A half-day tutorial surveying how large language models are reshaping mathematical optimization — translating natural-language problems into formal models, configuring solvers, and discovering new algorithms. Designed for a broad AI audience, with no prior optimization expertise required.
Mathematical optimization is a foundational pillar of modern AI, underpinning decision-making in supply chains, energy systems, finance, and scheduling. Despite its importance, building and deploying optimization models remains a challenging, expert-driven process that requires significant domain knowledge and technical expertise.
This tutorial surveys the emerging interface between LLMs and optimization along two synergistic themes. First, we examine how LLMs can act as copilots across the optimization pipeline — assisting with problem formulation, model construction, solver configuration, and validation. Second, we explore the growing role of LLMs in algorithmic discovery, generating, refining, and discovering new optimization algorithms and heuristics.
The tutorial bridges machine learning and optimization, covering foundational concepts, surveying state-of-the-art methods and systems, and highlighting key challenges such as correctness, robustness, and handling ambiguous problem specifications.
The tutorial bridges AI researchers new to optimization, OR researchers curious about how LLMs can assist modeling and algorithm design, and practitioners building AI-enabled decision-support systems.
{a.blurb}