流模型和分布式模型的最优矩估计

IF 0.9 3区 计算机科学 Q3 COMPUTER SCIENCE, THEORY & METHODS ACM Transactions on Algorithms Pub Date : 2023-06-24 DOI:https://dl.acm.org/doi/10.1145/3596494
Rajesh Jayaram, David P. Woodruff
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引用次数: 0

摘要

数据流模型中最古老的问题之一是近似底层非负向量\(\mathbf {X}\in \mathbb {R}^n\)的第p阶矩\(\Vert \mathbf {X}\Vert _p^p = \sum _{i=1}^n \mathbf {X}_i^p\),它表示为对其坐标的\(\mathrm{poly}(n)\)更新序列。特别有趣的是当\(p \in (0,2]\)。虽然当允许正更新和负更新时,已知这个问题的空间边界为\(\Theta (\epsilon ^{-2} \log n)\)位,但令人惊讶的是,当所有更新都是正更新时,这个问题的空间复杂性仍然存在差距。具体来说,上界为\(O(\epsilon ^{-2} \log n)\)位,下界仅为\(\Omega (\epsilon ^{-2} + \log n)\)位。最近,在假设更新以随机顺序到达的情况下,获得了\(\tilde{O}(\epsilon ^{-2} + \log n)\)位的上界。我们表明,对于\(p \in (0, 1]\),不需要随机顺序假设。也就是说,我们给出了用于估计\(\Vert \mathbf {X}\Vert _p^p\)的\(\tilde{O}(\epsilon ^{-2} + \log n)\)位的最坏情况流的上界。我们的技术还为估计流中的经验熵提供了新的上界。然而,我们证明了对于\(p \in (1,2]\),在自然协调器和黑板分布式通信拓扑中,存在一个基于随机舍入方案的\(\tilde{O}(\epsilon ^{-2})\)位最大通信上界。我们的协议也产生了重型打击协议和近似矩阵乘积。我们将结果推广到任意通信拓扑G,获得\(\tilde{O}(\epsilon ^{2} \log d)\)最大通信上界,其中d是G的直径。有趣的是,我们的上界排除了用于证明流算法\(p \in (1,2]\)的\(\Omega (\epsilon ^{-2} \log n)\)位下界的基于自然通信复杂度的方法。特别地,任何这样的下界必须来自具有大直径的拓扑。
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Towards Optimal Moment Estimation in Streaming and Distributed Models

One of the oldest problems in the data stream model is to approximate the pth moment \(\Vert \mathbf {X}\Vert _p^p = \sum _{i=1}^n \mathbf {X}_i^p\) of an underlying non-negative vector \(\mathbf {X}\in \mathbb {R}^n\), which is presented as a sequence of \(\mathrm{poly}(n)\) updates to its coordinates. Of particular interest is when \(p \in (0,2]\). Although a tight space bound of \(\Theta (\epsilon ^{-2} \log n)\) bits is known for this problem when both positive and negative updates are allowed, surprisingly, there is still a gap in the space complexity of this problem when all updates are positive. Specifically, the upper bound is \(O(\epsilon ^{-2} \log n)\) bits, while the lower bound is only \(\Omega (\epsilon ^{-2} + \log n)\) bits. Recently, an upper bound of \(\tilde{O}(\epsilon ^{-2} + \log n)\) bits was obtained under the assumption that the updates arrive in a random order.

We show that for \(p \in (0, 1]\), the random order assumption is not needed. Namely, we give an upper bound for worst-case streams of \(\tilde{O}(\epsilon ^{-2} + \log n)\) bits for estimating \(\Vert \mathbf {X}\Vert _p^p\). Our techniques also give new upper bounds for estimating the empirical entropy in a stream. However, we show that for \(p \in (1,2]\), in the natural coordinator and blackboard distributed communication topologies, there is an \(\tilde{O}(\epsilon ^{-2})\) bit max-communication upper bound based on a randomized rounding scheme. Our protocols also give rise to protocols for heavy hitters and approximate matrix product. We generalize our results to arbitrary communication topologies G, obtaining an \(\tilde{O}(\epsilon ^{2} \log d)\) max-communication upper bound, where d is the diameter of G. Interestingly, our upper bound rules out natural communication complexity-based approaches for proving an \(\Omega (\epsilon ^{-2} \log n)\) bit lower bound for \(p \in (1,2]\) for streaming algorithms. In particular, any such lower bound must come from a topology with large diameter.

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来源期刊
ACM Transactions on Algorithms
ACM Transactions on Algorithms COMPUTER SCIENCE, THEORY & METHODS-MATHEMATICS, APPLIED
CiteScore
3.30
自引率
0.00%
发文量
50
审稿时长
6-12 weeks
期刊介绍: ACM Transactions on Algorithms welcomes submissions of original research of the highest quality dealing with algorithms that are inherently discrete and finite, and having mathematical content in a natural way, either in the objective or in the analysis. Most welcome are new algorithms and data structures, new and improved analyses, and complexity results. Specific areas of computation covered by the journal include combinatorial searches and objects; counting; discrete optimization and approximation; randomization and quantum computation; parallel and distributed computation; algorithms for graphs, geometry, arithmetic, number theory, strings; on-line analysis; cryptography; coding; data compression; learning algorithms; methods of algorithmic analysis; discrete algorithms for application areas such as biology, economics, game theory, communication, computer systems and architecture, hardware design, scientific computing
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