The Ostrowski type moment integral inequalities and moment-bounds for continuous random variables

We establish Ostrowski type integral inequalities involving moments of a continuous random variable defined on a finite interval. We also derive bounds for moments from these inequalities. Further, we discuss applications of these bounds to the Euler's beta mappings and illustrate their behaviour.     

A workbench for computational geometry

We describe the design and implementation of a workbench for computational geometry. We discuss issues arising from this implementation, including comparisons of different algorithms for constant factors, code size, and ease of implementation. The workbench is not just a library of computational geometry algorithms and data structures, but is designed as a geometrical programming environment, providing tools for: creating, editing, and manipulating geometric objects; demonstrating and animating geometric algorithms; and, most importantly, for implementing and maintaining complex geometric algorithms.This research was partially supported by the Natural Sciences and Engineering Research Council of Canada, Carleton University, and the Univeristy of Passau. Work on this project was carried out in part while A. Knight and J.-R. Sack were at the University of Passau.     

Parallel computational geometry

We present efficient parallel algorithms for several basic problems in computational geometry: convex hulls, Voronoi diagrams, detecting line segment intersections, triangulating simple polygons, minimizing a circumscribing triangle, and recursive data-structures for three-dimensional queries.The work of C. Ó'Dúnlaing and C. Yap was supported by NSF Grants DCR-84-01898 and DCR-84-01633.     

Parallel computational geometry

We present efficient parallel algorithms for several basic problems in computational geometry: convex hulls, Voronoi diagrams, detecting line segment intersections, triangulating simple polygons, minimizing a circumscribing triangle, and recursive data-structures for three-dimensional queries.     

Sweep methods for parallel computational geometry

In this paper we give efficient parallel algorithms for a number of problems from computational geometry by using versions of parallel plane sweeping. We illustrate our approach with a number of applications, which include:

General hidden-surface elimination (even if the overlap relation contains cycles).

CSG boundary evaluation.

Computing the contour of a collection of rectangles.

Hidden-surface elimination for rectangles.

There are interesting subproblems that we solve as a part of each parallelization. For example, we give an optimal parallel method for building a data structure for line-stabbing queries (which, incidentally, improves the sequential complexity of this problem). Our algorithms are for the CREW PRAM, unless otherwise noted.     

Some inequalities for probability,expectation, and variance of random variables defined over a finite interval

Some recent inequalities for cumulative distribution functions, expectation, variance, and applications are presented.     

Optimal randomized parallel algorithms for computational geometry

We present parallel algorithms for some fundamental problems in computational geometry which have a running time ofO(logn) usingn processors, with very high probability (approaching 1 asn ). These include planar-point location, triangulation, and trapezoidal decomposition. We also present optimal algorithms for three-dimensional maxima and two-set dominance counting by an application of integer sorting. Most of these algorithms run on a CREW PRAM model and have optimal processor-time product which improve on the previously best-known algorithms of Atallah and Goodrich [5] for these problems. The crux of these algorithms is a useful data structure which emulates the plane-sweeping paradigm used for sequential algorithms. We extend some of the techniques used by Reischuk [26] and Reif and Valiant [25] for flashsort algorithm to perform divide and conquer in a plane very efficiently leading to the improved performance by our approach.This is a substantially revised version of the paper that appeared as Optimal Randomized Parallel Algorithms for Computational Geometry in theProceedings of the 16th International Conference on Parallel Processing, St. Charles, Illinois, August 1987.This research was supported by DARPA/ARO Contract DAAL03-88-K-0195, Air Force Contract AFOSR-87-0386, DARPA/ISTO Contracts N00014-88-K-0458 and N00014-91-J-1985, and by NASA Subcontract 550-63 of Primecontract NAS5-30428.     

Optimal randomized parallel algorithms for computational geometry

We present parallel algorithms for some fundamental problems in computational geometry which have a running time ofO(logn) usingn processors, with very high probability (approaching 1 asn → ∞). These include planar-point location, triangulation, and trapezoidal decomposition. We also present optimal algorithms for three-dimensional maxima and two-set dominance counting by an application of integer sorting. Most of these algorithms run on a CREW PRAM model and have optimal processor-time product which improve on the previously best-known algorithms of Atallah and Goodrich [5] for these problems. The crux of these algorithms is a useful data structure which emulates the plane-sweeping paradigm used for sequential algorithms. We extend some of the techniques used by Reischuk [26] and Reif and Valiant [25] for flashsort algorithm to perform divide and conquer in a plane very efficiently leading to the improved performance by our approach.     

