# Partial derivative

Last updated

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 used in vector calculus and differential geometry.

## Contents

The partial derivative of a function ${\displaystyle f(x,y,\dots )}$ with respect to the variable ${\displaystyle x}$ is variously denoted by

${\displaystyle f'_{x}}$, ${\displaystyle f_{x}}$, ${\displaystyle \partial _{x}f}$, ${\displaystyle \ D_{x}f}$, ${\displaystyle D_{1}f}$, ${\displaystyle {\frac {\partial }{\partial x}}f}$, or ${\displaystyle {\frac {\partial f}{\partial x}}}$.

Sometimes, for ${\displaystyle z=f(x,y,\ldots )}$, the partial derivative of ${\displaystyle z}$ with respect to ${\displaystyle x}$ is denoted as ${\displaystyle {\tfrac {\partial z}{\partial x}}.}$ Since a partial derivative generally has the same arguments as the original function, its functional dependence is sometimes explicitly signified by the notation, such as in:

${\displaystyle f_{x}(x,y,\ldots ),{\frac {\partial f}{\partial x}}(x,y,\ldots ).}$

The symbol used to denote partial derivatives is . One of the first known uses of this symbol in mathematics is by Marquis de Condorcet from 1770, who used it for partial differences. The modern partial derivative notation was created by Adrien-Marie Legendre (1786) (although he later abandoned it, Carl Gustav Jacob Jacobi reintroduced the symbol in 1841). [1]

## Definition

Like ordinary derivatives, the partial derivative is defined as a limit. Let U be an open subset of ${\displaystyle \mathbb {R} ^{n}}$ and ${\displaystyle f:U\to \mathbb {R} }$ a function. The partial derivative of f at the point ${\displaystyle \mathbf {a} =(a_{1},\ldots ,a_{n})\in U}$ with respect to the i-th variable xi is defined as

{\displaystyle {\begin{aligned}{\frac {\partial }{\partial x_{i}}}f(\mathbf {a} )&=\lim _{h\to 0}{\frac {f(a_{1},\ldots ,a_{i-1},a_{i}+h,a_{i+1},\ldots ,a_{n})-f(a_{1},\ldots ,a_{i},\dots ,a_{n})}{h}}\\&=\lim _{h\to 0}{\frac {f(\mathbf {a} +he_{i})-f(\mathbf {a} )}{h}}\end{aligned}}}

Even if all partial derivatives ∂f/∂xi(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, ${\displaystyle f:U\to \mathbb {R} ^{m}}$, by carefully using a componentwise argument.

The partial derivative ${\displaystyle {\frac {\partial f}{\partial x}}}$ 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:

${\displaystyle {\frac {\partial ^{2}f}{\partial x_{i}\partial x_{j}}}={\frac {\partial ^{2}f}{\partial x_{j}\partial x_{i}}}.}$

## Notation

For the following examples, let ${\displaystyle f}$ be a function in ${\displaystyle x,y}$ and ${\displaystyle z}$.

First-order partial derivatives:

${\displaystyle {\frac {\partial f}{\partial x}}=f_{x}=\partial _{x}f.}$

Second-order partial derivatives:

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

Second-order mixed derivatives:

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

Higher-order partial and mixed derivatives:

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

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

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

Conventionally, for clarity and simplicity of notation, the partial derivative function and the value of the function at a specific point are conflated by including the function arguments when the partial derivative symbol (Leibniz notation) is used. Thus, an expression like

${\displaystyle {\frac {\partial f(x,y,z)}{\partial x}}}$

is used for the function, while

${\displaystyle {\frac {\partial f(u,v,w)}{\partial u}}}$

might be used for the value of the function at the point ${\displaystyle (x,y,z)=(u,v,w)}$. However, this convention breaks down when we want to evaluate the partial derivative at a point like ${\displaystyle (x,y,z)=(17,u+v,v^{2})}$. In such a case, evaluation of the function must be expressed in an unwieldy manner as

