Plain-English statement
Independently exact-science65 VERIFIED ecd9d1f10ad0241312492cefacd5fe48c317e9c2. Original C2/two Hessians/positive capped eta produce SAME actual laws/e/U/T/root/centered inverse. Exact B0:HP0->kerP and V0=B0 Inv satisfy B0=V0 Gamma0,V0.adjoint composed V0=I and all-vector norm equality. Genuine original-input Test gives B0.adjoint=Gamma0 V0.adjoint, adjoint contraction and residual annihilation. No onto/reverse product. Rank0/alphaeta1 retained. B21 root commutation/rotation, B17/H1/operator inequalities,dynamics/main/errors/expected costs/composition remain open.
Read the mathematics first, then descend into Lean
You do not need to know Lean before opening this panel. The page keeps the paper-level theorem, rigorous proof obligations, exact Lean declaration, and proof dependencies as separate layers so a first-time reader can move down one layer at a time.
Proof architecture
Start with the tree when learning: prerequisites sit below the theorem and downstream results sit above it. Switch to the network when you want to understand where this declaration lives in the local formal library. Every mapped node is clickable.
Graph rule: only dependencies found by the ASTIS source scan are drawn. Missing tactic indirection is treated as an under-approximation; the site never invents an edge just to make a prettier graph.
How to read the exact Lean declaration
Read a Lean theorem left-to-right exactly as you would unpack a mathematical sentence: name → ambient types → automatically inferred structures → explicit hypotheses → conclusion → proof. Then read the proof top-to-bottom as transformations of the current goal.
- NameWhat reusable mathematical fact is being created?
- ParametersWhich symbols are arbitrary, and which structures are inferred by typeclass search?
- PropositionAfter the colon, translate the Lean expression back into a paper statement.
- Proof actionsAfter
by, ask what each tactic does to the mathematical goal—not only what syntax it uses.
Syntax used on this page
This glossary is filtered to syntax that actually occurs in the declaration above. Open a symbol only when you meet it, rather than memorizing Lean grammar in advance.
Source voice and ASTIS voice stay separate
When Samplinglib shows a short quotation from Chewi, it is labeled as a source excerpt and linked to the canonical book page. Intuition, expanded proof steps, hidden regularity assumptions, and Lean explanations are ASTIS-authored commentary. A quotation never substitutes for a formal proof, and an ASTIS explanation is never attributed to the textbook author.
Lean statement
theorem actual_centered_polar_isometry
{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) :
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‖) := by
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.CenteredRootOrderInverse.actual_centered_root_order_inverse
(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⟩
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
let B0 : HP0 →L[ℝ] Hperp :=
(B ∘L HP0.subtypeL).codRestrict Hperp (by intro f; exact hPB (f : HP))
have hB0 (f : HP0) : (B0 f : Lp ℝ 2 J)=B (f : HP) := rfl
let V0 : HP0 →L[ℝ] Hperp := B0 ∘L Inv
have hGramLocal : B.adjoint ∘L B=ΓP*ΓP := hGram
have hPosLocal : ΓP.IsPositive := hΓP
have hVnorm (f : HP0) : ‖V0 f‖=‖f‖ := by
let g : HP0 := Inv f
have hg : ΓP0 g=f := congrArg (fun C : HP0 →L[ℝ] HP0 => C f) hRight
have hb := B.apply_norm_sq_eq_inner_adjoint_right (g : HP)
have hr := ΓP.apply_norm_sq_eq_inner_adjoint_right (g : HP)
rw [hGramLocal] at hb
rw [hPosLocal.isSelfAdjoint.adjoint_eq] at hr
have hNorm : ‖B (g : HP)‖=‖ΓP (g : HP)‖ :=
(sq_eq_sq₀ (norm_nonneg _) (norm_nonneg _)).mp (hb.trans hr.symm)
calc
‖V0 f‖=‖B (g : HP)‖ := rfl
_ = ‖ΓP (g : HP)‖ := hNorm
_ = ‖ΓP0 g‖ := congrArg (fun z : HP => ‖z‖) (hΓP0 g).symm
_ = ‖f‖ := congrArg (fun z : HP0 => ‖z‖) hg
have hFactor : B0=V0 ∘L ΓP0 := by
apply ContinuousLinearMap.ext
intro f
change B0 f=B0 (Inv (ΓP0 f))
have hf : Inv (ΓP0 f)=f := congrArg (fun C : HP0 →L[ℝ] HP0 => C f) hLeft
rw [hf]
have hVadj : V0.adjoint ∘L V0=(1 : HP0 →L[ℝ] HP0) :=
(ContinuousLinearMap.norm_map_iff_adjoint_comp_self V0).mp hVnorm
exact ⟨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,rfl,
hFactor,hVadj,hVnorm⟩
end
end AutoSamplingTheory.ExampleCases.ProximalBPS.PolarIsometry
Open AutoSamplingTheory/ExampleCases/ProximalBPS/PolarIsometry.lean:10published source at ed709a7f320d
Proof architecture
Actual PBPS consumer; no SALD admission
Lean proof walkthrough
- Read the quantified variables and typeclass brackets as part of the mathematical statement; inferred arguments are not missing assumptions.
- `intro` introduces quantified hypotheses into the local proof context.
- `have` creates a named intermediate mathematical fact.
- `calc` records an equality or inequality chain matching a paper calculation.
- `rw` rewrites by an established identity.
- `apply` reduces the goal to the hypotheses of a reusable theorem.
- `exact` closes the current goal with an already typed term.
Why the statement has this shape
The declaration is kept at the reusable level recorded by its Registry tags and direct consumers. Explicit measures, spaces, wrappers, and regularity hypotheses expose interfaces that paper notation often infers. A theorem card explains those interfaces but never widens the compiled statement.
Hidden assumptions and non-claims
- Measurability is represented explicitly or must be supplied by a dependency.
- Totalized `fderiv` values must not be read as a differentiability theorem.
- Almost-everywhere hypotheses depend on the stated measure and representative.