Some problems in distributed computational geometry

In a planar geometric network vertices are located in the plane, and edges are straight line segments connecting pairs of vertices, such that no two of them intersect. In this paper we study distributed computing in asynchronous, failure-free planar geometric networks, where each vertex is associated to a processor, and each edge to a bidirectional message communication link. Processors are aware of their locations in the plane.We consider fundamental computational geometry problems from the distributed computing point of view, such as finding the convex hull of a geometric network and identification of the external face. We also study the classic distributed computing problem of leader election, to understand the impact that geometric information has on the message complexity of solving it.We obtain an O(nlog²n) message complexity algorithm to find the convex hull, and an O(nlogn) message complexity algorithm to identify the external face of a geometric network of n processors. We present a matching lower bound for the external face problem. We prove that the message complexity of leader election in a geometric ring is Ω(nlogn), hence geometric information does not help in reducing the message complexity of this problem.     

Huffman algebras for independent random variables

Based on a rearrangement inequality by Hardy, Littlewood, and Polya, we define two-operator algebras for independent random variables. These algebras are called Huffman algebras since the Huffman algorithm on these algebras produces an optimal binary tree that minimizes the weighted lengths of leaves. Many examples of such algebras are given. For the case with random weights of the leaves, we prove the optimality of the tree constructed by the power-of-2 rule, i.e., the Huffman algorithm assuming identical weights, when the weights of the leaves are independent and identically distributed.     

Parallel computational geometry of rectangles

Rectangles in a plane provide a very useful abstraction for a number of problems in diverse fields. In this paper we consider the problem of computing geometric properties of a set of rectangles in the plane. We give parallel algorithms for a number of problems usingn processors wheren is the number of upright rectangles. Specifically, we present algorithms for computing the area, perimeter, eccentricity, and moment of inertia of the region covered by the rectangles inO(logn) time. We also present algorithms for computing the maximum clique and connected components of the rectangles inO(logn) time. Finally, we give algorithms for finding the entire contour of the rectangles and the medial axis representation of a givenn × n binary image inO(n) time. Our results are faster than previous results and optimal (to within a constant factor).The work of Sung Kwan Kim was supported by NSF Grant CCR-87-03196 and the work of D. M. Mount was partially supported by National Science Foundation Grant CCR-89-08901.     

VLSI systems for some problems of computational geometry

Fast solution of the computational geometry problems is important for computer graphics, image processing and pattern recognition. The capability of the network Mesh of Trees for application in VLSI systems solving fastly the computational geometry problems is shown on two examples: determination of the convex hull of a weakly externally visible polygon and determination of the visibility polygon of a polygon.     

Some dynamic computational geometry problems

We consider some problems in computational geometry when every one of the input points is moving in a prescribed manner.     

Parallel computational geometry of rectangles

Rectangles in a plane provide a very useful abstraction for a number of problems in diverse fields. In this paper we consider the problem of computing geometric properties of a set of rectangles in the plane. We give parallel algorithms for a number of problems usingn processors wheren is the number of upright rectangles. Specifically, we present algorithms for computing the area, perimeter, eccentricity, and moment of inertia of the region covered by the rectangles inO(logn) time. We also present algorithms for computing the maximum clique and connected components of the rectangles inO(logn) time. Finally, we give algorithms for finding the entire contour of the rectangles and the medial axis representation of a givenn × n binary image inO(n) time. Our results are faster than previous results and optimal (to within a constant factor).     

Applications of power series in computational geometry

A number of algorithms are presented for obtaining power series expansions of curves and surfaces at a point. Some results on the radius of convergence are given. Two applications of series are given:

1. • for curve tracing algorithms, where a truncated series is used to approximate the curve of intersection of two surfaces

2. • to define nth degree geometric continuity, for arbitrary

An introduction to randomization in computational geometry

This paper is not a complete survey on randomized algorithms in computational geometry, but an introduction to this subject providing intuitions and references. First, some basic ideas are illustrated by the sorting problem, and then a few results on computational geometry are briefly explained.     

The general-to-specific method in computational geometry problems

Methods, algorithms, and software developed for solving computational geometry problems for bulk processing of large graphic data volumes are described. Computational geometry algorithms leverage the general-to-specific method based on hierarchical structures of graphic information representation and decision-making methods for these structures.     

Constant time algorithms for computational geometry on thereconfigurable mesh

The reconfigurable mesh consists of an array of processors interconnected by a reconfigurable bus system. The bus system can be used to dynamically obtain various interconnection patterns among the processors. Recently, this model has attracted a lot of attention. The authors show O(1) time solutions to the following computational geometry problems on the reconfigurable mesh: all-pairs nearest neighbors, convex hull, triangulation, two-dimensional maxima, two-set dominance counting, and smallest enclosing box. All these solutions accept N planar points as input and employ an N×N reconfigurable mesh. The basic scheme employed in the implementations is to recursively find an O(1) time solution. The number of recursion levels and the size of the subproblems at each level of recursion are optimized such that the problem decomposition and the solution to the problem can be obtained in constant time. As a result, they have developed some efficient merge techniques to combine the solutions for subproblems on the reconfigurable mesh. These techniques exploit reconfigurability in nontrivial ways leading to constant time solutions using optimal size of the mesh