Min-max heaps and generalized priority queues
Communications of the ACM
SIAM Journal on Computing
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
A note on the construction of data structure “deap”
Information Processing Letters
Compared to what?: an introduction to the analysis of algorithms
Compared to what?: an introduction to the analysis of algorithms
Information and Computation
The influence of caches on the performance of heaps
Journal of Experimental Algorithmics (JEA)
Theoretical Computer Science
Selection from read-only memory and sorting with minimum data movement
Theoretical Computer Science
The influence of caches on the performance of sorting
Journal of Algorithms
Performance engineering case study: heap construction
Journal of Experimental Algorithmics (JEA)
The Art of Computer Programming Volumes 1-3 Boxed Set
The Art of Computer Programming Volumes 1-3 Boxed Set
Sorting and Searching on the Word RAM
STACS '98 Proceedings of the 15th Annual Symposium on Theoretical Aspects of Computer Science
Optimal Time-Space Trade-Offs for Sorting
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Implementing HEAPSORT with (n logn - 0.9n) and QUICKSORT with (n logn + 0.2n) comparisons
Journal of Experimental Algorithmics (JEA)
An In-Place Sorting with O(n log n) Comparisons and O(n) Moves
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
ACM Transactions on Algorithms (TALG)
In-place heap construction with optimized comparisons, moves, and cache misses
MFCS'12 Proceedings of the 37th international conference on Mathematical Foundations of Computer Science
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A data structure, named a navigation pile, is described and exploited in the implementation of a sorting algorithm, a priority queue, and a priority deque. When carrying out these tasks, a linear number of bits is used in addition to the elements manipulated, and extra space for a sublinear number of elements is allocated if the grow and shrink operations are to be supported. Our viewpoint is to allow little extra space, make a low number of element moves, and still keep the efficiency in the number of element comparisons and machine instructions. In spite of low memory consumption, the worst-case bounds for the number of element comparisons, element moves, and machine instructions are close to the absolute minimum.