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

博客园 - Franky
Apple Machine Learning Research
Apple Machine Learning Research
月光博客
月光博客
Vercel News
Vercel News
Recent Announcements
Recent Announcements
B
Blog RSS Feed
Y
Y Combinator Blog
M
MIT News - Artificial intelligence
MongoDB | Blog
MongoDB | Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
雷峰网
雷峰网
D
Docker
Jina AI
Jina AI
IT之家
IT之家
人人都是产品经理
人人都是产品经理
L
LangChain Blog
G
Google Developers Blog
Google DeepMind News
Google DeepMind News
MyScale Blog
MyScale Blog
博客园 - 叶小钗
The GitHub Blog
The GitHub Blog
The Cloudflare Blog
A
About on SuperTechFans
Hugging Face - Blog
Hugging Face - Blog

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
Lift-and-Project Integrality Gaps for the Traveling Sales...
Thomas Watson · 2011-07-07 · via cs.DS updates on arXiv.org

We study the lift-and-project procedures of Lov{á}sz-Schrijver and Sherali-Adams applied to the standard linear programming relaxation of the traveling salesperson problem with triangle inequality. For the asymmetric TSP tour problem, Charikar, Goemans, and Karloff (FOCS 2004) proved that the integrality gap of the standard relaxation is at least 2. We prove that after one round of the Lov{á}sz-Schrijver or Sherali-Adams procedures, the integrality gap of the asymmetric TSP tour problem is at least 3/2, with a small caveat on which version of the standard relaxation is used. For the symmetric TSP tour problem, the integrality gap of the standard relaxation is known to be at least 4/3, and Cheung (SIOPT 2005) proved that it remains at least 4/3 after $o(n)$ rounds of the Lov{á}sz-Schrijver procedure, where $n$ is the number of nodes. For the symmetric TSP path problem, the integrality gap of the standard relaxation is known to be at least 3/2, and we prove that it remains at least 3/2 after $o(n)$ rounds of the Lov{á}sz-Schrijver procedure, by a simple reduction to Cheung's result.