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.     

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.     

A simple linear hidden-line algorithm for star-shaped polygons

A very simple, linear-running-time algorithm is presented for solving the hidden-line problem for star-shaped polygons. The algorithm first decomposes the visibility regions into edge-visible polygons and then solves the hidden-line problem for these simpler polygons. In addition to simplicity the algorithm possesses the virtue of affording a very easy proof of correctness. Some applications where this problem arises are mentioned.     

Optimal algorithms for computing the minimum distance between two finite planar sets

It is shown in this paper that the minimum distance between two finite planar sets of n points can be computer in O(n log n) worst-case running time and that this is optimal to within a constant factor. Furthermore, when the sets form a convex polygon this complexity can be reduced O(n).     

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(k^1+?) 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.     

Searching for empty convex polygons

A key problem in computational geometry is the identification of subsets of a point set having particular properties. We study this problem for the properties of convexity and emptiness. We show that finding empty triangles is related to the problem of determining pairs of vertices that see each other in a star-shaped polygon. A linear-time algorithm for this problem which is of independent interest yields an optimal algorithm for finding all empty triangles. This result is then extended to an algorithm for finding empty convex r-gons (r> 3) and for determining a largest empty convex subset. Finally, extensions to higher dimensions are mentioned.The first author is pleased to acknowledge support by the National Science Foundation under Grant CCR-8700917. The research of the second author was supported by Amoco Foundation Faculty Development Grant CS 1-6-44862 and by the National Science Foundation under Grant CCR-8714565.     

A new linear algorithm for triangulating monotone polygons

Let P = (p₁, p₂,…,p_n) be a monotone polygon whose vertices are specified in terms of cartesian coordinates in order. A new simple two-step procedure is presented for triangulating P, without the addition of new vertices, in O(n) time. Unlike the previous algorithm no specialized code is needed since the new approach uses well-known existing algorithms for first decomposing P into edge-visible polygons and subsequently triangulating these.     

A counterexample to an algorithm for computing monotone hulls of simple polygons

A two-stage algorithm was recently proposed by Sklansky (1982) for computing the convex hull of a simple polygon P. The first step is intended to compute a simple polygon $\text{P}{}^1$ which is monotonic in both the x and y directions and which contains the convex hull vertices of P. The second step applies a very simple convex hull algorithm on $\text{P}{}^1$. In this note we show that the first step does not always work correctly and can even yield non-simple polygons, invalidating the use of the second step. It is also shown that the first step can discard convex hull vertices thus invalidating the use of any convex hull algorithm in the second step.     

Scalable Parallel Algorithms for Geometric Pattern Recognition

This paper considers a variety of geometric pattern recognition problems on input sets of size n using a coarse grained multicomputer model consisting of p processors with Ω(n/p) local memory each (i.e., Ω(n/p) memory cells of Θ(log n) bits apiece), where the processors are connected to an arbitrary interconnection network. It introduces efficient scalable parallel algorithms for a number of geometric problems including the rectangle finding problem, the maximal equally spaced collinear points problem, and the point set pattern matching problem. All of the algorithms presented are scalable in that they are applicable and efficient over a very wide range of ratios of problem size to number of processors. In addition to the practicality imparted by scalability, these algorithms are easy to implement in that all required communications can be achieved by a small number of calls to standard global routing operations.     

Parallel algorithms for arrangements

We give the first efficient parallel algorithms for solving the arrangement problem. We give a deterministic algorithm for the CREW PRAM which runs in nearly optimal bounds ofO (logn log^* n) time andn ²/logn processors. We generalize this to obtain anO (logn log^* n)-time algorithm usingn ^d /logn processors for solving the problem ind dimensions. We also give a randomized algorithm for the EREW PRAM that constructs an arrangement ofn lines on-line, in which each insertion is done in optimalO (logn) time usingn/logn processors. Our algorithms develop new parallel data structures and new methods for traversing an arrangement.This work was supported by the National Science Foundation, under Grants CCR-8657562 and CCR-8858799, NSF/DARPA under Grant CCR-8907960, and Digital Equipment Corporation. A preliminary version of this paper appeared at the Second Annual ACM Symposium on Parallel Algorithms and Architectures [3].     

A simple proof of Pach's extremal theorem for convex polygons

A simple proof is given that the minimum-area triangle inscribed in a convex polygon has two sides which are edges of the polygon.     

Parallel algorithms for shortest path problems in polygons

Given ann-vertex simple polygon we address the following problems: (i) find the shortest path between two pointss andd insideP, and (ii) compute the shortestpath tree between a single points and each vertex ofP (which implicitly represents all the shortest paths). We show how to solve the first problem inO(logn) time usingO(n) processors, and the more general second problem inO(log² n) time usingO(n) processors, and the more general second problem inO(log² n) time usingO(n) processors for any simple polygonP. We assume the CREW RAM shared memory model of computation in which concurrent reads are allowed, but no two processors should attempt to simultaneously write in the same memory location. The algorithms are based on the divide-and-conquer paradigm and are quite different from the known sequential algorithmsResearch supported by the Faculty of Graduate Studies and Research (McGill University) grant 276-07     

On the approximation of convex hulls of finite grid point sets

The outer or inner approximation of the convex hull of a grid point set may be used to speed up the feature extraction process in a shape analysis system. Some properties of these approximations are given.     

Fast linear expected-time algorithms for computing maxima and convex hulls

This paper examines the expected complexity of boundary problems on a set ofN points inK-space. We assume that the points are chosen from a probability distribution in which each component of a point is chosen independently of all other components. We present an algorithm to find the maximal points usingKN + O (N^1–1/K log^1/K N) expected scalar comparisons, for fixedK 2. A lower bound shows that the algorithm is optimal in the leading term. We describe a simple maxima algorithm that is easy to code, and present experimental evidence that it has similar running time. For fixedK 2, an algorithm computes the convex hull of the set in 2KN + O(N^1–1/K log^1/KN) expected scalar comparisons. The history of the algorithms exhibits interesting interactions among consulting, algorithm design, data analysis, and mathematical analysis of algorithms.This work was performed while this author was visiting AT&T Bell Laboratories.     

Approximation algorithms for partitioning a rectangle with interior points

LetR be a rectangle and letP be a set of points located insideR. Our problem consists of introducing a set of line segments of least total length to partition the interior ofR into rectangles. Each rectangle in a valid partition must not contain points fromP as interior points. Since this partitioning problem is computationally intractable (NP-hard), we present efficient approximation algorithms for its solution. The solutions generated by our algorithms are guaranteed to be within three times the optimal solution value. Our algorithm also generates solutions within four times the optimal solution value whenR is a rectilinear polygon. Our algorithm can be generalized to generate good approximation solutions for the case whenR is a rectilinear polygon, there are rectilinear polygonal holes, and the sum of the length of the boundaries is not more than the sum of the length of the edges in an optimal solution.A preliminary version of this paper appeared in theProceedings of the Symposium on Computational Geometry, June 1985, pp. 281–287. This research was supported in part by the National Science Foundation under Grant DCR-8503163.