Optimization modeling
Optimization problems appear in industrial applications from manufacturing and distribution to healthcare. However, most such problems are still solved heuristically by hand rather than optimally by state-of-the-art solvers, as the expertise required to formulate and solve these problems limits the widespread adoption of optimization tools and techniques. My work has developed several tools to simplify optimization modeling. Most recently, we released a Large Language Model (LLM)-based agent to formulate and solve MILP problems from natural language descriptions.
Talks
AI and the future of optimization (Women in Data Science and Mathematics, 2024) video
Big Data is Low Rank using LowRankModels (keynote at JuliaCon, June 2019) video
The Type of Language for Mathematical Programming (JuliaCon, June 2017)
slides github video
Software
OptiMUS: model optimization problems using natural language
Linnaeus: detect equivalence between optimization algorithms
Convex.jl: disciplined convex programming in Julia
Papers
OptiMUS: Scalable Optimization Modeling Using MIP Solvers and Large Language Models
A. AhmadiTeshnizi, W. Gao, and M. Udell
International Conference on Machine Learning (ICML), 2024
[arxiv][url][bib][video]
OptiMUS: Optimization Modeling Using MIP Solvers and Large Language Models
A. AhmadiTeshnizi, W. Gao, and M. Udell
2023
[arxiv][url][bib][video]
An automatic system to detect equivalence between iterative algorithms
S. Zhao, L. Lessard, and M. Udell
2021
[arxiv][pdf][url][slides][bib]
Disciplined Multi-Convex Programming
X. Shen, S. Diamond, M. Udell, Y. Gu, and S. Boyd
Chinese Control and Decision Conference (CCDC), 2017
Best Student Paper
[arxiv][bib]
Convex Optimization in Julia
M. Udell, K. Mohan, D. Zeng, J. Hong, S. Diamond, and S. Boyd
SC14 Workshop on High Performance Technical Computing in Dynamic Languages, 2014
[arxiv][url][code][bib]
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