rsome 1.3.1

Robust Stochastic Optimization Made Easy

Robust Stochastic Optimization Made Easy

• Website: RSOME for Python
• PyPI: RSOME 1.3.1

RSOME (Robust Stochastic Optimization Made Easy) is an open-source Python package for generic modeling of optimization problems (subject to uncertainty). Models in RSOME are constructed by variables, constraints, and expressions that are formatted as N-dimensional arrays. These arrays are consistent with the NumPy library in terms of syntax and operations, including broadcasting, indexing, slicing, element-wise operations, and matrix calculation rules, among others. In short, RSOME provides a convenient platform to facilitate developments of robust optimization models and their applications.

Content

• Installation
• Solver interfaces
• Getting started
• Team
• Citation

Installation

The RSOME package can be installed by using the pip command:

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pip install rsome

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Solver interfaces

The RSOME package transforms robust or distributionally robust optimization models into deterministic linear or conic programming problems, and solved by external solvers. Details of compatible solvers and their interfaces are presented in the following table.

Solver	License type	Required version	RSOME interface	Second-order cone constraints	Exponential cone constraints	Semidefiniteness constraints
scipy.optimize	Open-source	>= 1.9.0	lpg_solver	No	No	No
CyLP	Open-source	>= 0.9.0	clp_solver	No	No	No
OR-Tools	Open-source	>= 7.5.7466	ort_solver	No	No	No
ECOS	Open-source	>= 2.0.10	eco_solver	Yes	Yes	No
Gurobi	Commercial	>= 9.1.0	grb_solver	Yes	No	No
Mosek	Commercial	>= 10.0.44	msk_solver	Yes	Yes	Yes
CPLEX	Commercial	>= 12.9.0.0	cpx_solver	Yes	No	No
COPT	Commercial	>= 7.0.3	cpt_solver	Yes	No	Yes

Getting started

Documents of RSOME are provided as follows:

• RSOME quick start
• RSOME users guide
• Application examples

Team

RSOME is a software project supported by Singapore Ministry of Education Tier 3 Grant Science of Prescriptive Analytics. It is primarly developed and maintained by Zhi Chen, Melvyn Sim, and Peng Xiong. Many other researchers, including Erick Delage, Zhaowei Hao, Long He, Zhenyu Hu, Jun Jiang, Brad Sturt, Qinshen Tang, as well as anonymous users and paper reviewers, have helped greatly in the way of developing RSOME.

Citation

If you use RSOME in your research, please cite our papers:

• Chen, Zhi, and Peng Xiong. 2023. RSOME in Python: an open-source package for robust stochastic optimization made easy. INFORMS Journal on Computing 35(4) 717-724.

• Chen, Zhi, Melvyn Sim, Peng Xiong. 2020. Robust stochastic optimization made easy with RSOME. Management Science 66(8) 3329-3339.

Bibtex entry:

@article{chen2021rsome,
  title={{RSOME} in {Python}: An open-source package for robust stochastic optimization made easy},
  author={Chen, Zhi and Xiong, Peng},
  journal={INFORMS Journal of Computing},
  volume={35},
  number={4},
  pages={717--724},
  year = {2023},
  publisher={INFORMS}
}

@article{chen2020robust,
  title={Robust stochastic optimization made easy with RSOME},
  author={Chen, Zhi and Sim, Melvyn and Xiong, Peng},
  journal={Management Science},
  volume={66},
  number={8},
  pages={3329--3339},
  year={2020},
  publisher={INFORMS}
}