${\displaystyle {\frac {\partial f(x,y,z)}{\partial x}}(17,u+v,v^{2})}$

or

${\displaystyle \left.{\frac {\partial f(x,y,z)}{\partial x}}\right|_{(x,y,z)=(17,u+v,v^{2})}}$

in order to use the Leibniz notation. Thus, in these cases, it may be preferable to use the Euler differential operator notation with ${\displaystyle D_{i}}$ as the partial derivative symbol with respect to the ith variable. For instance, one would write ${\displaystyle D_{1}f(17,u+v,v^{2})}$ for the example described above, while the expression ${\displaystyle D_{1}f}$ represents the partial derivative function with respect to the 1st variable. [2]

For higher order partial derivatives, the partial derivative (function) of ${\displaystyle D_{i}f}$ with respect to the jth variable is denoted ${\displaystyle D_{j}(D_{i}f)=D_{i,j}f}$. That is, ${\displaystyle D_{j}\circ D_{i}=D_{i,j}}$, so that the variables are listed in the order in which the derivatives are taken, and thus, in reverse order of how the composition of operators is usually notated. Of course, Clairaut's theorem implies that ${\displaystyle D_{i,j}=D_{j,i}}$ as long as comparatively mild regularity conditions on f are satisfied.

An important example of a function of several variables is the case of a scalar-valued function f(x1, …, xn) on a domain in Euclidean space ${\displaystyle \mathbb {R} ^{n}}$ (e.g., on ${\displaystyle \mathbb {R} ^{2}}$ or ${\displaystyle \mathbb {R} ^{3}}$). In this case f has a partial derivative ∂f/∂xj with respect to each variable xj. At the point a, these partial derivatives define the vector

${\displaystyle \nabla f(a)=\left({\frac {\partial f}{\partial x_{1}}}(a),\ldots ,{\frac {\partial f}{\partial x_{n}}}(a)\right).}$

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 ${\displaystyle \mathbb {R} ^{3}}$ with unit vectors ${\displaystyle {\hat {\mathbf {i} }},{\hat {\mathbf {j} }},{\hat {\mathbf {k} }}}$:

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

Or, more generally, for n-dimensional Euclidean space ${\displaystyle \mathbb {R} ^{n}}$ with coordinates ${\displaystyle x_{1},\ldots ,x_{n}}$ and unit vectors ${\displaystyle {\hat {\mathbf {e} }}_{1},\ldots ,{\hat {\mathbf {e} }}_{n}}$:

${\displaystyle \nabla =\sum _{j=1}^{n}\left[{\frac {\partial }{\partial x_{j}}}\right]{\hat {\mathbf {e} }}_{j}=\left[{\frac {\partial }{\partial x_{1}}}\right]{\hat {\mathbf {e} }}_{1}+\left[{\frac {\partial }{\partial x_{2}}}\right]{\hat {\mathbf {e} }}_{2}+\dots +\left[{\frac {\partial }{\partial x_{n}}}\right]{\hat {\mathbf {e} }}_{n}}$

## Directional derivative

The directional derivative of a scalar function

${\displaystyle f(\mathbf {x} )=f(x_{1},x_{2},\ldots ,x_{n})}$

along a vector

${\displaystyle \mathbf {v} =(v_{1},\ldots ,v_{n})}$

is the function ${\displaystyle \nabla _{\mathbf {v} }{f}}$ defined by the limit [3]

${\displaystyle \nabla _{\mathbf {v} }{f}(\mathbf {x} )=\lim _{h\to 0}{\frac {f(\mathbf {x} +h\mathbf {v} )-f(\mathbf {x} )}{h}}.}$
This definition is valid in a broad range of contexts, for example where the norm of a vector (and hence a unit vector) is undefined. [4]

## Example

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

${\displaystyle z=f(x,y)=x^{2}+xy+y^{2}}$.
A graph of z = x2 + xy + y2. For the partial derivative at (1, 1) that leaves y constant, the corresponding tangent line is parallel to the xz-plane.
A slice of the graph above showing the function in the xz-plane at y = 1. Note that the two axes are shown here with different scales. The slope of the tangent line is 3.

