Estimation.NPIV.Operator
The NPIV conditional-expectation operator: compactness, singular systems, and the source condition.
Operator 9 core · 6 supporting Extends the inverse-problem system with population operators for NPIV estimation. ★ T_inner_eq_integral
Extends the inverse-problem system with population operators for NPIV estimation. The module defines the operator interface connecting structural functions, instruments, and conditional-moment equations.
An operator system: an InverseProblemSystem enriched with the L² data needed to define the concrete population projection operator T = Π_{Q̄} ∘ E[· | σ(Z)]. On top of the base system it packages the σ-algebra generated by the instrument Z, with the facts that it is coarser than the ambient σ-algebra on the sample space and coincides with the σ-algebra pulled back along Z, that the ambient probability measure is finite, and closed submodules of the ambient L²(μ) space realizing the primal and dual candidate function classes, the dual one further required to admit an orthogonal projection; finally, every candidate primal or dual function is square-integrable once composed with the covariate or instrument map, with its L² class coherently landing inside the matching submodule.
Definition (Lean source)
Lift h ∈ Hbar to its L² class (h ∘ X)_{Lp} ∈ Lp ℝ 2 μ. This is just a name.
Definition (Lean source)
Lift q ∈ Qbar to its L² class (q ∘ Z)_{Lp} ∈ Lp ℝ 2 μ.
Definition (Lean source)
The conditional-expectation step E[· | σ(Z)], viewed as a self-map of Lp ℝ 2 μ (via the canonical inclusion of the σ(Z)-measurable subspace).
Definition (Lean source)
The concrete projection operator T = Π_{Q̄} ∘ E[· | σ(Z)].
Definition (Lean source)
Hypothesis used by T_inner_eq_integral: q ∘ Z is m_Z-strongly measurable (a.e.). Derivable from m_Z_eq and meas_of_Qbar; see qbar_aestrongly_measurable_mZ below. Kept as a def so existing call sites (which take it as a separate hypothesis) continue to compile.
Definition (Lean source)
Population identity for T. Consider an operator system S — an inverse-problem system enriched with an L² realization of the candidate sets — and fix two primal candidate functions h, h' in the primal candidate set Hbar together with a dual candidate function f in the dual candidate set Qbar. Writing T for the operator that conditions on the σ-algebra generated by the instrument Z and then orthogonally projects onto the L² realization of Qbar, the L²(μ) inner product of T applied to the lifted residual h − h' against the lifted f equals the expectation of the pointwise product (h(X) − h'(X))·f(Z).
Formal statement
Proof (Lean source)
Strong (L²) norm of an L² element.
Definition (Lean source)
Weak norm associated to T: ‖T f‖_{L²(μ)}.
Definition (Lean source)
6 supporting declarations (lemmas, instances)
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condExpStep_sublemma — condExpStep distributes over subtraction (linearity of condExpL2).hypothesesS :OperatorSystem Ω μf g :Lp ℝ 2 μconclusionS.condExpStep (f - g) = S.condExpStep f - S.condExpStep gProof (Lean source)
lemma condExpStep_sub (S : OperatorSystem Ω μ) (f g : Lp ℝ 2 μ) : S.condExpStep (f - g) = S.condExpStep f - S.condExpStep g := by haveI := S.isFiniteMeasure unfold condExpStep simp [map_sub] -
T_sublemma — T distributes over subtraction.Proof (Lean source)
lemma T_sub (S : OperatorSystem Ω μ) (f g : Lp ℝ 2 μ) : S.T (f - g) = S.T f - S.T g := by haveI := S.Qbar_L2_hasProj unfold T rw [S.condExpStep_sub] -- Reduce `orthogonalProjectionFn (a - b)` to -- `orthogonalProjectionFn a - orthogonalProjectionFn b` via the -- rfl-equality `orthogonalProjectionFn = ((orthogonalProjection ·) : _)` -- and `map_sub` on the bundled CLM `orthogonalProjection`. change ((S.Qbar_L2.orthogonalProjection (S.condExpStep f - S.condExpStep g)) : Lp ℝ 2 μ) = ((S.Qbar_L2.orthogonalProjection (S.condExpStep f)) : Lp ℝ 2 μ) - ((S.Qbar_L2.orthogonalProjection (S.condExpStep g)) : Lp ℝ 2 μ) rw [map_sub] rfl -
