Practical Algorithms for Generating a Random Ordering of the Elements of a Weighted Set

Abstract

This paper discusses the problem of efficiently generating a random ordering of the elements of an n-element weighted set, where the elements' weights are interpreted as relative probabilities. It is assumed that only the following randomness primitives are available: flipping a biased coin, and selecting a random non-negative integer smaller than n. We review the existing literature on this problem, focusing on several simple algorithms whose running times are O(n 2) and O(nlogn). Asymptotically faster algorithms do exist, but they are mostly either very complicated, or require a stronger computational model. The main contribution of this paper is a self-contained specification of and correctness proof for an O(nlogn) mergesort-like folklore algorithm that is often implemented incorrectly. The key piece of the correct algorithm can be viewed as the weighted generalization of the riffle merge operation which appears in mathematical models of card shuffling. Empirical results are also presented which suggest that this algorithm might be faster on a real computer than other simple algorithms which use the same randomness primitives, because its pattern of memory accesses results in fewer cache misses. Finally, three fancier but still implementable algorithms are described, analyzed, and simulated, which are asymptotically faster than O(nlogn), but require that the input weights to be partially sorted.