使用大语言模型诊断不可行的优化问题
Diagnosing Infeasible Optimization Problems Using Large Language Models
AI总结：
针对优化模型不可行时诊断难度高、需专业背景的问题，提出基于GPT-4、结合优化求解器与多种提示技术的OptiChat系统，可交互诊断不可行来源并给出改进建议，提升不同用户的模型理解效率。
摘要：
决策问题可被表示为数学优化模型，在经济、工程与制造、交通、医疗等领域有着广泛应用。优化模型是对在满足一系列要求或约束的前提下做出最优决策这一问题的数学抽象。在实际部署这些模型的主要障碍中，难点在于帮助从业者理解和解读这类模型，尤其是当模型不可行——即不存在能满足所有约束的决策——的时候。现有的诊断不可行优化模型的方法通常依赖专家系统，需要使用者具备大量优化领域的背景知识。\n本文提出了OptiChat，这是首个基于自然语言的系统，配备聊天机器人图形用户界面（GUI），可围绕不可行优化模型开展交互式对话。OptiChat能提供优化模型本身的自然语言描述，识别不可行性的潜在来源，并给出让模型变得可行的建议。OptiChat基于GPT-4实现，它与优化求解器对接，以识别导致整个优化问题不可行的最小约束子集，也就是不可约不可行子集（Irreducible Infeasible Subset, IIS）。我们利用少样本学习、专家思维链、关键信息检索和情感提示词来提升OptiChat的可靠性。实验表明，OptiChat能帮助专家和非专家用户提升对优化模型的理解，让他们能够快速定位不可行性的来源。
英文摘要：
Decision-making problems can be represented as mathematical optimization models, finding wide applications in fields such as economics, engineering and manufacturing, transportation, and health care. Optimization models are mathematical abstractions of the problem of making the best decision while satisfying a set of requirements or constraints. One of the primary barriers to deploying these models in practice is the challenge of helping practitioners understand and interpret such models, particularly when they are infeasible, meaning no decision satisfies all the constraints. Existing methods for diagnosing infeasible optimization models often rely on expert systems, necessitating significant background knowledge in optimization. In this paper, we introduce OptiChat, a first-of-its-kind natural language-based system equipped with a chatbot GUI for engaging in interactive conversations about infeasible optimization models. OptiChat can provide natural language descriptions of the optimization model itself, identify potential sources of infeasibility, and offer suggestions to make the model feasible. The implementation of OptiChat is built on GPT-4, which interfaces with an optimization solver to identify the minimal subset of constraints that render the entire optimization problem infeasible, also known as the Irreducible Infeasible Subset (IIS). We utilize few-shot learning, expert chain-of-thought, key-retrieve, and sentiment prompts to enhance OptiChat's reliability. Our experiments demonstrate that OptiChat assists both expert and non-expert users in improving their understanding of the optimization models, enabling them to quickly identify the sources of infeasibility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
