在所有有限域上快速进行多变量多点评估

IF 2.3 2区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Journal of the ACM Pub Date : 2024-03-21 DOI:10.1145/3652025
Vishwas Bhargava, Sumanta Ghosh, Zeyu Guo, Mrinal Kumar, Chris Umans
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引用次数: 0

摘要

多变量多点求值是在多个求值点同时求一个多变量多项式的问题,该多项式以系数向量的形式给出。在这项工作中,我们证明了在任意有限域 \(\mathbb {F} \)上存在一种多变量多点求值的确定性算法,它能在 N 个时间点上输出每个变量中阶数小于 d 的 m 变量多项式的求值结果 \[ (d^m+N)^{1+o(1)}\cdot {\rm poly}(m. d,\log |mathbb {F} \)、d,(log |\mathbb {F}|) \]对于所有的(m在 \mathbb {N} \)和所有足够大的(d在 \mathbb {N} \)。Kedlaya 和 Umans 之前的工作(FOCS 2008, SICOMP 2011)在变量数 m 最多为 do(1) 时达到了相同的时间复杂度,并将消除这一条件的问题作为一个未决问题。最近,Bhargava、Ghosh、Kumar 和 Mohapatra(STOC 2022)的一项研究回答了这个问题,即当底层字段不是太大且特征小于 do(1)时。在这项研究中,我们在所有有限域中取消了对变量数量的限制,从而在所有有限域中回答了 Kedlaya 和 Umans 的问题。我们的算法依赖于三种看似不同的先前已知多元多点求值算法的思想的非难结合,即 Kedlaya 和 Umans 的算法,Björklund、Kaski 和 Williams 的算法(IPEC 2017,Algorithmica 2019),以及 Bhargava、Ghosh、Kumar 和 Mohapatra 的算法,再加上 Bombieri 和 Vinogradov 从解析数论中得出的关于算术级数中素数分布的结果。我们还提出了多元多点求值的第二种算法,这种算法完全是基本算法,特别是避免了使用邦比利-维诺格拉多夫定理。不过,它需要一个温和的假设,即场的大小由一个高度有界的 d 指数塔来限定。更具体地说,我们的第二种算法求解有限域 \(\mathbb {F} \)上的多元多点求值问题所需的时间是 \[ (d^m+N)^{1+o(1)}\cdot {\rm poly}(m,d.) \log |\mathbb {F} \)、\log |mathbb {F}|) \]对于所有 \(m\in \mathbb {N} \)和所有足够大的(d\in \mathbb {N} \),只要有限域 \(\mathbb {F} \)的大小最多为(exp(exp(exp(⋅⋅⋅⋅(exp(d))))),其中这个指数塔的高度是固定的。
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Fast Multivariate Multipoint Evaluation Over All Finite Fields

Multivariate multipoint evaluation is the problem of evaluating a multivariate polynomial, given as a coefficient vector, simultaneously at multiple evaluation points. In this work, we show that there exists a deterministic algorithm for multivariate multipoint evaluation over any finite field \(\mathbb {F} \) that outputs the evaluations of an m-variate polynomial of degree less than d in each variable at N points in time \[ (d^m+N)^{1+o(1)}\cdot {\rm poly}(m,d,\log |\mathbb {F}|) \] for all \(m\in \mathbb {N} \) and all sufficiently large \(d\in \mathbb {N} \).

A previous work of Kedlaya and Umans (FOCS 2008, SICOMP 2011) achieved the same time complexity when the number of variables m is at most do(1) and had left the problem of removing this condition as an open problem. A recent work of Bhargava, Ghosh, Kumar and Mohapatra (STOC 2022) answered this question when the underlying field is not too large and has characteristic less than do(1). In this work, we remove this constraint on the number of variables over all finite fields, thereby answering the question of Kedlaya and Umans over all finite fields.

Our algorithm relies on a non-trivial combination of ideas from three seemingly different previously known algorithms for multivariate multipoint evaluation, namely the algorithms of Kedlaya and Umans, that of Björklund, Kaski and Williams (IPEC 2017, Algorithmica 2019), and that of Bhargava, Ghosh, Kumar and Mohapatra, together with a result of Bombieri and Vinogradov from analytic number theory about the distribution of primes in an arithmetic progression.

We also present a second algorithm for multivariate multipoint evaluation that is completely elementary and in particular, avoids the use of the Bombieri–Vinogradov Theorem. However, it requires a mild assumption that the field size is bounded by an exponential tower in d of bounded height. More specifically, our second algorithm solves the multivariate multipoint evaluation problem over a finite field \(\mathbb {F} \) in time \[ (d^m+N)^{1+o(1)}\cdot {\rm poly}(m,d,\log |\mathbb {F}|) \] for all \(m\in \mathbb {N} \) and all sufficiently large \(d\in \mathbb {N} \), provided that the size of the finite field \(\mathbb {F} \) is at most (exp(exp(exp(⋅⋅⋅(exp(d))))), where the height of this tower of exponentials is fixed.

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来源期刊
Journal of the ACM
Journal of the ACM 工程技术-计算机:理论方法
CiteScore
7.50
自引率
0.00%
发文量
51
审稿时长
3 months
期刊介绍: The best indicator of the scope of the journal is provided by the areas covered by its Editorial Board. These areas change from time to time, as the field evolves. The following areas are currently covered by a member of the Editorial Board: Algorithms and Combinatorial Optimization; Algorithms and Data Structures; Algorithms, Combinatorial Optimization, and Games; Artificial Intelligence; Complexity Theory; Computational Biology; Computational Geometry; Computer Graphics and Computer Vision; Computer-Aided Verification; Cryptography and Security; Cyber-Physical, Embedded, and Real-Time Systems; Database Systems and Theory; Distributed Computing; Economics and Computation; Information Theory; Logic and Computation; Logic, Algorithms, and Complexity; Machine Learning and Computational Learning Theory; Networking; Parallel Computing and Architecture; Programming Languages; Quantum Computing; Randomized Algorithms and Probabilistic Analysis of Algorithms; Scientific Computing and High Performance Computing; Software Engineering; Web Algorithms and Data Mining
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