Download lean/OAI/Probability/SKBarriers/Gaussian/GaussianLogStep.lean from roseking/openAI-math-dataset: direct link, hf CLI and curl.
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2.45 kB
| import Mathlib | |
| import OAI.Probability.SKBarriers.Gaussian.GaussianDomination | |
| namespace OAI | |
| section | |
| section | |
| noncomputable section | |
| open scoped BigOperators Topology | |
| open MeasureTheory ProbabilityTheory Filter | |
| noncomputable section | |
| open MeasureTheory Set Filter | |
| open scoped Topology Interval | |
| noncomputable section | |
| open MeasureTheory Set | |
| open scoped Interval | |
| noncomputable section | |
| open MeasureTheory Set Filter ProbabilityTheory | |
| open scoped Topology | |
| namespace SK.Analytic | |
| section UniformBounds | |
| variable {E : Type} [NormedAddCommGroup E] [NormedSpace ℝ E] | |
| theorem norm_le_of_fderiv_bound (f : E → ℝ) (hd : Differentiable ℝ f) | |
| (C : ℝ) (hC : ∀ x, ‖fderiv ℝ f x‖ ≤ C) (x : E) : | |
| ‖f x‖ ≤ ‖f 0‖ + C*‖x‖ := by | |
| have h := Convex.norm_image_sub_le_of_norm_fderiv_le | |
| (fun x (_ : x ∈ (Set.univ : Set E)) => hd x) | |
| (fun x (_ : x ∈ (Set.univ : Set E)) => hC x) convex_univ | |
| (mem_univ (0:E)) (mem_univ x) | |
| simp only [sub_zero] at h | |
| calc | |
| ‖f x‖ = ‖(f x-f 0)+f 0‖ := by rw [sub_add_cancel] | |
| _ ≤ ‖f x-f 0‖+‖f 0‖ := norm_add_le _ _ | |
| _ ≤ ‖f 0‖+C*‖x‖ := by linarith | |
| theorem exp_mul_growth_of_fderiv_bound (f : E → ℝ) (hd : Differentiable ℝ f) | |
| (C : ℝ) (hC₀ : 0 ≤ C) (hC : ∀ x, ‖fderiv ℝ f x‖ ≤ C) (m : ℝ) : | |
| HasExpGrowth (fun x => Real.exp (m*f x)) := by | |
| refine ⟨Real.exp (|m| *‖f 0‖), |m| *C, (Real.exp_pos _).le, | |
| mul_nonneg (abs_nonneg _) hC₀, ?_⟩ | |
| intro x | |
| rw [Real.norm_eq_abs, abs_of_pos (Real.exp_pos _), ← Real.exp_add] | |
| apply Real.exp_le_exp.mpr | |
| have hf := norm_le_of_fderiv_bound f hd C hC x | |
| have hb : m*f x ≤ |m| *‖f x‖ := by | |
| simpa only [Real.norm_eq_abs, abs_mul] using le_abs_self (m*f x) | |
| nlinarith [mul_le_mul_of_nonneg_left hf (abs_nonneg m)] | |
| def positiveGaussianLogStep (m : ℝ) (f : E × ℝ → ℝ) (x : E) : ℝ := | |
| Real.log (∫ y, Real.exp (m*f (x,y)) ∂gaussianReal 0 1) / m | |
| theorem positiveGaussianLogStep_partition_pos (f : E × ℝ → ℝ) | |
| (hd : Differentiable ℝ f) (C : ℝ) (hC₀ : 0 ≤ C) | |
| (hC : ∀ x, ‖fderiv ℝ f x‖ ≤ C) (m : ℝ) (x : E) : | |
| 0 < ∫ y, Real.exp (m*f (x,y)) ∂gaussianReal 0 1 := by | |
| apply integral_exp_pos | |
| exact (exp_mul_growth_of_fderiv_bound f hd C hC₀ hC m).integrable_gaussian_section | |
| (Real.continuous_exp.comp (continuous_const.mul hd.continuous)) x | |
| end UniformBounds | |
| end SK.Analytic | |
| end | |
| end | |
| end | |
| end | |
| end | |
| end | |
| end OAI | |