partial derivative: Definition from Answers.com

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In mathematics, a partial derivative of a function of several variables is its derivative with respect to one of those variables with the others held constant (as opposed to the total derivative, in which all variables are allowed to vary). Partial derivatives are useful in vector calculus and differential geometry.

The partial derivative of a function f with respect to the variable x is written as f '_x, ∂_xf, or ∂f/∂x. The partial-derivative symbol ∂ is a rounded letter, distinguished from the straight d of total-derivative notation. The notation was introduced by Adrien-Marie Legendre and gained general acceptance after its reintroduction by Carl Gustav Jacob Jacobi.^{[citation needed]}

1 Introduction
2 Definition
- 2.1 Basic definition
- 2.2 Formal definition
3 Examples
4 Notation
5 Anti-partial-derivative
6 See also
7 Notes
8 External links

Introduction

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

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

A graph of z = x² + xy + y². We want to find the partial derivative at (1, 1, 3) that leaves y constant; the corresponding tangent line is parallel to the x-axis.

It is difficult to describe the derivative of such a function, as there are an infinite number of tangent lines to every point on this surface. Partial differentiation is the act of choosing one of these lines and finding its slope. Usually, the lines of most interest are those that are parallel to the x-axis, and those that are parallel to the y-axis.

This is a slice of the graph at the right at y= 1.

A good way to find these parallel lines is to treat the other variable as a constant. For example, to find the tangent line of the above function at (1, 1, 3) that is parallel to the x-axis, we treat the y variable as constant. The graph and this plane are shown on the right. On the left, we see the way the function looks on the plane y= 1. By finding the derivative of the equation while assuming that y is a constant, we discover that the equation of the tangent line of ƒ is:

$\frac{\partial z}{\partial x} = 2x+y$

So at (1, 1, 3), by substitution, the slope is 3. Therefore

$\frac{\part z}{\part x} = 3$

at the point (1, 1, 3),

or as "The partial derivative of z with respect to x at (1, 1, 3) is 3."

Definition

Basic definition

The function f can be reinterpreted as a family of functions of one variable indexed by the other variables:

$f(x,y) = f_x(y) = \,\! x^2 + xy + y^2.\,$

In other words, every value of x defines a function, denoted f_x, which is a function of one real number.^[1] That is,

$f_x(y) = x^2 + xy + y^2.\,$

Once a value of x is chosen, say a, then f(x,y) determines a function f_a which sends y to a² + ay + y²:

$f_a(y) = a^2 + ay + y^2. \,$

In this expression, a is a constant, not a variable, so f_a is a function of only one real variable, that being y. Consequently, the definition of the derivative for a function of one variable applies:

$f_a'(y) = a + 2y. \,$

The above procedure can be performed for any choice of a. Assembling the derivatives together into a function gives a function which describes the variation of f in the y direction:

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

This is the partial derivative of f with respect to y. Here ∂ is a rounded d called the partial derivative symbol. To distinguish it from the letter d, ∂ is sometimes pronounced "del" or "partial" instead of "dee".

In general, the partial derivative of a function f(x₁,...,x_n) in the direction x_i at the point (a₁,...,a_n) is defined to be:

$\frac{\part f}{\part x_i}(a_1,\ldots,a_n) = \lim_{h \to 0}\frac{f(a_1,\ldots,a_i+h,\ldots,a_n) - f(a_1,\ldots,a_n)}{h}.$

In the above difference quotient, all the variables except x_i are held fixed. That choice of fixed values determines a function of one variable $f_{a_1,\ldots,a_{i-1},a_{i+1},\ldots,a_n}(x_i) = f(a_1,\ldots,a_{i-1},x_i,a_{i+1},\ldots,a_n)$ , and by definition,

$\frac{df_{a_1,\ldots,a_{i-1},a_{i+1},\ldots,a_n}}{dx_i}(x_i) = \frac{\part f}{\part x_i}(a_1,\ldots,a_n).$

In other words, the different choices of a index a family of one-variable functions just as in the example above. This expression also shows that the computation of partial derivatives reduces to the computation of one-variable derivatives.

