Download lean/OAI/Probability/SKGap/Matrix/LogDetConcentration.lean from roseking/openAI-math-dataset: direct link, hf CLI and curl.
- Browser
- Download file 3.45 kB
-
https://huggingface.co/datasets/roseking/openAI-math-dataset/resolve/main/lean/OAI/Probability/SKGap/Matrix/LogDetConcentration.lean
- Command line
-
hf download hf://datasets/roseking/openAI-math-dataset/lean/OAI/Probability/SKGap/Matrix/LogDetConcentration.lean
-
curl -L -o LogDetConcentration.lean https://huggingface.co/datasets/roseking/openAI-math-dataset/resolve/main/lean/OAI/Probability/SKGap/Matrix/LogDetConcentration.lean
3.45 kB
| import Mathlib | |
| import OAI.Probability.SKGap.Matrix.LogDetIntegral | |
| namespace OAI | |
| section | |
| noncomputable section | |
| namespace SKGap | |
| open Matrix MeasureTheory ProbabilityTheory Real Set Filter | |
| open scoped BigOperators Matrix.Norms.Frobenius NNReal Topology | |
| variable {ι : Type*} [Fintype ι] [DecidableEq ι] | |
| lemma regularizedLogDet_lipschitzWith [Nonempty ι] {γ : ℝ} (hγ : 0 < γ) : | |
| LipschitzWith ⟨1/sqrt ((Fintype.card ι:ℝ)*γ),by positivity⟩ | |
| (LogDet.regularizedLogDet γ : Matrix ι ι ℝ → ℝ) := by | |
| apply LipschitzWith.of_dist_le_mul | |
| intro M N | |
| change |LogDet.regularizedLogDet γ M-LogDet.regularizedLogDet γ N| ≤ | |
| (1/sqrt ((Fintype.card ι:ℝ)*γ))*‖M-N‖ | |
| exact LogDet.regularizedLogDet_lipschitz hγ M N | |
| lemma logPath_regularized_tail [Nonempty ι] {j γ u : ℝ} (hj : 0 < j) (hγ : 0 < γ) | |
| (hu : 0 ≤ u) {a : ι → ℝ} (ha : ∀ i, 0 ≤ a i) (ha1 : ∀ i, a i ≤ 1) : | |
| let F := fun g => LogDet.regularizedLogDet γ (logPath ((j/(Fintype.card ι:ℝ))*∑ i,a i) a | |
| (goeMatrix (j/(Fintype.card ι:ℝ)) g) 1) | |
| let μ := Measure.pi (fun _ : MatrixCoordinates ι => gaussianReal 0 1) | |
| μ.real {g | u ≤ |F g-∫ x,F x ∂μ|} ≤ | |
| 2*Real.exp (-γ*(Fintype.card ι:ℝ)^2*u^2/(π^2*j)) := by | |
| have hn : (0:ℝ) < Fintype.card ι := Nat.cast_pos.mpr Fintype.card_pos | |
| have hc := (regularizedLogDet_lipschitzWith hγ).comp | |
| ((logPath_one_lipschitz (j := j) ha ha1).comp (goeMatrix_L2_lipschitz (j/(Fintype.card ι:ℝ)))) | |
| let L : ℝ≥0 := ⟨(1/sqrt ((Fintype.card ι:ℝ)*γ))*sqrt (2*(j/(Fintype.card ι:ℝ))),by positivity⟩ | |
| have hc' : LipschitzWith L (fun x : EuclideanSpace ℝ (MatrixCoordinates ι) => | |
| LogDet.regularizedLogDet γ (logPath ((j/(Fintype.card ι:ℝ))*∑ i,a i) a | |
| (goeMatrix (j/(Fintype.card ι:ℝ)) x.ofLp) 1)) := by | |
| exact hc.weaken (by | |
| change (1/sqrt ((Fintype.card ι:ℝ)*γ))*(1*sqrt (2*(j/(Fintype.card ι:ℝ)))) ≤ | |
| (1/sqrt ((Fintype.card ι:ℝ)*γ))*sqrt (2*(j/(Fintype.card ι:ℝ))) | |
| rw [_root_.one_mul]) | |
| have hL : 0 < L := NNReal.coe_pos.mp (by | |
| change 0 < (1/sqrt ((Fintype.card ι:ℝ)*γ))*sqrt (2*(j/(Fintype.card ι:ℝ))) | |
| exact mul_pos (one_div_pos.mpr (sqrt_pos.mpr (mul_pos hn hγ))) | |
| (sqrt_pos.mpr (mul_pos (by norm_num) (div_pos hj hn)))) | |
| have h := gaussianProduct_tail_lipschitz | |
| (f := fun g => LogDet.regularizedLogDet γ (logPath ((j/(Fintype.card ι:ℝ))*∑ i,a i) a | |
| (goeMatrix (j/(Fintype.card ι:ℝ)) g) 1)) hc' hL hu | |
| apply h.trans_eq | |
| congr 2 | |
| change -2*u^2/(π^2*((1/sqrt ((Fintype.card ι:ℝ)*γ))*sqrt (2*(j/(Fintype.card ι:ℝ))))^2)=_ | |
| rw [mul_pow,_root_.one_div_pow,sq_sqrt (by positivity : 0 ≤ (Fintype.card ι:ℝ)*γ), | |
| sq_sqrt (by positivity : 0 ≤ 2*(j/(Fintype.card ι:ℝ)))] | |
| field_simp | |
| lemma regularizedLogDet_transpose {γ : ℝ} (hγ : γ ≠ 0) (M : Matrix ι ι ℝ) : | |
| LogDet.regularizedLogDet γ Mᵀ=LogDet.regularizedLogDet γ M := by | |
| have e₁ : γ • (1+(γ⁻¹ • M)*Mᵀ)=M*Mᵀ+γ • 1 := by | |
| rw [smul_add,smul_mul_assoc,smul_smul,mul_inv_cancel₀ hγ,one_smul] | |
| ac_rfl | |
| have e₂ : γ • (1+Mᵀ*(γ⁻¹ • M))=Mᵀ*M+γ • 1 := by | |
| rw [smul_add,mul_smul_comm,smul_smul,mul_inv_cancel₀ hγ,one_smul] | |
| ac_rfl | |
| unfold LogDet.regularizedLogDet LogDet.regularizedGram | |
| rw [transpose_transpose,← e₁,← e₂,det_smul,det_smul,det_one_add_mul_comm] | |
| end SKGap | |
| end | |
| end | |
| end OAI | |