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The Lebesgue Integral and the Convergence Theorems
1 · Prerequisites
- Areas of Elementary Plane Figures
- Binary Operations, Monoids, Groups and Subgroups
- Compactness
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Convexity
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Filters and Ultrafilters
- Finite Counting, Factorials and Binomial Coefficients
- Foundations of the Real Numbers for Analysis
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Lebesgue Measure on Euclidean Space
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Measures and Their Basic Properties
- Metric Spaces
- Monotone Functions, Discontinuities, and Continuity Sets
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Order, Zorn's Lemma, and the Axiom of Choice
- Outer Measure and the Caratheodory Extension Theorem
- Polynomial Rings, the Division Algorithm and Roots
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Rⁿ as a Normed Space; Vector-Valued Functions
- Roots, Rational Powers, and Classical Inequalities
- Sequences and Limits
- Series: Convergence and the Nonnegative Tests
- Sigma Algebras and Borel Sets
- Simple Field Extensions and the Construction of the Complex Numbers
- Subspaces, Products, and Quotients
- Suprema and Infima
- The Derivative and the Mean Value Theorems
- The Riemann Integral in Rᵐ and Jordan Content
- The Riemann Integral: Definition and Integrability
- The Topology of Euclidean Space
- The ZFC Axioms and the Basic Set Constructions
- Topological Spaces and Continuity
- Topology of ℝ
- Vector Spaces, Linear Subspaces, Span and Direct Sums
2 · Summary
This page builds the Lebesgue integral in the three-stage route fixed by the measure-theory design notes. First come nonnegative simple functions and the simple integral, then the nonnegative integral together with monotone convergence, Fatou, and the density construction, and only after that the signed and complex theories.
The page's false statements record the exact hypotheses the convergence theorems spend: monotonicity for monotone convergence, a dominating integrable majorant for dominated convergence, almost-everywhere rather than everywhere equality for zero-integral criteria, and probability normalization for Jensen's inequality.
3 · Logical flowchart
4 · Definitions, theorems and proofs
Extended-real-valued measurable functions
Definition
Let be a measure space (Measure spaces). Equip (The extended real line , its order, and the arithmetic that is left undefined) with its order topology and let be the corresponding Borel sigma-algebra (The Borel sigma-algebra of a topological space).
A function is measurable when for every .
Equivalently, it is enough to require because the rays generate .
Nonnegative simple measurable functions
Definition
Let be a measure space. A measurable function (Extended-real-valued measurable functions) is a nonnegative simple measurable function when it has finite range.
Equivalently, there are pairwise disjoint measurable sets and coefficients such that Any such display is a simple representation of .
Closure properties of measurable functions used by the integral
Statement
Let be a measure space.
- If are measurable and is defined pointwise, then is measurable.
- If and is measurable, then is measurable.
- If is measurable, then , , and are measurable.
- If and is measurable, then is measurable.
- If is a sequence of measurable functions , then is measurable; if moreover pointwise, then is measurable.
Facts & Assumptions
Given: A measure space and functions or sets as in the relevant clause.
A function is measurable exactly when for every real (Extended-real-valued measurable functions).
Proof
If is defined pointwise, then for every real , [L1, algebra] so clause 1 follows from [L1].
For one has when and [L1, algebra] when ; for the function is constant. Applying [L1] proves clause 2. The formulas and therefore give clause 3.
If and , then [L1, algebra] ∎ so clause 4 follows from [L1]. Also so clause 5 follows from [L1] as well, including the monotone-limit case .
Every nonnegative measurable function is the increasing limit of simple measurable functions
Statement
Let be measurable. Then there is an increasing sequence of nonnegative simple measurable functions such that for every .
Facts & Assumptions
Given: A measurable function .
For a measurable , the sets are measurable for every Borel (Extended-real-valued measurable functions).
A nonnegative measurable function with finite range is a nonnegative simple measurable function (Nonnegative simple measurable functions).
Increasing pointwise suprema of measurable functions are measurable, and measurable functions remain measurable under the elementary truncations used below (Closure properties of measurable functions used by the integral).
Proof
Set . For and , put [L1, L2, construct] and set Each and is measurable by [L1], the range of is finite, and therefore each is simple by [L2]; is also simple.
For each , one has . If and [step 1.1, algebra] , then ; if , then . Hence .
The functions are increasing. Indeed, is a dyadic multiple of [step 2.1, L3, algebra] ∎ below , hence also a dyadic multiple of below ; so the defining maximality of the -st dyadic truncation gives . Therefore , in accord with [L3].
The integral of a nonnegative simple function
Definition
Let be a simple representation of a nonnegative simple measurable function (Nonnegative simple measurable functions) on a measure space , so the are pairwise disjoint and . Its simple integral is where is the given measure (Measures on sigma-algebras) and the convention is fixed once and for all.
