Asymptotic granularity reduction and its application

It is well known that the inverse function of y = x with the derivative y′ = 1 is x = y, the inverse function of y = c with the derivative y′ = 0 is nonexistent, and so on. Hence, on the assumption that the noninvertibility of the univariate increasing function y = f (x) with x > 0 is in direct proportion to the growth rate reflected by its derivative, the authors put forward a method of comparing difficulties in inverting two functions on a continuous or discrete interval called asymptotic granularity reduction (AGR) which integrates asymptotic analysis with logarithmic granularities, and is an extension and a complement to polynomial time (Turing) reduction (PTR). Prove by AGR that inverting y ≡ x x (mod p) is computationally harder than inverting y ≡ g x (mod p), and inverting y ≡ g x n (mod p) is computationally equivalent to inverting y ≡ g x (mod p), which are compatible with the results from PTR. Besides, apply AGR to the comparison of inverting y ≡ x n (mod p) with y ≡ g x (mod p), y ≡ g g1 x (mod p) with y ≡ g x (mod p), and y ≡ x n + x + 1 (mod p) with y ≡ x n (mod p) in difficulty, and observe that the results are consistent with existing facts, which further illustrates that AGR is suitable for comparison of inversion problems in difficulty. Last, prove by AGR that inverting y ≡ x n g x (mod p) is computationally equivalent to inverting y ≡ g x (mod p) when PTR cannot be utilized expediently. AGR with the assumption partitions the complexities of problems more detailedly, and finds out some new evidence for the securities of cryptosystems.