In calculus, the chain rule is a formula that expresses the derivative of the composition of two differentiable functions  and  in terms of the derivatives  and .
More precisely, if h=f\circ g is the function such that h(x)=f(g(x)) for every , then the chain rule is, in Lagrange's notation,
h'(x) = f'(g(x)) g'(x).
or, equivalently,
h'=(f\circ g)'=(f'\circ g)\cdot g'.
The chain rule may also be expressed in Leibniz's notation.
If a variable  depends on the variable , which itself depends on the variable  (that is,  and  are dependent variables), then  depends on  as well, via the intermediate variable .
In this case, the chain rule is expressed as
\frac{dz}{dx} = \frac{dz}{dy} \cdot \frac{dy}{dx},
and
\left.
\frac{dz}{dx}\right|_{x} = \left.
\frac{dz}{dy}\right|_{y(x)} \cdot \left.
\frac{dy}{dx}\right|_{x} ,
for indicating at which points the derivatives have to be evaluated.
In integration, the counterpart to the chain rule is the substitution rule.
Intuitive explanation
Intuitively, the chain rule states that knowing the instantaneous rate of change of  relative to  and that of  relative to  allows one to calculate the instantaneous rate of change of  relative to  as the product of the two rates of change.
As put by George F. Simmons: "if a car travels twice as fast as a bicycle and the bicycle is four times as fast as a walking man, then the car travels 2 × 4 = 8 times as fast as the man."
George F. Simmons, Calculus with Analytic Geometry (1985), p.
93.
The relationship between this example and the chain rule is as follows.
Let ,   and  be the (variable) positions of the car, the bicycle, and the walking man, respectively.
The rate of change of relative positions of the car and the bicycle is \frac {dz}{dy}=2.
Similarly, \frac {dy}{dx}=4.
So, the rate of change of the relative positions of the car and the walking man is
\frac{dz}{dx}=\frac{dz}{dy}\cdot\frac{dy}{dx}=2\cdot 4=8.
The rate of change of positions is the ratio of the speeds, and the speed is the derivative of the position with respect to the time; that is,
\frac{dz}{dx}=\frac \frac{dz}{dt}\frac{dx}{dt},
or, equivalently,
\frac{dz}{dt}=\frac{dz}{dx}\cdot \frac{dx}{dt},
which is also an application of the chain rule.
History
The chain rule seems to have first been used by Gottfried Wilhelm Leibniz.
He used it to calculate the derivative of \sqrt{a + bz + cz^2} as the composite of the square root function and the function a + bz + cz^2\!.
He first mentioned it in a 1676 memoir (with a sign error in the calculation).
The common notation of the chain rule is due to Leibniz.
Guillaume de l'Hôpital used the chain rule implicitly in his Analyse des infiniment petits.
The chain rule does not appear in any of Leonhard Euler's analysis books, even though they were written over a hundred years after Leibniz's discovery.
Statement
The simplest form of the chain rule is for real-valued functions of one real variable.
It states that if  is a function that is differentiable at a point  (i.e. the derivative  exists) and  is a function that is differentiable at , then the composite function f\circ g is differentiable at , and the derivative is
(f\circ g)'(c) = f'(g(c))\cdot g'(c).
The rule is sometimes abbreviated as
(f\circ g)' = (f'\circ g) \cdot g'.
If  and , then this abbreviated form is written in Leibniz notation as:
\frac{dy}{dx} = \frac{dy}{du} \cdot \frac{du}{dx}.
The points where the derivatives are evaluated may also be stated explicitly:
\left.
\frac{dy}{dx}\right|_{x=c} = \left.
\frac{dy}{du}\right|_{u = g(c)} \cdot \left.
\frac{du}{dx}\right|_{x=c}.
