🤖 AI Summary
本文通过改进Bailey的四步FFT算法,提出了二进制扩展域上仿射子空间多项式评估的加法FFT技术,利用特定基和分解方法减少了计算中的乘法次数。
📝 Abstract
Motivated by Bailey's four-step FFT algorithm (1989), we develop additive FFT techniques for polynomial evaluation over affine subspaces of binary extension fields. Our key insight is that the Taylor expansion with respect to vanishing polynomials of subspaces provides a structural counterpart to Bailey's matrix formulation. It decomposes an additive FFT (AFFT) into independent sub-AFFTs associated with the columns and rows of a matrix. We first present a general-basis AFFT that applies to any ordered basis and any split of the dimension, providing a unified baseline for measuring the gains from specialization. We then specialize the framework to the Cantor special basis and obtain two AFFT algorithms. The first supports an arbitrary decomposition of the AFFT dimension and exploits the Cantor special basis structure to perform the Taylor expansion stage without finite field multiplications. The second uses a decomposition that preserves the binomial form of the relevant subspace polynomials. It requires exactly $\frac{1}{2}n\log_2 n$ multiplications, together with a closed-form addition count determined by the binary representation of $m$. Our implementation results show that this algorithm is faster than the LCH AFFT over a Cantor special basis in 37 of the 42 configurations tested across two hardware platforms. This performance advantage stems from its fully recursive structure, which provides memory locality by design and avoids separate basis-conversion and evaluation stages. Finally, in a separate analysis, we formalize the notion of partial Cantor special bases and identify parameter regimes in which both the von zur Gathen-Gerhard algorithm and our general-basis AFFT require fewer additions and multiplications than the first Gao-Mateer algorithm.