An Augmented Lagrangian Method for Optimization Problems in Banach Spaces
Christian Kanzow, Daniel Steck, Daniel Wachsmuth · SIAM Journal on Control and Optimization · 2018
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 it 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.