Journal ArticleDOI
Improvements in double ended priority queues
TLDR
Experimental results show that, in the average case, with the exception of creation phase data movement, the algorithm outperforms min-max heap of Strothotte in all other aspects.Abstract:
In this paper, we present improved algorithms for min–max pair heaps introduced by S. Olariu et al. (A Mergeable Double-ended Priority Queue – The Comp. J. 34, 423–427, 1991). We also show that in the worst case, this structure, though slightly costlier to create, is better than min–max heaps of Strothotte (Min–max Heaps and Generalized Priority Queues – CACM, 29(10), 996–1000, Oct, 1986) in respect of deletion, and is equally good for insertion when an improved technique using binary search is applied. Experimental results show that, in the average case, with the exception of creation phase data movement, our algorithm outperforms min–max heap of Strothotte in all other aspects.read more
Citations
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Journal ArticleDOI
Two new methods for constructing double-ended priority queues from priority queues
TL;DR: A meldable double-ended priority queue is obtained which guarantees the worst-case cost of O(1) for find-min, find-max, insert, extract; the best- case cost of Lg n + O(lg lg n) element comparisons for delete; and the Worst-Case cost ofO(min {lg m, lgN}) for meld.
Two new methods for transforming priority queues into double-ended priority queues
TL;DR: Two new ways of transforming a priority queue into a double-ended priority queue are introduced and can be used to improve all known bounds for the comparison complexity of double- ended priority-queue operations.
Journal ArticleDOI
The bounds of min-max pair heap construction☆
TL;DR: It has been shown that the construction of a min-max pair heap with n elements requires at least 2.07 n element comparisons, and a new algorithm has been devised that lowers the upper bound to 2.43 n.
References
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Book
The Design and Analysis of Computer Algorithms
Alfred V. Aho,John E. Hopcroft +1 more
TL;DR: This text introduces the basic data structures and programming techniques often used in efficient algorithms, and covers use of lists, push-down stacks, queues, trees, and graphs.
Journal ArticleDOI
Min-max heaps and generalized priority queues
TL;DR: The proposed structure, called a min-max heap, can be built in linear time and can be generalized to support other similar order-statistics operations efficiently (e.g., constant time and logarithmic time).