Abstract. We introduce a framework for reducing the number of element comparisons performed in priority-queue operations. In particular, we give a priority queue which guarantees the worst-case cost of O(1) per minimum finding and insertion, and the worst-case cost of O(log n) with at most log n + O(1) element comparisons per minimum deletion and deletion, improving the bound of 2log n + O(1) on the number of element comparisons known for binomial queues. Here, n denotes the number of elements stored in the data structure prior to the operation in question, and log n equals max {1,log 2 n}. We also give a priority queue that provides, in addition to the above-mentioned methods, the priority-decrease (or decrease-key) method. This priority queue achieves the worst-case cost of O(1) per minimum finding, insertion, and priority decrease; and the worst-case cost of O(log n) with at most log n + O(log log n) element comparisons per minimum deletion and deletion. CR Classification. E.1 [Data Structures]: Lists, stacks, and queues; E.2 [Data

Abstract. In this paper we study the time-space complexity of implicit priority queues supporting the decreasekey operation. Our first result is that by using one extra word of storage it is possible to match the performance of Fibonacci heaps: constant amortized time for insert and decreasekey and logarithmic time for deletemin. Our second result is a lower bound showing that that one extra word really is necessary. We reduce the decreasekey operation to a cell-probe type game called the Usher's Problem, where one must maintain a simple data structure without the aid of any auxiliary storage.

Abstract. We study the performance of several alternatives for implementing extendible arrays, which allow random access to elements stored in them, whilst allowing the arrays to be grown and shrunk. The study not only looks at the basic operations of grow/shrink and accessing data, but also the effects of memory fragmentation on performance. 1