Carrying the same reasoning further, given  functions f_1, \ldots, f_n\!
with the composite function f_1 \circ ( f_2 \circ \cdots (f_{n-1} \circ f_n) )\!, if each function f_i\!
is differentiable at its immediate input, then the composite function is also differentiable by the repeated application of Chain Rule, where the derivative is (in Leibniz's notation):
\frac{df_1}{dx} = \frac{df_1}{df_2}\frac{df_2}{df_3}\cdots\frac{df_n}{dx}.
Applications
Composites of more than two functions
The chain rule can be applied to composites of more than two functions.
To take the derivative of a composite of more than two functions, notice that the composite of , , and  (in that order) is the composite of  with .
The chain rule states that to compute the derivative of , it is sufficient to compute the derivative of  and the derivative of .
The derivative of  can be calculated directly, and the derivative of  can be calculated by applying the chain rule again.
For concreteness, consider the function
y = e^{\sin (x^2)}.
This can be decomposed as the composite of three functions:
\begin{align} y &= f(u) = e^u, \\[6pt] u &= g(v) = \sin v = \sin(x^2), \\[6pt] v &= h(x) = x^2.
\end{align}
Their derivatives are:
\begin{align} \frac{dy}{du} &= f'(u) = e^u = e^{\sin(x^2)}, \\[6pt] \frac{du}{dv} &= g'(v) = \cos v = \cos(x^2), \\[6pt] \frac{dv}{dx} &= h'(x) = 2x.
\end{align}
The chain rule states that the derivative of their composite at the point  is:
\begin{align} (f \circ g \circ h)'(a) & = f'((g \circ h)(a))\cdot (g \circ h)'(a) \\[10pt] & = f'((g \circ h)(a)) \cdot g'(h(a)) \cdot h'(a) = (f' \circ g \circ h)(a) \cdot (g' \circ h)(a) \cdot h'(a).
\end{align}
In Leibniz notation, this is:
\frac{dy}{dx} = \left.
\frac{dy}{du}\right|_{u=g(h(a))}\cdot\left.
\frac{du}{dv}\right|_{v=h(a)}\cdot\left.
\frac{dv}{dx}\right|_{x=a},
or for short,
\frac{dy}{dx} = \frac{dy}{du}\cdot\frac{du}{dv}\cdot\frac{dv}{dx}.
The derivative function is therefore:
\frac{dy}{dx} = e^{\sin(x^2)}\cdot\cos(x^2)\cdot 2x.
Another way of computing this derivative is to view the composite function  as the composite of  and h.
Applying the chain rule in this manner would yield:
(f \circ g \circ h)'(a) = (f \circ g)'(h(a))\cdot h'(a) = f'(g(h(a)))\cdot g'(h(a))\cdot h'(a).
This is the same as what was computed above.
This should be expected because .
Sometimes, it is necessary to differentiate an arbitrarily long composition of the form f_1 \circ f_2 \circ \cdots \circ f_{n-1} \circ f_n\!.
In this case, define
f_{a\,.\,.\,b} = f_{a} \circ f_{a+1} \circ \cdots \circ f_{b-1} \circ f_{b}
where f_{a\,.\,.\,a} = f_a and f_{a\,.\,.\,b}(x) = x when b < a.
Then the chain rule takes the form
Df_{1\,.\,.\,n} = (Df_1 \circ f_{2\,.\,.\,n}) (Df_2 \circ f_{3\,.\,.\,n}) \cdots (Df_{n-1} \circ f_{n\,.\,.\,n}) Df_n = \prod_{k=1}^n \left[Df_k \circ f_{(k+1)\,.\,.\,n}\right]
or, in the Lagrange notation,
f_{1\,.\,.\,n}'(x) = f_1' \left( f_{2\,.\,.\,n}(x) \right) \; f_2' \left( f_{3\,.\,.\,n}(x) \right) \cdots f_{n-1}' \left(f_{n\,.\,.\,n}(x)\right) \; f_n'(x) = \prod_{k=1}^{n} f_k' \left(f_{(k+1\,.\,.\,n)}(x) \right)
Quotient rule
The chain rule can be used to derive some well-known differentiation rules.
