production module
AutoSamplingTheory.ExampleCases.ProximalBPS.AmbientAdjointCorrector
Read the mathematical statements and proofs in order
2 named declarations scanned from AutoSamplingTheory/ExampleCases/ProximalBPS/AmbientAdjointCorrector.lean.
Declarations
def AutoSamplingTheory.ExampleCases.ProximalBPS.AmbientAdjointCorrector.actual_ambient_adjoint_centered_decomposition_statement Partial Not mapped
No declaration docstring.
private def actual_ambient_adjoint_centered_decomposition_statement
{E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
[FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
{V : E → ℝ} {α β : ℝ≥0} {η : ℝ}
(hα : 0 < (α : ℝ)) (hαβ : α ≤ β) (hV : ContDiff ℝ 2 V)
(hH : ∀ x v : E,
(α : ℝ)*‖v‖^2 ≤ (fderiv ℝ (fderiv ℝ V) x v) v ∧
(fderiv ℝ (fderiv ℝ V) x v) v ≤ (β : ℝ)*‖v‖^2)
(hη : 0 < η) (hβη : (β : ℝ)*η ≤ 1) : Prop :=
let μ := (volume : Measure E).tilted (fun x => -V x)
let J := (μ.prod (stdGaussian E)).map
(fun p : E × E => (p.1,p.1+Real.sqrt η • p.2))
let ν := J.snd
let Λ := J.map (fun p : E × E => (p.2,(2 : ℝ) • p.1-p.2))
let F := fun p : E × E => (p.1,(2 : ℝ) • p.1-p.2)
let mY := MeasurableSpace.comap Prod.snd (inferInstance : MeasurableSpace E)
letI : MeasurableSpace (E × E) := Prod.instMeasurableSpace
letI : Fact (mY ≤ (inferInstance : MeasurableSpace (E × E))) :=
⟨measurable_snd.comap_le⟩
let HP := lpMeas ℝ ℝ mY 2 J
let P : Lp ℝ 2 J →L[ℝ] Lp ℝ 2 J :=
HP.subtypeL ∘L condExpL2 ℝ ℝ (μ := J) measurable_snd.comap_le
let hp : MeasurePreserving (Prod.snd : E × E → E) J ν := ⟨measurable_snd,rfl⟩
let M : Lp ℝ 2 ν →ₗᵢ[ℝ] Lp ℝ 2 J := Lp.compMeasurePreservingₗᵢ ℝ Prod.snd hp
IsProbabilityMeasure μ ∧ IsProbabilityMeasure J ∧ IsProbabilityMeasure ν ∧
HP = P.toLinearMap.range ∧
(∀ f : HP, P (f : Lp ℝ 2 J) = f) ∧
∃ S : Kernel E E, IsMarkovKernel S ∧
(∀ y, S y = (volume : Measure E).tilted
(fun x => -V ((1/2 : ℝ) • (y+x)) - ‖y-x‖^2/(8*η))) ∧
Λ.IsCondKernel S ∧ Λ.fst = ν ∧ Λ.snd = ν ∧
∃ e : Lp ℝ 2 ν ≃ₗᵢ[ℝ] HP,
(∀ u : Lp ℝ 2 ν, (e u : Lp ℝ 2 J) = M u) ∧
∃ U : Lp ℝ 2 J →ₗᵢ[ℝ] Lp ℝ 2 J,
(∀ g : Lp ℝ 2 J, (U g : E × E → ℝ) =ᵐ[J] g ∘ F) ∧
Function.Involutive U ∧ IsSelfAdjoint U.toContinuousLinearMap ∧
∃ T : Lp ℝ 2 ν →L[ℝ] Lp ℝ 2 ν,
IsSelfAdjoint T ∧
(∀ u : Lp ℝ 2 ν,
‖T u‖ ≤ ‖u‖ ∧
(T u : E → ℝ) =ᵐ[ν] (fun y => ∫ x, u x ∂S y) ∧
(∫ y, T u y ∂ν) = ∫ y, u y ∂ν) ∧
let A : HP →L[ℝ] HP :=
HP.orthogonalProjectionOnto ∘L U.toContinuousLinearMap ∘L HP.subtypeL
-- Source excerpt truncated; follow the exact source link.
