In this paper, fully nonsmooth optimization problems in Banach spaces with
finitely many inequality constraints, an equality constraint within a Hilbert
space framework, and an additional abstract constraint are considered. First,
we suggest a (safeguarded) augmented Lagrangian method for the numerical
solution of such problems and provide a derivative-free global convergence
theory which applies in situations where the appearing subproblems can be
solved to approximate global minimality. Exemplary, the latter is possible in a
fully convex setting. As we do not rely on any tool of generalized
differentiation, the results are obtained under minimal continuity assumptions
on the data functions. We then consider two prominent and difficult
applications from image denoising and sparse optimal control where these
findings can be applied in a beneficial way. These two applications are
discussed and investigated in some detail. Due to the different nature of the
two applications, their numerical solution by the (safeguarded) augmented
Lagrangian approach requires problem-tailored techniques to compute approximate
minima of the resulting subproblems. The corresponding methods are discussed,
and numerical results visualize our theoretical findings.Comment: 36 pages, 4 figures, 1 tabl