For example, the quotient rule is a consequence of the chain rule and the product rule.
To see this, write the function  as the product .
First apply the product rule:
\begin{align} \frac{d}{dx}\left(\frac{f(x)}{g(x)}\right) &= \frac{d}{dx}\left(f(x)\cdot\frac{1}{g(x)}\right) \\ &= f'(x)\cdot\frac{1}{g(x)} + f(x)\cdot\frac{d}{dx}\left(\frac{1}{g(x)}\right).
\end{align}
To compute the derivative of , notice that it is the composite of  with the reciprocal function, that is, the function that sends  to .
The derivative of the reciprocal function is -1/x^2\!.
By applying the chain rule, the last expression becomes:
f'(x)\cdot\frac{1}{g(x)} + f(x)\cdot\left(-\frac{1}{g(x)^2}\cdot g'(x)\right) = \frac{f'(x) g(x) - f(x) g'(x)}{g(x)^2},
which is the usual formula for the quotient rule.
Derivatives of inverse functions
Suppose that  has an inverse function.
Call its inverse function  so that we have .
There is a formula for the derivative of  in terms of the derivative of .
To see this, note that  and  satisfy the formula
f(g(x)) = x.
And because the functions f(g(x)) and  are equal, their derivatives must be equal.
The derivative of  is the constant function with value 1, and the derivative of f(g(x)) is determined by the chain rule.
Therefore, we have that:
f'(g(x)) g'(x) = 1.
To express  as a function of an independent variable , we substitute f(y) for  wherever it appears.
Then we can solve for .
\begin{align} f'(g(f(y))) g'(f(y)) &= 1 \\[5pt] f'(y) g'(f(y)) &= 1 \\[5pt] f'(y) = \frac{1}{g'(f(y))}.
\end{align}
For example, consider the function .
It has an inverse .
Because , the above formula says that
\frac{d}{dy}\ln y = \frac{1}{e^{\ln y}} = \frac{1}{y}.
This formula is true whenever  is differentiable and its inverse  is also differentiable.
This formula can fail when one of these conditions is not true.
For example, consider .
Its inverse is , which is not differentiable at zero.
If we attempt to use the above formula to compute the derivative of  at zero, then we must evaluate .
Since  and , we must evaluate 1/0, which is undefined.
Therefore, the formula fails in this case.
This is not surprising because  is not differentiable at zero.
Higher derivatives
Faà di Bruno's formula generalizes the chain rule to higher derivatives.
Assuming that  and , then the first few derivatives are:
\begin{align} \frac{dy}{dx} & = \frac{dy}{du} \frac{du}{dx} \\[4pt] \frac{d^2 y }{d x^2} & = \frac{d^2 y}{d u^2} \left(\frac{du}{dx}\right)^2     + \frac{dy}{du} \frac{d^2 u}{dx^2} \\[4pt] \frac{d^3 y }{d x^3} & = \frac{d^3 y}{d u^3} \left(\frac{du}{dx}\right)^3     + 3 \, \frac{d^2 y}{d u^2} \frac{du}{dx} \frac{d^2 u}{d x^2}     + \frac{dy}{du} \frac{d^3 u}{d x^3} \\[4pt] \frac{d^4 y}{d x^4} & =\frac{d^4 y}{du^4} \left(\frac{du}{dx}\right)^4     + 6 \, \frac{d^3 y}{d u^3} \left(\frac{du}{dx}\right)^2 \frac{d^2 u}{d x^2}     + \frac{d^2 y}{d u^2} \left( 4 \, \frac{du}{dx} \frac{d^3 u}{dx^3}     + 3 \, \left(\frac{d^2 u}{dx^2}\right)^2\right)     + \frac{dy}{du} \frac{d^4 u}{dx^4}.
\end{align}
Proofs
First proof
One proof of the chain rule begins with the definition of the derivative:
(f \circ g)'(a) = \lim_{x \to a} \frac{f(g(x)) - f(g(a))}{x - a}.
