Bochner integral #
The Bochner integral extends the definition of the Lebesgue integral to functions that map from a measure space into a Banach space (complete normed vector space). It is constructed here using the L1 Bochner integral constructed in the file Mathlib/MeasureTheory/Integral/Bochner/L1.lean.
Main definitions #
The Bochner integral is defined through the extension process described in the file Mathlib/MeasureTheory/Integral/SetToL1.lean, which follows these steps:
MeasureTheory.integral: the Bochner integral on functions defined as the Bochner integral of its equivalence class in L1 space, if it is in L1, and 0 otherwise.
The result of that construction is ∫ a, f a ∂μ, which is definitionally equal to setToFun (dominatedFinMeasAdditive_weightedSMul μ) f. Some basic properties of the integral (like linearity) are particular cases of the properties of setToFun (which are described in the file Mathlib/MeasureTheory/Integral/SetToL1.lean).
Main statements #
- Basic properties of the Bochner integral on functions of type
α → E, whereαis a measure space andEis a real normed space.
integral_zero:∫ 0 ∂μ = 0integral_add:∫ x, f x + g x ∂μ = ∫ x, f ∂μ + ∫ x, g x ∂μintegral_neg:∫ x, - f x ∂μ = - ∫ x, f x ∂μintegral_sub:∫ x, f x - g x ∂μ = ∫ x, f x ∂μ - ∫ x, g x ∂μintegral_smul:∫ x, r • f x ∂μ = r • ∫ x, f x ∂μintegral_congr_ae:f =ᵐ[μ] g → ∫ x, f x ∂μ = ∫ x, g x ∂μnorm_integral_le_integral_norm:‖∫ x, f x ∂μ‖ ≤ ∫ x, ‖f x‖ ∂μ
- Basic order properties of the Bochner integral on functions of type
α → E, whereαis a measure space andEis a real ordered Banach space.
integral_nonneg_of_ae:0 ≤ᵐ[μ] f → 0 ≤ ∫ x, f x ∂μintegral_nonpos_of_ae:f ≤ᵐ[μ] 0 → ∫ x, f x ∂μ ≤ 0integral_mono_ae:f ≤ᵐ[μ] g → ∫ x, f x ∂μ ≤ ∫ x, g x ∂μintegral_nonneg:0 ≤ f → 0 ≤ ∫ x, f x ∂μintegral_nonpos:f ≤ 0 → ∫ x, f x ∂μ ≤ 0integral_mono:f ≤ᵐ[μ] g → ∫ x, f x ∂μ ≤ ∫ x, g x ∂μ
- Propositions connecting the Bochner integral with the integral on
ℝ≥0∞-valued functions, which is calledlintegraland has the notation∫⁻.
integral_eq_lintegral_pos_part_sub_lintegral_neg_part:∫ x, f x ∂μ = ∫⁻ x, f⁺ x ∂μ - ∫⁻ x, f⁻ x ∂μ, wheref⁺is the positive part offandf⁻is the negative part off.integral_eq_lintegral_of_nonneg_ae:0 ≤ᵐ[μ] f → ∫ x, f x ∂μ = ∫⁻ x, f x ∂μ
(In the file
Mathlib/MeasureTheory/Integral/DominatedConvergence.lean)tendsto_integral_of_dominated_convergence: the Lebesgue dominated convergence theorem(In
Mathlib/MeasureTheory/Integral/Bochner/Set.lean) integration commutes with continuous linear maps.
ContinuousLinearMap.integral_comp_commLinearIsometry.integral_comp_comm
Notes #
Some tips on how to prove a proposition if the API for the Bochner integral is not enough so that you need to unfold the definition of the Bochner integral and go back to simple functions.
One method is to use the theorem Integrable.induction in the file Mathlib/MeasureTheory/Function/SimpleFuncDenseLp.lean (or one of the related results, like Lp.induction for functions in Lp), which allows you to prove something for an arbitrary integrable function.
Another method is using the following steps. See integral_eq_lintegral_pos_part_sub_lintegral_neg_part for a complicated example, which proves that ∫ f = ∫⁻ f⁺ - ∫⁻ f⁻, with the first integral sign being the Bochner integral of a real-valued function f : α → ℝ, and second and third integral sign being the integral on ℝ≥0∞-valued functions (called lintegral). The proof of integral_eq_lintegral_pos_part_sub_lintegral_neg_part is scattered in sections with the name posPart.
