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Factoring Multivariate Polynomials with Many Factors and Huge Coefficients

机译:分解具有许多因素和巨大系数的多元多项式

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The standard approach to factor a multivariate polynomial in Z[x_1,x_2,...,x_n] is to factor a univariate image in Z[x_1] then recover the multivariate factors from their images using a process known as multivariate Hensel lifting. For the case when the factors are expected to be sparse, at CASC 2016, we introduced a new approach which uses sparse polynomial interpolation to solve the multivariate polynomial diophan-tine equations that arise inside Hensel lifting. In this work we extend our previous work to the case when the number of factors to be computed is more than 2. Secondly, for the case where the integer coefficients of the factors are large we develop an efficient p-adic method. We will argue that the probabilistic sparse interpolation method introduced by us provides new options to speed up the factorization for these two cases. Finally we present some experimental data comparing our new methods with previous methods.
机译:在Z [x_1,x_2,...,x_n]中分解多元多项式的标准方法是在Z [x_1]中分解单变量图像,然后使用称为多变量Hensel提升的过程从其图像中恢复多元因子。对于预期因子稀疏的情况,在CASC 2016上,我们引入了一种新方法,该方法使用稀疏多项式插值法来求解在Hensel提升过程中出现的多元多项式象素方程。在这项工作中,我们将先前的工作扩展到要计算的因子数量大于2的情况。其次,对于因子的整数系数较大的情况,我们开发了一种有效的p-adic方法。我们将争辩说,我们介绍的概率稀疏插值方法为加快这两种情况的因式分解提供了新的选择。最后,我们提供了一些实验数据,将我们的新方法与以前的方法进行了比较。

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