Assume for the moment that g(x)\!
does not equal g(a) for any  near .
Then the previous expression is equal to the product of two factors:
\lim_{x \to a} \frac{f(g(x)) - f(g(a))}{g(x) - g(a)} \cdot \frac{g(x) - g(a)}{x - a}.
If g oscillates near , then it might happen that no matter how close one gets to , there is always an even closer  such that .
For example, this happens near  for the continuous function  defined by  for  and  otherwise.
Whenever this happens, the above expression is undefined because it involves division by zero.
To work around this, introduce a function Q as follows:
Q(y) = \begin{cases} \displaystyle\frac{f(y) - f(g(a))}{y - g(a)}, & y \neq g(a), \\ f'(g(a)), & y = g(a).
\end{cases}
We will show that the difference quotient for  is always equal to:
Q(g(x)) \cdot \frac{g(x) - g(a)}{x - a}.
Whenever  is not equal to , this is clear because the factors of  cancel.
When  equals , then the difference quotient for  is zero because  equals , and the above product is zero because it equals  times zero.
So the above product is always equal to the difference quotient, and to show that the derivative of  at  exists and to determine its value, we need only show that the limit as  goes to  of the above product exists and determine its value.
To do this, recall that the limit of a product exists if the limits of its factors exist.
When this happens, the limit of the product of these two factors will equal the product of the limits of the factors.
The two factors are  and .
The latter is the difference quotient for  at , and because  is differentiable at  by assumption, its limit as  tends to  exists and equals .
As for , notice that  is defined wherever  is.
Furthermore,  is differentiable at  by assumption, so  is continuous at , by definition of the derivative.
The function  is continuous at  because it is differentiable at , and therefore  is continuous at .
So its limit as  goes to  exists and equals , which is .
This shows that the limits of both factors exist and that they equal  and , respectively.
Therefore, the derivative of  at a exists and equals .
Second proof
Another way of proving the chain rule is to measure the error in the linear approximation determined by the derivative.
This proof has the advantage that it generalizes to several variables.
It relies on the following equivalent definition of differentiability at a point: A function g is differentiable at a if there exists a real number g′(a) and a function ε(h) that tends to zero as h tends to zero, and furthermore
g(a + h) - g(a) = g'(a) h + \varepsilon(h) h.
Here the left-hand side represents the true difference between the value of g at a and at , whereas the right-hand side represents the approximation determined by the derivative plus an error term.
In the situation of the chain rule, such a function ε exists because g is assumed to be differentiable at a.
Again by assumption, a similar function also exists for f at g(a).
Calling this function η, we have
f(g(a) + k) - f(g(a)) = f'(g(a)) k + \eta(k) k.
The above definition imposes no constraints on η(0), even though it is assumed that η(k) tends to zero as k tends to zero.
If we set , then η is continuous at 0.
Proving the theorem requires studying the difference  as h tends to zero.
The first step is to substitute for  using the definition of differentiability of g at a:
f(g(a + h)) - f(g(a)) = f(g(a) + g'(a) h + \varepsilon(h) h) - f(g(a)).
The next step is to use the definition of differentiability of f at g(a).
This requires a term of the form  for some k.
In the above equation, the correct k varies with h.
Set  and the right hand side becomes .
Applying the definition of the derivative gives:
f(g(a) + k_h) - f(g(a)) = f'(g(a)) k_h + \eta(k_h) k_h.
To study the behavior of this expression as h tends to zero, expand kh.
After regrouping the terms, the right-hand side becomes:
f'(g(a)) g'(a)h + [f'(g(a)) \varepsilon(h) + \eta(k_h) g'(a) + \eta(k_h) \varepsilon(h)] h.
Because ε(h) and η(kh) tend to zero as h tends to zero, the first two bracketed terms tend to zero as h tends to zero.
Applying the same theorem on products of limits as in the first proof, the third bracketed term also tends zero.
