Download lean/OAI/NumberTheory/TwoPoint/Circuits/CircuitDyadicComparison.lean from roseking/openAI-math-dataset: direct link, hf CLI and curl.
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https://huggingface.co/datasets/roseking/openAI-math-dataset/resolve/main/lean/OAI/NumberTheory/TwoPoint/Circuits/CircuitDyadicComparison.lean
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hf download hf://datasets/roseking/openAI-math-dataset/lean/OAI/NumberTheory/TwoPoint/Circuits/CircuitDyadicComparison.lean
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curl -L -o CircuitDyadicComparison.lean https://huggingface.co/datasets/roseking/openAI-math-dataset/resolve/main/lean/OAI/NumberTheory/TwoPoint/Circuits/CircuitDyadicComparison.lean
3.15 kB
| import OAI.NumberTheory.TwoPoint.Circuits.CircuitDyadicApproximation | |
| import OAI.NumberTheory.TwoPoint.Circuits.CircuitDyadicErrors | |
| import OAI.NumberTheory.TwoPoint.Circuits.CircuitApproximationComparison | |
| namespace OAI | |
| /-! The fixed-depth bounded-independence theorem with a completely | |
| explicit polynomial budget, stated first at dyadic accuracy. -/ | |
| namespace TwoPointCorrelations | |
| open scoped Classical | |
| theorem AC0Circuit.dyadic_comparison_lower {n j t : ℕ} (c : AC0Circuit n) | |
| (hc : c.depth ≤ 22) (hm : c.size ≤ 2 ^ j) (ht : bravermanDegree j ≤ t) | |
| (g : BooleanCube n → ℝ) (hg : ∀ x, 0 ≤ g x) (hmean : cubeAverage g = 1) | |
| (hwise : TWiseUniformDensity g t) : | |
| cubeAverage c.indicator - (1 / 2 : ℝ) ^ j ≤ | |
| cubeAverage (fun x => g x * c.indicator x) := by | |
| obtain ⟨P, E, hP, hdepth, hsize, hprob, hexact, hnorm⟩ := | |
| c.dyadic_approximation (mixedCubeLaw g hg hmean) hc hm | |
| let D := switchingDegree 89 (bravermanSwitches j) | |
| have herror : cubeAverage (fun x => (E.indicator x - walshTruncation E.indicator D x) ^ 2) ≤ | |
| 4 * (2 : ℝ) ^ bravermanErrorExponent j * | |
| (1 / 2 : ℝ) ^ (bravermanSwitches j + 1) := by | |
| apply (E.fourier_tail_polynomial_degree 89 (bravermanSwitches j) hdepth | |
| (bravermanSwitches_pos j)).trans | |
| have hreal : (E.size : ℝ) ≤ (2 : ℝ) ^ bravermanErrorExponent j := by | |
| exact_mod_cast hsize | |
| gcongr | |
| have hF (x) : c.indicator x = 0 ∨ c.indicator x = 1 := by | |
| cases he : c.eval x <;> simp [AC0Circuit.indicator, he] | |
| have hbound : 1 ≤ (2 : ℝ) ^ bravermanNormExponent j := one_le_pow₀ (by norm_num) | |
| have hdegree : (bravermanBase j ^ 22 + D) + (bravermanBase j ^ 22 + D) ≤ t := by | |
| dsimp [bravermanDegree] at ht | |
| dsimp [D] | |
| omega | |
| have hh := hwise.approximation_lower hg hmean hF hP | |
| (walshTruncation_degree E.indicator D) hdegree | |
| (fun x hx => hexact x (by simp [hx])) hbound hnorm hprob herror | |
| have hloss := braverman_total_loss j | |
| linarith | |
| theorem AC0Circuit.dyadic_comparison {n j t : ℕ} (c : AC0Circuit n) | |
| (hc : c.depth ≤ 22) (hm : c.size ≤ 2 ^ j) (ht : bravermanDegree j ≤ t) | |
| (g : BooleanCube n → ℝ) (hg : ∀ x, 0 ≤ g x) (hmean : cubeAverage g = 1) | |
| (hwise : TWiseUniformDensity g t) : | |
| |cubeAverage (fun x => g x * c.indicator x) - cubeAverage c.indicator| ≤ | |
| (1 / 2 : ℝ) ^ j := by | |
| have hlo := c.dyadic_comparison_lower hc hm ht g hg hmean hwise | |
| have hhi := c.negate.dyadic_comparison_lower (by simpa [AC0Circuit.negate_depth] using hc) | |
| (by simpa [AC0Circuit.negate_size] using hm) ht g hg hmean hwise | |
| have hu : cubeAverage c.negate.indicator = 1 - cubeAverage c.indicator := by | |
| change cubeAverage (fun x => c.negate.indicator x) = _ | |
| simp_rw [AC0Circuit.negate_indicator] | |
| rw [cubeAverage_sub, cubeAverage_const] | |
| have hgneg : cubeAverage (fun x => g x * c.negate.indicator x) = | |
| 1 - cubeAverage (fun x => g x * c.indicator x) := by | |
| simp_rw [AC0Circuit.negate_indicator, mul_sub, mul_one] | |
| rw [cubeAverage_sub, hmean] | |
| rw [hu, hgneg] at hhi | |
| exact abs_le.mpr ⟨by linarith, by linarith⟩ | |
| end TwoPointCorrelations | |
| end OAI | |