In mathematics, differential refers to several related notions derived from the early days of calculus, put on a rigorous footing, such as infinitesimal differences and the derivatives of functions.
The term is used in various branches of mathematics such as calculus, differential geometry, algebraic geometry and algebraic topology.
Introduction
The term differential is used nonrigorously in calculus to refer to an infinitesimal ("infinitely small") change in some varying quantity. For example, if x is a variable, then a change in the value of x is often denoted Δx (pronounced delta x). The differential dx represents an infinitely small change in the variable x. The idea of an infinitely small or infinitely slow change is, intuitively, extremely useful, and there are a number of ways to make the notion mathematically precise.
Using calculus, it is possible to relate the infinitely small changes of various variables to each other mathematically using derivatives. If y is a function of x, then the differential dy of y is related to dx by the formula
where denotes not 'dy divided by dx' as one would intuitively read, but 'the derivative of y with respect to x '. This formula summarizes the idea that the derivative of y with respect to x is the limit of the ratio of differences Δy/Δx as Δx approaches zero.
Basic notions
In calculus, the differential represents a change in the linearization of a function. The total differential is its generalization for functions of multiple variables. In traditional approaches to calculus, differentials (e.g. dx, dy, dt, etc.) are interpreted as infinitesimals. There are several methods of defining infinitesimals rigorously, but it is sufficient to say that an infinitesimal number is smaller in absolute value than any positive real number, just as an infinitely large number is larger than any real number. The differential is another name for the Jacobian matrix of partial derivatives of a function from Rn to Rm (especially when this matrix is viewed as a linear map). More generally, the differential or pushforward refers to the derivative of a map between smooth manifolds and the pushforward operations it defines. The differential is also used to define the dual concept of pullback. Stochastic calculus provides a notion of stochastic differential and an associated calculus for stochastic processes. The integrator in a Stieltjes integral is represented as the differential of a function. Formally, the differential appearing under the integral behaves exactly as a differential: thus, the integration by substitution and integration by parts formulae for Stieltjes integral correspond, respectively, to the chain rule and product rule for the differential.
History and usage
Infinitesimal quantities played a significant role in the development of calculus. Archimedes used them, even though he did not believe that arguments involving infinitesimals were rigorous.. Isaac Newton referred to them as fluxions. However, it was Gottfried Leibniz who coined the term differentials for infinitesimal quantities and introduced the notation for them which is still used today.
In Leibniz's notation, if x is a variable quantity, then dx denotes an infinitesimal change in the variable x. Thus, if y is a function of x, then the derivative of y with respect to x is often denoted dy/dx, which would otherwise be denoted (in the notation of Newton or Lagrange) ẏ or y. The use of differentials in this form attracted much criticism, for instance in the famous pamphlet The Analyst by Bishop Berkeley. Nevertheless, the notation has remained popular because it suggests strongly the idea that the derivative of y at x is its instantaneous rate of change (the slope of the graph's tangent line), which may be obtained by taking the limit of the ratio Δy/Δx as Δx becomes arbitrarily small. Differentials are also compatible with dimensional analysis, where a differential such as dx has the same dimensions as the variable x.
Calculus evolved into a distinct branch of mathematics during the 17th century CE, although there were antecedents going back to antiquity. The presentations of, e.g., Newton, Leibniz, were marked by non-rigorous definitions of terms like differential, fluent and "infinitely small". While many of the arguments in Bishop Berkeley's 1734 The Analyst are theological in nature, modern mathematicians acknowledge the validity of his argument against "the Ghosts of departed Quantities"; however, the modern approaches do not have the same technical issues. Despite the lack of rigor, immense progress was made in the 17th and 18th centuries. In the 19th century, Cauchy and others gradually developed the epsilon-delta approach to continuity, limits and derivatives, giving a solid conceptual foundation for calculus.
In the 20th century, several new concepts in, e.g., multivariable calculus, differential geometry, seemed to encapsulate the intent of the old terms, especially differential; both differential and infinitesimal are used with new, more rigorous, meanings.
Differentials are also used in the notation for integrals because an integral can be regarded as an infinite sum of infinitesimal quantities: the area under a graph is obtained by subdividing the graph into infinitely thin strips and summing their areas. In an expression such as
the integral sign (which is a modified long s) denotes the infinite sum, f(x) denotes the "height" of a thin strip, and the differential dx denotes its infinitely thin width.
