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arXiv cs/0601104math.NAcs.NAcs.SCmath.NT
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类多项式计算的复杂度:基于浮点近似的方法

The complexity of class polynomial computation via floating point approximations

Andreas Enge

AI总结:

本文分析了基于浮点近似的类多项式计算复杂度,提出最快算法运行时间为O(h^{2+ε}),与多项式大小匹配,并依赖类群枚举与高度上界的新结果。

摘要:

我们分析了通过复数浮点近似其根来计算类多项式的复杂度,类多项式是椭圆曲线CM构造的重要成分。该算法的核心是在多个参数上评估模函数。所提出的方法中,最快的一种采用Dupont设计的技术,通过对涉及算术-几何平均的表达式进行牛顿迭代来评估模函数。其运行时间为 $O (|D| \log^5 |D| \log \log |D|) = O (|D|^{1 + ε}) = O (h^{2 + ε})$,对任意 $ε> 0$,其中 $D$ 是CM判别式,$h$ 是类多项式的次数。另一种快速算法使用符号计算中已知的多点评估技术;其渐近复杂度较差,多出一个因子 $\log |D|$。在忽略对数因子的情况下,该运行时间与所构造多项式的大小相匹配。该估计还依赖于关于虚二次阶类群枚举复杂度的新结果,以及类多项式高度的严格证明的上界。

英文摘要:

We analyse the complexity of computing class polynomials, that are an important ingredient for CM constructions of elliptic curves, via complex floating point approximations of their roots. The heart of the algorithm is the evaluation of modular functions in several arguments. The fastest one of the presented approaches uses a technique devised by Dupont to evaluate modular functions by Newton iterations on an expression involving the arithmetic-geometric mean. It runs in time $O (|D| \log^5 |D| \log \log |D|) = O (|D|^{1 + ε}) = O (h^{2 + ε})$ for any $ε> 0$, where $D$ is the CM discriminant and $h$ is the degree of the class polynomial. Another fast algorithm uses multipoint evaluation techniques known from symbolic computation; its asymptotic complexity is worse by a factor of $\log |D|$. Up to logarithmic factors, this running time matches the size of the constructed polynomials. The estimate also relies on a new result concerning the complexity of enumerating the class group of an imaginary-quadratic order and on a rigorously proven upper bound for the height of class polynomials.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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