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.