High-order lifting for polynomial Sylvester matrices

IF 1.8 2区 数学 Q1 MATHEMATICS
Clément Pernet , Hippolyte Signargout , Gilles Villard
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引用次数: 1

Abstract

A new algorithm is presented for computing the resultant of two generic bivariate polynomials over an arbitrary field. For p,q in K[x,y] of degree d in x and n in y, the resultant with respect to y is computed using O(n1.458d) arithmetic operations if d=O(n1/3). For d=1, the complexity estimate is therefore reconciled with the estimates of Neiger et al. 2021 for the related problems of modular composition and characteristic polynomial in a univariate quotient algebra. The 3/2 barrier in the exponent of n is crossed for the first time for the resultant. The problem is related to that of computing determinants of structured polynomial matrices. We identify new advanced aspects of structure for a polynomial Sylvester matrix. This enables to compute the determinant by mixing the baby steps/giant steps approach of Kaltofen and Villard 2005, until then restricted to the case d=1 for characteristic polynomials, and the high-order lifting strategy of Storjohann 2003 usually reserved for dense polynomial matrices.

多项式Sylvester矩阵的高阶提升
提出了一种计算任意域上两个一般二元多项式的结式的新算法。对于K[x,y]中的p,q在x中阶为d, n在y中阶为n,如果d=O(n1/3),则对y的结果使用O(n1.458d)算术运算计算。因此,对于d=1,复杂性估计与Neiger et al. 2021对单变量商代数中模组成和特征多项式相关问题的估计相一致。对于结果,n指数中的3/2势垒第一次被越过。这个问题与计算结构多项式矩阵的行列式有关。我们确定了多项式Sylvester矩阵结构的新高级方面。这使得通过混合Kaltofen和Villard 2005的小步法/大步法来计算行列式成为可能,直到那时仅限于d=1的特征多项式的情况,而Storjohann 2003的高阶提升策略通常用于密集多项式矩阵。
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来源期刊
Journal of Complexity
Journal of Complexity 工程技术-计算机:理论方法
CiteScore
3.10
自引率
17.60%
发文量
57
审稿时长
>12 weeks
期刊介绍: The multidisciplinary Journal of Complexity publishes original research papers that contain substantial mathematical results on complexity as broadly conceived. Outstanding review papers will also be published. In the area of computational complexity, the focus is on complexity over the reals, with the emphasis on lower bounds and optimal algorithms. The Journal of Complexity also publishes articles that provide major new algorithms or make important progress on upper bounds. Other models of computation, such as the Turing machine model, are also of interest. Computational complexity results in a wide variety of areas are solicited. Areas Include: • Approximation theory • Biomedical computing • Compressed computing and sensing • Computational finance • Computational number theory • Computational stochastics • Control theory • Cryptography • Design of experiments • Differential equations • Discrete problems • Distributed and parallel computation • High and infinite-dimensional problems • Information-based complexity • Inverse and ill-posed problems • Machine learning • Markov chain Monte Carlo • Monte Carlo and quasi-Monte Carlo • Multivariate integration and approximation • Noisy data • Nonlinear and algebraic equations • Numerical analysis • Operator equations • Optimization • Quantum computing • Scientific computation • Tractability of multivariate problems • Vision and image understanding.
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