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

Google DeepMind News
Google DeepMind News
B
Blog
博客园 - 三生石上(FineUI控件)
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
T
The Blog of Author Tim Ferriss
H
Hackread – Cybersecurity News, Data Breaches, AI and More
博客园_首页
Vercel News
Vercel News
量子位
A
About on SuperTechFans
博客园 - 聂微东
WordPress大学
WordPress大学
D
DataBreaches.Net
The Cloudflare Blog
M
MIT News - Artificial intelligence
Last Week in AI
Last Week in AI
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
D
Docker
雷峰网
雷峰网
C
Check Point Blog
S
SegmentFault 最新的问题
U
Unit 42
月光博客
月光博客
Apple Machine Learning Research
Apple Machine Learning Research

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
Degrees and Gaps: Tight Complexity Results of General Fac...
Dániel Marx, Govind S. Sankar, Philipp Schepper · 2021-05-19 · via cs.DS updates on arXiv.org

For the General Factor problem we are given an undirected graph $G$ and for each vertex $v\in V(G)$ a finite set $B_v$ of non-negative integers. The task is to decide if there is a subset $S\subseteq E(G)$ such that $deg_S(v)\in B_v$ for all vertices $v$ of $G$. The maxgap of a finite integer set $B$ is the largest $d\ge 0$ such that there is an $a\ge 0$ with $[a,a+d+1]\cap B=\{a,a+d+1\}$. Cornuéjols (1988) showed that if the maxgap of all sets $B_v$ is at most 1, then the decision version of General Factor is poly-time solvable. Dudycz and Paluch (2018) extended this result for the minimization and maximization versions. Using convolution techniques from van Rooij (2020), we improve upon the previous algorithm by Arulselvan et al. (2018) and present an algorithm counting the number of solutions of a certain size in time $O^*((M+1)^k)$, given a tree decomposition of width $k$, where $M=\max_v \max B_v$. We prove that this algorithm is essentially optimal for all cases that are not polynomial time solvable for the decision, minimization or maximization versions. We prove that such improvements are not possible even for $B$-Factor, which is General Factor on graphs where all sets $B_v$ agree with the fixed set $B$. We show that for every fixed $B$ where the problem is NP-hard, our new algorithm cannot be significantly improved: assuming the Strong Exponential Time Hypothesis (SETH), no algorithm can solve $B$-Factor in time $O^*((\max B+1-ε)^k)$ for any $ε>0$. We extend this bound to the counting version of $B$-Factor for arbitrary, non-trivial sets $B$, assuming #SETH. We also investigate the parameterization of the problem by cutwidth. Unlike for treewidth, a larger set $B$ does not make the problem harder: Given a linear layout of width $k$ we give a $O^*(2^k)$ algorithm for any $B$ and provide a matching lower bound that this is optimal for the NP-hard cases.