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Samplinglib mathematical lesson

Actual Gaussian-Gibbs square-root density and energy domains

AutoSamplingTheory.TechnicalLemmas.Measure.GaussianSqrtDensityDomain.gaussian_sqrt_density_domain · theorem · Teaching coverage

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Mathematical statement

For the actual covariance-identity Gaussian tilted by the normalized convex C2 residual, produce its positive finite partition, literal normalized positive density and C2 square root, actual volume Gibbs equality and probability, f and gradientf L2gamma, gradientrho L2r, entropy L1, explicit derivative identities and exact one-quarter Dirichlet-Fisher energy.

\[Z=\int e^{-\rho}d\gamma\in(0,1],\quad r=q\gamma\propto e^{-\|u\|^2/2-\rho}du,\quad q=e^{-\rho}/Z,\quad f=\sqrt q,\quad\int\|\nabla f\|^2d\gamma=\tfrac14\int\|\nabla\rho\|^2dr.\]
Lean statement · gaussian_sqrt_density_domain

For the actual covariance-identity Gaussian tilted by the normalized convex C2 residual, produce its positive finite partition, literal normalized positive density and C2 square root, actual volume Gibbs equality and probability, f and gradientf L2gamma, gradientrho L2r, entropy L1, explicit derivative identities and exact one-quarter Dirichlet-Fisher energy.

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 gaussian_sqrt_density_domain
    {E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
    [CompleteSpace E] [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
    {rho : E → ℝ} {L : ℝ≥0} (hrho : ContDiff ℝ 2 rho)
    (hrho0 : rho 0 = 0) (hgrad0 : gradient rho 0 = 0)
    (hH : ∀ u v : E, 0 ≤ fderiv ℝ (fderiv ℝ rho) u v v ∧
      fderiv ℝ (fderiv ℝ rho) u v v ≤ (L : ℝ)*‖v‖^2) :
    let gamma := stdGaussian E
    let Z := ∫ u, Real.exp (-rho u) ∂gamma
    let q := fun u => Real.exp (-rho u)/Z
    let f := fun u => Real.exp (-rho u/2)/Real.sqrt Z
    let r := gamma.tilted (fun u => -rho u)
    0 < Z ∧ Z ≤ 1 ∧
      r = (volume : Measure E).tilted (fun u => -(‖u‖^2/2+rho u)) ∧
      r = gamma.withDensity (fun u => ENNReal.ofReal (q u)) ∧
      IsProbabilityMeasure r ∧
      (∀ u, 0 < q u ∧ f u^2 = q u) ∧
      (∫ u, q u ∂gamma) = 1 ∧
      ContDiff ℝ 2 f ∧ MemLp f 2 gamma ∧
      MemLp (gradient f) 2 gamma ∧ MemLp (gradient rho) 2 r ∧
      Integrable (fun u => q u*Real.log (q u)) gamma ∧
      (∀ u, gradient f u = -(f u/2) • gradient rho u) ∧
      (∀ u, gradient (fun z => Real.log (q z)) u = -gradient rho u) ∧
      (∫ u, ‖gradient f u‖^2 ∂gamma) =
        (1/4 : ℝ)*(∫ u, ‖gradient rho u‖^2 ∂r)

Exact module and namespace context

Proof roadmap

  1. 1Derive the nonnegative residual and gradient growth

    Hessian0..L produces convexity and L-Lipschitz gradient through the canonical curvature provider. The normalized stationary origin then yields rho≥0 and gradientnorm≤Lnormu. No extra convexity or growth premise is introduced; L0 and dimension0 are included.

  2. 2Normalize against the actual standard Gaussian

    The real exponential is positive and bounded by1. Gaussian probability gives its L1 and strictly positive finite integral Z≤1. The literal tilt is exactly withDensity(exp(-rho)/Z), integrates to1 and is a genuine probability.

  3. 3Identify the canonical volume Gibbs law

    Use the compiled covariance-identity Gaussian density against canonical volume. Its actual mass1 forces the ordinary quadratic partition to be finite and positive; normalize that exact density. Consecutive tilts then identify the Gaussian-relative residual law with the volume Gibbs potential norm²/2+rho.

