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

B
Blog RSS Feed
J
Java Code Geeks
C
Check Point Blog
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
Google DeepMind News
Google DeepMind News
阮一峰的网络日志
阮一峰的网络日志
Engineering at Meta
Engineering at Meta
Blog — PlanetScale
Blog — PlanetScale
D
Docker
H
Hackread – Cybersecurity News, Data Breaches, AI and More
月光博客
月光博客
I
InfoQ
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
A
About on SuperTechFans
L
LangChain Blog
腾讯CDC
Y
Y Combinator Blog
MongoDB | Blog
MongoDB | Blog
Vercel News
Vercel News
MyScale Blog
MyScale Blog
博客园 - Franky
IT之家
IT之家
博客园_首页

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 Property Testers for Pattern Matching
Ce Jin, Tomasz Kociumaka · 2025-10-20 · via cs.DS updates on arXiv.org

The classic exact pattern matching problem, given two strings -- a pattern $P$ of length $m$ and a text $T$ of length $n$ -- asks whether $P$ occurs as a substring of $T$. A property tester for the problem needs to distinguish (with high probability) the following two cases for some threshold $k$: the YES case, where $P$ occurs as a substring of $T$, and the NO case, where $P$ has Hamming distance greater than $k$ from every substring of $T$, that is, $P$ has no $k$-mismatch occurrence in $T$. In this work, we provide adaptive and non-adaptive property testers for the exact pattern matching problem, jointly covering the whole spectrum of parameters. We further establish unconditional lower bounds demonstrating that the time and query complexities of our algorithms are optimal, up to $\mathrm{polylog}\, n$ factors hidden within the $\tilde O(\cdot)$ notation below. In the most studied regime of $n=m+Θ(m)$, our non-adaptive property tester has the time complexity of $\tilde O(n/\sqrt{k})$, and a matching lower bound remains valid for the query complexity of adaptive algorithms. This improves both upon a folklore solution that attains the optimal query complexity but requires $Ω(n)$ time, and upon the only previously known sublinear-time property tester, by Chan, Golan, Kociumaka, Kopelowitz, and Porat [STOC 2020], with time complexity $\tilde O(n/\sqrt[3]{k})$. The aforementioned results remain valid for $n=m+Ω(m)$, where our optimal running time $\tilde O(\sqrt{nm/k}+n/k)$ improves upon the previously best time complexity of $\tilde O(\sqrt[3]{n^2m/k}+n/k)$. In the regime of $n=m+o(m)$, which has not been targeted in any previous work, we establish a surprising separation between adaptive and non-adaptive algorithms, whose optimal time and query complexities are $\tilde O(\sqrt{(n-m+1)m/k}+n/k)$ and $\tilde O(\min(n\sqrt{n-m+1}/k,\sqrt{nm/k}+n/k))$, respectively.