4 papers
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…
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…
Tight Lower Bounds for Binary First-Order Oracles for Convex Optimization
Amitabh Basu, Phillip Kerger, Marco Molinaro
We establish new lower-bounds for the information complexity of mixed-integer convex optimization under two "bit-wise" oracles. The first oracle provides bits of first-order inform…
A Universal Transfer Theorem for Convex Optimization Algorithms Using Inexact First-order Oracles
Phillip Kerger, Marco Molinaro, Hongyi Jiang +1
Given any algorithm for convex optimization that uses exact first-order information (i.e., function values and subgradients), we show how to use such an algorithm to solve the prob…