3 papers
math.OC2026
TACIT: Optimization Models that Learn from Their Mistakes
Maxime Bouscary, Marco Molinaro, Sirui Li +3
Real-world optimization problems are difficult to model accurately because many objectives and constraints reside in domain experts' tacit knowledge, making them hard to formalize.…
cs.LG2025
Solve Smart, Not Often: Policy Learning for Costly MILP Re-solving
Rui Ai, Hugo De Oliveira Barbalho, Sirui Li +3
A common challenge in real-time operations is deciding whether to re-solve an optimization problem or continue using an existing solution. While modern data platforms may collect i…
cs.LG2025
OptiMind: Teaching LLMs to Think Like Optimization Experts
Xinzhi Zhang, Zeyi Chen, Humishka Zope +6
Mathematical programming -- the task of expressing operations and decision-making problems in precise mathematical language -- is fundamental across domains, yet remains a skill-in…