Finding the convex hull of a simple polygon

It is well known that the convex hull of a set of n points in the plane can be found by an algorithm having worst-case complexity O(n log n). A short linear-time algorithm for finding the convex hull when the points form the (ordered) vertices of a simple (i.e., non-self-intersecting) polygon is given.     

Automorphisms and Distinguishing Numbers of Geometric Cliques

A geometric automorphism is an automorphism of a geometric graph that preserves crossings and noncrossings of edges. We prove two theorems constraining the action of a geometric automorphism on the boundary of the convex hull of a geometric clique. First, any geometric automorphism that fixes the boundary of the convex hull fixes the entire clique. Second, if the boundary of the convex hull contains at least four vertices, then it is invariant under every geometric automorphism. We use these results, and the theory of determining sets, to prove that every geometric n-clique in which n≥7 and the boundary of the convex hull contains at least four vertices is 2-distinguishable.     

On the performance of peeling algorithms

The peeling of a d-dimensional set of points is usually performed with successive calls to a convex hull algorithm; the optimal worst-case convex hull algorithm, known to have an O(n^˙ Log (n)) execution time, may give an O(n^˙n^˙ Log (n)) to peel all the set; an O(n^˙n) convex hull algorithm, m being the number of extremal points, is shown to peel every set with an O(n-n) time, and proved to be optimal; an implementation of this algorithm is given for planar sets and spatial sets, but the latter give only an approximate O(n^˙n) performance.     

Optimal convex hull algorithms on enhanced meshes

In this paper we propose time-optimal convex hull algorithms for two classes of enhanced meshes. Our first algorithm computes the convex hull of an arbitrary set ofn points in the plane inO (logn) time on a mesh with multiple broadcasting of sizen×n. The second algorithm shows that the same problem can be solved inO (1) time on a reconfigurable mesh of sizen×n. Both algorithms achieve time lower bounds for their respective model of computation.This work was supported by NASA under grant NCCI-99.Additional support by the National Science Foundation under grant CCR-8909996 is gratefully acknowledged.     

Off-Line Maintenance of Planar Configurations

We achieve anO(log n) amortized time bound per operation for the off-line version of the dynamic convex hull problem in the plane. In this problem, a sequence ofninsert,delete, andqueryinstructions are to be processed, where each insert instruction adds a new point to the set, each delete instruction removes an existing point, and each query requests a standard convex hull search. We process the entire sequence in totalO(n log n) time andO(n) space. Alternatively, we can preprocess a sequence ofninsertions and deletions inO(n log n) time and space, then answer queries in history inO(log n) time apiece (a query in history means a query comes with a time parametert, and it must be answered with respect to the convex hull present at timet). The same bounds also hold for the off-line maintenance of several related structures, such as the maximal vectors, the intersection of half-planes, and the kernel of a polygon. Achieving anO(log n) per-operation time bound for theon-lineversions of these problems is a longstanding open problem in computational geometry.     

An optimal convex hull algorithm in any fixed dimension

We present a deterministic algorithm for computing the convex hull ofn points inE ^d in optimalO(n logn+n ^⌞d/2⌟ ) time. Optimal solutions were previously known only in even dimension and in dimension 3. A by-product of our result is an algorithm for computing the Voronoi diagram ofn points ind-space in optimalO(n logn+n ^⌜d/2⌝ ) time. This research was supported in part by the National Science Foundation under Grant CCR-9002352 and The Geometry Center, University of Minnesota, an STC funded by NSF, DOE, and Minnesota Technology, Inc. A preliminary version of this paper has appeared in “An optimal convex hull algorithm and new results on cuttings”,Proceedings of the 32nd Annual IEEE Symposium on the Foundations of Computer Science, October 1991, pp. 29–38. The convex hull algorithm given in the present paper, although similar in spirit, is considerably simpler than the one given in the proceedings.     

A geometric inequality on mixed volumes

We develop some geometric inequality for a kind of generalized convex set. The integral of (n – 2)-th mean curvature of the generalized convex set, the mixed volume of the convex hull of the set, and a reference convex set are involved in the inequality.Partially supported by grants from Kosef and BSRI-95-1419.     

Reconstructing polygons from x-rays

We present strategies for interactively reconstructing polygons from carefully chosen x-ray probes, generalizing previous results for convex polygons to a significantly larger class of objects. In particular, we show that n+h+2 parallel x-ray probes are sufficient to determine an n-gon P with h vertices on its convex hull, provided no three vertices of P are collinear. If given an upper bound n on the number of vertices of P, then 2n+2 parallel probes or 3n origin probes suffice. Further, we show that lg n–2 probes are necessary. Finally, we present verification strategies for arbitrary polygons. Interactive probing strategies have the potential to minimize radiation exposure in medical imaging.     

Finding Convex Sets in Convex Position

Let F{\cal{F}} denote a family of pairwise disjoint convex sets in the plane. F{\cal{F}} is said to be in convex position, if none of its members is contained in the convex hull of the union of the others. For any fixed k 3 5k\ge5, we give a linear upper bound on P_k(n)P_k(n), the maximum size of a family F{\cal{F}} with the property that any k members of F{\cal{F}} are in convex position, but no n are.     

Extreme points for convex mappings ofB n

We exhibit a collection of extreme points of the family of normalized convex mappings of the open unit ball of ℂ ⁿ forn≥2. These extreme points are defined in terms of the extreme points of a closed ball in the Banach space of homogeneous polynomials of degree 2 in ℂ ⁿ⁻¹, which are fully classified. Two examples are given to show that there are more convex mappings than those contained in the closed convex hull of the set of extreme points here exhibited.     

