Lebesgue integration
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In mathematics, the integral of a nonnegative function of a single variable can be regarded, in the simplest case, as the area between the graph of that function and the xaxis. The Lebesgue integral extends the integral to a larger class of functions. It also extends the domains on which these functions can be defined.
Mathematicians had long understood that for nonnegative functions with a smooth enough graph—such as continuous functions on closed bounded intervals—the area under the curve could be defined as the integral, and computed using approximation techniques on the region by polygons. However, as the need to consider more irregular functions arose—e.g., as a result of the limiting processes of mathematical analysis and the mathematical theory of probability—it became clear that more careful approximation techniques were needed to define a suitable integral. Also, one might wish to integrate on spaces more general than the real line. The Lebesgue integral provides the right abstractions needed to do this important job.
The Lebesgue integral plays an important role in probability theory, in the branch of mathematics called real analysis and in many other mathematical sciences fields. It is named after Henri Lebesgue (1875–1941), who introduced the integral (Lebesgue 1904). It is also a pivotal part of the axiomatic theory of probability.
The term Lebesgue integration can mean either the general theory of integration of a function with respect to a general measure, as introduced by Lebesgue, or the specific case of integration of a function defined on a subdomain of the real line with respect to Lebesgue measure.
Introduction
The integral of a function f between limits a and b can be interpreted as the area under the graph of f. This is easy to understand for familiar functions such as polynomials, but what does it mean for more exotic functions? In general, for which class of functions does "area under the curve" make sense? The answer to this question has great theoretical and practical importance.
As part of a general movement toward rigour in mathematics in the nineteenth century, mathematicians attempted to put integral calculus on a firm foundation. The Riemann integral—proposed by Bernhard Riemann (1826–1866)—is a broadly successful attempt to provide such a foundation. Riemann's definition starts with the construction of a sequence of easily calculated areas that converge to the integral of a given function. This definition is successful in the sense that it gives the expected answer for many alreadysolved problems, and gives useful results for many other problems.
However, Riemann integration does not interact well with taking limits of sequences of functions, making such limiting processes difficult to analyze. This is important, for instance, in the study of Fourier series, Fourier transforms, and other topics. The Lebesgue integral is better able to describe how and when it is possible to take limits under the integral sign (via the powerful monotone convergence theorem and dominated convergence theorem).
While the Riemann integral considers the area under a curve as made out of vertical rectangles, the Lebesgue definition considers horizontal slabs that are not necessarily just rectangles, and so it is more flexible. For this reason, the Lebesgue definition makes it possible to calculate integrals for a broader class of functions. For example, the Dirichlet function, which is 0 where its argument is irrational and 1 otherwise, has a Lebesgue integral, but does not have a Riemann integral. Furthermore, the Lebesgue integral of this function is zero, which agrees with the intuition that picking a real number uniformly at random from the unit interval, the probability of picking a rational number should be zero.
Lebesgue summarized his approach to integration in a letter to Paul Montel:
I have to pay a certain sum, which I have collected in my pocket. I take the bills and coins out of my pocket and give them to the creditor in the order I find them until I have reached the total sum. This is the Riemann integral. But I can proceed differently. After I have taken all the money out of my pocket I order the bills and coins according to identical values and then I pay the several heaps one after the other to the creditor. This is my integral.— Source: (SiegmundSchultze 2008)
The insight is that one should be able to rearrange the values of a function freely, while preserving the value of the integral. This process of rearrangement can convert a very pathological function into one that is "nice" from the point of view of integration, and thus let such pathological functions be integrated.
Intuitive interpretation
To get some intuition about the different approaches to integration, let us imagine that we want to find a mountain's volume (above sea level).
 The Riemann–Darboux approach
 Divide the base of the mountain into a grid of 1 meter squares. Measure the altitude of the mountain at the center of each square. The volume on a single grid square is approximately 1 m^{2} × (that square's altitude), so the total volume is 1 m^{2} times the sum of the altitudes.
 The Lebesgue approach
 Draw a contour map of the mountain, where adjacent contours are 1 meter of altitude apart. The volume of earth a single contour contains is approximately 1 m × (that contour's area), so the total volume is the sum of these areas times 1 m.
