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In this paper, we present Ray-shooting Quickhull, which is a simple, randomized, outputsensitive version of the Quickhull algorithm for constructing the convex hull of a set of n points in the plane. We show that the randomized Ray-shooting…

Computational Geometry · Computer Science 2024-10-01 Michael T. Goodrich , Ryuto Kitagawa

Convex hulls are a fundamental geometric tool used in a number of algorithms. A famous paper by Akl and Toussaint in 1978 described a way to reduce the number of points involved in the computation, which is since known as the Akl-Toussaint…

Computational Geometry · Computer Science 2013-04-10 Jean Souviron

This paper introduces a Delaunay triangulation algorithm based on the external incremental method. Unlike traditional random incremental methods, this approach uses convex hull and points as basic operational units instead of triangles.…

Computational Geometry · Computer Science 2025-03-20 Yifeng Cai

In this paper we present several results on the expected complexity of a convex hull of $n$ points chosen uniformly and independently from a convex shape. (i) We show that the expected number of vertices of the convex hull of $n$ points,…

Computational Geometry · Computer Science 2011-11-24 Sariel Har-Peled

Convex hulls are fundamental geometric tools used in a number of algorithms. This paper presents a fast, simple to implement and robust Smart Convex Hull (S-CH) algorithm for computing the convex hull of a set of points in E3. This…

Data Structures and Algorithms · Computer Science 2017-08-10 Vaclav Skala , Zuzana Majdisova , Michal Smolik

This paper presents an alternate choice of computing the convex hulls (CHs) for planar point sets. We firstly discard the interior points and then sort the remaining vertices by x- / y- coordinates separately, and later create a group…

Computational Geometry · Computer Science 2013-09-02 Gang Mei , John C. Tipper , Nengxiong Xu

This study presents a novel algorithm for identifying the set of extreme points that constitute the exact convex hull of a point set in high-dimensional Euclidean space. The proposed method iteratively solves a sequence of dynamically…

Computational Geometry · Computer Science 2025-11-11 Qianwei Zhuang

We study the convex-hull problem in a probabilistic setting, motivated by the need to handle data uncertainty inherent in many applications, including sensor databases, location-based services and computer vision. In our framework, the…

Computational Geometry · Computer Science 2014-06-26 Pankaj K. Agarwal , Sariel Har-Peled , Subhash Suri , Hakan Yildiz , Wuzhou Zhang

Convex hulls are a fundamental geometric tool used in a number of algorithms. As a side-effect of exhaustive tests for an algorithm for which a convex hull computation was the first step, interesting experimental results were found and are…

Computational Geometry · Computer Science 2013-04-10 Jean Souviron

Computationally efficient and automated generation of convex hulls is desirable for high throughput materials discovery of thermodynamically stable multi-species crystal structures. A convex hull genetic algorithm is proposed that uses…

Materials Science · Physics 2024-04-23 Scott Donaldson , Robert A. Lawrence , Matt I. J. Probert

J. J. Sylvester's four-point problem asks for the probability that four points chosen uniformly at random in the plane have a triangle as their convex hull. Using a combinatorial classification of points in the plane due to Goodman and…

Combinatorics · Mathematics 2010-10-20 Gregory S. Warrington

We investigate several computational problems related to the stochastic convex hull (SCH). Given a stochastic dataset consisting of $n$ points in $\mathbb{R}^d$ each of which has an existence probability, a SCH refers to the convex hull of…

Computational Geometry · Computer Science 2017-05-02 Jie Xue , Yuan Li , Ravi Janardan

We prove the existence of an algorithm $A$ for computing 2-d or 3-d convex hulls that is optimal for every point set in the following sense: for every sequence $\sigma$ of $n$ points and for every algorithm $A'$ in a certain class…

Computational Geometry · Computer Science 2015-05-04 Peyman Afshani , Jérémy Barbay , Timothy Chan

1) We introduce random discrete Morse theory as a computational scheme to measure the complicatedness of a triangulation. The idea is to try to quantify the frequence of discrete Morse matchings with a certain number of critical cells. Our…

Computational Geometry · Computer Science 2014-04-21 Bruno Benedetti , Frank H. Lutz

A randomised trapezoidal quadrature rule is proposed for continuous functions which enjoys less regularity than commonly required. Indeed, we consider functions in some fractional Sobolev space. Various error bounds for this randomised rule…

Numerical Analysis · Mathematics 2020-12-03 Yue Wu

Matrix completion is a well-studied problem with many machine learning applications. In practice, the problem is often solved by non-convex optimization algorithms. However, the current theoretical analysis for non-convex algorithms relies…

Machine Learning · Computer Science 2018-09-11 Yu Cheng , Rong Ge

A stochastic algorithm is proposed, finding some elements from the set of intrinsic $p$-mean(s) associated to a probability measure $\nu$ on a compact Riemannian manifold and to $p\in[1,\infty)$. It is fed sequentially with independent…

Probability · Mathematics 2016-06-24 Marc Arnaudon , Laurent Miclo

Pick $N$ random points $U_1,\cdots,U_{N}$ independently and uniformly in a triangle ABC with area 1, and take the convex hull of the set $\{A,B,U_1,\cdots,U_{N}\}$. The boundary of this convex hull is a convex chain $V_0=B,V_1,\cdots,$…

Probability · Mathematics 2025-10-31 Jean-François Marckert , Ludovic Morin

This survey explores modern approaches for computing low-rank approximations of high-dimensional matrices by means of the randomized SVD, randomized subspace iteration, and randomized block Krylov iteration. The paper compares the…

Numerical Analysis · Mathematics 2023-09-25 Joel A. Tropp , Robert J. Webber

We envision programmable matter as a system of nano-scale agents (called particles) with very limited computational capabilities that move and compute collectively to achieve a desired goal. We use the geometric amoebot model as our…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-08-15 Joshua J. Daymude , Robert Gmyr , Kristian Hinnenthal , Irina Kostitsyna , Christian Scheideler , Andréa W. Richa