The order of points on the second convex hull of a simple polygon

Given a setV of points on the plane, if {q ₁,...,q _n } is the set of points on the second convex hull ofV, the order in which these points are visited in anyV-gon is characterized. This order must verify two similar conditions to those of Kuratowski's theorem for planar graphs. Moreover, the number of possible orders that verify these conditions is obtained. It isO(5 ⁿ ).     

Finding the smallest triangles containing a given convex polygon

For a given convex n-gon P an O(n log²n) algorithm finds all local minima (with respect to area) among the triangles containing P. No areas are computed, for the algorithm is based on a simple geometric characterization of the local minima.     

Finding the convex hull facet by facet

Finding the convex hull of a finite set of points in Euclidean space was one of the first problems explored in the field of computational geometry. In two dimensions a variety of algorithms have been developed and analyzed. For higher dimensions the problems are less well understood. Several “convex hull” problems are defined, solved, and analyzed here in terms of the size of the input and output. In all cases these solutions are the best of the known algorthms. The problems include enumerating the facets of the convex hull, computing the facial lattice of the convex hull and producing a new compact structure representing the combinatorial type of the convex hull. In addition, deciding the combinatorial equivalence of two polytopes is shown to be coNP-hard.     

Decomposing a simple polygon into pseudo-triangles and convex polygons

In this paper we consider the problem of decomposing a simple polygon into subpolygons that exclusively use vertices of the given polygon. We allow two types of subpolygons: pseudo-triangles and convex polygons. We call the resulting decomposition PT-convex. We are interested in minimum decompositions, i.e., in decomposing the input polygon into the least number of subpolygons. Allowing subpolygons of one of two types has the potential to reduce the complexity of the resulting decomposition considerably.The problem of decomposing a simple polygon into the least number of convex polygons has been considered. We extend a dynamic-programming algorithm of Keil and Snoeyink for that problem to the case that both convex polygons and pseudo-triangles are allowed. Our algorithm determines such a decomposition in O(n³) time and space, where n is the number of the vertices of the polygon.     

A simple algorithm for building the 3-D convex hull

A new algorithm is given for finding the convex hull of a finite set of distinct points in three-dimensional space. The algorithm finds the faces of the hull one by one, thus gradually building the polyhedron that constitutes the hull. The algorithm is described as developed through stepwise refinement.     

On the convex hull of the simple integer recourse objective function

We consider the objective function of a simple integer recourse problem with fixed technology matrix.Using properties of the expected value function, we prove a relation between the convex hull of this function and the expected value function of a continuous simple recourse program.We present an algorithm to compute the convex hull of the expected value function in case of discrete right-hand side random variables. Allowing for restrictions on the first stage decision variables, this result is then extended to the convex hull of the objective function.Supported by the National Operations Research Network in the Netherlands (LNMB).     

Probabilities of hitting a convex hull

In this note, we consider the non-negative least-square method with a random matrix. This problem has connections with the probability that the origin is not in the convex hull of many random points. As related problems, suitable estimates are obtained as well on the probability that a small ball does not hit the convex hull.     

Illuminating a convex polygon with vertex lights

We prove that the interior of every convex polygon with n vertices (n ≥ 4) can be illuminated by four 45°-vertex lights. We restrict each vertex to anchoring at most one floodlight. This answers a question of O’Rourke, Shermer and Streinu [5].      

Minimization of convex functions on the convex hull of a point set

A basic algorithm for the minimization of a differentiable convex function (in particular, a strictly convex quadratic function) defined on the convex hull of m points in R ⁿ is outlined. Each iteration of the algorithm is implemented in barycentric coordinates, the number of which is equal to m. The method is based on a new procedure for finding the projection of the gradient of the objective function onto a simplicial cone in R ^m , which is the tangent cone at the current point to the simplex defined by the usual constraints on barycentric coordinates. It is shown that this projection can be computed in O(m log m) operations. For strictly convex quadratic functions, the basic method can be refined to a noniterative method terminating with the optimal solution.     