Folland summarizes the difference between the Riemann and Lebesgue approaches thus: "to compute the Riemann integral of f, one partitions the domain [a, b] into subintervals", while in the Lebesgue integral, "one is in effect partitioning the range of f ."^{[1]}
Towards a formal definition
To define the Lebesgue integral requires the formal notion of a measure that, roughly, associates to each set A of real numbers a nonnegative number μ(A) representing the "size" of A. This notion of "size" should agree with the usual length of an interval or disjoint union of intervals. Suppose that f : ℝ → ℝ^{+} is a nonnegative realvalued function. Using the "partitioning the range of f " philosophy, the integral of f should be the sum over t of the elementary area contained in the thin horizontal strip between y = t and y = t + dt. This elementary area is just
Let
The Lebesgue integral of f is then defined by^{[2]}
where the integral on the right is an ordinary improper Riemann integral (note that f^{∗} is a nonnegative decreasing function, and therefore has a welldefined improper Riemann integral). For a suitable class of functions (the measurable functions), this defines the Lebesgue integral.
A general (not necessarily positive) function f is Lebesgue integrable if the area between the graph of f and the xaxis is finite:
In that case, the integral is, as in the Riemannian case, the difference between the area above the xaxis and the area below the xaxis:
where is the unique decomposition of f into the difference of two nonnegative functions, given explicitly by
Construction
The discussion that follows parallels the most common expository approach to the Lebesgue integral. In this approach, the theory of integration has two distinct parts:
 A theory of measurable sets and measures on these sets
 A theory of measurable functions and integrals on these functions
The function whose integral is to be found is then approximated by certain socalled simple functions, whose integrals can be written in terms of the measure. The integral of the original function is then the limit of the integral of the simple functions.
Measure theory
Measure theory was initially created to provide a useful abstraction of the notion of length of subsets of the real line—and, more generally, area and volume of subsets of Euclidean spaces. In particular, it provided a systematic answer to the question of which subsets of ℝ have a length. As later set theory developments showed (see nonmeasurable set), it is actually impossible to assign a length to all subsets of ℝ in a way that preserves some natural additivity and translation invariance properties. This suggests that picking out a suitable class of measurable subsets is an essential prerequisite.
The Riemann integral uses the notion of length explicitly. Indeed, the element of calculation for the Riemann integral is the rectangle [a, b] × [c, d], whose area is calculated to be (b − a)(d − c). The quantity b − a is the length of the base of the rectangle and d − c is the height of the rectangle. Riemann could only use planar rectangles to approximate the area under the curve, because there was no adequate theory for measuring more general sets.
In the development of the theory in most modern textbooks (after 1950), the approach to measure and integration is axiomatic. This means that a measure is any function μ defined on a certain class X of subsets of a set E, which satisfies a certain list of properties. These properties can be shown to hold in many different cases.
Integration
We start with a measure space (E, X, μ) where E is a set, X is a σalgebra of subsets of E, and μ is a (nonnegative) measure on E defined on the sets of X.
For example, E can be Euclidean nspace ℝ^{n} or some Lebesgue measurable subset of it, X is the σalgebra of all Lebesgue measurable subsets of E, and μ is the Lebesgue measure. In the mathematical theory of probability, we confine our study to a probability measure μ, which satisfies μ(E) = 1.
Lebesgue's theory defines integrals for a class of functions called measurable functions. A realvalued function f on E is measurable if the preimage of every interval of the form (t, ∞) is in X:
We can show that this is equivalent to requiring that the preimage of any Borel subset of ℝ be in X. The set of measurable functions is closed under algebraic operations, but more importantly it is closed under various kinds of pointwise sequential limits:
are measurable if the original sequence (f_{k})_{k}, where k ∈ ℕ, consists of measurable functions.
We build up an integral
for measurable realvalued functions f defined on E in stages:
Indicator functions: To assign a value to the integral of the indicator function 1_{S} of a measurable set S consistent with the given measure μ, the only reasonable choice is to set:
Notice that the result may be equal to +∞, unless μ is a finite measure.
Simple functions: A finite linear combination of indicator functions
where the coefficients a_{k} are real numbers and the sets S_{k} are measurable, is called a measurable simple function. We extend the integral by linearity to nonnegative measurable simple functions. When the coefficients a_{k} are nonnegative, we set
The convention 0 × ∞ = 0 must be used, and the result may be infinite. Even if a simple function can be written in many ways as a linear combination of indicator functions, the integral is always the same. This can be shown using the additivity property of measures.
Some care is needed when defining the integral of a realvalued simple function, to avoid the undefined expression ∞ − ∞: one assumes that the representation
is such that μ(S_{k}) < ∞ whenever a_{k} ≠ 0. Then the above formula for the integral of f makes sense, and the result does not depend upon the particular representation of f satisfying the assumptions.
If B is a measurable subset of E and s is a measurable simple function one defines
Nonnegative functions: Let f be a nonnegative measurable function on E, which we allow to attain the value +∞, in other words, f takes nonnegative values in the extended real number line. We define
We need to show this integral coincides with the preceding one, defined on the set of simple functions, when E is a segment [a, b]. There is also the question of whether this corresponds in any way to a Riemann notion of integration. It is possible to prove that the answer to both questions is yes.