A geometric inequality and the complexity of computing volume

The volume of the convex hull of anym points of ann-dimensional ball with volumeV is at mostV·m/2 ⁿ . This implies that no polynomial time algorithm can compute the volume of a convex set (given by an oracle) with less than exponential relative error. A lower bound on the complexity of computing width can also be deduced.Dedicated to my teacher Kõváry Károly     

Voronoi Diagrams and Convex Hulls of Random Moving Points

This report considers the expected combinatorial complexity of the Euclidean Voronoi diagram and the convex hull of sets of n independent random points moving in unit time between two positions drawn independently from the same distribution in R ^d for fixed d\ge 2 as n→∈fty . It is proved that, when the source and destination distributions are the uniform distribution on the unit d -ball, these complexities are Θ(n ^(d+1)/d ) for the Voronoi diagram and O(n ^(d-1)/(d+1) log n) for the convex hull. Additional results for the convex hull are O( log ^d n) for the uniform distribution in the unit d -cube and O( log ^(d+1)/2 n) for the d -dimensional normal distribution. Received November 23, 1998, and in revised form July 8, 1999.     

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.     

Best restricted range approximation

A theory of best restricted range approximation is developed for an extended n-dimensional Chebyshev subspace of C[a,b] of order n without restricting the upper and lower restraining functions. This theory includes a “zero in the convex hull” characterization, an alternation theorem, and a strong uniqueness theorem.     

Counting the onion

Iteratively computing and discarding a set of convex hulls creates a structure known as an “onion.” In this paper, we show that the expected number of layers of a convex hull onion for n uniformly and independently distributed points in a disk is Θ(n^2/3). Additionally, we show that in general the bound is Θ(n^2/(d+1)) for points distributed in a d‐dimensional ball. Further, we show that this bound holds more generally for any fixed, bounded, full‐dimensional shape with a nonempty interior. © 2004 Wiley Periodicals, Inc. Random Struct. Alg., 2004     

On the volume of convex hulls of sets on spheres

We prove that for a measurable subset of S ^n–1 with fixed Haar measure, the volume of its convex hull is minimized for a cap (i.e. a ball with respect to the geodesic measure). We solve a similar problem for symmetric sets and n=2, 3. As a consequence, we deduce a result concerning Gaussian measures of dilatations of convex, symmetric sets in R ² and R ³.Partially supported by KBN (Poland), Grant No. 2 1094 91 01.     

Bounded linear regularity of convex sets in Banach spaces and its applications

This paper deals with bounded linear regularity, linear regularity and the strong conical hull intersection property (CHIP) of a collection of finitely many closed convex intersecting sets in Banach spaces. It is shown that, as in finite dimensional space setting (see [6]), the standard constraint qualification implies bounded linear regularity, which in turn yields the strong conical hull intersection property, and that the collection of closed convex sets {C ₁, . . . ,C _n } is bounded linearly regular if and only if the tangent cones of {C ₁, . . . ,C _n } has the CHIP and the normal cones of {C ₁, . . . ,C _n } has the property (G)(uniformly on a neighborhood in the intersection C). As applications, we study the global error bounds for systems of linear and convex inequalities. The work of this author was partially supported by the National Natural Sciences Grant (No. 10471032) and the Excellent Young Teachers Program of MOE, P.R.C The authors thank professor K.F.Ng for his helpful discussion and the referee for their helpful suggestions on improving the first version of this paper     

A model of the coNP-complete non-Hamilton tour decision problem for directed graphs

A truncated permutation matrix polytope is defined as the convex hull of a proper subset of n-permutations represented as 0/1 matrices. We present a linear system that models the coNP-complete non-Hamilton tour decision problem based upon constructing the convex hull of a set of truncated permutation matrix polytopes. Define polytope P^n–1 as the convex hull of all n-1 by n-1 permutation matrices. Each extreme point of P^n–1 is placed in correspondence (a bijection) with each Hamilton tour of a complete directed graph on n vertices. Given any n vertex graph G_n, a polynomial sized linear system F⁽ⁿ⁾ is constructed for which the image of its solution set, under an orthogonal projection, is the convex hull of the complete set of extrema of a subset of truncated permutation matrix polytopes, where each extreme point is in correspondence with each Hamilton tour not in G_n. The non-Hamilton tour decision problem is modeled by F⁽ⁿ⁾ such that G_n is non-Hamiltonian if and only if, under an orthogonal projection, the image of the solution set of F⁽ⁿ⁾ is P^n–1. The decision problem Is the projection of the solution set of F⁽ⁿ⁾=P^n–1? is therefore coNP-complete, and this particular model of the non-Hamilton tour problem appears to be new.Dedicated to the 250+ families in Kelowna BC, who lost their homes due to forest fires in 2003.I visited Ted at his home in Kelowna during this time - his family opened their home to evacuees and we shared happy and sad times with many wonderful people.     

Expectation of random polytopes

A random polytopeP _n in a convex bodyC is the convex hull ofn identically and independently distributed points inC. Its expectation is a convex body in the interior ofC. We study the deviation of the expectation ofP _n fromC asn→∞: while forC of classC ^k+1,k≥1, precise asymptotic expansions for the deviation exist, the behaviour of the deviation is extremely irregular for most convex bodiesC of classC ¹. Dedicated to my teacher and friend Professor Edmund Hlawka on the occasion of his 80^th birthday