Download lean/OAI/Probability/LogConcave/JetEstimates/Lipschitz.lean from roseking/openAI-math-dataset: direct link, hf CLI and curl.
- Browser
- Download file 2.33 kB
-
https://huggingface.co/datasets/roseking/openAI-math-dataset/resolve/main/lean/OAI/Probability/LogConcave/JetEstimates/Lipschitz.lean
- Command line
-
hf download hf://datasets/roseking/openAI-math-dataset/lean/OAI/Probability/LogConcave/JetEstimates/Lipschitz.lean
-
curl -L -o Lipschitz.lean https://huggingface.co/datasets/roseking/openAI-math-dataset/resolve/main/lean/OAI/Probability/LogConcave/JetEstimates/Lipschitz.lean
2.33 kB
| import Mathlib | |
| import OAI.Probability.LogConcave.JetEstimates.PolyC1 | |
| namespace OAI | |
| section | |
| section | |
| noncomputable section | |
| open MeasureTheory Filter | |
| open scoped ENNReal NNReal Topology | |
| section UpperProof | |
| open MeasureTheory ProbabilityTheory Filter | |
| open scoped ENNReal NNReal RealInnerProductSpace Topology | |
| open Function MeasureTheory Set Filter | |
| open scoped Topology NNReal | |
| namespace LogConcaveSampling | |
| open MeasureTheory | |
| open scoped RealInnerProductSpace | |
| lemma PolyC1.of_lipschitz {d : ℕ} {f : Point d → ℝ} {K : ℝ≥0} | |
| (hf : ContDiff ℝ 1 f) (hL : LipschitzWith K f) : PolyC1 f := by | |
| refine ⟨hf,growth_of_lipschitz hL,fun v => ⟨(K:ℝ)*‖v‖,by positivity,0,fun x => ?_⟩⟩ | |
| have hn := (fderiv ℝ f x).le_opNorm v | |
| have hb := norm_fderiv_le_of_lipschitz ℝ hL (x₀:=x) | |
| change ‖(fderiv ℝ f x) v‖ ≤ _ | |
| simp only [pow_zero] | |
| exact hn.trans ((mul_le_mul_of_nonneg_right hb (norm_nonneg v)).trans (by nlinarith [mul_nonneg K.coe_nonneg (norm_nonneg v)])) | |
| lemma polyC1_directional_potential {d : ℕ} {H : Point d → ℝ} {K : ℝ≥0} | |
| (hH : ContDiff ℝ (⊤ : ℕ∞) H) (hL : LipschitzWith K (gradient H)) (v : Point d) : | |
| PolyC1 (directional v H) := by | |
| apply PolyC1.of_lipschitz ((directional_smooth hH v).of_le (by simp)) | |
| have he : (fun x => (innerSL ℝ v) (gradient H x))=directional v H := by | |
| funext x | |
| rw [innerSL_apply_apply,real_inner_comm,directional_gradient] | |
| simpa only [Function.comp_def,he] using (innerSL ℝ v).lipschitzWith.comp hL | |
| lemma polyC1_adjointCoordinate {d : ℕ} {H f : Point d → ℝ} {K : ℝ≥0} | |
| (hH : ContDiff ℝ (⊤ : ℕ∞) H) (hL : LipschitzWith K (gradient H)) | |
| (hf : PolyC1 f) (v : Point d) (hd : PolyC1 (directional v f)) : | |
| PolyC1 (adjointCoordinate H v f) := | |
| hd.neg.add ((polyC1_directional_potential hH hL v).mul hf) | |
| lemma polyC1_tensorAdjoint {d : ℕ} {ι : Type*} [Fintype ι] | |
| {H : Point d → ℝ} {V : ι → Point d → ℝ} {K : ℝ≥0} | |
| (hH : ContDiff ℝ (⊤ : ℕ∞) H) (hL : LipschitzWith K (gradient H)) | |
| (b : ι → Point d) (hV : ∀i,PolyC1 (V i)) (hD : ∀i,PolyC1 (directional (b i) (V i))) : | |
| PolyC1 (tensorAdjoint H b V) := | |
| PolyC1.sum Finset.univ (fun i _ => polyC1_adjointCoordinate hH hL (hV i) (b i) (hD i)) | |
| end LogConcaveSampling | |
| end UpperProof | |
| end | |
| end | |
| end | |
| end OAI | |