We have defined the integral of f for any nonnegative extended realvalued measurable function on E. For some functions, this integral ∫_{E} f dμ is infinite.
Signed functions: To handle signed functions, we need a few more definitions. If f is a measurable function of the set E to the reals (including ±∞), then we can write
where
Note that both f^{+} and f^{−} are nonnegative measurable functions. Also note that
We say that the Lebesgue integral of the measurable function f exists, or is defined if at least one of and is finite:
In this case we define
If
we say that f is Lebesgue integrable.
It turns out that this definition gives the desirable properties of the integral.
Complex valued functions can be similarly integrated, by considering the real part and the imaginary part separately.
If h=f+ig for realvalued integrable functions f, g, then the integral of h is defined by
Example
Consider the indicator function of the rational numbers, 1_{Q}. This function is nowhere continuous.
 is not Riemannintegrable on [0, 1]: No matter how the set [0, 1] is partitioned into subintervals, each partition contains at least one rational and at least one irrational number, because rationals and irrationals are both dense in the reals. Thus the upper Darboux sums are all one, and the lower Darboux sums are all zero.
 is Lebesgueintegrable on [0, 1] using the Lebesgue measure: Indeed, it is the indicator function of the rationals so by definition
 because Q is countable.
Domain of integration
A technical issue in Lebesgue integration is that the domain of integration is defined as a set (a subset of a measure space), with no notion of orientation. In elementary calculus, one defines integration with respect to an orientation:
Generalizing this to higher dimensions yields integration of differential forms. By contrast, Lebesgue integration provides an alternative generalization, integrating over subsets with respect to a measure; this can be notated as
to indicate integration over a subset A. For details on the relation between these generalizations, see Differential form § Relation with measures.
Limitations of the Riemann integral
Here we discuss the limitations of the Riemann integral and the greater scope offered by the Lebesgue integral. This discussion presumes a working understanding of the Riemann integral.
With the advent of Fourier series, many analytical problems involving integrals came up whose satisfactory solution required interchanging limit processes and integral signs. However, the conditions under which the integrals
are equal proved quite elusive in the Riemann framework. There are some other technical difficulties with the Riemann integral. These are linked with the limittaking difficulty discussed above.
Failure of monotone convergence. As shown above, the indicator function 1_{Q} on the rationals is not Riemann integrable. In particular, the Monotone convergence theorem fails. To see why, let {a_{k}} be an enumeration of all the rational numbers in [0, 1] (they are countable so this can be done.) Then let
The function g_{k} is zero everywhere, except on a finite set of points. Hence its Riemann integral is zero. Each g_{k} is nonnegative, and this sequence of functions is monotonically increasing, but its limit as k → ∞ is 1_{Q}, which is not Riemann integrable.
Unsuitability for unbounded intervals. The Riemann integral can only integrate functions on a bounded interval. It can however be extended to unbounded intervals by taking limits, so long as this doesn't yield an answer such as ∞ − ∞.
Integrating on structures other than Euclidean space. The Riemann integral is inextricably linked to the order structure of the real line.
Basic theorems of the Lebesgue integral
The Lebesgue integral does not distinguish between functions that differ only on a set of μmeasure zero. To make this precise, functions f and g are said to be equal almost everywhere (a.e.) if
 If f, g are nonnegative measurable functions (possibly assuming the value +∞) such that f = g almost everywhere, then
To wit, the integral respects the equivalence relation of almosteverywhere equality.
 If f, g are functions such that f = g almost everywhere, then f is Lebesgue integrable if and only if g is Lebesgue integrable, and the integrals of f and g are the same if they exist.
The Lebesgue integral has the following properties:
Linearity: If f and g are Lebesgue integrable functions and a and b are real numbers, then af + bg is Lebesgue integrable and
Monotonicity: If f ≤ g, then
Monotone convergence theorem: Suppose { f_{k}}_{k ∈ ℕ} is a sequence of nonnegative measurable functions such that
Then, the pointwise limit f of f_{k} is Lebesgue measurable and
The value of any of the integrals is allowed to be infinite.
Fatou's lemma: If { f_{k}}_{k ∈ N} is a sequence of nonnegative measurable functions, then
Again, the value of any of the integrals may be infinite.
Dominated convergence theorem: Suppose { f_{k}}_{k ∈ N} is a sequence of complex measurable functions with pointwise limit f, and there is a Lebesgue integrable function g (i.e., g belongs to the space L^{1}) such that  f_{k}  ≤ g for all k.
