An active-set trust-region method for derivative-free nonlinear bound-constrained optimization

We consider an implementation of a recursive model-based active-set trust-region method for solving bound-constrained nonlinear non-convex optimization problems without derivatives using the technique of self-correcting geometry proposed in [24]. Considering an active-set method in modelbased optimization creates the opportunity of saving a substantial amount of function evaluations when maintaining smaller interpolation sets while proceeding optimization in lower dimensional subspaces. The resulting algorithm is shown to be numerically competitive.