# Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions

@article{Agarwal2014StochasticOA,
title={Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions},
author={Alekh Agarwal and Sahand N. Negahban and Martin J. Wainwright},
journal={2014 48th Annual Conference on Information Sciences and Systems (CISS)},
year={2014},
pages={1-2}
}
• Published 18 July 2012
• Computer Science
• 2014 48th Annual Conference on Information Sciences and Systems (CISS)
Summary form only given. Stochastic optimization algorithms have many desirable features for large-scale machine learning, and accordingly have been the focus of renewed and intensive study in the last several years (e.g., see the papers [2], [5], [14] and references therein). The empirical efficiency of these methods is backed with strong theoretical guarantees, providing sharp bounds on their convergence rates. These convergence rates are known to depend on the structure of the underlying…

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