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In this paper, we present fast algorithms for the product of two multivariate polynomials in sparse representation. The bit complexity of our algorithms are studied in detail for various types of coefficients, and we derive new complexity…

Data Structures and Algorithms · Computer Science 2009-01-28 Joris van der Hoeven , Grégoire Lecerf

In this paper, we present a probabilistic algorithm to multiply two sparse polynomials almost as efficiently as two dense univariate polynomials with a result of approximately the same size. The algorithm depends on unproven heuristics that…

Symbolic Computation · Computer Science 2025-08-25 Joris van der Hoeven

In this paper we present two algorithms for the multiplication of sparse Laurent polynomials and Poisson series (the latter being algebraic structures commonly arising in Celestial Mechanics from the application of perturbation theories).…

Symbolic Computation · Computer Science 2010-04-27 Francesco Biscani

Consider a sparse polynomial in several variables given explicitly as a sum of non-zero terms with coefficients in an effective field. In this paper, we present several algorithms for factoring such polynomials and related tasks (such as…

Symbolic Computation · Computer Science 2025-02-26 Alexander Demin , Joris van der Hoeven

We present a probabilistic algorithm to compute the product of two univariate sparse polynomials over a field with a number of bit operations that is quasi-linear in the size of the input and the output. Our algorithm works for any field of…

Symbolic Computation · Computer Science 2020-09-01 Pascal Giorgi , Bruno Grenet , Armelle Perret du Cray

Given a way to evaluate an unknown polynomial with integer coefficients, we present new algorithms to recover its nonzero coefficients and corresponding exponents. As an application, we adapt this interpolation algorithm to the problem of…

Symbolic Computation · Computer Science 2022-05-19 Pascal Giorgi , Bruno Grenet , Armelle Perret du Cray , Daniel S. Roche

Minkowski sums are of theoretical interest and have applications in fields related to industrial backgrounds. In this paper we focus on the specific case of summing polytopes as we want to solve the tolerance analysis problem described in…

Computational Geometry · Computer Science 2015-06-17 Vincent Delos , Denis Teissandier

We propose a fast greedy algorithm to compute sparse representations of signals from continuous dictionaries that are factorizable, i.e., with atoms that can be separated as a product of sub-atoms. Existing algorithms strongly reduce the…

Signal Processing · Electrical Eng. & Systems 2020-12-01 Gilles Monnoyer de Galland , Luc Vandendorpe , Laurent Jacques

Given a straight-line program whose output is a polynomial function of the inputs, we present a new algorithm to compute a concise representation of that unknown function. Our algorithm can handle any case where the unknown function is a…

Symbolic Computation · Computer Science 2014-12-16 Andrew Arnold , Mark Giesbrecht , Daniel S. Roche

We consider the problem of interpolating a sparse multivariate polynomial over a finite field, represented with a black box. Building on the algorithm of Ben-Or and Tiwari for interpolating polynomials over rings with characteristic zero,…

Symbolic Computation · Computer Science 2020-02-11 Qiao-Long Huang

We present new techniques for reducing a multivariate sparse polynomial to a univariate polynomial. The reduction works similarly to the classical and widely-used Kronecker substitution, except that we choose the degrees randomly based on…

Symbolic Computation · Computer Science 2014-05-05 Andrew Arnold , Daniel S. Roche

Consider a sparse multivariate polynomial f with integer coefficients. Assume that f is represented as a "modular black box polynomial", e.g. via an algorithm to evaluate f at arbitrary integer points, modulo arbitrary positive integers.…

Symbolic Computation · Computer Science 2024-01-01 Joris van der Hoeven , Grégoire Lecerf

We give a new probabilistic algorithm for interpolating a "sparse" polynomial f given by a straight-line program. Our algorithm constructs an approximation f* of f, such that their difference probably has at most half the number of terms of…

Symbolic Computation · Computer Science 2014-01-24 Andrew Arnold , Mark Giesbrecht , Daniel S. Roche

We present a new Monte Carlo algorithm for the interpolation of a straight-line program as a sparse polynomial $f$ over an arbitrary finite field of size $q$. We assume a priori bounds $D$ and $T$ are given on the degree and number of terms…

Symbolic Computation · Computer Science 2014-05-05 Andrew Arnold , Mark Giesbrecht , Daniel S. Roche

We introduce sparse polynomial zonotopes, a new set representation for formal verification of hybrid systems. Sparse polynomial zonotopes can represent non-convex sets and are generalizations of zonotopes, polytopes, and Taylor models.…

Systems and Control · Electrical Eng. & Systems 2024-12-20 Niklas Kochdumper , Matthias Althoff

In this paper, we give new sparse interpolation algorithms for black box polynomial f whose coefficients are from a finite set. In the univariate case, we recover f from one evaluation of f(a) for a sufficiently large number a. In the…

Symbolic Computation · Computer Science 2017-06-23 Qiao-Long Huang , Xiao-Shan Gao

In this paper, we propose two new interpolation algorithms for sparse multivariate polynomials represented by a straight-line program(SLP). Both of our algorithms work over any finite fields $F_q$ with large characteristic. The first one is…

Symbolic Computation · Computer Science 2020-02-11 Qiao-Long Huang

We present a new probabilistic algorithm that characterizes the equidimensional components of the affine algebraic variety defined by an arbitrary sparse polynomial system with prescribed supports. For each equidimensional component, the…

Algebraic Geometry · Mathematics 2026-01-19 Maria Isabel Herrero , Gabriela Jeronimo , Juan Sabia

The works presented in this habilitation concern the algorithmics of polynomials. This is a central topic in computer algebra, with numerous applications both within and outside the field - cryptography, error-correcting codes, etc. For…

Symbolic Computation · Computer Science 2026-03-09 Bruno Grenet

The computational cost of many signal processing and machine learning techniques is often dominated by the cost of applying certain linear operators to high-dimensional vectors. This paper introduces an algorithm aimed at reducing the…

Machine Learning · Computer Science 2016-03-30 Luc Le Magoarou , Rémi Gribonval
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