  In this paper we present a subgradient method with non-monotone line search
for the minimization of convex functions with simple convex constraints.
Different from the standard subgradient method with prefixed step sizes, the
new method selects the step sizes in an adaptive way. Under mild conditions
asymptotic convergence results and iteration-complexity bounds are obtained.
Preliminary numerical results illustrate the relative efficiency of the
proposed method.