Because the above expression is equal to the difference , by the definition of the derivative  is differentiable at a and its derivative is
The role of Q in the first proof is played by η in this proof.
They are related by the equation:
Q(y) = f'(g(a)) + \eta(y - g(a)).
The need to define Q at g(a) is analogous to the need to define η at zero.
Third proof
Constantin Carathéodory's alternative definition of the differentiability of a function can be used to give an elegant proof of the chain rule.
Under this definition, a function  is differentiable at a point  if and only if there is a function , continuous at  and such that .
There is at most one such function, and if  is differentiable at  then .
Given the assumptions of the chain rule and the fact that differentiable functions and compositions of continuous functions are continuous, we have that there exist functions , continuous at , and , continuous at , and such that,
f(g(x))-f(g(a))=q(g(x))(g(x)-g(a))
and
g(x)-g(a)=r(x)(x-a).
Therefore,
f(g(x))-f(g(a))=q(g(x))r(x)(x-a),
but the function given by  is continuous at , and we get, for this
(f(g(a)))'=q(g(a))r(a)=f'(g(a))g'(a).
A similar approach works for continuously differentiable (vector-)functions of many variables.
This method of factoring also allows a unified approach to stronger forms of differentiability, when the derivative is required to be Lipschitz continuous, Hölder continuous, etc.
Differentiation itself can be viewed as the polynomial remainder theorem (the little Bézout theorem, or factor theorem), generalized to an appropriate class of functions.
Proof via infinitesimals
If y=f(x) and x=g(t) then choosing infinitesimal \Delta t\not=0 we compute the corresponding \Delta x=g(t+\Delta t)-g(t) and then the corresponding \Delta y=f(x+\Delta x)-f(x), so that
\frac{\Delta y}{\Delta t}=\frac{\Delta y}{\Delta x} \frac{\Delta x}{\Delta t}
and applying the standard part we obtain
\frac{d y}{d t}=\frac{d y}{d x} \frac{dx}{dt}
which is the chain rule.
Multivariable case
The generalization of the chain rule to multi-variable functions is rather technical.
However, it is simpler to write in the case of functions of the form
f(g_1(x), \dots, g_k(x)).
As this case occurs often in the study of functions of a single variable, it is worth describing it separately.
Case of {{math|''f''(''g''{{sub|1}}(''x''), ... , ''g''{{sub|''k''}}(''x''))}}
For writing the chain rule for a function of the form
,
one needs the partial derivatives of  with respect to its  arguments.
The usual notations for partial derivatives involve names for the arguments of the function.
As these arguments are not named in the above formula, it is simpler and clearer to denote by
D_i f
the derivative of  with respect to its th argument, and by
D_i f(z)
the value of this derivative at .
With this notation, the chain rule is
\frac{d}{dx}f(g_1(x), \dots, g_k (x))=\sum_{i=1}^k  \left(\frac{d}{dx}{g_i}(x)\right) D_i f(g_1(x), \dots, g_k (x)).
Example: arithmetic operations
If the function  is addition, that is, if
f(u,v)=u+v,
then D_1 f = \frac{\partial f}{\partial u} = 1 and D_2 f = \frac{\partial f}{\partial v} = 1.
Thus, the chain rule gives
\frac{d}{dx}(g(x)+h(x)) = \left( \frac{d}{dx}g(x) \right) D_1 f+\left( \frac{d}{dx}h(x)\right) D_2 f=\frac{d}{dx}g(x) +\frac{d}{dx}h(x).
For multiplication
f(u,v)=uv,
the partials are D_1 f = v and D_2 f = u.
Thus,
\frac{d}{dx}(g(x)h(x)) = h(x) \frac{d}{dx} g(x) + g(x) \frac{d}{dx} h(x).
The case of exponentiation
f(u,v)=u^v
is slightly more complicated, as
D_1 f = vu^{v-1},
and, as u^v=e^{v\ln u},
D_2 f = u^v\ln u.