T_addlemma — T distributes over addition (companion to T_sub).Proof (Lean source)
lemma T_add (S : OperatorSystem Ω μ) (f g : Lp ℝ 2 μ) : S.T (f + g) = S.T f + S.T g := by haveI := S.Qbar_L2_hasProj haveI := S.isFiniteMeasure unfold T condExpStep change ((S.Qbar_L2.orthogonalProjection ((MeasureTheory.condExpL2 ℝ ℝ S.m_Z_le (f + g) : Lp ℝ 2 μ))) : Lp ℝ 2 μ) = ((S.Qbar_L2.orthogonalProjection ((MeasureTheory.condExpL2 ℝ ℝ S.m_Z_le f : Lp ℝ 2 μ))) : Lp ℝ 2 μ) + ((S.Qbar_L2.orthogonalProjection ((MeasureTheory.condExpL2 ℝ ℝ S.m_Z_le g : Lp ℝ 2 μ))) : Lp ℝ 2 μ) simp [map_add] -
qbar_aestrongly_measurable_mZlemma — q ∘ zOf ∘ W is m_Z-strongly measurable for every q ∈ Qbar. Direct consequence of m_Z_eq : m_Z = comap (zOf ∘ W) inst𝒵 and meas_of_Qbar: the canonical map (zOf ∘ W) : Ω → 𝒵 is measurable from comap (zOf ∘ W) inst𝒵 to inst𝒵 (comap_measurable), so its composition with the inst𝒵-measurable q : 𝒵 → ℝ is m_Z-measurable, hence m_Z-strongly measurable into ℝ.Proof (Lean source)
lemma qbar_aestrongly_measurable_mZ (S : OperatorSystem Ω μ) : S.QbarAEMeasurableMZ := by intro q hq have hZ : @Measurable Ω S.𝒵 S.m_Z S.inst𝒵 (fun ω => S.zOf (S.W ω)) := by rw [S.m_Z_eq] exact comap_measurable _ have hq_meas : @Measurable S.𝒵 ℝ S.inst𝒵 _ q := S.meas_of_Qbar q hq have h_meas : @Measurable Ω ℝ S.m_Z _ (fun ω => q (S.zOf (S.W ω))) := @comp Ω S.𝒵 ℝ S.m_Z S.inst𝒵 _ q (fun ω => S.zOf (S.W ω)) hq_meas hZ exact h_meas.aestronglyMeasurable -
weakNorm_le_strongNormlemma — The projected conditional-expectation norm is bounded by the ambient L² norm.Proof (Lean source)
lemma weakNorm_le_strongNorm (S : OperatorSystem Ω μ) (f : Lp ℝ 2 μ) : S.weakNorm f ≤ S.strongNorm f := by haveI := S.isFiniteMeasure haveI := S.Qbar_L2_hasProj calc S.weakNorm f = ‖S.Qbar_L2.starProjection (S.condExpStep f)‖ := by rw [weakNorm, T] _ ≤ ‖S.condExpStep f‖ := S.Qbar_L2.norm_starProjection_apply_le (S.condExpStep f) _ ≤ ‖f‖ := by simpa [condExpStep] using (MeasureTheory.norm_condExpL2_coe_le (E := ℝ) (𝕜 := ℝ) S.m_Z_le f) _ = S.strongNorm f := by rw [strongNorm] -
strongNorm_sq_hL2_eq_integrallemma — Squared strong norm of a lifted Hbar member, unfolded as the integral of the squared representative.hypothesesconclusion(S.strongNorm (S.hL2 hh)) ^ 2 = ∫ ω, (h (S.xOf (S.W ω))) ^ 2 ∂μProof (Lean source)
lemma strongNorm_sq_hL2_eq_integral (S : OperatorSystem Ω μ) {h : S.𝒳 → ℝ} (hh : h ∈ S.Hbar) : (S.strongNorm (S.hL2 hh)) ^ 2 = ∫ ω, (h (S.xOf (S.W ω))) ^ 2 ∂μ := by calc (S.strongNorm (S.hL2 hh)) ^ 2 = inner ℝ (S.hL2 hh) (S.hL2 hh) := by rw [OperatorSystem.strongNorm, real_inner_self_eq_norm_sq] _ = ∫ ω, ⟪(S.hL2 hh) ω, (S.hL2 hh) ω⟫_ℝ ∂μ := by rw [MeasureTheory.L2.inner_def] _ = ∫ ω, (h (S.xOf (S.W ω))) ^ 2 ∂μ := by refine integral_congr_ae ?_ filter_upwards [(S.toHbarL2 h hh).coeFn_toLp] with ω hω simp [OperatorSystem.hL2, hω, pow_two]
Adjoint 5 core · 1 supporting Builds the bundled continuous-linear-map representation of the NPIV operator and its adjoint. ★ Tstar_T_isSelfAdjoint
Builds the bundled continuous-linear-map representation of the NPIV operator and its adjoint. These operator-theoretic objects feed Tikhonov regularization and spectral arguments.
The conditional-expectation step f ↦ E[f | σ(Z)], viewed as a continuous ℝ-linear endomorphism of Lp ℝ 2 μ.
Definition (Lean source)
The bundled continuous-linear-map version of OperatorSystem.T:
Definition (Lean source)
The adjoint operator T† : Lp ℝ 2 μ →L[ℝ] Lp ℝ 2 μ.
Definition (Lean source)
The self-adjoint, positive-semidefinite composite T† T. This is the operator the β-source condition refers to: h₀ = (T*T)^{β/2} w₀ is interpreted via Mathlib's continuous functional calculus on this CLM.