The next lemma proves that this value is independent of the chosen simple representation.
The simple integral is independent of the chosen representation
Statement
If a nonnegative simple measurable function admits two representations then the two coefficient sums defining are equal. So The integral of a nonnegative simple function is well defined.
Facts & Assumptions
Given: Two simple representations of the same nonnegative simple measurable function .
The simple integral is defined by with the convention (The integral of a nonnegative simple function).
A measure is countably additive on pairwise disjoint measurable families, hence finitely additive on finite measurable partitions (Measures on sigma-algebras).
Proof
For each pair put . The family is [given, L2] measurable and pairwise disjoint, and Whenever , the two simple formulas for agree on , so .
Finite additivity over the partitions in step 1.1 gives[step 1.1, L1, L2, algebra] If some coefficient is on a cell of infinite measure, the convention in [L1] forces both corresponding terms to be , so no ambiguity occurs there either.
Therefore the simple integral does not depend on the chosen representation, [step 2.1, L1] ∎ and the definition in [L1] is well defined.
The simple integral is monotone, homogeneous, and additive
Statement
Let be nonnegative simple measurable functions and let .
- If pointwise, then .
- .
- .
Facts & Assumptions
Given: Nonnegative simple measurable functions and a scalar .
The simple integral is well defined, so any convenient common refinement of the chosen simple representations may be used to compute it (The simple integral is independent of the chosen representation).
The simple integral of is with (The integral of a nonnegative simple function).
Proof
Choose one finite measurable partition on which both and [L1, construct] are constant, say and . Then for every because .
Using the common partition from step 1.1 and [L2], [step 1.1, L2, algebra] and Termwise comparison gives monotonicity, while ordinary finite-sum algebra gives homogeneity and additivity.
Therefore all three properties hold for the simple integral.
The nonnegative Lebesgue integral
Definition
Let be measurable (Extended-real-valued measurable functions). Its nonnegative Lebesgue integral is where the simple integral on the right is the one from The integral of a nonnegative simple function.
The set of admissible simple minorants is nonempty because it contains the zero function, which is simple (Nonnegative simple measurable functions).
The nonnegative integral agrees with the simple integral on simple functions
Statement
If is a nonnegative simple measurable function, then its nonnegative Lebesgue integral equals its simple integral:
Facts & Assumptions
Given: A nonnegative simple measurable function .
The nonnegative integral is the supremum of the simple integrals of all simple minorants (The nonnegative Lebesgue integral).
The simple integral is monotone on nonnegative simple functions (The simple integral is monotone, homogeneous, and additive).
The simple integral itself is well defined on every nonnegative simple function (The integral of a nonnegative simple function, The simple integral is independent of the chosen representation).
Proof
The function is one of its own admissible simple minorants, so [L1] gives
If is any admissible simple minorant of , then , so [L2] gives Taking the supremum over all such in [L1] yields the reverse inequality.
The two inequalities from steps 1.1 and 1.2 are equalities, so the two [step 1.1, step 1.2] ∎ integrals agree on simple functions.
Integral over a measurable subset
Definition
Let be a measure space, let be measurable, and let . Since Closure properties of measurable functions used by the integral implies that is measurable, define the integral of over by where the integral on the right is the nonnegative Lebesgue integral of The nonnegative Lebesgue integral.
The indefinite integral of a nonnegative simple function is a measure
Statement
Let be a nonnegative simple measurable function on and define Then is a measure on .
Facts & Assumptions
Given: A nonnegative simple measurable function on .
For measurable , the set function is defined as (Integral over a measurable subset).
The simple integral is additive and homogeneous on nonnegative simple functions (The simple integral is monotone, homogeneous, and additive).
A measure is a set function with value at the empty set and countable additivity on pairwise disjoint measurable families (Measures on sigma-algebras).
Proof
Write . Then for every measurable ,[L1, L2, given, algebra]
Step 1.1 gives . If is a pairwise disjoint[step 1.1, L2, L3, algebra] measurable sequence, then each is pairwise disjoint, so
Therefore satisfies the two conditions in [L3], so it is a [step 2.1, L3] ∎ measure.
Monotonicity and nonnegative homogeneity of the nonnegative integral
Statement
Let be measurable and let .
- If , then .
- .
Facts & Assumptions
Given: Nonnegative measurable functions and a scalar .
The nonnegative integral is the supremum of simple minorants (The nonnegative Lebesgue integral).
On simple functions, the nonnegative and simple integrals agree (The nonnegative integral agrees with the simple integral on simple functions).
Proof
If , every simple minorant of is also a simple minorant of . [L1, given] Taking suprema in [L1] gives .
If , both sides are . Assume . Multiplication by carries [L1, L2, given, algebra] simple minorants of bijectively onto simple minorants of , and [L2] scales their integrals by the same factor. Taking suprema in [L1] yields .
