A simple linear algorithm for intersecting convex polygons

LetP andQ be two convex polygons withm andn vertices, respectively, which are specified by their cartesian coordinates in order. A simpleO(m+n) algorithm is presented for computing the intersection ofP andQ. Unlike previous algorithms, the new algorithm consists of a two-step combination of two simple algorithms for finding convex hulls and triangulations of polygons.     

Parallel algorithms for some functions of two convex polygons

Let P andQ be two convex,n-vertex polygons. We consider the problem of computing, in parallel, some functions ofP andQ whenP andQ are disjoint. The model of parallel computation we consider is the CREW-PRAM, i.e., it is the synchronous shared-memory model where concurrent reads are allowed but no two processors can simultaneously attempt to write in the same memory location (even if they are trying to write the same thing). We show that a CREW-PRAM havingn ^1/k processors can compute the following functions in O(k¹⁺) time: (i) the common tangents betweenP andQ, and (ii) the distance betweenP andQ (and hence a straight line separating them). The positive constant can be made arbitrarily close to zero. Even with a linear number of processors, it was not previously known how to achieve constant time performance for computing these functions. The algorithm for problem (ii) is easily modified to detect the case of zero distance as well.This research was supported by the Office of Naval Research under Grants N00014-84-K-0502 and N00014-86-K-0689, and the National Science Foundation under Grant DCR-8451393, with matching funds from AT&T.     

Constructing strongly convex hulls using exact or rounded arithmetic

One useful generalization of the convex hull of a setS ofn points is the -strongly convex -hull. It is defined to be a convex polygon with vertices taken fromS such that no point inS lies farther than outside and such that even if the vertices of are perturbed by as much as , remains convex. It was an open question as to whether an -strongly convexO()-hull existed for all positive . We give here anO(n logn) algorithm for constructing it (which thus proves its existence). This algorithm uses exact rational arithmetic. We also show how to construct an -strongly convexO( + )-hull inO(n logn) time using rounded arithmetic with rounding unit . This is the first rounded-arithmetic convex-hull algorithm which guarantees a convex output and which has error independent ofn.     

Finding the convex hull of a simple polygon in linear time

Though linear algorithms for finding the convex hull of a simply-connected polygon have been reported, not all are short and correct. A compact version based on Sklansky's original idea⁽⁷⁾ and Bykat's counter-example⁽⁸⁾ is given. Its complexity and correctness are also shown.     

Constructing strongly convex approximate hulls with inaccurate primitives

The first half of this paper introducesEpsilon Geometry, a framework for the development of robust geometric algorithms using inaccurate primitives. Epsilon Geometry is based on a very general model of imprecise computations, which includes floating-point and rounded-integer arithmetic as special cases. The second half of the paper introduces the notion of a (–)-convex polygon, a polygon that remains convex even if its vertices are all arbitrarily displaced by a distance of of less, and proves some interesting properties of such polygons. In particular, we prove that for every point set there exists a (–)-convex polygonH such that every point is at most 4 away fromH. Using the tools of Epsilon Geometry, we develop robust algorithms for testing whether a polygon is (–)-convex, for testing whether a point is inside a (–)-convex polygon, and for computing a (–)-convex approximate hull for a set of points.     

Optimal parallel algorithms for computing convex hulls and for sorting

Nonlinear equations are considered, where some input parameters are subjected to errors. By a class of monotone enclosing methods sequences of intervals are constructed, containing for each value of the perturbation parameter at least one zero of the problem. In finite dimensional spaces concrete realizations are given, e. g. of Newton-, Regula falsi- and Jacobi-Newton-type.     

Convex hulls of objects bounded by algebraic curves

We present an0(n ·d ^o(1)) algorithm to compute the convex hull of a curved object bounded by0(n) algebraic curve segments of maximum degreed.Research supported in part by NSF Grant MIP-85 21356, ARO Contract DAA G29-85-C0018 under Cornell MSI, and ONR Contract N00014-88-K-0402. This paper is an updated version of a part of [6].     