Definition (Lean source)
For any NPIV operator system, the composite operator T† T, obtained by composing the second-stage conditional-expectation operator with its adjoint, is self-adjoint.
Formal statement
Proof (Lean source)
1 supporting declaration (lemmas, instances)
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Tlin_applylemma — Coherence: applying the bundled CLM Tlin to f agrees with the function-level operator T from Causalean/Estimation/NPIV/Operator.lean. The proof unfolds both sides and uses Submodule.starProjection_apply = orthogonalProjectionFn.Proof (Lean source)
Complexification 9 core · 13 supporting Provides real-to-complex L2 operator glue for NPIV spectral calculations. ★ realCFC_resolvent_mul_self★ realCFC_norm_le
Provides real-to-complex L2 operator glue for NPIV spectral calculations. The module relates real conditional-moment operators to complexified continuous linear maps and their self-adjointness properties.
Local disambiguation of the real-scalar algebra structure on the complex operator algebra Lp ℂ 2 μ →L[ℂ] Lp ℂ 2 μ.
Pointwise real part on L²(Ω, ℂ), bundled as a continuous ℝ-linear map Lp ℂ 2 μ →L[ℝ] Lp ℝ 2 μ.
Pointwise imaginary part on L²(Ω, ℂ), bundled as a continuous ℝ-linear map Lp ℂ 2 μ →L[ℝ] Lp ℝ 2 μ. Same construction as reLp but with RCLike.imCLM.
Pointwise embedding of real-valued L² into complex-valued L², bundled as a continuous ℝ-linear map Lp ℝ 2 μ →L[ℝ] Lp ℂ 2 μ.
The function-level definition of the complex lift. Given a real CLM A : Lp ℝ 2 μ →L[ℝ] Lp ℝ 2 μ and a complex L² class f, the lift sends f = (Re f) + i (Im f) to A(Re f) + i · A(Im f) (with the real outputs re-embedded via ιLp).
Complex lift of a real CLM.
Definition (Lean source)
Real CFC by complexification.
Definition (Lean source)
Resolvent symbol for realCFC. For a self-adjoint bounded operator A whose real spectrum lies in the nonnegative reals, and for a strictly positive regularization parameter λ, composing the real-functional-calculus operator for the affine symbol x ↦ λ + x with that for the resolvent symbol x ↦ (λ + x)⁻¹ yields the identity operator on the ambient L² space.
Formal statement
Proof (Lean source)
Norm-via-spectrum bound for realCFC. For a self-adjoint bounded operator A and a continuous real-valued symbol f, if f is bounded in absolute value by a nonnegative constant c on the real spectrum of A, then the operator realCFC A f is a contraction up to c: for every vector g, ‖realCFC A f g‖ ≤ c · ‖g‖.
Formal statement
Proof (Lean source)
13 supporting declarations (lemmas, instances)
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reLp_comp_ιLplemma — reLp is a left-inverse of ιLp.Proof (Lean source)
lemma reLp_comp_ιLp (f : Lp ℝ 2 μ) : reLp (ιLp f) = f := by apply Lp.ext filter_upwards [ContinuousLinearMap.coeFn_compLpL (RCLike.reCLM (K := ℂ)) (ιLp f), ContinuousLinearMap.coeFn_compLpL (RCLike.ofRealCLM (K := ℂ)) f] with ω h₁ h₂ simpa [reLp, ιLp, h₂, RCLike.reCLM_apply, RCLike.ofRealCLM_apply, RCLike.ofReal_re] using h₁ -
imLp_comp_ιLplemma — The imaginary part vanishes on the image of ιLp (pointwise RCLike.im_ofReal : RCLike.im (r : ℂ) = 0).Proof (Lean source)
