# Probabilistic Analysis of Geometric Algorithms (thesis)

Report ID:
TR-266-90
Authors:
Date:
May 1990
Pages:
153
[PDF]

#### Abstract:

This thesis is divided into four chapters. In the first chapter we describe the subtleties involved in probabilistically analyzing simple algorithms in computational geometry. We also work through a few easy examples of such analyses. The first example analyzes Quicksort, the second analyzes Quickhull, and the third analyzes interpoint distances between uniformly distributed points in hypercubes or hypertori. In the second chapter we present a simple but effective preprocessing algorithm for calculating convex hulls. The algorithm is short and intuitive. It does a fast linear scan through the points, identifying many which are not on the convex hull and can therefore be eliminated. We perform an exact analysis of this algorithm showing that, given n points distributed uniformly in the unit square, only about 8sqrtn of them remain after the preprocessing step: in higher dimensions only cd(n1-1/d) will remain. We present the results of simulations comparing our mathematical analysis to reality. Finally, we end with a discussion of what distinguishes this algorithm from certain obvious variants. In the third chapter we analyze closest pair algorithms. First, we analyze a sweep-line closest-pair algorithm, one similar in spirit to Hoey and Shamos' divide and conquer algorithm for solving the same problem. Our result is that, given n points uniformly distributed in the unit square and then sorted, there is a six line algorithm
that finds the closest pair in O(n) expected time. Moreover, this algorithm uses no complicated data structures. We then analyze a second algorithm, one that finds the closest pair using a modified version of Bentley and Papadimitriou's nearest neighbor projection algorithm. Our result, again, is that after the sorting stage, the linear scan stage of this new algorithm also
finds the closest pair in O(n) expected time. The fourth chapter discusses the calculation of maxima. More specifically it analyzes a new heuristic, the Move-To-Front Algorithm, recently developed by Bentley, Clarkson and Levine. We prove
mathematically that the Move-To-Front Algorithm is extremely efficient; on average it performs only one comparison per input point except on those in an asymptotically negligible subset.