In real-world applications, optimization problems might arise where there is more than one objective.
Additionally, some objectives could be computationally expensive to evaluate, with no gradient information available.
I present a derivative-free local optimizer (written in Julia) aimed at such problems. It employs a trust-region strategy and local surrogate models (e.g., polynomials or radial basis function models) to save function evaluations.