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

H
Help Net Security
月光博客
月光博客
IT之家
IT之家
B
Blog RSS Feed
T
Tailwind CSS Blog
The GitHub Blog
The GitHub Blog
博客园 - 三生石上(FineUI控件)
MyScale Blog
MyScale Blog
J
Java Code Geeks
Stack Overflow Blog
Stack Overflow Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
博客园 - Franky
博客园 - 叶小钗
阮一峰的网络日志
阮一峰的网络日志
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
U
Unit 42
博客园_首页
B
Blog
V
V2EX
腾讯CDC
Vercel News
Vercel News
量子位
Microsoft Security Blog
Microsoft Security Blog

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
The CDAWG Index and Pattern Matching on Grammar-Compresse...
Alan M. Cleary, Joseph Winjum, Jordan Dood, Shunsuke Inenaga · 2024-07-12 · via cs.DS updates on arXiv.org

The compact directed acyclic word graph (CDAWG) is the minimal compact automaton that recognizes all the suffixes of a string. Classically the CDAWG has been implemented as an index of the string it recognizes, requiring $o(n)$ space for a copy of the string $T$ being indexed, where $n=|T|$. In this work, we propose using the CDAWG as an index for grammar-compressed strings. While this enables all analyses supported by the CDAWG on any grammar-compressed string, in this work we specifically consider pattern matching. Using the CDAWG index, pattern matching can be performed on any grammar-compressed string in $\mathcal{O}(\text{ra}(m)+\text{occ})$ time while requiring only $\mathcal{O}(\text{er}(T))$ additional space, where $m$ is the length of the pattern, $\text{ra}(m)$ is the grammar random access time, $\text{occ}$ is the number of occurrences of the pattern in $T$, and $\text{er}(T)$ is the number of right-extensions of the maximal repeats in $T$. Our experiments show that even when using a naïve random access algorithm, the CDAWG index achieves state of the art run-time performance for pattern matching on grammar-compressed strings. Additionally, we find that all of the grammars computed for our experiments are smaller than the number of right-extensions in the string they produce and, thus, their CDAWGs are within the best known $\mathcal{O}(\text{er}(T))$ space asymptotic bound.