In
mathematical analysis,
distributions are
generalized
functions that permit the
derivative to be
extended to
things which are not necessarily
smooth. The
theory of
distributions was founded in the middle of the
20th century by the
French analyst Laurent Schwartz, and
independently by
I. M. Gelfand and the
Russian school. In
Russian they are still called
generalized
functions.
Distributions on a
manifold X are defined as
continuous linear functionals on the
space of
smooth functions having
compact support. In
distribution theory this
space is
conventionally denoted by
D(X) or C
∞c(X), and the
distributions therefore by
D′(X) or C
-∞(X). Because
D(X) is not
metrizable (unless X is
compact) but only an
LF space, the
continuity condition is a little
technical to state. (An
LF space is the
inductive limit of an ascending sequence of
Fréchet spaces: roughly, the
completeness condition is as good as that of a
Fréchet space but you only have a
uniformity with which to state it rather than a
metric.)
The advantage of
distributions is that they can be
differentiated indefinitely even though they may not be "
smooth". We simply imagine that the
integration by parts formula is valid, and define the
derivative of a distribution u to be
u′(φ) = u(-&phi′). This notion of
differentiation turns out to yield a
calculus with the correct properties.
Note that the word distribution is used by statisticians to mean something very different.
For more information, consult a textbook on partial differential equations for pure mathematicians. I like the first volume of Michael Taylor's three-volume work.