Then, f is Lebesgue integrable and
Proof techniques
To illustrate some of the proof techniques used in Lebesgue integration theory, we sketch a proof of the abovementioned Lebesgue monotone convergence theorem. Let { f_{k}}_{k ∈ N} be a nondecreasing sequence of nonnegative measurable functions and put
By the monotonicity property of the integral, it is immediate that:
and the limit on the right exists, because the sequence is monotonic. We now prove the inequality in the other direction. It follows from the definition of integral that there is a nondecreasing sequence (g_{n}) of nonnegative simple functions such that g_{n} ≤ f and
Therefore, it suffices to prove that for each n ∈ ℕ,
We will show that if g is a simple function and
almost everywhere, then
By breaking up the function g into its constant value parts, this reduces to the case in which g is the indicator function of a set. The result we have to prove is then
Suppose A is a measurable set and { f_{k}}_{k ∈ ℕ} is a nondecreasing sequence of nonnegative measurable functions on E such that
for almost all x ∈ A. Then
To prove this result, fix ε > 0 and define the sequence of measurable sets
By monotonicity of the integral, it follows that for any k ∈ ℕ,
Because almost every x is in B_{k} for large enough k, we have
up to a set of measure 0. Thus by countable additivity of μ, and because B_{k} increases with k,
As this is true for any positive ε the result follows.
For another Proof of the Monotone Convergence Theorem, we follow:^{[1]}
Let (X, M, μ) be a measure space.
{ f_{n}} is an increasing sequence of numbers, therefore its limit exists, even if it is equal to ∞. We know that
for all n, so that
 .
Now we need to establish the reverse inequality. Fix α ∈ (0, 1), let ϕ be a simple function with 0 ≤ ϕ ≤ f and let
 .
Then {E_{n}} is an increasing sequence of measurable sets with . We know that
 .
This is true for all n, including the limit:
 .
Hence,
 .
This was true for all α ∈ (0, 1), so it remains true for α = 1, and taking the supremum over simple ϕ ≤ f by the definition of integration in L^{+},
 .
Now we have both inequalities, so we've shown the Monotone Convergence theorem:
for f_{{n+1}} ≥ f_{n}, and f_{n} → f pointwise, {f_{n}} ∈ L^{+}, the set of positive measurable functions from X → [0, ∞].
Alternative formulations
It is possible to develop the integral with respect to the Lebesgue measure without relying on the full machinery of measure theory. One such approach is provided by the Daniell integral.
There is also an alternative approach to developing the theory of integration via methods of functional analysis. The Riemann integral exists for any continuous function f of compact support defined on ℝ^{n} (or a fixed open subset). Integrals of more general functions can be built starting from these integrals.
Let C_{c} be the space of all realvalued compactly supported continuous functions of ℝ. Define a norm on C_{c} by
Then C_{c} is a normed vector space (and in particular, it is a metric space.) All metric spaces have Hausdorff completions, so let L^{1} be its completion. This space is isomorphic to the space of Lebesgue integrable functions modulo the subspace of functions with integral zero. Furthermore, the Riemann integral ∫ is a uniformly continuous functional with respect to the norm on C_{c}, which is dense in L^{1}. Hence ∫ has a unique extension to all of L^{1}. This integral is precisely the Lebesgue integral.
More generally, when the measure space on which the functions are defined is also a locally compact topological space (as is the case with the real numbers ℝ), measures compatible with the topology in a suitable sense (Radon measures, of which the Lebesgue measure is an example) an integral with respect to them can be defined in the same manner, starting from the integrals of continuous functions with compact support. More precisely, the compactly supported functions form a vector space that carries a natural topology, and a (Radon) measure is defined as a continuous linear functional on this space. The value of a measure at a compactly supported function is then also by definition the integral of the function. One then proceeds to expand the measure (the integral) to more general functions by continuity, and defines the measure of a set as the integral of its indicator function. This is the approach taken by Bourbaki (2004) and a certain number of other authors. For details see Radon measures.
Limitations of Lebesgue integral
The main purpose of Lebesgue integral is to provide an integral notion where limits of integrals hold under mild assumptions. There is no guarantee that every function is Lebesgue integrable. But it may happen that improper integrals exist for functions that are not Lebesgue integrable. One example would be
over the entire real line. This function is not Lebesgue integrable, as
On the other hand, exists as an improper integral and can be computed to be finite; it is twice the Dirichlet integral.
See also
 Henri Lebesgue, for a nontechnical description of Lebesgue integration
 Null set
 Integration
 Measure
 Sigmaalgebra
 Lebesgue space
 Lebesgue–Stieltjes integration
 Henstock–Kurzweil integral
Notes
 1 2 Folland, Gerald B. (1984). Real Analysis: Modern Techniques and Their Applications. Wiley. p. 56.
 ↑ Lieb & Loss 2001
References
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