Samplinglib
Lean gate not recorded for this source state main · 9bb7a7394ab0

Genuine nonlinear common-noise coupling and raw transport cost

Fan Chen, Sinho Chewi, Jianfeng Lu, Matthew S. Zhang; ASTIS explicit transport prerequisite/actual-kernel interface, not the full local-error or main result.

arXiv:2609.06906v1 · Theorem4.5 proof, HTML529-545; Proposition4.7 (4.13), HTML590-615; shared raw-infimum prerequisite · faithful paraphrase

Complete source statement (Samplinglib restatement)

The true randomized output laws map(Phi,mu×Gamma) and map(Phi,nu×Gamma) obey T_c(output_mu,output_nu)<=A T_c(mu,nu). Here T_c is the raw Kantorovich infimum over actual couplings. Input/output costs may be infinite and no optimizer or finite moments are assumed.

  • E is a normed additive Borel group with second-countable topology. These topological/algebraic conditions are exposed API requirements of the reused shared marginal interface, not hidden moment or optimizer assumptions. State and noise use this same space.
  • Phi:E×E->E and the extended nonnegative cost c:E×E->ENNReal are jointly measurable. The noise Gamma and both input laws mu,nu are probability measures. 0<A<infinity in extended reals.
  • For every x,y,zeta, c(Phi(x,zeta),Phi(y,zeta))<=A c(x,y). This is the explicit general lifting premise; the actual source algorithm consumer proves it from its genuine normalized potential, not a supplied kernel certificate.

Genuine randomized law and raw transport

\[\mathcal T_c(\Phi_\#(\mu\otimes\Gamma),\Phi_\#(\nu\otimes\Gamma))\le A\mathcal T_c(\mu,\nu),\qquad0<A<\infty.\]

Read the formalized proofs

Each statement and proof below has its own closed Lean disclosure. Samplinglib parents, Mathlib calls and external mathematical sources are distinguished in each proof.

Samplinglib mathematical lesson

Genuine nonlinear common-noise coupling and raw transport cost

AutoSamplingTheory.TechnicalLemmas.Measure.RandomizedMapTransport.transportCost_randomized_map_le · theorem · Teaching coverage

Read this first

Mathematical statement

The true randomized output laws map(Phi,mu×Gamma) and map(Phi,nu×Gamma) obey T_c(output_mu,output_nu)≤A T_c(mu,nu). Here T_c is the raw Kantorovich infimum over actual couplings. Input/output costs may be infinite and no optimizer or finite moments are assumed.

\[\mathcal T_c(\Phi_\#(\mu\otimes\Gamma),\Phi_\#(\nu\otimes\Gamma))\le A\mathcal T_c(\mu,\nu),\qquad0<A<\infty.\]
Lean statement · transportCost_randomized_map_le

Exact randomized law/actual Phi and Markov semantics, canonical raw source cost.

Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

theorem transportCost_randomized_map_le
    (Φ : E × E → E) (hΦ : Measurable Φ) (c : E × E → ℝ≥0∞) (hc : Measurable c)
    (A : ℝ≥0∞) (hA0 : A ≠ 0) (hAtop : A ≠ ⊤)
    (hcost : ∀ x y ζ, c (Φ (x,ζ),Φ (y,ζ)) ≤ A*c (x,y))
    (μ ν Γ : Measure E) [IsProbabilityMeasure μ] [IsProbabilityMeasure ν]
    [IsProbabilityMeasure Γ] :
    Transport.transportCost c ((μ.prod Γ).map Φ) ((ν.prod Γ).map Φ) ≤
      A * Transport.transportCost c μ ν

Exact module and namespace context

Proof roadmap

  1. 1Construct the actual common-noise coupling

    Start with any gamma coupling mu and nu. Normalization follows from its prescribed probability left marginal. Put rho=S#(gamma×Gamma), where S((x,y),zeta)=(Phi(x,zeta),Phi(y,zeta)). Joint measurability gives a genuine pushforward. Reuse the existing public left/right joint-noise marginal lemmas: (x,zeta) and (y,zeta) have laws mu×Gamma and nu×Gamma. Map composition therefore proves rho has exactly the two randomized output marginals.

