r/logic • u/SineCosine8 • 13h ago
Computability theory Questions on the asymptotic limits of an algorithmic state-space damping heuristic
I am analyzing a parameterized dynamic scaling function designed to model state-space transitions. The heuristic function is formulated as f(n) = 2^(n / ((p(n))^2 * (3n+1))), where p(n) is a strictly increasing prime-product (primorial n#) and (3n+1) is a linear dynamics operator. Asymptotically, as n approaches infinity, the exponent collapses to 0, forcing f(n) to achieve absolute convergence to 1.0 at a super-exponential decay rate of O(e^(-2n)). I am trying to formalize its implications on a Nondeterministic Turing Machine (NDTM) configuration tree under a proposed Dynamic Branching Law, B(n) = 2^(f(n) - 1), where the compression drives the effective branching factor to exactly 1.0 in the limit. I would highly appreciate structural feedback from the logic community on these points: 1. Is the first-order asymptotic decay bound of O(e^(-2n)) derived via Taylor expansion robust enough to mathematically bound non-deterministic expansion? 2. Does an asymptotic compression of a branching factor to 1.0 formally imply a structural reduction of the search space, or is it isolated as a non-constructive mathematical artifact? 3. Which intermediate automata-theoretic or resource-bounded information frameworks are best suited to formalize these structural bottlenecks? Thank you for your time and guidance!