In calculus, the differential represents the principal part of the change in a function with respect to changes in the independent variable. The differential is defined by
where is the derivative of with respect to , and is an additional real variable (so that is a function of and ). The notation is such that the equation
holds, where the derivative is represented in the Leibniz notation , and this is consistent with regarding the derivative as the quotient of the differentials. One also writes
The precise meaning of the variables and depends on the context of the application and the required level of mathematical rigor. The domain of these variables may take on a particular geometrical significance if the differential is regarded as a particular differential form, or analytical significance if the differential is regarded as a linear approximation to the increment of a function. Traditionally, the variables and are considered to be very small (infinitesimal), and this interpretation is made rigorous in non-standard analysis.
History and usage The differential was first introduced via an intuitive or heuristic definition by Isaac Newton and furthered by Gottfried Leibniz, who thought of the differential as an infinitely small (or infinitesimal) change in the value of the function, corresponding to an infinitely small change in the function's argument . For that reason, the instantaneous rate of change of with respect to , which is the value of the derivative of the function, is denoted by the fraction
in what is called the Leibniz notation for derivatives. The quotient is not infinitely small; rather it is a real number.
The use of infinitesimals in this form was widely criticized, for instance by the famous pamphlet The Analyst by Bishop Berkeley. Augustin-Louis Cauchy (1823) defined the differential without appeal to the atomism of Leibniz's infinitesimals.For a detailed historical account of the differential, see , especially page 275 for Cauchy's contribution on the subject. An abbreviated account appears in .Cauchy explicitly denied the possibility of actual infinitesimal and infinite quantities , and took the radically different point of view that "a variable quantity becomes infinitely small when its numerical value decreases indefinitely in such a way as to converge to zero" (; translation from ). Instead, Cauchy, following d'Alembert, inverted the logical order of Leibniz and his successors: the derivative itself became the fundamental object, defined as a limit of difference quotients, and the differentials were then defined in terms of it. That is, one was free to define the differential by an expression
in which and are simply new variables taking finite real values, not fixed infinitesimals as they had been for Leibniz.: "The differentials as thus defined are only new variables, and not fixed infinitesimals..."
According to , Cauchy's approach was a significant logical improvement over the infinitesimal approach of Leibniz because, instead of invoking the metaphysical notion of infinitesimals, the quantities and could now be manipulated in exactly the same manner as any other real quantities in a meaningful way. Cauchy's overall conceptual approach to differentials remains the standard one in modern analytical treatments,: "Here we remark merely in passing that it is possible to use this approximate representation of the increment by the linear expression to construct a logically satisfactory definition of a "differential", as was done by Cauchy in particular." although the final word on rigor, a fully modern notion of the limit, was ultimately due to Karl Weierstrass.
In physical treatments, such as those applied to the theory of thermodynamics, the infinitesimal view still prevails. reconcile the physical use of infinitesimal differentials with the mathematical impossibility of them as follows. The differentials represent finite non-zero values that are smaller than the degree of accuracy required for the particular purpose for which they are intended. Thus "physical infinitesimals" need not appeal to a corresponding mathematical infinitesimal in order to have a precise sense.
Following twentieth-century developments in mathematical analysis and differential geometry, it became clear that the notion of the differential of a function could be extended in a variety of ways. In real analysis, it is more desirable to deal directly with the differential as the principal part of the increment of a function. This leads directly to the notion that the differential of a function at a point is a linear functional of an increment . This approach allows the differential (as a linear map) to be developed for a variety of more sophisticated spaces, ultimately giving rise to such notions as the Fréchet or Gateaux derivative. Likewise, in differential geometry, the differential of a function at a point is a linear function of a tangent vector (an "infinitely small displacement"), which exhibits it as a kind of one-form: the exterior derivative of the function. In non-standard calculus, differentials are regarded as infinitesimals, which can themselves be put on a rigorous footing (see differential (infinitesimal)).
