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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
Snow Globe: An Advancing-Front 3D Delaunay Mesh Refinemen...
Shankar Prasad Sastry · 2015-08-01 · via cs.DS updates on arXiv.org

<incorrect proofs; does not consider an important case because of which the proofs are wrong. The paper was withdrawn from submission> One of the objectives of a Delaunay mesh refinement algorithm is to produce meshes with tetrahedral elements having a bounded aspect ratio, which is the ratio between the radius of the circumscribing and inscribing spheres. The refinement is carried out by inserting additional Steiner vertices inside the circumsphere of a poor-quality tetrahedron (to remove the tetrahedron) at a sufficient distance from existing vertices to guarantee the termination and size optimality of the algorithm. This technique eliminates tetrahedra whose ratio of the radius of the circumscribing sphere and the shortest side, the radius-edge ratio, is large. Slivers, almost-planar tetrahedra, which have a small radius-edge ratio, but a large aspect ratio, are avoided by small random perturbations of the Steiner vertices to improve the aspect ratio. Additionally, geometric constraints, called "petals", have been shown to produce smaller high-quality meshes in 2D Delaunay refinement algorithms. In this paper, we develop a deterministic nondifferentiable optimization routine to place the Steiner vertex inside geometrical constraints that we call "snow globes" for 3D Delaunay refinement. We explore why the geometrical constraints and an ordering on processing of poor-quality tetrahedra result in smaller meshes. The stricter analysis provides an improved constant associated with the size optimality of the generated meshes. Aided by the analysis, we present a modified algorithm to handle boundary encroachment. The final algorithm behaves like an advancing-front algorithms that are commonly used for quadrilateral and hexahedral meshing.