lemma imLp_comp_ιLp (f : Lp ℝ 2 μ) : imLp (ιLp f) = 0 := by apply Lp.ext filter_upwards [ContinuousLinearMap.coeFn_compLpL (RCLike.imCLM (K := ℂ)) (ιLp f), ContinuousLinearMap.coeFn_compLpL (RCLike.ofRealCLM (K := ℂ)) f, (Lp.coeFn_zero ℝ (2 : ENNReal) μ)] with ω h₁ h₂ hzero have hmain : (((imLp (ιLp f) : Lp ℝ 2 μ) : Ω → ℝ) ω) = (RCLike.imCLM (K := ℂ)) ((RCLike.ofRealCLM (K := ℂ)) (((f : Lp ℝ 2 μ) : Ω → ℝ) ω)) := by simpa [imLp, ιLp, h₂] using h₁ calc (((imLp (ιLp f) : Lp ℝ 2 μ) : Ω → ℝ) ω) = (RCLike.imCLM (K := ℂ)) ((RCLike.ofRealCLM (K := ℂ)) (((f : Lp ℝ 2 μ) : Ω → ℝ) ω)) := hmain _ = 0 := by change RCLike.im ((RCLike.ofRealCLM (K := ℂ)) (((f : Lp ℝ 2 μ) : Ω → ℝ) ω)) = 0 change RCLike.im ((((f : Lp ℝ 2 μ) : Ω → ℝ) ω : ℝ) : ℂ) = 0 exact RCLike.ofReal_im (((f : Lp ℝ 2 μ) : Ω → ℝ) ω) _ = ((0 : Lp ℝ 2 μ) : Ω → ℝ) ω := by rw [hzero]; rfl -
ιLp_isometrylemma — ιLp is an isometric embedding. Follows from RCLike.ofRealLI being a LinearIsometry and the fact that compLpL of a norm-one CLM preserves norms on L²; concretely, ‖ιLp f‖ = ‖f‖ reduces pointwise to ‖(r : ℂ)‖ = ‖r‖ (RCLike.norm_ofReal).Proof (Lean source)
lemma ιLp_isometry (f : Lp ℝ 2 μ) : ‖ιLp f‖ = ‖f‖ := by rw [Lp.norm_def, Lp.norm_def] apply congrArg toReal apply MeasureTheory.eLpNorm_congr_norm_ae filter_upwards [ContinuousLinearMap.coeFn_compLpL (RCLike.ofRealCLM (K := ℂ)) f] with ω h rw [show (((ιLp f : Lp ℂ 2 μ) : Ω → ℂ) ω) = (RCLike.ofRealCLM (K := ℂ)) (((f : Lp ℝ 2 μ) : Ω → ℝ) ω) by simpa [ιLp] using h] change ‖(RCLike.ofRealCLM (K := ℂ)) (((f : Lp ℝ 2 μ) : Ω → ℝ) ω)‖ = ‖(((f : Lp ℝ 2 μ) : Ω → ℝ) ω)‖ rw [show (RCLike.ofRealCLM (K := ℂ)) (((f : Lp ℝ 2 μ) : Ω → ℝ) ω) = ((((f : Lp ℝ 2 μ) : Ω → ℝ) ω : ℝ) : ℂ) by rw [RCLike.ofRealCLM_apply] rfl] exact RCLike.norm_ofReal (((f : Lp ℝ 2 μ) : Ω → ℝ) ω) -
reLp_add_smul_imLplemma — Reconstruction identity: every complex L²-class is the complex combination of its real and imaginary parts re-embedded via ιLp. Pointwise this is RCLike.re_add_im : (r.re : ℂ) + I * r.im = r.Proof (Lean source)
lemma reLp_add_smul_imLp (f : Lp ℂ 2 μ) : ιLp (reLp f) + (Complex.I : ℂ) • ιLp (imLp f) = f := by apply Lp.ext filter_upwards [Lp.coeFn_add (ιLp (reLp f)) ((Complex.I : ℂ) • ιLp (imLp f)), Lp.coeFn_smul (Complex.I : ℂ) (ιLp (imLp f)), ContinuousLinearMap.coeFn_compLpL (RCLike.ofRealCLM (K := ℂ)) (reLp f), ContinuousLinearMap.coeFn_compLpL (RCLike.ofRealCLM (K := ℂ)) (imLp f), ContinuousLinearMap.coeFn_compLpL (RCLike.reCLM (K := ℂ)) f, ContinuousLinearMap.coeFn_compLpL (RCLike.imCLM (K := ℂ)) f] with ω h_add h_smul h_re_embed h_im_embed h_re h_im rw [h_add] simp only [Pi.add_apply] rw [h_smul] simp only [Pi.smul_apply] rw [show (((ιLp (reLp f) : Lp ℂ 2 μ) : Ω → ℂ) ω) = (RCLike.ofRealCLM (K := ℂ)) (((reLp f : Lp ℝ 2 μ) : Ω → ℝ) ω) by simpa [ιLp] using h_re_embed] rw [show (((ιLp (imLp f) : Lp ℂ 2 μ) : Ω → ℂ) ω) = (RCLike.ofRealCLM (K := ℂ)) (((imLp f : Lp ℝ 2 μ) : Ω → ℝ) ω) by simpa [ιLp] using h_im_embed] rw [show (((reLp f : Lp ℝ 2 μ) : Ω → ℝ) ω) = (RCLike.reCLM (K := ℂ)) (((f : Lp ℂ 2 μ) : Ω → ℂ) ω) by simpa [reLp] using h_re] rw [show (((imLp f : Lp ℝ 2 μ) : Ω → ℝ) ω) = (RCLike.imCLM (K := ℂ)) (((f : Lp ℂ 2 μ) : Ω → ℂ) ω) by simpa [imLp] using h_im] change (RCLike.re (((f : Lp ℂ 2 μ) : Ω → ℂ) ω) : ℂ) + Complex.I * (RCLike.im (((f : Lp ℂ 2 μ) : Ω → ℂ) ω) : ℂ) = (((f : Lp ℂ 2 μ) : Ω → ℂ) ω) rw [mul_comm] exact RCLike.re_add_im (((f : Lp ℂ 2 μ) : Ω → ℂ) ω) -
complexLift_applytheorem — The action equation for complexLift: it agrees with complexLiftFun A on every input.hypothesesconclusionProof (Lean source)
theorem complexLift_apply (A : Lp ℝ 2 μ →L[ℝ] Lp ℝ 2 μ) (f : Lp ℂ 2 μ) : complexLift A f = ιLp (A (reLp f)) + (Complex.I : ℂ) • ιLp (A (imLp f)) := by rfl -
