Vector valued measures #
This file defines vector valued measures, which are σ-additive functions from a set to an add monoid
M
such that it maps the empty set and non-measurable sets to zero. In the case
that M = ℝ
, we called the vector measure a signed measure and write SignedMeasure α
.
Similarly, when M = ℂ
, we call the measure a complex measure and write ComplexMeasure α
(defined in MeasureTheory/Measure/Complex
).
Main definitions #
MeasureTheory.VectorMeasure
is a vector valued, σ-additive function that maps the empty and non-measurable set to zero.MeasureTheory.VectorMeasure.map
is the pushforward of a vector measure along a function.MeasureTheory.VectorMeasure.restrict
is the restriction of a vector measure on some set.
Notation #
v ≤[i] w
means that the vector measurev
restricted on the seti
is less than or equal to the vector measurew
restricted oni
, i.e.v.restrict i ≤ w.restrict i
.
Implementation notes #
We require all non-measurable sets to be mapped to zero in order for the extensionality lemma to only compare the underlying functions for measurable sets.
We use HasSum
instead of tsum
in the definition of vector measures in comparison to Measure
since this provides summability.
Tags #
vector measure, signed measure, complex measure
A vector measure on a measurable space α
is a σ-additive M
-valued function (for some M
an add monoid) such that the empty set and non-measurable sets are mapped to zero.
- measureOf' : Set α → M
- not_measurable' ⦃i : Set α⦄ : ¬MeasurableSet i → ↑self i = 0
Instances For
Equations
- MeasureTheory.VectorMeasure.instCoeFun = { coe := MeasureTheory.VectorMeasure.measureOf' }
Given a real number r
and a signed measure s
, smul r s
is the signed
measure corresponding to the function r • s
.
Equations
- MeasureTheory.VectorMeasure.smul r v = { measureOf' := r • ↑v, empty' := ⋯, not_measurable' := ⋯, m_iUnion' := ⋯ }
Instances For
Equations
- MeasureTheory.VectorMeasure.instSMul = { smul := MeasureTheory.VectorMeasure.smul }
Equations
- MeasureTheory.VectorMeasure.instZero = { zero := { measureOf' := 0, empty' := ⋯, not_measurable' := ⋯, m_iUnion' := ⋯ } }
Equations
- MeasureTheory.VectorMeasure.instInhabited = { default := 0 }
The sum of two vector measure is a vector measure.
Instances For
Equations
- MeasureTheory.VectorMeasure.instAdd = { add := MeasureTheory.VectorMeasure.add }
Equations
- MeasureTheory.VectorMeasure.instAddCommMonoid = Function.Injective.addCommMonoid MeasureTheory.VectorMeasure.measureOf' ⋯ ⋯ ⋯ ⋯
(⇑)
is an AddMonoidHom
.
Equations
- MeasureTheory.VectorMeasure.coeFnAddMonoidHom = { toFun := MeasureTheory.VectorMeasure.measureOf', map_zero' := ⋯, map_add' := ⋯ }
Instances For
The negative of a vector measure is a vector measure.
Instances For
Equations
- MeasureTheory.VectorMeasure.instNeg = { neg := MeasureTheory.VectorMeasure.neg }
The difference of two vector measure is a vector measure.
Instances For
Equations
- MeasureTheory.VectorMeasure.instSub = { sub := MeasureTheory.VectorMeasure.sub }
Equations
- MeasureTheory.VectorMeasure.instAddCommGroup = Function.Injective.addCommGroup MeasureTheory.VectorMeasure.measureOf' ⋯ ⋯ ⋯ ⋯ ⋯ ⋯ ⋯
Equations
- MeasureTheory.VectorMeasure.instDistribMulAction = Function.Injective.distribMulAction MeasureTheory.VectorMeasure.coeFnAddMonoidHom ⋯ ⋯
Equations
- MeasureTheory.VectorMeasure.instModule = Function.Injective.module R MeasureTheory.VectorMeasure.coeFnAddMonoidHom ⋯ ⋯
A finite measure coerced into a real function is a signed measure.
