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

D
Docker
小众软件
小众软件
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
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
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
酷 壳 – CoolShell
酷 壳 – CoolShell
Apple Machine Learning Research
Apple Machine Learning Research
月光博客
月光博客
人人都是产品经理
人人都是产品经理
大猫的无限游戏
大猫的无限游戏
V
V2EX
阮一峰的网络日志
阮一峰的网络日志
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
博客园 - Franky
WordPress大学
WordPress大学
有赞技术团队
有赞技术团队
Hugging Face - Blog
Hugging Face - Blog
Jina AI
Jina AI
博客园 - 聂微东
S
SegmentFault 最新的问题
量子位
宝玉的分享
宝玉的分享
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
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
The General Traveling Salesman Problem, Version 5
Howard Kleiman · 2011-10-19 · via cs.DS updates on arXiv.org

This paper is example 5 in chapter 5. Let H be an n-cycle. A permutation s is H-admissible if Hs = H' where H' is an n-cycle. Here we define a 19 X 19 matrix, M, in the following way: We obtain the remainders modulo 100 of each of the smallest 342 odd primes. we obtain the remainders modulo 100 of each of the primes. They are placed in M according to the original value of each prime. Thus their placement depends on the the original ordinal values of the primes according to size. We use this ordering to place the primes in M. Let H_0 be an initial 19 cycles arbitrarily chosen. We apply a sequence of up to [ln(n)+1] H_0 3-cycles to obtain a 19-cycle of smaller value than H_0, call the new 19-cycle H_1. We follow this procedure to obtain H_1. We call [ln(n)] + 1 a chain. We add up the values of the 19-cycles in each chain. This procedure continues until we cannot obtain a chain the sum of whose values is not negative. COMMENT. I've renamed the document "Yhe General Traveling Salesman Problem, Version 5". I preciously named it "The Traveling Salesman, Version 5". Although the algorithms work on the GTSP, I thought that more people would google it if it was named "The Traveling Salesman Problem." Rhar qas because my work is only available through arxiv.org,