Optimality conditions, approximate stationarity, and applications 'a story beyond lipschitzness

Approximate necessary optimality conditions in terms of Frechet subgradients and normals for a rather general optimization problem with a potentially non-Lipschitzian objective function are established with the aid of Ekeland's variational principle, the fuzzy Frechet subdifferential sum rule, and a novel notion of lower semicontinuity relative to a set-valued mapping or set. Feasible points satisfying these optimality conditions are referred to as approximately stationary. As applications, we derive a new general version of the extremal principle. Furthermore, we study approximate stationarity conditions for an optimization problem with a composite objective function and geometric constraints, a qualification condition guaranteeing that approximately stationary points of such a problem are M-stationary, and a multiplier-penalty-method which naturally computes approximately stationary points of the underlying problem. Finally, necessary optimality conditions for an optimal control problem with a non-Lipschitzian sparsity-promoting term in the objective function are established. © The authors

An Augmented Lagrangian Method for Non-Lipschitz Nonconvex Programming

2016-2017 > Academic research: refereed > Publication in refereed journal201804_a bcmaVersion of RecordRGCPublishedVoR allowe

An augmented lagrangian method for non-Lipschitz nonconvex programming

2016-2017 > Academic research: refereed > Publication in refereed journal201804_a bcmaVersion of RecordRGCPublishe