Approaches
There are several approaches for making the notion of differentials mathematically precise. Differentials as linear maps. This approach underlies the definition of the derivative and the exterior derivative in differential geometry.. 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.See and . Differentials as infinitesimals in hyperreal number systems, which are extensions of the real numbers that contain invertible infinitesimals and infinitely large numbers. This is the approach of nonstandard analysis pioneered by Abraham Robinson.See and .
These approaches are very different from each other, but they have in common the idea of being quantitative, i.e., saying not just that a differential is infinitely small, but how small it is.
Differentials as linear maps There is a simple way to make precise sense of differentials, first used on the Real line by regarding them as linear maps. It can be used on , , a Hilbert space, a Banach space, or more generally, a topological vector space. The case of the Real line is the easiest to explain. This type of differential is also known as a covariant vector or cotangent vector, depending on context.
Differentials as linear maps on R
Suppose is a real-valued function on . We can reinterpret the variable in as being a function rather than a number, namely the identity map on the real line, which takes a real number to itself: . Then is the composite of with , whose value at is . The differential (which of course depends on ) is then a function whose value at (usually denoted ) is not a number, but a linear map from to . Since a linear map from to is given by a matrix, it is essentially the same thing as a number, but the change in the point of view allows us to think of as an infinitesimal and compare it with the standard infinitesimal , which is again just the identity map from to (a matrix with entry ). The identity map has the property that if is very small, then is very small, which enables us to regard it as infinitesimal. The differential has the same property, because it is just a multiple of , and this multiple is the derivative by definition. We therefore obtain that , and hence . Thus we recover the idea that is the ratio of the differentials and .
This would just be a trick were it not for the fact that: it captures the idea of the derivative of at as the best linear approximation to at ; it has many generalizations.
Differentials as linear maps on Rn
If is a function from to , then we say that is differentiableSee, for instance, . at if there is a linear map from to such that for any , there is a neighbourhood of such that for ,
We can now use the same trick as in the one-dimensional case and think of the expression as the composite of with the standard coordinates on (so that is the -th component of ). Then the differentials at a point form a basis for the vector space of linear maps from to and therefore, if is differentiable at , we can write as a linear combination of these basis elements:
The coefficients are (by definition) the partial derivatives of at with respect to . Hence, if is differentiable on all of , we can write, more concisely:
In the one-dimensional case this becomes
as before.
This idea generalizes straightforwardly to functions from to . Furthermore, it has the decisive advantage over other definitions of the derivative that it is invariant under changes of coordinates. This means that the same idea can be used to define the differential of smooth maps between smooth manifolds.
Aside: Note that the existence of all the partial derivatives of at is a necessary condition for the existence of a differential at . However it is not a sufficient condition. For counterexamples, see Gateaux derivative.
Differentials as linear maps on a vector space
The same procedure works on a vector space with enough additional structure to reasonably talk about continuity. The most concrete case is a Hilbert space, also known as a complete inner product space, where the inner product and its associated norm define a suitable concept of distance. The same procedure works for a Banach space, also known as a complete normed vector space. However, for a more general topological vector space, some of the details are more abstract because there is no concept of distance.
For the important case of a finite dimension, any inner product space is a Hilbert space, any normed vector space is a Banach space and any topological vector space is complete. As a result, we can define a coordinate system from an arbitrary basis and use the same technique as for .
Differentials as germs of functions
This approach works on any differentiable manifold. If and are open sets containing is continuous is continuous then is equivalent to at , denoted , if and only if there is an open containing such that for every in . The germ of at , denoted , is the set of all real continuous functions equivalent to at ; if is smooth at then is a smooth germ. If , and are open sets containing , , and are smooth functions
is a real number then
This shows that the germs at p form an algebra.
Define to be the set of all smooth germs vanishing at and to be the product of ideals . Then a differential at (cotangent vector at ) is an element of . The differential of a smooth function at , denoted , is .
A similar approach is to define differential equivalence of first order in terms of derivatives in an arbitrary coordinate patch. Then the differential of at is the set of all functions differentially equivalent to at .
Algebraic geometry
In algebraic geometry, differentials and other infinitesimal notions are handled in a very explicit way by accepting that the coordinate ring or structure sheaf of a space may contain nilpotent elements. The simplest example is the ring of dual numbers R[ε], where ε2 = 0.