Equations
- μ.toSignedMeasure = { measureOf' := fun (s : Set α) => if MeasurableSet s then (μ s).toReal else 0, empty' := ⋯, not_measurable' := ⋯, m_iUnion' := ⋯ }
Instances For
A measure is a vector measure over ℝ≥0∞
.
Equations
- μ.toENNRealVectorMeasure = { measureOf' := fun (i : Set α) => if MeasurableSet i then μ i else 0, empty' := ⋯, not_measurable' := ⋯, m_iUnion' := ⋯ }
Instances For
A vector measure over ℝ≥0∞
is a measure.
Equations
- v.ennrealToMeasure = MeasureTheory.Measure.ofMeasurable (fun (s : Set α) (x : MeasurableSet s) => ↑v s) ⋯ ⋯
Instances For
The equiv between VectorMeasure α ℝ≥0∞
and Measure α
formed by
MeasureTheory.VectorMeasure.ennrealToMeasure
and
MeasureTheory.Measure.toENNRealVectorMeasure
.
Equations
- MeasureTheory.VectorMeasure.equivMeasure = { toFun := MeasureTheory.VectorMeasure.ennrealToMeasure, invFun := MeasureTheory.Measure.toENNRealVectorMeasure, left_inv := ⋯, right_inv := ⋯ }
Instances For
The pushforward of a vector measure along a function.
Equations
- v.map f = if hf : Measurable f then { measureOf' := fun (s : Set β) => if MeasurableSet s then ↑v (f ⁻¹' s) else 0, empty' := ⋯, not_measurable' := ⋯, m_iUnion' := ⋯ } else 0
Instances For
Given a vector measure v
on M
and a continuous AddMonoidHom
f : M → N
, f ∘ v
is a
vector measure on N
.
Equations
Instances For
Given a continuous AddMonoidHom
f : M → N
, mapRangeHom
is the AddMonoidHom
mapping the
vector measure v
on M
to the vector measure f ∘ v
on N
.
Equations
- MeasureTheory.VectorMeasure.mapRangeHom f hf = { toFun := fun (v : MeasureTheory.VectorMeasure α M) => v.mapRange f hf, map_zero' := ⋯, map_add' := ⋯ }
Instances For
Given a continuous linear map f : M → N
, mapRangeₗ
is the linear map mapping the
vector measure v
on M
to the vector measure f ∘ v
on N
.
Equations
- MeasureTheory.VectorMeasure.mapRangeₗ f hf = { toFun := fun (v : MeasureTheory.VectorMeasure α M) => v.mapRange f.toAddMonoidHom hf, map_add' := ⋯, map_smul' := ⋯ }
Instances For
The restriction of a vector measure on some set.
Equations
- v.restrict i = if hi : MeasurableSet i then { measureOf' := fun (s : Set α) => if MeasurableSet s then ↑v (s ∩ i) else 0, empty' := ⋯, not_measurable' := ⋯, m_iUnion' := ⋯ } else 0
Instances For
VectorMeasure.map
as an additive monoid homomorphism.
Equations
- MeasureTheory.VectorMeasure.mapGm f = { toFun := fun (v : MeasureTheory.VectorMeasure α M) => v.map f, map_zero' := ⋯, map_add' := ⋯ }
Instances For
VectorMeasure.restrict
as an additive monoid homomorphism.
Equations
- MeasureTheory.VectorMeasure.restrictGm i = { toFun := fun (v : MeasureTheory.VectorMeasure α M) => v.restrict i, map_zero' := ⋯, map_add' := ⋯ }
Instances For
VectorMeasure.map
as a linear map.
