Consider convex optimization problems subject to a large number of constraints. We focus on stochastic problems in which the objective takes the form of expected values and the feasible set is the intersection of a large number of convex sets. We propose a class of algorithms that perform both stochastic gradient descent and random feasibility updates… (More)

@article{Wang2015RandomMP,
title={Random Multi-Constraint Projection: Stochastic Gradient Methods for Convex Optimization with Many Constraints},
author={Mengdi Wang and Yichen Chen and Jialin Liu and Yuantao Gu},
journal={CoRR},
year={2015},
volume={abs/1511.03760}
}