AI for Discrete Optimization (IMEN891N, Fall 2025)

(♦: In-class presentation by students)

Part IV: Finale

Week 15: Recent topics

• Optimization over a trained neural network (Anderson et al. 2020, Bunel et al. 2020)
  • 2021 IPAM Workshop: Neural network verification as piecewise linear optimization (by Joseph Huchette @ Rice University) (link)
• ♦ Generative AI: Large language models (Wasserkrug et al. 2025, Ahmaditeshnizi et al. 2024, Huang et al. 2025), Foundation models (Li et al. 2024, Berto et al. 2024), Diffusion models, etc
  • 2024 Women in Data Science and Maths Seminar: AI and the Future of Optimization (by Madeleine Udell @ Stanford University) (link)
  • 2025 AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms (by AlphaEvolve team @ Google DeepMind) (link)
  • Combining Large Language Models and OR/MS to Make Smarter Decisions (by Boussioux @ University of Washington, and Wei Sun @ IBM Research) (link)
• (Optional) Explainable AI: Survey (De Bock et al. 2024, Bertsimas 2019), Counterfactual explanations (Guidotti 2022, Carrizosa et al. 2024, Maragno et al. 2024), Tree-based explanations, Neurosymbolic AI, …
  • 2022 NeEDS Seminar: The Counterfactual Explanation—Yet More Algorithms as a Solution to Explain Complex Models? (by David Martens @ University of Antwerp) (link)
  • 2021 GdR RO Seminar: Combinatorial optimization and interpretable machine learning (by Thibaut Vidal @ Ecole Polytechnique Montréal) (link)

Part IV — Reading list

• Anderson, Ross, Huchette, Joey, Ma, Will, Tjandraatmadja, Christian, Vielma, Juan Pablo (2020). Strong Mixed-Integer Programming Formulations for Trained Neural Networks. Mathematical Programming, 183(1), 3–39. (link)
• Bunel, Rudy, Lu, Jingyue, Turkaslan, Ilker, Torr, Philip H. S., Kohli, Pushmeet, Kumar, M. Pawan (2020). Branch and Bound for Piecewise Linear Neural Network Verification. Journal of Machine Learning Research, 21(42), 1–39.
• Wasserkrug, Segev, Boussioux, Leonard, den Hertog, Dick, Mirzazadeh, Farzaneh, Birbil, Ş. İlker, Kurtz, Jannis, Maragno, Donato (2025). Enhancing Decision Making Through the Integration of Large Language Models and Operations Research Optimization. Proceedings of the AAAI Conference on Artificial Intelligence, 39(27), 28643–28650. (link)
• Ahmaditeshnizi, Ali, Gao, Wenzhi, Udell, Madeleine (2024). OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models. Proceedings of the 41st International Conference on Machine Learning, 577–596.
• Huang, Chenyu, Tang, Zhengyang, Hu, Shixi, Jiang, Ruoqing, Zheng, Xin, Ge, Dongdong, Wang, Benyou, Wang, Zizhuo (2025). ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling. Operations Research. (link)
• Li, Sirui, Kulkarni, Janardhan, Menache, Ishai, Wu, Cathy, Li, Beibin (2024). Towards Foundation Models for Mixed Integer Linear Programming. The Thirteenth International Conference on Learning Representations.
• Berto, Federico, Hua, Chuanbo, Zepeda, Nayeli Gast, Hottung, Andre, Wouda, Niels, Lan, Leon, Tierney, Kevin, Park, Jinkyoo (2024). RouteFinder: Towards Foundation Models for Vehicle Routing Problems. ICML 2024 Workshop on Foundation Models in the Wild.
• De Bock, Koen W., Coussement, Kristof, Caigny, Arno De, Słowinski, Roman, Baesens, Bart, Boute, Robert N., Choi, Tsan-Ming, Delen, Dursun, Kraus, Mathias, Lessmann, Stefan, Maldonado, Sebastian, Martens, David, Oskarsdottir, Maria, Vairetti, Carla, Verbeke, Wouter, Weber, Richard (2024). Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda. European Journal of Operational Research, 317(2), 249–272. (link)
• Bertsimas, Dimitris (2019). Machine Learning Under a Modern Optimization Lens. Dynamic Ideas LLC.
• Guidotti, Riccardo (2022). Counterfactual Explanations and How to Find Them: Literature Review and Benchmarking. Data Mining and Knowledge Discovery. (link)
• Carrizosa, Emilio, Ramirez-Ayerbe, Jasone, Romero Morales, Dolores (2024). Mathematical Optimization Modelling for Group Counterfactual Explanations. European Journal of Operational Research, 319(2), 399–412. (link)
• Maragno, Donato, Kurtz, Jannis, Rober, Tabea E., Goedhart, Rob, Birbil, Ş. İlker, den Hertog, Dick (2024). Finding Regions of Counterfactual Explanations via Robust Optimization. INFORMS Journal on Computing, 36(5), 1316–1334. (link)