The graph of this function defines a surface in Euclidean space. To every point on this surface, there are an infinite number of tangent lines. 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 ${\displaystyle xz}$-plane, and those that are parallel to the ${\displaystyle yz}$-plane (which result from holding either ${\displaystyle y}$ or ${\displaystyle x}$ constant, respectively).

To find the slope of the line tangent to the function at ${\displaystyle P(1,1)}$ and parallel to the ${\displaystyle xz}$-plane, we treat ${\displaystyle y}$ as a constant. The graph and this plane are shown on the right. Below, we see how the function looks on the plane ${\displaystyle y=1}$ . By finding the derivative of the equation while assuming that ${\displaystyle y}$ is a constant, we find that the slope of ${\displaystyle f}$ at the point ${\displaystyle (x,y)}$ is:

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

So at ${\displaystyle (1,1)}$, by substitution, the slope is 3. Therefore,

${\displaystyle {\frac {\partial z}{\partial x}}=3}$

at the point ${\displaystyle (1,1)}$. That is, the partial derivative of ${\displaystyle z}$ with respect to ${\displaystyle x}$ at ${\displaystyle (1,1)}$ is 3, as shown in the graph.

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

${\displaystyle f(x,y)=f_{y}(x)=x^{2}+xy+y^{2}.}$

In other words, every value of y defines a function, denoted fy, which is a function of one variable x. [lower-alpha 1] That is,

${\displaystyle f_{y}(x)=x^{2}+xy+y^{2}.}$

In this section the subscript notation fy denotes a function contingent on a fixed value of y, and not a partial derivative.

Once a value of y is chosen, say a, then f(x,y) determines a function fa which traces a curve x2 + ax + a2 on the ${\displaystyle xz}$-plane:

${\displaystyle f_{a}(x)=x^{2}+ax+a^{2}}$.

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

${\displaystyle f_{a}'(x)=2x+a}$.

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 x direction:

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

This is the partial derivative of f with respect to x. Here is a rounded d called the partial derivative symbol ; to distinguish it from the letter d, is sometimes pronounced "partial".

## Higher order partial derivatives

Second and higher order partial derivatives are defined analogously to the higher order derivatives of univariate functions. For the function ${\displaystyle f(x,y,...)}$ the "own" second partial derivative with respect to x is simply the partial derivative of the partial derivative (both with respect to x): [5] :316–318

${\displaystyle {\frac {\partial ^{2}f}{\partial x^{2}}}\equiv \partial {\frac {\partial f/\partial x}{\partial x}}\equiv {\frac {\partial f_{x}}{\partial x}}\equiv f_{xx}.}$

The cross partial derivative with respect to x and y is obtained by taking the partial derivative of f with respect to x, and then taking the partial derivative of the result with respect to y, to obtain

${\displaystyle {\frac {\partial ^{2}f}{\partial y\,\partial x}}\equiv \partial {\frac {\partial f/\partial x}{\partial y}}\equiv {\frac {\partial f_{x}}{\partial y}}\equiv f_{xy}.}$

Schwarz's theorem states that if the second derivatives are continuous the expression for the cross partial derivative is unaffected by which variable the partial derivative is taken with respect to first and which is taken second. That is,

${\displaystyle {\frac {\partial ^{2}f}{\partial x\,\partial y}}={\frac {\partial ^{2}f}{\partial y\,\partial x}}}$

or equivalently ${\displaystyle f_{yx}=f_{xy}.}$

Own and cross partial derivatives appear in the Hessian matrix which is used in the second order conditions in optimization problems.

## Antiderivative analogue

There is a concept for partial derivatives that is analogous to antiderivatives for regular derivatives. Given a partial derivative, it allows for the partial recovery of the original function.

Consider the example of

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

The "partial" integral can be taken with respect to x (treating y as constant, in a similar manner to partial differentiation):

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

Here, the "constant" of integration is no longer a constant, but 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 ${\displaystyle x}$ 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 ${\displaystyle x^{2}+xy+g(y)}$, 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 ${\displaystyle 2x+y}$.

