  In this paper, we propose an inexact Augmented Lagrangian Method (ALM) for
the optimization of convex and nonsmooth objective functions subject to linear
equality constraints and box constraints where errors are due to fixed-point
data. To prevent data overflow we also introduce a projection operation in the
multiplier update. We analyze theoretically the proposed algorithm and provide
convergence rate results and bounds on the accuracy of the optimal solution.
Since iterative methods are often needed to solve the primal subproblem in ALM,
we also propose an early stopping criterion that is simple to implement on
embedded platforms, can be used for problems that are not strongly convex, and
guarantees the precision of the primal update. To the best of our knowledge,
this is the first fixed-point ALM that can handle non-smooth problems, data
overflow, and can efficiently and systematically utilize iterative solvers in
the primal update. Numerical simulation studies on a utility maximization
problem are presented that illustrate the proposed method.
