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. History: Ted Ralphs, Area Editor for Software Tools. Funding: The research of Z. Chen is funded by the Strategic Research Grant [Project 7005792] from the City University of Hong Kong. The research of P. Xiong is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call [Grant MOE-2019-T3-1-010]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1291 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0146 ) at ( http://dx.doi.org/10.5281/zenodo.7463845 ).
Published Version
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