  4. 4Differentiate the explicit positive square root

    The everywhere chosen f=exp(-rho/2)/sqrtZ is C2, strictly positive and satisfies f²=q. Ordinary Frechet chain rules and the Hilbert Riesz identification yield its gradient and the log-density gradient. No arbitrary pointwise RN representative is differentiated.

  5. 5Produce the finite gradient and entropy domains

    The actual Gaussian second moment dominates exp(-rho)normgradρ² by L²normu². This gives gradρ L2r and gradf L2gamma after multiplying by the proved normalizer. f L2 follows from q L1. The bound rho exp(-rho)≤1 and logq=-rho-logZ give entropy L1 without assuming its finiteness.

  6. 6Integrate the exact Dirichlet-Fisher identity

    Integrate normgradf²=(1/4)qnormgradρ² against gamma and rewrite the actual tilted integral. The domains have already been proved, so both energies are finite. This is the Euclidean Hilbert energy with the exact factor1/4; no LSI or transport inequality is asserted.

Full assumptions and notation

All objects and hypotheses

  • Complete finite-dimensional real Hilbert E with canonical volume and Borel structure; dimension zero included.
  • Actual global C2 rho, rho(0)=0, gradientrho(0)=0 and Hessian0..L for NNReal L including0. Probability/positive normalizer/entropy/gradient and L2 domains are conclusions.

Mathematical proof

1. Derive the nonnegative residual and gradient growth

Hessian0..L produces convexity and L-Lipschitz gradient through the canonical curvature provider. The normalized stationary origin then yields rho≥0 and gradientnorm≤Lnormu. No extra convexity or growth premise is introduced; L0 and dimension0 are included.

\[\rho(0)=0,\quad\nabla\rho(0)=0,\quad0\preceq\nabla^2\rho\preceq LI\ \Longrightarrow\ \rho\ge0,\ \|\nabla\rho(u)\|\le L\|u\|.\]
Corresponding Lean step

QuadraticRegularization.strongConvexOn_and_lipschitzWith_gradient_add_quadratic; StrongConvexFirstOrder.firstOrder_lower_bound_of_strongConvexOn

2. Normalize against the actual standard Gaussian

The real exponential is positive and bounded by1. Gaussian probability gives its L1 and strictly positive finite integral Z≤1. The literal tilt is exactly withDensity(exp(-rho)/Z), integrates to1 and is a genuine probability.

\[\gamma=\mathcal N(0,I),\quad Z=\int e^{-\rho}d\gamma\in(0,1],\quad q=e^{-\rho}/Z,\quad r=q\gamma,\quad\int q\,d\gamma=1.\]
Corresponding Lean step

weight_normalization; isProbabilityMeasure_tilted; integral_div

3. Identify the canonical volume Gibbs law

Use the compiled covariance-identity Gaussian density against canonical volume. Its actual mass1 forces the ordinary quadratic partition to be finite and positive; normalize that exact density. Consecutive tilts then identify the Gaussian-relative residual law with the volume Gibbs potential norm²/2+rho.

\[\gamma=Z_0^{-1}e^{-\|u\|^2/2}du,\quad r=Z_Q^{-1}e^{-\|u\|^2/2-\rho(u)}du.\]
Corresponding Lean step

IsotropicGaussianDensity.map_sqrt_smul_stdGaussian_eq_withDensity; integral_withDensity_eq_integral_toReal_smul; tilted_tilted

4. Differentiate the explicit positive square root

The everywhere chosen f=exp(-rho/2)/sqrtZ is C2, strictly positive and satisfies f²=q. Ordinary Frechet chain rules and the Hilbert Riesz identification yield its gradient and the log-density gradient. No arbitrary pointwise RN representative is differentiated.

\[f=\frac{e^{-\rho/2}}{\sqrt Z},\quad f^2=q,\quad\nabla f=-\tfrac12f\nabla\rho,\quad\nabla\log q=-\nabla\rho.\]
Corresponding Lean step

density_calculus; HasFDerivAt.exp; hasGradientAt_iff_hasFDerivAt; Real.log_div

5. Produce the finite gradient and entropy domains

The actual Gaussian second moment dominates exp(-rho)normgradρ² by L²normu². This gives gradρ L2r and gradf L2gamma after multiplying by the proved normalizer. f L2 follows from q L1. The bound rho exp(-rho)≤1 and logq=-rho-logZ give entropy L1 without assuming its finiteness.