  2. 2Integrate the explicit pointwise cost

    The canonical feasible-coupling bound gives T_c(outputs)≤integral c d rho. The measurable map formula and assumed pointwise bound give at most A integral c(x,y) d(gamma×Gamma). Since Gamma has mass one, mapping the product to its first coordinate reduces this exactly to A integral c d gamma. This uses nonnegative extended integrals, not an unproved finite expectation.

  3. 3Take the raw infimum

    A finite nonzero multiplier commutes with the raw extended-real infimum. Rewrite the input transport infimum as an indexed infimum over each feasible cost, and apply the already constructed candidate bound to every one. No existence of a minimizing coupling or continuity of c is required.

Full assumptions and notation

All objects and hypotheses

  • E is a normed additive Borel group with second-countable topology. These topological/algebraic conditions are exposed API requirements of the reused shared marginal interface, not hidden moment or optimizer assumptions. State and noise use this same space.
  • Phi:E×E→E and the extended nonnegative cost c:E×E→ENNReal are jointly measurable. The noise Gamma and both input laws mu,nu are probability measures. 0<A<infinity in extended reals.
  • For every x,y,zeta, c(Phi(x,zeta),Phi(y,zeta))≤A c(x,y). This is the explicit general lifting premise; the actual source algorithm consumer proves it from its genuine normalized potential, not a supplied kernel certificate.

Mathematical proof

1. Construct the actual common-noise coupling

Start with any gamma coupling mu and nu. Normalization follows from its prescribed probability left marginal. Put rho=S#(gamma×Gamma), where S((x,y),zeta)=(Phi(x,zeta),Phi(y,zeta)). Joint measurability gives a genuine pushforward. Reuse the existing public left/right joint-noise marginal lemmas: (x,zeta) and (y,zeta) have laws mu×Gamma and nu×Gamma. Map composition therefore proves rho has exactly the two randomized output marginals.

\[\rho=S_\#(\gamma\otimes\Gamma),\quad\rho_1=\Phi_\#(\mu\otimes\Gamma),\quad\rho_2=\Phi_\#(\nu\otimes\Gamma).\]
Corresponding Lean step

candidate; hS; hleft; hright; hρ; map_pairLeft_prod_eq; map_pairRight_prod_eq

2. Integrate the explicit pointwise cost

The canonical feasible-coupling bound gives T_c(outputs)≤integral c d rho. The measurable map formula and assumed pointwise bound give at most A integral c(x,y) d(gamma×Gamma). Since Gamma has mass one, mapping the product to its first coordinate reduces this exactly to A integral c d gamma. This uses nonnegative extended integrals, not an unproved finite expectation.

\[\mathcal T_c(\mathrm{outputs})\le\int c\,d\rho\le A\int c\,d\gamma.\]
Corresponding Lean step

transportCost_le_lintegral_of_isCoupling; lintegral_map; lintegral_mono; lintegral_const_mul; Measure.map_fst_prod

3. Take the raw infimum

A finite nonzero multiplier commutes with the raw extended-real infimum. Rewrite the input transport infimum as an indexed infimum over each feasible cost, and apply the already constructed candidate bound to every one. No existence of a minimizing coupling or continuity of c is required.

\[\mathcal T_c(\mathrm{outputs})\le\inf_{\gamma\in\mathrm{Coupl}(\mu,\nu)}A\int c\,d\gamma=A\mathcal T_c(\mu,\nu).\]
Corresponding Lean step

Transport.transportCost_eq_sInf; sInf_eq_iInf; ENNReal.mul_iInf_of_ne; le_iInf

Lean verification

The exact proof begins folded immediately after the mathematical argument; the statement is folded beside the claim above.

