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SDDP.jl : A Julia Package for Stochastic Dual Dynamic Programming

Author

Listed:
  • Oscar Dowson

    (Department of Industrial Engineering and Management Sciences, McCormick School of Engineering, Northwestern University, Evanston, Illinois 60208)

  • Lea Kapelevich

    (Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

Abstract

We present SDDP.jl , an open-source library for solving multistage stochastic programming problems using the stochastic dual dynamic programming algorithm. SDDP.jl is built on JuMP, an algebraic modeling language in Julia. JuMP provides SDDP.jl with a solver-agnostic, user-friendly interface. In addition, we leverage unique features of Julia, such as multiple dispatch, to provide an extensible framework for practitioners to build on our work. SDDP.jl is well tested, and accessible documentation is available at https://github.com/odow/SDDP.jl .

Suggested Citation

  • Oscar Dowson & Lea Kapelevich, 2021. "SDDP.jl : A Julia Package for Stochastic Dual Dynamic Programming," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 27-33, January.
    • Handle: RePEc:inm:orijoc:v:33:y:2021:i:1:p:27-33
    DOI: 10.1287/ijoc.2020.0987
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    References listed on IDEAS

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    Cited by:

    1. Haoxiang Yang & Harsha Nagarajan, 2022. "Optimal Power Flow in Distribution Networks Under N – 1 Disruptions: A Multistage Stochastic Programming Approach," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 690-709, March.
    2. Joaquim Dias Garcia & Guilherme Bodin & Alexandre Street, 2024. "BilevelJuMP.jl: Modeling and Solving Bilevel Optimization Problems in Julia," INFORMS Journal on Computing, INFORMS, vol. 36(2), pages 327-335, March.
    3. Arnab Bhattacharya & Jeffrey P. Kharoufeh & Bo Zeng, 2023. "A Nonconvex Regularization Scheme for the Stochastic Dual Dynamic Programming Algorithm," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1161-1178, September.
    4. Zhi Chen & Peng Xiong, 2023. "RSOME in Python: An Open-Source Package for Robust Stochastic Optimization Made Easy," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 717-724, July.
    5. Zhong, Zhiming & Fan, Neng & Wu, Lei, 2024. "Multistage Stochastic optimization for mid-term integrated generation and maintenance scheduling of cascaded hydroelectric system with renewable energy uncertainty," European Journal of Operational Research, Elsevier, vol. 318(1), pages 179-199.
    6. Martin Biel & Mikael Johansson, 2022. "Efficient Stochastic Programming in Julia," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 1885-1902, July.
    7. Phebe Vayanos & Qing Jin & George Elissaios, 2022. "ROC++: Robust Optimization in C++," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 2873-2888, November.
    8. Navarro, Andrés & Favereau, Marcel & Lorca, Álvaro & Olivares, Daniel & Negrete-Pincetic, Matías, 2024. "Medium-term stochastic hydrothermal scheduling with short-term operational effects for large-scale power and water networks," Applied Energy, Elsevier, vol. 358(C).
    9. D. Ávila & A. Papavasiliou & N. Löhndorf, 2022. "Parallel and distributed computing for stochastic dual dynamic programming," Computational Management Science, Springer, vol. 19(2), pages 199-226, June.

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