AutoSamplingTheory/ExampleCases/ProximalBPS/AmbientAdjointCorrector.lean:10published source at ed709a7f320d
Excerpt truncated; the exact source link is authoritative.
theorem AutoSamplingTheory.ExampleCases.ProximalBPS.AmbientAdjointCorrector.actual_ambient_adjoint_centered_decomposition Partial Not mapped
Read the complete mathematical statement and proof, with Lean below each
No declaration docstring.
theorem actual_ambient_adjoint_centered_decomposition
{E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
[FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
{V : E → ℝ} {α β : ℝ≥0} {η : ℝ}
(hα : 0 < (α : ℝ)) (hαβ : α ≤ β) (hV : ContDiff ℝ 2 V)
(hH : ∀ x v : E,
(α : ℝ)*‖v‖^2 ≤ (fderiv ℝ (fderiv ℝ V) x v) v ∧
(fderiv ℝ (fderiv ℝ V) x v) v ≤ (β : ℝ)*‖v‖^2)
(hη : 0 < η) (hβη : (β : ℝ)*η ≤ 1) : actual_ambient_adjoint_centered_decomposition_statement (E := E) (V := V) (α := α) (β := β) (η := η) hα hαβ hV hH hη hβη := by
unfold actual_ambient_adjoint_centered_decomposition_statement
classical
dsimp only
let μ := (volume : Measure E).tilted (fun x => -V x)
let J := (μ.prod (stdGaussian E)).map
(fun p : E × E => (p.1,p.1+Real.sqrt η • p.2))
let ν := J.snd
let mY : MeasurableSpace (E × E) := MeasurableSpace.comap Prod.snd (inferInstance : MeasurableSpace E)
letI : MeasurableSpace (E × E) := Prod.instMeasurableSpace
letI : Fact (mY ≤ (inferInstance : MeasurableSpace (E × E))) := ⟨measurable_snd.comap_le⟩
let HP := lpMeas ℝ ℝ mY 2 J
letI : NormedAddCommGroup HP := HP.normedAddCommGroup
letI : InnerProductSpace ℝ HP := HP.innerProductSpace
let P : Lp ℝ 2 J →L[ℝ] Lp ℝ 2 J :=
HP.subtypeL ∘L condExpL2 ℝ ℝ (μ := J) measurable_snd.comap_le
have hBase := AutoSamplingTheory.ExampleCases.ProximalBPS.PolarIsometry.actual_centered_polar_isometry
(E:=E) (V:=V) (α:=α) (β:=β) (η:=η) hα hαβ hV hH hη hβη
dsimp only at hBase
rcases hBase with
⟨hμ,hJ,hν,hRange,hPf,S,hS,hSd,hSc,hSf,hSs,e,he,U,hU,hUi,hUs,
T,hTs,hAll,hAeq,hAs,hAn,hPB,Γ,hΓ,hΓSq,hΓP,hΓPSq,hGram,
q,hqa,hTq,hq,hCenter,hΓPq,hγ,ΓP0,hΓP0,hPos0,hOrder0,hUnit,
Inv,hLeft,hRight,hNormInv,B0,hB0,V0,hVdef,hFactor,hVadj,hVnorm⟩
let A : HP →L[ℝ] HP := HP.orthogonalProjectionOnto ∘L U.toContinuousLinearMap ∘L HP.subtypeL
let B : HP →L[ℝ] Lp ℝ 2 J :=
(((1 : Lp ℝ 2 J →L[ℝ] Lp ℝ 2 J)-P)*U.toContinuousLinearMap*P) ∘L HP.subtypeL
let ΓP : HP →L[ℝ] HP := e.conjStarAlgEquiv Γ
let qP : HP := e q
let HP0 := (innerSL ℝ qP).ker
letI : NormedAddCommGroup HP0 := HP0.normedAddCommGroup
letI : InnerProductSpace ℝ HP0 := HP0.innerProductSpace
letI : CompleteSpace HP0 := (innerSL ℝ qP).isClosed_ker.completeSpace_coe
let Hperp := P.ker
letI : NormedAddCommGroup Hperp := Hperp.normedAddCommGroup
letI : InnerProductSpace ℝ Hperp := Hperp.innerProductSpace
-- Source excerpt truncated; follow the exact source link.
