A new feasible descent primal-dual interior point algorithm for nonlinear inequality constrained optimization

In this paper, a new feasible primal-dual interior point algorithm for solving inequality constrained optimization problems is presented. At each iteration, the algorithm solves only two or three reduced systems of linear equations with the same coefficient matrix. The searching direction is feasible and the object function is monotone decreasing. The proposed algorithm is proved to possess global and superlinear convergence under mild conditions. Finally, some numerical experiments with the algorithm are reported. (C) 2009 Elsevier Inc. All rights reserved.