\[e^{-\rho}\|\nabla\rho\|^2\le L^2\|u\|^2,\quad\int\|u\|^2d\gamma=d,\quad0\le\rho e^{-\rho}\le1,\quad q\log q=-Z^{-1}\rho e^{-\rho}-q\log Z.\]
Corresponding Lean step

StdGaussianMoment.integrable_norm_sq_and_integral_stdGaussian; integrable_tilted_iff; memLp_two_iff_integrable_sq_norm; entropy_integrable

6. Integrate the exact Dirichlet-Fisher identity

Integrate normgradf²=(1/4)qnormgradρ² against gamma and rewrite the actual tilted integral. The domains have already been proved, so both energies are finite. This is the Euclidean Hilbert energy with the exact factor1/4; no LSI or transport inequality is asserted.

\[\int\|\nabla f\|^2d\gamma=\frac14\int q\|\nabla\rho\|^2d\gamma=\frac14\int\|\nabla\rho\|^2dr.\]
Corresponding Lean step

integral_tilted; integral_const_mul; integral_congr_ae

Lean verification

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

Lean proof · gaussian_sqrt_density_domain

Derive the nonnegative residual and gradient growth; Normalize against the actual standard Gaussian; Identify the canonical volume Gibbs law; Differentiate the explicit positive square root; Produce the finite gradient and entropy domains; Integrate the exact Dirichlet-Fisher identity

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 gaussian_sqrt_density_domain
    {E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
    [CompleteSpace E] [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
    {rho : E → ℝ} {L : ℝ≥0} (hrho : ContDiff ℝ 2 rho)
    (hrho0 : rho 0 = 0) (hgrad0 : gradient rho 0 = 0)
    (hH : ∀ u v : E, 0 ≤ fderiv ℝ (fderiv ℝ rho) u v v ∧
      fderiv ℝ (fderiv ℝ rho) u v v ≤ (L : ℝ)*‖v‖^2) :
    let gamma := stdGaussian E
    let Z := ∫ u, Real.exp (-rho u) ∂gamma
    let q := fun u => Real.exp (-rho u)/Z
    let f := fun u => Real.exp (-rho u/2)/Real.sqrt Z
    let r := gamma.tilted (fun u => -rho u)
    0 < Z ∧ Z ≤ 1 ∧
      r = (volume : Measure E).tilted (fun u => -(‖u‖^2/2+rho u)) ∧
      r = gamma.withDensity (fun u => ENNReal.ofReal (q u)) ∧
      IsProbabilityMeasure r ∧
      (∀ u, 0 < q u ∧ f u^2 = q u) ∧
      (∫ u, q u ∂gamma) = 1 ∧
      ContDiff ℝ 2 f ∧ MemLp f 2 gamma ∧
      MemLp (gradient f) 2 gamma ∧ MemLp (gradient rho) 2 r ∧
      Integrable (fun u => q u*Real.log (q u)) gamma ∧
      (∀ u, gradient f u = -(f u/2) • gradient rho u) ∧
      (∀ u, gradient (fun z => Real.log (q z)) u = -gradient rho u) ∧
      (∫ u, ‖gradient f u‖^2 ∂gamma) =
        (1/4 : ℝ)*(∫ u, ‖gradient rho u‖^2 ∂r) := by
  let gamma := stdGaussian E
  let Z := ∫ u, Real.exp (-rho u) ∂gamma
  let q := fun u => Real.exp (-rho u)/Z
  let f := fun u => Real.exp (-rho u/2)/Real.sqrt Z
  let r := gamma.tilted (fun u => -rho u)
  obtain ⟨hn,hg⟩ := rho_nonneg_and_gradient_growth hrho hrho0 hgrad0 hH
  obtain ⟨hw,hZ,hZone⟩ := weight_normalization hrho.continuous hn
  obtain ⟨hf,hfq,hdf,hdlog⟩ := density_calculus hrho hZ
  have hprob : IsProbabilityMeasure r := isProbabilityMeasure_tilted hw
  have hq : Integrable q gamma := hw.div_const Z
  have hqone : (∫ u, q u ∂gamma) = 1 := by
    rw [integral_div]
    exact div_self hZ.ne'
  have hflp : MemLp f 2 gamma := by
    apply (memLp_two_iff_integrable_sq_norm hf.continuous.aestronglyMeasurable).mpr
    apply hq.congr
    exact ae_of_all _ (fun u => by
      change q u = ‖f u‖^2
      rw [Real.norm_eq_abs,sq_abs]
      exact (hfq u).2.symm)
  have hwg := weighted_gradient_square_integrable hrho hn hg
  have hgc := Analysis.Calculus.Gradient.continuous_gradient_of_contDiff_one
    (hrho.of_le (by norm_num))
  have hir : Integrable (fun u => ‖gradient rho u‖^2) r :=
    (integrable_tilted_iff hw (fun u => ‖gradient rho u‖^2)).mpr
      (by simpa only [smul_eq_mul] using hwg)
  have hrlp : MemLp (gradient rho) 2 r :=
    (memLp_two_iff_integrable_sq_norm hgc.aestronglyMeasurable).mpr hir
  have hpoint (u : E) : ‖gradient f u‖^2 =
      (1/(4*Z))*(Real.exp (-rho u)*‖gradient rho u‖^2) := by
    rw [hdf u,norm_smul,mul_pow,Real.norm_eq_abs,sq_abs,neg_sq,div_pow,(hfq u).2]
    change (Real.exp (-rho u)/Z)/(2:ℝ)^2*‖gradient rho u‖^2 = _
    ring
  have hfg : Integrable (fun u => ‖gradient f u‖^2) gamma :=
    (hwg.const_mul (1/(4*Z))).congr (ae_of_all _ (fun u => (hpoint u).symm))
  have hfgc := Analysis.Calculus.Gradient.continuous_gradient_of_contDiff_one
    (hf.of_le (by norm_num))
  have hfglp : MemLp (gradient f) 2 gamma :=
    (memLp_two_iff_integrable_sq_norm hfgc.aestronglyMeasurable).mpr hfg
  have he := entropy_integrable hrho.continuous hn hZ
  have henergy : (∫ u, ‖gradient f u‖^2 ∂gamma) =
      (1/4 : ℝ)*(∫ u, ‖gradient rho u‖^2 ∂r) := by
    rw [integral_tilted,← integral_const_mul]
    apply integral_congr_ae
    exact ae_of_all _ (fun u => by
      change ‖gradient f u‖^2 = (1/4 : ℝ)*(Real.exp (-rho u)/Z) • ‖gradient rho u‖^2
      rw [hpoint u]
      simp only [smul_eq_mul]
      ring)
  exact ⟨hZ,hZone,actual_gaussian_tilt_volume rho,rfl,hprob,hfq,hqone,hf,hflp,
    hfglp,hrlp,he,hdf,hdlog,henergy⟩