If all the partial derivatives of a function are known (for example, with the gradient), then the antiderivatives can be matched via the above process to reconstruct the original function up to a constant. Unlike in the single-variable case, however, not every set of functions can be the set of all (first) partial derivatives of a single function. In other words, not every vector field is conservative.

## Applications

### Geometry

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

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

The partial derivative of V with respect to r is

${\displaystyle {\frac {\partial V}{\partial r}}={\frac {2\pi rh}{3}},}$

which represents 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 ${\displaystyle h}$ equals ${\displaystyle {\frac {\pi r^{2}}{3}},}$ which 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

${\displaystyle {\frac {dV}{dr}}=\overbrace {\frac {2\pi rh}{3}} ^{\frac {\partial V}{\partial r}}+\overbrace {\frac {\pi r^{2}}{3}} ^{\frac {\partial V}{\partial h}}{\frac {dh}{dr}}}$

and

${\displaystyle {\frac {dV}{dh}}=\overbrace {\frac {\pi r^{2}}{3}} ^{\frac {\partial V}{\partial h}}+\overbrace {\frac {2\pi rh}{3}} ^{\frac {\partial V}{\partial r}}{\frac {dr}{dh}}}$

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

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

${\displaystyle k={\frac {h}{r}}={\frac {dh}{dr}}.}$

This gives the total derivative with respect to r:

${\displaystyle {\frac {dV}{dr}}={\frac {2\pi rh}{3}}+{\frac {\pi r^{2}}{3}}k}$

which simplifies to:

${\displaystyle {\frac {dV}{dr}}=k\pi r^{2}}$

Similarly, the total derivative with respect to h is:

${\displaystyle {\frac {dV}{dh}}=\pi r^{2}}$

The total derivative with respect to both r and h of the volume intended as scalar function of these two variables is given by the gradient vector

${\displaystyle \nabla V=\left({\frac {\partial V}{\partial r}},{\frac {\partial V}{\partial h}}\right)=\left({\frac {2}{3}}\pi rh,{\frac {1}{3}}\pi r^{2}\right)}$.

### Optimization

Partial derivatives appear in any calculus-based optimization problem with more than one choice variable. For example, in economics a firm may wish to maximize profit π(x, y) with respect to the choice of the quantities x and y of two different types of output. The first order conditions for this optimization are πx = 0 = πy. Since both partial derivatives πx and πy will generally themselves be functions of both arguments x and y, these two first order conditions form a system of two equations in two unknowns.

### Thermodynamics, quantum mechanics and mathematical physics

Partial derivatives appear in thermodynamic equations like Gibbs-Duhem equation, in quantum mechanics as Schrodinger wave equation as well in other equations from mathematical physics. Here the variables being held constant in partial derivatives can be ratio of simple variables like mole fractions xi in the following example involving the Gibbs energies in a ternary mixture system:

${\displaystyle {\bar {G_{2}}}=G+(1-x_{2})\left({\frac {\partial G}{\partial x_{2}}}\right)_{\frac {x_{1}}{x_{3}}}}$

Express mole fractions of a component as functions of other components' mole fraction and binary mole ratios:

${\displaystyle x_{1}={\frac {1-x_{2}}{1+{\frac {x_{3}}{x_{1}}}}}}$
${\displaystyle x_{3}={\frac {1-x_{2}}{1+{\frac {x_{1}}{x_{3}}}}}}$

Differential quotients can be formed at constant ratios like those above:

${\displaystyle \left({\frac {\partial x_{1}}{\partial x_{2}}}\right)_{\frac {x_{1}}{x_{3}}}=-{\frac {x_{1}}{1-x_{2}}}}$
${\displaystyle \left({\frac {\partial x_{3}}{\partial x_{2}}}\right)_{\frac {x_{1}}{x_{3}}}=-{\frac {x_{3}}{1-x_{2}}}}$

Ratios X, Y, Z of mole fractions can be written for ternary and multicomponent systems:

${\displaystyle X={\frac {x_{3}}{x_{1}+x_{3}}}}$
${\displaystyle Y={\frac {x_{3}}{x_{2}+x_{3}}}}$
${\displaystyle Z={\frac {x_{2}}{x_{1}+x_{2}}}}$

which can be used for solving partial differential equations like:

${\displaystyle \left({\frac {\partial \mu _{2}}{\partial n_{1}}}\right)_{n_{2},n_{3}}=\left({\frac {\partial \mu _{1}}{\partial n_{2}}}\right)_{n_{1},n_{3}}}$

This equality can be rearranged to have differential quotient of mole fractions on one side.

