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引用次数: 1

摘要

三角分解是一种经典的、广泛使用的和发展良好的表示代数变量的方法,具有许多应用。特别是,对于单个三角形集合,它所代表的变量的固有数据存在尖锐的度界;对于零维情况和不可约变量,存在强大的随机化算法来计算使用Hensel提升的三角形分解。然而,在一般情况下,大多数计算三角分解的算法产生嵌入分量,这使得无法直接应用固有度界。反过来,由于输出多项式的较高程度和较低的成功概率,这是有效应用Hensel提升的障碍。本文给出了C[x1, x2,…]中多项式集W的无冗余三角分解算法。, xn]。利用这种无冗余三角分解,我们能够给出三角集中出现的多项式的内阶界,并应用Hensel提升技术。我们的分解算法是随机的,我们分析成功的概率。
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Irredundant Triangular Decomposition
Triangular decomposition is a classic, widely used and well-developed way to represent algebraic varieties with many applications. In particular, there exist - sharp degree bounds for a single triangular set in terms of intrinsic data of the variety it represents, - powerful randomized algorithms for computing triangular decompositions using Hensel lifting in the zero-dimensional case and for irreducible varieties. However, in the general case, most of the algorithms computing triangular decompositions produce embedded components, which makes it impossible to directly apply the intrinsic degree bounds. This, in turn, is an obstacle for efficiently applying Hensel lifting due to the higher degrees of the output polynomials and the lower probability of success. In this paper, we give an algorithm to compute an irredundant triangular decomposition of an arbitrary algebraic set W defined by a set of polynomials in C[x1, x2, ..., xn]. Using this irredundant triangular decomposition, we are able to give intrinsic degree bounds for the polynomials appearing in the triangular sets and apply Hensel lifting techniques. Our decomposition algorithm is randomized, and we analyze the probability of success.
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