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Short Communication: A faster optimal algorithm for the measure problem

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TLDR
It is shown that the measure problem can be solved optimally in O(log n) time using O(n) processors in the same model of computation, thus settling an open problem posed in [8].
Abstract
The measure problem involves computing the area of the union of a set of n iso-oriented rectangles in the plane. Recently, it has been shown that for a set of n such rectangles, the measure problem can be solved in O(log n log log n) time, using O(n/log log n) processors in the CREW PRAM model of computation. In this note we show that the measure problem can be solved optimally in O(log n) time using O(n) processors in the same model of computation, thus settling an open problem posed in [8].

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References
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Computational geometry. an introduction

TL;DR: This book offers a coherent treatment, at the graduate textbook level, of the field that has come to be known in the last decade or so as computational geometry.
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Parallel merge sort

TL;DR: A parallel implementation of merge sort on a CREW PRAM that uses n processors and O(logn) time; the constant in the running time is small.
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Cascading divide-and-conquer: a technique for designing parallel algorithms

TL;DR: Improvements in parallel divide-and-conquer techniques are presented, resulting in improved parallel algorithms for a number of problems, including intersection detection, trapezoidal decomposition, and planar point location.
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On the complexity of computing the measure of ∪[ai,bi]

TL;DR: The decision tree complexity of computing the measure of the union of n (possibly overlapping) intervals is shown to be &OHgr;(n log n), even if comparisons between linear functions of the interval endpoints are allowed.
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An optimal speedup algorithm for the measure problem

TL;DR: The parallel algorithm presented in this paper is shown to have a tim complexity of O(log n log log n) with O(n/log log n ) processors.