Riemann integration, Lebesgue integration
line integral/contour integration
integration of differential forms
integration over supermanifolds, Berezin integral, fermionic path integral
Kontsevich integral, Selberg integral, elliptic Selberg integral
integration in ordinary differential cohomology
integration in differential K-theory
The term ‘Lebesgue space’ can stand for two distinct notions: one is the general notion of measure space (compare the Springer Encyclopaedia of Mathematics) and another is the notion of $L^p$ space (or $L_p$ space). Here we discuss the latter.
Lebesgue spaces $L^p$ in this sense are normed vector spaces of functions on a measure space, equipped with the suitable version of the p-norm.
Beware that sometimes the notation ‘$L_p$’ is used as a synonym for $L^p$; sometimes it is used to mean $L^{1/p}$.
If $1 \leq p \lt \infty$ is a real number and $(\Omega,\mu)$ is a measure space, one considers the $L^p$ space $L_p(\Omega)$, which is the vector space of equivalence classes of those measurable (complex- or real-valued) functions $f\colon \Omega \to \mathbb{K}$ whose (absolute values of) $p$th powers are integrable, in that the integral
exists. Two such are taken to be equivalent, $f \sim g$, if ${\|f-g\|_p} = 0$. For $p = 2$ this is the space $L^2$ of square integrable functions.
On these spaces $L^p(X)$ of equivalence classes of $p$-power integrable functions, the function ${\|f\|_p}$ satisfies the triangle inequality (due to Minkowski's inequality, see below) and hence defines a norm, the p-norm, making them normed vector spaces.
The $L^p$ spaces are examples of Banach spaces; they are continuous analogues of $l^p$ spaces of $p$-summable series. (Indeed, $l^p(S)$, for $S$ a set, is simply $L^p(S)$ if $S$ is equipped with counting measure?.)
For fixed $f$, the norm ${\|f\|_p}$ is continuous in $p$. Accordingly, for $p = \infty$, one may take the limit of ${\|f\|}_p$ as $p \to \infty$. However, this turns out to be the same as the essential supremum norm $\|f\|_\infty$. Therefore, $L^\infty(\Omega)$ makes sense as long as $\Omega$ is a measurable space equipped with a family of null sets (or full sets); the measure $\mu$ is otherwise irrelevant.
For $0 \leq p \lt 1$, one can modify the definition to make $L^p$ into an F-space (but not a Banach space). See the definitions at p-norm.
We offer here a proof that ${\|f\|_p}$ indeed defines a norm in the case $1 \lt p \lt \infty$, in that it satisfies the triangle inequality. This is usually known as Minkowski's inequality.
(The cases $p = 1$ and $p = \infty$ follow by continuity and are easy to check from first principles.)
The most usual textbook proofs involve a clever application of Hölder's inequality; the following proof is more straightforwardly geometric. All functions $f$ may be assumed to be real- or complex-valued.
Suppose $1 \leq p \leq \infty$, and suppose $\Omega$ is a measure space with measure $\mu$. Then the function ${|(-)|_p}\colon L^p(\Omega, \mu) \to \mathbb{R}$ defined by
defines a norm.
One must verify three things:
Separation axiom: ${\|f\|_p} = 0$ implies $f = 0$.
Scaling axiom: ${\|t f\|}_p = {|t|} \, {\|f\|_p}$.
Triangle inequality: ${\|f + g\|_p} \leq {\|f\|_p} + {\|g\|_p}$.
The first two properties are obvious, so it remains to prove the last, which is also called Minkowski's inequality.
Our proof of Minkowski’s inequality is broken down into a series of simple lemmas. The plan is to boil it down to two things: the scaling axiom, and convexity of the function $x \mapsto {|x|^p}$ (as a function from real or complex numbers to nonnegative real numbers).
First, some generalities. Let $V$ be a (real or complex) vector space equipped with a function ${\|(-)\|}\colon V \to [0, \infty]$ that satisfies the scaling axiom: ${\|t v\|} = {|t|} \, {\|v\|}$ for all scalars $t$, and the separation axiom: ${\|v\|} = 0$ implies $v = 0$. As usual, we define the unit ball in $V$ to be $\{v \in V \;|\; {\|v\|} \leq 1\}.$
Given that the scaling and separation axioms hold, the following conditions are equivalent:
Condition 1. implies condition 2. easily: if $u$ and $v$ are in the unit ball and $0 \leq t \leq 1$, we have
Now 2. implies 3. trivially, so it remains to prove that 3. implies 1. Suppose ${\|v\|}, {\|v'\|} \in (0, \infty)$. Let $u = \frac{v}{{\|v\|}}$ and $u' = \frac{v'}{{\|v'\|}}$ be the associated unit vectors. Then
where $t = \frac{{\|v\|}}{{\|v\|} + {\|v'\|}}$. If condition 3. holds, then
but by the scaling axiom, this is the same as saying
which is the triangle inequality.
Consider now $L^p$ with its $p$-norm ${\|f\|} = {|f|_p}$. By Lemma 1, this inequality is equivalent to
This allows us to remove the cumbersome exponent $1/p$ in the definition of the $p$-norm.
The next two lemmas may be proven by elementary calculus; we omit the proofs. (But you can also see the full details.)
Let $\alpha, \beta$ be two complex numbers, and define
for real $t$. Then $\gamma''(t)$ is nonnegative.
Define $\phi\colon \mathbb{C} \to \mathbb{R}$ by $\phi(x) = |x|^p$. Then $\phi$ is convex, i.e., for all $x, y$,
for all $t \in [0, 1]$.
Let $u$ and $v$ be unit vectors in $L^p$. By condition 4, it suffices to show that ${|t u + (1-t)v|_p} \leq 1$ for all $t \in [0, 1]$. But
by Lemma 3. Using $\int {|u|^p} = 1 = \int {|v|^p}$, we are done.
Named after Henri Lebesgue.
W. Rudin, Functional analysis, McGraw Hill 1991.
L. C. Evans, Partial differential equations, Amer. Math. Soc. 1998.
Wikipedia (English): Lp space