Random Multi-Constraint Projection: Stochastic Gradient Methods for Convex Optimization with Many Constraints

Abstract

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)

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Cite this paper

@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} }