Trust region methods are a class of numerical methods for optimization. Unlike line search
type methods where a line search is carried out in each iteration, trust region methods …

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 …

The current study shows a reliable stochastic computing heuristic approach for solving the
nonlinear Rabinovich-Fabrikant model. This nonlinear model contains three ordinary …

Interpolation-based trust-region methods are an important class of algorithms for Derivative-
Free Optimization which rely on locally approximating an objective function by quadratic …

This thesis studies derivative-free optimization (DFO), particularly model-based methods
and software. These methods are motivated by optimization problems for which it is …

Derivative-free optimization methods are numerical methods for optimization problems in
which no derivative information is used. Such optimization problems are widely seen in …

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 …

Derivative-free optimization (DFO) methods [502] are typically considered for the
minimization/maximization of functions for which the corresponding derivatives neither are …

We develop an iterative algorithm to recover the minimum p-norm solution of the functional
linear equation Ax= b, A x= b, where A: X ⟶ Y\, A: X⟶ Y is a continuous linear operator …