  Optimization problems are pervasive in sectors from manufacturing and
distribution to healthcare. However, most such problems are still solved
heuristically by hand rather than optimally by state-of-the art solvers because
the expertise required to formulate and solve these problems limits the
widespread adoption of optimization tools and techniques. We introduce a Large
Language Model (LLM)-based system designed to formulate and solve (mixed
integer) linear programming problems from their natural language descriptions.
Our system is capable of developing mathematical models, writing and debugging
solver code, evaluating the generated solutions, and improving efficiency and
correctness of its model and code based on these evaluations. OptiMUS-0.3
utilizes a modular structure to process problems, allowing it to handle
problems with long descriptions and complex data without long prompts.
Experiments demonstrate that OptiMUS-0.3 outperforms existing state-of-the-art
methods on easy datasets by more than 12% and on hard datasets (including a new
dataset, NLP4LP, released with this paper that features long and complex
problems) by more than 8%.
