Efficiency of Self-Adjusting Heaps

Since the invention of the pairing heap by Fredman, Sedgewick, Sleator, and Tarjan [ 8 ], it has been an open question whether this or any other simple “self-adjusting” heap supports decrease-key operations in \(\mathrm{O}(\log\log n)\) time, where \(n\) is the number of heap items. Using powerful new techniques, we answer this question in the affirmative. We prove that both slim and smooth heaps, recently introduced self-adjusting heaps, support heap operations in the following amortized time bounds: \(\mathrm{O}(\log n)\) for delete-min and delete, \(\mathrm{O}(\log\log n)\) for decrease-key, and \(\mathrm{O}(1)\) for all other heap operations, including insert and meld, where \(n\) is the number of heap items that are eventually deleted: Items inserted but never deleted do not count in the bounds. We also analyze the multipass pairing heap, a variant of pairing heaps. For this heap implementation, we obtain the same bounds except for decrease-key, for which our bound is \(\mathrm{O}(\log\log n\cdot\log\log\log n)\) , where again items that are never deleted do not count in \(n\) . Our bounds significantly improve the best previously known bounds for all three data structures. For slim and smooth heaps our bounds are tight, since they match lower bounds of Iacono and Özkan [ 13 ].

Authors

Institutions

Publication Details

Journal
ACM Transactions on Algorithms
Published
2024-12-23
DOI
https://doi.org/10.1145/3708989
Citations
1
Primary Topic
Computational Geometry and Mesh Generation
Type
article
Field-Weighted Citation Impact
0.63

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Efficiency of Self-Adjusting Heaps

Robert E. Tarjan, Corwin Sinnamon
1 citations
ACM Transactions on Algorithms
Computational Geometry and Mesh Generation
0.63
article

Efficiency of Self-Adjusting Heaps

Robert E. Tarjan, Corwin Sinnamon
article en
1 citations

Abstract

Since the invention of the pairing heap by Fredman, Sedgewick, Sleator, and Tarjan [ 8 ], it has been an open question whether this or any other simple “self-adjusting” heap supports decrease-key operations in \(\mathrm{O}(\log\log n)\) time, where \(n\) is the number of heap items. Using powerful new techniques, we answer this question in the affirmative. We prove that both slim and smooth heaps, recently introduced self-adjusting heaps, support heap operations in the following amortized time bounds: \(\mathrm{O}(\log n)\) for delete-min and delete, \(\mathrm{O}(\log\log n)\) for decrease-key, and \(\mathrm{O}(1)\) for all other heap operations, including insert and meld, where \(n\) is the number of heap items that are eventually deleted: Items inserted but never deleted do not count in the bounds. We also analyze the multipass pairing heap, a variant of pairing heaps. For this heap implementation, we obtain the same bounds except for decrease-key, for which our bound is \(\mathrm{O}(\log\log n\cdot\log\log\log n)\) , where again items that are never deleted do not count in \(n\) . Our bounds significantly improve the best previously known bounds for all three data structures. For slim and smooth heaps our bounds are tight, since they match lower bounds of Iacono and Özkan [ 13 ].

ACM Transactions on AlgorithmsVol. 21(4)
Princeton University (US)
Simons Institute for the Theory of Computing, University of California Berkeley
Openalex Percentile: Top 17%
Computational Geometry and Mesh Generation
0.63
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.