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

WordPress大学
WordPress大学
MyScale Blog
MyScale Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
人人都是产品经理
人人都是产品经理
C
Check Point Blog
宝玉的分享
宝玉的分享
B
Blog RSS Feed
博客园 - 三生石上(FineUI控件)
量子位
Martin Fowler
Martin Fowler
酷 壳 – CoolShell
酷 壳 – CoolShell
Jina AI
Jina AI
IT之家
IT之家
阮一峰的网络日志
阮一峰的网络日志
博客园 - 叶小钗
J
Java Code Geeks
The Cloudflare Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
腾讯CDC
P
Proofpoint News Feed
美团技术团队
H
Help Net Security
B
Blog
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
Improved Hardness for Cut, Interdiction, and Firefighter ...
Euiwoong Lee · 2016-07-18 · via cs.DS updates on arXiv.org

We study variants of the classic $s$-$t$ cut problem and prove the following improved hardness results assuming the Unique Games Conjecture (UGC). - For any constant $k \geq 2$ and $ε> 0$, we show that Directed Multicut with $k$ source-sink pairs is hard to approximate within a factor $k - ε$. This matches the trivial $k$-approximation algorithm. By a simple reduction, our result for $k = 2$ implies that Directed Multiway Cut with two terminals (also known as $s$-$t$ Bicut) is hard to approximate within a factor $2 - ε$, matching the trivial $2$-approximation algorithm. Previously, the best hardness factor for these problems (for constant $k$) was $1.5 - ε$ under the UGC. - For Length-Bounded Cut and Shortest Path Interdiction, we show that both problems are hard to approximate within any constant factor, even if we allow bicriteria approximation. If we want to cut vertices or the graph is directed, our hardness factor for Length-Bounded Cut matches the best approximation ratio up to a constant. Previously, the best hardness factor was $1.1377$ for Length-Bounded Cut and $2$ for Shortest Path Interdiction. - Assuming a variant of the UGC (implied by another variant of Bansal and Khot), we prove that it is hard to approximate Resource Minimization Fire Containment within any constant factor. Previously, the best hardness factor was $2$. Our results are based on a general method of converting an integrality gap instance to a length-control dictatorship test for variants of the $s$-$t$ cut problem, which may be useful for other problems.