In this paper, we bring the celebrated maxweight features (myopic and discrete actions) to mainstream convex optimization. Myopic actions are important in control because decisions need to be made in an online manner and without knowledge of future events and discrete actions, because many systems have a finite (so nonconvex) number of control decisions. For example, whether to transmit a packet or not in communication networks. Our results show that these two features can be encompassed in the subgradient method for the Lagrange dual problem by the use of stochastic and epsilon-subgradients. One of the appealing features of our approach is that it decouples the choice of a control action from a specific choice of subgradient, which allows us to design control policies without changing the underlying convex updates. Two classes of discrete control policies are presented: one that can make discrete actions by looking only at the system's current state, and another that selects actions using blocks. The latter class is useful for handling systems that have constraints on the order in which actions are selected.