Removing polynomial overhead from exponential inequalities #
The interpolation argument in Section 4 bounds tensor powers with an extra linear factor. Such a factor does not change their exponential growth rate. The statements below include eventual inequalities and constant factors, so the same argument also removes fixed counting losses in entropy limits.
theorem
MatrixMultiplicationAllFields.MatrixMultiplication.AuxiliarySeparation.exp_le_of_tendsto_log_div
{u : ℕ → ℝ}
{n : ℕ → ℕ}
{b C L : ℝ}
(hb : 0 < b)
(hC : 0 < C)
(hn : Filter.Tendsto n Filter.atTop Filter.atTop)
(hu : ∀ᶠ (k : ℕ) in Filter.atTop, 0 < u k)
(hlim : Filter.Tendsto (fun (k : ℕ) => Real.log (u k) / ↑(n k)) Filter.atTop (nhds L))
(hbound : ∀ᶠ (k : ℕ) in Filter.atTop, u k ≤ C * b ^ n k)
:
A fixed counting loss does not contribute to a normalized logarithmic growth rate. The exponent may run through any sequence tending to infinity, including the multiples of a common denominator used for rational types.