  In stochastic optimization, a common tool to deal sequentially with large
sample is to consider the well-known stochastic gradient algorithm.
Nevertheless, since the stepsequence is the same for each direction, this can
lead to bad results in practice in case of ill-conditionned problem. To
overcome this, adaptive gradient algorithms such that Adagrad or Stochastic
Newton algorithms should be prefered. This paper is devoted to the non
asymptotic analyis of these adaptive gradient algorithms for strongly convex
objective. All the theoretical results will be adapted to linear regression and
regularized generalized linear model for both Adagrad and Stochastic Newton
algorithms.