Steps 1.1 and 1.2 prove the monotonicity and nonnegative homogeneity rules.
Monotone convergence for the integral
Statement
Let be measurable and suppose for every . Then
Facts & Assumptions
Given: A nondecreasing sequence of nonnegative measurable functions with pointwise limit .
The nonnegative integral is monotone (Monotonicity and nonnegative homogeneity of the nonnegative integral).
For a nonnegative simple function , the set function is a measure (The indefinite integral of a nonnegative simple function is a measure).
Measures are continuous from below on increasing measurable sets (Continuity from below for measures).
The nonnegative integral agrees with the simple integral on simple functions, and the latter is homogeneous on nonnegative simple functions (The nonnegative integral agrees with the simple integral on simple functions, The simple integral is monotone, homogeneous, and additive).
Proof
By [L1], the numbers increase and satisfy[given, L1] for every . So their supremum exists in and .
Fix a nonnegative simple function and a real with .[given, L2, L3] Put . Then : if then for all , while if then , so eventually . Since is a measure by [L2], [L3] gives
On one has , hence . By [L1] and [L4],[step 1.2, L1, L4, algebra] Letting in step 1.2 yields Now choose and let ; then .
Step 2.1 holds for every simple minorant , so taking the supremum [step 1.1, step 2.1, given] ∎ over such gives by the definition of the nonnegative integral. Together with step 1.1, this proves , so .
Additivity of the nonnegative Lebesgue integral
Statement
If are measurable, then
Facts & Assumptions
Given: Nonnegative measurable functions and .
Nonnegative measurable functions admit increasing simple approximations (Every nonnegative measurable function is the increasing limit of simple measurable functions).
The sum of two measurable nonnegative functions is measurable, and pointwise increasing limits stay measurable (Closure properties of measurable functions used by the integral).
Monotone convergence holds for the nonnegative integral (Monotone convergence for the integral).
On simple functions, the nonnegative integral agrees with the simple integral, and the latter is additive (The nonnegative integral agrees with the simple integral on simple functions, The simple integral is monotone, homogeneous, and additive).
Proof
Choose simple functions and by [L1]. Then [L1, L2, construct] is simple for each , and by [L2].
By [L3] and [L4],[step 1.1, L3, L4, algebra] ∎
Beppo Levi's theorem for nonnegative series
Statement
Let be nonnegative measurable functions and let Then
Facts & Assumptions
Given: A sequence of nonnegative measurable functions.
Measurable nonnegative functions are closed under finite sums and increasing pointwise suprema (Closure properties of measurable functions used by the integral).
The nonnegative integral is additive (Additivity of the nonnegative Lebesgue integral).
Monotone convergence holds for the nonnegative integral (Monotone convergence for the integral).
Proof
Each partial sum is measurable by [L1], the sequence is [L1, given] increasing, and pointwise.
By [L2], for every . Applying [step 1.1, L2, L3, algebra] ∎ [L3] to step 1.1 gives which is exactly the displayed series identity.
The indefinite integral of a nonnegative measurable function is a measure
Statement
Let be measurable and define Then is a measure on .
Facts & Assumptions
Given: A nonnegative measurable function .
The set function is defined by (Integral over a measurable subset).
Monotone convergence holds for the nonnegative integral (Monotone convergence for the integral).
The nonnegative integral is additive on measurable sets (Additivity of the nonnegative Lebesgue integral).
A measure must vanish at the empty set and be countably additive on disjoint measurable sequences (Measures on sigma-algebras).
Proof
One has . If is a pairwise disjoint sequence,[L1, L2, given] put . Then , so and [L2] gives
Because the sets are disjoint, repeated use of [L3] gives [step 1.1, L3, algebra] Substituting this into step 1.1 proves countable additivity.
Steps 1.1 and 2.1 verify the two conditions in [L4], so is a [step 1.1, step 2.1, L4] ∎ measure.
The measure with density relative to
Definition
Let be a measure space and let be measurable. The measure with density relative to is the measure whose measure property is supplied by The indefinite integral of a nonnegative measurable function is a measure.
Integrating against a density agrees with integrating the product
Statement
Let be measurable. Then
Facts & Assumptions
Given: Nonnegative measurable functions and .
The density measure is defined by (The measure with density relative to ).
The nonnegative integral is additive on measurable sets (Additivity of the nonnegative Lebesgue integral).
Nonnegative measurable functions admit increasing simple approximations, and products with simple functions are measurable by finite sums of indicator products (Every nonnegative measurable function is the increasing limit of simple measurable functions, Closure properties of measurable functions used by the integral).
Monotone convergence holds for the nonnegative integral (Monotone convergence for the integral).
The nonnegative integral is homogeneous, and on simple functions it agrees with the simple integral for any measure. (Monotonicity and nonnegative homogeneity of the nonnegative integral, The nonnegative integral agrees with the simple integral on simple functions)
Proof
Suppose first that is a simple representation, so the sets are pairwise disjoint. Applying [L5] on the measure space and then using [L1], one gets.