Optimal and near-optimal algorithms for generalized intersection reporting on pointer machines

We develop efficient algorithms for a number of generalized intersection reporting problems, including orthogonal and general segment intersection, 2D range searching, rectangular point enclosure, and rectangle intersection search. Our results for orthogonal and general segment intersection, 3-sided 2D range searching, and rectangular pointer enclosure problems match the lower bounds for their corresponding standard versions under the pointer machine model. Our results for the remaining problems improve upon the best known previous algorithms.     

Solving the Multidimensional Multiple-choice Knapsack Problem by constructing convex hulls

This paper presents a heuristic to solve the Multidimensional Multiple-choice Knapsack Problem (MMKP), a variant of the classical 0–1 Knapsack Problem. We apply a transformation technique to map the multidimensional resource consumption to single dimension. Convex hulls are constructed to reduce the search space to find the near-optimal solution of the MMKP. We present the computational complexity of solving the MMKP using this approach. A comparative analysis of different heuristics for solving the MMKP has been presented based on the experimental results.     

A lower bound for computing Oja depth

Let S={s₁,…,sn} be a set of points in the plane. The Oja depth of a query point θ with respect to S is the sum of the areas of all triangles (θ,si,sj). This depth may be computed in O(nlogn) time in the RAM model of computation. We show that a matching lower bound holds in the algebraic decision tree model. This bound also applies to the computation of the Oja gradient, the Oja sign test, and to the problem of computing the sum of pairwise distances among points on a line.     

Clipping algorithms for solving the nearest point problem over reduced convex hulls

The nearest point problem (NPP), i.e., finding the closest points between two disjoint convex hulls, has two classical solutions, the Gilbert-Schlesinger-Kozinec (GSK) and Mitchell-Dem’yanov-Malozemov (MDM) algorithms. When the convex hulls do intersect, NPP has to be stated in terms of reduced convex hulls (RCHs), made up of convex pattern combinations whose coefficients are bound by a μ<1 value and that are disjoint for suitable μ. The GSK and MDM methods have recently been extended to solve NPP for RCHs using the particular structure of the extreme points of a RCH. While effective, their reliance on extreme points may make them computationally costly, particularly when applied in a kernel setting. In this work we propose an alternative clipped extension of classical MDM that results in a simpler algorithm with the same classification accuracy than that of the extensions already mentioned, but also with a much faster numerical convergence.     

A note on linear expected time algorithms for finding convex hulls

Considern independent identically distributed random vectors fromR ^d with common densityf, and letE (C) be the average complexity of an algorithm that finds the convex hull of these points. Most well-known algorithms satisfyE (C)=0(n) for certain classes of densities. In this note, we show thatE (C)=0(n) for algorithms that use a “throw-away” pre-processing step whenf is bounded away from 0 and ∞ on any nondegenerate rectangle ofR ².     

Computational geometry algorithms for the systolic screen

Adigitized plane of sizeM is a rectangular M × M array of integer lattice points called pixels. A M × M mesh-of-processors in which each processorP _ij represents pixel (i,j) is a natural architecture to store and manipulate images in ; such a parallel architecture is called asystolic screen. In this paper we consider a variety of computational-geometry problems on images in a digitized plane, and present optimal algorithms for solving these problems on a systolic screen. In particular, we presentO(M)-time algorithms for determining all contours of an image; constructing all rectilinear convex hulls of an image (peeling); solving the parallel and perspective visibility problem forn disjoint digitized images; and constructing the Voronoi diagram ofn planar objects represented by disjoint images, for a large class of object types (e.g., points, line segments, circles, ellipses, and polygons of constant size) and distance functions (e.g., allL _p metrics). These algorithms implyO(M)-time solutions to a number of other geometric problems: e.g., rectangular visibility, separability, detection of pseudo-star-shapedness, and optical clustering. One of the proposed techniques also leads to a new parallel algorithm for determining all longest common subsequences of two words.Research supported by the Naural Sciences and Engineering Research Council of Canada. With the Editor-in-Chief's permission, this paper was sent to the referees in a form which kept them unaware of the fact that the Guest Editor is one of the co-authors.     