end
end AutoSamplingTheory.TechnicalLemmas.Measure.GaussianSqrtDensityDomain

Exact module and namespace context

Scope and omitted-condition boundaries
  • Actual positive Gaussian relative-density and classical C2/L2 square-root/entropy/log-gradient/Dirichlet=Fisher/4 domains, genuinely consumed by the source standardized true RGO. No Gaussian LSI/T2/KL weak representative/Sobolev adapter/W2/bias/fullLemma/main/work/composition credit. Focused3748 passed with E0/L0 and actual eta2y3 posterior tests, standard3 axioms only. Independent proof/blind/source/commit review and integration pending; postmerge purification, renderedQA and Copy/Download remain open.
Sources, dependencies, and reuse

Source and reuse

Samplinglib parents called

Mathlib API called (external library)

  • MeasureTheory.isProbabilityMeasure_tilted
  • MeasureTheory.integrable_tilted_iff
  • MeasureTheory.integral_tilted
  • MeasureTheory.memLp_two_iff_integrable_sq_norm
  • Real.log_div
  • Real.add_one_le_exp

Mathematical sources

  • Actual source residual and omitted analytic prerequisites — Actual positive Gaussian relative-density and classical C2/L2 square-root/entropy/log-gradient/Dirichlet=Fisher/4 domains, genuinely consumed by the source standardized true RGO. No Gaussian LSI/T2/KL weak representative/Sobolev adapter/W2/bias/fullLemma/main/work/composition credit. Focused3748 passed with E0/L0 and actual eta2y3 posterior tests, standard3 axioms only. Independent proof/blind/source/commit review and integration pending; postmerge purification, renderedQA and Copy/Download remain open.

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