高效计算特征多项式

J. Dumas, Clément Pernet, Z. Wan
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引用次数: 48

摘要

我们处理了字长有限域和整数上密集矩阵特征多项式的计算。我们首先提出了两种有限域的算法:一种是基于Krylov迭代和高斯消去。我们将其与Keller-Gehrig第二种算法的改进进行了比较。然后我们证明了Keller-Gehrig的第三种算法的推广可以提高复杂度和计算时间。我们将这些结果作为计算整数矩阵特征多项式的基础。我们首先对密集矩阵使用了早终止和中文余数。然后,基于整数最小多项式和Hensel分解的概率方法特别适合于稀疏和/或结构化矩阵。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient computation of the characteristic polynomial
We deal with the computation of the characteristic polynomial of dense matrices over word size finite fields and over the integers. We first present two algorithms for finite fields: one is based on Krylov iterates and Gaussian elimination. We compare it to an improvement of the second algorithm of Keller-Gehrig. Then we show that a generalization of Keller-Gehrig's third algorithm could improve both complexity and computational time. We use these results as a basis for the computation of the characteristic polynomial of integer matrices. We first use early termination and Chinese remaindering for dense matrices. Then a probabilistic approach, based on integer minimal polynomial and Hensel factorization, is particularly well suited to sparse and/or structured matrices.
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