production module
AutoSamplingTheory.TechnicalLemmas.FunctionalInequalities.GibbsGradientKernelEquality
Read the mathematical statements and proofs in order
1 named declarations scanned from AutoSamplingTheory/TechnicalLemmas/FunctionalInequalities/GibbsGradientKernelEquality.lean.
Declarations
theorem AutoSamplingTheory.TechnicalLemmas.FunctionalInequalities.GibbsGradientKernelEquality.closed_gradient_zero_iff_ae_constant Partial Not mapped
Read the complete mathematical statement and proof, with Lean below each
No declaration docstring.
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
AutoSamplingTheory/TechnicalLemmas/FunctionalInequalities/GibbsGradientKernelEquality.lean:11published source at ed709a7f320d
Complete Lean source
AutoSamplingTheory/TechnicalLemmas/FunctionalInequalities/GibbsGradientKernelEquality.lean — full module with imports and context
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