Memoization
Memoization is a computational optimization technique within functional programming that belongs to the class of caching strategies, distinguished by its specific application of storing and reusing the results of pure function calls based on their input arguments to avoid redundant computation.
97
Publications
3.6K
Citations
225
Authors
104
Institutions
Publications per year
2017–2026
20
Parents
97
“Memo” Functions and Machine Learning
Donald Michie · Nature · 1968 · 554 citations
Proving acceptability properties of relaxed nondeterministic approximate programs
Michael Carbin, Deokhwan Kim, Saša Misailovíc et al. · 2012 · 104 citations · Full text
Umut A. Acar, Guy E. Blelloch, Robert Harper · 2003 · 99 citations
1–5 of 97
225
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Johns Hopkins University Applied Physics Laboratory, United States
3 publications · 54 citations
3
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3
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3
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1–5 of 225
104
Carnegie Mellon University
Pittsburgh, United States
7 h-index
18 publications
880 citations
Stanford University
Stanford, United States
3 h-index
6 publications
477 citations
Purdue University West Lafayette
West Lafayette, United States
3 h-index
7 publications
223 citations

Aarhus University
Aarhus, Denmark
3 h-index
8 publications
176 citations
Universitat Politècnica de Catalunya
Barcelona, Spain
3 h-index
12 publications
228 citations
Massachusetts Institute of Technology
Cambridge, United States
3 h-index
10 publications
512 citations
ETH Zurich
Zurich, Switzerland
3 h-index
4 publications
161 citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
2 h-index
6 publications
188 citations
1–8 of 104
5 publications · 124 citations
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3 publications · 124 citations
3
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TKDE · 2 publications · 58 citations
2
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2 publications · 613 citations
2
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Book series · 1 publications · 20 citations
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