Also , and the summands have pairwise disjoint supports. Therefore [L2] and [L5] give
Hence .
For general measurable , choose simple by [L3]. Then pointwise. Applying [L4] twice and step 1.1 to each yields.
Fatou's lemma
Statement
Let be nonnegative measurable functions. Then
Facts & Assumptions
Given: A sequence of nonnegative measurable functions.
Countable infima of measurable extended-real-valued functions are measurable, and monotone pointwise suprema are measurable (Closure properties of measurable functions used by the integral).
Monotone convergence holds for the nonnegative integral (Monotone convergence for the integral).
The nonnegative integral is monotone (Monotonicity and nonnegative homogeneity of the nonnegative integral).
Proof
For each , define [L1, given, construct] Then each is measurable by [L1], one has and pointwise. Also for every .
By [L2], [step 1.1, L2, L3] ∎ Since , [L3] gives for every . Taking the limit in yields the claimed inequality.
Reverse Fatou's lemma under an integrable majorant
Statement
Let be nonnegative measurable functions and let be a nonnegative measurable function with and for every . Then
Facts & Assumptions
Given: Nonnegative measurable functions dominated by a nonnegative measurable function with finite integral.
Fatou's lemma applies to every sequence of nonnegative measurable functions (Fatou's lemma).
The nonnegative integral is additive (Additivity of the nonnegative Lebesgue integral).
The nonnegative integral is monotone (Monotonicity and nonnegative homogeneity of the nonnegative integral).
Truncations of nonnegative measurable functions and pointwise limsups of measurable sequences are measurable (Closure properties of measurable functions used by the integral).
Monotone convergence holds for the nonnegative integral (Monotone convergence for the integral).
Proof
For each , put and . Then and are measurable, , and is a nonnegative measurable function.
Apply [L1] to the sequence . Since is finite-valued, and Rearranging Fatou's inequality therefore gives
Since , [L2] and [L3] give Taking and using step 2.1 yields
Because , [L5] gives . Applying [L2] to shows Letting in step 3.1 proves
A nonnegative measurable function has integral exactly when it vanishes almost everywhere
Statement
Let be measurable. Then
Facts & Assumptions
Given: A nonnegative measurable function .
The nonnegative integral is monotone and homogeneous (Monotonicity and nonnegative homogeneity of the nonnegative integral).
A statement holds almost everywhere when its exceptional set is contained in a measurable null set (Measure-null sets and almost-everywhere statements relative to a measure).
The nonnegative integral is the supremum of the integrals of simple minorants (The nonnegative Lebesgue integral).
Proof
Assume . For let . Then[L1, L2, given, algebra] , so [L1] gives Hence for every . Since , the exceptional set where is null, so almost everywhere by [L2].
Assume almost everywhere, and let be a measurable null set[L2, L3, given] containing . If is a simple minorant of , then every set with lies inside , so ; the remaining coefficients are . Therefore . Taking the supremum over all simple minorants in [L3] gives .
Step 1.1 proves the forward implication and step 1.2 proves the reverse [step 1.1, step 1.2] ∎ implication.
A nonnegative measurable function with finite integral is finite almost everywhere
Statement
If is measurable and , then for almost every .
Facts & Assumptions
Given: A nonnegative measurable function with finite integral.
The nonnegative integral is monotone and homogeneous (Monotonicity and nonnegative homogeneity of the nonnegative integral).
A nonnegative measurable function has integral exactly when it vanishes almost everywhere (A nonnegative measurable function has integral exactly when it vanishes almost everywhere).
Proof
Let . Then for every , so[L1, given] By [L1],
If , the inequality in step 1.1 would fail for large . [step 1.1, L2] ∎ Therefore , so the indicator has integral and hence vanishes almost everywhere by [L2]. Equivalently, almost everywhere.
A nonnegative integral over a null set vanishes
Statement
Let be measurable and let be measurable with . Then
Facts & Assumptions
Given: A nonnegative measurable function and a measurable null set .
The set function is a measure whenever is nonnegative simple (The indefinite integral of a nonnegative simple function is a measure).
The integral over a measurable set is defined by (Integral over a measurable subset).
The nonnegative integral is the supremum of the integrals of simple minorants (The nonnegative Lebesgue integral).
Proof
Let be a simple minorant of . If ,[L1, L2, given] then , so . Since is a measure by [L1], every positive-coefficient term contributes , and the zero-coefficient terms contribute as well. Hence .
Taking the supremum over all such simple minorants in [L3] gives[step 1.1, L2, L3] ∎
Almost-everywhere monotone convergence
Statement
Let be measurable, let be measurable, and suppose almost everywhere. Then
Facts & Assumptions
Given: A measurable nonnegative function and a nondecreasing sequence of nonnegative measurable functions with almost everywhere.