It follows that
\frac{d}{dx}\left(g(x)^{h(x)}\right) = h(x)g(x)^{h(x)-1} \frac{d}{dx}g(x) + g(x)^{h(x)} \ln g(x) \frac{d}{dx}h(x).
General rule
The simplest way for writing the chain rule in the general case is to use the total derivative, which is a linear transformation that captures all directional derivatives in a single formula.
Consider differentiable functions  and , and a point  in .
Let  denote the total derivative of  at  and  denote the total derivative of  at .
These two derivatives are linear transformations  and , respectively, so they can be composed.
The chain rule for total derivatives is that their composite is the total derivative of  at :
D_{\mathbf{a}}(f \circ g) = D_{g(\mathbf{a})}f \circ D_{\mathbf{a}}g,
or for short,
D(f \circ g) = Df \circ Dg.
The higher-dimensional chain rule can be proved using a technique similar to the second proof given above.
Because the total derivative is a linear transformation, the functions appearing in the formula can be rewritten as matrices.
The matrix corresponding to a total derivative is called a Jacobian matrix, and the composite of two derivatives corresponds to the product of their Jacobian matrices.
From this perspective the chain rule therefore says:
J_{f \circ g}(\mathbf{a}) = J_{f}(g(\mathbf{a})) J_{g}(\mathbf{a}),
or for short,
J_{f \circ g} = (J_f \circ g)J_g.
That is, the Jacobian of a composite function is the product of the Jacobians of the composed functions (evaluated at the appropriate points).
The higher-dimensional chain rule is a generalization of the one-dimensional chain rule.
If k, m, and n are 1, so that  and , then the Jacobian matrices of f and g are .
Specifically, they are:
\begin{align} J_g(a) &= \begin{pmatrix} g'(a) \end{pmatrix}, \\ J_{f}(g(a)) &= \begin{pmatrix} f'(g(a)) \end{pmatrix}.
\end{align}
The Jacobian of f ∘ g is the product of these  matrices, so it is , as expected from the one-dimensional chain rule.
In the language of linear transformations, Da(g) is the function which scales a vector by a factor of g′(a) and Dg(a)(f) is the function which scales a vector by a factor of f′(g(a)).
The chain rule says that the composite of these two linear transformations is the linear transformation , and therefore it is the function that scales a vector by f′(g(a))⋅g′(a).
Another way of writing the chain rule is used when f and g are expressed in terms of their components as  and .
In this case, the above rule for Jacobian matrices is usually written as:
\frac{\partial(y_1, \ldots, y_k)}{\partial(x_1, \ldots, x_n)} = \frac{\partial(y_1, \ldots, y_k)}{\partial(u_1, \ldots, u_m)} \frac{\partial(u_1, \ldots, u_m)}{\partial(x_1, \ldots, x_n)}.
The chain rule for total derivatives implies a chain rule for partial derivatives.
Recall that when the total derivative exists, the partial derivative in the ith coordinate direction is found by multiplying the Jacobian matrix by the ith basis vector.
By doing this to the formula above, we find:
\frac{\partial(y_1, \ldots, y_k)}{\partial x_i} = \frac{\partial(y_1, \ldots, y_k)}{\partial(u_1, \ldots, u_m)} \frac{\partial(u_1, \ldots, u_m)}{\partial x_i}.
Since the entries of the Jacobian matrix are partial derivatives, we may simplify the above formula to get:
\frac{\partial(y_1, \ldots, y_k)}{\partial x_i} = \sum_{\ell = 1}^m \frac{\partial(y_1, \ldots, y_k)}{\partial u_\ell} \frac{\partial u_\ell}{\partial x_i}.
More conceptually, this rule expresses the fact that a change in the xi direction may change all of g1 through gm, and any of these changes may affect f.
In the special case where , so that f is a real-valued function, then this formula simplifies even further:
\frac{\partial y}{\partial x_i} = \sum_{\ell = 1}^m \frac{\partial y}{\partial u_\ell} \frac{\partial u_\ell}{\partial x_i}.
