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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
A practical efficient and effective method for the Hamilt...
Eric Lewin Altschuler, Timothy J. Williams · 2017-01-12 · via cs.DS updates on arXiv.org

Given $N$ cities and $R < N^2 - N$ directed (unidirectional/one way) roads does there exist a tour of all $N$ cities stopping at each city exactly once using the given roads (a Hamiltonian cycle)? This Hamiltonian cycle problem (HCP) is an NP-complete problem, for which there is no known polynomial time solution algorithm. The HCP has important practical applications, for example, to logistical problems. It was claimed that an adiabatic quantum computer could solve an NP-complete problem faster than classical algorithms, but claim appears to have been debunked. Here we demonstrate an algorithm which runs on a standard computer that efficiently and effectively solves the HCP for at least up to 500 cities: We first optimized a simulated annealing based algorithm used for smaller sized HCP problems. Then we found that when a tour was deliberately inserted in a list of otherwise randomly chosen roads, crucially, if "extra" random roads are added to bring the total number of roads up to $0.58 N log_e N$ or more there is a 100% chance our algorithm will find a HC, but conversely when a list of roads does not include a pre-inserted tour random roads have to be added until there are $0.9 N log_e N$ roads to have a chance of finding a HC. We found similarly for a set of roads non-randomly chosen. Thus, the presence of a HC in a set of roads induces "connectivity" throughout the roads and a HC can be found with an insertion of a modest number of extra roads. Our algorithm also shows that only weakly non-local information is needed to find an HCP that is a global state.