NumGfun: a Package for Numerical and Analytic Computation with D-finite Functions

This article describes the implementation in the software package NumGfun of classical algorithms that operate on solutions of linear differential equations or recurrence relations with polynomial coefficients, including what seems to be the first general implementation of the fast high-precision numerical evaluation algorithms of Chudnovsky & Chudnovsky. In some cases, our descriptions contain improvements over existing algorithms. We also provide references to relevant ideas not currently used in NumGfun

Low Complexity Algorithms for Linear Recurrences

We consider two kinds of problems: the computation of polynomial and rational solutions of linear recurrences with coefficients that are polynomials with integer coefficients; indefinite and definite summation of sequences that are hypergeometric over the rational numbers. The algorithms for these tasks all involve as an intermediate quantity an integer $N$ (dispersion or root of an indicial polynomial) that is potentially exponential in the bit size of their input. Previous algorithms have a bit complexity that is at least quadratic in $N$. We revisit them and propose variants that exploit the structure of solutions and avoid expanding polynomials of degree $N$. We give two algorithms: a probabilistic one that detects the existence or absence of nonzero polynomial and rational solutions in $O(\sqrt{N}\log^{2}N)$ bit operations; a deterministic one that computes a compact representation of the solution in $O(N\log^{3}N)$ bit operations. Similar speed-ups are obtained in indefinite and definite hypergeometric summation. We describe the results of an implementation.Comment: This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistributio

Fast algorithms for differential equations in positive characteristic

We address complexity issues for linear differential equations in characteristic $p>0$: resolution and computation of the $p$-curvature. For these tasks, our main focus is on algorithms whose complexity behaves well with respect to $p$. We prove bounds linear in $p$ on the degree of polynomial solutions and propose algorithms for testing the existence of polynomial solutions in sublinear time $\tilde{O}(p^{1/2})$, and for determining a whole basis of the solution space in quasi-linear time $\tilde{O}(p)$; the $\tilde{O}$ notation indicates that we hide logarithmic factors. We show that for equations of arbitrary order, the $p$-curvature can be computed in subquadratic time $\tilde{O}(p^{1.79})$, and that this can be improved to $O(\log(p))$ for first order equations and to $\tilde{O}(p)$ for classes of second order equations

Fast Computation of the NN-th Term of a qq-Holonomic Sequence and Applications

33 pages. Long version of the conference paper Computing the $N$-th term of a $q$-holonomic sequence. Proceedings ISSAC'20, pp. 46–53, ACM Press, 2020 (https://hal.inria.fr/hal-02882885)International audienceIn 1977, Strassen invented a famous baby-step/giant-step algorithm that computes the factorial $N!$ in arithmetic complexity quasi-linear in $\sqrt{N}$. In 1988, the Chudnovsky brothers generalized Strassen’s algorithm to the computation of the $N$-th term of any holonomic sequence in essentially the same arithmetic complexity. We design $q$-analogues of these algorithms. We first extend Strassen’s algorithm to the computation of the $q$-factorial of $N$, then Chudnovskys' algorithm to the computation of the $N$-th term of any $q$-holonomic sequence. Both algorithms work in arithmetic complexity quasi-linear in $\sqrt{N}$; surprisingly, they are simpler than their analogues in the holonomic case. We provide a detailed cost analysis, in both arithmetic and bit complexity models. Moreover, we describe various algorithmic consequences, including the acceleration of polynomial and rational solving of linear $q$-differential equations, and the fast evaluation of large classes of polynomials, including a family recently considered by Nogneng and Schost

Fast algorithms for polynomial solutions of linear differential equations

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Algorithmes rapides pour les polynômes, séries formelles et matrices

Notes d'un cours dispensé aux Journées Nationales du Calcul Formel 2010International audienceLe calcul formel calcule des objets mathématiques exacts. Ce cours explore deux directions : la calculabilité et la complexité. La calculabilité étudie les classes d'objets mathématiques sur lesquelles des réponses peuvent être obtenues algorithmiquement. La complexité donne ensuite des outils pour comparer des algorithmes du point de vue de leur efficacité. Ce cours passe en revue l'algorithmique efficace sur les objets fondamentaux que sont les entiers, les polynômes, les matrices, les séries et les solutions d'équations différentielles ou de récurrences linéaires. On y montre que de nombreuses questions portant sur ces objets admettent une réponse en complexité (quasi-)optimale, en insistant sur les principes généraux de conception d'algorithmes efficaces. Ces notes sont dérivées du cours " Algorithmes efficaces en calcul formel " du Master Parisien de Recherche en Informatique (2004-2010), co-écrit avec Frédéric Chyzak, Marc Giusti, Romain Lebreton, Bruno Salvy et Éric Schost. Le support de cours complet est disponible à l'url https://wikimpri.dptinfo.ens-cachan.fr/doku.php?id=cours:c-2-2