Self-Adjusting Data Structures is a class of data structures that dynamically adapt their configuration in response to access patterns, distinguished by their use of amortized analysis to guarantee efficient performance without prior knowledge of the input distribution.
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2017–2026
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Publications
13
Amortized Computational Complexity
Robert E. Tarjan · SIAM Journal on Algebraic and Discrete Methods · 1985 · 525 citations
The pairing heap: A new form of self-adjusting heap
Michael L. Fredman, Robert Sedgewick, Daniel D. Sleator et al. · Algorithmica · 1986 · 274 citations
Daniel D. Sleator, Robert E. Tarjan · SIAM Journal on Computing · 1986 · 153 citations
Daniel D. Sleator, Robert E. Tarjan · 1983 · 112 citations · Full text
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Authors
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- RE
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Institutions
12
AT&T (United States)
Bedminster, United States
2 h-index
3 publications
561 citations
Rutgers, The State University of New Jersey
New Brunswick, United States
2 h-index
2 publications
51 citations
University of Pisa
Pisa, Italy
2 h-index
5 publications
63 citations
University of Illinois Chicago
Chicago, United States
1 h-index
2 publications
32 citations
Princeton University
Princeton, United States
1 h-index
2 publications
548 citations
University of Waterloo
Waterloo, Canada
1 h-index
2 publications
20 citations
University of California San Diego
San Diego, United States
1 h-index
1 publications
274 citations
Alcatel Lucent (Germany)
Stuttgart, Germany
1 h-index
2 publications
306 citations
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Venues
- SIAM Journal on Computing
2 publications · 167 citations
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- Algorithmica
1 publications · 274 citations
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- Journal of the ACM
JACM · 1 publications · 39 citations
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- Software Practice and Experience
1 publications · 32 citations
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- ACM Transactions on Algorithms
TALG · 1 publications · 12 citations
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