Continuous-time Dynamic Programming
Continuous-Time Dynamic Programming is a mathematical framework within dynamic programming that distinguishes itself by optimizing state trajectories over continuous time intervals using differential equations and Hamilton-Jacobi-Bellman equations, as opposed to discrete time steps.
119
Publications
9.6K
Citations
233
Authors
147
Institutions
Publications per year
2017–2026
22
Parents
119
Dynamic Programming and Markov Processes.
H. Kaufman, Ronald A. Howard · American Mathematical Monthly · 1961 · 648 citations
Bolin Liao, Yunong Zhang, Long Jin · IEEE Transactions on Neural Networks and Learning Systems · 2015 · 190 citations
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147

Imperial College London
London, United Kingdom
5 h-index
6 publications
319 citations
Georgia Institute of Technology
Atlanta, United States
4 h-index
11 publications
390 citations
Robotics Research (United States)
Cincinnati, United States
4 h-index
8 publications
366 citations
RAND Corporation
Santa Monica, United States
4 h-index
5 publications
438 citations

Centre National de la Recherche Scientifique
Paris, France
4 h-index
4 publications
153 citations
The University of Texas at Arlington
Arlington, United States
4 h-index
7 publications
336 citations

University of Liverpool
Liverpool, United Kingdom
3 h-index
4 publications
159 citations

Harbin Institute of Technology
Harbin, China
2 h-index
5 publications
210 citations
1–8 of 147
8 publications · 410 citations
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OR · 5 publications · 705 citations
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5 publications · 163 citations
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4 publications · 139 citations
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3 publications · 86 citations
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