Monotone convergence holds when the pointwise increase is everywhere (Monotone convergence for the integral).
A nonnegative integral over a measurable null set is (A nonnegative integral over a null set vanishes).
The nonnegative integral is additive (Additivity of the nonnegative Lebesgue integral).
Proof
Let be a measurable null set outside which , and define and .
Then everywhere, and is measurable because is. So [L1] gives .
Each difference and is supported on the null set .
Therefore [L2] and [L3] give
Substituting into step 1.1 yields the result. [step 1.1, L2, L3] ∎
Chebyshev-Markov inequality for the integral
Statement
Let be measurable and let . Then
Facts & Assumptions
Given: A nonnegative measurable function and a real number .
The nonnegative integral is monotone and homogeneous (Monotonicity and nonnegative homogeneity of the nonnegative integral).
Proof
If , then . Applying [L1] gives[L1, given]
Dividing by the positive number yields[step 1.1, algebra] ∎
Integrable real and complex functions, and their integrals
Definition
Let be measurable. Its positive and negative parts are they are measurable by Closure properties of measurable functions used by the integral and satisfy and .
The Lebesgue integral of a real measurable function is defined whenever at most one of and is , in which case The function is integrable when both integrals are finite, equivalently when .
For a complex measurable function with (Real and imaginary parts, complex conjugation, and modulus), define to be integrable when is integrable, and then define
The class of integrable functions
Definition
For a measure , write Here integrable is the notion introduced in Integrable real and complex functions, and their integrals.
On this page is the class of integrable representatives. The later Banach-space page will pass to almost-everywhere equivalence classes.
The Lebesgue integral is linear on
Statement
The class is a complex vector space, and the Lebesgue integral is complex-linear on it:
Facts & Assumptions
Given: Integrable functions and scalars .
Real and complex integrability, together with the decomposition into positive and negative parts and into real and imaginary parts, is defined in Integrable real and complex functions, and their integrals.
The nonnegative integral is additive (Additivity of the nonnegative Lebesgue integral).
The nonnegative integral is monotone and homogeneous (Monotonicity and nonnegative homogeneity of the nonnegative integral).
Sums and real scalar multiples of measurable real-valued functions are measurable (Closure properties of measurable functions used by the integral).
Proof
First treat real-valued and put . By [L4], the function is measurable. Also so [L2] and [L3] give Hence is integrable. Since another application of [L2] yields which rearranges to
Now let and let be real-valued. By [L4], is measurable. If , then if , then In both cases [L3] shows that is integrable and that
Let and . Then The real-valued functions and are integrable by steps 1.1 and 1.2, and their real-linear integral formulas combine into Writing and with real-valued integrable , step 1.1 gives
Two integrable functions are equal almost everywhere exactly when all of their indefinite integrals agree
Statement
Let . Then the following are equivalent:
- almost everywhere;
- for every measurable ,
For integrable real or complex , the notation in condition 2 means ; the product is integrable because .
Facts & Assumptions
Given: Integrable functions .
The integral over a null set vanishes for nonnegative integrands (A nonnegative integral over a null set vanishes).
The Lebesgue integral is linear on (The Lebesgue integral is linear on ).
A nonnegative measurable function has integral exactly when it vanishes almost everywhere (A nonnegative measurable function has integral exactly when it vanishes almost everywhere).
Real and imaginary parts of an integrable complex function are integrable (Integrable real and complex functions, and their integrals).
Proof
Assume almost everywhere, with exceptional null set . Then for [L1, L2, L4, given] every measurable , the real and imaginary parts of are supported on , so [L1] and [L2] give hence .
Assume instead that for every measurable [L2, L3, L4, given] . Apply this to the real part on the set and to on . In each case the corresponding nonnegative integral is , so [L3] gives almost everywhere. The same argument for shows almost everywhere. Hence almost everywhere.
Step 1.1 proves and step 1.2 proves .
The modulus of an integral is bounded by the integral of the modulus
Statement
If , then
Facts & Assumptions
Given: An integrable function .
The integral is linear on (The Lebesgue integral is linear on ).
Real and imaginary parts, complex conjugation, and modulus are as in Real and imaginary parts, complex conjugation, and modulus.
Real and complex integrability are defined in Integrable real and complex functions, and their integrals.
The nonnegative integral is monotone (Monotonicity and nonnegative homogeneity of the nonnegative integral).
Proof
For real-valued , the functions and are nonnegative. Therefore [L1] and [L4] give So and hence .
For complex-valued , let . If there is nothing to prove. Otherwise set , so by [L2]. Then and , so step 1.1 applies to the integrable real-valued function . Taking real parts gives because for every complex .
Dominated convergence
Statement
Let and be measurable complex-valued functions such that almost everywhere and almost everywhere for a single nonnegative measurable function with . Then , and hence
Facts & Assumptions
Given: Measurable complex-valued functions with almost everywhere and almost everywhere for one nonnegative measurable function of finite integral.