This can be rewritten as a dot product.
Recalling that , the partial derivative  is also a vector, and the chain rule says that:
\frac{\partial y}{\partial x_i} = \nabla y \cdot \frac{\partial \mathbf{u}}{\partial x_i}.
Example
Given  where  and , determine the value of  and  using the chain rule.
\frac{\partial u}{\partial r}=\frac{\partial u}{\partial x} \frac{\partial x}{\partial r}+\frac{\partial u}{\partial y} \frac{\partial y}{\partial r} = (2x)(\sin(t)) + (2)(0) = 2r \sin^2(t),
and
\begin{align}\frac{\partial u}{\partial t} &= \frac{\partial u}{\partial x} \frac{\partial x}{\partial t}+\frac{\partial u}{\partial y} \frac{\partial y}{\partial t} \\ &= (2x)(r\cos(t)) + (2)(2\sin(t)\cos(t)) \\ &= (2r\sin(t))(r\cos(t)) + 4\sin(t)\cos(t) \\ &= 2(r^2 + 2) \sin(t)\cos(t) \\ &= (r^2 + 2) \sin(2t).
\end{align}
Higher derivatives of multivariable functions
Faà di Bruno's formula for higher-order derivatives of single-variable functions generalizes to the multivariable case.
If  is a function of  as above, then the second derivative of  is:
\frac{\partial^2 y}{\partial x_i \partial x_j} = \sum_k \left(\frac{\partial y}{\partial u_k}\frac{\partial^2 u_k}{\partial x_i \partial x_j}\right) + \sum_{k, \ell} \left(\frac{\partial^2 y}{\partial u_k \partial u_\ell}\frac{\partial u_k}{\partial x_i}\frac{\partial u_\ell}{\partial x_j}\right).
Further generalizations
All extensions of calculus have a chain rule.
In most of these, the formula remains the same, though the meaning of that formula may be vastly different.
One generalization is to manifolds.
In this situation, the chain rule represents the fact that the derivative of  is the composite of the derivative of f and the derivative of g.
This theorem is an immediate consequence of the higher dimensional chain rule given above, and it has exactly the same formula.
The chain rule is also valid for Fréchet derivatives in Banach spaces.
The same formula holds as before.
This case and the previous one admit a simultaneous generalization to Banach manifolds.
In differential algebra, the derivative is interpreted as a morphism of modules of Kähler differentials.
A ring homomorphism of commutative rings  determines a morphism of Kähler differentials  which sends an element dr to d(f(r)), the exterior differential of f(r).
The formula  holds in this context as well.
The common feature of these examples is that they are expressions of the idea that the derivative is part of a functor.
A functor is an operation on spaces and functions between them.
It associates to each space a new space and to each function between two spaces a new function between the corresponding new spaces.
In each of the above cases, the functor sends each space to its tangent bundle and it sends each function to its derivative.
For example, in the manifold case, the derivative sends a Cr-manifold to a Cr−1-manifold (its tangent bundle) and a Cr-function to its total derivative.
There is one requirement for this to be a functor, namely that the derivative of a composite must be the composite of the derivatives.
This is exactly the formula .
There are also chain rules in stochastic calculus.
One of these, Itō's lemma, expresses the composite of an Itō process (or more generally a semimartingale) dXt with a twice-differentiable function f.
In Itō's lemma, the derivative of the composite function depends not only on dXt and the derivative of f but also on the second derivative of f.
The dependence on the second derivative is a consequence of the non-zero quadratic variation of the stochastic process, which broadly speaking means that the process can move up and down in a very rough way.
This variant of the chain rule is not an example of a functor because the two functions being composed are of different types.
See also
Integration by substitution
Leibniz integral rule
Quotient rule
Triple product rule
Product rule
Automatic differentiation, a computational method that makes heavy use of the chain rule to compute exact numerical derivatives.
References
External links