complexLift_realtheorem — Restriction of the complex lift to the real subspace recovers A. reLp_comp_ιLp gives reLp (ιLp g) = g and imLp_comp_ιLp gives imLp (ιLp g) = 0, so complexLift_apply collapses to ιLp (A g) + I • ιLp (A 0) = ιLp (A g).Proof (Lean source)
theorem complexLift_real (A : Lp ℝ 2 μ →L[ℝ] Lp ℝ 2 μ) (g : Lp ℝ 2 μ) : complexLift A (ιLp g) = ιLp (A g) := by rw [complexLift_apply, reLp_comp_ιLp, imLp_comp_ιLp] simp -
reLp_complexLift_realtheorem — Corollary of complexLift_real: projecting back via reLp gives exactly A g. Direct from reLp_comp_ιLp.Proof (Lean source)
theorem reLp_complexLift_real (A : Lp ℝ 2 μ →L[ℝ] Lp ℝ 2 μ) (g : Lp ℝ 2 μ) : reLp (complexLift A (ιLp g)) = A g := by rw [complexLift_real, reLp_comp_ιLp] -
complexLift_adjointtheorem — The complex lift commutes with taking adjoints.Proof (Lean source)
theorem complexLift_adjoint (A : Lp ℝ 2 μ →L[ℝ] Lp ℝ 2 μ) : (complexLift A).adjoint = complexLift A.adjoint := by ext f rw [← reLp_add_smul_imLp f] rw [map_add, map_smul] rw [complexLift_adjoint_apply_real, complexLift_adjoint_apply_real] rw [complexLift_apply] simp [map_add, reLp_comp_ιLp, imLp_comp_ιLp, reLp_I_smul, imLp_I_smul] -
complexLift_isSelfAdjointtheorem — Self-adjointness is preserved by the complex lift. Direct corollary of complexLift_adjoint (rewrite A.adjoint = A inside the RHS).hypotheseshA :conclusionProof (Lean source)
theorem complexLift_isSelfAdjoint {A : Lp ℝ 2 μ →L[ℝ] Lp ℝ 2 μ} (hA : IsSelfAdjoint A) : IsSelfAdjoint (complexLift A) := by rw [ContinuousLinearMap.isSelfAdjoint_iff'] rw [complexLift_adjoint, hA.adjoint_eq] -
realCFC_applytheorem — Action equation for realCFC — the basic rewrite rule used by spectral proofs.hypothesesconclusionProof (Lean source)
theorem realCFC_apply (A : Lp ℝ 2 μ →L[ℝ] Lp ℝ 2 μ) (hA : IsSelfAdjoint A) (f : ℝ → ℝ) (hf : Continuous f) (g : Lp ℝ 2 μ) : realCFC A f g = reLp (cfc (fun z : ℂ => (f z.re : ℂ)) (complexLift A) (ιLp g)) := by have _hA : IsSelfAdjoint A := hA have _hf : Continuous f := hf rfl -
realCFC_idtheorem — realCFC of the identity symbol recovers A.hypothesesconclusionrealCFC A id g = A gProof (Lean source)
theorem realCFC_id (A : Lp ℝ 2 μ →L[ℝ] Lp ℝ 2 μ) (hA : IsSelfAdjoint A) (g : Lp ℝ 2 μ) : realCFC A id g = A g := by rw [realCFC_apply A hA id continuous_id g] have hcl : IsSelfAdjoint (complexLift A) := complexLift_isSelfAdjoint hA rw [← cfc_real_eq_complex (a := complexLift A) (f := id) (ha := hcl)] rw [cfc_id ℝ (complexLift A) hcl] exact reLp_complexLift_real A g -
realCFC_isSelfAdjointtheorem — realCFC preserves self-adjointness.hypothesesconclusionIsSelfAdjoint (realCFC A f)Proof (Lean source)
theorem realCFC_isSelfAdjoint (A : Lp ℝ 2 μ →L[ℝ] Lp ℝ 2 μ) (hA : IsSelfAdjoint A) (f : ℝ → ℝ) (hf : Continuous f) : IsSelfAdjoint (realCFC A f) := by let C : Lp ℂ 2 μ →L[ℂ] Lp ℂ 2 μ := cfc (fun z : ℂ => (f z.re : ℂ)) (complexLift A) have hC : IsSelfAdjoint C := by have hcl : IsSelfAdjoint (complexLift A) := complexLift_isSelfAdjoint hA rw [show C = cfc (fun z : ℂ => (f z.re : ℂ)) (complexLift A) by rfl] rw [← cfc_real_eq_complex (a := complexLift A) (f := f) (ha := hcl)] exact IsSelfAdjoint.cfc rw [ContinuousLinearMap.isSelfAdjoint_iff_isSymmetric] intro x y change inner ℝ (realCFC A f x) y = inner ℝ x (realCFC A f y) rw [realCFC_apply A hA f hf x, realCFC_apply A hA f hf y] rw [inner_reLp_left (C (ιLp x)) y] change (inner ℂ (C (ιLp x)) (ιLp y)).re = inner ℝ x (reLp (C (ιLp y))) rw [real_inner_comm (reLp (C (ιLp y))) x] rw [inner_reLp_left (C (ιLp y)) x] calc (inner ℂ (C (ιLp x)) (ιLp y)).re = (inner ℂ (ιLp x) (C (ιLp y))).re := by exact congrArg re ((ContinuousLinearMap.isSelfAdjoint_iff_isSymmetric.mp hC) (ιLp x) (ιLp y)) _ = (inner ℂ (C (ιLp y)) (ιLp x)).re := by simpa using (inner_re_symm (𝕜 := ℂ) (x := C (ιLp y)) (y := ιLp x)).symm -
