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Table of Contents
1. INTRODUCTION
2. OPRO: LLM AS THE OPTIMIZER
3. MOTIVATING EXAMPLE: MATHEMATICAL OPTIMIZATION
3.1 LINEAR REGRESSION
3.2 TRAVELING SALESMAN PROBLEM (TSP)
Summary
Large Language Models as Optimizers presents Optimization by PROmpting (OPRO), a simple and effective approach that leverages large language models (LLMs) as optimizers by describing optimization tasks in natural language. The paper showcases OPRO on various optimization problems, demonstrating the ability of LLMs to generate new solutions based on prompts and past solutions. The potential of LLMs for optimization is highlighted through case studies on linear regression and the Traveling Salesman Problem (TSP), showing promising results in both continuous and discrete optimization tasks.