This manuscript reviews recent advances in deterministic global optimization for Mixed-
Integer Nonlinear Programming (MINLP), as well as Constrained Derivative-Free …

In many optimization problems arising from scientific, engineering and artificial intelligence
applications, objective and constraint functions are available only as the output of a black …

We present a model-based derivative-free method for optimization subject to general convex
constraints, which we assume are unrelaxable and accessed only through a projection …

The aim of this text is to highlight recent advances of trust-region-based methods for
nonlinear programming and to put them into perspective. An algorithmic framework provides …

We propose a trust-region algorithm for constrained optimization problems in which the
derivatives of the objective function are not available. In each iteration, the objective function …

The derivative-free trust-region algorithm proposed by Conn et al.(SIAM J Optim 20: 387–
415, 2009) is adapted to the problem of minimizing a composite function\varPhi (x)= f (x)+ h …

We consider the problem of optimizing the sum of a smooth, nonconvex function for which
derivatives are unavailable, and a convex, nonsmooth function with easy-to-evaluate …

This paper explores a method for solving constrained optimization problems when the
derivatives of the objective function are unavailable, while the derivatives of the constraints …

Data-driven optimization has been an emerging field of sciences, engineering, and applied
mathematics since the early 1960s. The increasing computation power, the ability to collect …

We consider derivative-free trust-region algorithms based on sampling approaches for
convex constrained problems and discuss two conditions on the quadratic models for …