realCFC_multheorem — Symbol-multiplication law for realCFC.hypothesesProof (Lean source)
theorem realCFC_mul (A : Lp ℝ 2 μ →L[ℝ] Lp ℝ 2 μ) (hA : IsSelfAdjoint A) (f g : ℝ → ℝ) (hf : Continuous f) (hg : Continuous g) : realCFC A (fun x => f x * g x) = (realCFC A f).comp (realCFC A g) := by ext v rw [realCFC_apply A hA (fun x => f x * g x) (hf.mul hg) v] rw [ContinuousLinearMap.comp_apply] rw [realCFC_apply A hA f hf (realCFC A g v), realCFC_apply A hA g hg v] rw [cfc_lifted_preserves_real A hA g hg v] rw [show (fun z : ℂ => ((f z.re * g z.re : ℝ) : ℂ)) = fun z : ℂ => (f z.re : ℂ) * (g z.re : ℂ) by funext z norm_num] rw [cfc_mul (fun z : ℂ => (f z.re : ℂ)) (fun z : ℂ => (g z.re : ℂ)) (complexLift A) (hf := (Complex.continuous_ofReal.comp (hf.comp Complex.continuous_re)).continuousOn) (hg := (Complex.continuous_ofReal.comp (hg.comp Complex.continuous_re)).continuousOn)] rfl
Tikhonov 6 core · 6 supporting Develops the Hilbert-space Tikhonov interface for NPIV inverse problems. ★ tikhonovMinimiserL2_optimality★ tikhonovMinimiserL2_strong_convexity
Develops the Hilbert-space Tikhonov interface for NPIV inverse problems.
The module defines OperatorSystem.tikhonovBilin,
OperatorSystem.tikhonovTargetSub, and
OperatorSystem.tikhonovMinimiserL2; proves coercivity and the minimizer's
variational identity; and exposes
OperatorSystem.tikhonovMinimiserL2_strong_convexity, the L²-level population
strong-convexity inequality used by the primal rate theorem.
The Tikhonov bilinear form on the ambient Lp ℝ 2 μ:
Definition (Lean source)
The bilinear form, as a bilinear form on the closed subspace Hbar_L2. We pre/post-compose tikhonovBilin with the subtype inclusion Hbar_L2 →L[ℝ] Lp ℝ 2 μ so the result lives on the Hilbert space Hbar_L2 itself. This is the form fed into Lax–Milgram.
Definition (Lean source)
The Tikhonov target functional evaluated on the closed subspace Hbar_L2:
Definition (Lean source)
The population Tikhonov minimiser at level λ (L² level).
Definition (Lean source)
Variational identity for the Tikhonov minimiser. For a strictly positive Tikhonov regularization level λ and any function v in the closed primal candidate subspace Hbar_L2, the population Tikhonov minimiser h*_λ at level λ satisfies the identity ⟪T h*_λ, T v⟫ + λ · ⟪h*_λ, v⟫ = ⟪T h₀, T v⟫, where T is the projection-composed conditional-expectation operator and h₀ is the L² class of the structural function.
Formal statement
Proof (Lean source)
Population strong convexity at the Tikhonov minimiser (L² level). For a strictly positive Tikhonov regularization level λ and any function h in the closed primal candidate subspace Hbar_L2, the non-negative excess λ‖h − h*_λ‖² + ‖T(h − h*_λ)‖² — the amount by which the quadratic Tikhonov objective at h exceeds its value at the population minimiser h*_λ — is bounded above by ‖T(h − h₀)‖² − ‖T(h*_λ − h₀)‖² + λ(‖h‖² − ‖h*_λ‖²), where T is the projection-composed conditional-expectation operator and h₀ is the structural function's L² class.
