Coordinate descent (CD) method is one of the most efficient algorithms in dealing with the large-scale optimization problems for its simple operation

cheap computational cost and practical efficiency.However until now

almost all the-state-of-art CD algorithms require the smoothness assumption of loss functions due to solving the subproblems in closed-form.In this paper

within the structural learning framework

we present a new stochastic CD(SCD)algorithm for non-smooth losses

in which the randomly selected single variable problem is solved using Comid method.Theoretical analysis indicates that the proposed algorithm has an

O

(

t

/

t

) convergence rate for general convex problems and an

O

(ln

t/t

) convergence rate for strongly convex problems.The experiments demonstrate the expected efficiency of our SCD algorithms when coping with the L1-regularized Hinge loss problems.