Reverse Fatou's lemma holds under an integrable majorant (Reverse Fatou's lemma under an integrable majorant).
The integral is linear on (The Lebesgue integral is linear on ).
The integral triangle inequality holds on (The modulus of an integral is bounded by the integral of the modulus).
Real and complex integrability are defined in Integrable real and complex functions, and their integrals.
The nonnegative integral is additive, and a nonnegative integral over a null set vanishes (Additivity of the nonnegative Lebesgue integral, A nonnegative integral over a null set vanishes).
A nonnegative measurable function with finite integral is finite almost everywhere (A nonnegative measurable function with finite integral is finite almost everywhere).
The nonnegative integral is monotone and homogeneous (Monotonicity and nonnegative homogeneity of the nonnegative integral).
Proof
Let be a measurable null set outside which and , and let . By [L6], the set is null. Put , , Then pointwise, , and is nonnegative, measurable, and finite everywhere. Also By [L5] and [L7], so by [L4].
The functions are nonnegative, converge pointwise to , and are dominated by the finite everywhere majorant . Applying [L1] therefore gives Hence .
Because is supported on the null set , [L5] gives Therefore, by [L2] and [L3], and the right-hand side tends to .
Bounded convergence on a finite measure space
Statement
Let be a finite measure space and let and be measurable complex-valued functions with almost everywhere. If almost everywhere for one real , then
Facts & Assumptions
Given: A finite measure space, measurable complex-valued functions with almost everywhere, and a uniform bound .
Dominated convergence applies whenever one integrable dominating function controls the whole sequence (Dominated convergence).
Proof
The constant function is integrable because It dominates every .
Apply [L1] with the dominating function from step 1.1.
Integrable simple functions are dense in
Statement
For every there is a sequence of integrable simple functions such that
Facts & Assumptions
Given: An integrable function .
Every nonnegative measurable function is the increasing limit of simple measurable functions (Every nonnegative measurable function is the increasing limit of simple measurable functions).
Real and complex integrability are defined in Integrable real and complex functions, and their integrals.
Dominated convergence gives convergence under an integrable majorant (Dominated convergence).
The nonnegative integral is additive, monotone, and homogeneous (Additivity of the nonnegative Lebesgue integral, Monotonicity and nonnegative homogeneity of the nonnegative integral).
Proof
Suppose first that is real-valued. Choose simple and by [L1], and put . Then is an integrable simple function and with . By [L3], .
For complex , apply step 1.1 separately to and to obtain real simple functions with Set . Then is a simple integrable function and By [L4], so converges to in .
Absolute continuity of the integral
Statement
Let and let . Then there is such that for every measurable ,
Facts & Assumptions
Given: An integrable function and a real number .
The truncations increase pointwise to , so their integrals converge to by monotone convergence (Monotone convergence for the integral).
The integral over a measurable set is defined by (Integral over a measurable subset).
The nonnegative integral is monotone and homogeneous (Monotonicity and nonnegative homogeneity of the nonnegative integral).
Integrability means (Integrable real and complex functions, and their integrals).
Proof
Choose so large that [L1, L4, choose] which is possible by [L1] and [L4]. Put .
If , then [step 1.1, L2, L3, algebra] ∎ using [L2] and [L3] for the first term.
The indefinite integral of an integrable function is countably additive on measurable sets
Statement
If and then is countably additive on pairwise disjoint measurable families. Here is integrable because ; this formula defines the notation for integrable real or complex .
Facts & Assumptions
Given: An integrable function .
For every nonnegative measurable , the set function is a measure (The indefinite integral of a nonnegative measurable function is a measure).
Real and complex integrability are defined by positive/negative parts and by real/imaginary parts (Integrable real and complex functions, and their integrals).
The Lebesgue integral is linear on (The Lebesgue integral is linear on ).
Proof
For real-valued , write . Then [L1, L2, L3] and both and are measures by [L1]. Because , the total masses of those measures are finite, so subtracting their countably additive values on a disjoint family is legitimate and gives countable additivity of .
For complex-valued , one has [step 1.1, L2, L3] ∎ and step 1.1 applies to the real-valued functions and . Therefore is countably additive as well.
Continuity under the integral sign
Statement
Let be an interval and let be such that:
- for every , the function is integrable;
- for almost every , the map is continuous on ;
- there is a nonnegative measurable function with and for every and almost every .
Then is continuous on .
Facts & Assumptions
Given: An interval and a function satisfying the three displayed hypotheses.
Dominated convergence applies to integrable complex-valued functions under a single majorant (Dominated convergence).
Proof
Fix and let be any sequence in with . For almost every , continuity in gives , and the dominating bound gives .
Apply [L1] to the sequence . Then Since every convergent sequence in has this property, is continuous at , and therefore on all of .