Formal statement
Proof (Lean source)
6 supporting declarations (lemmas, instances)
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tikhonovBilin_applylemma — Pointwise formula for tikhonovBilin.hypothesesProof (Lean source)
lemma tikhonovBilin_apply (S : OperatorSystem Ω μ) (lambda : ℝ) (u v : Lp ℝ 2 μ) : S.tikhonovBilin lambda u v = inner ℝ (S.Tlin u) (S.Tlin v) + lambda * inner ℝ u v := by change (((((innerSL ℝ : Lp ℝ 2 μ →L[ℝ] Lp ℝ 2 μ →L[ℝ] ℝ).comp S.Tlin).flip.comp S.Tlin).flip + lambda • (innerSL ℝ : Lp ℝ 2 μ →L[ℝ] Lp ℝ 2 μ →L[ℝ] ℝ)) u v) = inner ℝ (S.Tlin u) (S.Tlin v) + lambda * inner ℝ u v simp [coe_innerSL_apply] -
tikhonovBilin_symmlemma — Symmetry of tikhonovBilin.hypothesesconclusionS.tikhonovBilin lambda u v = S.tikhonovBilin lambda v uProof (Lean source)
lemma tikhonovBilin_symm (S : OperatorSystem Ω μ) (lambda : ℝ) (u v : Lp ℝ 2 μ) : S.tikhonovBilin lambda u v = S.tikhonovBilin lambda v u := by simp [tikhonovBilin_apply, real_inner_comm] -
tikhonovBilinSub_applylemma — Pointwise formula for the restricted bilinear form.hypothesesProof (Lean source)
lemma tikhonovBilinSub_apply (S : OperatorSystem Ω μ) (lambda : ℝ) (u v : S.Hbar_L2) : S.tikhonovBilinSub lambda u v = S.tikhonovBilin lambda (u : Lp ℝ 2 μ) (v : Lp ℝ 2 μ) := by rfl -
tikhonovBilinSub_isCoercivelemma — Coercivity of tikhonovBilinSub for 0 < λ. Witness constant: λ. Proof: bilin u u = ‖T u‖² + λ ‖u‖² ≥ λ ‖u‖² since ‖T u‖² ≥ 0.hypothesesconclusionIsCoercive (S.tikhonovBilinSub lambda)Proof (Lean source)
lemma tikhonovBilinSub_isCoercive (S : OperatorSystem Ω μ) [S.Hbar_L2.HasOrthogonalProjection] {lambda : ℝ} (lambda_pos : 0 < lambda) : IsCoercive (S.tikhonovBilinSub lambda) := by refine ⟨lambda, lambda_pos, ?_⟩ intro u change lambda * ‖u‖ * ‖u‖ ≤ S.tikhonovBilin lambda (u : Lp ℝ 2 μ) (u : Lp ℝ 2 μ) rw [tikhonovBilin_apply] simp nlinarith [sq_nonneg (‖S.Tlin (u : Lp ℝ 2 μ)‖)] -
tikhonovTargetSub_applylemma — Pointwise formula for the target functional.hypothesesS :OperatorSystem Ω μv :S.Hbar_L2Proof (Lean source)
lemma tikhonovTargetSub_apply (S : OperatorSystem Ω μ) (v : S.Hbar_L2) : S.tikhonovTargetSub v = inner ℝ (S.Tlin (S.hL2 S.h₀_mem)) (S.Tlin (v : Lp ℝ 2 μ)) := by simp [tikhonovTargetSub, coe_innerSL_apply] -
tikhonovMinimiserL2_memlemma — The minimiser lies in Hbar_L2.hypothesesconclusionS.tikhonovMinimiserL2 lambda ∈ S.Hbar_L2Proof (Lean source)
lemma tikhonovMinimiserL2_mem (S : OperatorSystem Ω μ) [S.Hbar_L2.HasOrthogonalProjection] (lambda : ℝ) : S.tikhonovMinimiserL2 lambda ∈ S.Hbar_L2 := by by_cases h : 0 < lambda · simp [tikhonovMinimiserL2, h] · simp [tikhonovMinimiserL2, h]
SpectralCalculus 10 core · 6 supporting This file proves the Tikhonov bias bounds for the primal NPIV estimator from a spectral β-source condition. ★ tikhonovMinimiserL2_eq_resolvent★ strong_bias★ weak_bias
Spectral Discharge of Tikhonov Bias
This file proves the Tikhonov bias bounds for the primal NPIV estimator from a
spectral β-source condition. It defines the strengthened
SpectralSourceCondition, builds the real functional-calculus operator
spectralPower = (T†T)^{β/2}, identifies the full-space Lax–Milgram
Tikhonov minimizer with its resolvent expression, proves the strong- and
weak-metric bias bounds, and packages them as tikhonov_bias_from_spectral
for the rate theorem.
Local disambiguation of the real-scalar algebra structure on the complex operator algebra Lp ℂ 2 μ →L[ℂ] Lp ℂ 2 μ.
Spectral β-source condition at the primal nuisance h₀: strengthens SourceCondition by additionally requiring that the primal candidate subspace coincides with the full ambient L² space, which is what lets the Lax–Milgram Tikhonov minimiser on the candidate class agree with the resolvent expression needed to run the spectral argument.