Here are the usual steps of proving that a property p, say ∫ f = ∫⁻ f⁺ - ∫⁻ f⁻, holds for all functions :
First go to the
L¹space.For example, if you see
ENNReal.toReal (∫⁻ a, ENNReal.ofReal <| ‖f a‖), that is the norm offinL¹space. Rewrite usingL1.norm_of_fun_eq_lintegral_norm.Show that the set
{f ∈ L¹ | ∫ f = ∫⁻ f⁺ - ∫⁻ f⁻}is closed inL¹usingisClosed_eq.Show that the property holds for all simple functions
sinL¹space.Typically, you need to convert various notions to their
SimpleFunccounterpart, using lemmas likeL1.integral_coe_eq_integral.Since simple functions are dense in
L¹,
univ = closure {s simple} = closure {s simple | ∫ s = ∫⁻ s⁺ - ∫⁻ s⁻} : the property holds for all simple functions ⊆ closure {f | ∫ f = ∫⁻ f⁺ - ∫⁻ f⁻} = {f | ∫ f = ∫⁻ f⁺ - ∫⁻ f⁻} : closure of a closed set is itself Use isClosed_property or DenseRange.induction_on for this argument.
Notation #
α →ₛ E: simple functions (defined inMathlib/MeasureTheory/Function/SimpleFunc.lean)α →₁[μ] E: functions in L1 space, i.e., equivalence classes of integrable functions (defined inMathlib/MeasureTheory/Function/LpSpace/Basic.lean)∫ a, f a ∂μ: integral offwith respect to a measureμ∫ a, f a: integral offwith respect tovolume, the default measure on the ambient type
We also define notations for integral on a set, which are described in the file Mathlib/MeasureTheory/Integral/Bochner/Set.lean.
Note : ₛ is typed using \_s. Sometimes it shows as a box if the font is missing.
Tags #
Bochner integral, simple function, function space, Lebesgue dominated convergence theorem
The Bochner integral on functions #
Define the Bochner integral on functions generally to be the L1 Bochner integral, for integrable functions, and 0 otherwise; prove its basic properties.
The Bochner integral
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- MeasureTheory.integral μ f = if x : CompleteSpace G then if hf : MeasureTheory.Integrable f μ then MeasureTheory.L1.integral (MeasureTheory.Integrable.toL1 f hf) else 0 else 0
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In the notation for integrals, an expression like ∫ x, g ‖x‖ ∂μ will not be parsed correctly, and needs parentheses. We do not set the binding power of r to 0, because then ∫ x, f x = 0 will be parsed incorrectly.
The Bochner integral
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The Bochner integral
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The Bochner integral
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The Bochner integral is linear. Note this requires 𝕜 to be a normed division ring, in order to ensure that for c ≠ 0, the function c • f is integrable iff f is. For an analogous statement for more general rings with an a priori integrability assumption on f, see MeasureTheory.Integrable.integral_smul.
Alias of MeasureTheory.integral_const_mul.
Alias of MeasureTheory.integral_mul_const.
If f has finite integral, then ∫ x in s, f x ∂μ is absolutely continuous in s: it tends to zero as μ s tends to zero.
If f is integrable, then ∫ x in s, f x ∂μ is absolutely continuous in s: it tends to zero as μ s tends to zero.
If F i → f in L1, then ∫ x, F i x ∂μ → ∫ x, f x ∂μ.
If F i → f in L1, then ∫ x, F i x ∂μ → ∫ x, f x ∂μ.
If F i → f in L1, then ∫ x in s, F i x ∂μ → ∫ x in s, f x ∂μ.
If F i → f in L1, then ∫ x in s, F i x ∂μ → ∫ x in s, f x ∂μ.
The Bochner integral of a real-valued function f : α → ℝ is the difference between the integral of the positive part of f and the integral of the negative part of f.
The integral of a function which is nonnegative almost everywhere is nonnegative.
Monotone convergence theorem for real-valued functions and Bochner integrals
Monotone convergence theorem for real-valued functions and Bochner integrals
If a monotone sequence of functions has an upper bound and the sequence of integrals of these functions tends to the integral of the upper bound, then the sequence of functions converges almost everywhere to the upper bound.
If an antitone sequence of functions has a lower bound and the sequence of integrals of these functions tends to the integral of the lower bound, then the sequence of functions converges almost everywhere to the lower bound.
Alias of MeasureTheory.setIntegral_measure_zero.
Markov's inequality also known as Chebyshev's first inequality.
Hölder's inequality for the integral of a product of norms. The integral of the product of two norms of functions is bounded by the product of their ℒp and ℒq seminorms when p and q are conjugate exponents.
Hölder's inequality for functions α → ℝ. The integral of the product of two nonnegative functions is bounded by the product of their ℒp and ℒq seminorms when p and q are conjugate exponents.
Simple function seen as simple function of a larger MeasurableSpace.
Equations
- MeasureTheory.SimpleFunc.toLargerSpace hm f = { toFun := f.toFun, measurableSet_fiber' := ⋯, finite_range' := ⋯ }
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Positivity extension for integrals.
This extension only proves non-negativity, strict positivity is more delicate for integration and requires more assumptions.