An important example of a function of several variables is the case of a scalar-valued function f(x₁,...x_n) on a domain in Euclidean space Rⁿ (e.g., on R² or R³). In this case f has a partial derivative ∂f/∂x_j with respect to each variable x_j. At the point a, these partial derivatives define the vector

$\nabla f(a) = \left(\frac{\partial f}{\partial x_1}(a), \ldots, \frac{\partial f}{\partial x_n}(a)\right).$

This vector is called the gradient of f at a. If f is differentiable at every point in some domain, then the gradient is a vector-valued function ∇f which takes the point a to the vector ∇f(a). Consequently, the gradient produces a vector field.

A common abuse of notation is to define the del operator (∇) as follows in three-dimensional Euclidean space R³ with unit vectors $\mathbf{\hat{i}}, \mathbf{\hat{j}}, \mathbf{\hat{k}}$ :

$\nabla = \bigg[{\frac{\partial}{\partial x}} \bigg] \mathbf{\hat{i}} + \bigg[{\frac{\partial}{\partial y}}\bigg] \mathbf{\hat{j}} + \bigg[{\frac{\partial}{\partial z}}\bigg] \mathbf{\hat{k}}$

Or, more generally, for n-dimensional Euclidean space Rⁿ with coordinates (x₁, x₂, x₃,...,x_n) and unit vectors ( $\mathbf{\hat{e}_1}, \mathbf{\hat{e}_2}, \mathbf{\hat{e}_3}, \dots , \mathbf{\hat{e}_n}$ ):

$\nabla = \sum_{j=1}^n \bigg[{\frac{\partial}{\partial x_j}}\bigg] \mathbf{\hat{e}_j} = \bigg[{\frac{\partial}{\partial x_1}}\bigg] \mathbf{\hat{e}_1} + \bigg[{\frac{\partial}{\partial x_2}}\bigg] \mathbf{\hat{e}_2} + \bigg[{\frac{\partial}{\partial x_3}}\bigg] \mathbf{\hat{e}_3} + \dots + \bigg[{\frac{\partial}{\partial x_n}}\bigg] \mathbf{\hat{e}_n}$

Formal definition

Like ordinary derivatives, the partial derivative is defined as a limit. Let U be an open subset of Rⁿ and f : U → R a function. The partial derivative of f at the point a = (a₁, ..., a_n) ∈ U with respect to the i-th variable a_i is defined as

$\frac{ \partial }{\partial a_i }f(\mathbf{a}) = \lim_{h \rightarrow 0}{ f(a_1, \dots , a_{i-1}, a_i+h, a_{i+1}, \dots ,a_n) - f(a_1, \dots ,a_n) \over h }$

Even if all partial derivatives ∂f/∂a_i(a) exist at a given point a, the function need not be continuous there. However, if all partial derivatives exist in a neighborhood of a and are continuous there, then f is totally differentiable in that neighborhood and the total derivative is continuous. In this case, it is said that f is a C¹ function. This can be used to generalize for vector valued functions (f : U → R'^m) by carefully using a componentwise argument.

The partial derivative $\frac{\partial f}{\partial x}$ can be seen as another function defined on U and can again be partially differentiated. If all mixed second order partial derivatives are continuous at a point (or on a set), f is termed a C² function at that point (or on that set); in this case, the partial derivatives can be exchanged by Clairaut's theorem:

$\frac{\partial^2f}{\partial x_i\, \partial x_j} = \frac{\partial^2f} {\partial x_j\, \partial x_i}.$

Examples

The volume of a cone depends on height and radius

The volume V of a cone depends on the cone's height h and its radius r according to the formula

$V(r, h) = \frac{\pi r^2 h}{3}.$

The partial derivative of V with respect to r is

$\frac{ \partial V}{\partial r} = \frac{ 2 \pi r h}{3}.$

It describes the rate with which a cone's volume changes if its radius is varied and its height is kept constant. The partial derivative with respect to h is

$\frac{ \partial V}{\partial h} = \frac{\pi r^2}{3}$

and represents the rate with which the volume changes if its height is varied and its radius is kept constant.