Differentiation under the integral sign
Statement
Let be an open interval and let be such that:
- for every , the function is integrable;
- for almost every , the map is differentiable on ;
- for every , the function is measurable;
- there are a measurable null set and a nonnegative measurable function with and for every and every .
Then is differentiable on , and
Facts & Assumptions
Given: An open interval , a function satisfying the first three displayed hypotheses, and a measurable null set together with a nonnegative measurable majorant satisfying hypothesis 4.
Dominated convergence applies to integrable complex-valued functions under a single majorant (Dominated convergence).
The mean value theorem bounds difference quotients by a derivative bound on an interval (The mean value theorem, as the case of Cauchy's: for continuous on with and differentiable on there is with ).
Proof
Fix and let with and . Define For every , differentiability in gives .
For each , hypothesis 1 makes and [step 1.1, L1, L2] integrable and therefore measurable, so is measurable. Fix and . If , then is immediate. Otherwise put so and Apply [L2] to the real-valued function on the segment joining to . For some interior point of that segment, Hypothesis 3 makes measurable. Therefore [L1] applies to .
By [L1], But so the difference quotients of converge to the displayed integral. Hence is differentiable at with the stated derivative.
Jensen's integral inequality for a probability measure
Statement
Let be a probability space, let be real-valued, let be an interval containing for almost every , and let be convex with . Then
Facts & Assumptions
Given: A probability space , a real-valued integrable , an interval containing its almost-everywhere range, and a convex with .
A probability measure is a measure with total mass (Probability measures and probability spaces).
The Lebesgue integral is linear on (The Lebesgue integral is linear on ).
Every slope between the one-sided derivatives of a convex function yields a supporting line at an interior point (Every slope between the left and right derivatives of a convex function gives a supporting line).
A nonnegative measurable function has integral exactly when it vanishes almost everywhere (A nonnegative measurable function has integral exactly when it vanishes almost everywhere).
Proof
Put . If lies in the interior of , apply [L3] to obtain a supporting line with on . Integrating and using [L1] and [L2] gives
Suppose instead that is an endpoint of , say the left endpoint. Then [L1, L2, L4, given] almost everywhere and by [L1] and [L2]. Therefore almost everywhere by [L4], so The right-endpoint case is identical.
Steps 1.1 and 1.2 cover the interior and endpoint cases, so Jensen's [step 1.1, step 1.2] ∎ inequality holds on the whole interval .
FALSE: monotone convergence holds without monotonicity
Statement
If are measurable and converge pointwise to , then
Facts & Assumptions
Given: The statement above.
Monotone convergence requires a nondecreasing hypothesis (Monotone convergence for the integral).
Refutation
On with Lebesgue measure, let [given, construct] ; then for every .
But for every , whereas . So the displayed conclusion fails, and [L1] shows that the missing monotonicity hypothesis is exactly what breaks.
FALSE: Fatou's lemma is always an equality
Statement
For every sequence of nonnegative measurable functions ,
Facts & Assumptions
Given: The statement above.
Fatou's lemma only asserts the inequality (Fatou's lemma).
Refutation
On with Lebesgue measure, let ; then pointwise.
Therefore , while for every . So the equality in the Statement fails, and [L1] is strict here.
FALSE: dominated convergence holds without a dominating function
Statement
If almost everywhere and each is integrable, then
Facts & Assumptions
Given: The statement above.
Dominated convergence spends one integrable majorant for the whole sequence (Dominated convergence).
Refutation
On with Lebesgue measure, let [given, construct] Then almost everywhere and every is integrable.
However for every , while . So the conclusion fails, and [L1] identifies the missing dominating function as the lost hypothesis.
FALSE: a nonnegative measurable function with integral vanishes everywhere
Statement
If is measurable and , then for every .
Facts & Assumptions
Given: The statement above.
A nonnegative measurable function has integral exactly when it vanishes almost everywhere, not everywhere (A nonnegative measurable function has integral exactly when it vanishes almost everywhere).
Degenerate intervals in have Lebesgue measure (A box in with parameters is Lebesgue measurable of measure , whichever of its faces are included).
Refutation
On with Lebesgue measure, let . By [L2], the [L2, given, construct] set is null, so .
But . Therefore the conclusion in the Statement is false, and [L1] shows that "almost everywhere" is the correct replacement.
FALSE: pointwise limits of integrable functions are integrable
Statement
Whenever integrable functions converge pointwise to , the limit is integrable.
Facts & Assumptions
Given: The statement above.
Counting measure is a measure on (Counting measure on an arbitrary set, Counting measure is a measure).
Integrability means finiteness of the integral of the modulus (Integrable real and complex functions, and their integrals).
Refutation
On , let [L1, L2, given, construct] Each is integrable because it has finite support, and for every .
