Minding the gap between Fast Heuristics and their Optimal Counterparts - Namyar, Pooria; Arzani, Behnaz; Beckett, Ryan; Segarra, Santiago; Kandula, Srikanth (Hot Topics in Networking, 2022)
Production systems use heuristics because they are faster or scale better than the corresponding optimal algorithms. Yet, practitioners are often unaware of how worse off a heuristic’s solution may be with respect to the optimum in realistic scenarios. Leveraging two-stage games and convex optimization, we present a provable framework that unveils settings where a given heuristic underperforms.