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AutoSamplingTheory.TechnicalLemmas.FunctionalInequalities.GibbsGradientKernelEquality

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theorem AutoSamplingTheory.TechnicalLemmas.FunctionalInequalities.GibbsGradientKernelEquality.closed_gradient_zero_iff_ae_constant Partial Not mapped

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theorem closed_gradient_zero_iff_ae_constant
    (W : E → ℝ) (hW : ContDiff ℝ 1 W)
    (hI : Integrable (fun x => Real.exp (-W x)))
    (D : Lp ℝ 2 ((volume : Measure E).tilted (fun x => -W x)) →ₗ.[ℝ]
      Lp E 2 ((volume : Measure E).tilted (fun x => -W x))) (hD : D.IsClosable)
    (hgraph : ∀ (a : Lp ℝ 2 ((volume : Measure E).tilted (fun x => -W x)))
      (H : Lp E 2 ((volume : Measure E).tilted (fun x => -W x))), (a,H) ∈ D.graph ↔
      ∃ φ : E → ℝ, ContDiff ℝ ∞ φ ∧ HasCompactSupport φ ∧
        a =ᵐ[(volume : Measure E).tilted (fun x => -W x)] φ ∧
        H =ᵐ[(volume : Measure E).tilted (fun x => -W x)] gradient φ)
    (u : D.closure.domain) :
    D.closure u=0 ↔ ∃ c : ℝ, (u : Lp ℝ 2 ((volume : Measure E).tilted (fun x => -W x)))
      =ᵐ[(volume : Measure E).tilted (fun x => -W x)] (fun _ => c) := by
  constructor
  · exact AutoSamplingTheory.TechnicalLemmas.FunctionalInequalities.GibbsGradientKernel.closed_gradient_zero_ae_constant
      W hW hI D hD hgraph u
  · rintro ⟨c,hc⟩
    obtain ⟨v,hv,hvz⟩ := AutoSamplingTheory.TechnicalLemmas.FunctionalInequalities.WeightedC1GradientDomain.constants_in_closed_gradient _ D hD hgraph c
    have he : u=v := Subtype.ext (Lp.ext (hc.trans hv.symm))
    rwa [he]

end AutoSamplingTheory.TechnicalLemmas.FunctionalInequalities.GibbsGradientKernelEquality

Complete Lean source

AutoSamplingTheory/TechnicalLemmas/FunctionalInequalities/GibbsGradientKernelEquality.lean — full module with imports and context
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import AutoSamplingTheory.TechnicalLemmas.FunctionalInequalities.WeightedC1GradientDomain
import AutoSamplingTheory.TechnicalLemmas.FunctionalInequalities.GibbsGradientKernel
/-! Actual SAME-original normalized Gibbs closed-gradient kernel equality combines genuine finite-weight constant-domain production with independently accepted zero-gradient AE constancy. No Poincare/BL/range/epsilon0/core follows. -/
set_option autoImplicit false
noncomputable section
open MeasureTheory Filter InnerProductSpace
open scoped Topology RealInnerProductSpace ContDiff
namespace AutoSamplingTheory.TechnicalLemmas.FunctionalInequalities.GibbsGradientKernelEquality
variable {E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
  [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
theorem closed_gradient_zero_iff_ae_constant
    (W : E → ℝ) (hW : ContDiff ℝ 1 W)
    (hI : Integrable (fun x => Real.exp (-W x)))
    (D : Lp ℝ 2 ((volume : Measure E).tilted (fun x => -W x)) →ₗ.[ℝ]
      Lp E 2 ((volume : Measure E).tilted (fun x => -W x))) (hD : D.IsClosable)
    (hgraph : ∀ (a : Lp ℝ 2 ((volume : Measure E).tilted (fun x => -W x)))
      (H : Lp E 2 ((volume : Measure E).tilted (fun x => -W x))), (a,H) ∈ D.graph ↔
      ∃ φ : E → ℝ, ContDiff ℝ ∞ φ ∧ HasCompactSupport φ ∧
        a =ᵐ[(volume : Measure E).tilted (fun x => -W x)] φ ∧
        H =ᵐ[(volume : Measure E).tilted (fun x => -W x)] gradient φ)
    (u : D.closure.domain) :
    D.closure u=0 ↔ ∃ c : ℝ, (u : Lp ℝ 2 ((volume : Measure E).tilted (fun x => -W x)))
      =ᵐ[(volume : Measure E).tilted (fun x => -W x)] (fun _ => c) := by
  constructor
  · exact AutoSamplingTheory.TechnicalLemmas.FunctionalInequalities.GibbsGradientKernel.closed_gradient_zero_ae_constant
      W hW hI D hD hgraph u
  · rintro ⟨c,hc⟩
    obtain ⟨v,hv,hvz⟩ := AutoSamplingTheory.TechnicalLemmas.FunctionalInequalities.WeightedC1GradientDomain.constants_in_closed_gradient _ D hD hgraph c
    have he : u=v := Subtype.ext (Lp.ext (hc.trans hv.symm))
    rwa [he]

end AutoSamplingTheory.TechnicalLemmas.FunctionalInequalities.GibbsGradientKernelEquality