By contrast, the total derivative of V with respect to r and h are respectively

$\frac{\operatorname dV}{\operatorname dr} = \overbrace{\frac{2 \pi r h}{3}}^\frac{ \partial V}{\partial r} + \overbrace{\frac{\pi r^2}{3}}^\frac{ \partial V}{\partial h}\frac{\operatorname d h}{\operatorname d r}$

and

$\frac{\operatorname dV}{\operatorname dh} = \overbrace{\frac{\pi r^2}{3}}^\frac{ \partial V}{\partial h} + \overbrace{\frac{2 \pi r h}{3}}^\frac{ \partial V}{\partial r}\frac{\operatorname d r}{\operatorname d h}$

The difference between the total and partial derivative is the elimination of indirect dependencies between variables in the latter.

If, for some reason, the cone's proportions have to stay the same, and the height and radius are in a fixed ratio k:

$k = \frac{h}{r} = \frac{\operatorname d h}{\operatorname d r}$

This gives the total derivative with respect to r:

$\frac{\operatorname dV}{\operatorname dr} = \frac{2 \pi r h}{3} + k\frac{\pi r^2}{3}$

Equations involving an unknown function's partial derivatives are called partial differential equations and are common in physics, engineering, and other sciences and applied disciplines.

Notation

For the following examples, let f be a function in x, y and z.

First-order partial derivatives:

$\frac{ \partial f}{ \partial x} = f_x = \partial_x f.$

Second-order partial derivatives:

$\frac{ \partial^2 f}{ \partial x^2} = f_{xx} = \partial_{xx} f.$

Second-order mixed derivatives:

$\frac{\partial^2 f}{\partial y \, \partial x} = \frac{\partial}{\partial y} \left( \frac{\partial f}{\partial x} \right) = f_{xy} = \partial_{yx} f.$

Higher-order partial and mixed derivatives:

$\frac{ \partial^{i+j+k} f}{ \partial x^i\, \partial y^j\, \partial z^k } = f^{(i, j, k)}.$

When dealing with functions of multiple variables, some of these variables may be related to each other, and it may be necessary to specify explicitly which variables are being held constant. In fields such as statistical mechanics, the partial derivative of f with respect to x, holding y and z constant, is often expressed as

$\left( \frac{\partial f}{\partial x} \right)_{y,z}.$

Anti-partial-derivative

There is also a concept for partial derivatives that is analogous to antiderivatives for regular derivatives. Given a partial derivative, it allows you to (partially) reconstruct the original function.

Consider the first example in this article, $\frac{\partial z}{\partial x} = 2x+y$ .

Then, you can take a "partial" integral with respect to $x$ (treating $y$ as constant, like when you take the partial derivative):

$z = \int \frac{\partial z}{\partial x} \,dx = x^2 + xy + g(y)$

Note that here, the "constant" of integration is no longer a constant, but is instead a function of all the variables of the original function except $x$ . The reason for this is that all the other variables are treated as constant when taking the partial derivative, so any function which does not involve $x$ will disappear when taking the partial derivative, and we have to account for this when we take the antiderivative. The most general way to represent this is to have the "constant" represent an unknown function of all the other variables.

Thus the set of functions $x 2 + x y + g (y)$ , where g is any one-argument function, represents the entire set of functions in variables $x, y$ that could have produced the $x$ -partial derivative $2 x + y$ .

If you have all the partial derivatives of a function (i.e. the gradient), then you can "match" the antiderivatives you get via the above process to reconstruct the original function up to a constant.

Notes

^ This can also be expressed as the adjointness between the product space and function space constructions.

External links

Partial Derivatives at MathWorld

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