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  • 1
    Book
    Book
    Amsterdam u.a. :Elsevier Science,
    Title: Handbook of computational geometry
    Contributer: Sack, J. R. , Urrutia, J.
    Publisher: Amsterdam u.a. :Elsevier Science,
    Year of publication: 2000
    Pages: 1027 S.
    Type of Medium: Book
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Algorithmica 11 (1994), S. 404-428 
    ISSN: 1432-0541
    Keywords: Algorithms ; Computational geometry ; Implementation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract 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.
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Algorithmica 14 (1995), S. 261-289 
    ISSN: 1432-0541
    Keywords: Computational geometry ; Algorithms and data structures ; Parallel computation ; Link distance ; Rectilinear polygons
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We provide optimal parallel solutions to several link-distance problems set in trapezoided rectilinear polygons. All our main parallel algorithms are deterministic and designed to run on the exclusive read exclusive write parallel random access machine (EREW PRAM). LetP be a trapezoided rectilinear simple polygon withn vertices. InO(logn) time usingO(n/logn) processors we can optimally compute: 1. Minimum réctilinear link paths, or shortest paths in theL 1 metric from any point inP to all vertices ofP. 2. Minimum rectilinear link paths from any segment insideP to all vertices ofP. 3. The rectilinear window (histogram) partition ofP. 4. Both covering radii and vertex intervals for any diagonal ofP. 5. A data structure to support rectilinear link-distance queries between any two points inP (queries can be answered optimally inO(logn) time by uniprocessor). Our solution to 5 is based on a new linear-time sequential algorithm for this problem which is also provided here. This improves on the previously best-known sequential algorithm for this problem which usedO(n logn) time and space.5 We develop techniques for solving link-distance problems in parallel which are expected to find applications in the design of other parallel computational geometry algorithms. We employ these parallel techniques, for example, to compute (on a CREW PRAM) optimally the link diameter, the link center, and the central diagonal of a rectilinear polygon.
    Type of Medium: Electronic Resource
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  • 4
    Electronic Resource
    Electronic Resource
    Springer
    Algorithmica 6 (1991), S. 734-761 
    ISSN: 1432-0541
    Keywords: Computational geometry ; Clustering ; Convex hull ; Digitized pictures ; Hulls ; Maxima ; Mesh-of-processors ; Parallel computing ; Separability ; Systolic array ; Visibility ; Voronoi diagram
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Adigitized plane Π 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 Π; 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.
    Type of Medium: Electronic Resource
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  • 5
    Electronic Resource
    Electronic Resource
    Springer
    BIT 27 (1987), S. 315-323 
    ISSN: 1572-9125
    Keywords: 4.34 ; 5.25 ; 5.31 ; 5.32 ; 8.1
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Very recently a new data structure, called a min-max heap, was presented for implementing the double-ended priority queue. A min-max heap onn keys is constructed inO(n) time; the minimum and maximum keys are found in constant time, and the operations of deleting the minimum, deleting the maximum and inserting a new key into the heap are performed inO(logn) time. In addition, the data structure can be stored implicitly, i.e. in an array ofn elements without using any additional pointers. In this paper, we present lower bound results on the number of comparisons required, in the worst case, for the operations i) to construct a min-max heap on a given set of keys; ii) to convert a min-max heap into a max-min heap; and iii) to merge two min-max heaps into one min-max heap. New upper bounds for the convert and merge operations are also derived. It is found that the main difference between traditional heaps and min-max heaps lies in the time needed to perform the merge operation. While traditional heaps can be merged efficiently, it is shown that min-max heaps are not sublinearly mergeable. Even the seemingly simple task of converting a min-max heap into a max-min heap cannot be performed in less than linear time.
    Type of Medium: Electronic Resource
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