  This letter investigates the convergence and concentration properties of the
Stochastic Mirror Descent (SMD) algorithm utilizing biased stochastic
subgradients. We establish the almost sure convergence of the algorithm's
iterates under the assumption of diminishing bias. Furthermore, we derive
concentration bounds for the discrepancy between the iterates' function values
and the optimal value, based on standard assumptions. Subsequently, leveraging
the assumption of Sub-Gaussian noise in stochastic subgradients, we present
refined concentration bounds for this discrepancy.
