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.
The partial derivative of a function
f(x,y,...)
x
f'_{x}
f_{x}
\partial_{x}f
D_{xf}
D_{1f}
\partial  
\partialx 
f
\partialf  
\partialx 
Sometimes, for
z=f(x,y,\ldots)
z
x
\tfrac{\partialz}{\partialx}.
f_{x(x,}y,\ldots),
\partialf  
\partialx 
(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 AdrienMarie Legendre (1786) (although he later abandoned it, Carl Gustav Jacob Jacobi reintroduced the symbol in 1841).^{[1]}
Like ordinary derivatives, the partial derivative is defined as a limit. Let U be an open subset of
\R^{n}
f:U\to\R
a=(a_{1,}\ldots,a_{n)}\inU
\begin{align}  \partial 
\partialx_{i} 
f(a)&=\lim_{h}
f(a_{1,}\ldots,a_{i1},a_{i+h,}a_{i+1},\ldots,a_{n)} f(a_{1,}\ldots,a_{i,}...,a_{n)}  
h 
\ &=\lim_{h}
 
h 
\end{align}
Even if all partial derivatives ∂f/∂x_{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 C^{1} function. This can be used to generalize for vector valued functions, by carefully using a componentwise argument.
The partial derivative
\partialf  
\partialx 
\partial^{2f}  
\partialx_{i}\partialx_{j} 
=
\partial^{2f}  
\partialx_{j}\partialx_{i} 
.
See also: ∂. For the following examples, let
f
x,y
z
Firstorder partial derivatives:
\partialf  
\partialx 
=f_{x}=\partial_{x}f.
Secondorder partial derivatives:
\partial^{2}f  
\partialx^{2} 
=f_{xx}=\partial_{xx}f=
2  
\partial  
x 
f.
Secondorder mixed derivatives:
\partial^{2}f  
\partialy\partialx 
=
\partial  
\partialy 
\left(
\partialf  
\partialx 
\right)=(f_{x})_{y}=f_{xy}=\partial_{yx}f=\partial_{y}\partial_{x}f.
Higherorder partial and mixed derivatives:
\partial^{i+j+k}f  
\partialx^{i}\partialy^{j}\partialz^{k} 
=f^{(i,}=
i  
\partial  
x 
j  
\partial  
y 
k  
\partial  
z 
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
f
x
y
z
\left(
\partialf  
\partialx 
\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
\partialf(x,y,z)  
\partialx 
is used for the function, while
\partialf(u,v,w)  
\partialu 
might be used for the value of the function at the point
(x,y,z)=(u,v,w)
(x,y,z)=(17,u+v,v^{2)}
\partialf(x,y,z)  
\partialx 
(17,u+v,v^{2)}
or
\left.
\partialf(x,y,z)  
\partialx 
\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
D_{i}
D_{1}f(17,u+v,v^{2)}
D_{1}f
For higher order partial derivatives, the partial derivative (function) of
D_{i}f
D_{j(D}_{i}f)=D_{i,j}f
D_{j\circ}D_{i}=D_{i,j}
D_{i,j}=D_{j,i}
An important example of a function of several variables is the case of a scalarvalued function f(x_{1}, …, x_{n}) on a domain in Euclidean space
\R^{n}
\R^{2}
\R^{3}
\nablaf(a)=\left(
\partialf  
\partialx_{1} 
(a),\ldots,
\partialf  
\partialx_{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 vectorvalued function ∇f which takes the point a to the vector ∇f(a). Consequently, the gradient produces a vector field.
\R^{3}
\hat{i
\nabla=\left[{
\partial  
\partialx 
Or, more generally, for ndimensional Euclidean space
\R^{n}
x_{1,}\ldots,x_{n}
\hat{e
\nabla=
n  
\sum  \left[  
j=1 
\partial  
\partialx_{j} 
\right]\hat{e
Suppose that f is a function of more than one variable. For instance,
z=f(x,y)=x^{2}+xy+y^{2}
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
xz
yz
y
x
To find the slope of the line tangent to the function at
P(1,1)
xz
y
y=1
y
f
(x,y)
\partialz  
\partialx 
=2x+y
So at
(1,1)
\partialz  
\partialx 
=3
at the point
(1,1)
z
x
(1,1)
The function f can be reinterpreted as a family of functions of one variable indexed by the other variables:
f(x,y)=f_{y(x)}=x^{2}+xy+y^{2.}
In other words, every value of y defines a function, denoted f_{y} , which is a function of one variable x. That is,
f_{y(x)}=x^{2}+xy+y^{2.}
In this section the subscript notation f_{y} 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 f_{a} which traces a curve x^{2} + ax + a^{2} on the
