In many situations across computational science and engineering, multiple computational
models are available that describe a system of interest. These different models have varying …

This paper addresses the solution of bound-constrained optimization problems using
algorithms that require only the availability of objective function values but no derivative …

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 fact that road transportation negatively affects the quality of the environment and
deteriorates its bearing capacity has drawn a wide range of concerns among researchers. In …

For many years all three of us have been interested in, and have tried to make contributions
to, derivative-free optimization. Our motivation for writing this book resulted from various …

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 …

We consider variants of trust-region and adaptive cubic regularization methods for non-
convex optimization, in which the Hessian matrix is approximated. Under certain condition …

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

We propose data profiles as a tool for analyzing the performance of derivative-free
optimization solvers when there are constraints on the computational budget. We use …

The adaptive momentum method (AdaMM), which uses past gradients to update descent
directions and learning rates simultaneously, has become one of the most popular first-order …