Applications of a semi-dynamic convex hull algorithm

We obtain new results for manipulating and searching semi-dynamic planar convex hulls (subject to deletions only), and apply them to derive improved bounds for two problems in geometry and scheduling. The new convex hull results are logarithmic time bounds for set splitting and for finding a tangent when the two convex hulls are not linearly separated. Using these results, we solve the following two problems optimally inO(n logn) time: (1) [matching] givenn red points andn blue points in the plane, find a matching of red and blue points (by line segments) in which no two edges cross, and (2) [scheduling] givenn jobs with due dates, linear penalties for late completion, and a single machine on which to process them, find a schedule of jobs that minimizes the maximum penalty.     

Computing the link center of a simple polygon

The link center of a simple polygonP is the set of pointsx insideP at which the maximal link-distance fromx to any other point inP is minimized. Here the link distance between two pointsx, y insideP is defined to be the smallest number of straight edges in a polygonal path insideP connectingx toy. We prove several geometric properties of the link center and present an algorithm that calculates this set in timeO(n ²), wheren is the number of sides ofP. We also give anO(n logn) algorithm for finding an approximate link center, that is, a pointx such that the maximal link distance fromx to any point inP is at most one more than the value attained from the true link center.Work on this paper by the second author has been supported by National Science Foundation Grant DMS-8501947. Work by the third author has been supported by the Canadian National Science and Engineering Research Council, Grant A0332. Work by the fifth author has been supported by Office of Naval Research Grant N00014-82-K-0381, National Science Foundation Grant DCR-83-20085, and by grants from the Digital Equipment Corporation, and the IBM Corporation. Work by the seventh author has been supported by a Killam Senior Research Fellowship from the Canada Council, and work by the ninth author has been supported by the National Science Foundation Grants DCR-84-01898 and DCR-84-01633. Part of the work on this paper has been carried out at the Workshop on Movable Separability of Sets at the Bellairs Research Institute of McGill University, Barbados, February 1986. Further acknowledgments can be obtained from the tenth author upon request.     

Algorithms for optimal area triangulations of a convex polygon

Given a convex polygon with n vertices in the plane, we are interested in triangulations of its interior, i.e., maximal sets of non-intersecting diagonals that subdivide the interior of the polygon into triangles. The MaxMin area triangulation is the triangulation of the polygon that maximizes the area of the smallest triangle in the triangulation. Similarly, the MinMax area triangulation is the triangulation that minimizes the area of the largest area triangle in the triangulation. We present algorithms that construct MaxMin and MinMax area triangulations of a convex polygon in O(n²logn) time and O(n²) space. The algorithms use dynamic programming and a number of geometric properties that are established within the paper.     

Computing the geodesic center of a simple polygon

The geodesic center of a simple polygon is a point inside the polygon which minimizes the maximum internal distance to any point in the polygon. We present an algorithm which calculates the geodesic center of a simple polygon withn vertices in timeO(n logn).Work on this paper by the first author has been supported by National Science Foundation Grant No. DMS-8501947. Work on this paper by the second author has been supported by Office of Naval Research Grant No. N00014-82-K-0381, National Science Foundation Grant No. NSF-DCR-83-20085, and by grants from the Digital Equipment Corporation, and the IBM Corporation. Part of the work on this paper by the first two authors has been carried out at the Workshop on Movable Separability of Sets at the Bellairs Research Institute of McGill University, Barbados, February 1986. Work on this paper by the third author has been supported by the Fonds zur Förderung der wissenschaftlichen Forschung (FWF), Project S32/01.     

On the volume of the convex hull of two convex bodies

In this note we examine the volume of the convex hull of two congruent copies of a convex body in Euclidean $n$ -space, under some subsets of the isometry group of the space. We prove inequalities for this volume if the two bodies are translates, or reflected copies of each other about a common point or a hyperplane containing it. In particular, we give a proof of a related conjecture of Rogers and Shephard.     

On the area and perimeter of a random convex hull in a bounded convex set

Suppose K is a compact convex set in ℝ² and X _i , 1≤i≤n, is a random sample of points in the interior of K. Under general assumptions on K and the distribution of the X _i we study the asymptotic properties of certain statistics of the convex hull of the sample. Received: 24 July 1996/Revised version: 24 February 1998