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RSOME in Python: An Open-Source Package for Robust Stochastic Optimization Made Easy

Author

Listed:
  • Zhi Chen

    (Department of Management Sciences, College of Business, City University of Hong Kong, Kowloon Tong, Hong Kong)

  • Peng Xiong

    (Department of Analytics & Operations, NUS Business School, National University of Singapore, Singapore 119245)

Abstract

We introduce a Python package called RSOME for modeling a wide spectrum of robust and distributionally robust optimization problems. RSOME serves as an open-source framework for modeling various optimization problems subject to distributional ambiguity in a highly readable and mathematically intuitive manner. It is versatile and fits well in the open-source software community in the sense that (i) it is consistent with NumPy arrays in indexing and slicing and; (ii) together with the rich Python libraries for machine learning, data analysis, and visualization, it is easy to implement data-driven models; and (iii) it provides convenient interfaces for users to switch and tune parameters among different solvers.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:orijoc:v:35:y:2023:i:4:p:717-724
    DOI: 10.1287/ijoc.2023.1291
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    References listed on IDEAS

    as
    1. Zhi Chen & Melvyn Sim & Peng Xiong, 2020. "Robust Stochastic Optimization Made Easy with RSOME," Management Science, INFORMS, vol. 66(8), pages 3329-3339, August.
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