx\}cos(x)=\; -sin(x).$

- $frac\{d\}\{dx\}tan(x)=\; sec^2(x).$

- $frac\{d\}\{dx\}arcsin(x)\; =\; frac\{1\}\{sqrt\{1-x^2\}\}.$

- $frac\{d\}\{dx\}arccos(x)=\; -frac\{1\}\{sqrt\{1-x^2\}\}.$

- $frac\{d\}\{dx\}arctan(x)=\; frac\{1\}.$

In many cases, complicated limit calculations by direct application of Newton's difference quotient can be avoided using differentiation rules. Some of the most basic rules are the following.

- Constant rule: if ƒ(x) is constant, then

- $f\text{'}\; =\; 0\; ,$

- $(af\; +\; bg)\text{'}\; =\; af\text{'}\; +\; bg\text{'}\; ,$ for all functions ƒ and g and all real numbers a and b.

- $(fg)\text{'}\; =\; f\; \text{'}g\; +\; fg\text{'}\; ,$ for all functions ƒ and g.

- $left(frac\{f\}\{g\}\; right)\text{'}\; =\; frac\{f\text{'}g\; -\; fg\text{'}\}\{g^2\}$ for all functions ƒ and g where g ≠ 0.

- Chain rule: If $f(x)\; =\; h(g(x))$, then

- $f\text{'}(x)\; =\; h\text{'}(g(x))\; cdot\; g\text{'}(x)\; ,$.

The derivative of

- $f(x)\; =\; x^4\; +\; sin\; (x^2)\; -\; ln(x)\; e^x\; +\; 7,$

is

- $$

Here the second term was computed using the chain rule and third using the product rule: the known derivatives of the elementary functions x^{2}, x^{4}, sin(x), ln(x) and exp(x) = e^{x} were also used.

A vector-valued function y(t) of a real variable is a function which sends real numbers to vectors in some vector space R^{n}. A vector-valued function can be split up into its coordinate functions y_{1}(t), y_{2}(t), …, y_{n}(t), meaning that y(t) = (y_{1}(t), ..., y_{n}(t)). This includes, for example, parametric curves in R^{2} or R^{3}. The coordinate functions are real valued functions, so the above definition of derivative applies to them. The derivative of y(t) is defined to be the vector, called the tangent vector, whose coordinates are the derivatives of the coordinate functions. That is,

- $mathbf\{y\}\text{'}(t)\; =\; (y\text{'}\_1(t),\; ldots,\; y\text{'}\_n(t)).$

Equivalently,

- $mathbf\{y\}\text{'}(t)=lim\_\{hto\; 0\}frac\{mathbf\{y\}(t+h)\; -\; mathbf\{y\}(t)\}\{h\},$

if the limit exists. The subtraction in the numerator is subtraction of vectors, not scalars. If the derivative of y exists for every value of t, then y′ is another vector valued function.

If e_{1}, …, e_{n} is the standard basis for R^{n}, then y(t) can also be written as y_{1}(t)e_{1} + … + y_{n}(t)e_{n}. If we assume that the derivative of a vector-valued function retains the linearity property, then the derivative of y(t) must be

- $y\text{'}\_1(t)mathbf\{e\}\_1\; +\; cdots\; +\; y\text{'}\_n(t)mathbf\{e\}\_n$

This generalization is useful, for example, if y(t) is the position vector of a particle at time t; then the derivative y′(t) is the velocity vector of the particle at time t.

Suppose that ƒ is a function that depends on more than one variable. For instance,

- $f(x,y)\; =\; x^2\; +\; xy\; +\; y^2.,$

- $f(x,y)\; =\; f\_x(y)\; =\; x^2\; +\; xy\; +\; y^2.,$

- $x\; mapsto\; f\_x,,$

- $f\_x(y)\; =\; x^2\; +\; xy\; +\; y^2.,$

- $f\_a(y)\; =\; a^2\; +\; ay\; +\; y^2.,$

- $f\_a\text{'}(y)\; =\; a\; +\; 2y.,$

- $frac\{part\; f\}\{part\; y\}(x,y)\; =\; x\; +\; 2y.$

In general, the partial derivative of a function ƒ(x_{1}, …, x_{n}) in the direction x_{i} at the point (a_{1} …, a_{n}) is defined to be:

- $frac\{part\; f\}\{part\; x\_i\}(a\_1,ldots,a\_n)\; =\; lim\_\{h\; to\; 0\}frac\{f(a\_1,ldots,a\_i+h,ldots,a\_n)\; -\; f(a\_1,ldots,a\_n)\}\{h\}.$

- $f\_\{a\_1,ldots,a\_\{i-1\},a\_\{i+1\},ldots,a\_n\}(x\_i)\; =\; f(a\_1,ldots,a\_\{i-1\},x\_i,a\_\{i+1\},ldots,a\_n)$

- $frac\{df\_\{a\_1,ldots,a\_\{i-1\},a\_\{i+1\},ldots,a\_n\}\}\{dx\_i\}(a\_1,ldots,a\_n)\; =\; frac\{part\; f\}\{part\; x\_i\}(a\_1,ldots,a\_n).$

An important example of a function of several variables is the case of a scalar-valued function ƒ(x_{1},...x_{n}) on a domain in Euclidean space R^{n} (e.g., on R² or R³). In this case ƒ has a partial derivative ∂ƒ/∂x_{j} with respect to each variable x_{j}. At the point a, these partial derivatives define the vector

- $nabla\; f(a)\; =\; left(frac\{partial\; f\}\{partial\; x\_1\}(a),\; ldots,\; frac\{partial\; f\}\{partial\; x\_n\}(a)right).$

If ƒ is a real-valued function on R^{n}, then the partial derivatives of ƒ measure its variation in the direction of the coordinate axes. For example, if ƒ is a function of x and y, then its partial derivatives measure the variation in ƒ in the x direction and the y direction. They do not, however, directly measure the variation of ƒ in any other direction, such as along the diagonal line y = x. These are measured using directional derivatives. Choose a vector

- $mathbf\{v\}\; =\; (v\_1,ldots,v\_n).$

- $D\_\{mathbf\{v\}\}\{f\}(boldsymbol\{x\})\; =\; lim\_\{h\; rightarrow\; 0\}\{frac\{f(boldsymbol\{x\}\; +\; hmathbf\{v\})\; -\; f(boldsymbol\{x\})\}\{h\}\}.$

If all the partial derivatives of ƒ exist and are continuous at x, then they determine the directional derivative of ƒ in the direction v by the formula:

- $D\_\{mathbf\{v\}\}\{f\}(boldsymbol\{x\})\; =\; sum\_\{j=1\}^n\; v\_j\; frac\{partial\; f\}\{partial\; x\_j\}.$

The same definition also works when ƒ is a function with values in R^{m}. We just use the above definition in each component of the vectors. In this case, the directional derivative is a vector in R^{m}.

Let ƒ be a function from a domain in R to R. The derivative of ƒ at a point a in its domain is the best linear approximation to ƒ at that point. As above, this is a number. Geometrically, if v is a unit vector starting at a, then f′ (a) , the best linear approximation to ƒ at a, should be the length of the vector found by moving v to the target space using ƒ. (This vector is called the pushforward of v by ƒ and is usually written $f\_*v$.) In other words, if v is measured in terms of distances on the target, then, because v can only be measured through ƒ, v no longer appears to be a unit vector because ƒ does not preserve unit vectors. Instead v appears to have length f′ (a). If m is greater than one, then by writing ƒ using coordinate functions, the length of v in each of the coordinate directions can be measured separately.

Suppose now that ƒ is a function from a domain in R^{n} to R^{m} and that a is a point in the domain of ƒ. The derivative of ƒ at a should still be the best linear approximation to ƒ at a. In other words, if v is a vector on R^{n}, then f′ (a) should be the linear transformation that best approximates ƒ at a. The linear transformation should contain all the information about how ƒ transforms vectors at a to vectors at f(a), and in symbols, this means it should be the linear transformation f′ (a) such that

- $lim\_\{|mathbf\{h\}|rightarrow\; 0\}\; frac\{|f(mathbf\{a\}+mathbf\{h\})\; -\; f(mathbf\{a\})\; -\; f\text{'}(mathbf\{a\})mathbf\{h\}|\}\{|mathbf\{h\}|\}\; =\; 0.$

Here h is a vector in R^{n}, so the norm in the denominator is the standard length on R^{n}. However, f′ (a)h is a vector in R^{m}, and the norm in the numerator is the standard length on R^{m}. The linear transformation f′ (a), if it exists, is called the total derivative of ƒ at a or the (total) differential of ƒ at a.

