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

Zhi Chen () and Peng Xiong ()
Additional contact information
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

INFORMS Journal on Computing, 2023, vol. 35, issue 4, 717-724

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.

Keywords: (distributionally) robust optimization; algebraic modeling package; adaptive decision making; data-driven analytics (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://dx.doi.org/10.1287/ijoc.2023.1291 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:35:y:2023:i:4:p:717-724

Access Statistics for this article

More articles in INFORMS Journal on Computing from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().