Definition (Lean source)
The symbol x ↦ Real.rpow (max x 0) (β/2) used by spectralPower. Continuous on all of ℝ for β ≥ 0, and agrees with x^{β/2} on [0, ∞).
Definition (Lean source)
The operator (T†T)^{β/2}, defined as the real CFC of T†T applied to the continuous symbol x ↦ Real.rpow (max x 0) (β/2).
Definition (Lean source)
Uniform constant absorbing both regimes (β ≤ 2 and β > 2) of the sup-on-spectrum analysis.
Definition (Lean source)
Resolvent identification of the Lax–Milgram minimiser. Given a spectral β-source condition sc whose primal candidate set coincides with the whole ambient L² space, for any strictly positive Tikhonov regularization level λ, the population Tikhonov minimiser at level λ equals the resolvent expression obtained by applying the real functional calculus of T†T to the symbol x ↦ x/(λ+x), evaluated at the L² class of the structural function h₀.
Formal statement
Proof (Lean source)
Strong-metric Tikhonov bias bound. Given a spectral β-source condition sc linking the structural function h₀ to a coefficient w₀ through the spectral power operator (T†T)^{β/2}, for any strictly positive Tikhonov regularization level λ, the squared strong-metric () distance between the Tikhonov minimiser h*_λ at level λ and h₀ is bounded by biasConst · ‖w₀‖² · λ^{min(β,2)}, where biasConst is a constant determined by T†T and β.
Formal statement
Proof (Lean source)
Weak-metric Tikhonov bias bound. Given the same spectral β-source condition sc linking h₀ to w₀ through (T†T)^{β/2}, for any strictly positive Tikhonov regularization level λ, the squared weak-metric () norm of the operator T applied to the Tikhonov-minimiser bias h*_λ − h₀ is bounded by biasConst · ‖w₀‖² · λ^{min(β+1,2)}.
Formal statement
Proof (Lean source)
Function-level pullback datum for the discharge. The user provides a Hbar-element h_lambda_star_fun whose L² class equals the Lax–Milgram minimiser tikhonovMinimiserL2 S λ constructed in Operator/Tikhonov.lean. This single pullback is the only function-level commitment needed: the bias and convexity inequalities all live at the L² level and transport along this equation.
Definition (Lean source)
Discharge of the Tikhonov bias bound from the spectral source condition.
Definition (Lean source)
6 supporting declarations (lemmas, instances)
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Hbar_L2_hasProjlemma — Closedness witness for the full primal candidate subspace.Proof (Lean source)
lemma Hbar_L2_hasProj (sc : SpectralSourceCondition S β) : S.Hbar_L2.HasOrthogonalProjection := by rw [sc.Hbar_L2_eq_top] infer_instance -
Tstar_T_isPositivelemma — T†T is a positive operator.Proof (Lean source)
lemma Tstar_T_isPositive (S : OperatorSystem Ω μ) : S.Tstar_T.IsPositive := by unfold OperatorSystem.Tstar_T OperatorSystem.Tadjoint simpa [ContinuousLinearMap.comp_def, ContinuousLinearMap.comp_apply] using ContinuousLinearMap.isPositive_adjoint_comp_self S.Tlin -
Tstar_T_spectrum_nonneglemma — Positivity of T†T: its real spectrum lies in [0, ∞).Proof (Lean source)
lemma Tstar_T_spectrum_nonneg (_sc : SpectralSourceCondition S β) : ∀ x ∈ spectrum ℝ S.Tstar_T, 0 ≤ x := by intro x hx exact spectrum_nonneg_of_nonneg (a := S.Tstar_T) (x := x) (by rw [ContinuousLinearMap.nonneg_iff_isPositive] exact Tstar_T_isPositive S) hx -
continuous_sourceSymbollemma — The source symbol is continuous whenever the source exponent is nonnegative.Proof (Lean source)
lemma continuous_sourceSymbol {β : ℝ} (h : 0 ≤ β) : Continuous (sourceSymbol β) := by unfold sourceSymbol refine (Real.continuous_rpow_const ?_).comp (continuous_id.max continuous_const) linarith -
spectral_identity_h₀lemma — Restated spectral identity using spectralPower.hypothesessc :conclusionS.hL2 S.h₀_mem = sc.spectralPower (S.hL2 sc.w₀_mem)Proof (Lean source)
lemma spectral_identity_h₀ (sc : SpectralSourceCondition S β) : S.hL2 S.h₀_mem = sc.spectralPower (S.hL2 sc.w₀_mem) := sc.spectral_identity -
biasConst_nonneglemma — The uniform Tikhonov bias constant is nonnegative.Proof (Lean source)
lemma biasConst_nonneg (sc : SpectralSourceCondition S β) : 0 ≤ sc.biasConst := by unfold biasConst exact Real.rpow_nonneg (le_max_of_le_left zero_le_one) _