A primal-dual interior point trust-region method for nonlinear semidefinite programming

Abstract

In this paper, we propose a primal-dual interior point trust-region method for solving nonlinear semidefinite programming problems. The method consists of the outer iteration (SDPIP) that finds a Karush–Kuhn–Tucker (KKT) point and the inner iteration (SDPTR) that calculates an approximate barrier KKT point. Algorithm SDPTR combines a commutative class of Newton-like directions with the steepest descent type direction within the framework of the trust-region strategy. We present a trust-region method in primal-dual space and prove the global convergence property of the proposed method. Some numerical experiments are given. In addition, we also present second-order approximations to the primal-dual merit function, and a trust-region method in primal space in Appendix.