近似微分多项式的gcrd计算

M. Giesbrecht, Joseph Haraldson
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引用次数: 5

摘要

引入近似多项式系数的微分(矿石)型多项式。这些提供了一种有用的近似微分算子的表示,它具有强的代数结构,已成功地用于精确的,符号的,设置。然后,我们提出了两个近似微分多项式的近似最大公右因子(GCRD)的算法,直观地说,它是微分算子,其解是两个输入算子的公数。更正式地说,给定近似微分多项式f和g,我们展示了如何找到具有非平凡GCRD的“邻近”多项式f和g。在这里,“附近”是在一个适当定义的标准之下。该算法是Corless et al.(1995)基于svd的正则多项式近似GCD方法的推广。我们处理一个适当的“线性化”的微分Sylvester矩阵,并对其应用块SVD。该算法已在Maple中实现,并证明了其鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computing GCRDs of approximate differential polynomials
Differential (Ore) type polynomials with approximate polynomial coefficients are introduced. These provide a useful representation of approximate differential operators with a strong algebraic structure, which has been used successfully in the exact, symbolic, setting. We then present an algorithm for the approximate Greatest Common Right Divisor (GCRD) of two approximate differential polynomials, which intuitively is the differential operator whose solutions are those common to the two inputs operators. More formally, given approximate differential polynomials f and g, we show how to find "nearby" polynomials f and g which have a non-trivial GCRD. Here "nearby" is under a suitably defined norm. The algorithm is a generalization of the SVD-based method of Corless et al. (1995) for the approximate GCD of regular polynomials. We work on an appropriately "linearized" differential Sylvester matrix, to which we apply a block SVD. The algorithm has been implemented in Maple and a demonstration of its robustness is presented.
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