Efficient convexity and domination algorithms for fine- and medium-grain hypercube computers

This paper gives hypercube algorithms for some simple problems involving geometric properties of sets of points. The properties considered emphasize aspects of convexity and domination. Efficient algorithms are given for both fine- and medium-grain hypercube computers, including a discussion of implementation, running times and results on an Intel iPSC hypercube, as well as theoretical results. For both serial and parallel computers, sorting plays an important role in geometric algorithms for determining simple properties, often being the dominant component of the running time. Since the time required to sort data on a hypercube computer is still not fully understood, the running times of some of our algorithms for unsorted data are not completely determined. For both the fine- and medium-grain models, we show that faster expected-case running time algorithms are possible for point sets generated randomly. Our algorithms are developed for sets of planar points, with several of them extending to sets of points in spaces of higher dimension.The research of E. Cohen, R. Miller, and E. M. Sarraf was partially supported by National Science Foundation Grant ASC-8705104. R. Miller was also partially supported by National Science Foundation Grants DCR-8608640 and IRI-8800514. Q. F. Stout's research was partially supported by National Science Foundation Grant DCR-85-07851, and an Incentives for Excellence Grant from the Digital Equipment Corporation.     

A linear time algorithm for obtaining the convex hull of a simple polygon

In this paper, a linear time algorithm is described for finding the convex hull of a simple (i.e. non-self intersecting) polygon.     

Algorithms for computing the center of area of a convex polygon

The center of area of a convex polygonP is the unique pointp ^* that maximizes the minimum area overlap betweenP and any halfplane that includesp ^*. We show thatp ^* is unique and present two algorithms for its computation. The first is a combinatorial algorithm that runs in timeO (n ⁶ log² n). The second is a numerical algorithm that runs in timeO(GK(n+K)) whereK represents the number of desired bits of precision in the output coordinates andG the number of bits used to represent the coordinates of the input polygon vertices. We conclude with a discussion of implementation issues and related results.Research partially supported by the second author's NSF grant CCR-8351468, at Johns Hopkins University and Smith College.     

Linear Data Structures for Fast Ray-Shooting amidst Convex Polyhedra

We consider the problem of ray shooting in a three-dimensional scene consisting of k (possibly intersecting) convex polyhedra with a total of n facets. That is, we want to preprocess them into a data structure, so that the first intersection point of a query ray and the given polyhedra can be determined quickly. We describe data structures that require preprocessing time and storage (where the notation hides polylogarithmic factors), and have polylogarithmic query time, for several special instances of the problem. These include the case when the ray origins are restricted to lie on a fixed line ℓ ₀, but the directions of the rays are arbitrary, the more general case when the supporting lines of the rays pass through ℓ ₀, and the case of rays orthogonal to some fixed line with arbitrary origins and orientations. We also present a simpler solution for the case of vertical ray-shooting with arbitrary origins. In all cases, this is a significant improvement over previously known techniques (which require Ω(n ²) storage, even when k ≪ n). Work by Haim Kaplan and Natan Rubin has been supported by Grant 975/06 from the Israel Science Fund. Work by Micha Sharir and Natan Rubin was partially supported by NSF Grant CCF-05-14079, by a grant from the U.S.–Israeli Binational Science Foundation, by grant 155/05 from the Israel Science Fund, Israeli Academy of Sciences, by a grant from the AFIRST French–Israeli program, and by the Hermann Minkowski–MINERVA Center for Geometry at Tel Aviv University. A preliminary version of this paper appeared in Proc. 15th Annu. Europ. Sympos. Alg. (2007), 287–298.