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RSOME is now available in Python!
Click to check the depository and the paper.

RSOME (Robust Stochastic Optimization Made Easy) is a MATLAB algebraic toolbox designed for generic optimization modeling under uncertainty.
Based on the robust stochastic optimization (RSO) framework proposed by Chen, Sim, Xiong (2020), RSOME unifies a wide variety of approaches for optimization under uncertainty, including the traditional scenario-tree based stochastic linear optimization, classical robust optimization as well as the emerging distributionally robust optimization that considers state-of-the-art data-driven ambiguity sets.

RSOME is consistent with the standard MATLAB syntax and data structure, and it is friendly to users familiar with MATLAB. It provides a powerful interface to specify optimization problems for both static and dynamic decision making under uncertainty. RSOME automatically transforms these optimization problems into their equivalent deterministic counterparts in forms of linear, second-order cone, or mixed-integer programs, and they are solved by commercial solvers such as CPLEX, Gurobi, and MOSEK.

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