Strongly convex stochastic online optimization on a unit simplex with application to the mixing least square regression
arXiv:1703.06770
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.
This paper has been withdrawn by the author due to a crucial error in Theorem 1 and in item 3