### Image resizing

Partial derivatives are key to target-aware image resizing algorithms. Widely known as seam carving, these algorithms require each pixel in an image to be assigned a numerical 'energy' to describe their dissimilarity against orthogonal adjacent pixels. The algorithm then progressively removes rows or columns with the lowest energy. The formula established to determine a pixel's energy (magnitude of gradient at a pixel) depends heavily on the constructs of partial derivatives.

### Economics

Partial derivatives play a prominent role in economics, in which most functions describing economic behaviour posit that the behaviour depends on more than one variable. For example, a societal consumption function may describe the amount spent on consumer goods as depending on both income and wealth; the marginal propensity to consume is then the partial derivative of the consumption function with respect to income.

## Notes

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

## Related Research Articles

In calculus, the chain rule is a formula that expresses the derivative of the composition of two differentiable functions f and g in terms of the derivatives f and g. More precisely, if is the function such that for every x, then the chain rule is, in Lagrange's notation,

In vector calculus, the curl is a vector operator that describes the infinitesimal circulation of a vector field in three-dimensional Euclidean space. The curl at a point in the field is represented by a vector whose length and direction denote the magnitude and axis of the maximum circulation. The curl of a field is formally defined as the circulation density at each point of the field.

In mathematics, the derivative of a function of a real variable measures the sensitivity to change of the function value with respect to a change in its argument. Derivatives are a fundamental tool of calculus. For example, the derivative of the position of a moving object with respect to time is the object's velocity: this measures how quickly the position of the object changes when time advances.

In vector calculus, divergence is a vector operator that operates on a vector field, producing a scalar field giving the quantity of the vector field's source at each point. More technically, the divergence represents the volume density of the outward flux of a vector field from an infinitesimal volume around a given point.

In vector calculus, the gradient of a scalar-valued differentiable function f of several variables is the vector field whose value at a point is the vector whose components are the partial derivatives of at . That is, for , its gradient is defined at the point in n-dimensional space as the vector:

In probability theory, a probability density function (PDF), or density of a continuous random variable, is a function whose value at any given sample in the sample space can be interpreted as providing a relative likelihood that the value of the random variable would equal that sample. In other words, while the absolute likelihood for a continuous random variable to take on any particular value is 0, the value of the PDF at two different samples can be used to infer, in any particular draw of the random variable, how much more likely it is that the random variable would equal one sample compared to the other sample.

In mathematics, Cauchy's integral formula, named after Augustin-Louis Cauchy, is a central statement in complex analysis. It expresses the fact that a holomorphic function defined on a disk is completely determined by its values on the boundary of the disk, and it provides integral formulas for all derivatives of a holomorphic function. Cauchy's formula shows that, in complex analysis, "differentiation is equivalent to integration": complex differentiation, like integration, behaves well under uniform limits – a result that does not hold in real analysis.

In vector calculus, the Jacobian matrix of a vector-valued function in several variables is the matrix of all its first-order partial derivatives. When this matrix is square, that is, when the function takes the same number of variables as input as the number of vector components of its output, its determinant is referred to as the Jacobian determinant. Both the matrix and the determinant are often referred to simply as the Jacobian in literature.

In vector calculus, Green's theorem relates a line integral around a simple closed curve C to a double integral over the plane region D bounded by C. It is the two-dimensional special case of Stokes' theorem.

In mathematics, a differential operator is an operator defined as a function of the differentiation operator. It is helpful, as a matter of notation first, to consider differentiation as an abstract operation that accepts a function and returns another function.

