惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

T
Tailwind CSS Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
雷峰网
雷峰网
量子位
有赞技术团队
有赞技术团队
阮一峰的网络日志
阮一峰的网络日志
The Cloudflare Blog
博客园 - Franky
罗磊的独立博客
宝玉的分享
宝玉的分享
博客园_首页
腾讯CDC
The GitHub Blog
The GitHub Blog
D
DataBreaches.Net
IT之家
IT之家
D
Docker
Microsoft Security Blog
Microsoft Security Blog
博客园 - 司徒正美
V
V2EX
月光博客
月光博客
N
Netflix TechBlog - Medium
爱范儿
爱范儿
I
InfoQ
P
Proofpoint News Feed

cs.DS updates on arXiv.org

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Near-Optimal Search Time in $δ$-Optimal Space, and Vice V...
Tomasz Kociumaka, Gonzalo Navarro, Francisco Olivares · 2022-06-02 · via cs.DS updates on arXiv.org

Two recent lower bounds on the compressibility of repetitive sequences, $δ\le γ$, have received much attention. It has been shown that a length-$n$ string $S$ over an alphabet of size $σ$ can be represented within the optimal $O(δ\log\tfrac{n\log σ}{δ\log n})$ space, and further, that within that space one can find all the $occ$ occurrences in $S$ of any length-$m$ pattern in time $O(m\log n + occ \log^εn)$ for any constant $ε>0$. Instead, the near-optimal search time $O(m+({occ+1})\log^εn)$ has been achieved only within $O(γ\log\frac{n}γ)$ space. Both results are based on considerably different locally consistent parsing techniques. The question of whether the better search time could be supported within the $δ$-optimal space remained open. In this paper, we prove that both techniques can indeed be combined to obtain the best of both worlds: $O(m+({occ+1})\log^εn)$ search time within $O(δ\log\tfrac{n\log σ}{δ\log n})$ space. Moreover, the number of occurrences can be computed in $O(m+\log^{2+ε}n)$ time within $O(δ\log\tfrac{n\log σ}{δ\log n})$ space. We also show that an extra sublogarithmic factor on top of this space enables optimal $O(m+occ)$ search time, whereas an extra logarithmic factor enables optimal $O(m)$ counting time.