A trust region method based on interior point techniques for nonlinear programming

An algorithm for minimizing a nonlinear function subject to nonlinear equality and inequality constraints is described. It can be seen as an extension of primal interior point methods to non-convex optimization. The new algorithm applies sequential quadratic programming techniques to a sequence of barrier problems, and uses trust regions to ensure the robustness of the iteration and to allow the direct use of second order derivatives. An analysis of the convergence properties of the new method is presented.