4 papers
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.…
HeurEvo: Agentic Evolution of Hybrid Solver-Augmented Heuristics for Time-Critical Mathematical Optimization
Feijie Wu, Hugo Barbalho, Konstantina Mellou +5
Recent advances in agentic heuristic design use AI agents and execution feedback to automate algorithm discovery for challenging optimization problems. In many practical settings,…
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…
Online Rack Placement in Large-Scale Data Centers: Online Sampling Optimization and Deployment
Saumil Baxi, Kayla Cummings, Alexandre Jacquillat +5
This paper optimizes the configuration of large-scale data centers toward cost-effective, reliable and sustainable cloud supply chains. The problem involves placing incoming racks…