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Factoring Multivariate Polynomials with Many Factors and Huge Coefficients

机译:与许多因素和巨大系数的分解多变量多项式

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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]中对多变量多项式的标准方法是在z [x_1]中的一个单变量图像,然后使用称为多变量Hensel提升的过程从其图像中恢复多变量因子。对于预期因素稀疏的情况,在2016年Casc,我们介绍了一种新方法,它使用稀疏多项式插值来解决亨利提升内部出现的多变量多项式二极管型型方程。在这项工作中,当要计算的因素的数量超过2.其次,我们将先前的工作扩展到案例,因为对于因子的整数系数大而言,我们开发了一种有效的P-ADIC方法。我们将争辩说,我们推出的概率稀疏插值方法提供了新选项,以加快这两种情况的分解。最后,我们介绍了一些实验数据,将我们的新方法与以前的方法进行比较。

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