AutoSamplingTheory/ExampleCases/ProximalBPS/AmbientAdjointCorrector.lean:107published source at ed709a7f320dOpen detailed card
Excerpt truncated; the exact source link is authoritative.
Complete Lean source
AutoSamplingTheory/ExampleCases/ProximalBPS/AmbientAdjointCorrector.lean — full module with imports and context
import AutoSamplingTheory.ExampleCases.ProximalBPS.PolarIsometry
open MeasureTheory ProbabilityTheory InnerProductSpace
open scoped RealInnerProductSpace ContDiff NNReal Topology ENNReal
namespace AutoSamplingTheory.ExampleCases.ProximalBPS.AmbientAdjointCorrector
noncomputable section
set_option autoImplicit false
set_option maxHeartbeats 2000000
private def actual_ambient_adjoint_centered_decomposition_statement
{E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
[FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
{V : E → ℝ} {α β : ℝ≥0} {η : ℝ}
(hα : 0 < (α : ℝ)) (hαβ : α ≤ β) (hV : ContDiff ℝ 2 V)
(hH : ∀ x v : E,
(α : ℝ)*‖v‖^2 ≤ (fderiv ℝ (fderiv ℝ V) x v) v ∧
(fderiv ℝ (fderiv ℝ V) x v) v ≤ (β : ℝ)*‖v‖^2)
(hη : 0 < η) (hβη : (β : ℝ)*η ≤ 1) : Prop :=
let μ := (volume : Measure E).tilted (fun x => -V x)
let J := (μ.prod (stdGaussian E)).map
(fun p : E × E => (p.1,p.1+Real.sqrt η • p.2))
let ν := J.snd
let Λ := J.map (fun p : E × E => (p.2,(2 : ℝ) • p.1-p.2))
let F := fun p : E × E => (p.1,(2 : ℝ) • p.1-p.2)
let mY := MeasurableSpace.comap Prod.snd (inferInstance : MeasurableSpace E)
letI : MeasurableSpace (E × E) := Prod.instMeasurableSpace
letI : Fact (mY ≤ (inferInstance : MeasurableSpace (E × E))) :=
⟨measurable_snd.comap_le⟩
let HP := lpMeas ℝ ℝ mY 2 J
let P : Lp ℝ 2 J →L[ℝ] Lp ℝ 2 J :=
HP.subtypeL ∘L condExpL2 ℝ ℝ (μ := J) measurable_snd.comap_le
let hp : MeasurePreserving (Prod.snd : E × E → E) J ν := ⟨measurable_snd,rfl⟩
let M : Lp ℝ 2 ν →ₗᵢ[ℝ] Lp ℝ 2 J := Lp.compMeasurePreservingₗᵢ ℝ Prod.snd hp
IsProbabilityMeasure μ ∧ IsProbabilityMeasure J ∧ IsProbabilityMeasure ν ∧
HP = P.toLinearMap.range ∧
(∀ f : HP, P (f : Lp ℝ 2 J) = f) ∧
∃ S : Kernel E E, IsMarkovKernel S ∧
(∀ y, S y = (volume : Measure E).tilted
(fun x => -V ((1/2 : ℝ) • (y+x)) - ‖y-x‖^2/(8*η))) ∧
Λ.IsCondKernel S ∧ Λ.fst = ν ∧ Λ.snd = ν ∧
∃ e : Lp ℝ 2 ν ≃ₗᵢ[ℝ] HP,
(∀ u : Lp ℝ 2 ν, (e u : Lp ℝ 2 J) = M u) ∧
∃ U : Lp ℝ 2 J →ₗᵢ[ℝ] Lp ℝ 2 J,