Equations
- MeasureTheory.VectorMeasure.mapₗ f = { toFun := fun (v : MeasureTheory.VectorMeasure α M) => v.map f, map_add' := ⋯, map_smul' := ⋯ }
Instances For
VectorMeasure.restrict
as an additive monoid homomorphism.
Equations
- MeasureTheory.VectorMeasure.restrictₗ i = { toFun := fun (v : MeasureTheory.VectorMeasure α M) => v.restrict i, map_add' := ⋯, map_smul' := ⋯ }
Instances For
Vector measures over a partially ordered monoid is partially ordered.
This definition is consistent with Measure.instPartialOrder
.
Equations
- MeasureTheory.VectorMeasure.instPartialOrder = PartialOrder.mk ⋯
Equations
- One or more equations did not get rendered due to their size.
Instances For
Pretty printer defined by notation3
command.
Equations
- One or more equations did not get rendered due to their size.
Instances For
A vector measure v
is absolutely continuous with respect to a measure μ
if for all sets
s
, μ s = 0
, we have v s = 0
.
Instances For
A vector measure v
is absolutely continuous with respect to a measure μ
if for all sets
s
, μ s = 0
, we have v s = 0
.
Equations
- MeasureTheory.«term_≪ᵥ_» = Lean.ParserDescr.trailingNode `MeasureTheory.«term_≪ᵥ_» 50 50 (Lean.ParserDescr.binary `andthen (Lean.ParserDescr.symbol " ≪ᵥ ") (Lean.ParserDescr.cat `term 51))
Instances For
Two vector measures v
and w
are said to be mutually singular if there exists a measurable
set s
, such that for all t ⊆ s
, v t = 0
and for all t ⊆ sᶜ
, w t = 0
.
We note that we do not require the measurability of t
in the definition since this makes it easier
to use. This is equivalent to the definition which requires measurability. To prove
MutuallySingular
with the measurability condition, use
MeasureTheory.VectorMeasure.MutuallySingular.mk
.
Equations
Instances For
Two vector measures v
and w
are said to be mutually singular if there exists a measurable
set s
, such that for all t ⊆ s
, v t = 0
and for all t ⊆ sᶜ
, w t = 0
.
We note that we do not require the measurability of t
in the definition since this makes it easier
to use. This is equivalent to the definition which requires measurability. To prove
MutuallySingular
with the measurability condition, use
MeasureTheory.VectorMeasure.MutuallySingular.mk
.
Equations
- MeasureTheory.«term_⟂ᵥ_» = Lean.ParserDescr.trailingNode `MeasureTheory.«term_⟂ᵥ_» 60 60 (Lean.ParserDescr.binary `andthen (Lean.ParserDescr.symbol " ⟂ᵥ ") (Lean.ParserDescr.cat `term 61))
Instances For
Restriction of a vector measure onto a sub-σ-algebra.
Equations
- v.trim hle = { measureOf' := fun (i : Set α) => if MeasurableSet i then ↑v i else 0, empty' := ⋯, not_measurable' := ⋯, m_iUnion' := ⋯ }
Instances For
The underlying function for SignedMeasure.toMeasureOfZeroLE
.
Equations
- s.toMeasureOfZeroLE' i hi j hj = ↑⟨↑(MeasureTheory.VectorMeasure.restrict s i) j, ⋯⟩
Instances For
Given a signed measure s
and a positive measurable set i
, toMeasureOfZeroLE
provides the measure, mapping measurable sets j
to s (i ∩ j)
.
Equations
- s.toMeasureOfZeroLE i hi₁ hi₂ = MeasureTheory.Measure.ofMeasurable (s.toMeasureOfZeroLE' i hi₂) ⋯ ⋯
Instances For
Given a signed measure s
and a negative measurable set i
, toMeasureOfLEZero
provides the measure, mapping measurable sets j
to -s (i ∩ j)
.
Instances For
SignedMeasure.toMeasureOfZeroLE
is a finite measure.
SignedMeasure.toMeasureOfLEZero
is a finite measure.