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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
A 2D Advancing-Front Delaunay Mesh Refinement Algorithm
Shankar Prasad Sastry · 2018-08-05 · via cs.DS updates on arXiv.org

I present a generalization of Chew's first algorithm for Delaunay mesh refinement. In his algorithm, Chew splits the line segments of the input planar straight line graph (PSLG) into shorter subsegments whose lengths are nearly identical. The constrained Delaunay triangulation of the subsegments is refined based on the length of the radii of the circumcircles of the triangles. This algorithm produces a uniform mesh, whose minimum angle can be at most $π/6$. My algorithm generates both truly Delaunay and constrained Delaunay size-optimal meshes. In my algorithm, I split the line segments of the input PSLG such that their lengths are asymptotically proportional to the local feature size (LFS) by solving ordinary differential equations (ODEs) that map points from a closed 1D interval to points on the input line segments in the PSLG. I then refine the Delaunay triangulation (truly or constrained) of the PSLG by inserting off-center Steiner vertices of "skinny" triangles while prioritizing such triangles with shortest edges first. As in Chew's algorithm, I show that the Steiner vertices do not encroach upon any subsegment of the PSLG. The off-center insertion algorithm places Steiner vertices in an advancing front manner such that we obtain a size-optimal Delaunay mesh (truly or constrained) if the desired minimum angle is less than $π/6$. In addition, even in the presence of a small angle $φ< π/2$ in the PSLG, the bound on the minimum angle "across" the small angle tends to $\arctan{((\sinφ)/(2-\cos(φ))}$ as the PSLG is progressively refined. Also, the bound on the maximum angle across any small input angle tends to $π/2 + φ/2$ as the PSLG is progressively refined.