xz
f_{a(x)}=x^{2}+ax+a^{2}
In this expression, a is a constant, not a variable, so f_{a} is a function of only one real variable, that being x. Consequently, the definition of the derivative for a function of one variable applies:
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:
\partialf  
\partialx 
(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".
Second and higher order partial derivatives are defined analogously to the higher order derivatives of univariate functions. For the function
f(x,y,...)
\partial^{2}f  
\partialx^{2} 
\equiv\partial
{\partialf/\partialx  
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
{\partial^{2}f  
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,
{\partial^{2}f  
or equivalently
f_{{yx}
Own and cross partial derivatives appear in the Hessian matrix which is used in the second order conditions in optimization problems.
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
\partialz  
\partialx 
=2x+y.
The "partial" integral can be taken with respect to x (treating y as constant, in a similar manner to partial differentiation):
z=\int
\partialz  
\partialx 
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
x
Thus the set of functions
x^{2}+xy+g(y)
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 singlevariable 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.
The volume V of a cone depends on the cone's height h and its radius r according to the formula
V(r,h)=
\pir^{2}h  
3 
.
The partial derivative of V with respect to r is
\partialV  
\partialr 
=
2\pirh  
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
h
\pir^{2}  
3 
,
By contrast, the total derivative of V with respect to r and h are respectively
dV  
dr 
=\overbrace{
2\pirh  
3 
and
dV  
dh 
=\overbrace{
\pir^{2}  
3 
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,
k=
h  
r 
=
dh  
dr 
.
This gives the total derivative with respect to r:
dV  
dr 
=
2\pirh  
3 
+
\pir^{2}  
3 
k
which simplifies to:
dV  
dr 
=k\pir^{2}
Similarly, the total derivative with respect to h is:
dV  
dh 
=\pir^{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
\nablaV=\left(
\partialV  ,  
\partialr 
\partialV  
\partialh 
\right)=\left(
2  
3 
\pirh,
1  
3 
\pir^{2\right)}
Partial derivatives appear in any calculusbased 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 π_{x} = 0 = π_{y}. Since both partial derivatives π_{x} and π_{y} 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.
Partial derivatives appear in thermodynamic equations like GibbsDuhem 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 x_{i} in the following example involving the Gibbs energies in a ternary mixture system:
\bar{G_{2}=}G+(1x_{2)}\left(
{\partialG  
Express mole fractions of a component as functions of other components' mole fraction and binary mole ratios:
x_{1}=
1x_{2}  

x_{3}=
1x_{2}  

Differential quotients can be formed at constant ratios like those above:
\left(  \partialx_{1} 
\partialx_{2} 
\right)  

=
x_{1}  
1x_{2} 
\left(  \partialx_{3} 
\partialx_{2} 
\right)  

=
x_{3}  
1x_{2} 
Ratios X, Y, Z of mole fractions can be written for ternary and multicomponent systems:
X=
x_{3}  
x_{1}+x_{3} 
Y=
x_{3}  
x_{2}+x_{3} 
Z=
x_{2}  
x_{1}+x_{2} 
which can be used for solving partial differential equations like:
\left(  \partial\mu_{2} 
\partialn_{1} 
\right)  
n_{2,}n_{3} 
=\left(
\partial\mu_{1}  
\partialn_{2} 
\right)  
n_{1,}n_{3} 
This equality can be rearranged to have differential quotient of mole fractions on one side.
Partial derivatives are key to targetaware 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.
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.