OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models
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. This paper introduces OptiMUS, a Large Language Model (LLM)-based agent designed to formulate and solve (mixed integer) linear programming problems from their natural language descriptions. OptiMUS can develop mathematical models, write and debug solver code, evaluate the generated solutions, and improve its model and code based on these evaluations. OptiMUS 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 outperforms existing state-of-the-art methods on easy datasets by more than $20\%$ and on hard datasets (including a new dataset, NLP4LP, released with this paper that features long and complex problems) by more than $30\%$.
Code (1)
Tasks
Language ModelingLanguage ModellingLarge Language ModelSimilar Papers 제목 키워드 기반
OptiMUS: Optimization Modeling Using MIP Solvers and large language models
Optimization problems are pervasive across various 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 …
Language ModelingLanguage ModellingLarge Language ModelOptiMUS-0.3: Using Large Language Models to Model and Solve Optimization Problems at Scale
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 becau…
Language ModelingLanguage ModellingLarge Language ModelAn Efficient 2D Method for Training Super-Large Deep Learning Models
Huge neural network models have shown unprecedented performance in real-world applications. However, due to memory constraints, model parallelism must be utilized to host large models that would otherwise not fit into th…
Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space
When trained effectively, the Variational Autoencoder (VAE) can be both a powerful generative model and an effective representation learning framework for natural language. In this paper, we propose the first large-scale…
Language ModelingLanguage ModellingRepresentation LearningText GenerationOptimus-3: Towards Generalist Multimodal Minecraft Agents with Scalable Task Experts
Recently, agents based on multimodal large language models (MLLMs) have achieved remarkable progress across various domains. However, building a generalist agent with capabilities such as perception, planning, action, gr…
DiversityMinecraftMixture-of-ExpertsMultimodal Reasoning