概率搜索技术的两个应用:排序X+Y和构建平衡搜索树

M. Fredman
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引用次数: 67

摘要

令X = {x1,…,xN}和Y = {y1,…, N}是N个实数的集合。我们用X + Y表示多集{xi + yj;1≤i, j≤N},大小为N2。Berklekamp提出了对X + Y排序的问题。Harper, Payne, Savage和Strauss[1]表明,N21og2N比较足以对X + Y排序,从而在不利用X + Y结构的情况下节省了2倍的排序(给定X + Y中的u,我们假设我们知道i,j个指标,使得u = xi + yj)。进一步地,他们证明了这个界对于一类受限的比较算法是紧的。然而,如果没有它们的限制,这个问题的数量级比较的复杂性仍然是一个悬而未决的问题。本文证明了X + Y可以用O(N2)次比较排序。对于这类问题,我们的证明是不寻常的,因为我们没有明确地展示算法。相反,它是一种更一般的搜索技术的特殊应用,其行为很容易与信息理论下界相关。在排序上下文中,这种搜索方法转换为插入排序,其中插入不是通过通常的二进制搜索执行的,而是作为偏离中心的搜索执行的,因此,粗略地说,每次比较都平分剩余可能性的空间。我们要注意这种搜索技术,因为它可以应用于其他问题,我们用第二个应用程序来说明这种可能性。我们的第二个应用涉及到概率平衡二叉搜索树的构造。
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Two applications of a probabilistic search technique: Sorting X+Y and building balanced search trees
Let X = {x1,...,xN} and Y = {y1,...,yN} be sets of N real numbers. We denote by X + Y the multiset {xi + yj; 1 ≤ i, j ≤ N} of size N2. Berklekamp has posed the problem of sorting X + Y. Harper, Payne, Savage and Strauss [1] show that N21og2N comparisons suffice to sort X + Y, thereby saving a factor of 2 over sorting without exploiting the structure of X + Y. (Given u in X + Y, we assume that we know the i,j indices such that u = xi + yj.) Furthermore, they show that this bound is tight for a restricted class of comparison algorithms. However, without their restriction the order of magnitude comparison complexity of this problem has remained an open question. In this paper we show that X + Y can be sorted with O(N2) comparisons. Our proof is unusual for this type of problem in that we do not explicitly exhibit an algorithm. Instead, it is a particular application of a more general search technique whose behavior is easily related to information theoretic lower bounds. In the context of sorting, this search method translates into an insertion sort, where the insertions are not performed by means of the usual binary search, but rather as off-centered searches designed so that each comparison, roughly speaking, equally divides the space of remaining possibilities. We draw attention to this search technique because it might find application to other problems, and we illustrate this possibility with a second application. Our second application concerns the construction of probabilistically balanced binary search trees.
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