Differential of a function
Introduction 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 bywhere  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 equationholds, 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 writesThe 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 fractionin 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. 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 expressionin which  and  are simply new variables taking finite real values, not fixed infinitesimals as they had been for Leibniz.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 quantitiesin a meaningful way. Cauchy's overall conceptual approach to differentials remains the standard one in modern analytical treatments, 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 of a function  at a point .The differential is defined in modern treatments of differential calculus as follows. The differential of a function  of a single real variable  is the function  of two independent real variables  and  given byOne 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 -valuessatisfieswhere the error  in the approximation satisfies  as . In other words, one has the approximate identityin 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 derivativeFollowing , 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 thereforeinvolving 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 incrementwhere the error terms  tend to zero as the increments  jointly tend to zero. The total differential is then rigorously defined asSince, with this definition,one hasAs in the case of one variable, the approximate identity holdsin 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 all variables are independent, then for all variables,This is because the derivative  with respect to the particular parameter  gives the sensitivity of the function  to a change in , in particular the error . As they are assumed to be independent, the analysis describes the worst-case scenario. The absolute values of the component errors are used, because after simple computation, the derivative may have a negative sign. From this principle the 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 iswith an extra '' factor not found in the case of a simple product. This additional factor tends to make the error smaller, as  is not as large as a bare .
Higher-order differentials Higher-order differentials of a function  of a single variable  can be defined via:and, in general,Informally, this motivates Leibniz's notation for higher-order derivativesWhen 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 , thenwhere  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 hasBecause 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 these contexts, the -th order differential of the function  applied to an increment  is defined byor an equivalent expression, such aswhere  is an ''n''th 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 byThe 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 ruleIn addition, various forms of the chain rule hold, in increasing level of generality:* If  is a differentiable function of the variable  and  is a differentiable function of , then * If  and all of the variables  depend on another variable , then by the chain rule for partial derivatives, one has  Heuristically, the chain rule for several variables can itself be understood by dividing through both sides of this equation by the infinitely small quantity . * More general analogous expressions hold, in which the intermediate variables  depend on more than one variable.
General formulation A consistent notion of differential can be developed for a function  between two Euclidean spaces. Let  be a pair of Euclidean vectors. The increment in the function  isIf there exists an  matrix  such thatin which the vector  as , then  is by definition differentiable at the point . The matrix  is sometimes known as the Jacobian matrix, and the linear transformation that associates to the increment  the vector  is, in this general setting, known as the differential  of  at the point . This is precisely the Fréchet derivative, and the same construction can be made to work for a function between any Banach spaces.Another fruitful point of view is to define the differential directly as a kind of directional derivative:which is the approach already taken for defining higher order differentials (and is most nearly the definition set forth by Cauchy). If  represents time and '''x''' position, then '''h''' represents a velocity instead of a displacement as we have heretofore regarded it. This yields yet another refinement of the notion of differential: that it should be a linear function of a kinematic velocity. The set of all velocities through a given point of space is known as the tangent space, and so  gives a linear function on the tangent space: a differential form. With this interpretation, the differential of  is known as the exterior derivative, and has broad application in differential geometry because the notion of velocities and the tangent space makes sense on any differentiable manifold. If, in addition, the output value of  also represents a position (in a Euclidean space), then a dimensional analysis confirms that the output value of ''df'' must be a velocity. If one treats the differential in this manner, then it is known as the pushforward since it "pushes" velocities from a source space into velocities in a target space.
Other approaches Although the notion of having an infinitesimal increment  is not well-defined in modern mathematical analysis, a variety of techniques exist for defining the infinitesimal differential so that the differential of a function can be handled in a manner that does not clash with the Leibniz notation. These include:* Defining the differential as a kind of differential form, specifically the exterior derivative of a function. The infinitesimal increments are then identified with vectors in the tangent space at a point. This approach is popular in differential geometry and related fields, because it readily generalizes to mappings between differentiable manifolds. * Differentials as nilpotent elements of commutative rings. This approach is popular in algebraic geometry. * Differentials in smooth models of set theory. This approach is known as synthetic differential geometry or smooth infinitesimal analysis and is closely related to the algebraic geometric approach, except that ideas from topos theory are used to ''hide'' the mechanisms by which nilpotent infinitesimals are introduced. * Differentials as infinitesimals in hyperreal number systems, which are extensions of the real numbers which contain invertible infinitesimals and infinitely large numbers. This is the approach of nonstandard analysis pioneered by Abraham Robinson.
Examples and applications Differentials may be effectively used in numerical analysis to study the propagation of experimental errors in a calculation, and thus the overall numerical stability of a problem . Suppose that the variable  represents the outcome of an experiment and  is the result of a numerical computation applied to ''x''. The question is to what extent errors in the measurement of  influence the outcome of the computation of ''y''. If the  is known to within Δ''x'' of its true value, then Taylor's theorem gives the following estimate on the error Δ''y'' in the computation of ''y'':where  for some . If  is small, then the second order term is negligible, so that Δ''y'' is, for practical purposes, well-approximated by .The differential is often useful to rewrite a differential equationin the formin particular when one wants to separate the variables.
Notes
See also * Notation for differentiation
References *.*.*.*. ** . *.*.*.*.*.*.*. ** . * . * . * . *.
External links * Differential Of A Function at Wolfram Demonstrations Project