In mathematics, the Hessian matrix or Hessian is a square matrix of second-order partial derivatives of a scalar-valued function, or scalar field. It describes the local curvature of a function of many variables. The Hessian matrix was developed in the 19th century by the German mathematician Ludwig Otto Hesse and later named after him. Hesse originally used the term "functional determinants".

In mathematics, the covariant derivative is a way of specifying a derivative along tangent vectors of a manifold. Alternatively, the covariant derivative is a way of introducing and working with a connection on a manifold by means of a differential operator, to be contrasted with the approach given by a principal connection on the frame bundle – see affine connection. In the special case of a manifold isometrically embedded into a higher-dimensional Euclidean space, the covariant derivative can be viewed as the orthogonal projection of the Euclidean directional derivative onto the manifold's tangent space. In this case the Euclidean derivative is broken into two parts, the extrinsic normal component and the intrinsic covariant derivative component.

In mathematics, more specifically in multivariable calculus, the implicit function theorem is a tool that allows relations to be converted to functions of several real variables. It does so by representing the relation as the graph of a function. There may not be a single function whose graph can represent the entire relation, but there may be such a function on a restriction of the domain of the relation. The implicit function theorem gives a sufficient condition to ensure that there is such a function.

The following are important identities involving derivatives and integrals in vector calculus.

A vector-valued function, also referred to as a vector function, is a mathematical function of one or more variables whose range is a set of multidimensional vectors or infinite-dimensional vectors. The input of a vector-valued function could be a scalar or a vector ; the dimension of the function's domain is not defined by the dimension of the range.

In differential calculus, there is no single uniform notation for differentiation. Instead, several different notations for the derivative of a function or variable have been proposed by different mathematicians. The usefulness of each notation varies with the context, and it is sometimes advantageous to use more than one notation in a given context. The most common notations for differentiation are listed below.

In complex analysis of one and several complex variables, Wirtinger derivatives, named after Wilhelm Wirtinger who introduced them in 1927 in the course of his studies on the theory of functions of several complex variables, are partial differential operators of the first order which behave in a very similar manner to the ordinary derivatives with respect to one real variable, when applied to holomorphic functions, antiholomorphic functions or simply differentiable functions on complex domains. These operators permit the construction of a differential calculus for such functions that is entirely analogous to the ordinary differential calculus for functions of real variables.

In mathematical analysis its applications, a function of several real variables or real multivariate function is a function with more than one argument, with all arguments being real variables. This concept extends the idea of a function of a real variable to several variables. The "input" variables take real values, while the "output", also called the "value of the function", may be real or complex. However, the study of the complex valued functions may be easily reduced to the study of the real valued functions, by considering the real and imaginary parts of the complex function; therefore, unless explicitly specified, only real valued functions will be considered in this article.

Lagrangian field theory is a formalism in classical field theory. It is the field-theoretic analogue of Lagrangian mechanics. Lagrangian mechanics is used to analyze the motion of a system of discrete particles each with a finite number of degrees of freedom. Lagrangian field theory applies to continua and fields, which have an infinite number of degrees of freedom.

In theoretical physics, Hamiltonian field theory is the field-theoretic analogue to classical Hamiltonian mechanics. It is a formalism in classical field theory alongside Lagrangian field theory. It also has applications in quantum field theory.

## References

1. Miller, Jeff (2009-06-14). "Earliest Uses of Symbols of Calculus". Earliest Uses of Various Mathematical Symbols. Retrieved 2009-02-20.
2. Spivak, M. (1965). Calculus on Manifolds. New York: W. A. Benjamin, Inc. p. 44. ISBN   9780805390216.
3. R. Wrede; M.R. Spiegel (2010). Advanced Calculus (3rd ed.). Schaum's Outline Series. ISBN   978-0-07-162366-7.
4. The applicability extends to functions over spaces without a metric and to differentiable manifolds, such as in general relativity.
5. Chiang, Alpha C. Fundamental Methods of Mathematical Economics, McGraw-Hill, third edition, 1984.