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Quickselect and Dickman function
 Combinatorics, Probability and Computing
, 2000
"... We show that the limiting distribution of the number of comparisons used by Hoare's quickselect algorithm when given a random permutation of n elements for finding the mth smallest element, where m = o(n), is the Dickman function. The limiting distribution of the number of exchanges is also de ..."
Abstract

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We show that the limiting distribution of the number of comparisons used by Hoare's quickselect algorithm when given a random permutation of n elements for finding the mth smallest element, where m = o(n), is the Dickman function. The limiting distribution of the number of exchanges is also derived. 1 Quickselect Quickselect is one of the simplest and e#cient algorithms in practice for finding specified order statistics in a given sequence. It was invented by Hoare [19] and uses the usual partitioning procedure of quicksort: choose first a partitioning key, say x; regroup the given sequence into two parts corresponding to elements whose values are less than and larger than x, respectively; then decide, according to the size of the smaller subgroup, which part to continue recursively or to stop if x is the desired order statistics; see Figure 1 for an illustration in terms of binary search trees. For more details, see Guibas [15] and Mahmoud [26]. This algorithm , although ine#cient in the worst case, has linear mean when given a sequence of n independent and identically distributed continuous random variables, or equivalently, when given a random permutation of n elements, where, here and throughout this paper, all n! permutations are equally likely. Let C n,m denote the number of comparisons used by quickselect for finding the mth smallest element in a random permutation, where the first partitioning stage uses n 1 comparisons. Knuth [23] was the first to show, by some di#erencing argument, that E(C n,m ) = 2 (n + 3 + (n + 1)H n (m + 2)Hm (n + 3 m)H n+1m ) , n, where Hm = 1#k#m k 1 . A more transparent asymptotic approximation is E(C n,m ) (#), (#) := 2 #), # Part of the work of this author was done while he was visiting School of C...
On the diminishing process of B. Tóth
, 2014
"... Let K and K0 be convex bodies in Rd, such that K contains the origin, and define the process (Kn, pn), n ≥ 0, as follows: let pn+1 be a uniform random point in Kn, and set Kn+1 = Kn ∩ (pn+1 + K). Clearly, (Kn) is a nested sequence of convex bodies which converge to a nonempty limit object, again a ..."
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Let K and K0 be convex bodies in Rd, such that K contains the origin, and define the process (Kn, pn), n ≥ 0, as follows: let pn+1 be a uniform random point in Kn, and set Kn+1 = Kn ∩ (pn+1 + K). Clearly, (Kn) is a nested sequence of convex bodies which converge to a nonempty limit object, again a convex body in Rd. We study this process for K being a regular simplex, a cube, or a regular convex polygon with an odd number of vertices. We also derive some new results in one dimension for nonuniform distributions. 1