convex optimization 1cutting-plane methods 1first-order oracles 1information complexity 1lower bounds 1mixed-integer programming 1
From the 1 of 3 linked papers with an AI index.
3 papers
math.OC2026
Tight Lower Bounds for Binary First-Order Oracles for Convex Optimization
Amitabh Basu, Phillip Kerger, Marco Molinaro
The paper proves that solving mixed-integer convex optimization with bit‑wise first‑order oracles requires a number of bits that grows quadratically with the number of continuous v…
cs.LG2026
Sample Complexity of Stochastic Optimization with Integer Variables
Hongyu Cheng, Yinghao Zheng, Marco Molinaro +1
We establish sample complexity results for stochastic optimization over the integers, especially with a view to understand the complexity with respect to the corresponding continuo…
math.OC2026
Probabilistic analysis of dual decomposition on two-stage stochastic integer programs
Santanu S. Dey, Marco Molinaro, Jingye Xu
Two-stage stochastic integer programs provide a powerful framework for modeling decision-making under uncertainty, but they are notoriously difficult to solve at scale due to their…