Lean proof · transportCost_randomized_map_le

Genuine synchronous coupling / actual kernel-law producer, measurable nonnegative integrals and positive finite scaled raw infimum.

Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

Download module (.lean)
theorem transportCost_randomized_map_le
    (Φ : E × E → E) (hΦ : Measurable Φ) (c : E × E → ℝ≥0∞) (hc : Measurable c)
    (A : ℝ≥0∞) (hA0 : A ≠ 0) (hAtop : A ≠ ⊤)
    (hcost : ∀ x y ζ, c (Φ (x,ζ),Φ (y,ζ)) ≤ A*c (x,y))
    (μ ν Γ : Measure E) [IsProbabilityMeasure μ] [IsProbabilityMeasure ν]
    [IsProbabilityMeasure Γ] :
    Transport.transportCost c ((μ.prod Γ).map Φ) ((ν.prod Γ).map Φ) ≤
      A * Transport.transportCost c μ ν := by
  classical
  have candidate (γ : Measure (E × E)) (hγ : Transport.IsCoupling γ μ ν) :
      Transport.transportCost c ((μ.prod Γ).map Φ) ((ν.prod Γ).map Φ) ≤
        A * ∫⁻ z, c z ∂γ := by
    letI : IsProbabilityMeasure γ := Transport.isProbabilityMeasure_of_isCoupling_left hγ
    let S : ((E × E) × E) → E × E := fun w => (Φ (w.1.1,w.2),Φ (w.1.2,w.2))
    have hS : Measurable S := hΦ.comp (by fun_prop) |>.prodMk (hΦ.comp (by fun_prop))
    let ρ := (γ.prod Γ).map S
    have hleft : (γ.prod Γ).map (fun w : ((E × E) × E) => (w.1.1,w.2)) = μ.prod Γ :=
      CommonNoiseContraction.map_pairLeft_prod_eq hγ
    have hright : (γ.prod Γ).map (fun w : ((E × E) × E) => (w.1.2,w.2)) = ν.prod Γ :=
      CommonNoiseContraction.map_pairRight_prod_eq hγ
    have hρ : Transport.IsCoupling ρ ((μ.prod Γ).map Φ) ((ν.prod Γ).map Φ) := by
      constructor
      · rw [Measure.fst, show ρ = (γ.prod Γ).map S from rfl,
          Measure.map_map measurable_fst hS]
        rw [← hleft, Measure.map_map hΦ (by fun_prop)]
        rfl
      · rw [Measure.snd, show ρ = (γ.prod Γ).map S from rfl,
          Measure.map_map measurable_snd hS]
        rw [← hright, Measure.map_map hΦ (by fun_prop)]
        rfl
    calc
      Transport.transportCost c ((μ.prod Γ).map Φ) ((ν.prod Γ).map Φ) ≤
          ∫⁻ z, c z ∂ρ := Transport.transportCost_le_lintegral_of_isCoupling _ _ _ _ hρ
      _ = ∫⁻ w, c (S w) ∂γ.prod Γ := lintegral_map hc hS
      _ ≤ ∫⁻ w : (E × E) × E, A*c w.1 ∂γ.prod Γ := by
        apply lintegral_mono
        intro w
        exact hcost _ _ _
      _ = A * ∫⁻ w : (E × E) × E, c w.1 ∂γ.prod Γ :=
        lintegral_const_mul _ (hc.comp measurable_fst)
      _ = A * ∫⁻ z, c z ∂γ := by
        rw [← lintegral_map hc measurable_fst, Measure.map_fst_prod, measure_univ, one_smul]
  conv_rhs => rw [Transport.transportCost_eq_sInf, sInf_eq_iInf,
    ENNReal.mul_iInf_of_ne hA0 hAtop]
  apply le_iInf
  intro r
  rw [ENNReal.mul_iInf_of_ne hA0 hAtop]
  apply le_iInf
  intro hrmem
  rcases hrmem with ⟨γ,hγ,hr⟩
  simpa only [hr] using candidate γ hγ
end
end AutoSamplingTheory.TechnicalLemmas.Measure.RandomizedMapTransport

