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 #
Cslib.Complexity.SubexponentialSAT.interpolationScale: the shifted interpolation exponent.
Main results #
Cslib.Complexity.SubexponentialSAT.isLittleO_interpolationScale: the exponent iso(n).Cslib.Complexity.SubexponentialSAT.eventually_mul_interpolationScale_le: every fixed multiple is eventually bounded by any positive linear exponent.Cslib.Complexity.SubexponentialSAT.interpolationScale_zero,Cslib.Complexity.SubexponentialSAT.interpolationScale_pos,Cslib.Complexity.SubexponentialSAT.one_lt_log_add_sixteen,Cslib.Complexity.SubexponentialSAT.one_lt_log_log_add_sixteen: zero value and strict positivity of the shifted logarithms and interpolation scale.
References #
- OpenAI, The Exponential Time Hypothesis is False, Proposition 6.4, pp. 43–44.
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.
At zero variables, the shifted interpolation scale vanishes.
The interpolation scale is strictly positive for 1 ≤ n.
For any multiplier A and any ε > 0, 2 ^ (A * interpolationScale n) is globally bounded
by a constant multiple of 2 ^ (ε * n).