  We adapt the quasi-monotone method from [2] for composite convex minimization
in the stochastic setting. For the proposed numerical scheme we derive the
optimal convergence rate in terms of the last iterate, rather than on average
as it is standard for subgradient methods. The theoretical guarantee for
individual convergence of the regularized quasi-monotone method is confirmed by
numerical experiments on l1-regularized robust linear regression.
