An augmented penalty function method with penalty parameter updates for nonconvex optimization

Abstract

Given an augmented Lagrangian scheme for a general optimization problem, we use an epsilon subgradient step for improving the dual function. This can be seen as an update for an augmented penalty method, which is more stable because it does not force the penalty parameter to tend to infinity. We establish for this update primal–dual convergence for our augmented penalty method. As illustration, we apply our method to the test-bed kissing number problem.