Efficient randomized mirror descents in stochastic online convex optimization
Optimization and Control
2016-12-12 v9
Abstract
In the paper we consider an application of mirror descent (dual averaging) to the stochastic online convex optimization problems. We compare classical mirror descent (Nemirovski-Yudin, 1979) with dual averaging (Nesterov, 2005) and Grigoriadis-Khachiyan algorithm (1995). Grigoriadis-Khachiyan algorithm has just proved to be a randomized mirror descent (dual averaging) with randomization in KL-projection of (sub)gradient to a unit simplex. We found out that this randomization is an optimal way of solving sparse matrix games and some other problems arising in convex optimization and experts weighting.
Cite
@article{arxiv.1410.3118,
title = {Efficient randomized mirror descents in stochastic online convex optimization},
author = {Alexander Gasnikov and Yurii Nesterov and Vladimir Spokoiny},
journal= {arXiv preprint arXiv:1410.3118},
year = {2016}
}
Comments
26 pages, in Russian