Extension of a linear function from indicators to L1 #
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Let T : set α → E →L[ℝ] F be additive for measurable sets with finite measure, in the sense that
for s, t two such sets, s ∩ t = ∅ → T (s ∪ t) = T s + T t. T is akin to a bilinear map on
set α × E, or a linear map on indicator functions.
This file constructs an extension of T to integrable simple functions, which are finite sums of
indicators of measurable sets with finite measure, then to integrable functions, which are limits of
integrable simple functions.
The main result is a continuous linear map (α →₁[μ] E) →L[ℝ] F. This extension process is used to
define the Bochner integral in the measure_theory.integral.bochner file and the conditional
expectation of an integrable function in measure_theory.function.conditional_expectation.
Main Definitions #
fin_meas_additive μ T: the property thatTis additive on measurable sets with finite measure. For two such sets,s ∩ t = ∅ → T (s ∪ t) = T s + T t.dominated_fin_meas_additive μ T C:fin_meas_additive μ T ∧ ∀ s, ‖T s‖ ≤ C * (μ s).to_real. This is the property needed to perform the extension from indicators to L1.set_to_L1 (hT : dominated_fin_meas_additive μ T C) : (α →₁[μ] E) →L[ℝ] F: the extension ofTfrom indicators to L1.set_to_fun μ T (hT : dominated_fin_meas_additive μ T C) (f : α → E) : F: a version of the extension which applies to functions (with value 0 if the function is not integrable).
Properties #
For most properties of set_to_fun, we provide two lemmas. One version uses hypotheses valid on
all sets, like T = T', and a second version which uses a primed name uses hypotheses on
measurable sets with finite measure, like ∀ s, measurable_set s → μ s < ∞ → T s = T' s.
The lemmas listed here don't show all hypotheses. Refer to the actual lemmas for details.
Linearity:
set_to_fun_zero_left : set_to_fun μ 0 hT f = 0set_to_fun_add_left : set_to_fun μ (T + T') _ f = set_to_fun μ T hT f + set_to_fun μ T' hT' fset_to_fun_smul_left : set_to_fun μ (λ s, c • (T s)) (hT.smul c) f = c • set_to_fun μ T hT fset_to_fun_zero : set_to_fun μ T hT (0 : α → E) = 0set_to_fun_neg : set_to_fun μ T hT (-f) = - set_to_fun μ T hT fIffandgare integrable:set_to_fun_add : set_to_fun μ T hT (f + g) = set_to_fun μ T hT f + set_to_fun μ T hT gset_to_fun_sub : set_to_fun μ T hT (f - g) = set_to_fun μ T hT f - set_to_fun μ T hT gIfTis verifies∀ c : 𝕜, ∀ s x, T s (c • x) = c • T s x:set_to_fun_smul : set_to_fun μ T hT (c • f) = c • set_to_fun μ T hT f
Other:
set_to_fun_congr_ae (h : f =ᵐ[μ] g) : set_to_fun μ T hT f = set_to_fun μ T hT gset_to_fun_measure_zero (h : μ = 0) : set_to_fun μ T hT f = 0
If the space is a normed_lattice_add_comm_group and T is such that 0 ≤ T s x for 0 ≤ x, we
also prove order-related properties:
set_to_fun_mono_left (h : ∀ s x, T s x ≤ T' s x) : set_to_fun μ T hT f ≤ set_to_fun μ T' hT' fset_to_fun_nonneg (hf : 0 ≤ᵐ[μ] f) : 0 ≤ set_to_fun μ T hT fset_to_fun_mono (hfg : f ≤ᵐ[μ] g) : set_to_fun μ T hT f ≤ set_to_fun μ T hT g
Implementation notes #
The starting object T : set α → E →L[ℝ] F matters only through its restriction on measurable sets
with finite measure. Its value on other sets is ignored.
A set function is fin_meas_additive if its value on the union of two disjoint measurable
sets with finite measure is the sum of its values on each set.
A fin_meas_additive set function whose norm on every set is less than the measure of the
set (up to a multiplicative constant).
Extend set α → (F →L[ℝ] F') to (α →ₛ F) → F'.
Extend set α → (E →L[ℝ] F') to (α →₁ₛ[μ] E) → F'.
set_to_L1s does not change if we replace the measure μ by μ' with μ ≪ μ'. The statement
uses two functions f and f' because they have to belong to different types, but morally these
are the same function (we have f =ᵐ[μ] f').
Extend set α → E →L[ℝ] F to (α →₁ₛ[μ] E) →L[𝕜] F.
Equations
- measure_theory.L1.simple_func.set_to_L1s_clm' α E 𝕜 μ hT h_smul = {to_fun := measure_theory.L1.simple_func.set_to_L1s T, map_add' := _, map_smul' := _}.mk_continuous C _
Extend set α → E →L[ℝ] F to (α →₁ₛ[μ] E) →L[ℝ] F.
Equations
- measure_theory.L1.simple_func.set_to_L1s_clm α E μ hT = {to_fun := measure_theory.L1.simple_func.set_to_L1s T, map_add' := _, map_smul' := _}.mk_continuous C _
Extend set α → (E →L[ℝ] F) to (α →₁[μ] E) →L[𝕜] F.
Equations
- measure_theory.L1.set_to_L1' 𝕜 hT h_smul = (measure_theory.L1.simple_func.set_to_L1s_clm' α E 𝕜 μ hT h_smul).extend (measure_theory.Lp.simple_func.coe_to_Lp α E 𝕜) measure_theory.L1.set_to_L1'._proof_6 measure_theory.L1.set_to_L1'._proof_7
Extend set α → E →L[ℝ] F to (α →₁[μ] E) →L[ℝ] F.
Equations
- measure_theory.L1.set_to_L1 hT = (measure_theory.L1.simple_func.set_to_L1s_clm α E μ hT).extend (measure_theory.Lp.simple_func.coe_to_Lp α E ℝ) measure_theory.L1.set_to_L1._proof_6 measure_theory.L1.set_to_L1._proof_7
If fs i → f in L1, then set_to_L1 hT (fs i) → set_to_L1 hT f.
Extend T : set α → E →L[ℝ] F to (α → E) → F (for integrable functions α → E). We set it to
0 if the function is not integrable.
Equations
- measure_theory.set_to_fun μ T hT f = dite (measure_theory.integrable f μ) (λ (hf : measure_theory.integrable f μ), ⇑(measure_theory.L1.set_to_L1 hT) (measure_theory.integrable.to_L1 f hf)) (λ (hf : ¬measure_theory.integrable f μ), 0)
If F i → f in L1, then set_to_fun μ T hT (F i) → set_to_fun μ T hT f.
Auxiliary lemma for set_to_fun_congr_measure: the function sending f : α →₁[μ] G to
f : α →₁[μ'] G is continuous when μ' ≤ c' • μ for c' ≠ ∞.
Lebesgue dominated convergence theorem provides sufficient conditions under which almost
everywhere convergence of a sequence of functions implies the convergence of their image by
set_to_fun.
We could weaken the condition bound_integrable to require has_finite_integral bound μ instead
(i.e. not requiring that bound is measurable), but in all applications proving integrability
is easier.
Lebesgue dominated convergence theorem for filters with a countable basis