Strongly convex stochastic online optimization on a unit simplex with application to the mixing least square regression
Optimization and Control
2017-03-24 v2
Abstract
In this paper we propose a new approach to obtain mixing least square regression estimate by means of stochastic online mirror descent in non-euclidian set-up.
Keywords
Cite
@article{arxiv.1703.06770,
title = {Strongly convex stochastic online optimization on a unit simplex with application to the mixing least square regression},
author = {Anastasia Bayandina and Elena Chernousova and Alexander Gasnikov and Ekaterina Krymova},
journal= {arXiv preprint arXiv:1703.06770},
year = {2017}
}
Comments
This paper has been withdrawn by the author due to a crucial error in Theorem 1 and in item 3