183 citations · 295 across the 13 of their papers we have counts for
Showing 2026Show all
2 papers · 1 filter
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.CL2026
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,…