Exact module and namespace context

Source assumptions versus formal assumptions

SourceActual LeanDifference kindWhy it matters
Common-noise coupling of the source phase and its induced law.E is a normed additive Borel group with second-countable topology. These topological/algebraic conditions are exposed API requirements of the reused shared marginal interface, not hidden moment or optimizer assumptions. State and noise use this same space. Phi:E×E->E and the extended nonnegative cost c:E×E->ENNReal are jointly measurable. The noise Gamma and both input laws mu,nu are probability measures. 0<A<infinity in extended reals. For every x,y,zeta, c(Phi(x,zeta),Phi(y,zeta))<=A c(x,y). This is the explicit general lifting premise; the actual source algorithm consumer proves it from its genuine normalized potential, not a supplied kernel certificate.generalizationShared prerequisite abstracts measurable maps/costs with an explicit pointwise premise and API-only Borel group structure; the actual source consumer proves that premise for its genuine Phi. At the actual consumer, f=V_eta remains an exposed unresolved producer, and source Q/Gaussian kernel semantics are exact. Arbitrary probability inputs may have infinite raw costs; no moment/invariance claim.
Source universal constant c and squared source metric costs.The true randomized output laws map(Phi,mu×Gamma) and map(Phi,nu×Gamma) obey T_c(output_mu,output_nu)<=A T_c(mu,nu). Here T_c is the raw Kantorovich infimum over actual couplings. Input/output costs may be infinite and no optimizer or finite moments are assumed.sameActual consumer retains the simultaneously valid c=1/65536, squares its pointwise norm contraction, and constructs a real synchronous coupling. Shared A>0 finite is explicit; no zero-times-infinity or hidden optimizer argument.

Actual Gaussian exact-gradient numerical K at the explicit normalized global C2 curvature interface. Actual f=V_eta smoothing/C2/curvature remains separate. No target invariance/stationarity, numerical target bias, stochastic Khat/Kbar, local errors/history/expected query cost, Wp/proxy-warmness/initialization, PBPS or either complete main/composition result. TV proximity does not transfer unbounded cost.

Encoder–denoiser: accepted · equivalent-after-elaboration

A generalization is not a source correction. Proposed missing conditions require separate independent repair review. No proposed repair silently changes the original theorem.

Scope and omitted-condition boundaries
  • Actual Gaussian exact-gradient numerical K at the explicit normalized global C2 curvature interface. Actual f=V_eta smoothing/C2/curvature remains separate. No target invariance/stationarity, numerical target bias, stochastic Khat/Kbar, local errors/history/expected query cost, Wp/proxy-warmness/initialization, PBPS or either complete main/composition result. TV proximity does not transfer unbounded cost.
Sources, dependencies, and reuse

Source and reuse

Samplinglib parents called

Mathlib API called (external library)

  • Measure.map_map
  • lintegral_map
  • lintegral_mono
  • lintegral_const_mul
  • Measure.map_fst_prod
  • ENNReal.mul_iInf_of_ne
  • sInf_eq_iInf
  • le_iInf

Mathematical sources

  • SPHMC actual common-noise transport prerequisite — Actual Gaussian exact-gradient numerical K at the explicit normalized global C2 curvature interface. Actual f=V_eta smoothing/C2/curvature remains separate. No target invariance/stationarity, numerical target bias, stochastic Khat/Kbar, local errors/history/expected query cost, Wp/proxy-warmness/initialization, PBPS or either complete main/composition result. TV proximity does not transfer unbounded cost.

Samplinglib prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

Which proof obligations and prerequisites are covered?

  • Independently verified prerequisite Actual common-noise law/transport prerequisite
  • TODO — not closed by these contributions Actual f=V_eta smoothing/C2/curvature (remaining)
  • TODO — not closed by these contributions Local errors/invariance/history/cost and full main/composition (remaining)