Advances in trust region algorithms for constrained optimization Original Research Article

Constrained optimization problems occur in many applications of engineering, science and medicine. Much attention has recently been devoted to solving this class of problems using trust region algorithms with strong convergence properties, in part because of the availability of reliable software. This paper presents a survey of recent advances in trust region algorithms. We then explain the different choices of penalty function, Lagrange function and expanded Lagrangian function used for modeling constrained optimization problems and solving these equations using trust region algorithms. Finally, some numerical results for the implementation of our proposed method on different test problems with various sizes are presented.