(∀ g : Lp ℝ 2 J, (U g : E × E → ℝ) =ᵐ[J] g ∘ F) ∧
Function.Involutive U ∧ IsSelfAdjoint U.toContinuousLinearMap ∧
∃ T : Lp ℝ 2 ν →L[ℝ] Lp ℝ 2 ν,
IsSelfAdjoint T ∧
(∀ u : Lp ℝ 2 ν,
‖T u‖ ≤ ‖u‖ ∧
(T u : E → ℝ) =ᵐ[ν] (fun y => ∫ x, u x ∂S y) ∧
(∫ y, T u y ∂ν) = ∫ y, u y ∂ν) ∧
let A : HP →L[ℝ] HP :=
HP.orthogonalProjectionOnto ∘L U.toContinuousLinearMap ∘L HP.subtypeL
let B : HP →L[ℝ] Lp ℝ 2 J :=
(((1 : Lp ℝ 2 J →L[ℝ] Lp ℝ 2 J)-P)*U.toContinuousLinearMap*P) ∘L HP.subtypeL
A = e.conjStarAlgEquiv T ∧ IsSelfAdjoint A ∧
(∀ f : HP, ‖A f‖ ≤ ‖f‖) ∧
(∀ f : HP, P (B f) = 0) ∧
∃ Γ : Lp ℝ 2 ν →L[ℝ] Lp ℝ 2 ν,
Γ.IsPositive ∧ Γ*Γ=(1 : Lp ℝ 2 ν →L[ℝ] Lp ℝ 2 ν)-T*T ∧
let ΓP : HP →L[ℝ] HP := e.conjStarAlgEquiv Γ
ΓP.IsPositive ∧ ΓP*ΓP=(1 : HP →L[ℝ] HP)-A*A ∧
B.adjoint ∘L B = ΓP*ΓP ∧
∃ q : Lp ℝ 2 ν,
(q : E → ℝ) =ᵐ[ν] (fun _ => (1 : ℝ)) ∧ T q = q ∧
(∀ u : Lp ℝ 2 ν, inner ℝ q u = ∫ y, u y ∂ν) ∧
let qP : HP := e q
let HP0 := (innerSL ℝ qP).ker
letI : NormedAddCommGroup HP0 := HP0.normedAddCommGroup
letI : InnerProductSpace ℝ HP0 := HP0.innerProductSpace
letI : CompleteSpace HP0 := (innerSL ℝ qP).isClosed_ker.completeSpace_coe
let γ : ℝ := 2*Real.sqrt ((α : ℝ)*η)/(1+(α : ℝ)*η)
(∀ f : HP, f ∈ HP0 ↔ (∫ y, (e.symm f) y ∂ν) = 0) ∧
ΓP qP = 0 ∧ 0 < γ ∧
∃ ΓP0 : HP0 →L[ℝ] HP0,
(∀ f : HP0, (ΓP0 f : HP) = ΓP (f : HP)) ∧
ΓP0.IsPositive ∧ (ΓP0-γ • (1 : HP0 →L[ℝ] HP0)).IsPositive ∧
IsUnit ΓP0 ∧
∃ Inv : HP0 →L[ℝ] HP0,
Inv*ΓP0=(1 : HP0 →L[ℝ] HP0) ∧
ΓP0*Inv=(1 : HP0 →L[ℝ] HP0) ∧ ‖Inv‖ ≤ 1/γ ∧
let Hperp := P.ker
letI : NormedAddCommGroup Hperp := Hperp.normedAddCommGroup
letI : InnerProductSpace ℝ Hperp := Hperp.innerProductSpace
letI : CompleteSpace Hperp := P.isClosed_ker.completeSpace_coe
∃ B0 : HP0 →L[ℝ] Hperp,
(∀ f : HP0, (B0 f : Lp ℝ 2 J) = B (f : HP)) ∧
∃ V0 : HP0 →L[ℝ] Hperp,
V0 = B0 ∘L Inv ∧ B0 = V0 ∘L ΓP0 ∧
V0.adjoint ∘L V0 = (1 : HP0 →L[ℝ] HP0) ∧
(∀ f : HP0, ‖V0 f‖ = ‖f‖) ∧
∃ R : Lp ℝ 2 J →L[ℝ] Hperp,
(∀ g : Lp ℝ 2 J, (R g : Lp ℝ 2 J)=g-P g) ∧
(∀ g : Lp ℝ 2 J,
B.adjoint g=HP0.subtypeL (B0.adjoint (R g))) ∧
(∀ f : Lp ℝ 2 J, (∫ z, f z ∂J)=0 →
∃ fP : HP0,
HP0.subtypeL fP=condExpL2 ℝ ℝ (μ:=J) measurable_snd.comap_le f ∧
let fperp : Hperp := R f
let fV : HP0 := V0.adjoint fperp
B.adjoint (fperp : Lp ℝ 2 J)=HP0.subtypeL (ΓP0 fV) ∧
HP0.subtypeL (ΓP0 fV)=ΓP (HP0.subtypeL fV) ∧
