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arXiv · 1507.00852

Stochastic inertial primal-dual algorithms

Lorenzo Rosasco·Silvia Villa·Bang Cong Vu
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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.

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Lorenzo Rosasco, Silvia Villa, Bang Cong Vu. 2015-07-03. Stochastic inertial primal-dual algorithms. https://arxiv.org/abs/1507.00852

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