Dynamic Programming Principles
Dynamic Programming Principles is a theoretical framework in dynamic optimization that distinguishes itself by decomposing sequential decision problems into recursively solvable subproblems through the systematic application of Bellman’s principle of optimality.
1K
Publications
72.2K
Citations
2.2K
Authors
849
Institutions
Publications per year
2017–2026
125
1K
A Greedy Algorithm for Aligning DNA Sequences
Zheng Zhang, Scott Schwartz, Lukas Wagner et al. · Journal of Computational Biology · 2000 · 5.6K citations
Automated generation of heuristics for biological sequence comparison
Guy Slater, Ewan Birney · BMC Bioinformatics · 2005 · 3.2K citations · Full text
1–5 of 1K
2.2K
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6
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5
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Massachusetts Institute of Technology, United States
5 publications · 1.4K citations
5
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1–5 of 2.2K
849
University of Southern California
Los Angeles, United States
15 h-index
39 publications
3.7K citations

Cornell University
Ithaca, United States
14 h-index
34 publications
1.3K citations
RAND Corporation
Santa Monica, United States
14 h-index
21 publications
2.5K citations
Massachusetts Institute of Technology
Cambridge, United States
13 h-index
19 publications
1.6K citations
Stanford University
Stanford, United States
13 h-index
23 publications
1.8K citations
University of California, Berkeley
Berkeley, United States
12 h-index
19 publications
953 citations
Purdue University West Lafayette
West Lafayette, United States
11 h-index
20 publications
801 citations

Chinese Academy of Sciences
Beijing, China
11 h-index
20 publications
1.6K citations
1–8 of 849
32 publications · 1.8K citations
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22 publications · 1.5K citations
17
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OR · 24 publications · 1.5K citations
17
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26 publications · 835 citations
15
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17 publications · 2.4K citations
14
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