‖f‖^2=‖fP‖^2+‖fperp‖^2 ∧ ‖fV‖≤‖fperp‖)
theorem actual_ambient_adjoint_centered_decomposition
{E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
[FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
{V : E → ℝ} {α β : ℝ≥0} {η : ℝ}
(hα : 0 < (α : ℝ)) (hαβ : α ≤ β) (hV : ContDiff ℝ 2 V)
(hH : ∀ x v : E,
(α : ℝ)*‖v‖^2 ≤ (fderiv ℝ (fderiv ℝ V) x v) v ∧
(fderiv ℝ (fderiv ℝ V) x v) v ≤ (β : ℝ)*‖v‖^2)
(hη : 0 < η) (hβη : (β : ℝ)*η ≤ 1) : actual_ambient_adjoint_centered_decomposition_statement (E := E) (V := V) (α := α) (β := β) (η := η) hα hαβ hV hH hη hβη := by
unfold actual_ambient_adjoint_centered_decomposition_statement
classical
dsimp only
let μ := (volume : Measure E).tilted (fun x => -V x)
let J := (μ.prod (stdGaussian E)).map
(fun p : E × E => (p.1,p.1+Real.sqrt η • p.2))
let ν := J.snd
let mY : MeasurableSpace (E × E) := MeasurableSpace.comap Prod.snd (inferInstance : MeasurableSpace E)
letI : MeasurableSpace (E × E) := Prod.instMeasurableSpace
letI : Fact (mY ≤ (inferInstance : MeasurableSpace (E × E))) := ⟨measurable_snd.comap_le⟩
let HP := lpMeas ℝ ℝ mY 2 J
letI : NormedAddCommGroup HP := HP.normedAddCommGroup
letI : InnerProductSpace ℝ HP := HP.innerProductSpace
let P : Lp ℝ 2 J →L[ℝ] Lp ℝ 2 J :=
HP.subtypeL ∘L condExpL2 ℝ ℝ (μ := J) measurable_snd.comap_le
have hBase := AutoSamplingTheory.ExampleCases.ProximalBPS.PolarIsometry.actual_centered_polar_isometry
(E:=E) (V:=V) (α:=α) (β:=β) (η:=η) hα hαβ hV hH hη hβη
dsimp only at hBase
rcases hBase with
⟨hμ,hJ,hν,hRange,hPf,S,hS,hSd,hSc,hSf,hSs,e,he,U,hU,hUi,hUs,
T,hTs,hAll,hAeq,hAs,hAn,hPB,Γ,hΓ,hΓSq,hΓP,hΓPSq,hGram,
q,hqa,hTq,hq,hCenter,hΓPq,hγ,ΓP0,hΓP0,hPos0,hOrder0,hUnit,
Inv,hLeft,hRight,hNormInv,B0,hB0,V0,hVdef,hFactor,hVadj,hVnorm⟩
let A : HP →L[ℝ] HP := HP.orthogonalProjectionOnto ∘L U.toContinuousLinearMap ∘L HP.subtypeL
let B : HP →L[ℝ] Lp ℝ 2 J :=
(((1 : Lp ℝ 2 J →L[ℝ] Lp ℝ 2 J)-P)*U.toContinuousLinearMap*P) ∘L HP.subtypeL
let ΓP : HP →L[ℝ] HP := e.conjStarAlgEquiv Γ
let qP : HP := e q
let HP0 := (innerSL ℝ qP).ker
letI : NormedAddCommGroup HP0 := HP0.normedAddCommGroup
letI : InnerProductSpace ℝ HP0 := HP0.innerProductSpace
letI : CompleteSpace HP0 := (innerSL ℝ qP).isClosed_ker.completeSpace_coe
let Hperp := P.ker
letI : NormedAddCommGroup Hperp := Hperp.normedAddCommGroup
