OptimAI: Optimization from Natural Language Using LLM-Powered AI Agents

• URL: http://arxiv.org/abs/2504.16918v1
• Date: Wed, 23 Apr 2025 17:45:05 GMT
• Title: OptimAI: Optimization from Natural Language Using LLM-Powered AI Agents
• Authors: Raghav Thind, Youran Sun, Ling Liang, Haizhao Yang,
• Abstract summary: We introduce textbfOptimAI, a framework for solving underlineOptimization problems described in natural language.<n>Our framework is built upon four key roles: (1) a emphformulator; (2) a emphplanner; and (3) a emphcoder and a emphcode critic.<n>Our approach attains 88.1% accuracy on the NLP4LP dataset and 71.2% on the Optibench subset, reducing error rates by 58% and 50% respectively over prior best results.
• Score: 8.441638148384389
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optimization plays a vital role in scientific research and practical applications, but formulating a concrete optimization problem described in natural language into a mathematical form and selecting a suitable solver to solve the problem requires substantial domain expertise. We introduce \textbf{OptimAI}, a framework for solving \underline{Optim}ization problems described in natural language by leveraging LLM-powered \underline{AI} agents, achieving superior performance over current state-of-the-art methods. Our framework is built upon four key roles: (1) a \emph{formulator} that translates natural language problem descriptions into precise mathematical formulations; (2) a \emph{planner} that constructs a high-level solution strategy prior to execution; and (3) a \emph{coder} and a \emph{code critic} capable of interacting with the environment and reflecting on outcomes to refine future actions. Ablation studies confirm that all roles are essential; removing the planner or code critic results in $5.8\times$ and $3.1\times$ drops in productivity, respectively. Furthermore, we introduce UCB-based debug scheduling to dynamically switch between alternative plans, yielding an additional $3.3\times$ productivity gain. Our design emphasizes multi-agent collaboration, allowing us to conveniently explore the synergistic effect of combining diverse models within a unified system. Our approach attains 88.1\% accuracy on the NLP4LP dataset and 71.2\% on the Optibench (non-linear w/o table) subset, reducing error rates by 58\% and 50\% respectively over prior best results.

Related papers

• Autoformulation of Mathematical Optimization Models Using LLMs [50.030647274271516]
  We develop an automated approach to creating optimization models from natural language descriptions for commercial solvers. We identify the three core challenges of autoformulation: (1) defining the vast, problem-dependent hypothesis space, (2) efficiently searching this space under uncertainty, and (3) evaluating formulation correctness.
  arXiv  Detail & Related papers  (2024-11-03T20:41:38Z)
• LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch [16.174567164068037]
  We propose a unified learning-based framework called LLMOPT to boost optimization generalization.<n>LLMOPT constructs the introduced five-element formulation as a universal model for learning to define diverse optimization problem types.<n>LLMOPT is able to model various optimization problem types such as linear/nonlinear programming mixed integer programming.
  arXiv  Detail & Related papers  (2024-10-17T04:37:37Z)
• Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System [75.25394449773052]
  Large Language Model (LLM) based multi-agent systems (MAS) show remarkable potential in collaborative problem-solving. Yet they still face critical challenges: low communication efficiency, poor scalability, and a lack of effective parameter-updating optimization methods. We present Optima, a novel framework that addresses these issues by significantly enhancing both communication efficiency and task effectiveness.
  arXiv  Detail & Related papers  (2024-10-10T17:00:06Z)
• OptiBench Meets ReSocratic: Measure and Improve LLMs for Optimization Modeling [62.19438812624467]
  Large language models (LLMs) have exhibited their problem-solving abilities in mathematical reasoning. We propose OptiBench, a benchmark for End-to-end optimization problem-solving with human-readable inputs and outputs.
  arXiv  Detail & Related papers  (2024-07-13T13:27:57Z)
• Solving General Natural-Language-Description Optimization Problems with Large Language Models [34.50671063271608]
  We propose a novel framework called OptLLM that augments LLMs with external solvers. OptLLM accepts user queries in natural language, convert them into mathematical formulations and programming codes, and calls the solvers to calculate the results. Some features of OptLLM framework have been available for trial since June 2023.
  arXiv  Detail & Related papers  (2024-07-09T07:11:10Z)
• LLM as a Complementary Optimizer to Gradient Descent: A Case Study in Prompt Tuning [69.95292905263393]
  We show that gradient-based and high-level LLMs can effectively collaborate a combined optimization framework. In this paper, we show that these complementary to each other and can effectively collaborate a combined optimization framework.
  arXiv  Detail & Related papers  (2024-05-30T06:24:14Z)
• Large Language Models as Optimizers [106.52386531624532]
  We propose Optimization by PROmpting (OPRO), a simple and effective approach to leverage large language models (LLMs) as prompts. In each optimization step, the LLM generates new solutions from the prompt that contains previously generated solutions with their values. We demonstrate that the best prompts optimized by OPRO outperform human-designed prompts by up to 8% on GSM8K, and by up to 50% on Big-Bench Hard tasks.
  arXiv  Detail & Related papers  (2023-09-07T00:07:15Z)
• NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions [19.01388243205877]
  The goal of the competition is to increase the accessibility and usability of optimization solvers by allowing non-experts to interface with them using natural language. We present the LP word problem dataset and shared tasks for the NeurIPS 2022 competition.
  arXiv  Detail & Related papers  (2023-03-14T20:59:04Z)
• A Novel Approach for Auto-Formulation of Optimization Problems [66.94228200699997]
  In the Natural Language for Optimization (NL4Opt) NeurIPS 2022 competition, competitors focus on improving the accessibility and usability of optimization solvers. In this paper, we present the solution of our team. Our proposed methods have achieved the F1-score of 0.931 in subtask 1 and the accuracy of 0.867 in subtask 2, which won the fourth and third places respectively in this competition.
  arXiv  Detail & Related papers  (2023-02-09T13:57:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.