The pointwise limit is the constant function , whose counting-measure [step 1.1, L1, L2, algebra] ∎ integral is , so it is not integrable by [L2]. Therefore the Statement is false.
FALSE: the Lebesgue integral extends linearly to all measurable functions
Statement
Whenever measurable real-valued functions , , and all have defined extended Lebesgue integrals, the extended-real sum is defined and equals .
Facts & Assumptions
Given: The statement above.
The actual linearity theorem only applies on (The Lebesgue integral is linear on ).
Refutation
On with Lebesgue measure, let ; its nonnegative Lebesgue integral is .
The integrals of , , and are individually defined, with [step 1.1, L1, algebra] values , , and . But is undefined, so the claimed unrestricted linearity identity fails. This is why [L1] restricts linearity to .
FALSE: Jensen's inequality holds on an infinite measure space without normalization
Statement
For every convex and every nonnegative measurable one has even when .
Facts & Assumptions
Given: The statement above.
Jensen's inequality is stated for probability measures, so the normalization is part of the theorem (Jensen's integral inequality for a probability measure).
Counting measure is a measure on (Counting measure on an arbitrary set, Counting measure is a measure).
Refutation
On , let and[L2, given, construct] . Then
Therefore [step 1.1, L1, algebra] ∎ so the displayed inequality fails on this infinite measure space. This is why [L1] requires probability normalization.
5 · Examples, counterexamples and false statements
None yet.
Sources
- Richard F. Bass, Real Analysis for Graduate Students, ch. 6
- Gerald B. Folland, Real Analysis, 2nd ed., §2.2
- John K. Hunter, Measure Theory Notes, Definition 4.1
- Richard F. Bass, Real Analysis for Graduate Students, Proposition 6.3
- John K. Hunter, Measure Theory Notes, §4.2
- Richard F. Bass, Real Analysis for Graduate Students, Theorem 7.1 setup
- John K. Hunter, Measure Theory Notes, Definition 4.4
- John K. Hunter, Measure Theory Notes, Proposition 4.3
- Richard F. Bass, Real Analysis for Graduate Students, ch. 7
- John K. Hunter, Measure Theory Notes, Proposition 4.5
- Richard F. Bass, Real Analysis for Graduate Students, Theorem 7.1
- John K. Hunter, Measure Theory Notes, Theorem 4.6
- Gerald B. Folland, Real Analysis, 2nd ed., Theorem 2.14
- John K. Hunter, Measure Theory Notes, Proposition 4.7
- Gerald B. Folland, Real Analysis, 2nd ed., Theorem 2.15
- Richard F. Bass, Real Analysis for Graduate Students, Proposition 7.6
- Richard F. Bass, Real Analysis for Graduate Students, Theorem 7.8
- John K. Hunter, Measure Theory Notes, Theorem 4.22
- Richard F. Bass, Real Analysis for Graduate Students, Proposition 8.1
- Gerald B. Folland, Real Analysis, 2nd ed., Proposition 2.16
- John K. Hunter, Measure Theory Notes, Proposition 4.14
- Gerald B. Folland, Real Analysis, 2nd ed., Proposition 2.20
- Richard F. Bass, Real Analysis for Graduate Students, Proposition 6.3(4)
- Gerald B. Folland, Real Analysis, 2nd ed., Corollary 2.17
- John K. Hunter, Measure Theory Notes, ch. 4
- Richard F. Bass, Real Analysis for Graduate Students, Definition 6.2
- John K. Hunter, Measure Theory Notes, Definition 4.8
- Gerald B. Folland, Real Analysis, 2nd ed., §2.3
- Richard F. Bass, Real Analysis for Graduate Students, Theorem 7.4
- John K. Hunter, Measure Theory Notes, Proposition 4.9
- Richard F. Bass, Real Analysis for Graduate Students, Proposition 8.2
- Gerald B. Folland, Real Analysis, 2nd ed., Proposition 2.23(b)
- Gerald B. Folland, Real Analysis, 2nd ed., Proposition 2.22
- John K. Hunter, Measure Theory Notes, Theorem 4.24
- Gerald B. Folland, Real Analysis, 2nd ed., Theorem 2.24
- Gerald B. Folland, Real Analysis, 2nd ed., Theorem 2.26
- John K. Hunter, Measure Theory Notes, Proposition 4.16
- Gerald B. Folland, Real Analysis, 2nd ed., Proposition 2.23
- Gerald B. Folland, Real Analysis, 2nd ed., Theorem 2.27
- Richard L. Wheeden and Antoni Zygmund, Measure and Integral, Theorem (7.44)
- Richard F. Bass, Real Analysis for Graduate Students, Example 7.3
- Gerald B. Folland, Real Analysis, 2nd ed., Fatou's Lemma 2.18
- John K. Hunter, Measure Theory Notes, Example 4.18
- John K. Hunter, Measure Theory Notes, Chapter 4