Min-max heaps and generalized priority queues
Communications of the ACM
Fibonacci heaps and their uses in improved network optimization algorithms
Journal of the ACM (JACM)
The Deap—A double-ended heap to implement double-ended priority queues
Information Processing Letters
Relaxed heaps: an alternative to Fibonacci heaps with applications to parallel computation
Communications of the ACM
Data-structural bootstrapping and catenable deques
Data-structural bootstrapping and catenable deques
A generalization of binomial queues
Information Processing Letters
Worst-case efficient priority queues
Proceedings of the seventh annual ACM-SIAM symposium on Discrete algorithms
A data structure for manipulating priority queues
Communications of the ACM
ISAAC '93 Proceedings of the 4th International Symposium on Algorithms and Computation
WADS '95 Proceedings of the 4th International Workshop on Algorithms and Data Structures
A Note on Worst Case Efficient Meldable Priority Queues
A Note on Worst Case Efficient Meldable Priority Queues
A general technique for implementation of efficient priority queues
ISTCS '95 Proceedings of the 3rd Israel Symposium on the Theory of Computing Systems (ISTCS'95)
Multidimensional heaps and complementary range searching
Information Processing Letters
On self-stabilizing search trees
DISC'06 Proceedings of the 20th international conference on Distributed Computing
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In this paper we present a simple and efficient 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 Høyer.