letI : InnerProductSpace ℝ Hperp := Hperp.innerProductSpace
letI : CompleteSpace Hperp := P.isClosed_ker.completeSpace_coe
change HP0 →L[ℝ] Hperp at B0 V0
change HP0 →L[ℝ] HP0 at ΓP0 Inv
letI : IsProbabilityMeasure J := hJ
letI : IsProbabilityMeasure ν := hν
let C : Lp ℝ 2 J →L[ℝ] HP := condExpL2 ℝ ℝ (μ:=J) measurable_snd.comap_le
have hPself (g k : Lp ℝ 2 J) : inner ℝ (P g) k=inner ℝ g (P k) :=
inner_condExpL2_left_eq_right measurable_snd.comap_le
have hPid (g : Lp ℝ 2 J) : P (P g)=P g := hPf (C g)
let R : Lp ℝ 2 J →L[ℝ] Hperp :=
((1 : Lp ℝ 2 J →L[ℝ] Lp ℝ 2 J)-P).codRestrict Hperp (by
intro g
change P (g-P g)=0
rw [map_sub,hPid,sub_self])
have hR (g : Lp ℝ 2 J) : (R g : Lp ℝ 2 J)=g-P g := rfl
have hRself (g : Hperp) : R (g : Lp ℝ 2 J)=g := by
apply Subtype.ext
change (g : Lp ℝ 2 J)-P g=g
rw [show P (g : Lp ℝ 2 J)=0 from g.property,sub_zero]
have hGramLocal : B.adjoint ∘L B=ΓP*ΓP := hGram
have hRootq : ΓP qP=0 := hΓPq
have hBq : B qP=0 := by
have hn := B.apply_norm_sq_eq_inner_adjoint_right qP
rw [hGramLocal] at hn
have hz : (ΓP*ΓP) qP=0 := by
change ΓP (ΓP qP)=0
rw [hRootq,map_zero]
rw [hz,inner_zero_right] at hn
exact norm_eq_zero.mp (sq_eq_zero_iff.mp hn)
have hBCenter (g : Lp ℝ 2 J) : B.adjoint g ∈ HP0 := by
change inner ℝ qP (B.adjoint g)=0
rw [B.adjoint_inner_right,hBq,inner_zero_left]
have hAmbient (g : Lp ℝ 2 J) :
B.adjoint g=HP0.subtypeL (B0.adjoint (R g)) := by
let b : HP0 := ⟨B.adjoint g,hBCenter g⟩
have heq : b=B0.adjoint (R g) := by
apply ext_inner_left ℝ
intro u
change inner ℝ (u : HP) (B.adjoint g)=inner ℝ u (B0.adjoint (R g))
rw [B.adjoint_inner_right,B0.adjoint_inner_right]
change inner ℝ (B (u : HP)) g=inner ℝ (B0 u : Lp ℝ 2 J) (R g : Lp ℝ 2 J)
rw [hB0,hR,inner_sub_right]
have ho : inner ℝ (B (u : HP)) (P g)=0 := by
rw [← hPself,hPB,inner_zero_left]
rw [ho,sub_zero]
exact congrArg (fun z : HP0 => HP0.subtypeL z) heq
have hPosLocal : ΓP0.IsPositive := hPos0
have hBAdj : B0.adjoint=ΓP0 ∘L V0.adjoint := by
calc
B0.adjoint = (V0 ∘L ΓP0).adjoint :=
congrArg (fun D : HP0 →L[ℝ] Hperp => D.adjoint) hFactor
_ = ΓP0.adjoint ∘L V0.adjoint := ContinuousLinearMap.adjoint_comp V0 ΓP0
_ = ΓP0 ∘L V0.adjoint :=
congrArg (fun D : HP0 →L[ℝ] HP0 => D ∘L V0.adjoint)
hPosLocal.isSelfAdjoint.adjoint_eq
