7,997 research outputs found

    On the complexity of computing with zero-dimensional triangular sets

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    We study the complexity of some fundamental operations for triangular sets in dimension zero. Using Las-Vegas algorithms, we prove that one can perform such operations as change of order, equiprojectable decomposition, or quasi-inverse computation with a cost that is essentially that of modular composition. Over an abstract field, this leads to a subquadratic cost (with respect to the degree of the underlying algebraic set). Over a finite field, in a boolean RAM model, we obtain a quasi-linear running time using Kedlaya and Umans' algorithm for modular composition. Conversely, we also show how to reduce the problem of modular composition to change of order for triangular sets, so that all these problems are essentially equivalent. Our algorithms are implemented in Maple; we present some experimental results

    Computing Puiseux series : a fast divide and conquer algorithm

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    Let F∈K[X,Y]F\in \mathbb{K}[X, Y ] be a polynomial of total degree DD defined over a perfect field K\mathbb{K} of characteristic zero or greater than DD. Assuming FF separable with respect to YY , we provide an algorithm that computes the singular parts of all Puiseux series of FF above X=0X = 0 in less than O~(Dδ)\tilde{\mathcal{O}}(D\delta) operations in K\mathbb{K}, where δ\delta is the valuation of the resultant of FF and its partial derivative with respect to YY. To this aim, we use a divide and conquer strategy and replace univariate factorization by dynamic evaluation. As a first main corollary, we compute the irreducible factors of FF in K[[X]][Y]\mathbb{K}[[X]][Y ] up to an arbitrary precision XNX^N with O~(D(δ+N))\tilde{\mathcal{O}}(D(\delta + N )) arithmetic operations. As a second main corollary, we compute the genus of the plane curve defined by FF with O~(D3)\tilde{\mathcal{O}}(D^3) arithmetic operations and, if K=Q\mathbb{K} = \mathbb{Q}, with O~((h+1)D3)\tilde{\mathcal{O}}((h+1)D^3) bit operations using a probabilistic algorithm, where hh is the logarithmic heigth of FF.Comment: 27 pages, 2 figure

    Improved algorithm for computing separating linear forms for bivariate systems

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    We address the problem of computing a linear separating form of a system of two bivariate polynomials with integer coefficients, that is a linear combination of the variables that takes different values when evaluated at the distinct solutions of the system. The computation of such linear forms is at the core of most algorithms that solve algebraic systems by computing rational parameterizations of the solutions and this is the bottleneck of these algorithms in terms of worst-case bit complexity. We present for this problem a new algorithm of worst-case bit complexity \sOB(d^7+d^6\tau) where dd and Ï„\tau denote respectively the maximum degree and bitsize of the input (and where \sO refers to the complexity where polylogarithmic factors are omitted and OBO_B refers to the bit complexity). This algorithm simplifies and decreases by a factor dd the worst-case bit complexity presented for this problem by Bouzidi et al. \cite{bouzidiJSC2014a}. This algorithm also yields, for this problem, a probabilistic Las-Vegas algorithm of expected bit complexity \sOB(d^5+d^4\tau).Comment: ISSAC - 39th International Symposium on Symbolic and Algebraic Computation (2014

    Roots of bivariate polynomial systems via determinantal representations

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    We give two determinantal representations for a bivariate polynomial. They may be used to compute the zeros of a system of two of these polynomials via the eigenvalues of a two-parameter eigenvalue problem. The first determinantal representation is suitable for polynomials with scalar or matrix coefficients, and consists of matrices with asymptotic order n2/4n^2/4, where nn is the degree of the polynomial. The second representation is useful for scalar polynomials and has asymptotic order n2/6n^2/6. The resulting method to compute the roots of a system of two bivariate polynomials is competitive with some existing methods for polynomials up to degree 10, as well as for polynomials with a small number of terms.Comment: 22 pages, 9 figure
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