  We propose a variant of the classical augmented Lagrangian method for
constrained optimization problems in Banach spaces. Our theoretical framework
does not require any convexity or second-order assumptions and allows the
treatment of inequality constraints with infinite-dimensional image space.
Moreover, we discuss the convergence properties of our algorithm with regard to
feasibility, global optimality, and KKT conditions. Some numerical results are
given to illustrate the practical viability of the method.
