openAI-math-dataset / lean /OAI /NumberTheory /TwoPoint /Circuits /CircuitLogarithmicBudget.lean
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import OAI.NumberTheory.TwoPoint.Circuits.CircuitPolynomialGrowth
namespace OAI
/-! Convert a dyadic accuracy index to the natural logarithm used in the
published fixed-depth bounded-independence input. -/
namespace TwoPointCorrelations
lemma dyadic_index_of_ratio (m : ℕ) (hm : 1 ≤ m) (ε : ℝ)
(hε : 0 < ε) (hεhalf : ε ≤ 1 / 2) :
∃ j : ℕ, m ≤ 2 ^ j ∧ (1 / 2 : ℝ) ^ j ≤ ε ∧
(j : ℝ) + 1 ≤ 6 * Real.log ((m : ℝ) / ε) := by
let L := Real.log ((m : ℝ) / ε)
have hmreal : (1 : ℝ) ≤ m := by exact_mod_cast hm
have hratio : (2 : ℝ) ≤ (m : ℝ) / ε := by
apply (le_div_iff₀ hε).mpr
linarith
have hlogtwo : (1 / 2 : ℝ) ≤ Real.log 2 := by
have h := Real.one_sub_inv_le_log_of_pos (by norm_num : (0 : ℝ) < 2)
norm_num at h ⊢
exact h
have hL : 1 / 2 ≤ L := hlogtwo.trans (Real.log_le_log (by norm_num) hratio)
let j := ⌈2 * L⌉₊
have hjlo : 2 * L ≤ (j : ℝ) := Nat.le_ceil _
have hjhi : (j : ℝ) < 2 * L + 1 := Nat.ceil_lt_add_one (by linarith)
have hlogbound : L ≤ (j : ℝ) * Real.log 2 := by
nlinarith [show (0 : ℝ) ≤ j from Nat.cast_nonneg j]
have hx : (m : ℝ) / ε ≤ (2 : ℝ) ^ j := by
apply (Real.log_le_log_iff (by positivity) (by positivity)).mp
simpa only [Real.log_pow] using hlogbound
have hmul : (m : ℝ) ≤ (2 : ℝ) ^ j * ε := (div_le_iff₀ hε).mp hx
have hmle : (m : ℝ) ≤ (2 : ℝ) ^ j := by nlinarith [show (0 : ℝ) < 2 ^ j by positivity]
have heps : (1 / 2 : ℝ) ^ j ≤ ε := by
have hh : (1 : ℝ) / (2 : ℝ) ^ j ≤ ε :=
(div_le_iff₀ (by positivity)).mpr (by nlinarith)
simpa only [div_pow, one_pow] using hh
exact ⟨j, by exact_mod_cast hmle, heps, by linarith⟩
theorem bravermanDegree_logarithmic_budget :
∃ K C : ℕ, 0 < K ∧ 0 < C ∧ ∀ m : ℕ, 1 ≤ m →
∀ ε : ℝ, 0 < ε → ε ≤ 1 / 2 → ∀ t : ℕ,
(K : ℝ) * (Real.log ((m : ℝ) / ε)) ^ C ≤ (t : ℝ) →
∃ j : ℕ, m ≤ 2 ^ j ∧ (1 / 2 : ℝ) ^ j ≤ ε ∧ bravermanDegree j ≤ t := by
obtain ⟨K, C, hK, hC, hbound⟩ := bravermanDegree_polynomial_positive
refine ⟨K * 6 ^ C, C, by positivity, hC, ?_⟩
intro m hm ε hε hεhalf t ht
obtain ⟨j, hmj, hjε, hjlog⟩ := dyadic_index_of_ratio m hm ε hε hεhalf
refine ⟨j, hmj, hjε, ?_⟩
have hj : (bravermanDegree j : ℝ) ≤ (K : ℝ) * ((j : ℝ) + 1) ^ C := by
exact_mod_cast hbound j
have hdeg : (bravermanDegree j : ℝ) ≤ (t : ℝ) := by
calc
_ ≤ _ := hj
_ ≤ (K : ℝ) * (6 * Real.log ((m : ℝ) / ε)) ^ C := by gcongr
_ = ((K * 6 ^ C : ℕ) : ℝ) * (Real.log ((m : ℝ) / ε)) ^ C := by
push_cast
rw [mul_pow]
ring
_ ≤ _ := ht
exact_mod_cast hdeg
end TwoPointCorrelations
end OAI