Stochastic inertial primal-dual algorithms
Optimization and Control
2015-07-06 v1
Abstract
We propose and study a novel stochastic inertial primal-dual approach to solve composite optimization problems. These latter problems arise naturally when learning with penalized regularization schemes. Our analysis provide convergence results in a general setting, that allows to analyze in a unified framework a variety of special cases of interest. Key in our analysis is considering the framework of splitting algorithm for solving a monotone inclusions in suitable product spaces and for a specific choice of preconditioning operators.
Cite
@article{arxiv.1507.00852,
title = {Stochastic inertial primal-dual algorithms},
author = {Lorenzo Rosasco and Silvia Villa and Bang Cong Vu},
journal= {arXiv preprint arXiv:1507.00852},
year = {2015}
}