Global convergence of a primal-dual interior-point method for nonlinear programming

Many recent convergence results obtained for primal-dual interior-point methods for nonlinear programming, use assumptions of the boundedness of generated iterates. In this paper we replace such assumptions by new assumptions on the NLP problem, develop a modification of a primal-dual interior-point method implemented in software package LOQO and analyze convergence of the new method from any initial guess

OPTIMIZATION AND DYNAMICAL SYSTEMS ALGORITHMS FOR FINDING EQUILIBRIA OF STOCHASTIC GAMES

Abstract. We present two new algorithms for computing Nash equilibria of stochastic games. One is a global random start algorithm based on nonlinear programming, while the other combines a dynamical system with nonlinear programming to find stable equilibria. Promising numerical results are presented. 1

Interior-point methods for nonconvex nonlinear programming: filter-methods and merit functions.

Abstract. In this paper, we present a barrier method for solving nonlinear programming problems. It employs a Levenberg-Marquardt perturbation to the Karush-Kuhn-Tucker (KKT) matrix to handle indefinite Hessians and a line search to obtain sufficient descent at each iteration. We show that the Levenberg-Marquardt perturbation is equivalent to replacing the Newton step by a cubic regularization step with an appropriately chosen regularization parameter. This equivalence allows us to use the favorable theoretical results o

Interior-point methods for nonconvex nonlinear programming: Orderings and higher-order methods

Abstract. In this paper, we investigate the use of an exact primal-dual penalty approach within the framework of an interior-point method for nonconvex nonlinear programming. This approach provides regularization and relaxation, which can aid in solving ill-behaved problems and in warmstarting the algorithm. We present details of our implementation within the loqo algorithm and provide extensive numerical results on the CUTEr test set and on warmstarting in the context of quadratic, nonlinear, mixed integer nonlinear, and goal programming. 1