Abstract. Two new ways of transforming a priority queue into a double-ended priority queue are introduced. These methods can be used to improve all known bounds for the comparison complexity of double-ended priority-queue operations. Using an efficient priority queue, the first transformation can produce a doubleended priority queue which guarantees the worst-case cost of O(1) for find-min, find-max, and insert; and the worst-case cost of O(lg n) including at most lg n + O(1) element comparisons for delete, but the data structure cannot support meld efficiently. Using a meldable priority queue that supports decrease efficiently, the second transformation can produce a meldable double-ended priority queue which guarantees the worst-case cost of O(1) for find-min, find-max, and insert; the worst-case cost of O(lg n) including at most lg n + O(lg lg n) element comparisons for delete; and the worst-case cost of O(min {lg m, lg n}) for meld. Here, m and n denote the number of elements stored in the data structures prior to the operation in question, and lg n is a shorthand for log 2 (max {2, n}). 1.

The Fibonacci heap was devised to provide an especially efficient implementation of Dijkstra’s shortest path algorithm. Although asyptotically efficient, it is not as fast in practice as other heap implementations. Expanding on ideas of Høyer, we describe three heap implementations (two versions of thin heaps and one of thick heaps) that have the same amortized efficiency as Fibonacci heaps but need less space and promise better practical performance. As part of our development, we fill in a gap in Høyer’s analysis.

In this paper we present a simple and e#cient implementation of a min-max priority queue, reflected min-max priority queues. The main merits of our construction are threefold. First, the space utilization of the reflected min-max heaps is much better than the naive solution of putting two heaps back-to-back. Second, the methods applied in this structure can be easily used to transform ordinary priority queues into min-max priority queues. Third, when considering only the setting of min-max priority queues, we support merging in constant worst-case time which is a clear improvement over the best worst-case bounds achieved by Hyer.

Abstract. We introduce the rank-pairing heap, a heap (priority queue) implementation that combines the asymptotic efficiency of Fibonacci heaps with much of the simplicity of pairing heaps. Unlike all other heap implementations that match the bounds of Fibonacci heaps, our structure needs only one cut and no other structural changes per key decrease; the trees representing the heap can evolve to have arbitrary structure. Our initial experiments indicate that rankpairing heaps perform almost as well as pairing heaps on typical input sequences and better on worst-case sequences. 1

Wepresentthefirstpointer-basedheapimplementationwith time bounds matching those of Fibonacci heaps in the worst case. We support make-heap, insert, find-min, meld and decrease-key in worst-case O(1) time, and delete and deletemin in worst-case O(lgn) time, where n is the size of the heap. The data structure uses linear space. A previous, very complicated, solution achieving the same time bounds in the RAM model made essential use of arrays and extensive use of redundant counter schemes to maintain balance. Our solution uses neither. Our key simplification is to discard the structure of the smaller heap when doing a meld. We use the pigeonhole principle in place of the redundant counter mechanism.