Download lean/OAI/Computability/MinUncut/Analysis/RowProjectedGradient.lean from roseking/openAI-math-dataset: direct link, hf CLI and curl.
- Browser
- Download file 3 kB
-
https://huggingface.co/datasets/roseking/openAI-math-dataset/resolve/main/lean/OAI/Computability/MinUncut/Analysis/RowProjectedGradient.lean
- Command line
-
hf download hf://datasets/roseking/openAI-math-dataset/lean/OAI/Computability/MinUncut/Analysis/RowProjectedGradient.lean
-
curl -L -o RowProjectedGradient.lean https://huggingface.co/datasets/roseking/openAI-math-dataset/resolve/main/lean/OAI/Computability/MinUncut/Analysis/RowProjectedGradient.lean
3 kB
| import Mathlib | |
| import OAI.Computability.MinUncut.Estimates.Convolution | |
| import OAI.Computability.MinUncut.Estimates.CoupledAnswer | |
| namespace OAI | |
| noncomputable section | |
| open scoped BigOperators | |
| open MeasureTheory ProbabilityTheory Filter | |
| open scoped Topology NNReal | |
| open scoped BigOperators | |
| open MeasureTheory ProbabilityTheory Polynomial Filter | |
| open scoped BigOperators Topology | |
| open MeasureTheory ProbabilityTheory WithLp | |
| open scoped BigOperators RealInnerProductSpace | |
| open scoped BigOperators | |
| namespace MinUncut.Inner | |
| open MeasureTheory ProbabilityTheory | |
| open scoped BigOperators | |
| attribute [local instance] Classical.propDecidable | |
| variable {V A : Type*} [AddCommGroup V] [Module F₂ V] [AddTorsor V A] [Fintype A] | |
| variable {m n : ℕ} | |
| def rowProjectedGradient (D : ℕ) (f : FoldedProof A) (σ η : ℝ) | |
| (B : FaceArray A m n) (c : Point m n → ℝ) (x : Point m n) : ℝ := | |
| RowNoise.project D (fun C => gradient f C σ η c x) B | |
| theorem gradient_row_tail (f : FoldedProof A) (hn : 0 < n) | |
| {σ a : ℝ} (hσ : σ≠0) (ha : 0≤a) (ha1 : a≤1) (η : ℝ) (D : ℕ) | |
| (hD : ∀ k : ℕ, D < k → 1≤(k:ℝ)*a) (c : Point m n → ℝ) : | |
| (𝔼 B : FaceArray A m n, spatialEnergy (fun x => | |
| gradient f B σ η c x-rowProjectedGradient D f σ η B c x)) ≤ | |
| 4*σ⁻¹^2*(𝔼 B : FaceArray A m n, 𝔼 C : FaceArray A m n, | |
| RowNoise.density a B C*secondRejection f B C σ η c) := by | |
| have htail (x : Point m n) := RowNoise.row_tail_le ha ha1 D hD | |
| (fun B : FaceArray A m n => gradient f B σ η c x) | |
| calc | |
| _ = ((n^m:ℕ):ℝ)⁻¹*∑ x : Point m n, | |
| 𝔼 B : FaceArray A m n, (gradient f B σ η c x-rowProjectedGradient D f σ η B c x)^2 := by | |
| simp only [spatialEnergy,← Finset.mul_expect,Finset.expect_sum_comm] | |
| _ ≤ ((n^m:ℕ):ℝ)⁻¹*∑ x : Point m n, | |
| 𝔼 B : FaceArray A m n, 𝔼 C : FaceArray A m n, | |
| RowNoise.density a B C*(gradient f B σ η c x-gradient f C σ η c x)^2 := | |
| mul_le_mul_of_nonneg_left (Finset.sum_le_sum (fun x _ => htail x)) (by positivity) | |
| _ = 𝔼 B : FaceArray A m n, 𝔼 C : FaceArray A m n, | |
| RowNoise.density a B C*spatialEnergy | |
| (fun x => gradient f B σ η c x-gradient f C σ η c x) := by | |
| simp only [spatialEnergy,Finset.mul_sum,Finset.expect_sum_comm, | |
| ← Finset.mul_expect,mul_left_comm] | |
| _ ≤ 𝔼 B : FaceArray A m n, 𝔼 C : FaceArray A m n, | |
| RowNoise.density a B C*(4*σ⁻¹^2*secondRejection f B C σ η c) := | |
| Finset.expect_le_expect (fun B _ => Finset.expect_le_expect (fun C _ => | |
| mul_le_mul_of_nonneg_left (gradient_second_comparison f B C hn hσ η c) | |
| (RowNoise.density_nonneg ha ha1 B C))) | |
| _ = _ := by | |
| have he (B C : FaceArray A m n) : | |
| RowNoise.density a B C*(4*σ⁻¹^2*secondRejection f B C σ η c) = | |
| (4*σ⁻¹^2)*(RowNoise.density a B C*secondRejection f B C σ η c) := by ring | |
| simp_rw [he,← Finset.mul_expect] | |
| end MinUncut.Inner | |
| end | |
| end OAI | |