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

MyScale Blog
MyScale Blog
A
About on SuperTechFans
G
Google Developers Blog
B
Blog RSS Feed
F
Fortinet All Blogs
WordPress大学
WordPress大学
Recent Announcements
Recent Announcements
Hugging Face - Blog
Hugging Face - Blog
Y
Y Combinator Blog
MongoDB | Blog
MongoDB | Blog
小众软件
小众软件
人人都是产品经理
人人都是产品经理
博客园 - 叶小钗
T
The Blog of Author Tim Ferriss
Jina AI
Jina AI
IT之家
IT之家
P
Proofpoint News Feed
美团技术团队
量子位
Microsoft Azure Blog
Microsoft Azure Blog
Engineering at Meta
Engineering at Meta
B
Blog
有赞技术团队
有赞技术团队
U
Unit 42

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
Asymptotically Optimal Inapproximability of E$k$-SAT Reco...
Shuichi Hirahara, Naoto Ohsaka · 2025-08-01 · via cs.DS updates on arXiv.org

In the Maxmin E$k$-SAT Reconfiguration problem, we are given a satisfiable $k$-CNF formula $\varphi$ where each clause contains exactly $k$ literals, along with a pair of its satisfying assignments. The objective is transform one satisfying assignment into the other by repeatedly flipping the value of a single variable, while maximizing the minimum fraction of satisfied clauses of $\varphi$ throughout the transformation. In this paper, we demonstrate that the optimal approximation factor for Maxmin E$k$-SAT Reconfiguration is $1 - Θ\left(\frac{1}{k}\right)$. On the algorithmic side, we develop a deterministic $\left(1-\frac{1}{k-1}-\frac{1}{k}\right)$-factor approximation algorithm for every $k \geq 3$. On the hardness side, we show that it is $\mathsf{PSPACE}$-hard to approximate this problem within a factor of $1-\frac{1}{10k}$ for every sufficiently large $k$. Note that an ``$\mathsf{NP}$ analogue'' of Maxmin E$k$-SAT Reconfiguration is Max E$k$-SAT, whose approximation threshold is $1-\frac{1}{2^k}$ shown by Håstad (JACM 2001). To the best of our knowledge, this is the first reconfiguration problem whose approximation threshold is (asymptotically) worse than that of its $\mathsf{NP}$ analogue. To prove the hardness result, we introduce a new ``non-monotone'' test, which is specially tailored to reconfiguration problems, despite not being helpful in the PCP regime.