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

Apple Machine Learning Research
Apple Machine Learning Research
aimingoo的专栏
aimingoo的专栏
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - 聂微东
Engineering at Meta
Engineering at Meta
N
Netflix TechBlog - Medium
Blog — PlanetScale
Blog — PlanetScale
大猫的无限游戏
大猫的无限游戏
Vercel News
Vercel News
D
DataBreaches.Net
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
WordPress大学
WordPress大学
L
LangChain Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
F
Fortinet All Blogs
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
J
Java Code Geeks
Recent Announcements
Recent Announcements
Jina AI
Jina AI
G
Google Developers Blog
腾讯CDC
博客园_首页
博客园 - 【当耐特】

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
Twin-width V: linear minors, modular counting, and matrix...
Édouard Bonnet, Ugo Giocanti, Patrice Ossona de Mendez, Stéphan · 2022-09-24 · via cs.DS updates on arXiv.org

We continue developing the theory around the twin-width of totally ordered binary structures, initiated in the previous paper of the series. We first introduce the notion of parity and linear minors of a matrix, which consists of iteratively replacing consecutive rows or consecutive columns with a linear combination of them. We show that a matrix class has bounded twin-width if and only if its linear-minor closure does not contain all matrices. We observe that the fixed-parameter tractable algorithm for first-order model checking on structures given with an $O(1)$-sequence (certificate of bounded twin-width) and the fact that first-order transductions of bounded twin-width classes have bounded twin-width, both established in Twin-width I, extend to first-order logic with modular counting quantifiers. We make explicit a win-win argument obtained as a by-product of Twin-width IV, and somewhat similar to bidimensionality, that we call rank-bidimensionality. Armed with the above-mentioned extension to modular counting, we show that the twin-width of the product of two conformal matrices $A, B$ over a finite field is bounded by a function of the twin-width of $A$, of $B$, and of the size of the field. Furthermore, if $A$ and $B$ are $n \times n$ matrices of twin-width $d$ over $\mathbb F_q$, we show that $AB$ can be computed in time $O_{d,q}(n^2 \log n)$. We finally present an ad hoc algorithm to efficiently multiply two matrices of bounded twin-width, with a single-exponential dependence in the twin-width bound: If the inputs are given in a compact tree-like form, called twin-decomposition (of width $d$), then two $n \times n$ matrices $A, B$ over $\mathbb F_2$, a twin-decomposition of $AB$ with width $2^{d+o(d)}$ can be computed in time $4^{d+o(d)}n$ (resp. $4^{d+o(d)}n^{1+\varepsilon}$), and entries queried in doubly-logarithmic (resp. constant) time.