About RobustOPT

From uncertainty to a model you can solve.

RobustOPT helps you formulate, reformulate, implement and review optimization models under uncertainty.

Start with a problem description, or bring an existing mathematical model directly to Step 6. Review its reformulation, generate implementation code, solve a numerical instance, and inspect the results and report.

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Method 01

Memory-augmented LLM

Relevant reformulation experience is retrieved from memory and supplied to a large language model to help derive a tractable counterpart. You review the resulting mathematics before continuing to code and numerical solving.

This implementation adapts the AutoREM approach to RobustOPT’s workflow. It does not reproduce the paper’s experimental pipeline verbatim.

AutoREM paper

Method 02

RSOME

RSOME provides an algebraic modeling language and a compiler for supported robust and distributionally robust models. Begin with RSOME code you provide, or review code translated from your mathematical model.

The compiler produces a deterministic optimization representation. A separate numerical solver computes the solution; successful compilation and a successful solve are distinct results.

RSOME in Python paper

References

The bibliography requires the local reference script. The core papers are AutoREM (2026) and RSOME in Python (2023).