Robust and Adaptive Optimization Under a Large Language Model Lens

Abstract

We present a novel approach to robust and adaptive optimization that leverages large language models (LLMs) to enhance decision-making under uncertainty. Our framework combines traditional robust optimization techniques with the natural language processing capabilities of LLMs to provide more interpretable and flexible solutions to complex optimization problems. We demonstrate the effectiveness of our approach on several benchmark problems and real-world applications.