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We provide sharp estimates for the probabilistic behaviour of the main parameters of the Euclid Algorithms, both on polynomials and on integer numbers. We study in particular the distribution of the bit-complexity which involves two main parameters: digit-costs and length of remainders. We first show here that an asymptotic Gaussian law holds for the length of remainders at a fraction of the execution, which exhibits a deep regularity phenomenon. Then, we study in each framework—polynomials (P) and integer numbers (I)—two gcd algorithms, the standard one (S) which only computes the gcd, and the extended one (E) which also computes the Bezout pair, and is widely used for computing modular inverses. The extended algorithm is more regular than the standard one, and this explains that our results are more precise for the Extended algorithm: we exhibit an asymptotic Gaussian law for the bit-complexity of the extended algorithm, in both cases (P) and (I). We also prove that an asymptotic Gaussian law for the bit-complexity of the standard gcd in case (P), but we do not succeed obtaining a similar result in case (I). The integer study is more involved than the polynomial study, as it is usually the case. In the polynomial case, we deal with the central tools of the distributional analysis of algorithms, namely bivariate generating functions. In the integer case, we are led to dynamical methods, which heavily use the dynamical system underlying the number Euclidean algorithm, and its transfer operator. Baladi and Vallée (J. Number Theory 110(2):331–386, 2005) have recently designed a general framework for “distributional dynamical analysis”, where they have exhibited asymptotic Gaussian laws for a large family of parameters. However, this family does not contain neither the bit-complexity cost nor the size of remainders, and we have to extend their methods for obtaining our results. Even if these dynamical methods are not necessary in case (P), we explain how the polynomial dynamical system can be also used for proving our results. This provides a common framework for both analyses, which well explains the similarities and the differences between the two cases (P) and (I), for the algorithms themselves, and also for their analysis. An extended abstract of this paper can be found in Lhote and Vallée (Proceedings of LATIN’06, Lecture Notes in Computer Science, vol. 3887, pp. 689–702, 2006).

Gaussian Laws for the Main Parameters of the Euclid Algorithms