Stochastic Three-Composite Convex Minimization with a Linear Operator

We develop a primal-dual convex minimization framework to solve a class of stochastic convex three-composite problem with a linear operator. We consider the cases where the problem is both convex and strongly convex and analyze the convergence of the proposed algorithm in both cases. In addition, we extend the proposed framework to deal with additional constraint sets and multiple non-smooth terms. We provide numerical evidence on graph-guided sparse logistic regression, fused lasso and overlapped group lasso, to demonstrate the superiority of our approach to the state-of-the-art.


Published in:
Proceedings of the 21st International Conference on Artificial Intelligence and Statistics
Presented at:
21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018,, Lanzarotte, Spain, April 9-11, 2018
Year:
2018
Publisher:
Lanzarotte, JMLR: W&CP
Laboratories:




 Record created 2018-02-15, last modified 2019-03-17

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