If the total derivative exists at a, then all the partial derivatives of ƒ exist at a. If we write ƒ using coordinate functions, so that ƒ = (ƒ_{1}, ƒ_{2}, ..., ƒ_{m}), then the total derivative can be expressed as a matrix called the Jacobian matrix of ƒ at a:

- $f\text{'}(mathbf\{a\})\; =\; text\{Jac\}\_\{mathbf\{a\}\}\; =\; left(frac\{partial\; f\_i\}\{partial\; x\_j\}right)\_\{ij\}.$

The existence of the Jacobian is strictly stronger than existence of all the partial derivatives, but if the partial derivatives exist and satisfy mild smoothness conditions, then the total derivative exists and is given by the Jacobian.

The definition of the total derivative subsumes the definition of the derivative in one variable. In this case, the total derivative exists if and only if the usual derivative exists. The Jacobian matrix reduces to a 1×1 matrix whose only entry is the derivative f′ (x). This 1×1 matrix satisfies the property that ƒ(a + h) − ƒ(a) − f′(a)h is approximately zero, in other words that

- $f(a+h)\; approx\; f(a)\; +\; f\text{'}(a)h.$

Up to changing variables, this is the statement that the function $x\; mapsto\; f(a)\; +\; f\text{'}(a)(x-a)$ is the best linear approximation to ƒ at a.

The total derivative of a function does not give another function in the same way as the one-variable case. This is because the total derivative of a multivariable function has to record much more information than the derivative of a single-variable function. Instead, the total derivative gives a function from the tangent bundle of the source to the tangent bundle of the target.

The concept of a derivative can be extended to many other settings. The common thread is that the derivative of a function at a point serves as a linear approximation of the function at that point.

- An important generalization of the derivative concerns complex functions of complex variables, such as functions from (a domain in) the complex numbers C to C. The notion of the derivative of such a function is obtained by replacing real variables with complex variables in the definition. However, this innocent definition hides some very deep properties. If C is identified with R² by writing a complex number z as x + i y, then a differentiable function from C to C is certainly differentiable as a function from R² to R² (in the sense that its partial derivatives all exist), but the converse is not true in general: the complex derivative only exists if the real derivative is complex linear and this imposes relations between the partial derivatives called the Cauchy Riemann equations — see holomorphic functions.
- Another generalization concerns functions between differentiable or smooth manifolds. Intuitively speaking such a manifold M is a space which can be approximated near each point x by a vector space called its tangent space: the prototypical example is a smooth surface in R³. The derivative (or differential) of a (differentiable) map ƒ: M → N between manifolds, at a point x in M, is then a linear map from the tangent space of M at x to the tangent space of N at ƒ(x). The derivative function becomes a map between the tangent bundles of M and N. This definition is fundamental in differential geometry and has many uses — see pushforward (differential) and pullback (differential geometry).
- Differentiation can also be defined for maps between infinite dimensional vector spaces such as Banach spaces and Fréchet spaces. There is a generalization both of the directional derivative, called the Gâteaux derivative, and of the differential, called the Fréchet derivative.
- One deficiency of the classical derivative is that not very many functions are differentiable. Nevertheless, there is a way of extending the notion of the derivative so that all continuous functions and many other functions can be differentiated using a concept known as the weak derivative. The idea is to embed the continuous functions in a larger space called the space of distributions and only require that a function is differentiable "on average".
- The properties of the derivative have inspired the introduction and study of many similar objects in algebra and topology — see, for example, differential algebra.
- Also see arithmetic derivative.

- Calculus
- Symmetric derivative
- Automatic differentiation
- Differentiability class
- Differintegral
- Integral
- Linearization
- Numerical differentiation
- Techniques for differentiation
- Table of derivatives

- WIMS Function Calculator makes online calculation of derivatives; this software also enables interactive exercises.
- Mathematical Assistant on Web online calculation of derivatives, including explanation of steps in the solution.
- Practice finding derivatives of randomly generated functions

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Last updated on Friday October 10, 2008 at 14:47:30 PDT (GMT -0700)

View this article at Wikipedia.org - Edit this article at Wikipedia.org - Donate to the Wikimedia Foundation

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