Definition
The differential is defined in modern treatments of differential calculus as follows.See, for instance, the influential treatises of , , , and . Tertiary sources for this definition include also and . The differential of a function of a single real variable is the function of two independent real variables and given by
One or both of the arguments may be suppressed, i.e., one may see or simply . If , the differential may also be written as . Since , it is conventional to write so that the following equality holds:
This notion of differential is broadly applicable when a linear approximation to a function is sought, in which the value of the increment is small enough. More precisely, if is a differentiable function at , then the difference in -values
satisfies
where the error in the approximation satisfies as . In other words, one has the approximate identity
in which the error can be made as small as desired relative to by constraining to be sufficiently small; that is to say,
as . For this reason, the differential of a function is known as the principal (linear) part in the increment of a function: the differential is a linear function of the increment , and although the error may be nonlinear, it tends to zero rapidly as tends to zero.
Differentials in several variables +Operator / FunctionDifferential1: 2:
3: Partial derivativeTotal derivative Following , for functions of more than one independent variable,
the partial differential of with respect to any one of the variables is the principal part of the change in resulting from a change in that one variable. The partial differential is therefore
involving the partial derivative of with respect to . The sum of the partial differentials with respect to all of the independent variables is the total differential
which is the principal part of the change in resulting from changes in the independent variables .
More precisely, in the context of multivariable calculus, following , if is a differentiable function, then by the definition of differentiability, the increment
where the error terms tend to zero as the increments jointly tend to zero. The total differential is then rigorously defined as
Since, with this definition,
one has
As in the case of one variable, the approximate identity holds
in which the total error can be made as small as desired relative to by confining attention to sufficiently small increments.
Application of the total differential to error estimation In measurement, the total differential is used in estimating the error of a function based on the errors of the parameters . Assuming that the interval is short enough for the change to be approximately linear:
and that the parameters are functionally independent, so that the total differential applies, the change in induced by simultaneous changes in all parameters is
The derivative with respect to the parameter gives the sensitivity of the function to a change in , in particular to the error . How the individual errors combine into a bound on depends on what is known about them.
If the errors are known only as bounds on magnitude (i.e., , etc.) and may have arbitrary signs, the corresponding worst-case bound on is obtained by taking absolute values, since signed contributions could otherwise cancel:
If instead the errors are modelled as statistically independent random variables with standard deviations , then by propagation of variance the standard deviation of adds in quadrature:
The first formula is a deterministic upper bound, the second a typical (one-standard-deviation) estimate; the two coincide only in the trivial single-variable case.
From the worst-case principle the familiar error rules of summation, multiplication, etc. are derived, e.g.:
That is to say, in multiplication, the total relative error is the sum of the relative errors of the parameters.
To illustrate how this depends on the function considered, consider the case where the function is instead. Then, it can be computed that the error estimate is
with an extra factor not found in the case of a simple product. This additional factor tends to make the error smaller, as the denominator is larger than a bare .
Higher-order differentials Higher-order differentials of a function of a single variable can be defined via:. See also, for instance, .
and, in general,
Informally, this motivates Leibniz's notation for higher-order derivatives
When the independent variable itself is permitted to depend on other variables, then the expression becomes more complicated, as it must include also higher order differentials in itself. Thus, for instance,
and so forth.
Similar considerations apply to defining higher order differentials of functions of several variables. For example, if is a function of two variables and , then
where is a binomial coefficient. In more variables, an analogous expression holds, but with an appropriate multinomial expansion rather than binomial expansion.
Higher order differentials in several variables also become more complicated when the independent variables are themselves allowed to depend on other variables. For instance, for a function of and which are allowed to depend on auxiliary variables, one has
Because of this notational awkwardness, the use of higher order differentials was roundly criticized by , who concluded:
That is: Finally, what is meant, or represented, by the equality [...]? In my opinion, nothing at all. In spite of this skepticism, higher order differentials did emerge as an important tool in analysis.In particular to infinite dimensional holomorphy and numerical analysis via the calculus of finite differences.
In these contexts, the -th order differential of the function applied to an increment is defined by
or an equivalent expression, such as
where is an nth forward difference with increment .
This definition makes sense as well if is a function of several variables (for simplicity taken here as a vector argument). Then the -th differential defined in this way is a homogeneous function of degree in the vector increment . Furthermore, the Taylor series of at the point is given by
The higher order Gateaux derivative generalizes these considerations to infinite dimensional spaces.
Properties A number of properties of the differential follow in a straightforward manner from the corresponding properties of the derivative, partial derivative, and total derivative. These include:
Linearity: For constants and and differentiable functions and , Product rule: For two differentiable functions and ,
An operation with these two properties is known in abstract algebra as a derivation. They imply the power rule