have hNormV : ‖V0‖ ≤ (1 : ℝ) :=
ContinuousLinearMap.opNorm_le_bound _ zero_le_one (by intro u; rw [hVnorm u,one_mul])
have hNormAdj : ‖V0.adjoint‖ ≤ (1 : ℝ) :=
(ContinuousLinearMap.adjoint.norm_map V0).trans_le hNormV
have hVcontract (g : Hperp) : ‖V0.adjoint g‖≤‖g‖ := by
calc
‖V0.adjoint g‖ ≤ ‖V0.adjoint‖*‖g‖ := V0.adjoint.le_opNorm g
_ ≤ 1*‖g‖ := mul_le_mul_of_nonneg_right hNormAdj (norm_nonneg _)
_ = ‖g‖ := one_mul _
let hp : MeasurePreserving (Prod.snd : E × E → E) J ν := ⟨measurable_snd,rfl⟩
let M : Lp ℝ 2 ν →ₗᵢ[ℝ] Lp ℝ 2 J := Lp.compMeasurePreservingₗᵢ ℝ Prod.snd hp
have hqOne : (qP : Lp ℝ 2 J)=
AutoSamplingTheory.TechnicalLemmas.Measure.L2Expectation.one J := by
rw [show (qP : Lp ℝ 2 J)=M q from he q]
apply Lp.ext
have hqaPull : (fun z : E × E => q z.2)=ᵐ[J] (fun _ => (1 : ℝ)) :=
hp.quasiMeasurePreserving.ae hqa
exact (Lp.coeFn_compMeasurePreserving q hp).trans
(hqaPull.trans (Lp.coeFn_const (p:=(2 : ℝ≥0∞)) (μ:=J) (c:=(1 : ℝ))).symm)
have hqIntegral (g : Lp ℝ 2 J) : inner ℝ (qP : Lp ℝ 2 J) g=∫ z, g z ∂J := by
rw [hqOne]
exact AutoSamplingTheory.TechnicalLemmas.Measure.L2Expectation.inner_one_eq_integral J g
have hPq : P (qP : Lp ℝ 2 J)=qP := hPf qP
refine ⟨hμ,hJ,hν,hRange,hPf,S,hS,hSd,hSc,hSf,hSs,e,he,U,hU,hUi,hUs,T,hTs,hAll,
hAeq,hAs,hAn,hPB,Γ,hΓ,hΓSq,hΓP,hΓPSq,hGram,q,hqa,hTq,hq,hCenter,hΓPq,hγ,
ΓP0,hΓP0,hPos0,hOrder0,hUnit,Inv,hLeft,hRight,hNormInv,B0,hB0,V0,hVdef,
hFactor,hVadj,hVnorm,R,hR,hAmbient,?_⟩
intro f hf
have hCf : C f ∈ HP0 := by
change inner ℝ qP (C f)=0
change inner ℝ (qP : Lp ℝ 2 J) (P f)=0
rw [← hPself,hPq,hqIntegral,hf]
let fP : HP0 := ⟨C f,hCf⟩
refine ⟨fP,rfl,?_,?_,?_,hVcontract (R f)⟩
· calc
B.adjoint (R f : Lp ℝ 2 J)=HP0.subtypeL (B0.adjoint (R (R f))) := hAmbient _
_ = HP0.subtypeL (B0.adjoint (R f)) := congrArg
(fun z : Hperp => HP0.subtypeL (B0.adjoint z)) (hRself (R f))
_ = HP0.subtypeL (ΓP0 (V0.adjoint (R f))) :=
congrArg (fun z : HP0 => HP0.subtypeL z)
(congrArg (fun D : Hperp →L[ℝ] HP0 => D (R f)) hBAdj)
· exact hΓP0 _
· have hn := HP.norm_sq_eq_add_norm_sq_starProjection f
have hProj : HP.starProjection=P := rfl
rw [hProj,HP.starProjection_orthogonal_val] at hn
change ‖f‖^2=‖(C f : Lp ℝ 2 J)‖^2+‖f-P f‖^2 at hn
exact hn
end
end AutoSamplingTheory.ExampleCases.ProximalBPS.AmbientAdjointCorrector