A Probabilistic Algorithm for Computation of Polynomial Greatest Common with Smaller Factors

Yang Zhang, Xin Qian, Qidi You, Xuan Zhou, Xiyong Zhang, Yang Wang
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Abstract

In the earlier work, Knuth present an algorithm to decrease the coefficient growth in the Euclidean algorithm of polynomials called subresultant algorithm. However, the output polynomials may have a small factor which can be removed. Then later, Brown of Bell Telephone Laboratories showed the subresultant in another way by adding a variant called 𝜏 and gave a way to compute the variant. Nevertheless, the way failed to determine every𝜏 correctly. In this paper, we will give a probabilistic algorithm to determine the variant 𝜏 correctly in most cases by adding a few steps instead of computing 𝑡(𝑥) when given 𝑓(𝑥) and𝑔(𝑥) ∈ ℤ[𝑥], where 𝑡(𝑥) satisfies that 𝑠(𝑥)𝑓(𝑥) + 𝑡(𝑥)𝑔(𝑥) = 𝑟(𝑥), here 𝑡(𝑥), 𝑠(𝑥) ∈ ℤ[𝑥]
一种计算小因子多项式最大公项的概率算法
在早期的工作中,Knuth提出了一种算法来减少多项式的欧几里得算法中的系数增长,称为子结式算法。然而,输出多项式可能有一个小的因子,可以被删除。后来,贝尔电话实验室的布朗以另一种方式展示了子结果,他添加了一个叫做“”的变体,并给出了一种计算变体的方法。然而,这种方法并没有正确地确定每一个地名。在本文中,我们将给出一个概率算法来确定变异𝜏正确在大多数情况下,通过添加几个步骤,而不是计算𝑡(𝑥)当给定𝑓(𝑥)和𝑔(𝑥)∈ℤ[𝑥],在𝑡(𝑥)满足𝑠(𝑥)𝑓(𝑥)+𝑡(𝑥)𝑔(𝑥)=𝑟(𝑥),这里𝑡(𝑥)𝑠(𝑥)∈ℤ[𝑥]
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