Documentation

Complexity.Computability.Complexity.FineGrained.CountingScale

The sublinear exponent of the interpolation counting algorithm #

The rational-interpolation route of OpenAI's The Exponential Time Hypothesis is False, Proposition 6.4, uses the scale n (log log n)² / √log n. Shifting the logarithm's argument by sixteen gives a real-valued scale defined at every natural input. This module proves that the scale is sublinear. It does not construct a counting algorithm or certify its running time.

Main definitions #

Main results #

References #

The interpolation exponent with a positive logarithm argument at every natural input.

Instances For

    The interpolation exponent grows more slowly than the number of variables.

    Every fixed multiple of the exponent is eventually at most any positive linear exponent.

    The interpolation scale is nonnegative at every natural input.

    @[simp]

    At zero variables, the shifted interpolation scale vanishes.

    For every natural n, the shifted logarithm log (n + 16) exceeds 1.

    For every natural n, 0 < log (n + 16).

    For every natural n, the shifted iterated logarithm log (log (n + 16)) exceeds 1 (since 16 = 2 ^ 4 and 4 * log 2 > e).

    For every natural n, 0 < log (log (n + 16)).

    The interpolation scale is strictly positive for 1 ≤ n.

    theorem Cslib.Complexity.SubexponentialSAT.exists_two_pow_mul_interpolationScale_le (A : ℝ) {ε : ℝ} (hε : 0 < ε) :
    ∃ (K : ℝ), 1 ≤ K ∧ ∀ (n : ℕ), 2 ^ (A * interpolationScale n) ≤ K * 2 ^ (ε * ↑n)

    For any multiplier A and any ε > 0, 2 ^ (A * interpolationScale n) is globally bounded by a constant multiple of 2 ^ (ε * n).