Summary of Optimus: Optimization Modeling Using Mip Solvers and Large Language Models, by Ali Ahmaditeshnizi et al.
OptiMUS: Optimization Modeling Using MIP Solvers and large language models
by Ali AhmadiTeshnizi, Wenzhi Gao, Madeleine Udell
First submitted to arxiv on: 9 Oct 2023
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Large Language Model (LLM)-based agent, OptiMUS, is introduced to formulate and solve Mixed Integer Linear Programming (MILP) problems from natural language descriptions. This agent can develop mathematical models, write solver code, create tests, and verify solution validity. To evaluate its performance, a novel dataset called NLP4LP, comprising linear programming (LP) and MILP problems, is presented. Experimental results show that OptiMUS outperforms a basic LLM prompting strategy by solving nearly twice as many problems. The code for OptiMUS and the NLP4LP dataset are available at this URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary OptiMUS is a special computer program that can understand and solve math problems written in words, like “Maximize profit by producing X units of Y.” This helps people who aren’t experts in math to use optimization tools. The program can make the math problem into an equation, write code to solve it, test its answer, and check if it’s correct. To see how well OptiMUS does, researchers created a dataset with many math problems. They found that OptiMUS is much better than a simpler approach at solving these problems. |
Keywords
» Artificial intelligence » Large language model » Optimization » Prompting
