Discrete-time Dynamic Programming

Discrete-Time Dynamic Programming is a mathematical optimization method that distinguishes itself by solving sequential decision-making problems through recursive value functions defined over discrete time intervals, thereby enabling optimal policy computation in systems with discrete state and action spaces.

159

Publications

23.3K

Citations

307

Authors

171

Institutions

Publications per year

2017–2026

33

Authors

307

  1. QW
    Qinglai Wei

    Chinese Academy of Sciences, China

    15 publications · 1.4K citations

    15

    h-index

  2. DL
    Derong Liu

    Chinese Academy of Sciences, China

    13 publications · 1.3K citations

    12

    h-index

  3. RS
    Ruizhuo Song

    University of Science and Technology Beijing, China

    6 publications · 624 citations

    6

    h-index

  4. EV
    Eric V. Denardo

    Yale University, United States

    4 publications · 680 citations

    4

    h-index

  5. FL
    Frank L. Lewis

    The University of Texas at Arlington, United States

    3 publications · 282 citations

    3

    h-index

1–5 of 307

Venues

  1. IEEE Transactions on Automatic Control

    9 publications · 628 citations

    9

    h-index

  2. 7

    h-index

  3. The Annals of Mathematical Statistics

    6 publications · 1.4K citations

    6

    h-index

  4. Mathematics of Operations Research

    5 publications · 710 citations

    5

    h-index

  5. IEEE Transactions on Cybernetics

    4 publications · 529 citations

    4

    h-index

1–5